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repo_name stringlengths 6 92	path stringlengths 7 220	copies stringclasses 78 values	size stringlengths 2 9	content stringlengths 15 1.05M ⌀	license stringclasses 15 values
oseledets/fastpde	lecture-7.ipynb	1	150631	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Lecture 7: Fast sparse solvers" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##Sparse matrix \n", "\n", " DEF: Sparse matrix is a matrix that contains $\\mathcal{O}(n)$ nonzero elements.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "###Sparse matrices are ubiquitous in PDEs\n", "\n", "Consider for example a 3D Poisson equation:\n", "\n", "\n", "\n", "$$\\Delta T = \\frac{\\partial^2T}{\\partial x^2}+\\frac{\\partial^2T}{\\partial y^2}+\\frac{\\partial^2T}{\\partial z^2}=f.$$\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "After discretization we obtain five diagonal matrix A:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.cm as cm\n", "%matplotlib inline\n", "N = 3\n", "B = np.diag(2np.ones(N)) + np.diag((-1)np.ones(N-1),k=-1)+ np.diag((-1)np.ones(N-1),k = 1)\n", "Id = np.diag(np.ones(N));\n", "# Assembling a 3D operator:\n", "A = np.kron(Id,np.kron(Id,B)) + np.kron(Id,np.kron(B,Id)) +np.kron(B,np.kron(Id,Id))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x12ad428d0>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAPwAAAD7CAYAAABOrvnfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAD3tJREFUeJzt3U+sXOV5x/Hfr0AXCZUIorUtZGKitpsqEqgRUhsqz6KK\n", 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"fragment" } }, "source": [ "this matrix has $\\mathcal{O}(1)$ elements in a row, therefore it is sparse." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Finite elements method is also likely to give you a system with a sparse matrix." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##How to store a sparse matrix" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Coordinate format (coo)\n", "\n", "(i, j, value)\n", "\n", "i.e. store two integer arrays and one real array.\n", "Easy* to add elements.\n", "But how to multiply a matrix by a vector?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "###CSR format\n", "\n", "A matrix is stored as 3 different arrays:\n", "\n", " sa, ja, ia\n", "\n", "where:\n", "\n", "* nnz is the total number of non-zeros for the matrix\n", "* sa is an real-value array of non-zeros for the matrix (length nnz)\n", "* ja is an integer array of column number of the non-zeros (length nnz)\n", "* ia is an integer array of locations of the first non-zero element in each row (length n+1)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "(Blackboard figure)\n", "###Idea behind CSR\n", "* For each row i we store the column number of the non-zeros (and their) values\n", "* We stack this all together into ja and sa arrays\n", "* We save the location of the first non-zero element in each row" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "###CSR helps for matrix-by-vector product as well\n", "```\n", " for i in xrange(n):\n", " for k in xrange(ia(i):ia(i+1)-1):\n", " y(i) += sa(k) * x(ja(k))\n", "```\n", "Let us do a short timing test" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import numpy as np\n", "import scipy as sp\n", "import scipy.sparse\n", "import scipy.sparse.linalg\n", "from scipy.sparse import csc_matrix, csr_matrix, coo_matrix, lil_matrix\n", " \n", "A = csr_matrix([10,10])\n", "B = lil_matrix([10,10])\n", "A[0,0] = 1\n", "#print A\n", "B[0,0] = 1\n", "#print B" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 10.5 ms per loop\n", "100 loops, best of 3: 14 ms per loop\n" ] } ], "source": [ "import numpy as np\n", "import scipy as sp\n", "import scipy.sparse\n", "import scipy.sparse.linalg\n", "from scipy.sparse import csc_matrix, csr_matrix, coo_matrix\n", "import matplotlib.pyplot as plt\n", "import time\n", "%matplotlib inline\n", "n = 1000\n", "ex = np.ones(n);\n", "lp1 = sp.sparse.spdiags(np.vstack((ex, -2ex, ex)), [-1, 0, 1], n, n, 'csr'); \n", "e = sp.sparse.eye(n)\n", "A = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)\n", "A = csr_matrix(A)\n", "rhs = np.ones(n n)\n", "B = coo_matrix(A)\n", "#t0 = time.time()\n", "%timeit A.dot(rhs)\n", "#print time.time() - t0\n", "#t0 = time.time()\n", "%timeit B.dot(rhs)\n", "#print time.time() - t0" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "As you see, CSR is faster, and for more unstructured patterns the gain will be larger. \n", "\n", "CSR format has difficulties with adding new elements." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "##How to solve linear systems?\n", "\n", "\n", "\n", "###Direct or iterative solvers\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "###Direct solvers\n", "\n", "The direct methods use sparse Gaussian elimination, i.e.\n", "they eliminate variables while trying to keep the matrix as sparse as possible. \n", "\n", "And often, the inverse of a sparse matrix is not \n", "sparse:\n", "(it corresponds to some integral operator, so it has block low-rank structure. Details will be later in this course)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x10fd4eed0>" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAWwAAAC0CAYAAACwhRZPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAEyRJREFUeJztnW+sZGV9xz/fu4tbYO3d3Td3USALxA2WWArRSg2GKyyG\n", "JpYS0igmEjSprzSiTSyLfbFP0rQhJAb7J31RW+1KYlsXlT9JY1lxR9PUWKyAyIJbawmi2btrKX8K\n", "qQr764s5u84d5uydvTPnnOeZ+X6SCc+cO3Oe78z+5vD8PufMvYoIjDHG5M9C1wGMMcaMhw/YxhhT\n", "CD5gG2NMIfiAbYwxheADtjHGFIIP2MYYUwiNHbAlXSPpCUn/IemWBuc5R9IBSY9J+p6kj1Tbt0na\n", "L+mQpPslbWkwwwZJD0m6r625JW2RdJekxyUdlPTWtl6zpFur9/tRSZ+XtKnN97tr5qW2u6jrap5O\n", "aruEum7kgC1pA/CXwDXArwHvlfTGJuYCfgF8LCIuAi4DPlTNtRvYHxE7gQeq+01xM3AQOH5Rextz\n", 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np.array(np.linalg.inv(a))\n", "fig,axes = plt.subplots(1, 2)\n", "axes[0].spy(a)\n", "axes[1].spy(b,markersize=2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Looks woefully." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-0.83333333 -0.66666667 -0.5 -0.33333333 -0.16666667]\n", " [-0.66666667 -1.33333333 -1. -0.66666667 -0.33333333]\n", " [-0.5 -1. -1.5 -1. -0.5 ]\n", " [-0.33333333 -0.66666667 -1. -1.33333333 -0.66666667]\n", " [-0.16666667 -0.33333333 -0.5 -0.66666667 -0.83333333]]\n" ] } ], "source": [ "N = n = 5\n", "ex = np.ones(n);\n", "A = sp.sparse.spdiags(np.vstack((ex, -2ex, ex)), [-1, 0, 1], n, n, 'csr'); \n", "A = A.todense()\n", "B = np.array(np.linalg.inv(A))\n", "\n", "print B" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "But occasionally L and U factors can be sparse." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x10d719350>" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAWwAAAC0CAYAAACwhRZPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAADthJREFUeJzt3V+MnFd9xvHvU2/4E9Jm8c3aBUdOad0ERFAsWgJCYogS\n", "Ka1QyFUIEpGFFK5ABC4Ca268V6WJFKVIFTeAkBWJVmlAVizR1kvIiItKEBoHQhzj8icCg7yGIFPK\n", "VaL8ejHvbibj2Z135533zznv85Eszf/z7u7jZ885M7OjiMDMzLrvT9o+ADMzK8eFbWaWCBe2mVki\n", "XNhmZolwYZuZJcKFbWaWiNoKW9Jtks5K+h9Jn61xnAOSnpD0rKQfSfpkcfleSeuSzkk6JWm5xmPY\n", "I+m0pJNNjS1pWdKjkp6TdEbSu5r6miUdLb7fz0j6mqTXNvn9bltfst1GrotxWsl2CrmupbAl7QH+\n", "GbgNeCvwYUnX1zEW8CLw6Yh4G3AT8PFirFVgPSIOAY8X5+tyL3AG2HxRexNjfwH4ZkRcD9wAnG1i\n", 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"XunBpfoe3AyIiMan2s61NWFatmstbDMzWxxviZiZJcKFbWaWCBe2mVkiXNhmZolwYZuZJeL/ASVo\n", "yXzI9OE3AAAAAElFTkSuQmCC\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x12b5d01d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p, l, u = scipy.linalg.lu(a)\n", "fig,axes = plt.subplots(1, 2)\n", "axes[0].spy(l)\n", "axes[1].spy(u)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "In 1D factors L and U are bidiagonal." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In 2D factors L and U looks less optimistic, but still ok.) " ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x110e0a110>" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAWwAAAC0CAYAAACwhRZPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAEaZJREFUeJzt3V2MXdV5xvH/U0NSDCmuL2ZMwcg0LYVGCYo1aVw6EU5E\n", "JYpSF+WCDwVi05KrVAGkJti5qHzVlkhRkrbKRUI+DGmaUgchkFLFA8GNrLYpEwwlGMdNVeRA5CGF\n", "AiGtIihvL/Y+nj3jc2b2+Jy1P5+fZDFnzzlnrRn2fr38nL3WUkRgZmbN9wt1d8DMzMpxwTYzawkX\n", "bDOzlnDBNjNrCRdsM7OWcME2M2uJZAVb0lWSjkr6d0l3JGxns6RHJD0l6fuSPpof3yhpTtIxSQck\n", "bUjYh3WSDkt6sKq2JW2QtF/S05KOSHp3VT+zpD357/tJSV+T9OYqf99NVtV5v0ofhl4TdVl+fdTU\n", "h+XXy7aa+nHKtbOW1ycp2JLWAX8NXAX8JnCDpEtTtAW8BtweEW8DtgEfydvaDcxFxMXAw/njVG4F\n", "jgCDm9qraPuzwDcj4lLgHcDRKtqVtAX4MLA1It4OrAOur6Ltpqv4vF/JqGuiLsuvjzosv16erroD\n", 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"mU2OIxEzs5ZwwTYzawkXbDOzlnDBNjNrCRdsM7OW+H/jzBspelO+SwAAAABJRU5ErkJggg==\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x110954750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 3\n", "\n", "ex = np.ones(n);\n", "lp1 = sp.sparse.spdiags(np.vstack((ex, -2ex, ex)), [-1, 0, 1], n, n, 'csr'); \n", "e = sp.sparse.eye(n)\n", "A = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)\n", "A = csc_matrix(A)\n", "T = scipy.sparse.linalg.splu(A)\n", "fig,axes = plt.subplots(1, 2)\n", "axes[0].spy(a, markersize=1)\n", "axes[1].spy(T.L, marker='.', markersize=0.4)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Sparse matrices and graph ordering\n", "The number of non-zeros in the LU decomposition has a deep connection to the graph theory. \n", "(I.e., there is an edge between $(i, j)$ if $a_{ij} \\ne 0$." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "slideshow": { 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"n = 13\n", "ex = np.ones(n);\n", "lp1 = sp.sparse.spdiags(np.vstack((ex, -2ex, ex)), [-1, 0, 1], n, n, 'csr'); \n", "e = sp.sparse.eye(n)\n", "A = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)\n", "A = csc_matrix(A)\n", "G = nx.Graph(A)\n", "nx.draw(G, pos=nx.spring_layout(G), node_size=10)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Strategies for elimination\n", "The reordering that minimizes the fill-in is important, so we can use graph theory to find one.\n", "\n", "- Minimum degree ordering - order by the degree of the vertex\n", "- Cuthill–McKee algorithm (and reverse Cuthill-McKee) -- order for a small bandwidth\n", "- Nested dissection: split the graph into two with minimal number of vertices on the separator" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 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"uAS5iBRoCIBJKVtLYUUdwqX5C6gjACZmxVoKZTMb7UurLzUu9dIQZBMqzh6fwwKfw0Lq51BSgLSc\n", "AblSsVFfEcFeT687NHt9n8BA7PV9AgOx1+TgssuJVWZBpmsATNdurhH4Da1pDGgIgIlZ0VXY12Lp\n", "wtLuQS9XDTp9QQAHDOLyIYDhoWsAgIYAAA0BANEQABANAQBJ/w/BNel+dFGzIQAAAABJRU5ErkJg\n", "gg==\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x10f55d8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import networkx as nx\n", "from networkx.utils import reverse_cuthill_mckee_ordering, cuthill_mckee_ordering\n", "n = 13\n", "ex = np.ones(n);\n", "lp1 = sp.sparse.spdiags(np.vstack((ex, -2ex, ex)), [-1, 0, 1], n, n, 'csr'); \n", "e = sp.sparse.eye(n)\n", "A = sp.sparse.kron(lp1, e) + sp.sparse.kron(e, lp1)\n", "A = csc_matrix(A)\n", "G = nx.Graph(A)\n", "#rcm = list(reverse_cuthill_mckee_ordering(G))\n", "rcm = list(reverse_cuthill_mckee_ordering(G))\n", "A1 = A[rcm, :][:, rcm]\n", "plt.spy(A1, marker='.', markersize=3)\n", "#p, L, U = scipy.linalg.lu(A1.todense())\n", "#plt.spy(L, marker='.', markersize=0.8)\n", "#nx.draw(G, pos=nx.spring_layout(G), node_size=10)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Florida sparse matrix collection\n", "[Florida sparse matrix collection](http://www.cise.ufl.edu/research/sparse/matrices/) which contains all sorts of matrices for different applications. It also allows for finding test matrices as well! Let's have a look." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "<iframe src=http://yifanhu.net/GALLERY/GRAPHS/search.html width=700 height=450></iframe>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "HTML('<iframe src=http://yifanhu.net/GALLERY/GRAPHS/search.html width=700 height=450></iframe>')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Test some\n", "Let us check some sparse matrix (and its LU)." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2015-04-13 17:45:26-- http://www.cise.ufl.edu/research/sparse/mat/Boeing/crystm02.mat\n", "Resolving www.cise.ufl.edu... 128.227.248.40\n", "Connecting to www.cise.ufl.edu\|128.227.248.40\|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 797624 (779K) [text/plain]\n", "Saving to: 'crystm02.mat.3'\n", "\n", "crystm02.mat.3 100%[=====================>] 778.93K 464KB/s in 1.7s \n", "\n", "2015-04-13 17:45:29 (464 KB/s) - 'crystm02.mat.3' saved [797624/797624]\n", "\n" ] } ], "source": [ "fname = 'crystm02.mat'\n", "!wget http://www.cise.ufl.edu/research/sparse/mat/Boeing/$fname" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "from scipy.io import loadmat\n", "import scipy.sparse\n", "q = loadmat(fname)\n", "#print q\n", "mat = q['Problem']['A'][0, 0]\n", "T = scipy.sparse.linalg.splu(mat)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x10efc0c90>" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAD7CAYAAAB0WxGFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAGOFJREFUeJzt3X+wHWV9x/H3R0IiGkyMMuFHAmTwosRprYQh4OiQVqAR\n", "LaFTC+lUBiV1OpO2oJ2qCc6U/NFxQMdB+CO0VYGAEkkBEQZEAjXVGYHgiDQQQhI1lVyaGxoExKk1\n", "kW//2OeazeHce8/Zs7vn1+c1c4fd5+zusyfDfs7zPPtLEYGZWbte1+0dMLP+5PAws0IcHmZWiMPD\n", "zApxeJhZIQ4PMyuk58ND0lJJ2yTtkPSZkrY5X9J3JT0l6UlJl6XyOZI2Stou6QFJs3PrrE77sE3S\n", "ubnyRZK2pM+ubXM/DpP0uKR76q5f0mxJt0t6WtJWSYtrrn91+vffIulWSTOqrF/SDZLGJG3JlZVW\n", "X9r/21L5I5JOaKH+L6R//yck3SlpVlX1VyIievYPOAzYCZwIHA78GDilhO0eDfxBmp4JPAOcAnwe\n", "+HQq/wxwVZpemOo+PO3LTkDps83A6Wn6PmBpG/vx98DXgbvTfG31A+uAS9P0NGBWXfWnbfwUmJHm\n", "bwMuqbJ+4H3Au4EtubLS6gNWAmvT9EXAN1qo/xzgdWn6qirrr+T4rLqCjnYOzgTuz82vAlZVUM9d\n", 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"slide_type": "slide" } }, "source": [ "##Iterative solvers\n", "\n", "The main disadvantage of factorization methods is there computational complexity.\n", "\n", "A more efficient solution of linear systems can be obtained by iterative methods.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This requires a high convergence rate* of the iterative process and low arithmetic cost of each iteration." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Modern iterative methods are mainly based on the idea of iteration on Krylov subspace.\n", "\n", "$$ \\mathcal{K}_i = span\\{b,~Ab,~A^2b,~ ..,~ A^{i-1}b\\}, ~~ i = 1,2,..$$\n", "$$ x_i = argmin\\{ \\\|b-Ax\\\|_{\\text{some norm}}:x\\in \\mathcal{K}_i\\} $$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "In fact, to apply iterative solver to a system with matrix $~A$ all you need to know is\n", "\n", "* how to multiply matrix by vector\n", "\n", "* how to apply preconditioner" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "###Preconditioners\n", "\n", "If A is ill conditioned then iterative methods give you a lot of iterations." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "You can reduce number of iterations if you find matrix $~B$ (called preconditioner), such that $~AB$ or $~BA$ matrices has less conditional number.\n", "\n", "$$Ax=y \\Rightarrow BAx= By$$\n", "\n", "$$ABz= y, x= Bz.$$\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "To be a good preconditioner matrix $~B$ must be somehow close to inverse matrix of $~A$\n", "\n", "$$B \\approx A^{-1}.$$\n", "\n", "Note that $B = A^{-1}$ is a perfect preconditioner and gives you 1 iteration to converge.\n", "\n", "But building this preconditioner requires as much operations as the direct solution requires." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Building a preconditioner requires some compromise between time for building it and iterations time." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "##Two basic strategies for building preconditioner:\n", "\n", "\n", "* Use information about elements of matrix $A$\n", " \n", "\n", "* Use additional information about problem." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "###The first strategy, where we use information about elements of matrix $A$\n", "\n", "For sparse matrices we use only non-zero elements.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Good example is a method of Incomplete matrix factorization\n", "\n", "The main idea here is to avoid full factorization by dropping some elements in the factorization.\n", "Drop rules specify type of incomplete factorization and type of preconditioner.\n", "\n", "Standard ILU preconditioners:\n", "\n", "* [ILU($0$)](http://en.wikipedia.org/wiki/Incomplete_LU_factorization)\n", "* [ILU(k)](http://en.wikipedia.org/wiki/Incomplete_LU_factorization)\n", "* [ILUt](http://www-users.cs.umn.edu/~saad/PDF/umsi-92-38.pdf)\n", "* [ILU2](https://scholar.google.ru/citations?view_op=view_citation&hl=ru&user=2HaTUbkAAAAJ&citation_for_view=2HaTUbkAAAAJ:u5HHmVD_uO8C)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "###The second strategy, where we use additional information about a problem\n", "\n", "Here we use additional information about where the matrix came from.\n", "\n", "For example, Multigrid and Domain Decomposition methods (see next lecture for multigrid)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "<link href='http://fonts.googleapis.com/css?family=Fenix' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' type='text/css'>\n", "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " /width:80%;/\n", " /margin-left:auto !important;\n", " margin-right:auto;/\n", " }\n", " h1 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " h2 {\n", " font-family: 'Fenix', serif;\n", " }\n", " h3{\n", "\t\tfont-family: 'Fenix', 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block;\n", " }\n", " \n", " li {\n", " line-height: 110%;\n", " }\n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\$\",\"\\\$\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"./styles/custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "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 }	cc0-1.0
nholtz/structural-analysis	Devel/Old/v04-old/Milestones/Frame2D-v04-Milestone2.ipynb	1	24098	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Milestone 2 - this version has all the input completed, individually and each tested." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2-Dimensional Frame Analysis - Version 04\n", "This program performs an elastic analysis of 2-dimensional structural frames. It has the following features:\n", "1. Input is provided by a set of CSV files (and cell-magics exist so you can specifiy the CSV data\n", "in a notebook cell). See the example below for an, er, example.\n", "1. Handles concentrated forces on nodes, and concentrated forces, concentrated moments, and linearly varying distributed loads applied transversely anywhere along the member (i.e., there is as yet no way to handle longitudinal\n", "load components).\n", "1. It handles fixed, pinned, roller supports and member end moment releases (internal pins). The former are\n", "handled by assigning free or fixed global degrees of freedom, and the latter are handled by adjusting the \n", "member stiffness matrix.\n", "1. It has the ability to handle named sets of loads with factored combinations of these.\n", "1. The DOF #'s are assigned by the program, with the fixed DOF #'s assigned after the non-fixed. The equilibrium\n", "equation is then partitioned for solution. Among other advantages, this means that support settlement could be\n", "easily added (there is no UI for that, yet).\n", "1. A non-linear analysis can be performed using the P-Delta method (fake shears are computed at column ends due to the vertical load acting through horizontal displacement differences, and these shears are applied as extra loads\n", "to the nodes).\n", "1. A full non-linear (2nd order) elastic analysis will soon be available by forming the equilibrium equations \n", "on the deformed structure. This is very easy to add, but it hasn't been done yet. Shouldn't be too long.\n", "1. There is very little no documentation below, but that will improve, slowly." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import salib as sl\n", "sl.import_notebooks()\n", "from Tables import Table\n", "from Nodes import Node\n", "from Members import Member\n", "from LoadSets import LoadSet, LoadCombination\n", "from NodeLoads import makeNodeLoad\n", "from MemberLoads import makeMemberLoad\n", "from collections import OrderedDict, defaultdict\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Object(object):\n", " pass\n", "\n", "class Frame2D(object):\n", " \n", " def __init__(self,dsname=None):\n", " self.dsname = dsname\n", " self.rawdata = Object()\n", " self.nodes = OrderedDict()\n", " self.members = OrderedDict()\n", " self.nodeloads = LoadSet()\n", " self.memberloads = LoadSet()\n", " self.loadcombinations = LoadCombination()\n", " #self.dofdesc = []\n", " #self.nodeloads = defaultdict(list)\n", " #self.membloads = defaultdict(list)\n", " self.ndof = 0\n", " self.nfree = 0\n", " self.ncons = 0\n", " self.R = None\n", " self.D = None\n", " self.PDF = None # P-Delta forces\n", " \n", " COLUMNS_xxx = [] # list of column names for table 'xxx'\n", " \n", " def get_table(self,tablename,extrasok=False,optional=False):\n", " columns = getattr(self,'COLUMNS_'+tablename)\n", " t = Table(tablename,columns=columns,optional=optional)\n", " t.read(optional=optional)\n", " reqdl= columns\n", " reqd = set(reqdl)\n", " prov = set(t.columns)\n", " if reqd-prov:\n", " raise Exception('Columns missing {} for table \"{}\". Required columns are: {}'\\\n", " .format(list(reqd-prov),tablename,reqdl))\n", " if not extrasok:\n", " if prov-reqd:\n", " raise Exception('Extra columns {} for table \"{}\". Required columns are: {}'\\\n", " .format(list(prov-reqd),tablename,reqdl))\n", " return t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Frame\n", "![test frame](img/frame-6b.svg)\n", "## Nodes" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%Table nodes\n", "NODEID,X,Y,Z\n", "A,0,0,5000\n", "B,0,4000,5000\n", "C,8000,4000,5000\n", "D,8000,0,5000" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@sl.extend(Frame2D)\n", "class Frame2D:\n", " \n", " COLUMNS_nodes = ('NODEID','X','Y')\n", " \n", " def install_nodes(self):\n", " node_table = self.get_table('nodes')\n", " for ix,r in node_table.data.iterrows():\n", " if r.NODEID in self.nodes:\n", " raise Exception('Multiply defined node: {}'.format(r.NODEID))\n", " n = Node(r.NODEID,r.X,r.Y)\n", " self.nodes[n.id] = n\n", " self.rawdata.nodes = node_table\n", " \n", " def get_node(self,id):\n", " try:\n", " return self.nodes[id]\n", " except KeyError:\n", " raise Exception('Node not defined: {}'.format(id))\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "f = Frame2D()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "f.install_nodes()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('A', Node(\"A\",0,0)),\n", " ('B', Node(\"B\",0,4000)),\n", " ('C', Node(\"C\",8000,4000)),\n", " ('D', Node(\"D\",8000,0))])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##test:\n", "f.nodes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Node(\"C\",8000,4000)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##test:\n", "f.get_node('C')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Supports" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%Table supports\n", "NODEID,C0,C1,C2\n", "A,FX,FY,MZ\n", "D,FX,FY" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def isnan(x):\n", " if x is None:\n", " return True\n", " try:\n", " return np.isnan(x)\n", " except TypeError:\n", " return False" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@sl.extend(Frame2D)\n", "class Frame2D:\n", " \n", " COLUMNS_supports = ('NODEID','C0','C1','C2')\n", " \n", " def install_supports(self):\n", " table = self.get_table('supports')\n", " for ix,row in table.data.iterrows():\n", " node = self.get_node(row.NODEID)\n", " for c in [row.C0,row.C1,row.C2]:\n", " if not isnan(c):\n", " node.add_constraint(c)\n", " self.rawdata.supports = table" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "f.install_supports()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'constraints': {'FX', 'FY'},\n", " 'dofnums': [None, None, None],\n", " 'id': 'D',\n", " 'x': 8000L,\n", " 'y': 0L}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vars(f.get_node('D'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Members" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%Table members\n", "MEMBERID,NODEJ,NODEK\n", "AB,A,B\n", "BC,B,C\n", "DC,D,C" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@sl.extend(Frame2D)\n", "class Frame2D:\n", " \n", " COLUMNS_members = ('MEMBERID','NODEJ','NODEK')\n", " \n", " def install_members(self):\n", " table = self.get_table('members')\n", " for ix,m in table.data.iterrows():\n", " if m.MEMBERID in self.members:\n", " raise Exception('Multiply defined member: {}'.format(m.MEMBERID))\n", " memb = Member(m.MEMBERID,self.get_node(m.NODEJ),self.get_node(m.NODEK))\n", " self.members[memb.id] = memb\n", " self.rawdata.members = table\n", " \n", " def get_member(self,id):\n", " try:\n", " return self.members[id]\n", " except KeyError:\n", " raise Exception('Member not defined: {}'.format(id))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('AB', Member(\"AB\",\"Node(\"A\",0,0)\",\"Node(\"B\",0,4000)\")),\n", " ('BC', Member(\"BC\",\"Node(\"B\",0,4000)\",\"Node(\"C\",8000,4000)\")),\n", " ('DC', Member(\"DC\",\"Node(\"D\",8000,0)\",\"Node(\"C\",8000,4000)\"))])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##test:\n", "f.install_members()\n", "f.members" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('BC', 8000.0, 1.0, 0.0)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##test:\n", "m = f.get_member('BC')\n", "m.id, m.L, m.dcx, m.dcy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Releases" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%Table releases\n", "MEMBERID,RELEASE\n", "AB,MZK" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@sl.extend(Frame2D)\n", "class Frame2D:\n", " \n", " COLUMNS_releases = ('MEMBERID','RELEASE')\n", " \n", " def install_releases(self):\n", " table = self.get_table('releases',optional=True)\n", " for ix,r in table.data.iterrows():\n", " memb = self.get_member(r.MEMBERID)\n", " memb.add_release(r.RELEASE)\n", " self.rawdata.releases = table" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "f.install_releases()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'A': None,\n", " 'Ix': None,\n", " 'KG': None,\n", " 'KL': None,\n", " 'L': 4000.0,\n", " 'Tm': None,\n", " 'dcx': 0.0,\n", " 'dcy': 1.0,\n", " 'fefsl': None,\n", " 'id': 'AB',\n", " 'mefs': None,\n", " 'nodej': Node(\"A\",0,0),\n", " 'nodek': Node(\"B\",0,4000),\n", " 'releases': {'MZK'}}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##test:\n", "vars(f.get_member('AB'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Properties" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the SST module is loadable, member properties may be specified by giving steel shape designations\n", "(such as 'W310x97') in the member properties data. If the module is not available, you may still give $A$ and\n", "$I_x$ directly (it only tries to lookup the properties if these two are not provided)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "try:\n", " from sst import SST\n", " __SST = SST()\n", " get_section = __SST.section\n", "except ImportError:\n", " def get_section(dsg,fields):\n", " raise ValueError('Cannot lookup property SIZE because SST is not available. SIZE = {}'.format(dsg))\n", " ##return [1.] * len(fields.split(',')) # in case you want to do it that way" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%Table properties\n", "MEMBERID,SIZE,IX,A\n", "BC,W460x106,,\n", "AB,W310x97,,\n", "DC,," ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@sl.extend(Frame2D)\n", "class Frame2D:\n", " \n", " COLUMNS_properties = ('MEMBERID','SIZE','IX','A')\n", " \n", " def install_properties(self):\n", " table = self.get_table('properties')\n", " table = self.fill_properties(table)\n", " for ix,row in table.data.iterrows():\n", " memb = self.get_member(row.MEMBERID)\n", " memb.size = row.SIZE\n", " memb.Ix = row.IX\n", " memb.A = row.A\n", " self.rawdata.properties = table\n", " \n", " def fill_properties(self,table):\n", " data = table.data\n", " for ix,row in data.iterrows():\n", " if type(row.SIZE) in [type(''),type(u'')]:\n", " if isnan(row.IX) or isnan(row.A):\n", " Ix,A = get_section(row.SIZE,'Ix,A')\n", " if isnan(row.IX):\n", " data.loc[ix,'IX'] = Ix\n", " if isnan(row.A):\n", " data.loc[ix,'A'] = A\n", " elif isnan(row.SIZE):\n", " data.loc[ix,'SIZE'] = ''\n", " table.data = data.fillna(method='ffill')\n", " return table" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "f.install_properties()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'A': 12300.0,\n", " 'Ix': 222000000.0,\n", " 'KG': None,\n", " 'KL': None,\n", " 'L': 4000.0,\n", " 'Tm': None,\n", " 'dcx': 0.0,\n", " 'dcy': 1.0,\n", " 'fefsl': None,\n", " 'id': 'DC',\n", " 'mefs': None,\n", " 'nodej': Node(\"D\",8000,0),\n", " 'nodek': Node(\"C\",8000,4000),\n", " 'releases': set(),\n", " 'size': ''}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##test:\n", "vars(f.get_member('DC'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Node Loads" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%Table node_loads\n", "LOAD,NODEID,DIRN,F\n", "Wind,B,FX,-200000." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@sl.extend(Frame2D)\n", "class Frame2D:\n", " \n", " COLUMNS_node_loads = ('LOAD','NODEID','DIRN','F')\n", " \n", " def install_node_loads(self):\n", " table = self.get_table('node_loads')\n", " dirns = ['FX','FY','FZ']\n", " for ix,row in table.data.iterrows():\n", " n = self.get_node(row.NODEID)\n", " if row.DIRN not in dirns:\n", " raise ValueError(\"Invalid node load direction: {} for load {}, node {}; must be one of '{}'\"\n", " .format(row.DIRN, row.LOAD, row.NODEID, ', '.join(dirns)))\n", " l = makeNodeLoad({row.DIRN:row.F})\n", " self.nodeloads.append(row.LOAD,n,l)\n", " self.rawdata.node_loads = table" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "f.install_node_loads()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "for o,l,fact in f.nodeloads.iterloads('Wind'):\n", " print(o,l,fact,lfact)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Member Loads" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%Table member_loads\n", "LOAD,MEMBERID,TYPE,W1,W2,A,B,C\n", "Live,BC,UDL,-50,,,,\n", "Live,BC,PL,-200000,,5000" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@sl.extend(Frame2D)\n", "class Frame2D:\n", " \n", " COLUMNS_member_loads = ('LOAD','MEMBERID','TYPE','W1','W2','A','B','C')\n", " \n", " def install_member_loads(self):\n", " table = self.get_table('member_loads')\n", " for ix,row in table.data.iterrows():\n", " m = self.get_member(row.MEMBERID)\n", " l = makeMemberLoad(m.L,row)\n", " self.memberloads.append(row.LOAD,m,l)\n", " self.rawdata.member_loads = table" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "##test:\n", "f.install_member_loads()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "for o,l,fact in f.memberloads.iterloads('Live'):\n", " print(o.id,l,fact,l.fefs()fact)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Combinations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%Table load_combinations\n", "COMBO,LOAD,FACTOR\n", "One,Live,1.5\n", "One,Wind,1.75" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@sl.extend(Frame2D)\n", "class Frame2D:\n", " \n", " COLUMNS_load_combinations = ('COMBO','LOAD','FACTOR')\n", " \n", " def install_load_combinations(self):\n", " table = self.get_table('load_combinations')\n", " for ix,row in table.data.iterrows():\n", " self.loadcombinations.append(row.COMBO,row.LOAD,row.FACTOR)\n", " self.rawdata.load_combinations = table" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "f.install_load_combinations()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "for o,l,fact in f.loadcombinations.iterloads('One',f.nodeloads):\n", " print(o.id,l,fact)\n", "for o,l,fact in f.loadcombinations.iterloads('One',f.memberloads):\n", " print(o.id,l,fact,l.fefs()*fact)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load Iterators" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@sl.extend(Frame2D)\n", "class Frame2D:\n", "\n", " def iter_nodeloads(self,comboname):\n", " for o,l,f in self.loadcombinations.iterloads(comboname,self.nodeloads):\n", " yield o,l,f\n", " \n", " def iter_memberloads(self,comboname):\n", " for o,l,f in self.loadcombinations.iterloads(comboname,self.memberloads):\n", " yield o,l,f" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "for o,l,fact in f.iter_nodeloads('One'):\n", " print(o.id,l,fact)\n", "for o,l,fact in f.iter_memberloads('One'):\n", " print(o.id,l,fact)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Support Constraints" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%Table supports\n", "NODEID,C0,C1,C2\n", "A,FX,FY,MZ\n", "D,FX,FY" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accumulated Cell Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "##test:\n", "Table.CELLDATA" ] } ], "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": "2.7.6" }, "widgets": { "state": {}, "version": "1.1.1" } }, "nbformat": 4, "nbformat_minor": 0 }	cc0-1.0
6KidsGames/Prog	Test2.ipynb	1	1174	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This is a test notebook for the IJavascript kernel." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "// This is a bit of JavaScript code.\n", "// Press Shift+Enter while your focus is here to run this code and see the result.\n", "var i = 0;\n", "while (i < 22) {\n", " console.log(`Current: ${i}`);\n", " i++;\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Running code locally\n", "This is a test of markdown.\n", "\n", "- asdasd\n", "- asdas\n", "\n", "1. one\n", "1. two\n", "3. three\n", "\n", ":+1:\n", "\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Javascript (Node.js)", "language": "javascript", "name": "javascript" }, "language_info": { "file_extension": ".js", "mimetype": "application/javascript", "name": "javascript", "version": "7.4.0" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
Chris7/pyquant	notebooks/ML.ipynb	1	267109	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This explores different ways to analyze the quality of PSM quantifications" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from itertools import chain\n", "\n", "bad_data = [\n", " ('ELcSAAITMSDNTAANLLLTTIGGPk', 8846),\n", " ('FVESVDVAVNLGIDAR',7466 ),\n", " ('ELcSAAITMSDNTAANLLLTTIGGPK', 9209),\n", " ('FVESVDVAVNLGIDAR', 9213),\n", " ('FVESVDVAVNLGIDAR', 9426),\n", " ('AVTLYLGAVAATVR', 6660),\n", " ('AVTLYLGAVAATVR', 8958),\n", " ('IVVIYTTGSQATMDER', 4505),\n", " ('VGYIELDLNSGk', 5624),\n", " ('LLTGELLTLASR', 6942),\n", " ('FVESVDVAVNLGIDAr', 9184),\n", " ('ELcSAAITMSDNTAANLLLTTIGGPk', 9458),\n", " ('VGYIELDLNSGk', 5238),\n", " ('IVVIYTTGSQATMDERNR', 4024),\n", " ('AVTLYLGAVAATVR', 9652),\n", " ('ELcSAAITMSDNTAANLLLTTIGGPk', 8883),\n", " ('IVVIYTTGSQATMDERNR', 4005),\n", " ('FVESVDVAVNLGIDAR', 9950),\n", " ('AQHSALDDIPR', 2510),\n", " ('FVESVDVAVNLGIDAR', 9980),\n", " ('VGYIELDLNSGk', 9546),\n", " ('IVVIYTTGSQATMDER', 9933),\n", " ('HFESTPDTPEIIATIHGEGYR', 4488),\n", " ('YYLGNADEIAAK', 3703),\n", " ('FVESVDVAVNLGIDAR', 6879),\n", " ('RDDSILLAQHTR', 1849),\n", " ('EQGYALDSEENEQGVR', 2536),\n", " ('VLLcGAVLSR', 4541),\n", " ('LGYPITDDLDIYTr', 5790),\n", " ('VGYIELDLNSGk', 8965),\n", " ('FVESVDVAVNLGIDAR', 7796),\n", "]\n", "\n", "good_data = [\n", " ('VHIINLEK', 2373),\n", " ('HITDRDVR', 863),\n", " ('GATVLPHGTGr', 1244),\n", " ('GATVLPHGTGR', 1238),\n", " ('EQGLHFYAAGHHATER', 1570),\n", " ('VPLHTLr', 1371),\n", " ('IHVAVAQEVPGTGVDTPEDLER', 4157),\n", " ('cIFDNISLTVPR', 6174),\n", " ('HLTDGmTVR', 974),\n", " ('AGVHFGHQTR', 1002),\n", " ('AHHYPSELSGGQQQR', 1142),\n", " ('HYGALQGLNk', 1738),\n", " ('HITGLHYNPITNTFk', 3590),\n", " ('IGLLEHANR', 2008),\n", " ('ALEINSQSLDNNAAFIR', 5217),\n", " ('RIYGVLER', 2188),\n", " ('FQDVGSFDYGR', 3734),\n", " ('AVQNAMR', 995),\n", " ('IGVGGTITYPR', 3358),\n", " ('GmGESNPVTGNTcDNVk', 1558),\n", " ('MVEEDPAHPr', 1177),\n", " ('AIENQAYVAGcNr', 1914),\n", " ('FIAQQLGVSR', 3332),\n", " ('MPEDLLTr', 3424),\n", " ('mVEEDPAHPr', 1016),\n", " ('GFSVNFER', 3790),\n", " ('TPVGNTAAIcIYPR', 4031),\n", " ('IDAILVDR', 3375),\n", " ('LVAVGNTFVYPIAGYSk', 5966),\n", "]\n", "\n", "peptides = ' '.join(i[0] for i in chain(bad_data, good_data))\n", "scans = ' '.join(str(i[1]) for i in chain(bad_data, good_data))\n", "out = 'ml_train'" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:pyQuant:Reader done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "msparser not found, Mascot DAT files unable to be parsed\n", "Loading Scans:\n", ".\n", "Scans loaded.\n", "Beginning quantification.\n", "Processing /home/chris/gdrive/Dropbox/Manuscripts/SILAC Fix/EColi/Chris_Ecoli_1-2-4.mzML.\n", "........................................................................................................../home/chris/Devel/pyquant/pyquant/worker.py:49: FutureWarning: sort is deprecated, use sort_values(inplace=True) for INPLACE sorting\n", " self.msn_rt_map.sort()\n", "Chris_Ecoli_1-2-4 processed and placed into queue.\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "\r", "16.67% Completed/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/lib/nanfunctions.py:675: RuntimeWarning: Mean of empty slice\n", " warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "\r", "33.33% Completed\r", "50.00% Completed\r", "66.67% Completed\r", "83.33% Completed\r", "100.00% CompletedUnable to calculate statistics for Heavy/Light.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n", "Unable to calculate statistics for Heavy/Medium.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n", "Unable to calculate statistics for Light/Heavy.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n", "Unable to calculate statistics for Light/Medium.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n", "Unable to calculate statistics for Medium/Heavy.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n", "Unable to calculate statistics for Medium/Light.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n" ] } ], "source": [ "# %%bash -s \"$peptides\" \"$scans\" \"$out\"\n", "# pyQuant --search-file \"/home/chris/gdrive/Dropbox/Manuscripts/SILAC Fix/EColi/PD/Chris_Ecoli_1-2-4-(01).msf\" \\\n", "# --scan-file \"/home/chris/gdrive/Dropbox/Manuscripts/SILAC Fix/EColi/Chris_Ecoli_1-2-4.mzML\" \\\n", "# --peptide $1 --scan $2 \\\n", "# -o $3 \\\n", "# -p 9" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ERROR ON IGSDAYNQGLSER: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/worker.py\", line 702, in quantify_peaks\n", " peak_index = peaks.find_nearest_index(merged_x, valid_peaks[0]['mean'])\n", "IndexError: list index out of range\n", "\n", "INFO:pyQuant:Reader done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "msparser not found, Mascot DAT files unable to be parsed\n", "In file included from /home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1777:0,\n", " from /home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:18,\n", " from /home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n", " from /home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:266:\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: warning: #warning \"Using deprecated NumPy API, disable it by \" \"#defining NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n", " #warning \"Using deprecated NumPy API, disable it by \" \\\n", " ^\n", "In file included from /home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:27:0,\n", " from /home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,\n", " from /home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:266:\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/include/numpy/__multiarray_api.h:1448:1: warning: ‘_import_array’ defined but not used [-Wunused-function]\n", " _import_array(void)\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c: In function ‘__pyx_pf_7pyquant_6cpeaks_12gauss_jac_old.isra.52’:\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:6003:37: warning: ‘__pyx_v_mu’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n", " __pyx_t_1 = PyFloat_FromDouble(__pyx_v_mu); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 125, __pyx_L1_error)\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:5702:9: note: ‘__pyx_v_mu’ was declared here\n", " float __pyx_v_mu;\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:6056:37: warning: ‘__pyx_v_amp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n", " __pyx_t_4 = PyFloat_FromDouble(__pyx_v_amp); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 126, __pyx_L1_error)\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:5701:9: note: ‘__pyx_v_amp’ was declared here\n", " float __pyx_v_amp;\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c: In function ‘__pyx_pf_7pyquant_6cpeaks_14gauss_jac.isra.50’:\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:6789:39: warning: ‘__pyx_v_mu’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n", " __pyx_t_1 = PyFloat_FromDouble((-__pyx_v_mu)); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 144, __pyx_L1_error)\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:6440:9: note: ‘__pyx_v_mu’ was declared here\n", " float __pyx_v_mu;\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:6897:38: warning: ‘__pyx_v_amp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n", " __pyx_t_15 = PyFloat_FromDouble(__pyx_v_amp); if (unlikely(!__pyx_t_15)) __PYX_ERR(0, 146, __pyx_L1_error)\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:6439:9: note: ‘__pyx_v_amp’ was declared here\n", " float __pyx_v_amp;\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c: In function ‘__pyx_pf_7pyquant_6cpeaks_10bigauss_jac.isra.54’:\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:5062:37: warning: ‘__pyx_v_amp’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n", " __pyx_t_4 = PyFloat_FromDouble(__pyx_v_amp); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 104, __pyx_L1_error)\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:4510:9: note: ‘__pyx_v_amp’ was declared here\n", " float __pyx_v_amp;\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:4931:49: warning: ‘__pyx_v_sigma1’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n", " __pyx_t_3 = PyFloat_FromDouble((2.0 * powf(__pyx_v_sigma1, 2.0))); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 101, __pyx_L1_error)\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:4512:9: note: ‘__pyx_v_sigma1’ was declared here\n", " float __pyx_v_sigma1;\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:4920:39: warning: ‘__pyx_v_mu’ may be used uninitialized in this function [-Wmaybe-uninitialized]\n", " __pyx_t_3 = PyFloat_FromDouble((-__pyx_v_mu)); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 101, __pyx_L1_error)\n", " ^\n", "/home/chris/.pyxbld/temp.linux-x86_64-2.7/pyrex/pyquant/cpeaks.c:4511:9: note: ‘__pyx_v_mu’ was declared here\n", " float __pyx_v_mu;\n", " ^\n", "Loading Scans:\n", "..\n", "Scans loaded.\n", "Beginning quantification.\n", "Processing /home/chris/gdrive/Dropbox/Manuscripts/SILAC Fix/EColi/Chris_Ecoli_1-2-4.mzML.\n", "........................................................................................................../home/chris/Devel/pyquant/pyquant/worker.py:49: FutureWarning: sort is deprecated, use sort_values(inplace=True) for INPLACE sorting\n", " self.msn_rt_map.sort()\n", "Chris_Ecoli_1-2-4 processed and placed into queue.\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n", " warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/lib/nanfunctions.py:675: RuntimeWarning: Mean of empty slice\n", " warnings.warn(\"Mean of empty slice\", RuntimeWarning)\n", "/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 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Completed\r", "83.01% Completed\r", "83.79% Completed\r", "84.56% Completed\r", "85.34% Completed\r", "86.11% Completed\r", "86.89% Completed\r", "87.66% Completed\r", "88.44% Completed\r", "89.22% Completed\r", "89.99% Completed\r", "90.77% Completed\r", "91.54% Completed\r", "92.32% Completed\r", "93.10% Completed\r", "93.87% Completed\r", "94.65% Completed\r", "95.42% Completed\r", "96.20% Completed\r", "96.97% Completed\r", "97.75% Completed\r", "98.53% Completed\r", "99.30% CompletedUnable to calculate statistics for Heavy/Light.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n", "Unable to calculate statistics for Heavy/Medium.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n", "Unable to calculate statistics for Light/Heavy.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n", "Unable to calculate statistics for Light/Medium.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n", "Unable to calculate statistics for Medium/Heavy.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n", "Unable to calculate statistics for Medium/Light.\n", " Traceback: Traceback (most recent call last):\n", " File \"/home/chris/Devel/pyquant/pyquant/command_line.py\", line 1148, in run_pyquant\n", " conf_ass = classifier.predict_proba(fit_predictors)[:,1]10\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 537, in predict_proba\n", " X = self._validate_X_predict(X)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py\", line 319, in _validate_X_predict\n", " return self.estimators_[0]._validate_X_predict(X, check_input=True)\n", " File \"/home/chris/.virtualenvs/pyquant/local/lib/python2.7/site-packages/sklearn/tree/tree.py\", line 376, in _validate_X_predict\n", " % (self.n_features_, n_features))\n", "ValueError: Number of features of the model must match the input. Model n_features is 19 and input n_features is 11 \n" ] } ], "source": [ "# %%bash -s \"$peptides\" \"$scans\" \"$out\"\n", "# pyQuant --search-file \"/home/chris/gdrive/Dropbox/Manuscripts/SILAC Fix/EColi/PD/Chris_Ecoli_1-2-4-(01).msf\" \\\n", "# --scan-file \"/home/chris/gdrive/Dropbox/Manuscripts/SILAC Fix/EColi/Chris_Ecoli_1-2-4.mzML\" \\\n", "# -o $3 \\\n", "# -p 9" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>Label1 Isotopes Found</th>\n", " <th>Label1 Intensity</th>\n", " <th>Label1 RT Width</th>\n", " <th>Label1 Mean Offset</th>\n", " <th>Label1 Residual</th>\n", " <th>Label1 R^2</th>\n", " <th>Label1 SNR</th>\n", " <th>Label2 Isotopes Found</th>\n", " <th>Label2 Intensity</th>\n", " <th>Label2 RT Width</th>\n", " <th>Label2 Mean Offset</th>\n", " <th>Label2 Residual</th>\n", " <th>Label2 R^2</th>\n", " <th>Label2 SNR</th>\n", " <th>Deviation</th>\n", " <th>Class</th>\n", " </tr>\n", " <tr>\n", " <th>Peptide</th>\n", " <th>MS2 Spectrum ID</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>GcImGSAHQr</th>\n", " <th>779</th>\n", " <td>24.0</td>\n", " <td>14.985734</td>\n", " <td>0.053936</td>\n", " <td>9.931775e-01</td>\n", " <td>0.085489</td>\n", " <td>3.528190</td>\n", " <td>21.114721</td>\n", " <td>32.0</td>\n", " <td>16.703665</td>\n", " <td>0.056111</td>\n", " <td>1.434795e+00</td>\n", " <td>0.416770</td>\n", " <td>2.361091</td>\n", " <td>9.658139</td>\n", " <td>0.094592</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">GcImGSAHQR</th>\n", " <th>783</th>\n", " <td>17.0</td>\n", " <td>12.924555</td>\n", " <td>0.042203</td>\n", " <td>2.686645e+00</td>\n", " <td>0.492796</td>\n", " <td>3.038507</td>\n", " <td>8.359946</td>\n", " <td>26.0</td>\n", " <td>14.711507</td>\n", " <td>0.055908</td>\n", " <td>4.517510e-01</td>\n", " <td>1.063376</td>\n", " <td>2.148553</td>\n", " <td>6.236617</td>\n", " <td>0.000523</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>777</th>\n", " <td>28.0</td>\n", " <td>14.996194</td>\n", " <td>0.043803</td>\n", " <td>2.569664e+00</td>\n", " <td>0.084761</td>\n", " <td>3.823541</td>\n", " <td>20.474859</td>\n", " <td>34.0</td>\n", " <td>16.708224</td>\n", " <td>0.056213</td>\n", " <td>1.406006e+00</td>\n", " <td>0.412675</td>\n", " <td>2.329501</td>\n", " <td>9.082110</td>\n", " <td>0.094830</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>TQDATHGNSLSHR</th>\n", " <th>811</th>\n", " <td>39.0</td>\n", " <td>13.991486</td>\n", " <td>0.048815</td>\n", " <td>2.128489e+00</td>\n", " <td>0.972111</td>\n", " <td>1.881489</td>\n", " <td>6.062496</td>\n", " <td>50.0</td>\n", " <td>15.962065</td>\n", " <td>0.061264</td>\n", " <td>4.103946e-01</td>\n", " <td>0.238537</td>\n", " <td>3.268813</td>\n", " <td>2.282480</td>\n", " <td>-0.101995</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>IEQAPGQHGAR</th>\n", " <th>887</th>\n", " <td>7.0</td>\n", " <td>16.167331</td>\n", " <td>0.050244</td>\n", " <td>7.230988e-02</td>\n", " <td>0.012362</td>\n", " <td>13.026475</td>\n", " <td>1.012433</td>\n", " <td>11.0</td>\n", " <td>17.872230</td>\n", " <td>0.045521</td>\n", " <td>6.600722e-02</td>\n", " <td>0.034781</td>\n", " <td>11.525348</td>\n", " <td>1.143215</td>\n", " <td>0.290681</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>AGVTGAENr</th>\n", " <th>904</th>\n", " <td>8.0</td>\n", " <td>17.969158</td>\n", " <td>0.067676</td>\n", " <td>2.493468e-01</td>\n", " <td>0.025712</td>\n", " <td>5.443686</td>\n", " <td>1.053801</td>\n", " <td>10.0</td>\n", " <td>19.819117</td>\n", " <td>0.055905</td>\n", " <td>1.362417e-01</td>\n", " <td>0.015580</td>\n", " <td>8.858486</td>\n", " <td>1.034260</td>\n", " <td>-0.065885</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>AGVTGAENR</th>\n", " <th>903</th>\n", " <td>8.0</td>\n", " <td>17.969158</td>\n", " <td>0.067676</td>\n", " <td>2.493468e-01</td>\n", " <td>0.025712</td>\n", " <td>5.443686</td>\n", " <td>1.053801</td>\n", " <td>10.0</td>\n", " <td>19.819117</td>\n", " <td>0.055905</td>\n", " <td>1.362417e-01</td>\n", " <td>0.015580</td>\n", " <td>8.858486</td>\n", " <td>1.034260</td>\n", " <td>-0.065885</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>GTAmNPVDHPHGGGEGR</th>\n", " <th>917</th>\n", " <td>8.0</td>\n", " <td>16.550535</td>\n", " <td>0.050721</td>\n", " <td>1.833391e-01</td>\n", " <td>0.019163</td>\n", " <td>7.692895</td>\n", " <td>1.021964</td>\n", " <td>12.0</td>\n", " <td>18.294151</td>\n", " <td>0.051766</td>\n", " <td>9.084471e-02</td>\n", " <td>0.014866</td>\n", " <td>9.459570</td>\n", " <td>1.234008</td>\n", " <td>0.085492</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>ALVSHPR</th>\n", " <th>933</th>\n", " <td>4.0</td>\n", " <td>15.729831</td>\n", " <td>0.090572</td>\n", " <td>1.991482e-01</td>\n", " <td>0.059474</td>\n", " <td>3.887081</td>\n", " <td>NaN</td>\n", " <td>8.0</td>\n", " <td>17.857216</td>\n", " <td>0.070174</td>\n", " <td>9.030660e-01</td>\n", " <td>0.067431</td>\n", " <td>4.512238</td>\n", " <td>1.013494</td>\n", " <td>0.111110</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>mTGDNPDAPR</th>\n", " <th>944</th>\n", " <td>2.0</td>\n", " <td>13.843877</td>\n", " <td>0.054663</td>\n", " <td>1.666761e-01</td>\n", " <td>0.034627</td>\n", " <td>8.161228</td>\n", " <td>NaN</td>\n", " <td>7.0</td>\n", " <td>15.876904</td>\n", " <td>0.063909</td>\n", " <td>6.023946e-01</td>\n", " <td>0.034698</td>\n", " <td>4.959971</td>\n", " <td>1.001678</td>\n", " <td>0.007644</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>VHPNGIR</th>\n", " <th>898</th>\n", " <td>6.0</td>\n", " <td>15.131996</td>\n", " <td>0.056340</td>\n", " <td>4.975481e-01</td>\n", " <td>0.247207</td>\n", " <td>3.758966</td>\n", " <td>NaN</td>\n", " <td>11.0</td>\n", " <td>17.245997</td>\n", " <td>0.058525</td>\n", " <td>1.444357e-01</td>\n", " <td>0.052027</td>\n", " <td>5.027183</td>\n", " <td>1.406236</td>\n", " <td>-0.135307</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>SVANAEQmDR</th>\n", " <th>959</th>\n", " <td>9.0</td>\n", " <td>16.980617</td>\n", " <td>0.069780</td>\n", " <td>3.405412e-01</td>\n", " <td>0.119328</td>\n", " <td>4.002708</td>\n", " <td>1.026194</td>\n", " <td>12.0</td>\n", " <td>18.786625</td>\n", " <td>0.075726</td>\n", " <td>4.197732e-01</td>\n", " <td>0.104451</td>\n", " <td>3.851186</td>\n", " <td>0.931681</td>\n", " <td>0.071929</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>SVANAEQmDr</th>\n", " <th>962</th>\n", " <td>9.0</td>\n", " <td>16.980617</td>\n", " <td>0.069780</td>\n", " <td>3.405412e-01</td>\n", " <td>0.119328</td>\n", " <td>4.002708</td>\n", " <td>1.026194</td>\n", " <td>12.0</td>\n", " <td>18.786625</td>\n", " <td>0.075726</td>\n", " <td>4.197732e-01</td>\n", " <td>0.104451</td>\n", " <td>3.851186</td>\n", " <td>0.931681</td>\n", " <td>0.071929</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>AAASHLVR</th>\n", " <th>961</th>\n", " <td>14.0</td>\n", " <td>17.324065</td>\n", " <td>0.058804</td>\n", " <td>1.981803e+00</td>\n", " <td>0.189402</td>\n", " <td>8.559042</td>\n", " <td>15.804382</td>\n", " <td>27.0</td>\n", " <td>19.420032</td>\n", " <td>0.075272</td>\n", " <td>1.019746e+00</td>\n", " <td>1.396346</td>\n", " <td>2.584104</td>\n", " <td>19.286076</td>\n", " <td>0.091354</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>AAASHLVr</th>\n", " <th>964</th>\n", " <td>15.0</td>\n", " <td>17.324065</td>\n", " <td>0.058804</td>\n", " <td>1.981799e+00</td>\n", " <td>0.203778</td>\n", " <td>8.558997</td>\n", " <td>10.880751</td>\n", " <td>28.0</td>\n", " <td>19.424549</td>\n", " <td>0.075731</td>\n", " <td>9.812200e-01</td>\n", " <td>1.535988</td>\n", " <td>2.584007</td>\n", " <td>15.079136</td>\n", " <td>0.083129</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>HLTDGmTVr</th>\n", " <th>975</th>\n", " <td>28.0</td>\n", " <td>23.746521</td>\n", " <td>0.093142</td>\n", " <td>4.456008e-01</td>\n", " <td>0.021245</td>\n", " <td>5.482953</td>\n", " <td>15.104723</td>\n", " <td>35.0</td>\n", " <td>25.416597</td>\n", " <td>0.084778</td>\n", " <td>1.511559e+00</td>\n", " <td>0.072245</td>\n", " <td>5.677385</td>\n", " <td>16.507001</td>\n", " <td>0.080686</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>HLTDGmTVR</th>\n", " <th>974</th>\n", " <td>28.0</td>\n", " <td>23.746521</td>\n", " <td>0.093142</td>\n", " <td>4.456008e-01</td>\n", " <td>0.021245</td>\n", " <td>5.482953</td>\n", " <td>15.104723</td>\n", " <td>35.0</td>\n", " <td>25.416597</td>\n", " <td>0.084778</td>\n", " <td>1.511559e+00</td>\n", " <td>0.072245</td>\n", " <td>5.677385</td>\n", " <td>16.507001</td>\n", " <td>0.080686</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>AVQNAMR</th>\n", " <th>995</th>\n", " <td>9.0</td>\n", " <td>17.682340</td>\n", " <td>0.061553</td>\n", " <td>1.262045e+00</td>\n", " <td>0.062885</td>\n", " <td>4.914437</td>\n", " <td>1.126810</td>\n", " <td>18.0</td>\n", " <td>19.446729</td>\n", " <td>0.067331</td>\n", " <td>9.486762e-01</td>\n", " <td>0.099772</td>\n", " <td>5.123132</td>\n", " <td>3.522732</td>\n", " <td>0.142120</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>AGVHFGHQTR</th>\n", " <th>1002</th>\n", " <td>12.0</td>\n", " <td>21.008434</td>\n", " <td>0.083990</td>\n", " <td>5.339253e-01</td>\n", " <td>0.055843</td>\n", " <td>4.154514</td>\n", " <td>1.224810</td>\n", " <td>20.0</td>\n", " <td>22.994444</td>\n", " <td>0.075378</td>\n", " <td>2.275157e-01</td>\n", " <td>0.024981</td>\n", " <td>5.482815</td>\n", " <td>1.207646</td>\n", " <td>-0.189688</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>mVEEDPAHPr</th>\n", " <th>1016</th>\n", " <td>13.0</td>\n", " <td>18.121198</td>\n", " <td>0.092191</td>\n", " <td>7.536466e-01</td>\n", " <td>0.071224</td>\n", " <td>4.389010</td>\n", " <td>6.852894</td>\n", " <td>20.0</td>\n", " <td>20.084497</td>\n", " <td>0.093785</td>\n", " <td>3.310712e-01</td>\n", " <td>0.046965</td>\n", " <td>4.964579</td>\n", " <td>10.238337</td>\n", " <td>-0.049676</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>mVEEDPAHPR</th>\n", " <th>1020</th>\n", " <td>12.0</td>\n", " <td>18.117252</td>\n", " <td>0.092013</td>\n", " <td>7.461029e-01</td>\n", " <td>0.072749</td>\n", " <td>4.389664</td>\n", " <td>1.142899</td>\n", " <td>20.0</td>\n", " <td>20.084497</td>\n", " <td>0.093785</td>\n", " <td>3.310712e-01</td>\n", " <td>0.046965</td>\n", " <td>4.964579</td>\n", " <td>10.238337</td>\n", " <td>-0.227445</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>AGVHFGHQTR</th>\n", " <th>992</th>\n", " <td>23.0</td>\n", " <td>23.282827</td>\n", " <td>0.076255</td>\n", " <td>1.544298e-01</td>\n", " <td>0.013500</td>\n", " <td>7.192932</td>\n", " <td>15.622094</td>\n", " <td>30.0</td>\n", " <td>25.061076</td>\n", " <td>0.080505</td>\n", " <td>3.480352e-01</td>\n", " <td>0.063421</td>\n", " <td>4.765415</td>\n", " <td>14.052629</td>\n", " <td>0.021684</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>AGVHFGHQTr</th>\n", " <th>994</th>\n", " <td>23.0</td>\n", " <td>23.282827</td>\n", " <td>0.076255</td>\n", " <td>1.544298e-01</td>\n", " <td>0.013500</td>\n", " <td>7.192932</td>\n", " <td>15.622094</td>\n", " <td>31.0</td>\n", " <td>25.061076</td>\n", " <td>0.080505</td>\n", " <td>3.483843e-01</td>\n", " <td>0.064882</td>\n", " <td>4.765081</td>\n", " <td>14.052629</td>\n", " <td>0.021684</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>mVEEDPAHPr</th>\n", " <th>1025</th>\n", " <td>6.0</td>\n", " <td>15.698337</td>\n", " <td>0.069287</td>\n", " <td>4.783882e-01</td>\n", " <td>0.046070</td>\n", " <td>5.193861</td>\n", " <td>2.638442</td>\n", " <td>11.0</td>\n", " <td>18.316519</td>\n", " <td>0.079541</td>\n", " <td>1.710395e-01</td>\n", " <td>0.137727</td>\n", " <td>5.748694</td>\n", " <td>1.114434</td>\n", " <td>-0.720857</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>AGVHFGHQTr</th>\n", " <th>1004</th>\n", " <td>12.0</td>\n", " <td>21.008434</td>\n", " <td>0.083990</td>\n", " <td>5.339253e-01</td>\n", " <td>0.055843</td>\n", " <td>4.154514</td>\n", " <td>1.224810</td>\n", " <td>20.0</td>\n", " <td>22.994444</td>\n", " <td>0.075378</td>\n", " <td>2.275157e-01</td>\n", " <td>0.024981</td>\n", " <td>5.482815</td>\n", " <td>1.207646</td>\n", " <td>-0.189688</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>GTAMNPVDHPHGGGEGR</th>\n", " <th>1087</th>\n", " <td>6.0</td>\n", " <td>16.798970</td>\n", " <td>0.052111</td>\n", " <td>2.076855e-01</td>\n", " <td>0.042727</td>\n", " <td>4.882561</td>\n", " <td>NaN</td>\n", " <td>12.0</td>\n", " <td>18.816326</td>\n", " <td>0.051097</td>\n", " <td>5.237172e-01</td>\n", " <td>0.099033</td>\n", " <td>5.016891</td>\n", " <td>1.092491</td>\n", " <td>-0.049417</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>TDLHGTAVR</th>\n", " <th>1078</th>\n", " <td>12.0</td>\n", " <td>18.357713</td>\n", " <td>0.060047</td>\n", " <td>6.581272e-01</td>\n", " <td>0.053875</td>\n", " <td>4.749313</td>\n", " <td>1.380350</td>\n", " <td>8.0</td>\n", " <td>19.553958</td>\n", " <td>0.051518</td>\n", " <td>1.114874e-01</td>\n", " <td>0.032910</td>\n", " <td>7.299707</td>\n", " <td>1.038474</td>\n", " <td>1.064196</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">AHHYPSELSGGQQQR</th>\n", " <th>1137</th>\n", " <td>19.0</td>\n", " <td>20.740120</td>\n", " <td>0.069004</td>\n", " <td>7.869672e-01</td>\n", " <td>0.034991</td>\n", " <td>5.765859</td>\n", " <td>1.344427</td>\n", " <td>28.0</td>\n", " <td>22.611332</td>\n", " <td>0.080211</td>\n", " <td>3.786946e-01</td>\n", " <td>0.056959</td>\n", " <td>4.790247</td>\n", " <td>1.299816</td>\n", " <td>0.020353</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1142</th>\n", " <td>13.0</td>\n", " <td>18.501803</td>\n", " <td>0.071385</td>\n", " <td>6.950947e-01</td>\n", " <td>0.036237</td>\n", " <td>5.550402</td>\n", " <td>1.138348</td>\n", " <td>22.0</td>\n", " <td>20.373860</td>\n", " <td>0.069400</td>\n", " <td>3.310561e-01</td>\n", " <td>0.041349</td>\n", " <td>5.465852</td>\n", " <td>1.339840</td>\n", " <td>0.052759</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1145</th>\n", " <td>5.0</td>\n", " <td>18.857965</td>\n", " <td>0.087171</td>\n", " <td>7.221403e-01</td>\n", " <td>0.016279</td>\n", " <td>4.229913</td>\n", " <td>1.098967</td>\n", " <td>23.0</td>\n", " <td>21.817347</td>\n", " <td>0.073249</td>\n", " <td>3.987897e-01</td>\n", " <td>0.079853</td>\n", " <td>4.701050</td>\n", " <td>1.190595</td>\n", " <td>0.222085</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>LAQmQIPADDYFIWITGEGk</th>\n", " <th>7850</th>\n", " <td>9.0</td>\n", " <td>18.757221</td>\n", " <td>0.077344</td>\n", " <td>1.544988e+00</td>\n", " <td>0.442809</td>\n", " <td>2.354658</td>\n", " <td>1.138290</td>\n", " <td>5.0</td>\n", " <td>17.276999</td>\n", " <td>0.116156</td>\n", " <td>8.156097e-01</td>\n", " <td>1.656970</td>\n", " <td>NaN</td>\n", " <td>1.651758</td>\n", " <td>-0.327431</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>EGAFVPFVTLGDPGIEQSLK</th>\n", " <th>7849</th>\n", " <td>43.0</td>\n", " <td>23.650085</td>\n", " <td>0.193217</td>\n", " <td>1.206463e+00</td>\n", " <td>0.729791</td>\n", " <td>1.709437</td>\n", " <td>6.358218</td>\n", " <td>41.0</td>\n", " <td>23.037434</td>\n", " <td>0.207459</td>\n", " <td>2.615308e-01</td>\n", " <td>0.228140</td>\n", " <td>3.322470</td>\n", " <td>6.108227</td>\n", " <td>-0.002491</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>ELcSAAITmSDNTAANLLLTTIGGPk</th>\n", " <th>7866</th>\n", " <td>23.0</td>\n", " <td>20.358045</td>\n", " <td>0.143019</td>\n", " <td>9.635800e-01</td>\n", " <td>1.476684</td>\n", " <td>1.071712</td>\n", " <td>10.838309</td>\n", " <td>17.0</td>\n", " <td>18.233260</td>\n", " <td>0.076696</td>\n", " <td>4.096748e+00</td>\n", " <td>1.956180</td>\n", " <td>1.403417</td>\n", " <td>221.445566</td>\n", " <td>1.077706</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>TQGAAAFEGAVIAYEPVWAIGTGk</th>\n", " <th>7884</th>\n", " <td>49.0</td>\n", " <td>23.298585</td>\n", " <td>0.219064</td>\n", " <td>1.977094e-01</td>\n", " <td>0.204182</td>\n", " <td>3.837238</td>\n", " <td>4.173747</td>\n", " <td>39.0</td>\n", " <td>22.537990</td>\n", " <td>0.180326</td>\n", " <td>1.371329e+00</td>\n", " <td>0.624656</td>\n", " <td>2.240398</td>\n", " <td>6.524064</td>\n", " <td>0.063856</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">ELcSAAITmSDNTAANLLLTTIGGPk</th>\n", " <th>7874</th>\n", " <td>49.0</td>\n", " <td>24.919943</td>\n", " <td>0.206087</td>\n", " <td>2.303474e-01</td>\n", " <td>0.853150</td>\n", " <td>1.620717</td>\n", " <td>4.859298</td>\n", " <td>47.0</td>\n", " <td>24.124365</td>\n", " <td>0.212079</td>\n", " <td>1.652292e-01</td>\n", " <td>0.197110</td>\n", " <td>3.682382</td>\n", " <td>4.434152</td>\n", " <td>0.058754</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7886</th>\n", " <td>21.0</td>\n", " <td>19.999417</td>\n", " <td>0.114682</td>\n", " <td>3.075215e-01</td>\n", " <td>1.977056</td>\n", " <td>1.436670</td>\n", " <td>7.416190</td>\n", " <td>34.0</td>\n", " <td>19.573735</td>\n", " <td>0.056227</td>\n", " <td>1.608774e+00</td>\n", " <td>6.064123</td>\n", " <td>2.089884</td>\n", " <td>6.699860</td>\n", " <td>0.739104</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>VLALAENYQPLYAALGLHPGMLEk</th>\n", " <th>7915</th>\n", " <td>18.0</td>\n", " <td>19.815205</td>\n", " <td>0.095440</td>\n", " <td>5.583934e-01</td>\n", " <td>0.903778</td>\n", " <td>1.903067</td>\n", " <td>120.276338</td>\n", " <td>12.0</td>\n", " <td>19.124448</td>\n", " <td>0.071011</td>\n", " <td>1.931950e+00</td>\n", " <td>0.545626</td>\n", " <td>2.040685</td>\n", " <td>15.321044</td>\n", " <td>0.767542</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>FGASSLLASLLk</th>\n", " <th>8084</th>\n", " <td>34.0</td>\n", " <td>23.119274</td>\n", " <td>0.148651</td>\n", " <td>2.196329e+00</td>\n", " <td>0.141995</td>\n", " <td>3.944355</td>\n", " <td>5.234995</td>\n", " <td>35.0</td>\n", " <td>22.498940</td>\n", " <td>0.188970</td>\n", " <td>4.770330e-01</td>\n", " <td>0.213794</td>\n", " <td>3.080469</td>\n", " <td>4.816335</td>\n", " <td>-0.080325</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>TqGAAAFEGAVIAYEPVWAIGTGk</th>\n", " <th>8094</th>\n", " <td>23.0</td>\n", " <td>16.250988</td>\n", " <td>0.041308</td>\n", " <td>4.430558e-01</td>\n", " <td>0.092574</td>\n", " <td>11.158354</td>\n", " <td>-0.743560</td>\n", " <td>27.0</td>\n", " <td>18.138290</td>\n", " <td>0.040326</td>\n", " <td>2.672755e+01</td>\n", " <td>1.164123</td>\n", " <td>12.381305</td>\n", " <td>0.775548</td>\n", " <td>-1.095661</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>DGVGLLPTVLDVVENPk</th>\n", " <th>8559</th>\n", " <td>38.0</td>\n", " <td>21.899157</td>\n", " <td>0.169400</td>\n", " <td>1.452420e-01</td>\n", " <td>0.516881</td>\n", " <td>3.197701</td>\n", " <td>7.688767</td>\n", " <td>27.0</td>\n", " <td>21.228191</td>\n", " <td>0.184760</td>\n", " <td>3.650728e-01</td>\n", " <td>0.534657</td>\n", " <td>2.116104</td>\n", " <td>6.450245</td>\n", " <td>-0.088636</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">ELcSAAITMSDNTAANLLLTTIGGPk</th>\n", " <th>8695</th>\n", " <td>29.0</td>\n", " <td>22.895231</td>\n", " <td>0.273095</td>\n", " <td>3.034958e-01</td>\n", " <td>0.280452</td>\n", " <td>2.329319</td>\n", " <td>4.004378</td>\n", " <td>52.0</td>\n", " <td>23.293799</td>\n", " <td>0.232344</td>\n", " <td>4.787828e-01</td>\n", " <td>1.796918</td>\n", " <td>0.960049</td>\n", " <td>4.702896</td>\n", " <td>-0.195882</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>8686</th>\n", " <td>32.0</td>\n", " <td>23.059216</td>\n", " <td>0.201174</td>\n", " <td>5.944131e+00</td>\n", " <td>2.126078</td>\n", " <td>0.805061</td>\n", " <td>4.347489</td>\n", " <td>39.0</td>\n", " <td>22.595297</td>\n", " <td>0.129570</td>\n", " <td>2.685297e+00</td>\n", " <td>3.346062</td>\n", " <td>1.681354</td>\n", " <td>2.784739</td>\n", " <td>0.025548</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>VGYIELDLNSGk</th>\n", " <th>8965</th>\n", " <td>36.0</td>\n", " <td>18.805564</td>\n", " <td>0.332078</td>\n", " <td>2.099859e+00</td>\n", " <td>7.771240</td>\n", " <td>NaN</td>\n", " <td>6.158339</td>\n", " <td>29.0</td>\n", " <td>17.510583</td>\n", " <td>0.145803</td>\n", " <td>2.394400e+00</td>\n", " <td>4.397509</td>\n", " <td>-0.054590</td>\n", " <td>2.641730</td>\n", " <td>0.394377</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>SLDDAQIALAVINTTYASQIGLTPAk</th>\n", " <th>9089</th>\n", " <td>26.0</td>\n", " <td>20.788147</td>\n", " <td>0.102492</td>\n", " <td>2.694714e-01</td>\n", " <td>0.586520</td>\n", " <td>3.116046</td>\n", " <td>15.176564</td>\n", " <td>31.0</td>\n", " <td>20.371373</td>\n", " <td>0.114255</td>\n", " <td>6.046525e-01</td>\n", " <td>1.046882</td>\n", " <td>2.549166</td>\n", " <td>6.614207</td>\n", " <td>-0.124315</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">ELcSAAITMSDNTAANLLLTTIGGPk</th>\n", " <th>8846</th>\n", " <td>216.0</td>\n", " <td>23.835211</td>\n", " <td>0.850754</td>\n", " <td>3.325143e-01</td>\n", " <td>1.893201</td>\n", " <td>NaN</td>\n", " <td>-0.022767</td>\n", " <td>216.0</td>\n", " <td>23.428056</td>\n", " <td>0.756642</td>\n", " <td>2.127841e+00</td>\n", " <td>2.621094</td>\n", " <td>-3.001260</td>\n", " <td>0.851808</td>\n", " <td>-0.127975</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>8883</th>\n", " <td>264.0</td>\n", " <td>24.355262</td>\n", " <td>0.235376</td>\n", " <td>1.270539e-01</td>\n", " <td>2.377913</td>\n", " <td>1.738366</td>\n", " <td>4.411485</td>\n", " <td>268.0</td>\n", " <td>24.149613</td>\n", " <td>0.279444</td>\n", " <td>2.810016e-01</td>\n", " <td>1.998608</td>\n", " <td>1.920195</td>\n", " <td>3.695578</td>\n", " <td>-0.300475</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>9458</th>\n", " <td>71.0</td>\n", " <td>19.814885</td>\n", " <td>0.067602</td>\n", " <td>1.056360e+00</td>\n", " <td>4.460319</td>\n", " <td>1.553299</td>\n", " <td>8.495942</td>\n", " <td>55.0</td>\n", " <td>19.899775</td>\n", " <td>0.081068</td>\n", " <td>7.621572e-01</td>\n", " <td>4.665191</td>\n", " <td>2.026509</td>\n", " <td>16.984387</td>\n", " <td>-1.687464</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>LANEGIFTQQELYDELLTLADEAk</th>\n", " <th>9522</th>\n", " <td>4.0</td>\n", " <td>18.594230</td>\n", " <td>0.047298</td>\n", " <td>9.488388e-02</td>\n", " <td>0.128509</td>\n", " <td>3.940116</td>\n", " <td>NaN</td>\n", " <td>5.0</td>\n", " <td>18.353222</td>\n", " <td>0.046340</td>\n", " <td>7.130651e-01</td>\n", " <td>0.196605</td>\n", " <td>3.942563</td>\n", " <td>NaN</td>\n", " <td>0.062293</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">AIHTLWNVLDELDQAWLPVEk</th>\n", " <th>9560</th>\n", " <td>11.0</td>\n", " <td>22.513893</td>\n", " <td>0.058378</td>\n", " <td>2.969589e-01</td>\n", " <td>0.072579</td>\n", " <td>4.455892</td>\n", " <td>1.022964</td>\n", " <td>11.0</td>\n", " <td>21.834118</td>\n", " <td>0.062201</td>\n", " <td>4.901800e-01</td>\n", " <td>0.119166</td>\n", " <td>3.774949</td>\n", " <td>1.024549</td>\n", " <td>-0.038950</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>9586</th>\n", " <td>6.0</td>\n", " <td>21.728629</td>\n", " <td>0.058892</td>\n", " <td>3.079504e-01</td>\n", " <td>0.046923</td>\n", " <td>4.161881</td>\n", " <td>0.963363</td>\n", " <td>8.0</td>\n", " <td>21.320748</td>\n", " <td>0.054593</td>\n", " <td>9.095046e-01</td>\n", " <td>0.092096</td>\n", " <td>4.274263</td>\n", " <td>1.208662</td>\n", " <td>-0.070930</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>ELcSAAITMSDNTAANLLLTTIGGPK</th>\n", " <th>9148</th>\n", " <td>104.0</td>\n", " <td>18.329059</td>\n", " <td>0.041806</td>\n", " <td>6.509279e+00</td>\n", " <td>2.474097</td>\n", " <td>3.799134</td>\n", " <td>2.287613</td>\n", " <td>182.0</td>\n", " <td>21.163595</td>\n", " <td>0.263017</td>\n", " <td>9.294961e-01</td>\n", " <td>3.052862</td>\n", " <td>0.857173</td>\n", " <td>1.730295</td>\n", " <td>-3.928888</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>TAPDGEHGVNLVHLEDVIGAITLLLQAPk</th>\n", " <th>9656</th>\n", " <td>4.0</td>\n", " <td>17.701533</td>\n", " <td>0.040175</td>\n", " <td>6.339672e-04</td>\n", " <td>0.016543</td>\n", " <td>inf</td>\n", " <td>NaN</td>\n", " <td>6.0</td>\n", " <td>17.895217</td>\n", " <td>0.040175</td>\n", " <td>6.339672e-04</td>\n", " <td>0.024815</td>\n", " <td>inf</td>\n", " <td>NaN</td>\n", " <td>-0.150357</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk</th>\n", " <th>9688</th>\n", " <td>3.0</td>\n", " <td>15.521233</td>\n", " <td>0.052455</td>\n", " <td>1.120198e-01</td>\n", " <td>0.068335</td>\n", " <td>9.743359</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>13.981914</td>\n", " <td>0.052455</td>\n", " <td>1.120198e-01</td>\n", " <td>0.022778</td>\n", " <td>9.743359</td>\n", " <td>NaN</td>\n", " <td>-0.548328</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK</th>\n", " <th>9696</th>\n", " <td>0.0</td>\n", " <td>-inf</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>4.0</td>\n", " <td>16.293770</td>\n", " <td>0.067028</td>\n", " <td>1.165689e-01</td>\n", " <td>0.042940</td>\n", " <td>4.309249</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>ELcSAAITMSDNTAANLLLTTIGGPK</th>\n", " <th>9209</th>\n", " <td>216.0</td>\n", " <td>20.944411</td>\n", " <td>0.236307</td>\n", " <td>9.643571e+00</td>\n", " <td>3.447382</td>\n", " <td>0.541612</td>\n", " <td>2.741174</td>\n", " <td>142.0</td>\n", " <td>20.709059</td>\n", " <td>0.299294</td>\n", " <td>1.026466e+01</td>\n", " <td>5.649428</td>\n", " <td>-2.158764</td>\n", " <td>8.410005</td>\n", " <td>-0.675744</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">VLAPINDFINTLNAFFSAGGk</th>\n", " <th>9765</th>\n", " <td>6.0</td>\n", " <td>19.314618</td>\n", " <td>0.053537</td>\n", " <td>1.655816e-01</td>\n", " <td>0.013354</td>\n", " <td>6.934276</td>\n", " <td>NaN</td>\n", " <td>6.0</td>\n", " <td>18.662179</td>\n", " <td>0.053537</td>\n", " <td>2.025805e-01</td>\n", " <td>0.013438</td>\n", " <td>6.934400</td>\n", " <td>NaN</td>\n", " <td>-0.090769</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>9767</th>\n", " <td>5.0</td>\n", " <td>18.794496</td>\n", " <td>0.058808</td>\n", " <td>2.308393e-01</td>\n", " <td>0.013254</td>\n", " <td>6.645728</td>\n", " <td>1.003997</td>\n", " <td>7.0</td>\n", " <td>18.154701</td>\n", " <td>0.054971</td>\n", " <td>1.785362e-01</td>\n", " <td>0.006698</td>\n", " <td>7.625821</td>\n", " <td>NaN</td>\n", " <td>0.235751</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>VGYIELDLNSGk</th>\n", " <th>9546</th>\n", " <td>93.0</td>\n", " <td>19.127481</td>\n", " <td>0.184934</td>\n", " <td>1.256568e+01</td>\n", " <td>12.800832</td>\n", " <td>-1.205556</td>\n", " <td>3.856789</td>\n", " <td>83.0</td>\n", " <td>17.154424</td>\n", " <td>0.098624</td>\n", " <td>3.966709e+00</td>\n", " <td>9.656604</td>\n", " <td>-3.448946</td>\n", " <td>8.693567</td>\n", " <td>1.260810</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>FVQAYQSDEVYEAANk</th>\n", " <th>10015</th>\n", " <td>1.0</td>\n", " <td>12.242579</td>\n", " <td>0.030985</td>\n", " <td>1.733472e-07</td>\n", " <td>0.036631</td>\n", " <td>3.687593</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>12.242579</td>\n", " <td>0.030985</td>\n", " <td>1.733472e-07</td>\n", " <td>0.036631</td>\n", " <td>3.687593</td>\n", " <td>NaN</td>\n", " <td>-0.721485</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>ELcSAAITMSDNTAAnLLLTTIGGPk</th>\n", " <th>9818</th>\n", " <td>65.0</td>\n", " <td>17.013110</td>\n", " <td>0.113016</td>\n", " <td>5.642632e+00</td>\n", " <td>0.907013</td>\n", " <td>0.174813</td>\n", " <td>0.286011</td>\n", " <td>39.0</td>\n", " <td>16.018433</td>\n", " <td>0.050737</td>\n", " <td>1.074356e+01</td>\n", " <td>0.859082</td>\n", " <td>10.182012</td>\n", " <td>0.436856</td>\n", " <td>1.423368</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2138 rows × 16 columns</p>\n", "</div>" ], "text/plain": [ " Label1 Isotopes Found \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 24.0 \n", "GcImGSAHQR 783 17.0 \n", " 777 28.0 \n", "TQDATHGNSLSHR 811 39.0 \n", "IEQAPGQHGAR 887 7.0 \n", "AGVTGAENr 904 8.0 \n", "AGVTGAENR 903 8.0 \n", "GTAmNPVDHPHGGGEGR 917 8.0 \n", "ALVSHPR 933 4.0 \n", "mTGDNPDAPR 944 2.0 \n", "VHPNGIR 898 6.0 \n", "SVANAEQmDR 959 9.0 \n", "SVANAEQmDr 962 9.0 \n", "AAASHLVR 961 14.0 \n", "AAASHLVr 964 15.0 \n", "HLTDGmTVr 975 28.0 \n", "HLTDGmTVR 974 28.0 \n", "AVQNAMR 995 9.0 \n", "AGVHFGHQTR 1002 12.0 \n", "mVEEDPAHPr 1016 13.0 \n", "mVEEDPAHPR 1020 12.0 \n", "AGVHFGHQTR 992 23.0 \n", "AGVHFGHQTr 994 23.0 \n", "mVEEDPAHPr 1025 6.0 \n", "AGVHFGHQTr 1004 12.0 \n", "GTAMNPVDHPHGGGEGR 1087 6.0 \n", "TDLHGTAVR 1078 12.0 \n", "AHHYPSELSGGQQQR 1137 19.0 \n", " 1142 13.0 \n", " 1145 5.0 \n", "... ... \n", "LAQmQIPADDYFIWITGEGk 7850 9.0 \n", "EGAFVPFVTLGDPGIEQSLK 7849 43.0 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 23.0 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 49.0 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 49.0 \n", " 7886 21.0 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 18.0 \n", "FGASSLLASLLk 8084 34.0 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 23.0 \n", "DGVGLLPTVLDVVENPk 8559 38.0 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 29.0 \n", " 8686 32.0 \n", "VGYIELDLNSGk 8965 36.0 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 26.0 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 216.0 \n", " 8883 264.0 \n", " 9458 71.0 \n", "LANEGIFTQQELYDELLTLADEAk 9522 4.0 \n", "AIHTLWNVLDELDQAWLPVEk 9560 11.0 \n", " 9586 6.0 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 104.0 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 4.0 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 3.0 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 0.0 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 216.0 \n", "VLAPINDFINTLNAFFSAGGk 9765 6.0 \n", " 9767 5.0 \n", "VGYIELDLNSGk 9546 93.0 \n", "FVQAYQSDEVYEAANk 10015 1.0 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 65.0 \n", "\n", " Label1 Intensity \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 14.985734 \n", "GcImGSAHQR 783 12.924555 \n", " 777 14.996194 \n", "TQDATHGNSLSHR 811 13.991486 \n", "IEQAPGQHGAR 887 16.167331 \n", "AGVTGAENr 904 17.969158 \n", "AGVTGAENR 903 17.969158 \n", "GTAmNPVDHPHGGGEGR 917 16.550535 \n", "ALVSHPR 933 15.729831 \n", "mTGDNPDAPR 944 13.843877 \n", "VHPNGIR 898 15.131996 \n", "SVANAEQmDR 959 16.980617 \n", "SVANAEQmDr 962 16.980617 \n", "AAASHLVR 961 17.324065 \n", "AAASHLVr 964 17.324065 \n", "HLTDGmTVr 975 23.746521 \n", "HLTDGmTVR 974 23.746521 \n", "AVQNAMR 995 17.682340 \n", "AGVHFGHQTR 1002 21.008434 \n", "mVEEDPAHPr 1016 18.121198 \n", "mVEEDPAHPR 1020 18.117252 \n", "AGVHFGHQTR 992 23.282827 \n", "AGVHFGHQTr 994 23.282827 \n", "mVEEDPAHPr 1025 15.698337 \n", "AGVHFGHQTr 1004 21.008434 \n", "GTAMNPVDHPHGGGEGR 1087 16.798970 \n", "TDLHGTAVR 1078 18.357713 \n", "AHHYPSELSGGQQQR 1137 20.740120 \n", " 1142 18.501803 \n", " 1145 18.857965 \n", "... ... \n", "LAQmQIPADDYFIWITGEGk 7850 18.757221 \n", "EGAFVPFVTLGDPGIEQSLK 7849 23.650085 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 20.358045 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 23.298585 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 24.919943 \n", " 7886 19.999417 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 19.815205 \n", "FGASSLLASLLk 8084 23.119274 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 16.250988 \n", "DGVGLLPTVLDVVENPk 8559 21.899157 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 22.895231 \n", " 8686 23.059216 \n", "VGYIELDLNSGk 8965 18.805564 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 20.788147 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 23.835211 \n", " 8883 24.355262 \n", " 9458 19.814885 \n", "LANEGIFTQQELYDELLTLADEAk 9522 18.594230 \n", "AIHTLWNVLDELDQAWLPVEk 9560 22.513893 \n", " 9586 21.728629 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 18.329059 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 17.701533 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 15.521233 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 -inf \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 20.944411 \n", "VLAPINDFINTLNAFFSAGGk 9765 19.314618 \n", " 9767 18.794496 \n", "VGYIELDLNSGk 9546 19.127481 \n", "FVQAYQSDEVYEAANk 10015 12.242579 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 17.013110 \n", "\n", " Label1 RT Width \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 0.053936 \n", "GcImGSAHQR 783 0.042203 \n", " 777 0.043803 \n", "TQDATHGNSLSHR 811 0.048815 \n", "IEQAPGQHGAR 887 0.050244 \n", "AGVTGAENr 904 0.067676 \n", "AGVTGAENR 903 0.067676 \n", "GTAmNPVDHPHGGGEGR 917 0.050721 \n", "ALVSHPR 933 0.090572 \n", "mTGDNPDAPR 944 0.054663 \n", "VHPNGIR 898 0.056340 \n", "SVANAEQmDR 959 0.069780 \n", "SVANAEQmDr 962 0.069780 \n", "AAASHLVR 961 0.058804 \n", "AAASHLVr 964 0.058804 \n", "HLTDGmTVr 975 0.093142 \n", "HLTDGmTVR 974 0.093142 \n", "AVQNAMR 995 0.061553 \n", "AGVHFGHQTR 1002 0.083990 \n", "mVEEDPAHPr 1016 0.092191 \n", "mVEEDPAHPR 1020 0.092013 \n", "AGVHFGHQTR 992 0.076255 \n", "AGVHFGHQTr 994 0.076255 \n", "mVEEDPAHPr 1025 0.069287 \n", "AGVHFGHQTr 1004 0.083990 \n", "GTAMNPVDHPHGGGEGR 1087 0.052111 \n", "TDLHGTAVR 1078 0.060047 \n", "AHHYPSELSGGQQQR 1137 0.069004 \n", " 1142 0.071385 \n", " 1145 0.087171 \n", "... ... \n", "LAQmQIPADDYFIWITGEGk 7850 0.077344 \n", "EGAFVPFVTLGDPGIEQSLK 7849 0.193217 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 0.143019 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 0.219064 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 0.206087 \n", " 7886 0.114682 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 0.095440 \n", "FGASSLLASLLk 8084 0.148651 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 0.041308 \n", "DGVGLLPTVLDVVENPk 8559 0.169400 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 0.273095 \n", " 8686 0.201174 \n", "VGYIELDLNSGk 8965 0.332078 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 0.102492 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 0.850754 \n", " 8883 0.235376 \n", " 9458 0.067602 \n", "LANEGIFTQQELYDELLTLADEAk 9522 0.047298 \n", "AIHTLWNVLDELDQAWLPVEk 9560 0.058378 \n", " 9586 0.058892 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 0.041806 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 0.040175 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 0.052455 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 NaN \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 0.236307 \n", "VLAPINDFINTLNAFFSAGGk 9765 0.053537 \n", " 9767 0.058808 \n", "VGYIELDLNSGk 9546 0.184934 \n", "FVQAYQSDEVYEAANk 10015 0.030985 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 0.113016 \n", "\n", " Label1 Mean Offset \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 9.931775e-01 \n", "GcImGSAHQR 783 2.686645e+00 \n", " 777 2.569664e+00 \n", "TQDATHGNSLSHR 811 2.128489e+00 \n", "IEQAPGQHGAR 887 7.230988e-02 \n", "AGVTGAENr 904 2.493468e-01 \n", "AGVTGAENR 903 2.493468e-01 \n", "GTAmNPVDHPHGGGEGR 917 1.833391e-01 \n", "ALVSHPR 933 1.991482e-01 \n", "mTGDNPDAPR 944 1.666761e-01 \n", "VHPNGIR 898 4.975481e-01 \n", "SVANAEQmDR 959 3.405412e-01 \n", "SVANAEQmDr 962 3.405412e-01 \n", "AAASHLVR 961 1.981803e+00 \n", "AAASHLVr 964 1.981799e+00 \n", "HLTDGmTVr 975 4.456008e-01 \n", "HLTDGmTVR 974 4.456008e-01 \n", "AVQNAMR 995 1.262045e+00 \n", "AGVHFGHQTR 1002 5.339253e-01 \n", "mVEEDPAHPr 1016 7.536466e-01 \n", "mVEEDPAHPR 1020 7.461029e-01 \n", "AGVHFGHQTR 992 1.544298e-01 \n", "AGVHFGHQTr 994 1.544298e-01 \n", "mVEEDPAHPr 1025 4.783882e-01 \n", "AGVHFGHQTr 1004 5.339253e-01 \n", "GTAMNPVDHPHGGGEGR 1087 2.076855e-01 \n", "TDLHGTAVR 1078 6.581272e-01 \n", "AHHYPSELSGGQQQR 1137 7.869672e-01 \n", " 1142 6.950947e-01 \n", " 1145 7.221403e-01 \n", "... ... \n", "LAQmQIPADDYFIWITGEGk 7850 1.544988e+00 \n", "EGAFVPFVTLGDPGIEQSLK 7849 1.206463e+00 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 9.635800e-01 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 1.977094e-01 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 2.303474e-01 \n", " 7886 3.075215e-01 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 5.583934e-01 \n", "FGASSLLASLLk 8084 2.196329e+00 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 4.430558e-01 \n", "DGVGLLPTVLDVVENPk 8559 1.452420e-01 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 3.034958e-01 \n", " 8686 5.944131e+00 \n", "VGYIELDLNSGk 8965 2.099859e+00 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 2.694714e-01 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 3.325143e-01 \n", " 8883 1.270539e-01 \n", " 9458 1.056360e+00 \n", "LANEGIFTQQELYDELLTLADEAk 9522 9.488388e-02 \n", "AIHTLWNVLDELDQAWLPVEk 9560 2.969589e-01 \n", " 9586 3.079504e-01 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 6.509279e+00 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 6.339672e-04 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 1.120198e-01 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 NaN \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 9.643571e+00 \n", "VLAPINDFINTLNAFFSAGGk 9765 1.655816e-01 \n", " 9767 2.308393e-01 \n", "VGYIELDLNSGk 9546 1.256568e+01 \n", "FVQAYQSDEVYEAANk 10015 1.733472e-07 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 5.642632e+00 \n", "\n", " Label1 Residual \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 0.085489 \n", "GcImGSAHQR 783 0.492796 \n", " 777 0.084761 \n", "TQDATHGNSLSHR 811 0.972111 \n", "IEQAPGQHGAR 887 0.012362 \n", "AGVTGAENr 904 0.025712 \n", "AGVTGAENR 903 0.025712 \n", "GTAmNPVDHPHGGGEGR 917 0.019163 \n", "ALVSHPR 933 0.059474 \n", "mTGDNPDAPR 944 0.034627 \n", "VHPNGIR 898 0.247207 \n", "SVANAEQmDR 959 0.119328 \n", "SVANAEQmDr 962 0.119328 \n", "AAASHLVR 961 0.189402 \n", "AAASHLVr 964 0.203778 \n", "HLTDGmTVr 975 0.021245 \n", "HLTDGmTVR 974 0.021245 \n", "AVQNAMR 995 0.062885 \n", "AGVHFGHQTR 1002 0.055843 \n", "mVEEDPAHPr 1016 0.071224 \n", "mVEEDPAHPR 1020 0.072749 \n", "AGVHFGHQTR 992 0.013500 \n", "AGVHFGHQTr 994 0.013500 \n", "mVEEDPAHPr 1025 0.046070 \n", "AGVHFGHQTr 1004 0.055843 \n", "GTAMNPVDHPHGGGEGR 1087 0.042727 \n", "TDLHGTAVR 1078 0.053875 \n", "AHHYPSELSGGQQQR 1137 0.034991 \n", " 1142 0.036237 \n", " 1145 0.016279 \n", "... ... \n", "LAQmQIPADDYFIWITGEGk 7850 0.442809 \n", "EGAFVPFVTLGDPGIEQSLK 7849 0.729791 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 1.476684 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 0.204182 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 0.853150 \n", " 7886 1.977056 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 0.903778 \n", "FGASSLLASLLk 8084 0.141995 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 0.092574 \n", "DGVGLLPTVLDVVENPk 8559 0.516881 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 0.280452 \n", " 8686 2.126078 \n", "VGYIELDLNSGk 8965 7.771240 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 0.586520 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 1.893201 \n", " 8883 2.377913 \n", " 9458 4.460319 \n", "LANEGIFTQQELYDELLTLADEAk 9522 0.128509 \n", "AIHTLWNVLDELDQAWLPVEk 9560 0.072579 \n", " 9586 0.046923 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 2.474097 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 0.016543 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 0.068335 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 NaN \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 3.447382 \n", "VLAPINDFINTLNAFFSAGGk 9765 0.013354 \n", " 9767 0.013254 \n", "VGYIELDLNSGk 9546 12.800832 \n", "FVQAYQSDEVYEAANk 10015 0.036631 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 0.907013 \n", "\n", " Label1 R^2 Label1 SNR \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 3.528190 21.114721 \n", "GcImGSAHQR 783 3.038507 8.359946 \n", " 777 3.823541 20.474859 \n", "TQDATHGNSLSHR 811 1.881489 6.062496 \n", "IEQAPGQHGAR 887 13.026475 1.012433 \n", "AGVTGAENr 904 5.443686 1.053801 \n", "AGVTGAENR 903 5.443686 1.053801 \n", "GTAmNPVDHPHGGGEGR 917 7.692895 1.021964 \n", "ALVSHPR 933 3.887081 NaN \n", "mTGDNPDAPR 944 8.161228 NaN \n", "VHPNGIR 898 3.758966 NaN \n", "SVANAEQmDR 959 4.002708 1.026194 \n", "SVANAEQmDr 962 4.002708 1.026194 \n", "AAASHLVR 961 8.559042 15.804382 \n", "AAASHLVr 964 8.558997 10.880751 \n", "HLTDGmTVr 975 5.482953 15.104723 \n", "HLTDGmTVR 974 5.482953 15.104723 \n", "AVQNAMR 995 4.914437 1.126810 \n", "AGVHFGHQTR 1002 4.154514 1.224810 \n", "mVEEDPAHPr 1016 4.389010 6.852894 \n", "mVEEDPAHPR 1020 4.389664 1.142899 \n", "AGVHFGHQTR 992 7.192932 15.622094 \n", "AGVHFGHQTr 994 7.192932 15.622094 \n", "mVEEDPAHPr 1025 5.193861 2.638442 \n", "AGVHFGHQTr 1004 4.154514 1.224810 \n", "GTAMNPVDHPHGGGEGR 1087 4.882561 NaN \n", "TDLHGTAVR 1078 4.749313 1.380350 \n", "AHHYPSELSGGQQQR 1137 5.765859 1.344427 \n", " 1142 5.550402 1.138348 \n", " 1145 4.229913 1.098967 \n", "... ... ... \n", "LAQmQIPADDYFIWITGEGk 7850 2.354658 1.138290 \n", "EGAFVPFVTLGDPGIEQSLK 7849 1.709437 6.358218 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 1.071712 10.838309 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 3.837238 4.173747 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 1.620717 4.859298 \n", " 7886 1.436670 7.416190 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 1.903067 120.276338 \n", "FGASSLLASLLk 8084 3.944355 5.234995 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 11.158354 -0.743560 \n", "DGVGLLPTVLDVVENPk 8559 3.197701 7.688767 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 2.329319 4.004378 \n", " 8686 0.805061 4.347489 \n", "VGYIELDLNSGk 8965 NaN 6.158339 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 3.116046 15.176564 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 NaN -0.022767 \n", " 8883 1.738366 4.411485 \n", " 9458 1.553299 8.495942 \n", "LANEGIFTQQELYDELLTLADEAk 9522 3.940116 NaN \n", "AIHTLWNVLDELDQAWLPVEk 9560 4.455892 1.022964 \n", " 9586 4.161881 0.963363 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 3.799134 2.287613 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 inf NaN \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 9.743359 NaN \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 NaN NaN \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 0.541612 2.741174 \n", "VLAPINDFINTLNAFFSAGGk 9765 6.934276 NaN \n", " 9767 6.645728 1.003997 \n", "VGYIELDLNSGk 9546 -1.205556 3.856789 \n", "FVQAYQSDEVYEAANk 10015 3.687593 NaN \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 0.174813 0.286011 \n", "\n", " Label2 Isotopes Found \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 32.0 \n", "GcImGSAHQR 783 26.0 \n", " 777 34.0 \n", "TQDATHGNSLSHR 811 50.0 \n", "IEQAPGQHGAR 887 11.0 \n", "AGVTGAENr 904 10.0 \n", "AGVTGAENR 903 10.0 \n", "GTAmNPVDHPHGGGEGR 917 12.0 \n", "ALVSHPR 933 8.0 \n", "mTGDNPDAPR 944 7.0 \n", "VHPNGIR 898 11.0 \n", "SVANAEQmDR 959 12.0 \n", "SVANAEQmDr 962 12.0 \n", "AAASHLVR 961 27.0 \n", "AAASHLVr 964 28.0 \n", "HLTDGmTVr 975 35.0 \n", "HLTDGmTVR 974 35.0 \n", "AVQNAMR 995 18.0 \n", "AGVHFGHQTR 1002 20.0 \n", "mVEEDPAHPr 1016 20.0 \n", "mVEEDPAHPR 1020 20.0 \n", "AGVHFGHQTR 992 30.0 \n", "AGVHFGHQTr 994 31.0 \n", "mVEEDPAHPr 1025 11.0 \n", "AGVHFGHQTr 1004 20.0 \n", "GTAMNPVDHPHGGGEGR 1087 12.0 \n", "TDLHGTAVR 1078 8.0 \n", "AHHYPSELSGGQQQR 1137 28.0 \n", " 1142 22.0 \n", " 1145 23.0 \n", "... ... \n", "LAQmQIPADDYFIWITGEGk 7850 5.0 \n", "EGAFVPFVTLGDPGIEQSLK 7849 41.0 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 17.0 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 39.0 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 47.0 \n", " 7886 34.0 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 12.0 \n", "FGASSLLASLLk 8084 35.0 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 27.0 \n", "DGVGLLPTVLDVVENPk 8559 27.0 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 52.0 \n", " 8686 39.0 \n", "VGYIELDLNSGk 8965 29.0 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 31.0 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 216.0 \n", " 8883 268.0 \n", " 9458 55.0 \n", "LANEGIFTQQELYDELLTLADEAk 9522 5.0 \n", "AIHTLWNVLDELDQAWLPVEk 9560 11.0 \n", " 9586 8.0 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 182.0 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 6.0 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 1.0 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 4.0 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 142.0 \n", "VLAPINDFINTLNAFFSAGGk 9765 6.0 \n", " 9767 7.0 \n", "VGYIELDLNSGk 9546 83.0 \n", "FVQAYQSDEVYEAANk 10015 1.0 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 39.0 \n", "\n", " Label2 Intensity \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 16.703665 \n", "GcImGSAHQR 783 14.711507 \n", " 777 16.708224 \n", "TQDATHGNSLSHR 811 15.962065 \n", "IEQAPGQHGAR 887 17.872230 \n", "AGVTGAENr 904 19.819117 \n", "AGVTGAENR 903 19.819117 \n", "GTAmNPVDHPHGGGEGR 917 18.294151 \n", "ALVSHPR 933 17.857216 \n", "mTGDNPDAPR 944 15.876904 \n", "VHPNGIR 898 17.245997 \n", "SVANAEQmDR 959 18.786625 \n", "SVANAEQmDr 962 18.786625 \n", "AAASHLVR 961 19.420032 \n", "AAASHLVr 964 19.424549 \n", "HLTDGmTVr 975 25.416597 \n", "HLTDGmTVR 974 25.416597 \n", "AVQNAMR 995 19.446729 \n", "AGVHFGHQTR 1002 22.994444 \n", "mVEEDPAHPr 1016 20.084497 \n", "mVEEDPAHPR 1020 20.084497 \n", "AGVHFGHQTR 992 25.061076 \n", "AGVHFGHQTr 994 25.061076 \n", "mVEEDPAHPr 1025 18.316519 \n", "AGVHFGHQTr 1004 22.994444 \n", "GTAMNPVDHPHGGGEGR 1087 18.816326 \n", "TDLHGTAVR 1078 19.553958 \n", "AHHYPSELSGGQQQR 1137 22.611332 \n", " 1142 20.373860 \n", " 1145 21.817347 \n", "... ... \n", "LAQmQIPADDYFIWITGEGk 7850 17.276999 \n", "EGAFVPFVTLGDPGIEQSLK 7849 23.037434 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 18.233260 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 22.537990 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 24.124365 \n", " 7886 19.573735 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 19.124448 \n", "FGASSLLASLLk 8084 22.498940 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 18.138290 \n", "DGVGLLPTVLDVVENPk 8559 21.228191 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 23.293799 \n", " 8686 22.595297 \n", "VGYIELDLNSGk 8965 17.510583 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 20.371373 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 23.428056 \n", " 8883 24.149613 \n", " 9458 19.899775 \n", "LANEGIFTQQELYDELLTLADEAk 9522 18.353222 \n", "AIHTLWNVLDELDQAWLPVEk 9560 21.834118 \n", " 9586 21.320748 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 21.163595 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 17.895217 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 13.981914 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 16.293770 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 20.709059 \n", "VLAPINDFINTLNAFFSAGGk 9765 18.662179 \n", " 9767 18.154701 \n", "VGYIELDLNSGk 9546 17.154424 \n", "FVQAYQSDEVYEAANk 10015 12.242579 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 16.018433 \n", "\n", " Label2 RT Width \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 0.056111 \n", "GcImGSAHQR 783 0.055908 \n", " 777 0.056213 \n", "TQDATHGNSLSHR 811 0.061264 \n", "IEQAPGQHGAR 887 0.045521 \n", "AGVTGAENr 904 0.055905 \n", "AGVTGAENR 903 0.055905 \n", "GTAmNPVDHPHGGGEGR 917 0.051766 \n", "ALVSHPR 933 0.070174 \n", "mTGDNPDAPR 944 0.063909 \n", "VHPNGIR 898 0.058525 \n", "SVANAEQmDR 959 0.075726 \n", "SVANAEQmDr 962 0.075726 \n", "AAASHLVR 961 0.075272 \n", "AAASHLVr 964 0.075731 \n", "HLTDGmTVr 975 0.084778 \n", "HLTDGmTVR 974 0.084778 \n", "AVQNAMR 995 0.067331 \n", "AGVHFGHQTR 1002 0.075378 \n", "mVEEDPAHPr 1016 0.093785 \n", "mVEEDPAHPR 1020 0.093785 \n", "AGVHFGHQTR 992 0.080505 \n", "AGVHFGHQTr 994 0.080505 \n", "mVEEDPAHPr 1025 0.079541 \n", "AGVHFGHQTr 1004 0.075378 \n", "GTAMNPVDHPHGGGEGR 1087 0.051097 \n", "TDLHGTAVR 1078 0.051518 \n", "AHHYPSELSGGQQQR 1137 0.080211 \n", " 1142 0.069400 \n", " 1145 0.073249 \n", "... ... \n", "LAQmQIPADDYFIWITGEGk 7850 0.116156 \n", "EGAFVPFVTLGDPGIEQSLK 7849 0.207459 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 0.076696 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 0.180326 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 0.212079 \n", " 7886 0.056227 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 0.071011 \n", "FGASSLLASLLk 8084 0.188970 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 0.040326 \n", "DGVGLLPTVLDVVENPk 8559 0.184760 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 0.232344 \n", " 8686 0.129570 \n", "VGYIELDLNSGk 8965 0.145803 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 0.114255 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 0.756642 \n", " 8883 0.279444 \n", " 9458 0.081068 \n", "LANEGIFTQQELYDELLTLADEAk 9522 0.046340 \n", "AIHTLWNVLDELDQAWLPVEk 9560 0.062201 \n", " 9586 0.054593 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 0.263017 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 0.040175 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 0.052455 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 0.067028 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 0.299294 \n", "VLAPINDFINTLNAFFSAGGk 9765 0.053537 \n", " 9767 0.054971 \n", "VGYIELDLNSGk 9546 0.098624 \n", "FVQAYQSDEVYEAANk 10015 0.030985 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 0.050737 \n", "\n", " Label2 Mean Offset \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 1.434795e+00 \n", "GcImGSAHQR 783 4.517510e-01 \n", " 777 1.406006e+00 \n", "TQDATHGNSLSHR 811 4.103946e-01 \n", "IEQAPGQHGAR 887 6.600722e-02 \n", "AGVTGAENr 904 1.362417e-01 \n", "AGVTGAENR 903 1.362417e-01 \n", "GTAmNPVDHPHGGGEGR 917 9.084471e-02 \n", "ALVSHPR 933 9.030660e-01 \n", "mTGDNPDAPR 944 6.023946e-01 \n", "VHPNGIR 898 1.444357e-01 \n", "SVANAEQmDR 959 4.197732e-01 \n", "SVANAEQmDr 962 4.197732e-01 \n", "AAASHLVR 961 1.019746e+00 \n", "AAASHLVr 964 9.812200e-01 \n", "HLTDGmTVr 975 1.511559e+00 \n", "HLTDGmTVR 974 1.511559e+00 \n", "AVQNAMR 995 9.486762e-01 \n", "AGVHFGHQTR 1002 2.275157e-01 \n", "mVEEDPAHPr 1016 3.310712e-01 \n", "mVEEDPAHPR 1020 3.310712e-01 \n", "AGVHFGHQTR 992 3.480352e-01 \n", "AGVHFGHQTr 994 3.483843e-01 \n", "mVEEDPAHPr 1025 1.710395e-01 \n", "AGVHFGHQTr 1004 2.275157e-01 \n", "GTAMNPVDHPHGGGEGR 1087 5.237172e-01 \n", "TDLHGTAVR 1078 1.114874e-01 \n", "AHHYPSELSGGQQQR 1137 3.786946e-01 \n", " 1142 3.310561e-01 \n", " 1145 3.987897e-01 \n", "... ... \n", "LAQmQIPADDYFIWITGEGk 7850 8.156097e-01 \n", "EGAFVPFVTLGDPGIEQSLK 7849 2.615308e-01 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 4.096748e+00 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 1.371329e+00 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 1.652292e-01 \n", " 7886 1.608774e+00 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 1.931950e+00 \n", "FGASSLLASLLk 8084 4.770330e-01 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 2.672755e+01 \n", "DGVGLLPTVLDVVENPk 8559 3.650728e-01 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 4.787828e-01 \n", " 8686 2.685297e+00 \n", "VGYIELDLNSGk 8965 2.394400e+00 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 6.046525e-01 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 2.127841e+00 \n", " 8883 2.810016e-01 \n", " 9458 7.621572e-01 \n", "LANEGIFTQQELYDELLTLADEAk 9522 7.130651e-01 \n", "AIHTLWNVLDELDQAWLPVEk 9560 4.901800e-01 \n", " 9586 9.095046e-01 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 9.294961e-01 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 6.339672e-04 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 1.120198e-01 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 1.165689e-01 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 1.026466e+01 \n", "VLAPINDFINTLNAFFSAGGk 9765 2.025805e-01 \n", " 9767 1.785362e-01 \n", "VGYIELDLNSGk 9546 3.966709e+00 \n", "FVQAYQSDEVYEAANk 10015 1.733472e-07 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 1.074356e+01 \n", "\n", " Label2 Residual \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 0.416770 \n", "GcImGSAHQR 783 1.063376 \n", " 777 0.412675 \n", "TQDATHGNSLSHR 811 0.238537 \n", "IEQAPGQHGAR 887 0.034781 \n", "AGVTGAENr 904 0.015580 \n", "AGVTGAENR 903 0.015580 \n", "GTAmNPVDHPHGGGEGR 917 0.014866 \n", "ALVSHPR 933 0.067431 \n", "mTGDNPDAPR 944 0.034698 \n", "VHPNGIR 898 0.052027 \n", "SVANAEQmDR 959 0.104451 \n", "SVANAEQmDr 962 0.104451 \n", "AAASHLVR 961 1.396346 \n", "AAASHLVr 964 1.535988 \n", "HLTDGmTVr 975 0.072245 \n", "HLTDGmTVR 974 0.072245 \n", "AVQNAMR 995 0.099772 \n", "AGVHFGHQTR 1002 0.024981 \n", "mVEEDPAHPr 1016 0.046965 \n", "mVEEDPAHPR 1020 0.046965 \n", "AGVHFGHQTR 992 0.063421 \n", "AGVHFGHQTr 994 0.064882 \n", "mVEEDPAHPr 1025 0.137727 \n", "AGVHFGHQTr 1004 0.024981 \n", "GTAMNPVDHPHGGGEGR 1087 0.099033 \n", "TDLHGTAVR 1078 0.032910 \n", "AHHYPSELSGGQQQR 1137 0.056959 \n", " 1142 0.041349 \n", " 1145 0.079853 \n", "... ... \n", "LAQmQIPADDYFIWITGEGk 7850 1.656970 \n", "EGAFVPFVTLGDPGIEQSLK 7849 0.228140 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 1.956180 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 0.624656 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 0.197110 \n", " 7886 6.064123 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 0.545626 \n", "FGASSLLASLLk 8084 0.213794 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 1.164123 \n", "DGVGLLPTVLDVVENPk 8559 0.534657 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 1.796918 \n", " 8686 3.346062 \n", "VGYIELDLNSGk 8965 4.397509 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 1.046882 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 2.621094 \n", " 8883 1.998608 \n", " 9458 4.665191 \n", "LANEGIFTQQELYDELLTLADEAk 9522 0.196605 \n", "AIHTLWNVLDELDQAWLPVEk 9560 0.119166 \n", " 9586 0.092096 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 3.052862 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 0.024815 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 0.022778 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 0.042940 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 5.649428 \n", "VLAPINDFINTLNAFFSAGGk 9765 0.013438 \n", " 9767 0.006698 \n", "VGYIELDLNSGk 9546 9.656604 \n", "FVQAYQSDEVYEAANk 10015 0.036631 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 0.859082 \n", "\n", " Label2 R^2 Label2 SNR \\\n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 2.361091 9.658139 \n", "GcImGSAHQR 783 2.148553 6.236617 \n", " 777 2.329501 9.082110 \n", "TQDATHGNSLSHR 811 3.268813 2.282480 \n", "IEQAPGQHGAR 887 11.525348 1.143215 \n", "AGVTGAENr 904 8.858486 1.034260 \n", "AGVTGAENR 903 8.858486 1.034260 \n", "GTAmNPVDHPHGGGEGR 917 9.459570 1.234008 \n", "ALVSHPR 933 4.512238 1.013494 \n", "mTGDNPDAPR 944 4.959971 1.001678 \n", "VHPNGIR 898 5.027183 1.406236 \n", "SVANAEQmDR 959 3.851186 0.931681 \n", "SVANAEQmDr 962 3.851186 0.931681 \n", "AAASHLVR 961 2.584104 19.286076 \n", "AAASHLVr 964 2.584007 15.079136 \n", "HLTDGmTVr 975 5.677385 16.507001 \n", "HLTDGmTVR 974 5.677385 16.507001 \n", "AVQNAMR 995 5.123132 3.522732 \n", "AGVHFGHQTR 1002 5.482815 1.207646 \n", "mVEEDPAHPr 1016 4.964579 10.238337 \n", "mVEEDPAHPR 1020 4.964579 10.238337 \n", "AGVHFGHQTR 992 4.765415 14.052629 \n", "AGVHFGHQTr 994 4.765081 14.052629 \n", "mVEEDPAHPr 1025 5.748694 1.114434 \n", "AGVHFGHQTr 1004 5.482815 1.207646 \n", "GTAMNPVDHPHGGGEGR 1087 5.016891 1.092491 \n", "TDLHGTAVR 1078 7.299707 1.038474 \n", "AHHYPSELSGGQQQR 1137 4.790247 1.299816 \n", " 1142 5.465852 1.339840 \n", " 1145 4.701050 1.190595 \n", "... ... ... \n", "LAQmQIPADDYFIWITGEGk 7850 NaN 1.651758 \n", "EGAFVPFVTLGDPGIEQSLK 7849 3.322470 6.108227 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 1.403417 221.445566 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 2.240398 6.524064 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 3.682382 4.434152 \n", " 7886 2.089884 6.699860 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 2.040685 15.321044 \n", "FGASSLLASLLk 8084 3.080469 4.816335 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 12.381305 0.775548 \n", "DGVGLLPTVLDVVENPk 8559 2.116104 6.450245 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 0.960049 4.702896 \n", " 8686 1.681354 2.784739 \n", "VGYIELDLNSGk 8965 -0.054590 2.641730 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 2.549166 6.614207 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 -3.001260 0.851808 \n", " 8883 1.920195 3.695578 \n", " 9458 2.026509 16.984387 \n", "LANEGIFTQQELYDELLTLADEAk 9522 3.942563 NaN \n", "AIHTLWNVLDELDQAWLPVEk 9560 3.774949 1.024549 \n", " 9586 4.274263 1.208662 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 0.857173 1.730295 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 inf NaN \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 9.743359 NaN \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 4.309249 NaN \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 -2.158764 8.410005 \n", "VLAPINDFINTLNAFFSAGGk 9765 6.934400 NaN \n", " 9767 7.625821 NaN \n", "VGYIELDLNSGk 9546 -3.448946 8.693567 \n", "FVQAYQSDEVYEAANk 10015 3.687593 NaN \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 10.182012 0.436856 \n", "\n", " Deviation Class \n", "Peptide MS2 Spectrum ID \n", "GcImGSAHQr 779 0.094592 1 \n", "GcImGSAHQR 783 0.000523 1 \n", " 777 0.094830 1 \n", "TQDATHGNSLSHR 811 -0.101995 1 \n", "IEQAPGQHGAR 887 0.290681 1 \n", "AGVTGAENr 904 -0.065885 1 \n", "AGVTGAENR 903 -0.065885 1 \n", "GTAmNPVDHPHGGGEGR 917 0.085492 1 \n", "ALVSHPR 933 0.111110 1 \n", "mTGDNPDAPR 944 0.007644 1 \n", "VHPNGIR 898 -0.135307 1 \n", "SVANAEQmDR 959 0.071929 1 \n", "SVANAEQmDr 962 0.071929 1 \n", "AAASHLVR 961 0.091354 1 \n", "AAASHLVr 964 0.083129 1 \n", "HLTDGmTVr 975 0.080686 1 \n", "HLTDGmTVR 974 0.080686 1 \n", "AVQNAMR 995 0.142120 1 \n", "AGVHFGHQTR 1002 -0.189688 1 \n", "mVEEDPAHPr 1016 -0.049676 1 \n", "mVEEDPAHPR 1020 -0.227445 1 \n", "AGVHFGHQTR 992 0.021684 1 \n", "AGVHFGHQTr 994 0.021684 1 \n", "mVEEDPAHPr 1025 -0.720857 1 \n", "AGVHFGHQTr 1004 -0.189688 1 \n", "GTAMNPVDHPHGGGEGR 1087 -0.049417 1 \n", "TDLHGTAVR 1078 1.064196 0 \n", "AHHYPSELSGGQQQR 1137 0.020353 1 \n", " 1142 0.052759 1 \n", " 1145 0.222085 1 \n", "... ... ... \n", "LAQmQIPADDYFIWITGEGk 7850 -0.327431 1 \n", "EGAFVPFVTLGDPGIEQSLK 7849 -0.002491 1 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7866 1.077706 0 \n", "TQGAAAFEGAVIAYEPVWAIGTGk 7884 0.063856 1 \n", "ELcSAAITmSDNTAANLLLTTIGGPk 7874 0.058754 1 \n", " 7886 0.739104 1 \n", "VLALAENYQPLYAALGLHPGMLEk 7915 0.767542 1 \n", "FGASSLLASLLk 8084 -0.080325 1 \n", "TqGAAAFEGAVIAYEPVWAIGTGk 8094 -1.095661 0 \n", "DGVGLLPTVLDVVENPk 8559 -0.088636 1 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8695 -0.195882 1 \n", " 8686 0.025548 1 \n", "VGYIELDLNSGk 8965 0.394377 1 \n", "SLDDAQIALAVINTTYASQIGLTPAk 9089 -0.124315 1 \n", "ELcSAAITMSDNTAANLLLTTIGGPk 8846 -0.127975 1 \n", " 8883 -0.300475 1 \n", " 9458 -1.687464 0 \n", "LANEGIFTQQELYDELLTLADEAk 9522 0.062293 1 \n", "AIHTLWNVLDELDQAWLPVEk 9560 -0.038950 1 \n", " 9586 -0.070930 1 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9148 -3.928888 0 \n", "TAPDGEHGVNLVHLEDVIGAITLLLQAPk 9656 -0.150357 1 \n", "NADGLGMLVAqAAHAFLLWHGVLPDVEPVIk 9688 -0.548328 1 \n", "FLQFMVSPAFQNAIPTGnWMYPVANVTLPAGFEK 9696 NaN 0 \n", "ELcSAAITMSDNTAANLLLTTIGGPK 9209 -0.675744 1 \n", "VLAPINDFINTLNAFFSAGGk 9765 -0.090769 1 \n", " 9767 0.235751 1 \n", "VGYIELDLNSGk 9546 1.260810 0 \n", "FVQAYQSDEVYEAANk 10015 -0.721485 1 \n", "ELcSAAITMSDNTAAnLLLTTIGGPk 9818 1.423368 0 \n", "\n", "[2138 rows x 16 columns]" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "from tpot import TPOT\n", "from sklearn.cross_validation import train_test_split\n", "import numpy as np\n", "from scipy.special import logit\n", "import pandas as pd\n", "pd.options.display.max_columns = None\n", "from patsy import dmatrix\n", "\n", "dat = pd.read_table(out)\n", "dat = dat[dat['Peptide'].str.count('R')+dat['Peptide'].str.count('K')+dat['Peptide'].str.count('k')+dat['Peptide'].str.count('r') == 1]\n", "dat['Class'] = None\n", "dat.loc[dat['Peptide'].str.count('R')+dat['Peptide'].str.count('r') == 1, 'Class'] = 'R'\n", "dat.loc[dat['Peptide'].str.count('K')+dat['Peptide'].str.count('k') == 1, 'Class'] = 'K'\n", "dat.set_index(['Peptide', 'MS2 Spectrum ID'], inplace=True)\n", "dat.drop(['Modifications', 'Raw File', 'Accession', 'MS1 Spectrum ID', 'Charge', 'Medium Calibrated Precursor', 'Medium Precursor', 'Heavy/Medium', 'Heavy Calibrated Precursor', 'Heavy Precursor', 'Light Calibrated Precursor', 'Light Precursor', 'Retention Time', 'Heavy/Light Confidence', 'Medium/Heavy', 'Medium/Heavy Confidence', 'Medium/Light Confidence', 'Light/Medium Confidence', 'Heavy/Medium Confidence', 'Light/Heavy Confidence'], inplace=True, axis=1)\n", "# Arg H/L -> -1.86\n", "# Arg M/L = -1\n", "# Lys H/L -> 1.89\n", "# Lys M/L = 0.72\n", "nds = []\n", "for numerator, denominator in zip(['Heavy', 'Medium'], ['Light', 'Light']):\n", " ratio = '{}/{}'.format(numerator, denominator)\n", " cols=['Isotopes Found', 'Intensity', 'RT Width', 'Mean Offset', 'Residual', 'R^2', 'SNR']\n", " nd = pd.DataFrame([], columns=[\n", " 'Label1 Isotopes Found',\n", " 'Label1 Intensity',\n", " 'Label1 RT Width',\n", " 'Label1 Mean Offset',\n", " 'Label1 Residual',\n", " 'Label1 R^2',\n", " 'Label1 SNR',\n", " 'Label2 Isotopes Found',\n", " 'Label2 Intensity',\n", " 'Label2 RT Width',\n", " 'Label2 Mean Offset',\n", " 'Label2 Residual',\n", " 'Label2 R^2',\n", " 'Label2 SNR',\n", " 'Deviation',\n", " 'Class',\n", " ])\n", " median, std = np.log2(dat[dat['Class']=='R'][ratio]).median(), np.log2(dat[dat['Class']=='R'][ratio]).std()\n", " expected = median\n", " nd['Deviation'] = np.log2(dat[dat['Class']=='R'][ratio])-expected\n", " nd['Class'] = np.abs(np.log2(dat[dat['Class']=='R'][ratio])-median).apply(lambda x: 1 if x < std else 0)\n", " for label, new_label in zip([numerator, denominator], ['Label1', 'Label2']):\n", " for col in cols:\n", " nd['{} {}'.format(new_label, col)] = dat['{} {}'.format(label, col)]\n", " nd['Label1 Intensity'] = np.log2(nd['Label1 Intensity'])\n", " nd['Label2 Intensity'] = np.log2(nd['Label2 Intensity'])\n", " nd['Label1 R^2'] = logit(nd['Label1 R^2'])\n", " nd['Label2 R^2'] = logit(nd['Label2 R^2'])\n", " nds.append(nd)\n", "for numerator, denominator in zip(['Heavy', 'Medium'], ['Light', 'Light']):\n", " ratio = '{}/{}'.format(numerator, denominator)\n", " cols=['Isotopes Found', 'Intensity', 'RT Width', 'Mean Offset', 'Residual', 'R^2', 'SNR']\n", " nd = pd.DataFrame([], columns=[\n", " 'Label1 Isotopes Found',\n", " 'Label1 Intensity',\n", " 'Label1 RT Width',\n", " 'Label1 Mean Offset',\n", " 'Label1 Residual',\n", " 'Label1 R^2',\n", " 'Label1 SNR',\n", " 'Label2 Isotopes Found',\n", " 'Label2 Intensity',\n", " 'Label2 RT Width',\n", " 'Label2 Mean Offset',\n", " 'Label2 Residual',\n", " 'Label2 R^2',\n", " 'Label2 SNR',\n", " 'Deviation',\n", " 'Class'\n", " ])\n", " median, std = np.log2(dat[dat['Class']=='K'][ratio]).median(), np.log2(dat[dat['Class']=='K'][ratio]).std()\n", " expected = median\n", " nd['Deviation'] = np.log2(dat[dat['Class']=='K'][ratio])-expected\n", " nd['Class'] = np.abs(np.log2(dat[dat['Class']=='K'][ratio])-median).apply(lambda x: 1 if x < std else 0)\n", " for label, new_label in zip([numerator, denominator], ['Label1', 'Label2']):\n", " for col in cols:\n", " nd['{} {}'.format(new_label, col)] = dat['{} {}'.format(label, col)]\n", " nd['Label1 Intensity'] = np.log2(nd['Label1 Intensity'])\n", " nd['Label2 Intensity'] = np.log2(nd['Label2 Intensity'])\n", " nd['Label1 R^2'] = logit(nd['Label1 R^2'])\n", " nd['Label2 R^2'] = logit(nd['Label2 R^2'])\n", " nds.append(nd)\n", "pd.concat(nds)" ] }, { "cell_type": "code", "execution_count": 223, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Peptide MS2 Spectrum ID\n", "GcImGSAHQr 779 1\n", "GcImGSAHQR 783 1\n", " 777 1\n", "TQDATHGNSLSHR 811 1\n", "IEQAPGQHGAR 887 1\n", "AGVTGAENr 904 1\n", "AGVTGAENR 903 1\n", "GTAmNPVDHPHGGGEGR 917 1\n", "ALVSHPR 933 1\n", "mTGDNPDAPR 944 1\n", "VHPNGIR 898 1\n", "SVANAEQmDR 959 1\n", "SVANAEQmDr 962 1\n", "AAASHLVR 961 1\n", "AAASHLVr 964 1\n", "HLTDGmTVr 975 1\n", "HLTDGmTVR 974 1\n", "AVQNAMR 995 1\n", "AGVHFGHQTR 1002 1\n", "mVEEDPAHPr 1016 1\n", "mVEEDPAHPR 1020 1\n", "AGVHFGHQTR 992 1\n", "AGVHFGHQTr 994 1\n", "mVEEDPAHPr 1025 1\n", "AGVHFGHQTr 1004 1\n", "GTAMNPVDHPHGGGEGR 1087 1\n", "TDLHGTAVR 1078 0\n", "AHHYPSELSGGQQQR 1137 1\n", " 1142 1\n", " 1145 1\n", " ..\n", "TIPSVLTALFcAR 8606 0\n", "AMLTLIVFSFTVSVYSSATVTPGSLnLAPIAIADMDQSqLSnr 8994 0\n", "GVLLPLLSLDcAVTITNR 8966 1\n", "ILELAGFLDSYIPEPER 8999 0\n", "FVESVDVAVNLGIDAr 9057 0\n", "IEGGEWLVETVQmLTER 9375 1\n", " 9363 1\n", "IEGGEWLVETVQmLTEr 9381 1\n", " 9377 1\n", "GDMLSMEDVLEILR 9469 1\n", "IEGGEWLVETVQmLTEr 9388 1\n", "GDMLSMEDVLEILr 9471 1\n", "HLEFFNTQPFVAAPILGVTLALEEQR 9514 1\n", "SVPGYSNIISMIGmLAER 9526 1\n", "SVPGYSNIISMIGMLAER 9593 1\n", "IEGGEWLVETVQMLTER 9611 1\n", "IEGGEWLVETVQMLTEr 9612 1\n", "IEGGEWLVETVQMLTER 9613 1\n", " 9625 0\n", "FVESVDVAVnLGIDAR 9677 0\n", "ATFVVDPQGIIQAIEVTAEGIGR 9724 1\n", " 9729 1\n", "AVTLYLGAVAATVR 9652 0\n", " 8958 0\n", "IVVIYTTGSQATMDER 9933 1\n", "FVESVDVAVNLGIDAR 9213 1\n", " 9426 0\n", "FVESVDVAVNLGIDAr 9184 0\n", "FVESVDVAVNLGIDAR 9950 0\n", " 9980 0\n", "Name: Heavy/Light, dtype: int64" ] }, "execution_count": 223, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "df = pd.concat(nds)\n", "df = df.replace([np.inf,-np.inf], np.nan).dropna()\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import preprocessing\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "X = preprocessing.scale(df.drop('Deviation', axis=1).drop('Class', axis=1).values)\n", "y = df.loc[:, ['Deviation', 'Class']].values\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)\n", "y_test_reg = y_test[:, 0]\n", "y_test_class = y_test[:, 1]\n", "y_train_reg = y_train[:, 0]\n", "y_train_class = y_train[:, 1]" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.884615384615\n" ] } ], "source": [ "from sklearn.svm import SVC as Classifier\n", "\n", "clf = Classifier()\n", "clf = clf.fit(X_train, y_train_class)\n", "from sklearn.metrics import accuracy_score\n", "print accuracy_score(y_test_class, clf.predict(X_test))" ] }, { "cell_type": "code", "execution_count": 341, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.926799007444\n" ] } ], "source": [ "from sklearn.qda import QDA as Classifier\n", "\n", "clf = Classifier()\n", "clf = clf.fit(X_train, y_train_class)\n", "from sklearn.metrics import accuracy_score\n", "print accuracy_score(y_test_class, clf.predict(X_test))" ] }, { "cell_type": "code", "execution_count": 349, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.918114143921\n" ] } ], "source": [ "from sklearn.gaussian_process import GaussianProcessClassifier as Classifier\n", "\n", "clf = Classifier()\n", "clf = clf.fit(X_train, y_train_class)\n", "from sklearn.metrics import accuracy_score\n", "print accuracy_score(y_test_class, clf.predict(X_test))" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.890818858561\n" ] } ], "source": [ "from sklearn.neural_network import MLPClassifier as Classifier\n", "\n", "clf = Classifier()\n", "clf = clf.fit(X_train, y_train_class)\n", "from sklearn.metrics import accuracy_score\n", "print accuracy_score(y_test_class, clf.predict(X_test))" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "import pickle\n", "pickle.dump(clf, open('/home/chris/Devel/pyquant/pyquant/static/new_classifier2.pickle', 'wb'))" ] }, { "cell_type": "code", "execution_count": 375, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.217232977277\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f2df55edf50>]" ] }, "execution_count": 375, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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jGdn9462trbz88ssln/TLfCNYu3YlsVgnx48froqOELUdSjmV2s3yLME1uX4W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\nIiJ14P8DHU8X1qMyudkAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2df569fc90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.ensemble import AdaBoostRegressor as Regressor\n", "\n", "clf = Regressor()\n", "clf = clf.fit(X_train, y_train_reg)\n", "from sklearn import metrics\n", "print metrics.median_absolute_error(y_test_reg, clf.predict(X_test))\n", "from matplotlib import pyplot as plt\n", "plt.scatter(y_test_reg, clf.predict(X_test))\n", "plt.plot([-6, 6], [-6, 6], 'r-')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.206629642862\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f6fe8eda150>]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Iudxmjh59XAld2lrcNouOAG0mbZjGI6Ojo2SzeYJCDrCOTKaf0dHRxg1KpIXo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\nKuYiIinw/wH2P+Tuonv1vQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6fe8e0b950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.neural_network import MLPRegressor\n", "reg = MLPRegressor()\n", "clf = GridSearchCV(reg, {})\n", "clf.fit(X_train, y_train_reg)\n", "print metrics.median_absolute_error(y_test_reg, clf.predict(X_test))\n", "plt.scatter(y_test_reg, clf.predict(X_test))\n", "plt.plot([-6, 6], [-6, 6], 'r-')" ] }, { "cell_type": "code", "execution_count": 377, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f2df570ad10>]" ] }, "execution_count": 377, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AirmISASomIuIRICKuYhIBKiYi4hEgIq5iEgEqJiLiESAirmISASomIuIREBR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\nMUlL0AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2df56aaf90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.ensemble import GradientBoostingRegressor\n", "reg = GradientBoostingRegressor()\n", "parameters = {\n", " 'loss': ['ls', 'lad'], \n", " 'learning_rate': [0.01, 0.1, 0.5],\n", " 'n_estimators': [50, 100, 200],\n", "}\n", "clf = GridSearchCV(reg, parameters)\n", "clf.fit(X_train, y_train_reg)\n", "from sklearn.metrics import r2_score\n", "r2_score(y_test_reg, clf.predict(X_test))\n", "plt.scatter(y_test_reg, clf.predict(X_test))\n", "plt.plot([-6, 6], [-6, 6], 'r-')" ] }, { "cell_type": "code", "execution_count": 379, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.302797515251\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f2df53bee10>]" ] }, "execution_count": 379, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Dg08A0NGxiNWrr6GjYxE7d+4a2W/09XYBW4BVwEJSqbfR3b2l6MzLKH5fVf2d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\nUDAXEYkBBXMRkRhQMBcRiQEFcxGRGPj/2X09ARKPu2YAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2df55a1290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.tree import DecisionTreeRegressor as Regressor\n", "clf = Regressor()\n", "clf.fit(X_train, y_train_reg)\n", "print r2_score(y_test_reg, clf.predict(X_test))\n", "plt.scatter(y_test_reg, clf.predict(X_test))\n", "plt.plot([-6, 6], [-6, 6], 'r-')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7feb98e65b90>" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZleTKUdYmSVrYqHsMdwM/c1jbBmB7VZ0PPAxsBEhyAXAdsB64CrgrSUZcn8Zo7dqzSTL2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\nBoMkqcNgkCR1GAySpI7/Dwv3/t+Fnnj9AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7feb98d39150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.log2(dat[dat['Class']=='R']['Heavy/Light']).plot(kind='hist')" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Heavy Isotopes Found',\n", " 'Heavy Intensity',\n", " 'Heavy RT Width',\n", " 'Heavy Mean Offset',\n", " 'Heavy Residual',\n", " 'Heavy R^2',\n", " 'Heavy SNR',\n", " 'Heavy/Light',\n", " 'Light Isotopes Found',\n", " 'Light Intensity',\n", " 'Light RT Width',\n", " 'Light Mean Offset',\n", " 'Light Residual',\n", " 'Light R^2',\n", " 'Light SNR',\n", " 'Medium Isotopes Found',\n", " 'Medium Intensity',\n", " 'Medium RT Width',\n", " 'Medium Mean Offset',\n", " 'Medium Residual',\n", " 'Medium R^2',\n", " 'Medium SNR',\n", " 'Medium/Light',\n", " 'Class']" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dat.columns.tolist()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 9%\|▉ \| 100/1100 [00:00<04:56, 3.37pipeline/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 1 - Current best internal CV score: 1.00000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 18%\|█▊ \| 196/1100 [00:00<06:04, 2.48pipeline/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 2 - Current best internal CV score: 1.00000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 26%\|██▋ \| 290/1100 [00:00<07:17, 1.85pipeline/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 3 - Current best internal CV score: 1.00000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 36%\|███▌ \| 394/1100 [00:00<05:00, 2.35pipeline/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 4 - Current best internal CV score: 1.00000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 44%\|████▍ \| 488/1100 [00:00<03:11, 3.20pipeline/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 5 - Current best internal CV score: 1.00000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 54%\|█████▍ \| 594/1100 [00:00<03:19, 2.54pipeline/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 6 - Current best internal CV score: 1.00000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 63%\|██████▎ \| 692/1100 [00:00<03:19, 2.04pipeline/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 7 - Current best internal CV score: 1.00000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 72%\|███████▏ \| 789/1100 [00:00<01:52, 2.76pipeline/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 8 - Current best internal CV score: 1.00000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 81%\|████████▏ \| 894/1100 [00:00<01:26, 2.39pipeline/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 9 - Current best internal CV score: 1.00000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "GP Progress: 90%\|█████████ \| 990/1100 [00:00<00:50, 2.19pipeline/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation 10 - Current best internal CV score: 1.00000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Best pipeline: _linear_svc(input_df, 0.97999999999999998, 60, False)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r" ] }, { "data": { "text/plain": [ "1.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from tpot import TPOT\n", "from sklearn.cross_validation import train_test_split\n", "import numpy as np\n", "import pandas as pd\n", "from patsy import dmatrix\n", "\n", "dat = pd.read_table(out)\n", "dat.set_index(['Peptide', 'MS2 Spectrum ID'], inplace=True)\n", "dat.drop(['Modifications', 'Raw File', 'Accession', 'MS1 Spectrum ID', 'Charge', 'Retention Time', 'Heavy/Light', 'Heavy/Light Confidence', 'Medium/Light', 'Medium/Heavy', 'Medium/Heavy Confidence', 'Medium/Light', 'Medium/Light Confidence', 'Light/Medium', 'Light/Medium Confidence', 'Heavy/Medium', 'Heavy/Medium Confidence', 'Light/Heavy Confidence', 'Light/Heavy'], inplace=True, axis=1)\n", "for i in ['Heavy', 'Medium', 'Light']:\n", " for j in ['Precursor', 'Calibrated Precursor']:\n", " dat.drop(i + ' ' +j, inplace=True, axis=1)\n", " to_drop = []\n", "\n", "for j in dat.columns:\n", " if j.startswith('Heavy'):\n", " to_drop.append(j)\n", "dat.drop(to_drop, inplace=True, axis=1)\n", "\n", "dat['Class'] = None\n", "for i in bad_data:\n", " dat.loc[i, 'Class'] = 0\n", "for i in good_data:\n", " dat.loc[i, 'Class'] = 1\n", "\n", "dat.dropna(inplace=True)\n", "labels = dat['Class']\n", "\n", "# # preprocess\n", "dat['Medium Intensity'] = np.log2(dat['Medium Intensity'])\n", "dat['Light Intensity'] = np.log2(dat['Light Intensity'])\n", "\n", "# extra info\n", "for i in ['RT Width', 'Isotopes Found']:\n", " dat['Medium/Light {}'.format(i)] = dat['Medium {}'.format(i)]/dat['Light {}'.format(i)]\n", "\n", "# dat = dat.loc[:, ['Medium R^2', 'Light R^2', 'Class']]\n", "dat.reset_index(drop=True, inplace=True)\n", "training_indices, testing_indices = train_test_split(dat.index, stratify = labels.values, train_size=0.5, test_size=0.5)\n", "\n", "tpot = TPOT(verbosity=2, generations=10)\n", "tpot.fit(dat.drop('Class',axis=1).loc[training_indices].values, dat.loc[training_indices,'Class'].values.astype(int))\n", "tpot.score(dat.drop('Class',axis=1).loc[testing_indices].values, dat.loc[testing_indices, 'Class'].values.astype(int))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.90740740740740744" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# %matplotlib inline\n", "# from sklearn.svm import SVC\n", "\n", "# predictor = SVC()\n", "# predictor.fit(dat.drop('Class',axis=1).loc[training_indices].values, dat.loc[training_indices,'Class'].values.astype(int))\n", "# predictor.score(dat.drop('Class',axis=1).loc[training_indices].values, dat.loc[training_indices,'Class'].values.astype(int))\n", "# # plt.scatter(dat.iloc[:, 0], dat.iloc[:, 1], c=dat.iloc[:, 2])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpot.export('pipe.py')" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "dat = pd.read_table('/home/chris/Devel/pyquant/ml_test_cl2_stats')\n", "dat = dat[dat['Peptide'].str.count('R')+dat['Peptide'].str.count('K')+dat['Peptide'].str.count('k')+dat['Peptide'].str.count('r') == 1]\n", "dat['Class'] = None\n", "dat.loc[dat['Peptide'].str.count('R')+dat['Peptide'].str.count('r') == 1, 'Class'] = 'R'\n", "dat.loc[dat['Peptide'].str.count('K')+dat['Peptide'].str.count('k') == 1, 'Class'] = 'K'\n" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fe2a4a210>" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NT8aTCgaROSzm0Z7HUrmeoXadryxxPSJLzkuQ6hucNT+ZTFEqeRMqEmltjd52+6vAEcA9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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6fe2fbe690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.log2(dat.loc[dat['Class']=='R','Heavy/Light']).plot(kind='density', c='r')\n", "np.log2(dat.loc[(dat['Class']=='R') & (dat['Heavy/Light Confidence']>5),'Heavy/Light']).plot(kind='density', c='g')\n", "np.log2(dat.loc[(dat['Class']=='R') & (dat['Heavy/Light Confidence']>8),'Heavy/Light']).plot(kind='density', c='k')" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fe2bb5fd0>" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RXr49km8EABXS6/LtkYUzAGB0aGsAQIIIZwBIEOG8DTObNbN1M/uFzc8Pm9lLm6+Pmtnf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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6fe2ccedd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "isotope = 'K'\n", "ratio = 'Heavy/Light'\n", "df_1 = np.log2(dat.loc[dat['Class']==isotope,ratio])\n", "df_2 = np.log2(dat.loc[(dat['Class']==isotope) & (dat['{} Confidence'.format(ratio)]>5),ratio])\n", "df_3 = np.log2(dat.loc[(dat['Class']==isotope) & (dat['{} Confidence'.format(ratio)]>9),ratio])\n", "df = pd.concat([df_1, df_2, df_3], axis=1)\n", "df.columns=['All', '5', '8']\n", "df.plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fe2d99ad0>" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6fe2e93e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dat.loc[dat['Class']=='K', '{} Confidence'.format('Heavy/Light')].plot(kind='density')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
ipython-toolbox/nbconvert	test/.ipynb_checkpoints/21-checkpoint.ipynb	1	2599	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "import sys\n", "import re" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "'''\n", "This is the docstring\n", "over two lines\n", "'''\n", "\n", "#\n", "# This should be a comment\n", "#\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class SampleClass1():\n", " def __init__(self):\n", " return" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class SampleClass2():\n", "\n", " def __init__(self):\n", " '''\n", " TEST\n", " '''\n", " return\n", "\n", " def func1(self, par1, par2):\n", " return\n", "\n", " def func2():\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def func1():\n", " '''\n", " Docstring Line1\n", " '''\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def func2():\n", " '''\n", " Docstring Line1\n", " Docstring Line2\n", " '''\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sys.exit(0)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
mssalvador/notebooks	notebooks/cvr/.ipynb_checkpoints/CombineMedarbejdstal-checkpoint.ipynb	1	25488	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Always Pyspark first!\n", "ErhvervsPath = \"/home/svanhmic/workspace/Python/Erhvervs\"\n", "cvrPath = \"/home/svanhmic/workspace/Python/Erhvervs/data/cdata/parquet\"\n", "from pyspark.sql import functions as F, Window, WindowSpec\n", "from pyspark.sql import Row\n", "from pyspark.sql.types import StringType,ArrayType,IntegerType,DoubleType,StructField,StructType\n", "sc.addPyFile(ErhvervsPath+\"/src/RegnSkabData/ImportRegnskabData.py\")\n", "sc.addPyFile(ErhvervsPath+'/src/RegnSkabData/RegnskabsClass.py')\n", "sc.addPyFile(ErhvervsPath+'/src/cvr/GetNextJsonLayer.py')\n", "\n", "import sys\n", "import re\n", "import os\n", "import ImportRegnskabData\n", "import GetNextJsonLayer\n", "import itertools\n", "import functools" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def createManPowerTabel(aarsDf,kvartDf,maanedDf):\n", " '''\n", " Combines medarbejdstabels to one tabel and makes sure that all is sampel once a month\n", " \n", " Input:\n", " \n", " Output:\n", " \n", " \n", " '''\n", " def assignPrefix(df,prefix):\n", " excluded = [\"langBeskrivelse\",\"virksomhedsformkode\",\"periode_gyldigFra\",\"periode_gyldigTil\",\"rank\"]\n", " return df.select([F.col(i).alias(prefix+i) for i in df.columns if i not in excluded])\n", " \n", " def assignKvartLowerBound(col):\n", " \n", " if col == 1: \n", " return 1\n", " elif col == 2:\n", " return 4\n", " elif col == 3:\n", " return 7\n", " else:\n", " return 10\n", " \n", " def assignKvartUpperBound(col):\n", " \n", " if col == 1:\n", " return 3\n", " elif col == 2:\n", " return 6\n", " elif col == 3:\n", " return 9\n", " else:\n", " return 12\n", " \n", " nameCols = [\"final_cvrNummer\"\n", " ,\"aar\"\n", " ,\"maaned\"\n", " ,\"final_lower_intervalKodeAntalAarsvaerk\"\n", " ,\"final_lower_intervalKodeAntalAnsatte\"\n", " ,\"final_kortBeskrivelse\"]\n", " \n", " #what we want to get is as dataframe that looks like this\n", " #\| cvr \| year \| month \| x \| y \| aps/as\| \n", " # where years below 2015 are currently represented as year or qarter data. \n", " \n", " attributesCols = [\"aar\"]\n", " minMaxCols = [F.min,F.max]\n", " combinedCols = [f(i) for i in attributesCols for f in minMaxCols]\n", " \n", " minYear = aarsDf.groupBy().agg(combinedCols).collect()[0]\n", " years = [Row(aar=i,maaned=j) for i in range(minYear[0],minYear[1]+1) for j in range(1,13)]\n", " \n", " lowerUdf = F.udf(lambda x: float(assignKvartLowerBound(x)),DoubleType())\n", " upperUdf = F.udf(lambda x: float(assignKvartUpperBound(x)),DoubleType())\n", " \n", " aDf = (assignPrefix(aarsDf,\"aar_\")\n", " .withColumnRenamed(existing=\"aar_lower_intervalKodeAntalInklusivEjere\",new=\"aar_lower_intervalKodeAntalAnsatte\")\n", " .drop(\"aar_ansvarligDataleverandoer\")\n", " .repartition(\"aar_aar\")\n", " )\n", " \n", " kDf = (assignPrefix(kvartDf,\"kvart_\")\n", " .withColumn(col=lowerUdf(\"kvart_kvartal\"),colName =\"l\")\n", " .withColumn(col=upperUdf(\"kvart_kvartal\"),colName =\"u\")\n", " .repartition(\"kvart_aar\"))\n", " #aDf.show()\n", "\n", " mDf = (maanedDf\n", " .select([F.col(re.sub(\"final_\",\"\",c)).alias(nameCols[i]) for i,c in enumerate(nameCols)]))\n", " \n", " yearsAndMonthDf = sqlContext.createDataFrame(years).repartition(\"aar\")\n", "\n", " secondCols = [kDf.columns[0]]+yearsAndMonthDf.columns+kDf.columns[3:5]+[kDf.columns[6]]\n", " secondJoinDf = (yearsAndMonthDf\n", " .join(kDf,((yearsAndMonthDf[\"aar\"] == kDf[\"kvart_aar\"])\n", " & ( yearsAndMonthDf[\"maaned\"].between(kDf[\"l\"],kDf[\"u\"]) )),how=\"left\")\n", " .select([F.col(c).alias(nameCols[i]) for i,c in enumerate(secondCols)])\n", " )\n", " \n", " #secondJoinDf.orderBy(F.col(\"final_cvrNummer\").desc()).show()\n", "\n", " coalseSecondCol = [secondJoinDf.columns[0]]+secondJoinDf.columns[3:]\n", " aCoalseCol = [aDf.columns[0]]+aDf.columns[2:]\n", " combinedCols = list(zip(coalseSecondCol,aCoalseCol))\n", " #print(combinedCols)\n", " actionCols = [F.coalesce(combinedCols[0][0],combinedCols[0][1]).alias(combinedCols[0][0])]+yearsAndMonthDf.columns+[F.coalesce(i,j).alias(i) for i,j in combinedCols[1:]]\n", " #print(actionCols)\n", " \n", " lastJoinDf = (secondJoinDf\n", " .join(aDf,((secondJoinDf[\"aar\"] == aDf[\"aar_aar\"]) \n", " & (secondJoinDf[\"final_cvrNummer\"] == None)),how=\"left\")\n", " .select(actionCols)\n", " )\n", " #lastJoinDf.show()\n", " \n", " return (lastJoinDf\n", " .na\n", " .drop(how=\"all\",subset=[\"final_cvrNummer\"])\n", " \n", " # remove all that don't have any cvr-nummber\n", " ) #extract only relevant" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "dfA = sqlContext.read.parquet(cvrPath+\"/AarsVaerker.parquet\")\n", "dfK = sqlContext.read.parquet(cvrPath+\"/KvartalsVaerker.parquet\")\n", "dfM = sqlContext.read.parquet(cvrPath+\"/MaanedsVaerker.parquet\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "#xDf = createManPowerTabel(dfA,dfK,dfM)\n", "#xDf.write.parquet(mode=\"overwrite\",path=\"/home/svanhmic/workspace/Python/Erhvervs/data/cdata/TotalAarsVaerker.parquet\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "def assignPrefix(df,prefix):\n", " excluded = [\"langBeskrivelse\",\"virksomhedsformkode\",\"periode_gyldigFra\",\"periode_gyldigTil\",\"rank\"]\n", " return df.select([F.col(i).alias(prefix+i) for i in df.columns if i not in excluded])\n", " \n", "def assignKvartLowerBound(col):\n", " \n", " if col == 1: \n", " return 1\n", " elif col == 2:\n", " return 4\n", " elif col == 3:\n", " return 7\n", " else:\n", " return 10\n", " \n", "def assignKvartUpperBound(col):\n", " \n", " if col == 1:\n", " return 3\n", " elif col == 2:\n", " return 6\n", " elif col == 3:\n", " return 9\n", " else:\n", " return 12\n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def createYearTabel(df):\n", " \n", " attributesCols = [\"aar\"]\n", " minMaxCols = [F.min,F.max]\n", " combinedCols = [f(i) for i in attributesCols for f in minMaxCols]\n", " minYear = df.groupBy().agg(combinedCols).collect()[0]\n", " years = [Row(aar=i,maaned=j) for i in range(minYear[0],minYear[1]+1) for j in range(1,13)]\n", " yearDf = sqlContext.createDataFrame(years)\n", " \n", " return yearDf" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---------+----+------+--------------------------------+------------------------------+---------------+\n", "\|cvrNummer\| aar\|maaned\|lower_intervalKodeAntalAarsvaerk\|lower_intervalKodeAntalAnsatte\|kortBeskrivelse\|\n", "+---------+----+------+--------------------------------+------------------------------+---------------+\n", "\| 29214654\|2007\| 1\| null\| 5\| APS\|\n", "\| 14339094\|2007\| 1\| null\| 2\| A/S\|\n", "\| 87136213\|2007\| 1\| null\| 10\| A/S\|\n", "\| 21763403\|2007\| 1\| null\| 5\| APS\|\n", "\| 10099366\|2007\| 1\| null\| 5\| APS\|\n", "\| 20888601\|2007\| 1\| null\| 2\| APS\|\n", "\| 13075549\|2007\| 1\| null\| 5\| APS\|\n", "\| 52107016\|2007\| 1\| null\| 10\| A/S\|\n", "\| 26059216\|2007\| 1\| null\| 5\| APS\|\n", "\| 30078993\|2007\| 1\| null\| 1\| APS\|\n", "\| 26677130\|2007\| 1\| null\| 5\| APS\|\n", "\| 25773950\|2007\| 1\| null\| 0\| APS\|\n", "\| 25795687\|2007\| 1\| null\| 2\| APS\|\n", "\| 28112122\|2007\| 1\| null\| 2\| APS\|\n", "\| 70446111\|2007\| 1\| null\| 10\| A/S\|\n", "\| 28697783\|2007\| 1\| null\| 1\| APS\|\n", "\| 25685083\|2007\| 1\| null\| 5\| A/S\|\n", "\| 25826523\|2007\| 1\| null\| 5\| A/S\|\n", "\| 51033019\|2007\| 1\| null\| 2\| APS\|\n", "\| 45532410\|2007\| 1\| null\| 5\| A/S\|\n", "+---------+----+------+--------------------------------+------------------------------+---------------+\n", "only showing top 20 rows\n", "\n" ] }, { "data": { "text/plain": [ "13403772" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "lowerUdf = F.udf(lambda x: float(assignKvartLowerBound(x)),DoubleType())\n", "upperUdf = F.udf(lambda x: float(assignKvartUpperBound(x)),DoubleType())\n", "\n", "y = createYearTabel(dfK)\n", "kCols = [\"cvrNummer\",\"aar\",\"maaned\",\"lower_intervalKodeAntalAarsvaerk\",\"lower_intervalKodeAntalAnsatte\",\"kortBeskrivelse\"]\n", "expandKDf = (y\n", " .join(dfK\n", " .withColumn(col=lowerUdf(\"kvartal\"),colName=\"l\")\n", " .withColumn(col=upperUdf(\"kvartal\"),colName=\"u\")\n", " ,((y[\"aar\"] == dfK[\"aar\"]) & (y[\"maaned\"].between(F.col(\"l\"),F.col(\"u\"))))\n", " ,\"inner\"\n", " )\n", " .drop(dfK[\"aar\"])\n", " .drop(dfK[\"kvartal\"])\n", " .select(kCols)\n", " )\n", "\n", "expandKDf.show()\n", "expandKDf.count()\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---------+----+------+--------------------------------+------------------------------------+---------------+\n", "\|cvrNummer\| aar\|maaned\|lower_intervalKodeAntalAarsvaerk\|lower_intervalKodeAntalInklusivEjere\|kortBeskrivelse\|\n", "+---------+----+------+--------------------------------+------------------------------------+---------------+\n", "\| 83072911\|1997\| 1\| 1\| 1\| APS\|\n", "\| 82552316\|1997\| 1\| 1\| 1\| A/S\|\n", "\| 70346419\|1997\| 1\| 5\| 5\| APS\|\n", "\| 19302695\|1997\| 1\| 2\| 2\| APS\|\n", "\| 10667607\|1997\| 1\| 1\| 2\| APS\|\n", "\| 10967899\|1997\| 1\| 1\| 2\| APS\|\n", "\| 89461812\|1997\| 1\| 5\| 5\| APS\|\n", "\| 15678577\|1997\| 1\| 5\| 5\| APS\|\n", "\| 13224188\|1997\| 1\| 2\| 2\| APS\|\n", "\| 83888016\|1997\| 1\| 1\| 1\| APS\|\n", "\| 73396719\|1997\| 1\| 5\| 5\| APS\|\n", "\| 20199083\|1997\| 1\| 2\| 5\| APS\|\n", "\| 12467893\|1997\| 1\| 2\| 5\| A/S\|\n", "\| 17713396\|1997\| 1\| 2\| 5\| APS\|\n", "\| 71276619\|1997\| 1\| 10\| 10\| A/S\|\n", "\| 76699119\|1997\| 1\| 20\| 20\| A/S\|\n", "\| 40234012\|1997\| 1\| 10\| 10\| A/S\|\n", "\| 21862843\|1997\| 1\| 5\| 10\| APS\|\n", "\| 16244201\|1997\| 1\| 50\| 100\| A/S\|\n", "\| 61786619\|1997\| 1\| 1\| 2\| APS\|\n", "+---------+----+------+--------------------------------+------------------------------------+---------------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "x = createYearTabel(dfA)\n", "cols = [\"cvrNummer\",\"aar\",\"maaned\",\"lower_intervalKodeAntalAarsvaerk\",\"lower_intervalKodeAntalInklusivEjere\",\"kortBeskrivelse\"]\n", "expandedADf = (x\n", " .join(dfA,(x[\"aar\"] == dfA[\"aar\"] ),\"left\")\n", " .drop(dfA[\"aar\"])\n", " .select(cols)\n", " )\n", "\n", "expandedADf.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mCols= [\"cvrNummer\",\"aar\",\"maaned\",\"lower_intervalKodeAntalAarsvaerk\",\"lower_intervalKodeAntalAnsatte\",\"kortBeskrivelse\"]\n", "MaanedsDf = dfM.select(mCols)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "kvartCvrDf = expandKDf.select(\"cvrNummer\",\"aar\",\"maaned\")\n", "aarCvrDf = expandedADf.select(\"cvrNummer\",\"aar\",\"maaned\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "onlyInAar = aarCvrDf.subtract(kvartCvrDf)\n", "onlyInDataDf = (onlyInAar\n", " .join(expandedADf,((onlyInAar[\"cvrNummer\"] == expandedADf[\"cvrNummer\"]) \n", " & (onlyInAar[\"aar\"] == expandedADf[\"aar\"])\n", " & (onlyInAar[\"maaned\"] == expandedADf[\"maaned\"])),\"inner\")\n", " .drop(expandedADf[\"cvrNummer\"])\n", " .drop(expandedADf[\"aar\"])\n", " .drop(expandedADf[\"maaned\"])\n", " )\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---------+----+------+--------------------------------+------------------------------------+---------------+\n", "\|cvrNummer\| aar\|maaned\|lower_intervalKodeAntalAarsvaerk\|lower_intervalKodeAntalInklusivEjere\|kortBeskrivelse\|\n", "+---------+----+------+--------------------------------+------------------------------------+---------------+\n", "\| 10000009\|1999\| 8\| 1\| 1\| APS\|\n", "\| 10001706\|1998\| 1\| 2\| 2\| APS\|\n", "\| 10001765\|2009\| 3\| 1\| 2\| APS\|\n", "\| 10002923\|1999\| 4\| 1\| 0\| APS\|\n", "\| 10002966\|2006\| 12\| 0\| 1\| APS\|\n", "\| 10003164\|2001\| 1\| 0\| 1\| APS\|\n", "\| 10003652\|2011\| 1\| 1\| 0\| APS\|\n", "\| 10004500\|1998\| 4\| 20\| 20\| A/S\|\n", "\| 10004861\|2004\| 7\| 1\| 0\| A/S\|\n", "\| 10005205\|1999\| 7\| 1\| 1\| APS\|\n", "\| 10005620\|2003\| 8\| 2\| 2\| A/S\|\n", "\| 10006120\|2000\| 6\| 0\| 0\| A/S\|\n", "\| 10006481\|1997\| 8\| 10\| 10\| A/S\|\n", "\| 10006716\|1999\| 12\| 2\| 5\| APS\|\n", "\| 10010535\|1997\| 7\| 2\| 5\| APS\|\n", "\| 10011221\|2003\| 7\| 1\| 1\| A/S\|\n", "\| 10011574\|1997\| 1\| 1\| 1\| APS\|\n", "\| 10011574\|1997\| 11\| 1\| 1\| APS\|\n", "\| 10012090\|1997\| 7\| 5\| 5\| A/S\|\n", "\| 10012090\|1998\| 6\| 5\| 10\| A/S\|\n", "+---------+----+------+--------------------------------+------------------------------------+---------------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "onlyInDataDf.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "combineAllKADf = expandKDf.unionAll(onlyInDataDf)\n", "combineAllKAMDf = combineAllKADf.unionAll(MaanedsDf)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---------+----+------+--------------------------------+------------------------------+---------------+\n", "\|cvrNummer\| aar\|maaned\|lower_intervalKodeAntalAarsvaerk\|lower_intervalKodeAntalAnsatte\|kortBeskrivelse\|\n", "+---------+----+------+--------------------------------+------------------------------+---------------+\n", "\| 29214654\|2007\| 1\| null\| 5\| APS\|\n", "\| 14339094\|2007\| 1\| null\| 2\| A/S\|\n", "\| 87136213\|2007\| 1\| null\| 10\| A/S\|\n", "\| 21763403\|2007\| 1\| null\| 5\| APS\|\n", "\| 10099366\|2007\| 1\| null\| 5\| APS\|\n", "\| 20888601\|2007\| 1\| null\| 2\| APS\|\n", "\| 13075549\|2007\| 1\| null\| 5\| APS\|\n", "\| 52107016\|2007\| 1\| null\| 10\| A/S\|\n", "\| 26059216\|2007\| 1\| null\| 5\| APS\|\n", "\| 30078993\|2007\| 1\| null\| 1\| APS\|\n", "\| 26677130\|2007\| 1\| null\| 5\| APS\|\n", "\| 25773950\|2007\| 1\| null\| 0\| APS\|\n", "\| 25795687\|2007\| 1\| null\| 2\| APS\|\n", "\| 28112122\|2007\| 1\| null\| 2\| APS\|\n", "\| 70446111\|2007\| 1\| null\| 10\| A/S\|\n", "\| 28697783\|2007\| 1\| null\| 1\| APS\|\n", "\| 25685083\|2007\| 1\| null\| 5\| A/S\|\n", "\| 25826523\|2007\| 1\| null\| 5\| A/S\|\n", "\| 51033019\|2007\| 1\| null\| 2\| APS\|\n", "\| 45532410\|2007\| 1\| null\| 5\| A/S\|\n", "+---------+----+------+--------------------------------+------------------------------+---------------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "combineAllKAMDf.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "15729030" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combineAllKADf.count()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16836855" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combineAllKAMDf.count()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "combineAllKAMDf.write.parquet(cvrPath+\"/TotalAarsVaerker.parquet\",mode=\"overwrite\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
eogasawara/mylibrary	Introduction.ipynb	1	170840	{ "cells": [ { "cell_type": "markdown", "id": "8d42fc52", "metadata": {}, "source": [ "#### Installation of R packages" ] }, { "cell_type": "code", "execution_count": 1, "id": "52ac0f54", "metadata": {}, "outputs": [], "source": [ "#install.packages(\"ISwR\")" ] }, { "cell_type": "markdown", "id": "3a957ba3", "metadata": {}, "source": [ "#### Package loading" ] }, { "cell_type": "code", "execution_count": 2, "id": "de33e724", "metadata": {}, "outputs": [], "source": [ "library(ISwR)" ] }, { "cell_type": "markdown", "id": "99bcc6a7", "metadata": {}, "source": [ "#### Variable definition and assignment" ] }, { "cell_type": "code", "execution_count": 3, "id": "1d0c4a11", "metadata": {}, "outputs": [], "source": [ "weight <- 60\n", "height = 1.75\n", "subject <- \"A\"\n", "healthy <- TRUE" ] }, { "cell_type": "markdown", "id": "bcee452b", "metadata": {}, "source": [ "#### Variable evaluation" ] }, { "cell_type": "code", "execution_count": 4, "id": "ad938581", "metadata": {}, "outputs": [ { "data": { "text/html": [ "60" ], "text/latex": [ "60" ], "text/markdown": [ "60" ], "text/plain": [ "[1] 60" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight" ] }, { "cell_type": "markdown", "id": "57dd208e", "metadata": {}, "source": [ "#### Functions for type checking" ] }, { "cell_type": "code", "execution_count": 5, "id": "1778ae3b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "FALSE" ], "text/latex": [ "FALSE" ], "text/markdown": [ "FALSE" ], "text/plain": [ "[1] FALSE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "is.numeric(weight) # variable \n", "is.double(weight)\n", "is.integer(weight)\n", "is.character(subject)" ] }, { "cell_type": "markdown", "id": "5dcfe56a", "metadata": {}, "source": [ "#### Functions for variable conversion" ] }, { "cell_type": "code", "execution_count": 6, "id": "9de5f0fd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight <- as.integer(weight)\n", "is.integer(weight)" ] }, { "cell_type": "markdown", "id": "ecb65b7e", "metadata": {}, "source": [ "#### Computing the body mass index (BMI) from the weight and height" ] }, { "cell_type": "code", "execution_count": 7, "id": "5bb310e3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "19.5918367346939" ], "text/latex": [ "19.5918367346939" ], "text/markdown": [ "19.5918367346939" ], "text/plain": [ "[1] 19.59184" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Body mass index (BMI)\n", "bmi <- weight/height^2 \n", "bmi " ] }, { "cell_type": "markdown", "id": "1d6b02ea", "metadata": {}, "source": [ "#### Functions for string manipulation" ] }, { "cell_type": "code", "execution_count": 8, "id": "2a5c0c64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"19.6\"\n" ] } ], "source": [ "message <- sprintf(\"%.1f\", bmi)\n", "print(message)" ] }, { "cell_type": "markdown", "id": "f465aa65", "metadata": {}, "source": [ "#### Vector definition" ] }, { "cell_type": "code", "execution_count": 9, "id": "f5792a41", "metadata": {}, "outputs": [], "source": [ "weight <- c(60, 72, 57, 90, 95, 72) \n", "height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)\n", "subject <- c(\"A\", \"B\", \"C\", \"D\", \"E\", \"F\")\n" ] }, { "cell_type": "markdown", "id": "1c86ae38", "metadata": {}, "source": [ "#### Vector evaluation" ] }, { "cell_type": "code", "execution_count": 10, "id": "ea73ae27", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>60</li><li>72</li><li>57</li><li>90</li><li>95</li><li>72</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 60\n", "\\item 72\n", "\\item 57\n", "\\item 90\n", "\\item 95\n", "\\item 72\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 60\n", "2. 72\n", "3. 57\n", "4. 90\n", "5. 95\n", "6. 72\n", "\n", "\n" ], "text/plain": [ "[1] 60 72 57 90 95 72" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>1.75</li><li>1.8</li><li>1.65</li><li>1.9</li><li>1.74</li><li>1.91</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 1.75\n", "\\item 1.8\n", "\\item 1.65\n", "\\item 1.9\n", "\\item 1.74\n", "\\item 1.91\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 1.75\n", "2. 1.8\n", "3. 1.65\n", "4. 1.9\n", "5. 1.74\n", "6. 1.91\n", "\n", "\n" ], "text/plain": [ "[1] 1.75 1.80 1.65 1.90 1.74 1.91" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>'A'</li><li>'B'</li><li>'C'</li><li>'D'</li><li>'E'</li><li>'F'</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 'A'\n", "\\item 'B'\n", "\\item 'C'\n", "\\item 'D'\n", "\\item 'E'\n", "\\item 'F'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 'A'\n", "2. 'B'\n", "3. 'C'\n", "4. 'D'\n", "5. 'E'\n", "6. 'F'\n", "\n", "\n" ], "text/plain": [ "[1] \"A\" \"B\" \"C\" \"D\" \"E\" \"F\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight\n", "height\n", "subject" ] }, { "cell_type": "markdown", "id": "5502a330", "metadata": {}, "source": [ "#### Creating a vector with a particular size" ] }, { "cell_type": "code", "execution_count": 11, "id": "4e59d217", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li><li>0</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\item 0\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 0\n", "2. 0\n", "3. 0\n", "4. 0\n", "5. 0\n", "6. 0\n", "7. 0\n", "8. 0\n", "9. 0\n", "10. 0\n", "\n", "\n" ], "text/plain": [ " [1] 0 0 0 0 0 0 0 0 0 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vec <- rep(0, 10)\n", "vec" ] }, { "cell_type": "markdown", "id": "6689bea8", "metadata": {}, "source": [ "#### Vector length" ] }, { "cell_type": "code", "execution_count": 12, "id": "8b9a291b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "6" ], "text/latex": [ "6" ], "text/markdown": [ "6" ], "text/plain": [ "[1] 6" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "length(weight)\n" ] }, { "cell_type": "markdown", "id": "e2bfeacd", "metadata": {}, "source": [ "#### Vector indexes: from one to the length of the vector" ] }, { "cell_type": "code", "execution_count": 13, "id": "be37ba67", "metadata": {}, "outputs": [ { "data": { "text/html": [ "60" ], "text/latex": [ "60" ], "text/markdown": [ "60" ], "text/plain": [ "[1] 60" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "72" ], "text/latex": [ "72" ], "text/markdown": [ "72" ], "text/plain": [ "[1] 72" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weight[1]\n", "weight[length(weight)]" ] }, { "cell_type": "markdown", "id": "8e052e4d", "metadata": {}, "source": [ "#### Iteration: for loop\n", "from one to the length of weight" ] }, { "cell_type": "code", "execution_count": 14, "id": "d89d1ae9", "metadata": {}, "outputs": [], "source": [ "bmi <- 0\n", "for (i in 1:length(weight)) {\n", " bmi[i] <- weight[i]/height[i]^2\n", "}\n" ] }, { "cell_type": "markdown", "id": "44062e04", "metadata": {}, "source": [ "evaluation of the bmi vector" ] }, { "cell_type": "code", "execution_count": 15, "id": "3bc6e592", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>19.5918367346939</li><li>22.2222222222222</li><li>20.9366391184573</li><li>24.9307479224377</li><li>31.3779891663364</li><li>19.7363010882377</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi" ] }, { "cell_type": "markdown", "id": "78af2d5a", "metadata": {}, "source": [ "#### Iteration: while loop\n", "run while i is below or equal to the length of weight" ] }, { "cell_type": "code", "execution_count": 16, "id": "708de5df", "metadata": {}, "outputs": [], "source": [ "bmi <- 0\n", "i <- 1\n", "while (i <= length(weight)) {\n", " bmi[i] <- weight[i]/height[i]^2\n", " i <- i + 1\n", "}\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "7449e07e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>19.5918367346939</li><li>22.2222222222222</li><li>20.9366391184573</li><li>24.9307479224377</li><li>31.3779891663364</li><li>19.7363010882377</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi" ] }, { "cell_type": "markdown", "id": "9394fcf5", "metadata": {}, "source": [ "#### Remove a variable" ] }, { "cell_type": "code", "execution_count": 18, "id": "2c181a87", "metadata": {}, "outputs": [ { "data": { "text/html": [ "FALSE" ], "text/latex": [ "FALSE" ], "text/markdown": [ "FALSE" ], "text/plain": [ "[1] FALSE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rm(bmi)\n", "exists(\"bmi\")" ] }, { "cell_type": "markdown", "id": "6203ac32", "metadata": {}, "source": [ "#### Right way of manipulating vectors: assigning at once" ] }, { "cell_type": "code", "execution_count": 19, "id": "6e6712a2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>19.5918367346939</li><li>22.2222222222222</li><li>20.9366391184573</li><li>24.9307479224377</li><li>31.3779891663364</li><li>19.7363010882377</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi <- weight/height^2 \n", "bmi " ] }, { "cell_type": "markdown", "id": "f654b0ff", "metadata": {}, "source": [ "#### Creating a function\n", "name <- function(parameters) { body }" ] }, { "cell_type": "code", "execution_count": 20, "id": "682911c9", "metadata": {}, "outputs": [], "source": [ "compute_bmi <- function(weight, height) {\n", " bmi <- weight/height^2 \n", " return(bmi)\n", "}" ] }, { "cell_type": "markdown", "id": "4cd72edd", "metadata": {}, "source": [ "#### Using a function with scalars" ] }, { "cell_type": "code", "execution_count": 21, "id": "abd58d44", "metadata": {}, "outputs": [ { "data": { "text/html": [ "19.5918367346939" ], "text/latex": [ "19.5918367346939" ], "text/markdown": [ "19.5918367346939" ], "text/plain": [ "[1] 19.59184" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "bmi <- compute_bmi(60, 1.75)\n", "bmi\n" ] }, { "cell_type": "markdown", "id": "9df8b472", "metadata": {}, "source": [ "#### Using the same function with vectors" ] }, { "cell_type": "code", "execution_count": 22, "id": "90db70cb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>19.5918367346939</li><li>22.2222222222222</li><li>20.9366391184573</li><li>24.9307479224377</li><li>31.3779891663364</li><li>19.7363010882377</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 19.5918367346939\n", "\\item 22.2222222222222\n", "\\item 20.9366391184573\n", "\\item 24.9307479224377\n", "\\item 31.3779891663364\n", "\\item 19.7363010882377\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 19.5918367346939\n", "2. 22.2222222222222\n", "3. 20.9366391184573\n", "4. 24.9307479224377\n", "5. 31.3779891663364\n", "6. 19.7363010882377\n", "\n", "\n" ], "text/plain": [ "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmi <- compute_bmi(weight, height)\n", "bmi" ] }, { "cell_type": "markdown", "id": "c7e11f53", "metadata": {}, "source": [ "#### Example of a function to compute the average\n", "(iterating in all elements of the vector)" ] }, { "cell_type": "code", "execution_count": 23, "id": "67374bc5", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " s <- 0\n", " n <- length(vec)\n", " for (x in vec) {\n", " s <- s + x \n", " }\n", " return(s/n)\n", "}" ] }, { "cell_type": "markdown", "id": "b683d41d", "metadata": {}, "source": [ "invoking the function" ] }, { "cell_type": "code", "execution_count": 24, "id": "8af439bb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "23.1326227087309" ], "text/latex": [ "23.1326227087309" ], "text/markdown": [ "23.1326227087309" ], "text/plain": [ "[1] 23.13262" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_bmi <- average(bmi)\n", "avg_bmi" ] }, { "cell_type": "markdown", "id": "7925f58c", "metadata": {}, "source": [ "#### Example of a function to compute the average\n", "(manipulating vectors at once)" ] }, { "cell_type": "code", "execution_count": 25, "id": "d81ade80", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " s <- sum(vec)\n", " n <- length(vec)\n", " return(s/n)\n", "}" ] }, { "cell_type": "markdown", "id": "e85fe370", "metadata": {}, "source": [ "invoking the function" ] }, { "cell_type": "code", "execution_count": 26, "id": "acd8835e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "23.1326227087309" ], "text/latex": [ "23.1326227087309" ], "text/markdown": [ "23.1326227087309" ], "text/plain": [ "[1] 23.13262" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_bmi <- average(bmi)\n", "avg_bmi" ] }, { "cell_type": "markdown", "id": "29d0264b", "metadata": {}, "source": [ "#### Average function using mean function\n", "Major statistical functions are available in R" ] }, { "cell_type": "code", "execution_count": 27, "id": "f46e574c", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " return(mean(vec))\n", "}" ] }, { "cell_type": "markdown", "id": "0a6609d2", "metadata": {}, "source": [ "invoking the function" ] }, { "cell_type": "code", "execution_count": 28, "id": "82116583", "metadata": {}, "outputs": [ { "data": { "text/html": [ "23.1326227087309" ], "text/latex": [ "23.1326227087309" ], "text/markdown": [ "23.1326227087309" ], "text/plain": [ "[1] 23.13262" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "avg_bmi <- average(bmi)\n", "avg_bmi" ] }, { "cell_type": "markdown", "id": "cd0eded4", "metadata": {}, "source": [ "#### Working with vectors with NA\n", "Operations with NA lead to NA." ] }, { "cell_type": "code", "execution_count": 29, "id": "a76dcffa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<NA>" ], "text/latex": [ "<NA>" ], "text/markdown": [ "<NA>" ], "text/plain": [ "[1] NA" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- c(10, NA, 13)\n", "y <- average(x)\n", "y" ] }, { "cell_type": "markdown", "id": "ad2e1300", "metadata": {}, "source": [ "#### addressing NA with na.rm=TRUE" ] }, { "cell_type": "code", "execution_count": 30, "id": "38a57461", "metadata": {}, "outputs": [], "source": [ "average <- function(vec) {\n", " return(mean(vec, na.rm=TRUE))\n", "}" ] }, { "cell_type": "code", "execution_count": 31, "id": "6fc66146", "metadata": {}, "outputs": [ { "data": { "text/html": [ "11.5" ], "text/latex": [ "11.5" ], "text/markdown": [ "11.5" ], "text/plain": [ "[1] 11.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- c(10, NA, 13)\n", "y <- average(x)\n", "y" ] }, { "cell_type": "markdown", "id": "5143eaa8", "metadata": {}, "source": [ "#### Plotting graphics\n", "scatter plots" ] }, { "cell_type": "code", "execution_count": 32, "id": "5bc25389", "metadata": {}, "outputs": [ { "data": { "image/png": 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pkf/uNqdOXRT/pkfviPq9GVRz/pk/nh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\ngj1i/OZO9ogAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "plot(height, weight)" ] }, { "cell_type": "markdown", "id": "f6c2f13d", "metadata": {}, "source": [ "#### Most functions contain many default parameters" ] }, { "cell_type": "code", "execution_count": 33, "id": "de2fc532", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyM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\nACFBgJAgQEgQICQIEBIECAkC/gMAWV/ScE8QGwAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "plot(height, weight, pch=2)" ] }, { "cell_type": "markdown", "id": "3beca799", "metadata": {}, "source": [ "#### Default function arguments can be shown with args" ] }, { "cell_type": "code", "execution_count": 34, "id": "a03fe7dc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre class=language-r><code>function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", "<span style=white-space:pre-wrap> log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, </span>\n", "<span style=white-space:pre-wrap> ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, </span>\n", "<span style=white-space:pre-wrap> panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, </span>\n", "<span style=white-space:pre-wrap> ...) </span>\n", "NULL</code></pre>" ], "text/latex": [ "\\begin{minted}{r}\n", "function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", " log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, \n", " ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, \n", " panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, \n", " ...) \n", "NULL\n", "\\end{minted}" ], "text/markdown": [ "```r\n", "function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", " log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, \n", " ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, \n", " panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, \n", " ...) \n", "NULL\n", "```" ], "text/plain": [ "function (x, y = NULL, type = \"p\", xlim = NULL, ylim = NULL, \n", " log = \"\", main = NULL, sub = NULL, xlab = NULL, ylab = NULL, \n", " ann = par(\"ann\"), axes = TRUE, frame.plot = axes, panel.first = NULL, \n", " panel.last = NULL, asp = NA, xgap.axis = NA, ygap.axis = NA, \n", " ...) \n", "NULL" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "args(plot.default)" ] }, { "cell_type": "markdown", "id": "87d4c7ee", "metadata": {}, "source": [ "#### All functions in R that belongs to packages have help with examples" ] }, { "cell_type": "code", "execution_count": 35, "id": "6226b10f", "metadata": {}, "outputs": [], "source": [ "?base::plot" ] }, { "cell_type": "markdown", "id": "8a6491dd", "metadata": {}, "source": [ "#### Canvas for plotting is still active until a new plot" ] }, { "cell_type": "code", "execution_count": 36, "id": "c37b7038", "metadata": {}, "outputs": [ { "data": { "image/png": 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CsVcTsh/XYXKrgPDIeQfr4Dvd8DhkVI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\nCAkChAQBQoIAIUHAf9lCPtXZOcSjAAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "plot(height, weight)\n", "hh = c(1.65, 1.70, 1.75, 1.80, 1.85, 1.90)\n", "lines(hh, 22.5 * hh^2)" ] }, { "cell_type": "markdown", "id": "caf65b7c", "metadata": {}, "source": [ "#### Factors\n", "Factors are used to handle categorical data." ] }, { "cell_type": "code", "execution_count": 37, "id": "38fa7999", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>0</li><li>3</li><li>2</li><li>2</li><li>1</li></ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<style>\n", "\t.list-inline {list-style: none; margin:0; padding: 0}\n", "\t.list-inline>li {display: inline-block}\n", "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\t</style>\n", "\t<ol class=list-inline><li>'0'</li><li>'1'</li><li>'2'</li><li>'3'</li></ol>\n", "</details>" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 0\n", "\\item 3\n", "\\item 2\n", "\\item 2\n", "\\item 1\n", "\\end{enumerate}\n", "\n", "\\emph{Levels}: \\begin{enumerate}\n", "\\item '0'\n", "\\item '1'\n", "\\item '2'\n", "\\item '3'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 0\n", "2. 3\n", "3. 2\n", "4. 2\n", "5. 1\n", "\n", "\n", "\n", "Levels: 1. '0'\n", "2. '1'\n", "3. '2'\n", "4. '3'\n", "\n", "\n" ], "text/plain": [ "[1] 0 3 2 2 1\n", "Levels: 0 < 1 < 2 < 3" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pain <- c(0,3,2,2,1)\n", "fpain <- factor(pain,levels=0:3, ordered=TRUE)\n", "fpain" ] }, { "cell_type": "markdown", "id": "300d0d78", "metadata": {}, "source": [ "#### Levels provide correspondence between numerical values and categorical labels" ] }, { "cell_type": "code", "execution_count": 38, "id": "9a683e5e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>none</li><li>severe</li><li>medium</li><li>medium</li><li>mild</li></ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<style>\n", "\t.list-inline {list-style: none; margin:0; padding: 0}\n", "\t.list-inline>li {display: inline-block}\n", "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\t</style>\n", "\t<ol class=list-inline><li>'none'</li><li>'mild'</li><li>'medium'</li><li>'severe'</li></ol>\n", "</details>" ], "text/latex": [ "\\begin{enumerate}\n", "\\item none\n", "\\item severe\n", "\\item medium\n", "\\item medium\n", "\\item mild\n", "\\end{enumerate}\n", "\n", "\\emph{Levels}: \\begin{enumerate}\n", "\\item 'none'\n", "\\item 'mild'\n", "\\item 'medium'\n", "\\item 'severe'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. none\n", "2. severe\n", "3. medium\n", "4. medium\n", "5. mild\n", "\n", "\n", "\n", "Levels: 1. 'none'\n", "2. 'mild'\n", "3. 'medium'\n", "4. 'severe'\n", "\n", "\n" ], "text/plain": [ "[1] none severe medium medium mild \n", "Levels: none < mild < medium < severe" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "levels(fpain) <- c(\"none\",\"mild\",\"medium\",\"severe\")\n", "fpain" ] }, { "cell_type": "markdown", "id": "d019846e", "metadata": {}, "source": [ "#### Convert height to factor\n", "Levels: small, medium, high" ] }, { "cell_type": "markdown", "id": "f1dfffae", "metadata": {}, "source": [ "#### coding setting element by element" ] }, { "cell_type": "code", "execution_count": 39, "id": "07d36349", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>medium</li><li>medium</li><li>short</li><li>tall</li><li>medium</li><li>tall</li></ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<style>\n", "\t.list-inline {list-style: none; margin:0; padding: 0}\n", "\t.list-inline>li {display: inline-block}\n", "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\t</style>\n", "\t<ol class=list-inline><li>'medium'</li><li>'short'</li><li>'tall'</li></ol>\n", "</details>" ], "text/latex": [ "\\begin{enumerate}\n", "\\item medium\n", "\\item medium\n", "\\item short\n", "\\item tall\n", "\\item medium\n", "\\item tall\n", "\\end{enumerate}\n", "\n", "\\emph{Levels}: \\begin{enumerate}\n", "\\item 'medium'\n", "\\item 'short'\n", "\\item 'tall'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. medium\n", "2. medium\n", "3. short\n", "4. tall\n", "5. medium\n", "6. tall\n", "\n", "\n", "\n", "Levels: 1. 'medium'\n", "2. 'short'\n", "3. 'tall'\n", "\n", "\n" ], "text/plain": [ "[1] medium medium short tall medium tall \n", "Levels: medium short tall" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lev <- rep(\"\", length(height))\n", "\n", "for (i in 1:length(height)) {\n", " if (height[i] < 1.7)\n", " lev[i] <- \"short\"\n", " else if (height[i] < 1.9)\n", " lev[i] <- \"medium\"\n", " else \n", " lev[i] <- \"tall\"\n", "}\n", "lev <- as.factor(lev)\n", "lev" ] }, { "cell_type": "markdown", "id": "569660bc", "metadata": {}, "source": [ "#### coding setting the vector at once\n", "It uses the cut function." ] }, { "cell_type": "code", "execution_count": 40, "id": "d81b0ae6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>(1.7,1.9]</li><li>(1.7,1.9]</li><li>(0,1.7]</li><li>(1.7,1.9]</li><li>(1.7,1.9]</li><li>(1.9,1.8e+308]</li></ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<style>\n", "\t.list-inline {list-style: none; margin:0; padding: 0}\n", "\t.list-inline>li {display: inline-block}\n", "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\t</style>\n", "\t<ol class=list-inline><li>'(0,1.7]'</li><li>'(1.7,1.9]'</li><li>'(1.9,1.8e+308]'</li></ol>\n", "</details>" ], "text/latex": [ "\\begin{enumerate}\n", "\\item (1.7,1.9{]}\n", "\\item (1.7,1.9{]}\n", "\\item (0,1.7{]}\n", "\\item (1.7,1.9{]}\n", "\\item (1.7,1.9{]}\n", "\\item (1.9,1.8e+308{]}\n", "\\end{enumerate}\n", "\n", "\\emph{Levels}: \\begin{enumerate}\n", "\\item '(0,1.7{]}'\n", "\\item '(1.7,1.9{]}'\n", "\\item '(1.9,1.8e+308{]}'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. (1.7,1.9]\n", "2. (1.7,1.9]\n", "3. (0,1.7]\n", "4. (1.7,1.9]\n", "5. (1.7,1.9]\n", "6. (1.9,1.8e+308]\n", "\n", "\n", "\n", "Levels: 1. '(0,1.7]'\n", "2. '(1.7,1.9]'\n", "3. '(1.9,1.8e+308]'\n", "\n", "\n" ], "text/plain": [ "[1] (1.7,1.9] (1.7,1.9] (0,1.7] (1.7,1.9] (1.7,1.9] \n", "[6] (1.9,1.8e+308]\n", "Levels: (0,1.7] < (1.7,1.9] < (1.9,1.8e+308]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>medium</li><li>medium</li><li>short</li><li>medium</li><li>medium</li><li>tall</li></ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<style>\n", "\t.list-inline {list-style: none; margin:0; padding: 0}\n", "\t.list-inline>li {display: inline-block}\n", "\t.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "\t</style>\n", "\t<ol class=list-inline><li>'short'</li><li>'medium'</li><li>'tall'</li></ol>\n", "</details>" ], "text/latex": [ "\\begin{enumerate}\n", "\\item medium\n", "\\item medium\n", "\\item short\n", "\\item medium\n", "\\item medium\n", "\\item tall\n", "\\end{enumerate}\n", "\n", "\\emph{Levels}: \\begin{enumerate}\n", "\\item 'short'\n", "\\item 'medium'\n", "\\item 'tall'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. medium\n", "2. medium\n", "3. short\n", "4. medium\n", "5. medium\n", "6. tall\n", "\n", "\n", "\n", "Levels: 1. 'short'\n", "2. 'medium'\n", "3. 'tall'\n", "\n", "\n" ], "text/plain": [ "[1] medium medium short medium medium tall \n", "Levels: short < medium < tall" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lev <- cut(height, breaks=c(0, 1.7, 1.9, .Machine$double.xmax), ordered=TRUE)\n", "lev\n", "levels(lev) <- c(\"short\", \"medium\", \"tall\")\n", "lev" ] }, { "cell_type": "markdown", "id": "a7b75411", "metadata": {}, "source": [ "#### Matrix\n", "Matrices can be filled from vectors or data frames. " ] }, { "cell_type": "code", "execution_count": 41, "id": "d900a570", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>1</li><li>2</li><li>3</li><li>4</li><li>5</li><li>6</li><li>7</li><li>8</li><li>9</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 1\n", "\\item 2\n", "\\item 3\n", "\\item 4\n", "\\item 5\n", "\\item 6\n", "\\item 7\n", "\\item 8\n", "\\item 9\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 1\n", "2. 2\n", "3. 3\n", "4. 4\n", "5. 5\n", "6. 6\n", "7. 7\n", "8. 8\n", "9. 9\n", "\n", "\n" ], "text/plain": [ "[1] 1 2 3 4 5 6 7 8 9" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- 1:9\n", "x" ] }, { "cell_type": "markdown", "id": "fab3bf6e", "metadata": {}, "source": [ "#### Converting a vector to matrix" ] }, { "cell_type": "code", "execution_count": 42, "id": "36dd40b8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type int</caption>\n", "<tbody>\n", "\t<tr><td>1</td><td>4</td><td>7</td></tr>\n", "\t<tr><td>2</td><td>5</td><td>8</td></tr>\n", "\t<tr><td>3</td><td>6</td><td>9</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type int\n", "\\begin{tabular}{lll}\n", "\t 1 & 4 & 7\\\\\n", "\t 2 & 5 & 8\\\\\n", "\t 3 & 6 & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type int\n", "\n", "\| 1 \| 4 \| 7 \|\n", "\| 2 \| 5 \| 8 \|\n", "\| 3 \| 6 \| 9 \|\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 1 4 7 \n", "[2,] 2 5 8 \n", "[3,] 3 6 9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim(x) <- c(3,3)\n", "x" ] }, { "cell_type": "markdown", "id": "013a9fa5", "metadata": {}, "source": [ "#### accessing elements from a matrix" ] }, { "cell_type": "code", "execution_count": 43, "id": "31aad9ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] 1\n", "[1] 4\n", "[1] 7\n", "[1] 2\n", "[1] 5\n", "[1] 8\n", "[1] 3\n", "[1] 6\n", "[1] 9\n" ] } ], "source": [ "for (i in 1:nrow(x)) \n", " for (j in 1:ncol(x))\n", " print(x[i,j])\n", " \n" ] }, { "cell_type": "markdown", "id": "582a8811", "metadata": {}, "source": [ "#### Iterating and assigning values to each element" ] }, { "cell_type": "code", "execution_count": 44, "id": "ad2239bd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type dbl</caption>\n", "<tbody>\n", "\t<tr><td>3</td><td>12</td><td>21</td></tr>\n", "\t<tr><td>6</td><td>15</td><td>24</td></tr>\n", "\t<tr><td>9</td><td>18</td><td>27</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type dbl\n", "\\begin{tabular}{lll}\n", "\t 3 & 12 & 21\\\\\n", "\t 6 & 15 & 24\\\\\n", "\t 9 & 18 & 27\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type dbl\n", "\n", "\| 3 \| 12 \| 21 \|\n", "\| 6 \| 15 \| 24 \|\n", "\| 9 \| 18 \| 27 \|\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 3 12 21 \n", "[2,] 6 15 24 \n", "[3,] 9 18 27 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y <- x\n", "for (i in 1:nrow(y)) \n", " for (j in 1:ncol(y))\n", " y[i,j] <- 3 * y[i, j]\n", " \n", "y" ] }, { "cell_type": "markdown", "id": "ea7e0255", "metadata": {}, "source": [ "#### Assigning the values of a matrix at once" ] }, { "cell_type": "code", "execution_count": 45, "id": "a6c41f00", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type dbl</caption>\n", "<tbody>\n", "\t<tr><td>3</td><td>12</td><td>21</td></tr>\n", "\t<tr><td>6</td><td>15</td><td>24</td></tr>\n", "\t<tr><td>9</td><td>18</td><td>27</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type dbl\n", "\\begin{tabular}{lll}\n", "\t 3 & 12 & 21\\\\\n", "\t 6 & 15 & 24\\\\\n", "\t 9 & 18 & 27\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type dbl\n", "\n", "\| 3 \| 12 \| 21 \|\n", "\| 6 \| 15 \| 24 \|\n", "\| 9 \| 18 \| 27 \|\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 3 12 21 \n", "[2,] 6 15 24 \n", "[3,] 9 18 27 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y <- 3x\n", "y" ] }, { "cell_type": "markdown", "id": "e4503b07", "metadata": {}, "source": [ "#### Converting a vector to a matrix by row" ] }, { "cell_type": "code", "execution_count": 46, "id": "6c1a2c28", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type int</caption>\n", "<tbody>\n", "\t<tr><td>1</td><td>2</td><td>3</td></tr>\n", "\t<tr><td>4</td><td>5</td><td>6</td></tr>\n", "\t<tr><td>7</td><td>8</td><td>9</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type int\n", "\\begin{tabular}{lll}\n", "\t 1 & 2 & 3\\\\\n", "\t 4 & 5 & 6\\\\\n", "\t 7 & 8 & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type int\n", "\n", "\| 1 \| 2 \| 3 \|\n", "\| 4 \| 5 \| 6 \|\n", "\| 7 \| 8 \| 9 \|\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 1 2 3 \n", "[2,] 4 5 6 \n", "[3,] 7 8 9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- matrix(1:9,nrow=3,byrow=T)\n", "x" ] }, { "cell_type": "markdown", "id": "2f8b5706", "metadata": {}, "source": [ "#### transposing a matrix" ] }, { "cell_type": "code", "execution_count": 47, "id": "2b2d54df", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 3 × 3 of type int</caption>\n", "<tbody>\n", "\t<tr><td>1</td><td>4</td><td>7</td></tr>\n", "\t<tr><td>2</td><td>5</td><td>8</td></tr>\n", "\t<tr><td>3</td><td>6</td><td>9</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 3 × 3 of type int\n", "\\begin{tabular}{lll}\n", "\t 1 & 4 & 7\\\\\n", "\t 2 & 5 & 8\\\\\n", "\t 3 & 6 & 9\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 3 × 3 of type int\n", "\n", "\| 1 \| 4 \| 7 \|\n", "\| 2 \| 5 \| 8 \|\n", "\| 3 \| 6 \| 9 \|\n", "\n" ], "text/plain": [ " [,1] [,2] [,3]\n", "[1,] 1 4 7 \n", "[2,] 2 5 8 \n", "[3,] 3 6 9 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x <- t(x)\n", "x" ] }, { "cell_type": "markdown", "id": "4eb25f6b", "metadata": {}, "source": [ "#### computing the determinant of a matrix" ] }, { "cell_type": "code", "execution_count": 48, "id": "10b6b6fa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "0" ], "text/latex": [ "0" ], "text/markdown": [ "0" ], "text/plain": [ "[1] 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "det(x)" ] }, { "cell_type": "markdown", "id": "a8dd0d35", "metadata": {}, "source": [ "#### Lists\n", "Lists are used to work with \"objects\"" ] }, { "cell_type": "code", "execution_count": 49, "id": "354a54cd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<ol>\n", "\t<li><style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>5260</li><li>5470</li><li>5640</li><li>6180</li><li>6390</li><li>6515</li><li>6805</li><li>7515</li><li>7515</li><li>8230</li><li>8770</li></ol>\n", "</li>\n", "\t<li><style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>3910</li><li>4220</li><li>3885</li><li>5160</li><li>5645</li><li>4680</li><li>5265</li><li>5975</li><li>6790</li><li>6900</li><li>7335</li></ol>\n", "</li>\n", "\t<li>0</li>\n", "\t<li>'a'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item \\begin{enumerate}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\item 6515\n", "\\item 6805\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate}\n", "\n", "\\item \\begin{enumerate}\n", "\\item 3910\n", "\\item 4220\n", "\\item 3885\n", "\\item 5160\n", "\\item 5645\n", "\\item 4680\n", "\\item 5265\n", "\\item 5975\n", "\\item 6790\n", "\\item 6900\n", "\\item 7335\n", "\\end{enumerate}\n", "\n", "\\item 0\n", "\\item 'a'\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "6. 6515\n", "7. 6805\n", "8. 7515\n", "9. 7515\n", "10. 8230\n", "11. 8770\n", "\n", "\n", "\n", "2. 1. 3910\n", "2. 4220\n", "3. 3885\n", "4. 5160\n", "5. 5645\n", "6. 4680\n", "7. 5265\n", "8. 5975\n", "9. 6790\n", "10. 6900\n", "11. 7335\n", "\n", "\n", "\n", "3. 0\n", "4. 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'b'\n", "\n", "\n" ], "text/plain": [ "[[1]]\n", " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770\n", "\n", "[[2]]\n", " [1] 3910 4220 3885 5160 5645 4680 5265 5975 6790 6900 7335\n", "\n", "[[3]]\n", "[1] 0\n", "\n", "[[4]]\n", "[1] \"a\"\n", "\n", "[[5]]\n", "[1] \"b\"\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n <- length(mybag)\n", "mybag[[n+1]] <- \"b\"\n", "mybag" ] }, { "cell_type": "markdown", "id": "99098b73", "metadata": {}, "source": [ "#### List slicing" ] }, { "cell_type": "code", "execution_count": 51, "id": "8fd47b0b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<ol>\n", "\t<li><style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>5260</li><li>5470</li><li>5640</li><li>6180</li><li>6390</li><li>6515</li><li>6805</li><li>7515</li><li>7515</li><li>8230</li><li>8770</li></ol>\n", "</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item \\begin{enumerate}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\item 6515\n", "\\item 6805\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate}\n", "\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "6. 6515\n", "7. 6805\n", "8. 7515\n", "9. 7515\n", "10. 8230\n", "11. 8770\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "[[1]]\n", " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "slice <- mybag[1]\n", "slice\n", "is.list(slice)" ] }, { "cell_type": "markdown", "id": "ec157ec8", "metadata": {}, "source": [ "#### Slicing is also a list" ] }, { "cell_type": "code", "execution_count": 52, "id": "946d60f4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<ol>\n", "\t<li><style>\n", ".list-inline {list-style: none; 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margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>5260</li><li>5470</li><li>5640</li><li>6180</li><li>6390</li><li>6515</li><li>6805</li><li>7515</li><li>7515</li><li>8230</li><li>8770</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 5260\n", "\\item 5470\n", "\\item 5640\n", "\\item 6180\n", "\\item 6390\n", "\\item 6515\n", "\\item 6805\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 5260\n", "2. 5470\n", "3. 5640\n", "4. 6180\n", "5. 6390\n", "6. 6515\n", "7. 6805\n", "8. 7515\n", "9. 7515\n", "10. 8230\n", "11. 8770\n", "\n", "\n" ], "text/plain": [ " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h <- mybag[[1]]\n", "h" ] }, { "cell_type": "markdown", "id": "aa046dc8", "metadata": {}, "source": [ "An element can be evaluated. \n", "In this case, it is a vector." ] }, { "cell_type": "code", "execution_count": 55, "id": "95eda4bb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "TRUE" ], "text/latex": [ "TRUE" ], "text/markdown": [ "TRUE" ], "text/plain": [ "[1] TRUE" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "FALSE" ], "text/latex": [ "FALSE" ], "text/markdown": [ "FALSE" ], "text/plain": [ "[1] FALSE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "is.vector(h)\n", "is.list(h)" ] }, { "cell_type": "markdown", "id": "38a8fabf", "metadata": {}, "source": [ "#### Naming variables\n", "They are properties on the list" ] }, { "cell_type": "code", "execution_count": 56, "id": "4da009f5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<dl>\n", "\t<dt>$x</dt>\n", "\t\t<dd><style>\n", ".list-inline {list-style: none; 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" ] }, { "cell_type": "code", "execution_count": 58, "id": "c3e56d5d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>A</th><th scope=col>B</th></tr>\n", "\t<tr><th></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>5260</td><td>3910</td></tr>\n", "\t<tr><th scope=row>2</th><td>5470</td><td>4220</td></tr>\n", "\t<tr><th scope=row>3</th><td>5640</td><td>3885</td></tr>\n", "\t<tr><th scope=row>4</th><td>6180</td><td>5160</td></tr>\n", "\t<tr><th scope=row>5</th><td>6390</td><td>5645</td></tr>\n", "\t<tr><th scope=row>6</th><td>6515</td><td>4680</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r\|ll}\n", " & A & B\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 5260 & 3910\\\\\n", "\t2 & 5470 & 4220\\\\\n", "\t3 & 5640 & 3885\\\\\n", "\t4 & 6180 & 5160\\\\\n", "\t5 & 6390 & 5645\\\\\n", "\t6 & 6515 & 4680\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "\| <!--/--> \| A <dbl> \| B <dbl> \|\n", "\|---\|---\|---\|\n", "\| 1 \| 5260 \| 3910 \|\n", "\| 2 \| 5470 \| 4220 \|\n", "\| 3 \| 5640 \| 3885 \|\n", "\| 4 \| 6180 \| 5160 \|\n", "\| 5 \| 6390 \| 5645 \|\n", "\| 6 \| 6515 \| 4680 \|\n", "\n" ], "text/plain": [ " A B \n", "1 5260 3910\n", "2 5470 4220\n", "3 5640 3885\n", "4 6180 5160\n", "5 6390 5645\n", "6 6515 4680" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d <- data.frame(A=a, B=b)\n", "head(d)" ] }, { "cell_type": "markdown", "id": "cf05166f", "metadata": {}, "source": [ "#### Adding a column in the data frame " ] }, { "cell_type": "code", "execution_count": 59, "id": "8d526668", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 3</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>A</th><th scope=col>B</th><th scope=col>c</th></tr>\n", "\t<tr><th></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>5260</td><td>3910</td><td> 9170</td></tr>\n", "\t<tr><th scope=row>2</th><td>5470</td><td>4220</td><td> 9690</td></tr>\n", "\t<tr><th scope=row>3</th><td>5640</td><td>3885</td><td> 9525</td></tr>\n", "\t<tr><th scope=row>4</th><td>6180</td><td>5160</td><td>11340</td></tr>\n", "\t<tr><th scope=row>5</th><td>6390</td><td>5645</td><td>12035</td></tr>\n", "\t<tr><th scope=row>6</th><td>6515</td><td>4680</td><td>11195</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 3\n", "\\begin{tabular}{r\|lll}\n", " & A & B & c\\\\\n", " & <dbl> & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 5260 & 3910 & 9170\\\\\n", "\t2 & 5470 & 4220 & 9690\\\\\n", "\t3 & 5640 & 3885 & 9525\\\\\n", "\t4 & 6180 & 5160 & 11340\\\\\n", "\t5 & 6390 & 5645 & 12035\\\\\n", "\t6 & 6515 & 4680 & 11195\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 3\n", "\n", "\| <!--/--> \| A <dbl> \| B <dbl> \| c <dbl> \|\n", "\|---\|---\|---\|---\|\n", "\| 1 \| 5260 \| 3910 \| 9170 \|\n", "\| 2 \| 5470 \| 4220 \| 9690 \|\n", "\| 3 \| 5640 \| 3885 \| 9525 \|\n", "\| 4 \| 6180 \| 5160 \| 11340 \|\n", "\| 5 \| 6390 \| 5645 \| 12035 \|\n", "\| 6 \| 6515 \| 4680 \| 11195 \|\n", "\n" ], "text/plain": [ " A B c \n", "1 5260 3910 9170\n", "2 5470 4220 9690\n", "3 5640 3885 9525\n", "4 6180 5160 11340\n", "5 6390 5645 12035\n", "6 6515 4680 11195" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d$c <- d$A + d$B\n", "head(d)" ] }, { "cell_type": "code", "execution_count": 60, "id": "ed285c21", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>B</th><th scope=col>c</th></tr>\n", "\t<tr><th></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>3910</td><td> 9170</td></tr>\n", "\t<tr><th scope=row>2</th><td>4220</td><td> 9690</td></tr>\n", "\t<tr><th scope=row>3</th><td>3885</td><td> 9525</td></tr>\n", "\t<tr><th scope=row>4</th><td>5160</td><td>11340</td></tr>\n", "\t<tr><th scope=row>5</th><td>5645</td><td>12035</td></tr>\n", "\t<tr><th scope=row>6</th><td>4680</td><td>11195</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r\|ll}\n", " & B & c\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 3910 & 9170\\\\\n", "\t2 & 4220 & 9690\\\\\n", "\t3 & 3885 & 9525\\\\\n", "\t4 & 5160 & 11340\\\\\n", "\t5 & 5645 & 12035\\\\\n", "\t6 & 4680 & 11195\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "\| <!--/--> \| B <dbl> \| c <dbl> \|\n", "\|---\|---\|---\|\n", "\| 1 \| 3910 \| 9170 \|\n", "\| 2 \| 4220 \| 9690 \|\n", "\| 3 \| 3885 \| 9525 \|\n", "\| 4 \| 5160 \| 11340 \|\n", "\| 5 \| 5645 \| 12035 \|\n", "\| 6 \| 4680 \| 11195 \|\n", "\n" ], "text/plain": [ " B c \n", "1 3910 9170\n", "2 4220 9690\n", "3 3885 9525\n", "4 5160 11340\n", "5 5645 12035\n", "6 4680 11195" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d$A <- NULL\n", "head(d)" ] }, { "cell_type": "markdown", "id": "b3c2ac80", "metadata": {}, "source": [ "#### Reading csv file\n", "There are many functions for reading CSV, Excel, and RData formats." ] }, { "cell_type": "code", "execution_count": 61, "id": "cb4df166", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 14</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>Type</th><th scope=col>Alcohol</th><th scope=col>Malic</th><th scope=col>Ash</th><th scope=col>Alcalinity</th><th scope=col>Magnesium</th><th scope=col>Phenols</th><th scope=col>Flavanoids</th><th scope=col>Nonflavanoids</th><th scope=col>Proanthocyanins</th><th scope=col>Color</th><th scope=col>Hue</th><th scope=col>Dilution</th><th scope=col>Proline</th></tr>\n", "\t<tr><th></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><int></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>1</td><td>13.20</td><td>1.78</td><td>2.14</td><td>11.2</td><td>100</td><td>2.65</td><td>2.76</td><td>0.26</td><td>1.28</td><td>4.38</td><td>1.05</td><td>3.40</td><td>1050</td></tr>\n", "\t<tr><th scope=row>2</th><td>1</td><td>13.16</td><td>2.36</td><td>2.67</td><td>18.6</td><td>101</td><td>2.80</td><td>3.24</td><td>0.30</td><td>2.81</td><td>5.68</td><td>1.03</td><td>3.17</td><td>1185</td></tr>\n", "\t<tr><th scope=row>3</th><td>1</td><td>14.37</td><td>1.95</td><td>2.50</td><td>16.8</td><td>113</td><td>3.85</td><td>3.49</td><td>0.24</td><td>2.18</td><td>7.80</td><td>0.86</td><td>3.45</td><td>1480</td></tr>\n", "\t<tr><th scope=row>4</th><td>1</td><td>13.24</td><td>2.59</td><td>2.87</td><td>21.0</td><td>118</td><td>2.80</td><td>2.69</td><td>0.39</td><td>1.82</td><td>4.32</td><td>1.04</td><td>2.93</td><td> 735</td></tr>\n", "\t<tr><th scope=row>5</th><td>1</td><td>14.20</td><td>1.76</td><td>2.45</td><td>15.2</td><td>112</td><td>3.27</td><td>3.39</td><td>0.34</td><td>1.97</td><td>6.75</td><td>1.05</td><td>2.85</td><td>1450</td></tr>\n", "\t<tr><th scope=row>6</th><td>1</td><td>14.39</td><td>1.87</td><td>2.45</td><td>14.6</td><td> 96</td><td>2.50</td><td>2.52</td><td>0.30</td><td>1.98</td><td>5.25</td><td>1.02</td><td>3.58</td><td>1290</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 14\n", "\\begin{tabular}{r\|llllllllllllll}\n", " & Type & Alcohol & Malic & Ash & Alcalinity & Magnesium & Phenols & Flavanoids & Nonflavanoids & Proanthocyanins & Color & Hue & Dilution & Proline\\\\\n", " & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n", "\\hline\n", "\t1 & 1 & 13.20 & 1.78 & 2.14 & 11.2 & 100 & 2.65 & 2.76 & 0.26 & 1.28 & 4.38 & 1.05 & 3.40 & 1050\\\\\n", "\t2 & 1 & 13.16 & 2.36 & 2.67 & 18.6 & 101 & 2.80 & 3.24 & 0.30 & 2.81 & 5.68 & 1.03 & 3.17 & 1185\\\\\n", "\t3 & 1 & 14.37 & 1.95 & 2.50 & 16.8 & 113 & 3.85 & 3.49 & 0.24 & 2.18 & 7.80 & 0.86 & 3.45 & 1480\\\\\n", "\t4 & 1 & 13.24 & 2.59 & 2.87 & 21.0 & 118 & 2.80 & 2.69 & 0.39 & 1.82 & 4.32 & 1.04 & 2.93 & 735\\\\\n", "\t5 & 1 & 14.20 & 1.76 & 2.45 & 15.2 & 112 & 3.27 & 3.39 & 0.34 & 1.97 & 6.75 & 1.05 & 2.85 & 1450\\\\\n", "\t6 & 1 & 14.39 & 1.87 & 2.45 & 14.6 & 96 & 2.50 & 2.52 & 0.30 & 1.98 & 5.25 & 1.02 & 3.58 & 1290\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 14\n", "\n", "\| <!--/--> \| Type <int> \| Alcohol <dbl> \| Malic <dbl> \| Ash <dbl> \| Alcalinity <dbl> \| Magnesium <int> \| Phenols <dbl> \| Flavanoids <dbl> \| Nonflavanoids <dbl> \| Proanthocyanins <dbl> \| Color <dbl> \| Hue <dbl> \| Dilution <dbl> \| Proline <int> \|\n", "\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n", "\| 1 \| 1 \| 13.20 \| 1.78 \| 2.14 \| 11.2 \| 100 \| 2.65 \| 2.76 \| 0.26 \| 1.28 \| 4.38 \| 1.05 \| 3.40 \| 1050 \|\n", "\| 2 \| 1 \| 13.16 \| 2.36 \| 2.67 \| 18.6 \| 101 \| 2.80 \| 3.24 \| 0.30 \| 2.81 \| 5.68 \| 1.03 \| 3.17 \| 1185 \|\n", "\| 3 \| 1 \| 14.37 \| 1.95 \| 2.50 \| 16.8 \| 113 \| 3.85 \| 3.49 \| 0.24 \| 2.18 \| 7.80 \| 0.86 \| 3.45 \| 1480 \|\n", "\| 4 \| 1 \| 13.24 \| 2.59 \| 2.87 \| 21.0 \| 118 \| 2.80 \| 2.69 \| 0.39 \| 1.82 \| 4.32 \| 1.04 \| 2.93 \| 735 \|\n", "\| 5 \| 1 \| 14.20 \| 1.76 \| 2.45 \| 15.2 \| 112 \| 3.27 \| 3.39 \| 0.34 \| 1.97 \| 6.75 \| 1.05 \| 2.85 \| 1450 \|\n", "\| 6 \| 1 \| 14.39 \| 1.87 \| 2.45 \| 14.6 \| 96 \| 2.50 \| 2.52 \| 0.30 \| 1.98 \| 5.25 \| 1.02 \| 3.58 \| 1290 \|\n", "\n" ], "text/plain": [ " Type Alcohol Malic Ash Alcalinity Magnesium Phenols Flavanoids Nonflavanoids\n", "1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 \n", "2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 \n", "3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 \n", "4 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 \n", "5 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 \n", "6 1 14.39 1.87 2.45 14.6 96 2.50 2.52 0.30 \n", " Proanthocyanins Color Hue Dilution Proline\n", "1 1.28 4.38 1.05 3.40 1050 \n", "2 2.81 5.68 1.03 3.17 1185 \n", "3 2.18 7.80 0.86 3.45 1480 \n", "4 1.82 4.32 1.04 2.93 735 \n", "5 1.97 6.75 1.05 2.85 1450 \n", "6 1.98 5.25 1.02 3.58 1290 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wine = read.table(\n", " \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\", \n", " header = TRUE, sep = \",\")\n", " colnames(wine) <- c('Type', 'Alcohol', 'Malic', 'Ash', \n", " 'Alcalinity', 'Magnesium', 'Phenols', \n", " 'Flavanoids', 'Nonflavanoids',\n", " 'Proanthocyanins', 'Color', 'Hue', \n", " 'Dilution', 'Proline')\n", "head(wine)" ] }, { "cell_type": "markdown", "id": "11892ffc", "metadata": {}, "source": [ "#### saving in binary format " ] }, { "cell_type": "code", "execution_count": 62, "id": "2143afee", "metadata": {}, "outputs": [], "source": [ "save(wine, file=\"wine.RData\")" ] }, { "cell_type": "markdown", "id": "837380f2", "metadata": {}, "source": [ "#### removing data frame from memory" ] }, { "cell_type": "code", "execution_count": 63, "id": "cd5bdb2d", "metadata": {}, "outputs": [], "source": [ "rm(wine)" ] }, { "cell_type": "markdown", "id": "c5b982a0", "metadata": {}, "source": [ "#### load binary format" ] }, { "cell_type": "code", "execution_count": 64, "id": "4bb77c1f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 3 × 14</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>Type</th><th scope=col>Alcohol</th><th scope=col>Malic</th><th scope=col>Ash</th><th scope=col>Alcalinity</th><th scope=col>Magnesium</th><th scope=col>Phenols</th><th scope=col>Flavanoids</th><th scope=col>Nonflavanoids</th><th scope=col>Proanthocyanins</th><th scope=col>Color</th><th scope=col>Hue</th><th scope=col>Dilution</th><th scope=col>Proline</th></tr>\n", "\t<tr><th></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><int></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>1</td><td>13.20</td><td>1.78</td><td>2.14</td><td>11.2</td><td>100</td><td>2.65</td><td>2.76</td><td>0.26</td><td>1.28</td><td>4.38</td><td>1.05</td><td>3.40</td><td>1050</td></tr>\n", "\t<tr><th scope=row>2</th><td>1</td><td>13.16</td><td>2.36</td><td>2.67</td><td>18.6</td><td>101</td><td>2.80</td><td>3.24</td><td>0.30</td><td>2.81</td><td>5.68</td><td>1.03</td><td>3.17</td><td>1185</td></tr>\n", "\t<tr><th scope=row>3</th><td>1</td><td>14.37</td><td>1.95</td><td>2.50</td><td>16.8</td><td>113</td><td>3.85</td><td>3.49</td><td>0.24</td><td>2.18</td><td>7.80</td><td>0.86</td><td>3.45</td><td>1480</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 3 × 14\n", "\\begin{tabular}{r\|llllllllllllll}\n", " & Type & Alcohol & Malic & Ash & Alcalinity & Magnesium & Phenols & Flavanoids & Nonflavanoids & Proanthocyanins & Color & Hue & Dilution & Proline\\\\\n", " & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n", "\\hline\n", "\t1 & 1 & 13.20 & 1.78 & 2.14 & 11.2 & 100 & 2.65 & 2.76 & 0.26 & 1.28 & 4.38 & 1.05 & 3.40 & 1050\\\\\n", "\t2 & 1 & 13.16 & 2.36 & 2.67 & 18.6 & 101 & 2.80 & 3.24 & 0.30 & 2.81 & 5.68 & 1.03 & 3.17 & 1185\\\\\n", "\t3 & 1 & 14.37 & 1.95 & 2.50 & 16.8 & 113 & 3.85 & 3.49 & 0.24 & 2.18 & 7.80 & 0.86 & 3.45 & 1480\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 14\n", "\n", "\| <!--/--> \| Type <int> \| Alcohol <dbl> \| Malic <dbl> \| Ash <dbl> \| Alcalinity <dbl> \| Magnesium <int> \| Phenols <dbl> \| Flavanoids <dbl> \| Nonflavanoids <dbl> \| Proanthocyanins <dbl> \| Color <dbl> \| Hue <dbl> \| Dilution <dbl> \| Proline <int> \|\n", "\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n", "\| 1 \| 1 \| 13.20 \| 1.78 \| 2.14 \| 11.2 \| 100 \| 2.65 \| 2.76 \| 0.26 \| 1.28 \| 4.38 \| 1.05 \| 3.40 \| 1050 \|\n", "\| 2 \| 1 \| 13.16 \| 2.36 \| 2.67 \| 18.6 \| 101 \| 2.80 \| 3.24 \| 0.30 \| 2.81 \| 5.68 \| 1.03 \| 3.17 \| 1185 \|\n", "\| 3 \| 1 \| 14.37 \| 1.95 \| 2.50 \| 16.8 \| 113 \| 3.85 \| 3.49 \| 0.24 \| 2.18 \| 7.80 \| 0.86 \| 3.45 \| 1480 \|\n", "\n" ], "text/plain": [ " Type Alcohol Malic Ash Alcalinity Magnesium Phenols Flavanoids Nonflavanoids\n", "1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 \n", "2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 \n", "3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 \n", " Proanthocyanins Color Hue Dilution Proline\n", "1 1.28 4.38 1.05 3.40 1050 \n", "2 2.81 5.68 1.03 3.17 1185 \n", "3 2.18 7.80 0.86 3.45 1480 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "load(\"wine.RData\")\n", "head(wine, 3)" ] }, { "cell_type": "markdown", "id": "dfd0d49f", "metadata": {}, "source": [ "#### exporting data.frame into csv file" ] }, { "cell_type": "code", "execution_count": 65, "id": "99c07fd0", "metadata": {}, "outputs": [], "source": [ "write.table(wine, file=\"wine.csv\", row.names=FALSE, quote = FALSE, sep = \",\")" ] }, { "cell_type": "markdown", "id": "f5147833", "metadata": {}, "source": [ "#### filtering vectors" ] }, { "cell_type": "code", "execution_count": 66, "id": "82575667", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>FALSE</li><li>FALSE</li><li>FALSE</li><li>FALSE</li><li>FALSE</li><li>FALSE</li><li>FALSE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item FALSE\n", "\\item TRUE\n", "\\item TRUE\n", "\\item TRUE\n", "\\item TRUE\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. FALSE\n", "2. FALSE\n", "3. FALSE\n", "4. FALSE\n", "5. FALSE\n", "6. FALSE\n", "7. FALSE\n", "8. TRUE\n", "9. TRUE\n", "10. TRUE\n", "11. TRUE\n", "\n", "\n" ], "text/plain": [ " [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a <- c(5260,5470,5640,6180,6390,6515,6805,7515,7515,8230,8770)\n", "b <- c(3910,4220,3885,5160,5645,4680,5265,5975,6790,6900,7335)\n", "\n", "# logical vector\n", "bool <- (a > 7000)\n", "bool" ] }, { "cell_type": "code", "execution_count": 67, "id": "a03b1408", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>7515</li><li>7515</li><li>8230</li><li>8770</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 7515\n", "\\item 7515\n", "\\item 8230\n", "\\item 8770\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 7515\n", "2. 7515\n", "3. 8230\n", "4. 8770\n", "\n", "\n" ], "text/plain": [ "[1] 7515 7515 8230 8770" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# selecting elements from positions that are true\n", "a[bool] " ] }, { "cell_type": "code", "execution_count": 68, "id": "20ba5879", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>3910</li><li>4220</li><li>3885</li><li>5975</li><li>6790</li><li>6900</li><li>7335</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 3910\n", "\\item 4220\n", "\\item 3885\n", "\\item 5975\n", "\\item 6790\n", "\\item 6900\n", "\\item 7335\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 3910\n", "2. 4220\n", "3. 3885\n", "4. 5975\n", "5. 6790\n", "6. 6900\n", "7. 7335\n", "\n", "\n" ], "text/plain": [ "[1] 3910 4220 3885 5975 6790 6900 7335" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# filtering with logical expressions\n", "b[a < 6000 \| a > 7000]" ] }, { "cell_type": "code", "execution_count": 69, "id": "d976d8c5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>5160</li><li>5645</li><li>4680</li><li>5265</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 5160\n", "\\item 5645\n", "\\item 4680\n", "\\item 5265\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 5160\n", "2. 5645\n", "3. 4680\n", "4. 5265\n", "\n", "\n" ], "text/plain": [ "[1] 5160 5645 4680 5265" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "b[6000 <= a & a <= 7000]" ] }, { "cell_type": "markdown", "id": "8c969809", "metadata": {}, "source": [ "#### filtering data frames" ] }, { "cell_type": "code", "execution_count": 70, "id": "f0175992", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 11 × 3</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>a</th><th scope=col>b</th><th scope=col>c</th></tr>\n", "\t<tr><th></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>5260</td><td>3910</td><td>1350</td></tr>\n", "\t<tr><th scope=row>2</th><td>5470</td><td>4220</td><td>1250</td></tr>\n", "\t<tr><th scope=row>3</th><td>5640</td><td>3885</td><td>1755</td></tr>\n", "\t<tr><th scope=row>4</th><td>6180</td><td>5160</td><td>1020</td></tr>\n", "\t<tr><th scope=row>5</th><td>6390</td><td>5645</td><td> 745</td></tr>\n", "\t<tr><th scope=row>6</th><td>6515</td><td>4680</td><td>1835</td></tr>\n", "\t<tr><th scope=row>7</th><td>6805</td><td>5265</td><td>1540</td></tr>\n", "\t<tr><th scope=row>8</th><td>7515</td><td>5975</td><td>1540</td></tr>\n", "\t<tr><th scope=row>9</th><td>7515</td><td>6790</td><td> 725</td></tr>\n", "\t<tr><th scope=row>10</th><td>8230</td><td>6900</td><td>1330</td></tr>\n", "\t<tr><th scope=row>11</th><td>8770</td><td>7335</td><td>1435</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 11 × 3\n", "\\begin{tabular}{r\|lll}\n", " & a & b & c\\\\\n", " & <dbl> & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 5260 & 3910 & 1350\\\\\n", "\t2 & 5470 & 4220 & 1250\\\\\n", "\t3 & 5640 & 3885 & 1755\\\\\n", "\t4 & 6180 & 5160 & 1020\\\\\n", "\t5 & 6390 & 5645 & 745\\\\\n", "\t6 & 6515 & 4680 & 1835\\\\\n", "\t7 & 6805 & 5265 & 1540\\\\\n", "\t8 & 7515 & 5975 & 1540\\\\\n", "\t9 & 7515 & 6790 & 725\\\\\n", "\t10 & 8230 & 6900 & 1330\\\\\n", "\t11 & 8770 & 7335 & 1435\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 11 × 3\n", "\n", "\| <!--/--> \| a <dbl> \| b <dbl> \| c <dbl> \|\n", "\|---\|---\|---\|---\|\n", "\| 1 \| 5260 \| 3910 \| 1350 \|\n", "\| 2 \| 5470 \| 4220 \| 1250 \|\n", "\| 3 \| 5640 \| 3885 \| 1755 \|\n", "\| 4 \| 6180 \| 5160 \| 1020 \|\n", "\| 5 \| 6390 \| 5645 \| 745 \|\n", "\| 6 \| 6515 \| 4680 \| 1835 \|\n", "\| 7 \| 6805 \| 5265 \| 1540 \|\n", "\| 8 \| 7515 \| 5975 \| 1540 \|\n", "\| 9 \| 7515 \| 6790 \| 725 \|\n", "\| 10 \| 8230 \| 6900 \| 1330 \|\n", "\| 11 \| 8770 \| 7335 \| 1435 \|\n", "\n" ], "text/plain": [ " a b c \n", "1 5260 3910 1350\n", "2 5470 4220 1250\n", "3 5640 3885 1755\n", "4 6180 5160 1020\n", "5 6390 5645 745\n", "6 6515 4680 1835\n", "7 6805 5265 1540\n", "8 7515 5975 1540\n", "9 7515 6790 725\n", "10 8230 6900 1330\n", "11 8770 7335 1435" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data <- data.frame(a=a, b=b)\n", "data$c <- data$a - data$b\n", "head(data, nrow(data))" ] }, { "cell_type": "code", "execution_count": 71, "id": "3f08fc76", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 4 × 3</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>a</th><th scope=col>b</th><th scope=col>c</th></tr>\n", "\t<tr><th></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>8</th><td>7515</td><td>5975</td><td>1540</td></tr>\n", "\t<tr><th scope=row>9</th><td>7515</td><td>6790</td><td> 725</td></tr>\n", "\t<tr><th scope=row>10</th><td>8230</td><td>6900</td><td>1330</td></tr>\n", "\t<tr><th scope=row>11</th><td>8770</td><td>7335</td><td>1435</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 4 × 3\n", "\\begin{tabular}{r\|lll}\n", " & a & b & c\\\\\n", " & <dbl> & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t8 & 7515 & 5975 & 1540\\\\\n", "\t9 & 7515 & 6790 & 725\\\\\n", "\t10 & 8230 & 6900 & 1330\\\\\n", "\t11 & 8770 & 7335 & 1435\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 3\n", "\n", "\| <!--/--> \| a <dbl> \| b <dbl> \| c <dbl> \|\n", "\|---\|---\|---\|---\|\n", "\| 8 \| 7515 \| 5975 \| 1540 \|\n", "\| 9 \| 7515 \| 6790 \| 725 \|\n", "\| 10 \| 8230 \| 6900 \| 1330 \|\n", "\| 11 \| 8770 \| 7335 \| 1435 \|\n", "\n" ], "text/plain": [ " a b c \n", "8 7515 5975 1540\n", "9 7515 6790 725\n", "10 8230 6900 1330\n", "11 8770 7335 1435" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(data[data$a > 7000,])" ] }, { "cell_type": "code", "execution_count": 72, "id": "9bbfd456", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 4 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>a</th><th scope=col>b</th></tr>\n", "\t<tr><th></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>8</th><td>7515</td><td>5975</td></tr>\n", "\t<tr><th scope=row>9</th><td>7515</td><td>6790</td></tr>\n", "\t<tr><th scope=row>10</th><td>8230</td><td>6900</td></tr>\n", "\t<tr><th scope=row>11</th><td>8770</td><td>7335</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 4 × 2\n", "\\begin{tabular}{r\|ll}\n", " & a & b\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t8 & 7515 & 5975\\\\\n", "\t9 & 7515 & 6790\\\\\n", "\t10 & 8230 & 6900\\\\\n", "\t11 & 8770 & 7335\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 2\n", "\n", "\| <!--/--> \| a <dbl> \| b <dbl> \|\n", "\|---\|---\|---\|\n", "\| 8 \| 7515 \| 5975 \|\n", "\| 9 \| 7515 \| 6790 \|\n", "\| 10 \| 8230 \| 6900 \|\n", "\| 11 \| 8770 \| 7335 \|\n", "\n" ], "text/plain": [ " a b \n", "8 7515 5975\n", "9 7515 6790\n", "10 8230 6900\n", "11 8770 7335" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(data[data$a > 7000,c(1,2)])" ] }, { "cell_type": "markdown", "id": "a5454520", "metadata": {}, "source": [ "#### performance with matrix and data frames" ] }, { "cell_type": "code", "execution_count": 73, "id": "cdc8c347", "metadata": {}, "outputs": [], "source": [ "#install.packages(\"pryr\")\n", "library(pryr)\n", "rheight <- rnorm(100000, 1.8, sd=0.2)\n", "rweight <- rnorm(100000, 72, sd=15)" ] }, { "cell_type": "markdown", "id": "f228be75", "metadata": {}, "source": [ "#### computing a entire column at once" ] }, { "cell_type": "code", "execution_count": 74, "id": "dae10733", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Time difference of 0.005038977 secs" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "2,400,984 B" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time <- Sys.time()\n", "hw <- data.frame(height=rheight, weight=rweight)\n", "hw$bmi <- hw$weight/hw$height^2\n", "end_time <- Sys.time()\n", "end_time - start_time\n", "object_size(hw)" ] }, { "cell_type": "markdown", "id": "740b7c7c", "metadata": {}, "source": [ "#### processing cell by cell" ] }, { "cell_type": "code", "execution_count": 75, "id": "f0590ce1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Time difference of 15.38209 secs" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time <- Sys.time()\n", "hw <- data.frame(height=rheight, weight=rweight)\n", "for (i in 1:nrow(hw)) {\n", " hw$bmi[i] <- hw$weight[i]/hw$height[i]^2\n", "}\n", "end_time <- Sys.time()\n", "end_time - start_time" ] }, { "cell_type": "markdown", "id": "feadffd7", "metadata": {}, "source": [ "#### convert the entire column" ] }, { "cell_type": "code", "execution_count": 76, "id": "b5e4b09a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Time difference of 0.233 secs" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start_time <- Sys.time()\n", "hw <- data.frame(height=rheight, weight=rweight)\n", "hw <- as.matrix(hw)\n", "hw <- cbind(hw, 0)\n", "for (i in 1:nrow(hw)) {\n", " hw[i,3] <- hw[i,2]/hw[i,1]^2\n", "}\n", "end_time <- Sys.time()\n", "end_time - start_time\n" ] }, { "cell_type": "markdown", "id": "8314bae7", "metadata": {}, "source": [ "#### apply family\n", "\n", "apply functions can be applied for all rows or columns. \n", "\n", "The first character of the function name establishes the return type (s: simple, l: list)." ] }, { "cell_type": "code", "execution_count": 77, "id": "fa3b939a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>blood.glucose</th><th scope=col>short.velocity</th></tr>\n", "\t<tr><th></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>15.3</td><td>1.76</td></tr>\n", "\t<tr><th scope=row>2</th><td>10.8</td><td>1.34</td></tr>\n", "\t<tr><th scope=row>3</th><td> 8.1</td><td>1.27</td></tr>\n", "\t<tr><th scope=row>4</th><td>19.5</td><td>1.47</td></tr>\n", "\t<tr><th scope=row>5</th><td> 7.2</td><td>1.27</td></tr>\n", "\t<tr><th scope=row>6</th><td> 5.3</td><td>1.49</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r\|ll}\n", " & blood.glucose & short.velocity\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 15.3 & 1.76\\\\\n", "\t2 & 10.8 & 1.34\\\\\n", "\t3 & 8.1 & 1.27\\\\\n", "\t4 & 19.5 & 1.47\\\\\n", "\t5 & 7.2 & 1.27\\\\\n", "\t6 & 5.3 & 1.49\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "\| <!--/--> \| blood.glucose <dbl> \| short.velocity <dbl> \|\n", "\|---\|---\|---\|\n", "\| 1 \| 15.3 \| 1.76 \|\n", "\| 2 \| 10.8 \| 1.34 \|\n", "\| 3 \| 8.1 \| 1.27 \|\n", "\| 4 \| 19.5 \| 1.47 \|\n", "\| 5 \| 7.2 \| 1.27 \|\n", "\| 6 \| 5.3 \| 1.49 \|\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "1 15.3 1.76 \n", "2 10.8 1.34 \n", "3 8.1 1.27 \n", "4 19.5 1.47 \n", "5 7.2 1.27 \n", "6 5.3 1.49 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(ISwR)\n", "data(thuesen)\n", "head(thuesen)" ] }, { "cell_type": "code", "execution_count": 78, "id": "0a409147", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<dl>\n", "\t<dt>$blood.glucose</dt>\n", "\t\t<dd>10.3</dd>\n", "\t<dt>$short.velocity</dt>\n", "\t\t<dd>1.32565217391304</dd>\n", "</dl>\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$blood.glucose] 10.3\n", "\\item[\\$short.velocity] 1.32565217391304\n", "\\end{description}\n" ], "text/markdown": [ "$blood.glucose\n", ": 10.3\n", "$short.velocity\n", ": 1.32565217391304\n", "\n", "\n" ], "text/plain": [ "$blood.glucose\n", "[1] 10.3\n", "\n", "$short.velocity\n", "[1] 1.325652\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#lapply returns a list\n", "lapply(thuesen, mean, na.rm=T)" ] }, { "cell_type": "code", "execution_count": 79, "id": "37e7c1b7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".dl-inline {width: auto; margin:0; padding: 0}\n", ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n", ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n", ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n", "</style><dl class=dl-inline><dt>blood.glucose</dt><dd>10.3</dd><dt>short.velocity</dt><dd>1.32565217391304</dd></dl>\n" ], "text/latex": [ "\\begin{description}\n", "\\item[blood.glucose] 10.3\n", "\\item[short.velocity] 1.32565217391304\n", "\\end{description}\n" ], "text/markdown": [ "blood.glucose\n", ": 10.3short.velocity\n", ": 1.32565217391304\n", "\n" ], "text/plain": [ " blood.glucose short.velocity \n", " 10.300000 1.325652 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#sapply returns a vector\n", "sapply(thuesen, mean, na.rm=T)" ] }, { "cell_type": "code", "execution_count": 80, "id": "a91b1d25", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>1.76</li><li>1.34</li><li>1.27</li><li>1.47</li><li>1.27</li><li>1.49</li><li>1.31</li><li>1.09</li><li>1.18</li><li>1.22</li><li>1.25</li><li>1.19</li><li>1.95</li><li>1.28</li><li>1.52</li><li>8.6</li><li>1.12</li><li>1.37</li><li>1.19</li><li>1.05</li><li>1.32</li><li>1.03</li><li>1.12</li><li>1.7</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 1.76\n", "\\item 1.34\n", "\\item 1.27\n", "\\item 1.47\n", "\\item 1.27\n", "\\item 1.49\n", "\\item 1.31\n", "\\item 1.09\n", "\\item 1.18\n", "\\item 1.22\n", "\\item 1.25\n", "\\item 1.19\n", "\\item 1.95\n", "\\item 1.28\n", "\\item 1.52\n", "\\item 8.6\n", "\\item 1.12\n", "\\item 1.37\n", "\\item 1.19\n", "\\item 1.05\n", "\\item 1.32\n", "\\item 1.03\n", "\\item 1.12\n", "\\item 1.7\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 1.76\n", "2. 1.34\n", "3. 1.27\n", "4. 1.47\n", "5. 1.27\n", "6. 1.49\n", "7. 1.31\n", "8. 1.09\n", "9. 1.18\n", "10. 1.22\n", "11. 1.25\n", "12. 1.19\n", "13. 1.95\n", "14. 1.28\n", "15. 1.52\n", "16. 8.6\n", "17. 1.12\n", "18. 1.37\n", "19. 1.19\n", "20. 1.05\n", "21. 1.32\n", "22. 1.03\n", "23. 1.12\n", "24. 1.7\n", "\n", "\n" ], "text/plain": [ " [1] 1.76 1.34 1.27 1.47 1.27 1.49 1.31 1.09 1.18 1.22 1.25 1.19 1.95 1.28 1.52\n", "[16] 8.60 1.12 1.37 1.19 1.05 1.32 1.03 1.12 1.70" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<style>\n", ".dl-inline {width: auto; margin:0; padding: 0}\n", ".dl-inline>dt, .dl-inline>dd {float: none; width: auto; display: inline-block}\n", ".dl-inline>dt::after {content: \":\\0020\"; padding-right: .5ex}\n", ".dl-inline>dt:not(:first-of-type) {padding-left: .5ex}\n", "</style><dl class=dl-inline><dt>blood.glucose</dt><dd>4.2</dd><dt>short.velocity</dt><dd>1.03</dd></dl>\n" ], "text/latex": [ "\\begin{description}\n", "\\item[blood.glucose] 4.2\n", "\\item[short.velocity] 1.03\n", "\\end{description}\n" ], "text/markdown": [ "blood.glucose\n", ": 4.2short.velocity\n", ": 1.03\n", "\n" ], "text/plain": [ " blood.glucose short.velocity \n", " 4.20 1.03 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# apply - second parameter (1: by rows, 2: by columns)\n", "m <- as.matrix(thuesen)\n", "apply(m, 1, min, na.rm=TRUE)\n", "apply(m, 2, min, na.rm=TRUE)" ] }, { "cell_type": "markdown", "id": "331fe9d8", "metadata": {}, "source": [ "### sort and order" ] }, { "cell_type": "code", "execution_count": 81, "id": "7be5fb8f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>blood.glucose</th><th scope=col>short.velocity</th></tr>\n", "\t<tr><th></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>15.3</td><td>1.76</td></tr>\n", "\t<tr><th scope=row>2</th><td>10.8</td><td>1.34</td></tr>\n", "\t<tr><th scope=row>3</th><td> 8.1</td><td>1.27</td></tr>\n", "\t<tr><th scope=row>4</th><td>19.5</td><td>1.47</td></tr>\n", "\t<tr><th scope=row>5</th><td> 7.2</td><td>1.27</td></tr>\n", "\t<tr><th scope=row>6</th><td> 5.3</td><td>1.49</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r\|ll}\n", " & blood.glucose & short.velocity\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t1 & 15.3 & 1.76\\\\\n", "\t2 & 10.8 & 1.34\\\\\n", "\t3 & 8.1 & 1.27\\\\\n", "\t4 & 19.5 & 1.47\\\\\n", "\t5 & 7.2 & 1.27\\\\\n", "\t6 & 5.3 & 1.49\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "\| <!--/--> \| blood.glucose <dbl> \| short.velocity <dbl> \|\n", "\|---\|---\|---\|\n", "\| 1 \| 15.3 \| 1.76 \|\n", "\| 2 \| 10.8 \| 1.34 \|\n", "\| 3 \| 8.1 \| 1.27 \|\n", "\| 4 \| 19.5 \| 1.47 \|\n", "\| 5 \| 7.2 \| 1.27 \|\n", "\| 6 \| 5.3 \| 1.49 \|\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "1 15.3 1.76 \n", "2 10.8 1.34 \n", "3 8.1 1.27 \n", "4 19.5 1.47 \n", "5 7.2 1.27 \n", "6 5.3 1.49 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(ISwR)\n", "data(thuesen)\n", "head(thuesen)" ] }, { "cell_type": "code", "execution_count": 82, "id": "f7f7f7a1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>4.2</li><li>4.9</li><li>5.2</li><li>5.3</li><li>6.7</li><li>6.7</li><li>7.2</li><li>7.5</li><li>8.1</li><li>8.6</li><li>8.8</li><li>9.3</li><li>9.5</li><li>10.3</li><li>10.8</li><li>11.1</li><li>12.2</li><li>12.5</li><li>13.3</li><li>15.1</li><li>15.3</li><li>16.1</li><li>19</li><li>19.5</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 4.2\n", "\\item 4.9\n", "\\item 5.2\n", "\\item 5.3\n", "\\item 6.7\n", "\\item 6.7\n", "\\item 7.2\n", "\\item 7.5\n", "\\item 8.1\n", "\\item 8.6\n", "\\item 8.8\n", "\\item 9.3\n", "\\item 9.5\n", "\\item 10.3\n", "\\item 10.8\n", "\\item 11.1\n", "\\item 12.2\n", "\\item 12.5\n", "\\item 13.3\n", "\\item 15.1\n", "\\item 15.3\n", "\\item 16.1\n", "\\item 19\n", "\\item 19.5\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 4.2\n", "2. 4.9\n", "3. 5.2\n", "4. 5.3\n", "5. 6.7\n", "6. 6.7\n", "7. 7.2\n", "8. 7.5\n", "9. 8.1\n", "10. 8.6\n", "11. 8.8\n", "12. 9.3\n", "13. 9.5\n", "14. 10.3\n", "15. 10.8\n", "16. 11.1\n", "17. 12.2\n", "18. 12.5\n", "19. 13.3\n", "20. 15.1\n", "21. 15.3\n", "22. 16.1\n", "23. 19\n", "24. 19.5\n", "\n", "\n" ], "text/plain": [ " [1] 4.2 4.9 5.2 5.3 6.7 6.7 7.2 7.5 8.1 8.6 8.8 9.3 9.5 10.3 10.8\n", "[16] 11.1 12.2 12.5 13.3 15.1 15.3 16.1 19.0 19.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sort(thuesen$blood.glucose)" ] }, { "cell_type": "code", "execution_count": 83, "id": "fa5a3d0e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>17</li><li>22</li><li>12</li><li>6</li><li>11</li><li>15</li><li>5</li><li>9</li><li>3</li><li>16</li><li>23</li><li>7</li><li>24</li><li>18</li><li>2</li><li>8</li><li>10</li><li>19</li><li>21</li><li>14</li><li>1</li><li>20</li><li>13</li><li>4</li></ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 17\n", "\\item 22\n", "\\item 12\n", "\\item 6\n", "\\item 11\n", "\\item 15\n", "\\item 5\n", "\\item 9\n", "\\item 3\n", "\\item 16\n", "\\item 23\n", "\\item 7\n", "\\item 24\n", "\\item 18\n", "\\item 2\n", "\\item 8\n", "\\item 10\n", "\\item 19\n", "\\item 21\n", "\\item 14\n", "\\item 1\n", "\\item 20\n", "\\item 13\n", "\\item 4\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 17\n", "2. 22\n", "3. 12\n", "4. 6\n", "5. 11\n", "6. 15\n", "7. 5\n", "8. 9\n", "9. 3\n", "10. 16\n", "11. 23\n", "12. 7\n", "13. 24\n", "14. 18\n", "15. 2\n", "16. 8\n", "17. 10\n", "18. 19\n", "19. 21\n", "20. 14\n", "21. 1\n", "22. 20\n", "23. 13\n", "24. 4\n", "\n", "\n" ], "text/plain": [ " [1] 17 22 12 6 11 15 5 9 3 16 23 7 24 18 2 8 10 19 21 14 1 20 13 4" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "order(thuesen$blood.glucose)" ] }, { "cell_type": "code", "execution_count": 84, "id": "ca65c09c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>blood.glucose</th><th scope=col>short.velocity</th></tr>\n", "\t<tr><th></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>17</th><td>4.2</td><td>1.12</td></tr>\n", "\t<tr><th scope=row>22</th><td>4.9</td><td>1.03</td></tr>\n", "\t<tr><th scope=row>12</th><td>5.2</td><td>1.19</td></tr>\n", "\t<tr><th scope=row>6</th><td>5.3</td><td>1.49</td></tr>\n", "\t<tr><th scope=row>11</th><td>6.7</td><td>1.25</td></tr>\n", "\t<tr><th scope=row>15</th><td>6.7</td><td>1.52</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r\|ll}\n", " & blood.glucose & short.velocity\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t17 & 4.2 & 1.12\\\\\n", "\t22 & 4.9 & 1.03\\\\\n", "\t12 & 5.2 & 1.19\\\\\n", "\t6 & 5.3 & 1.49\\\\\n", "\t11 & 6.7 & 1.25\\\\\n", "\t15 & 6.7 & 1.52\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "\| <!--/--> \| blood.glucose <dbl> \| short.velocity <dbl> \|\n", "\|---\|---\|---\|\n", "\| 17 \| 4.2 \| 1.12 \|\n", "\| 22 \| 4.9 \| 1.03 \|\n", "\| 12 \| 5.2 \| 1.19 \|\n", "\| 6 \| 5.3 \| 1.49 \|\n", "\| 11 \| 6.7 \| 1.25 \|\n", "\| 15 \| 6.7 \| 1.52 \|\n", "\n" ], "text/plain": [ " blood.glucose short.velocity\n", "17 4.2 1.12 \n", "22 4.9 1.03 \n", "12 5.2 1.19 \n", "6 5.3 1.49 \n", "11 6.7 1.25 \n", "15 6.7 1.52 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "o <- order(thuesen$blood.glucose)\n", "sorted <- thuesen[o,]\n", "head(sorted)" ] }, { "cell_type": "markdown", "id": "b73fab4d", "metadata": {}, "source": [ "#### Pipelines\n", "The operator $\\%$>$\\%$ creates a pipeline. \n", "\n", "The first parameter of the next invoked function receives the data from the pipeline. \n", "\n", "Library $dplyr$ contains a set of functions that support relational algebra operations." ] }, { "cell_type": "code", "execution_count": 85, "id": "30b5e039", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 4</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>Year</th><th scope=col>Quarter</th><th scope=col>Flights</th><th scope=col>Delays</th></tr>\n", "\t<tr><th></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2016</td><td>1</td><td>11</td><td>6</td></tr>\n", "\t<tr><th scope=row>2</th><td>2016</td><td>2</td><td>12</td><td>5</td></tr>\n", "\t<tr><th scope=row>3</th><td>2016</td><td>3</td><td>13</td><td>3</td></tr>\n", "\t<tr><th scope=row>4</th><td>2016</td><td>4</td><td>12</td><td>5</td></tr>\n", "\t<tr><th scope=row>5</th><td>2017</td><td>1</td><td>10</td><td>4</td></tr>\n", "\t<tr><th scope=row>6</th><td>2017</td><td>2</td><td> 9</td><td>3</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r\|llll}\n", " & Year & Quarter & Flights & Delays\\\\\n", " & <int> & <int> & <int> & <int>\\\\\n", "\\hline\n", "\t1 & 2016 & 1 & 11 & 6\\\\\n", "\t2 & 2016 & 2 & 12 & 5\\\\\n", "\t3 & 2016 & 3 & 13 & 3\\\\\n", "\t4 & 2016 & 4 & 12 & 5\\\\\n", "\t5 & 2017 & 1 & 10 & 4\\\\\n", "\t6 & 2017 & 2 & 9 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "\| <!--/--> \| Year <int> \| Quarter <int> \| Flights <int> \| Delays <int> \|\n", "\|---\|---\|---\|---\|---\|\n", "\| 1 \| 2016 \| 1 \| 11 \| 6 \|\n", "\| 2 \| 2016 \| 2 \| 12 \| 5 \|\n", "\| 3 \| 2016 \| 3 \| 13 \| 3 \|\n", "\| 4 \| 2016 \| 4 \| 12 \| 5 \|\n", "\| 5 \| 2017 \| 1 \| 10 \| 4 \|\n", "\| 6 \| 2017 \| 2 \| 9 \| 3 \|\n", "\n" ], "text/plain": [ " Year Quarter Flights Delays\n", "1 2016 1 11 6 \n", "2 2016 2 12 5 \n", "3 2016 3 13 3 \n", "4 2016 4 12 5 \n", "5 2017 1 10 4 \n", "6 2017 2 9 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flight_data <- read.table(text = \"Year Quarter Flights Delays\n", " 2016 1 11 6\n", " 2016 2 12 5\n", " 2016 3 13 3\n", " 2016 4 12 5\n", " 2017 1 10 4\n", " 2017 2 9 3\n", " 2017 3 11 4\n", " 2017 4 25 15\n", " 2018 1 14 3\n", " 2018 2 12 5\n", " 2018 3 13 3\n", " 2018 4 15 4\",\n", " header = TRUE,sep = \"\") \n", "head(flight_data)" ] }, { "cell_type": "code", "execution_count": 86, "id": "5b900bf8", "metadata": {}, "outputs": [], "source": [ "#install.packages(\"dplyr\")" ] }, { "cell_type": "code", "execution_count": 87, "id": "b25f0481", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "Attaching package: 'dplyr'\n", "\n", "\n", "The following objects are masked from 'package:stats':\n", "\n", " filter, lag\n", "\n", "\n", "The following objects are masked from 'package:base':\n", "\n", " intersect, setdiff, setequal, union\n", "\n", "\n" ] }, { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 2 × 3</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>Year</th><th scope=col>Quarter</th><th scope=col>Flights</th></tr>\n", "\t<tr><th></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2016</td><td>1</td><td>11</td></tr>\n", "\t<tr><th scope=row>2</th><td>2017</td><td>4</td><td>25</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 2 × 3\n", "\\begin{tabular}{r\|lll}\n", " & Year & Quarter & Flights\\\\\n", " & <int> & <int> & <int>\\\\\n", "\\hline\n", "\t1 & 2016 & 1 & 11\\\\\n", "\t2 & 2017 & 4 & 25\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 2 × 3\n", "\n", "\| <!--/--> \| Year <int> \| Quarter <int> \| Flights <int> \|\n", "\|---\|---\|---\|---\|\n", "\| 1 \| 2016 \| 1 \| 11 \|\n", "\| 2 \| 2017 \| 4 \| 25 \|\n", "\n" ], "text/plain": [ " Year Quarter Flights\n", "1 2016 1 11 \n", "2 2017 4 25 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(dplyr)\n", "result <- flight_data %>% \n", " filter(Delays > 5) %>% \n", " select(Year, Quarter, Flights)\n", "head(result)" ] }, { "cell_type": "code", "execution_count": 88, "id": "eddf7163", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A tibble: 3 × 3</caption>\n", "<thead>\n", "\t<tr><th scope=col>Year</th><th scope=col>mean</th><th scope=col>sd</th></tr>\n", "\t<tr><th scope=col><int></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><td>2016</td><td>12.00</td><td>0.8164966</td></tr>\n", "\t<tr><td>2017</td><td>13.75</td><td>7.5443135</td></tr>\n", "\t<tr><td>2018</td><td>13.50</td><td>1.2909944</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A tibble: 3 × 3\n", "\\begin{tabular}{lll}\n", " Year & mean & sd\\\\\n", " <int> & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t 2016 & 12.00 & 0.8164966\\\\\n", "\t 2017 & 13.75 & 7.5443135\\\\\n", "\t 2018 & 13.50 & 1.2909944\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 3 × 3\n", "\n", "\| Year <int> \| mean <dbl> \| sd <dbl> \|\n", "\|---\|---\|---\|\n", "\| 2016 \| 12.00 \| 0.8164966 \|\n", "\| 2017 \| 13.75 \| 7.5443135 \|\n", "\| 2018 \| 13.50 \| 1.2909944 \|\n", "\n" ], "text/plain": [ " Year mean sd \n", "1 2016 12.00 0.8164966\n", "2 2017 13.75 7.5443135\n", "3 2018 13.50 1.2909944" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(dplyr)\n", "result <- flight_data %>% \n", " group_by(Year) %>% \n", " summarize(mean = mean(Flights), sd = sd(Flights))\n", "head(result)" ] }, { "cell_type": "code", "execution_count": 89, "id": "a3ac3f6e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "12" ], "text/latex": [ "12" ], "text/markdown": [ "12" ], "text/plain": [ "[1] 12" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 4</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>Year</th><th scope=col>Quarter</th><th scope=col>Flights</th><th scope=col>Delays</th></tr>\n", "\t<tr><th></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th><th scope=col><int></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2016</td><td>1</td><td>11</td><td>6</td></tr>\n", "\t<tr><th scope=row>2</th><td>2016</td><td>2</td><td>12</td><td>5</td></tr>\n", "\t<tr><th scope=row>3</th><td>2016</td><td>3</td><td>13</td><td>3</td></tr>\n", "\t<tr><th scope=row>4</th><td>2016</td><td>4</td><td>12</td><td>5</td></tr>\n", "\t<tr><th scope=row>5</th><td>2017</td><td>1</td><td>10</td><td>4</td></tr>\n", "\t<tr><th scope=row>6</th><td>2017</td><td>2</td><td> 9</td><td>3</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r\|llll}\n", " & Year & Quarter & Flights & Delays\\\\\n", " & <int> & <int> & <int> & <int>\\\\\n", "\\hline\n", "\t1 & 2016 & 1 & 11 & 6\\\\\n", "\t2 & 2016 & 2 & 12 & 5\\\\\n", "\t3 & 2016 & 3 & 13 & 3\\\\\n", "\t4 & 2016 & 4 & 12 & 5\\\\\n", "\t5 & 2017 & 1 & 10 & 4\\\\\n", "\t6 & 2017 & 2 & 9 & 3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "\| <!--/--> \| Year <int> \| Quarter <int> \| Flights <int> \| Delays <int> \|\n", "\|---\|---\|---\|---\|---\|\n", "\| 1 \| 2016 \| 1 \| 11 \| 6 \|\n", "\| 2 \| 2016 \| 2 \| 12 \| 5 \|\n", "\| 3 \| 2016 \| 3 \| 13 \| 3 \|\n", "\| 4 \| 2016 \| 4 \| 12 \| 5 \|\n", "\| 5 \| 2017 \| 1 \| 10 \| 4 \|\n", "\| 6 \| 2017 \| 2 \| 9 \| 3 \|\n", "\n" ], "text/plain": [ " Year Quarter Flights Delays\n", "1 2016 1 11 6 \n", "2 2016 2 12 5 \n", "3 2016 3 13 3 \n", "4 2016 4 12 5 \n", "5 2017 1 10 4 \n", "6 2017 2 9 3 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nrow(flight_data)\n", "head(flight_data)" ] }, { "cell_type": "code", "execution_count": 90, "id": "f644b850", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "Attaching package: 'reshape'\n", "\n", "\n", "The following object is masked from 'package:dplyr':\n", "\n", " rename\n", "\n", "\n" ] }, { "data": { "text/html": [ "24" ], "text/latex": [ "24" ], "text/markdown": [ "24" ], "text/plain": [ "[1] 24" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 4</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>Year</th><th scope=col>Quarter</th><th scope=col>variable</th><th scope=col>value</th></tr>\n", "\t<tr><th></th><th scope=col><int></th><th scope=col><int></th><th scope=col><fct></th><th scope=col><int></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>2016</td><td>1</td><td>Flights</td><td>11</td></tr>\n", "\t<tr><th scope=row>2</th><td>2016</td><td>2</td><td>Flights</td><td>12</td></tr>\n", "\t<tr><th scope=row>3</th><td>2016</td><td>3</td><td>Flights</td><td>13</td></tr>\n", "\t<tr><th scope=row>17</th><td>2017</td><td>1</td><td>Delays </td><td> 4</td></tr>\n", "\t<tr><th scope=row>18</th><td>2017</td><td>2</td><td>Delays </td><td> 3</td></tr>\n", "\t<tr><th scope=row>19</th><td>2017</td><td>3</td><td>Delays </td><td> 4</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r\|llll}\n", " & Year & Quarter & variable & value\\\\\n", " & <int> & <int> & <fct> & <int>\\\\\n", "\\hline\n", "\t1 & 2016 & 1 & Flights & 11\\\\\n", "\t2 & 2016 & 2 & Flights & 12\\\\\n", "\t3 & 2016 & 3 & Flights & 13\\\\\n", "\t17 & 2017 & 1 & Delays & 4\\\\\n", "\t18 & 2017 & 2 & Delays & 3\\\\\n", "\t19 & 2017 & 3 & Delays & 4\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "\| <!--/--> \| Year <int> \| Quarter <int> \| variable <fct> \| value <int> \|\n", "\|---\|---\|---\|---\|---\|\n", "\| 1 \| 2016 \| 1 \| Flights \| 11 \|\n", "\| 2 \| 2016 \| 2 \| Flights \| 12 \|\n", "\| 3 \| 2016 \| 3 \| Flights \| 13 \|\n", "\| 17 \| 2017 \| 1 \| Delays \| 4 \|\n", "\| 18 \| 2017 \| 2 \| Delays \| 3 \|\n", "\| 19 \| 2017 \| 3 \| Delays \| 4 \|\n", "\n" ], "text/plain": [ " Year Quarter variable value\n", "1 2016 1 Flights 11 \n", "2 2016 2 Flights 12 \n", "3 2016 3 Flights 13 \n", "17 2017 1 Delays 4 \n", "18 2017 2 Delays 3 \n", "19 2017 3 Delays 4 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#install.packages(reshape)\n", "library(reshape)\n", "result <- melt(flight_data[,c('Year', 'Quarter', 'Flights', 'Delays')], \n", " id.vars = c(1,2))\n", "nrow(result)\n", "head(result[c(1:3,17:19), ])" ] }, { "cell_type": "markdown", "id": "9bfa9ee8", "metadata": {}, "source": [ "#### merge\n", "\n", "The function $merge$ can be used to join data frames. It can be used to produce inner, left, right, and outer joins. " ] }, { "cell_type": "code", "execution_count": 91, "id": "156b7407", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 5 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>city</th><th scope=col>value</th></tr>\n", "\t<tr><th></th><th scope=col><chr></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Rio de Janeiro</td><td>10</td></tr>\n", "\t<tr><th scope=row>2</th><td>Sao Paulo </td><td>12</td></tr>\n", "\t<tr><th scope=row>3</th><td>Paris </td><td>20</td></tr>\n", "\t<tr><th scope=row>4</th><td>New York </td><td>25</td></tr>\n", "\t<tr><th scope=row>5</th><td>Tokyo </td><td>18</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 5 × 2\n", "\\begin{tabular}{r\|ll}\n", " & city & value\\\\\n", " & <chr> & <dbl>\\\\\n", "\\hline\n", "\t1 & Rio de Janeiro & 10\\\\\n", "\t2 & Sao Paulo & 12\\\\\n", "\t3 & Paris & 20\\\\\n", "\t4 & New York & 25\\\\\n", "\t5 & Tokyo & 18\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 2\n", "\n", "\| <!--/--> \| city <chr> \| value <dbl> \|\n", "\|---\|---\|---\|\n", "\| 1 \| Rio de Janeiro \| 10 \|\n", "\| 2 \| Sao Paulo \| 12 \|\n", "\| 3 \| Paris \| 20 \|\n", "\| 4 \| New York \| 25 \|\n", "\| 5 \| Tokyo \| 18 \|\n", "\n" ], "text/plain": [ " city value\n", "1 Rio de Janeiro 10 \n", "2 Sao Paulo 12 \n", "3 Paris 20 \n", "4 New York 25 \n", "5 Tokyo 18 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stores <- data.frame(\n", " city = c(\"Rio de Janeiro\", \"Sao Paulo\", \"Paris\", \"New York\", \"Tokyo\"),\n", " value = c(10, 12, 20, 25, 18))\n", "head(stores)\n" ] }, { "cell_type": "code", "execution_count": 92, "id": "96d229c2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 5 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>city</th><th scope=col>country</th></tr>\n", "\t<tr><th></th><th scope=col><chr></th><th scope=col><chr></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Rio de Janeiro</td><td>Brazil</td></tr>\n", "\t<tr><th scope=row>2</th><td>Sao Paulo </td><td>Brazil</td></tr>\n", "\t<tr><th scope=row>3</th><td>Paris </td><td>France</td></tr>\n", "\t<tr><th scope=row>4</th><td>New York </td><td>US </td></tr>\n", "\t<tr><th scope=row>5</th><td>Tokyo </td><td>Japan </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 5 × 2\n", "\\begin{tabular}{r\|ll}\n", " & city & country\\\\\n", " & <chr> & <chr>\\\\\n", "\\hline\n", "\t1 & Rio de Janeiro & Brazil\\\\\n", "\t2 & Sao Paulo & Brazil\\\\\n", "\t3 & Paris & France\\\\\n", "\t4 & New York & US \\\\\n", "\t5 & Tokyo & Japan \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 2\n", "\n", "\| <!--/--> \| city <chr> \| country <chr> \|\n", "\|---\|---\|---\|\n", "\| 1 \| Rio de Janeiro \| Brazil \|\n", "\| 2 \| Sao Paulo \| Brazil \|\n", "\| 3 \| Paris \| France \|\n", "\| 4 \| New York \| US \|\n", "\| 5 \| Tokyo \| Japan \|\n", "\n" ], "text/plain": [ " city country\n", "1 Rio de Janeiro Brazil \n", "2 Sao Paulo Brazil \n", "3 Paris France \n", "4 New York US \n", "5 Tokyo Japan " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "divisions <- data.frame(\n", " city = c(\"Rio de Janeiro\", \"Sao Paulo\", \"Paris\", \"New York\", \"Tokyo\"),\n", " country = c(\"Brazil\", \"Brazil\", \"France\", \"US\", \"Japan\"))\n", "head(divisions)" ] }, { "cell_type": "code", "execution_count": 93, "id": "f60cefb0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 5 × 3</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>city</th><th scope=col>value</th><th scope=col>country</th></tr>\n", "\t<tr><th></th><th scope=col><chr></th><th scope=col><dbl></th><th scope=col><chr></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>New York </td><td>25</td><td>US </td></tr>\n", "\t<tr><th scope=row>2</th><td>Paris </td><td>20</td><td>France</td></tr>\n", "\t<tr><th scope=row>3</th><td>Rio de Janeiro</td><td>10</td><td>Brazil</td></tr>\n", "\t<tr><th scope=row>4</th><td>Sao Paulo </td><td>12</td><td>Brazil</td></tr>\n", "\t<tr><th scope=row>5</th><td>Tokyo </td><td>18</td><td>Japan </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 5 × 3\n", "\\begin{tabular}{r\|lll}\n", " & city & value & country\\\\\n", " & <chr> & <dbl> & <chr>\\\\\n", "\\hline\n", "\t1 & New York & 25 & US \\\\\n", "\t2 & Paris & 20 & France\\\\\n", "\t3 & Rio de Janeiro & 10 & Brazil\\\\\n", "\t4 & Sao Paulo & 12 & Brazil\\\\\n", "\t5 & Tokyo & 18 & Japan \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 3\n", "\n", "\| <!--/--> \| city <chr> \| value <dbl> \| country <chr> \|\n", "\|---\|---\|---\|---\|\n", "\| 1 \| New York \| 25 \| US \|\n", "\| 2 \| Paris \| 20 \| France \|\n", "\| 3 \| Rio de Janeiro \| 10 \| Brazil \|\n", "\| 4 \| Sao Paulo \| 12 \| Brazil \|\n", "\| 5 \| Tokyo \| 18 \| Japan \|\n", "\n" ], "text/plain": [ " city value country\n", "1 New York 25 US \n", "2 Paris 20 France \n", "3 Rio de Janeiro 10 Brazil \n", "4 Sao Paulo 12 Brazil \n", "5 Tokyo 18 Japan " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "stdiv <- merge(stores, divisions, by.x=\"city\", by.y=\"city\")\n", "head(stdiv)" ] }, { "cell_type": "code", "execution_count": 94, "id": "4cb961bd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A tibble: 4 × 3</caption>\n", "<thead>\n", "\t<tr><th scope=col>country</th><th scope=col>count</th><th scope=col>amount</th></tr>\n", "\t<tr><th scope=col><chr></th><th scope=col><int></th><th scope=col><dbl></th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><td>Brazil</td><td>2</td><td>22</td></tr>\n", "\t<tr><td>France</td><td>1</td><td>20</td></tr>\n", "\t<tr><td>Japan </td><td>1</td><td>18</td></tr>\n", "\t<tr><td>US </td><td>1</td><td>25</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A tibble: 4 × 3\n", "\\begin{tabular}{lll}\n", " country & count & amount\\\\\n", " <chr> & <int> & <dbl>\\\\\n", "\\hline\n", "\t Brazil & 2 & 22\\\\\n", "\t France & 1 & 20\\\\\n", "\t Japan & 1 & 18\\\\\n", "\t US & 1 & 25\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 4 × 3\n", "\n", "\| country <chr> \| count <int> \| amount <dbl> \|\n", "\|---\|---\|---\|\n", "\| Brazil \| 2 \| 22 \|\n", "\| France \| 1 \| 20 \|\n", "\| Japan \| 1 \| 18 \|\n", "\| US \| 1 \| 25 \|\n", "\n" ], "text/plain": [ " country count amount\n", "1 Brazil 2 22 \n", "2 France 1 20 \n", "3 Japan 1 18 \n", "4 US 1 25 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result <- stdiv %>% group_by(country) %>% \n", " summarize(count = n(), amount = sum(value))\n", "head(result)" ] }, { "cell_type": "markdown", "id": "d2e3a3a0", "metadata": {}, "source": [ "#### statistical tests: t-test\n", "There are many statistical tests in R.\n", "One of the most used is the t-test. It checks if the mean of observations is not different from a theoretical value." ] }, { "cell_type": "code", "execution_count": 95, "id": "580b4fab", "metadata": {}, "outputs": [], "source": [ "weight <- c(60, 72, 57, 90, 95, 72) \n", "height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91)\n", "bmi <- weight/height^2 " ] }, { "cell_type": "code", "execution_count": 96, "id": "0d0b8c52", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\tOne Sample t-test\n", "\n", "data: bmi\n", "t = 0.34488, df = 5, p-value = 0.7442\n", "alternative hypothesis: true mean is not equal to 22.5\n", "95 percent confidence interval:\n", " 18.41734 27.84791\n", "sample estimates:\n", "mean of x \n", " 23.13262 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t.test(bmi, mu=22.5)" ] }, { "cell_type": "code", "execution_count": null, "id": "115d05d0", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.0.5" } }, "nbformat": 4, "nbformat_minor": 5 }	mit
robblack007/clase-dinamica-robot	Practicas/practica2/numerico.ipynb	1	6573	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Práctica 2 - Cinemática directa y dinámica de manipuladores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Una vez obtenida la dinámica del manipulador, tenemos la necesidad de construir una función ```f``` para poder simular el comportamiento del manipulador, empecemos escribiendo la ecuación:\n", "\n", "$$\n", "\\tau =\n", "\\begin{bmatrix}\n", "J_1 + J_2 + m_1 l_1^2 + m_2 l_1^2 + \\mu_1 + 2 \\mu_2 c_2 & J_2 + \\mu_1 + \\mu_2 c_2 \\\\\n", "J_2 + \\mu_1 + \\mu_2 c_2 & J_2 + \\mu_1\n", "\\end{bmatrix}\\ddot{q} - \\mu_2 s_2\n", "\\begin{bmatrix}\n", "2 \\dot{q}_2 & \\dot{q}_2 \\\\ -\\dot{q}_1 & 0\n", "\\end{bmatrix} + g\n", "\\begin{bmatrix}\n", "m_1 l_1 c_1 + m_2 l_1 c_1 + m_2 l_2 c_{12} \\\\ m_2 l_2 c_{12}\n", "\\end{bmatrix}\n", "$$\n", "\n", "en donde $\\mu_1 = m_2 l_2^2$ y $\\mu_2 = m_2 l_1 l_2$; por lo que de aqui en adelante, podemos caracterizar la dinámica de este manipulador como la siguiente ecuación:\n", "\n", "$$\n", "\\tau = M(q)\\ddot{q} + C(q, \\dot{q}) + G(q)\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si ahora cambiamos nuestra atención al problema de contruir la función\n", "\n", "$$\n", "\\dot{x} = f(x, t)\n", "$$\n", "\n", "tenemos que empezar por que representa el estado $x$.\n", "\n", "En el ejercicio pasado nuestro manipulador tenía un solo grado de libertad, por lo que el estado terminaba siendo:\n", "\n", "$$\n", "x =\n", "\\begin{pmatrix}\n", "q_1 \\\\ \\dot{q}_1\n", "\\end{pmatrix}\n", "$$\n", "\n", "En este caso, nuestro manipulador tiene dos grados de libertad, por lo que necesitamos que el estado incluya a la posición de ambos grados de libertad, así como su velocidad:\n", "\n", "$$\n", "x =\n", "\\begin{pmatrix}\n", "q_1 \\\\ q_2 \\\\ \\dot{q}_1 \\\\ \\dot{q}_2\n", "\\end{pmatrix}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por lo que para construir $f(x,t)$, necesitamos calcular los siguientes terminos:\n", "\n", "$$\n", "\\dot{x} =\n", "\\begin{pmatrix}\n", "\\dot{q}_1 \\\\ \\dot{q}_2 \\\\ \\ddot{q}_1 \\\\ \\ddot{q}_2\n", "\\end{pmatrix}\n", "$$\n", "\n", "en donde los primeros dos terminos son triviales, ya que son los mismos que obtenemos del estado del sistema ($\\dot{q}_1$, $\\dot{q}_2$), y los segundos dos terminos los podemos obtener de la ecuación de movimiento del manipulador:\n", "\n", "$$\n", "\\ddot{q} = M^{-1}\\left( \\tau - C(q, \\dot{q})\\dot{q} - G(q) \\right)\n", "$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "nbgrader": { "checksum": "c9de019e865123e8505f7cfcbe98751b", "grade": false, "grade_id": "cell-f34c4d5201ef0e67", "locked": false, "schema_version": 1, "solution": true } }, "outputs": [], "source": [ "def f(t, x):\n", " # Se importan funciones matematicas necesarias\n", " from numpy import matrix, sin, cos\n", " # Se desenvuelven las variables que componen al estado\n", " q1, q2, q̇1, q̇2 = x\n", " # Se definen constantes del sistema\n", " g = 9.81\n", " m1, m2, J1, J2 = 0.3, 0.2, 0.0005, 0.0002\n", " l1, l2 = 0.4, 0.3\n", " τ1, τ2 = 0, 0\n", " # Se agrupan terminos en vectores\n", " q̇ = matrix([[q̇1], [q̇2]])\n", " τ = matrix([[τ1], [τ2]])\n", " # Se calculan terminos comúnes\n", " μ1 = m2l22\n", " μ2 = m2l1*l2\n", " c1 = cos(q1)\n", " c2 = cos(q2)\n", " s2 = sin(q2)\n", " c12 = cos(q1 + q2)\n", " # Se calculan las matrices de la ecuación de movimiento\n", " # ESCRIBE TU CODIGO AQUI\n", " raise NotImplementedError\n", " # Se calculan las variables a devolver por el sistema\n", " # ESCRIBE TU CODIGO AQUI\n", " raise NotImplementedError\n", " q1pp = qpp.item(0)\n", " q2pp = qpp.item(1)\n", " # Se devuelve la derivada de las variables de entrada\n", " return [q1p, q2p, q1pp, q2pp]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": false, "editable": false, "nbgrader": { "checksum": "6a6ab5d022ba694ae431461e4abfa39a", "grade": true, "grade_id": "cell-38c4d0977442c793", "locked": true, "points": 3, "schema_version": 1, "solution": false } }, "outputs": [], "source": [ "from numpy.testing import assert_almost_equal\n", "assert_almost_equal(f(0, [0, 0, 0, 0]), [0,0,-1392.38, 3196.16], 2)\n", "assert_almost_equal(f(0, [1, 1, 0, 0]), [0,0,-53.07, 104.34], 2)\n", "print(\"Sin errores\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mandamos llamar al simulador" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from robots.simuladores import simulador" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib widget\n", "ts, xs = simulador(puerto_zmq=\"5551\", f=f, x0=[0, 0, 0, 0], dt=0.02)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> El argumento ```puerto_zmq``` se refiere al puerto por el cual esta mandando datos, para el visualizador descrito a continuación." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estos datos se estan actualizando en tiempo real, y si bien es posible ver el comportamiento general con esta gráfica, tambien es posible ver una visualización en tiempo real de este manipulador, para lo cual es necesario mantener este documento abierto, mientras se abre el documento ```visualizacion.ipynb```." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
Cyb3rWard0g/HELK	docker/helk-jupyter/notebooks/sigma/proxy_telegram_api.ipynb	1	3221	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Telegram API Access\n", "Detects suspicious requests to Telegram API without the usual Telegram User-Agent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", "- title: Telegram API Access\n", " id: b494b165-6634-483d-8c47-2026a6c52372\n", " status: experimental\n", " description: Detects suspicious requests to Telegram API without the usual Telegram\n", " User-Agent\n", " references:\n", " - https://researchcenter.paloaltonetworks.com/2018/03/unit42-telerat-another-android-trojan-leveraging-telegrams-bot-api-to-target-iranian-users/\n", " - https://blog.malwarebytes.com/threat-analysis/2016/11/telecrypt-the-ransomware-abusing-telegram-api-defeated/\n", " - https://www.welivesecurity.com/2016/12/13/rise-telebots-analyzing-disruptive-killdisk-attacks/\n", " author: Florian Roth\n", " date: 2018/06/05\n", " logsource:\n", " category: proxy\n", " product: null\n", " service: null\n", " detection:\n", " selection:\n", " r-dns:\n", " - api.telegram.org\n", " filter:\n", " c-useragent:\n", " - 'Telegram'\n", " - 'Bot'\n", " condition: selection and not filter\n", " fields:\n", " - ClientIP\n", " - c-uri\n", " - c-useragent\n", " falsepositives:\n", " - Legitimate use of Telegram bots in the company\n", " level: medium\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying Elasticsearch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from elasticsearch import Elasticsearch\n", "from elasticsearch_dsl import Search\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize Elasticsearch client" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "es = Elasticsearch(['http://helk-elasticsearch:9200'])\n", "searchContext = Search(using=es, index='logs-', doc_type='doc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run Elasticsearch Query" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = searchContext.query('query_string', query='(r-dns:(\"api.telegram.org\") AND (NOT (c-useragent.keyword:(Telegram* OR Bot))))')\n", "response = s.execute()\n", "if response.success():\n", " df = pd.DataFrame((d.to_dict() for d in s.scan()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.head()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 4 }	gpl-3.0
Mayurji/Machine-Learning	Bank Marketing Machine Learning Solution/Bank_Marketing_ML.ipynb	1	321611	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predictive Analysis on Bank Marketing Dataset :\n", "\n", "### Bank Marketing Dataset contains both type variables 'Categorical' and 'Numerical'.\n", " \n", "#### Categorical Variable includes :\n", "\n", " * Marital - (Married , Single , Divorced)\",\n", " * Job - (Management,Blue-Collar,Technician,entrepreneur,retired,admin.,services,selfemployed,housemaid,student,unemployed,unknown)\n", " * Contact - (Telephone,Cellular,Unknown)\n", " * Education - (Primary,Secondary,Tertiary,Unknown)\n", " * Month - (Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec)\n", " * Poutcome - (Success,Failure,Other,Unknown)\n", " * Housing - (Yes/No)\n", " * Loan - (Yes/No)\n", " * is_success - (Yes/No)\n", " * Default - (Yes/No)\n", "\n", "#### Numerical Variable:\n", " \n", " * Age\n", " * Balance\n", " * Day\n", " * Duration\n", " * Campaign\n", " * Pdays\n", " * Previous\n", " \n", "#### Mean, Standard Deviation, Min, Max, Quantile output of all numerical variable:\n", "\n", "\n", " \|Description\|age \|balance \|duration\|campaign\|pdays \|previous\|day \|\n", " \|-----------\|--------\|---------\|--------\|--------\|---------\|--------\|---------\|\n", " \|count \|45211.00\|45211.00 \|45211.00\|45211.00\|45211.00 \|45211.00\|45211.00 \|\n", " \|-----------\|--------\|---------\|--------\|--------\|---------\|--------\|---------\|\n", " \|mean \|40.93 \|1362.27 \|258.16 \|2.76 \|40.19 \|0.58 \|15.80 \|\n", " \|-----------\|--------\|---------\|--------\|--------\|---------\|--------\|---------\| \n", " \|std \|10.61 \|3044.76 \|257.52 \|3.09 \|100.12 \|2.30 \|8.32 \|\n", " \|-----------\|--------\|---------\|--------\|--------\|---------\|--------\|---------\|\n", " \|min \|18.00 \|-8019.00 \|0.00 \|1.00 \|-1.00 \|0.00 \|1.00 \|\n", " \|-----------\|--------\|---------\|--------\|--------\|---------\|--------\|---------\|\n", " \|25% \|33.00 \|72.00 \|103.00 \|1.00 \|-1.00 \|0.00 \|8.00 \|\n", " \|-----------\|--------\|---------\|--------\|--------\|---------\|--------\|---------\|\n", " \|50% \|39.00 \|448.00 \|180.00 \|2.00 \|-1.00 \|0.00 \|16.00 \|\n", " \|-----------\|--------\|---------\|--------\|--------\|---------\|--------\|---------\|\n", " \|75% \|48.00 \|1428.00 \|319.00 \|3.00 \|-1.00 \|0.00 \|21.00 \|\n", " \|-----------\|--------\|---------\|--------\|--------\|---------\|--------\|---------\|\n", " \|max \|95.00 \|102127.00\|4918.00 \|63.00 \|871.00 \|275.00 \|31.00 \|\n", " \|-----------\|--------\|---------\|--------\|--------\|---------\|--------\|---------\|\n", "\n", "\n", "#### Understanding above table :\n", " \n", " ** Outlier : data_point > (Q3 * 1.5) is said to be outlier where Q3 is 75% Quantile !\n", " \n", "#### Age:\n", "\n", " Average age of the people in the dataset is ~41 with std of 10.61\n", " Min. age is 18\n", " Max. age is 95\n", " quantile 75%(percentile) refers that 75 percentage of the people have 48 or less age.\n", " ** As 95 is max, there is great chance that its a outlier \"48(3/2) = 72\". So anything greater than 72 is outlier.\n", "\n", "#### Balance: \n", "\n", " * Average balance of the people in the dataset is (approx)1326.27 with std of 3044.76, as standard deviation is quite huge it means that balance is wide spread across the dataset.\n", " Min. balance is -8019\n", " Max. balance is 102127\n", " quantile 75%(percentile) refers that 75 percentage of the people have 1428 or less balance.\n", " while comparing with 75% quantile, 102127 is very huge and its a outlier data point.\n", "\n", "#### Duration: \n", "\n", " Average duration of the people speaking in the dataset is (approx)258.16 with std of 257.52, as standard deviation is quite huge it means that duration is wide spread across the dataset.\n", " Min. duration is 0\n", " Max. duration is 4918\n", " quantile 75%(percentile) refers that 75 percentage of the people spoke for 319 seconds or less.\n", " while comparing with 75% quantile, 4918 is a outlier data point.\n", "\n", "#### Pdays:\n", "\n", " Average no. of days passed after the client was contacted from previous campaign in the dataset is (approx)40.19 with std of 100.12.\n", " Min. pdays is -1\n", " Max. pdays is 871\n", " quantile 75%(percentile),for 75% of records it is -1 days, which means the Client was not contacted.\n", "\n", "#### Campaign: \n", " \n", " Average no. of contacts performed during the current campaign for a client in the dataset is (approx)2.76 with std of 3.09.\n", " Min. balance is 1\n", " Max. balance is 63\n", " quantile 75%(percentile),for 75% of records, 3 times the client has been contacted in the current campaign for a client.\n", " while comparing with 75% quantile,63 is a outlier data point.\n", " \n", "#### Previous:\n", " \n", " Average no. of contacts performed before this campaign for a client in the dataset is (approx)0.58 with std of 2.30.\n", " Min. balance is 0.\n", " Max. balance is 275.\n", " quantile 75%(percentile),for 75% of records, 0 times the client has been contacted before this campaign.\n", " while comparing with 75% quantile,275 is a outlier data point.\n", " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/mayurjain/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0 39922\n", "1 5289\n", "Name: is_success, dtype: int64\n", " age marital education default balance \\\n", "count 45211.000000 45211.000000 45211.000000 45211.000000 45211.000000 \n", "mean 40.936210 1.513238 2.224813 0.018027 1362.272058 \n", "std 10.618762 0.692948 0.747997 0.133049 3044.765829 \n", "min 18.000000 1.000000 1.000000 0.000000 -8019.000000 \n", "25% 33.000000 1.000000 2.000000 0.000000 72.000000 \n", "50% 39.000000 1.000000 2.000000 0.000000 448.000000 \n", "75% 48.000000 2.000000 3.000000 0.000000 1428.000000 \n", "max 95.000000 3.000000 4.000000 1.000000 102127.000000 \n", "\n", " housing loan contact month duration \\\n", "count 45211.000000 45211.000000 45211.000000 45211.000000 45211.000000 \n", "mean 0.555838 0.160226 2.223707 6.144655 258.163080 \n", "std 0.496878 0.366820 0.549747 2.408034 257.527812 \n", "min 0.000000 0.000000 1.000000 1.000000 0.000000 \n", "25% 0.000000 0.000000 2.000000 5.000000 103.000000 \n", "50% 1.000000 0.000000 2.000000 6.000000 180.000000 \n", "75% 1.000000 0.000000 3.000000 8.000000 319.000000 \n", "max 1.000000 1.000000 3.000000 12.000000 4918.000000 \n", "\n", " campaign pdays previous is_success \n", "count 45211.000000 45211.000000 45211.000000 45211.000000 \n", "mean 2.763841 40.197828 0.580323 0.116985 \n", "std 3.098021 100.128746 2.303441 0.321406 \n", "min 1.000000 -1.000000 0.000000 0.000000 \n", "25% 1.000000 -1.000000 0.000000 0.000000 \n", "50% 2.000000 -1.000000 0.000000 0.000000 \n", "75% 3.000000 -1.000000 0.000000 0.000000 \n", "max 63.000000 871.000000 275.000000 1.000000 \n", " age job marital education default balance housing loan \\\n", "0 58 management 1 3 0 2143 1 0 \n", "1 44 technician 2 2 0 29 1 0 \n", "2 33 entrepreneur 1 2 0 2 1 1 \n", "3 47 blue-collar 1 4 0 1506 1 0 \n", "4 33 unknown 2 4 0 1 0 0 \n", "\n", " contact month duration campaign pdays previous is_success \n", "0 3 5 261 1 -1 0 0 \n", "1 3 5 151 1 -1 0 0 \n", "2 3 5 76 1 -1 0 0 \n", "3 3 5 92 1 -1 0 0 \n", "4 3 5 198 1 -1 0 0 \n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn import metrics as m\n", "from sklearn.model_selection import StratifiedShuffleSplit\n", "from sklearn.model_selection import ShuffleSplit\n", "from sklearn.metrics import roc_auc_score\n", "\n", "data = pd.read_csv('/Users/mayurjain/Documents/Fragma ML TEST/August 13/marketing-data.csv',sep=',',header='infer')\n", "data = data.drop(['day','poutcome'],axis=1)\n", "\n", "def binaryType_(data):\n", " \n", " data.is_success.replace(('yes', 'no'), (1, 0), inplace=True)\n", " data.default.replace(('yes','no'),(1,0),inplace=True)\n", " data.housing.replace(('yes','no'),(1,0),inplace=True)\n", " data.loan.replace(('yes','no'),(1,0),inplace=True)\n", " data.marital.replace(('married','single','divorced'),(1,2,3),inplace=True)\n", " data.contact.replace(('telephone','cellular','unknown'),(1,2,3),inplace=True)\n", " data.month.replace(('jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec'),(1,2,3,4,5,6,7,8,9,10,11,12),inplace=True)\n", " data.education.replace(('primary','secondary','tertiary','unknown'),(1,2,3,4),inplace=True)\n", " \n", " return data\n", "\n", "data = binaryType_(data)\n", "\n", "# for i in range(len(data.marital.unique())):\n", "# data[\"marital_\"+str(data.marital.unique()[i])] = (data.marital == data.marital.unique()[i]).astype(int)\n", "\n", "# for j in range(len(data.job.unique())):\n", "# data[\"job_\"+str(data.job.unique()[j])] = (data.job == data.job.unique()[j]).astype(int)\n", "\n", "# for k in range(len(data.contact.unique())):\n", "# data[\"contact_\"+str(data.contact.unique()[k])] = (data.contact == data.contact.unique()[k]).astype(int)\n", "\n", "# for l in range(len(data.education.unique())):\n", "# data['education_'+str(data.education.unique()[l])] = (data.education == data.education.unique()[l]).astype(int)\n", "\n", "# for n in range(len(data.month.unique())):\n", "# data['month_'+str(data.month.unique()[n])] = (data.month == data.month.unique()[n]).astype(int)\n", "\n", "\n", "print(data.is_success.value_counts())\n", "print(data.describe())\n", "print(data.head())\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Below are set of Graph for greater insight into data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11020b828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist((data.duration),bins=100)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x115528c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(data.age,bins=10)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1102276d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(data.balance,bins=1000)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Above, All the Histogram suggest that data is skewed towards left i.e. existence of skewness brings us to a point that we need to sample the data efficiently while classifiying the train_data and test_data !" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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SNDw8rHXr1mn9+vXat2/fp5waAJO9MNKRUh+ApSvl0DI9Pa0nn3xS1113nSRp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\nMAKhBQAAGOH/AfovfTntpjoLAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11be72978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(1, figsize=(9, 6))\n", "ax1 = fig.add_subplot(211)\n", "bp1 = ax1.boxplot(data.balance,0,'')\n", "ax2 = fig.add_subplot(212)\n", "bp2 = ax2.boxplot(data.balance,0,'gD')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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FQvffFmElgyCJ/d7/O8WR/+CxsAgGv5V32XjPBeKNICHesGJW2yYAgOAh3gBgEPEGAIOI\nNwAYRLwBwCDiDQAGEW8AMIh4A4BBxBsADCLeAGAQ8QYAg4g36o7fs0t4rgms4MFUmFeSyQ6dPn1q\nah+cluT889eepN1T+7S2tu8rm83PYDpgeniqIHAfsd9HJU8a6WbFjeAh3oCPWCyqkRHijeDhkbAA\nMM8QbwAwiHgDgEHEGwAMIt4AYBDxBgCDiDcAGES8AcAg4g0ABhFvADCIeAOAQcQbAAwKT+WD9u/f\nr6NHj8p1XW3atEmJREK9vb1yHEetra1Kp9NqaOB1AACqpWxx8/m8PvnkE73zzjs6ePCgLl++rD17\n9iiVSmloaEie52l4eLgaswIA/qlsvD/66COtWLFC3d3d2r59u9atW6dCoaBEIiFJSiaTyuVyFR8U\nAPCNstsmxWJRFy9e1L59+3T+/Hnt2LFDnufJcb46giQSiWh83P953c3NjQqHQ3MzMVABfs9NBoKo\nbLybmpoUj8e1YMECxeNxLVy4UJcvX568XiqVFI1Gfb9GsTgx+0mBCuLAEATRrA5jeOSRR3T8+HF5\nnqcrV67oxo0bWrNmjfL5r87wy2azam9vn7tpAQBlTekYtDfeeEP5fF6e5+mll17S0qVL1dfXJ9d1\nFY/HlclkFArdf1uEVQ2CjGPQEFScYQn4IN4IKs6wBIB5hngDgEHEGwAMIt4AYBDxBgCDiDcAGES8\nAcAg4g0ABhFvADCIeAOAQcQbAAwi3gBgEPEGAIOINwAYRLwBwCDiDQAGlT3DEqilFSuWaWxsrOL3\nicX8z2GdraamJn322T8qeg/UF+KNQBsbG6v4KTctLUsqftpTpV8cUH/YNgEAg4g3ABhEvAHAIOIN\nAAYRbwAwiHgDgEHEGwAMIt4AYBDxBgCDphTvzz//XI899pjOnDmjc+fOadOmTdq8ebPS6bTu3LlT\n6RkBAN9SNt6u66q/v1+LFi2SJO3Zs0epVEpDQ0PyPE/Dw8MVHxIAcLey8R4YGNCzzz6rWCwmSSoU\nCkokEpKkZDKpXC5X2QkBAPfwfTDVoUOH9OCDD2rt2rV68803JUme58lxHElSJBLR+Hj5B/o0Nzcq\nHA7NwbioRy0tS7gH8C2+8X7vvffkOI5OnjypU6dOqaenR1988cXk9VKppGi0/NPSisWJ2U+KuvT4\n4AZt+O8dtR5j1h4f3FDxJxdi/vF7wfeN99tvvz356y1btui1117T4OCg8vm8Ojo6lM1mtXr16rmb\nFPiWP+78n/nzSNjn/6ui90B9mfaPCvb09Gjv3r3auHGjXNdVV1dXJeYCAPhwPM/zKn0Tvl3ETMVi\n0Xmz8q703wPzj9+2CW/SAQCDiDcAGES8AcAg4g0ABhFvADCIeAOAQcQbAAzyfYclEASxWPlHMARd\nU1NTrUfAPEO8EWjVeGMLb6CBRWybAIBBxBsADCLeAGAQ8QYAg4g3ABhEvAHAIOINAAYRbwAwiHgD\ngEHEGwAMIt4AYBDxBgCDiDcAGES8AcAg4g0ABhFvADCIeAOAQcQbAAzyPQbNdV3t2rVLFy5c0K1b\nt7Rjxw49/PDD6u3tleM4am1tVTqdVkMDrwEAUE2+8T5y5Iiampo0ODiosbExPf3002pra1MqlVJH\nR4f6+/s1PDyszs7Oas0LAFCZbZMnnnhCL774oiTJ8zyFQiEVCgUlEglJUjKZVC6Xq/yUAIC7+K68\nI5GIJOn69et64YUXlEqlNDAwIMdxJq+Pj4+XvUlzc6PC4dAcjAtURkvLklqPAEyLb7wl6dKlS+ru\n7tbmzZv15JNPanBwcPJaqVRSNBote5NicWJ2UwIVNjpafhECVJvfosJ32+Tq1avaunWrdu7cqWee\neUaStHLlSuXzeUlSNptVe3v7HI4KAJgKx/M8734XM5mMPvjgA8Xj8ck/e/XVV5XJZOS6ruLxuDKZ\njEIh/y0RVjWolmSyQ6dPn6roPdravq9sNl/RewCS/8rbN95zhXgjyFpalvBvFIE0420TAEAwEW8A\nMIh4A4BBxBsADCLeAGBQVX7aBAAwt1h5A4BBxBsADCLeAGAQ8QYAg4g3ABhEvAHAIOKNuvbpp59q\ny5YttR4DmLayhzEA89WBAwd05MgRLV68uNajANPGyht1a9myZdq7d2+txwBmhHijbnV1dSkc5ptP\n2ES8AcAg4g0ABhFvADCIpwoCgEGsvAHAIOINAAYRbwAwiHgDgEHEGwAMIt4AYBDxBgCDiDcAGPR/\nF2uHWwxjRlUAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11ba4ed68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(1, figsize=(6, 6))\n", "ax = fig.add_subplot(211)\n", "bp = ax.boxplot(data.age,0,'')\n", "ax = fig.add_subplot(212)\n", "bp = ax.boxplot(data.age,0,'gD')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11b547c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(1, figsize=(9, 6))\n", "ax1 = fig.add_subplot(211)\n", "bp1 = ax1.boxplot(data.duration,0,'')\n", "ax2 = fig.add_subplot(212)\n", "bp2 = ax2.boxplot(data.duration,0,'gD')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Above boxplot suggest how the data is spread across the dataset\n", "\n", "\n", "** Most of the data is lying above the 3rd quantile by multiplication factor of 1.5 i.e. by theortical aspect the data points are outlier for most of the data points.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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55pl8+tOfzqZNm/L888/nwAMPzIc+9KG+mxgAAGCA6jHQJk+enLvuuisf+MAHsmbNmkyY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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10fec7f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.housing, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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fzmmnnZaNGzfm2Wefzf7775/3ve99/TcxAADAINVroE2ePDl33HFH3v3ud2ft2rWZMGFC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\nUPk2mgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11c00ff28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.default, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10feefd68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.loan, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### By looking at the bar graph, we can observe that Feature vs Label the data is wide spread i.e. we cannot predict completely based on feature alone.\n", "\n", "### Feature Engineering \n", "\n", "\n", " * First, We can convert the duration from Seconds to Minutes and then making it as categorical feature.\n", " * Converting the age of the person into categorical feature by segregating the age as Adult , Middle Aged and old.\n", " * Similarly we can converting the continous feature value into discrete feature value." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n", " 'loan', 'contact', 'month', 'duration', 'campaign', 'pdays', 'previous',\n", " 'is_success', 'Adult', 'Middle_Aged', 'old', 'Neg_Balance',\n", " 'No_Balance', 'Pos_Balance', 'Not_Contacted', 'Contacted', 't_min',\n", " 't_e_min', 'e_min', 'pdays_not_contacted', 'months_passed'],\n", " dtype='object')\n", " 0 3514\n", " 1 195\n", " 2 156\n", " 4 139\n", " 3 134\n", " 5 113\n", " 6 88\n", " 8 81\n", " 23 75\n", " 10 69\n", " 7 69\n", " 11 65\n", " 25 63\n", " 20 62\n", " 19 60\n", " 15 59\n", " 47 59\n", " 21 59\n", " 49 59\n", " 33 58\n", " 13 57\n", " 53 56\n", " 16 56\n", " 24 55\n", " 79 55\n", " 91 54\n", " 145 54\n", " 46 54\n", " 9 54\n", " 14 53\n", " ... \n", " 6205 1\n", " 6320 1\n", " 4394 1\n", " 2327 1\n", " 4404 1\n", " 8460 1\n", " 29312 1\n", " 4362 1\n", "-2049 1\n", " 17332 1\n", " 2633 1\n", " 6571 1\n", " 1338 1\n", "-568 1\n", " 8402 1\n", " 8863 1\n", " 10451 1\n", " 29184 1\n", " 13242 1\n", " 4586 1\n", " 51439 1\n", " 4092 1\n", "-472 1\n", " 8652 1\n", " 4554 1\n", " 4305 1\n", " 6352 1\n", " 18881 1\n", " 14889 1\n", " 7218 1\n", "Name: balance, Length: 7168, dtype: int64\n", "2.066667 188\n", "1.500000 184\n", "1.483333 177\n", "1.900000 175\n", "1.733333 175\n", "2.033333 175\n", "1.866667 174\n", "2.266667 174\n", "2.316667 174\n", "2.016667 173\n", "1.983333 170\n", "1.466667 170\n", "1.516667 170\n", "1.716667 169\n", "1.533333 168\n", "1.616667 168\n", "2.050000 168\n", "1.350000 166\n", "1.416667 166\n", "1.883333 166\n", "1.216667 166\n", "1.850000 166\n", "1.766667 165\n", "1.366667 165\n", "2.083333 165\n", "1.800000 165\n", "1.333333 164\n", "1.450000 163\n", "1.683333 163\n", "2.100000 163\n", " ... \n", "32.766667 1\n", "26.050000 1\n", "24.400000 1\n", "27.633333 1\n", "17.450000 1\n", "21.633333 1\n", "22.216667 1\n", "21.966667 1\n", "51.266667 1\n", "23.283333 1\n", "25.000000 1\n", "23.616667 1\n", "31.116667 1\n", "23.533333 1\n", "32.850000 1\n", "25.316667 1\n", "32.083333 1\n", "24.833333 1\n", "39.816667 1\n", "31.950000 1\n", "38.350000 1\n", "46.250000 1\n", "40.483333 1\n", "16.650000 1\n", "24.850000 1\n", "32.833333 1\n", "21.050000 1\n", "21.033333 1\n", "17.983333 1\n", "33.883333 1\n", "Name: duration, Length: 1573, dtype: int64\n", "-1 36954\n", " 182 167\n", " 92 147\n", " 183 126\n", " 91 126\n", " 181 117\n", " 370 99\n", " 184 85\n", " 364 77\n", " 95 74\n", " 350 73\n", " 94 72\n", " 175 71\n", " 185 68\n", " 93 65\n", " 343 65\n", " 188 64\n", " 189 60\n", " 186 60\n", " 174 57\n", " 96 57\n", " 349 57\n", " 363 55\n", " 97 54\n", " 90 54\n", " 196 51\n", " 365 51\n", " 368 49\n", " 342 49\n", " 98 49\n", " ... \n", " 774 1\n", " 550 1\n", " 396 1\n", " 492 1\n", " 466 1\n", " 45 1\n", " 434 1\n", " 18 1\n", " 529 1\n", " 465 1\n", " 401 1\n", " 784 1\n", " 656 1\n", " 592 1\n", " 528 1\n", " 464 1\n", " 432 1\n", " 655 1\n", " 495 1\n", " 543 1\n", " 47 1\n", " 782 1\n", " 686 1\n", " 558 1\n", " 526 1\n", " 749 1\n", " 717 1\n", " 589 1\n", " 493 1\n", " 32 1\n", "Name: pdays, Length: 559, dtype: int64\n", "2 18026\n", "1 17544\n", "3 9641\n", "Name: campaign, dtype: int64\n", "32 2085\n", "31 1996\n", "33 1972\n", "34 1930\n", "35 1894\n", "36 1806\n", "30 1757\n", "37 1696\n", "39 1487\n", "38 1466\n", "40 1355\n", "41 1291\n", "42 1242\n", "45 1216\n", "29 1185\n", "46 1175\n", "43 1161\n", "44 1136\n", "47 1088\n", "28 1038\n", "48 997\n", "49 994\n", "50 939\n", "51 936\n", "52 911\n", "27 909\n", "53 891\n", "57 828\n", "54 811\n", "55 806\n", " ... \n", "67 54\n", "71 54\n", "72 52\n", "20 50\n", "77 44\n", "73 44\n", "69 44\n", "75 39\n", "74 37\n", "68 36\n", "19 35\n", "76 32\n", "80 31\n", "78 30\n", "79 25\n", "83 22\n", "82 19\n", "81 17\n", "18 12\n", "84 9\n", "86 9\n", "85 5\n", "87 4\n", "89 3\n", "88 2\n", "90 2\n", "92 2\n", "93 2\n", "95 2\n", "94 1\n", "Name: age, Length: 77, dtype: int64\n" ] } ], "source": [ "data['duration'] = data['duration']/60\n", "def age_(data):\n", " \n", " data['Adult'] = 0\n", " data['Middle_Aged'] = 0\n", " data['old'] = 0 \n", " data.loc[(data['age'] <= 35) & (data['age'] >= 18),'Adult'] = 1\n", " data.loc[(data['age'] <= 60) & (data['age'] >= 36),'Middle_Aged'] = 1\n", " #data.loc[(data['age'] <= 60) & (data['age'] >= 46),'Elderly'] = 1\n", " data.loc[data['age'] >=61,'old'] = 1\n", " \n", " return data\n", "\n", "def campaign_(data):\n", " \n", " \n", " data.loc[data['campaign'] == 1,'campaign'] = 1\n", " data.loc[(data['campaign'] >= 2) & (data['campaign'] <= 3),'campaign'] = 2\n", " data.loc[data['campaign'] >= 4,'campaign'] = 3\n", " \n", " return data\n", "\n", "def duration_(data):\n", " \n", " data['t_min'] = 0\n", " data['t_e_min'] = 0\n", " data['e_min']=0\n", " data.loc[data['duration'] <= 5,'t_min'] = 1\n", " data.loc[(data['duration'] > 5) & (data['duration'] <= 10),'t_e_min'] = 1\n", " data.loc[data['duration'] > 10,'e_min'] = 1\n", " \n", " return data\n", "\n", "def pdays_(data):\n", " data['pdays_not_contacted'] = 0\n", " data['months_passed'] = 0\n", " data.loc[data['pdays'] == -1 ,'pdays_not_contacted'] = 1\n", " data['months_passed'] = data['pdays']/30\n", " data.loc[(data['months_passed'] >= 0) & (data['months_passed'] <=2) ,'months_passed'] = 1\n", " data.loc[(data['months_passed'] > 2) & (data['months_passed'] <=6),'months_passed'] = 2\n", " data.loc[data['months_passed'] > 6 ,'months_passed'] = 3\n", " \n", " return data\n", "\n", "def previous_(data):\n", " \n", " data['Not_Contacted'] = 0\n", " data['Contacted'] = 0\n", " data.loc[data['previous'] == 0 ,'Not_Contacted'] = 1\n", " data.loc[(data['previous'] >= 1) & (data['pdays'] <=99) ,'Contacted'] = 1\n", " data.loc[data['previous'] >= 100,'Contacted'] = 2\n", " \n", " return data\n", "\n", "def balance_(data):\n", " data['Neg_Balance'] = 0\n", " data['No_Balance'] = 0\n", " data['Pos_Balance'] = 0\n", " \n", " data.loc[~data['balance']<0,'Neg_Balance'] = 1\n", " data.loc[data['balance'] == 0,'No_Balance'] = 1\n", " data.loc[(data['balance'] >= 1) & (data['balance'] <= 100),'Pos_Balance'] = 1\n", " data.loc[(data['balance'] >= 101) & (data['balance'] <= 500),'Pos_Balance'] = 2\n", " data.loc[(data['balance'] >= 501) & (data['balance'] <= 2000),'Pos_Balance'] = 3\n", " data.loc[(data['balance'] >= 2001) & (data['balance'] <= 10000),'Pos_Balance'] = 4\n", " data.loc[data['balance'] >= 10001,'Pos_Balance'] = 5\n", " \n", " return data\n", "\n", "def job_(data):\n", " \n", " data.loc[data['job'] == \"management\",'job'] = 1\n", " data.loc[data['job'] == \"technician\",'job'] = 2\n", " data.loc[data['job'] == \"entrepreneur\",'job'] = 3\n", " data.loc[data['job'] == \"blue-collar\",'job'] = 4\n", " data.loc[data['job'] == \"retired\",'job'] = 5\n", " data.loc[data['job'] == \"admin.\",'job'] = 6\n", " data.loc[data['job'] == \"services\",'job'] = 7\n", " data.loc[data['job'] == \"self-employed\",'job'] = 8\n", " data.loc[data['job'] == \"unemployed\",'job'] = 9\n", " data.loc[data['job'] == \"student\",'job'] = 10\n", " data.loc[data['job'] == \"housemaid\",'job'] = 11\n", " data.loc[data['job'] == \"unknown\",'job'] = 12\n", " \n", " return data\n", "\n", "def marital_(data):\n", " \n", " data['married'] = 0\n", " data['singles'] = 0\n", " data['divorced'] = 0\n", " data.loc[data['marital'] == 'married','married'] = 1\n", " data.loc[data['marital'] == 'singles','singles'] = 1\n", " data.loc[data['marital'] == 'divorced','divorced'] = 1\n", " \n", " return data\n", "\n", "def education_(data):\n", " \n", " data['primary'] = 0\n", " data['secondary'] = 0\n", " data['tertiary'] = 0\n", " data['unknown'] = 0\n", " data.loc[data['education'] == 'primary','primary'] = 1\n", " data.loc[data['education'] == 'secondary','secondary'] = 1\n", " data.loc[data['education'] == 'tertiary','tertiary'] = 1\n", " data.loc[data['education'] == 'unknown','unknown'] = 1 \n", " \n", " return data\n", "\n", "data = campaign_(data)\n", "data = age_(data)\n", "#data = education_(data)\n", "data = balance_(data)\n", "data = job_(data)\n", "data = previous_(data)\n", "data = duration_(data)\n", "data = pdays_(data)\n", "#data = marital_(data)\n", "print(data.columns)\n", "\n", "print(data.balance.value_counts())\n", "print(data.duration.value_counts())\n", "print(data.pdays.value_counts())\n", "print(data.campaign.value_counts())\n", "print(data.age.value_counts())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting bar chart :\n", "\n", "##### data.Adult vs data.is_success :\n", " \n", " The data is spread equally opting for term deposit or not.\n", "\n", "##### data.Middle_Aged vs data.is_success :\n", " \n", " The data is points out that people opt less for term deposit.\n", "\n", "##### data.old vs data.is_success :\n", "\n", " The data is points out that people opt more for term deposit as it covers people who are retired.\n", "\n", "##### data.t_min vs data.is_success :\n", "\n", " The data point brings out the fact that if th client is less interested in enrolling for term deposit, he/she is ready to invest less time on call with the agent.\n", " \n", " Note : t_min - Five minutes or less\n", "\n", "##### data.t_e_min vs data.is_success :\n", "\n", " The data points brings out the fact that if th client is interested in enrolling for term deposit, he/she is ready to investing minimum of 5 to 10 minute time on call with the agent.\n", " \n", " Note : t_e_min - greater than Five minutes or more\n", "\n", "##### data.e_min vs data.is_success :\n", "\n", " The data points suggest that if th client is very much interested in enrolling for term deposit, he/she is ready to investing more than 10 minute of time on call with the agent.\n", " \n", " Note : e_min - greater than ten minutes or more\n", "\n", "##### data.pdays_not_contacted vs data.is_success :\n", "\n", " The data points refers to the client who were not contacted in the previous campaign.And it looks like the people are contaced in current campaign are not contacted previously.\n", "\n", "##### data.months_passed vs data.is_success :\n", "\n", " The data points refers to the months passed after the client has been contacted before the current campaign.\n", "\n", "##### data.Contacted vs data.is_success :\n", " \n", " The data points refers to the no. of contact for a client has been contacted before this campaign.Fewer no. of contacts are more likely to enroll for term deposit\n", " \n", "##### data.not_Contacted vs data.is_success :\n", "\n", " The data points refers that no contact is made for a client before this campaign. Not contacted Clients are less likely to enroll for term deposit\n", "\n", "##### data.Pos_Balance vs data.is_success :\n", "\n", " Here, We can clearly see as the balance in the account increases the no. of client enrolling for the term deposit is more and more.\n", "\n", "##### data.No_Balance vs data.is_success :\n", "\n", " Here, We can see as the balance in the account is zero, the no. of client enrolling for the term deposit are less.\n", "\n", "##### data.Neg_Balance vs data.is_success :\n", "\n", " We can infer that as the balance in the account is -ve, the no. of client enrolling for the term deposit are very less and feature come in place while classifying such data points\n", "\n", "##### data.campaign vs data.is_success :\n", "\n", " The data points refers that no. of contact made to a client in this campaign. If a client is contacted once or twice are more likely to enroll than clients who are contacted more than 3 times." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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TPPHEE/n4xz+eHTt25Omnn87hhx+eU045ZfgmBgAAGKUGDbTZs2fnpptuyoknnpjNmzen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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12089eda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.Adult, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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x2I9//OO8+OKL+exnP5sNGzbklVdeyejRo/Pxj3+85yYGAADoo7oMtEmTJuXee+/NUUcd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\nAFCI/wtACZ5wSwb+9QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11b54f550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.Middle_Aged, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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OE42DHdDU1JSenp6B7UqlksbGF172gx/8IE8//XQ++tGPZvfu3Xn22Wdz9NFH56yzzhq+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\nAABQCIEGAABQCIEGAABQiP8H1WdFMnGcavsAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11c9d10f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.old, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2gAAAE4CAYAAAAuB9DjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nswx4ap4hwQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x120974550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.t_min, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2gAAAE4CAYAAAAuB9DjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\neUYlHLBK0AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11b545eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.t_e_min, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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AAKAQAg0AAKAQAg0AAKAQAg0AAKAQAg0AAKAQAg0AAKAQAg0AAKAQAg0AAKAQAg0AAKAQ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\nhRBoAAAAhRBoAAAAhfh/TDwaX8BDvkYAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11becb550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.e_min, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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AoBACDQAAoBACDQAAoBACDQAAoBACDQAAoBACDQAAoBACDQAAoBACDQAAoBACDQAAoBAC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\nY7xYAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x120b7a898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.pdays_not_contacted, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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CQQMAACgIBQ0AAKAgFDQAAICCUNAAAAAKQkEDAAAoCAUNAACgIBqqHQAA4H87Yc7iakfY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\nAAAUxP8FXq+9ObCcwp8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11b4ce9e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.months_passed, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(int), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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2dmxXKpXU17/+sB/84Ad5+eWXM3ny5GzcuDGbN2/OIYcckk9/+tNdNzEAAEAP1WmgjR49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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11b4cea58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.Contacted, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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BAAAUQqABAAAUQqABAAAUQqABAAAUQqABAAAUQqABAAAUQqABAAAUQqABAAAUQqABAAAU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\nABRCoAEAABRCoAEAABRCoAEAABTi/wJGLJYjnIFQNQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10ff57b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.Not_Contacted, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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AFGHy5I/n0UcfyZFHfiCf//y0tLQ8m2ef/eOL7r9mzaOZNGlCBg9+cyZNOiUf/vCJeeyx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\nAIBCCDQAAIBCCDQAAIBCCDQAAIBC/D9wFSmYmmJolgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11be6fc18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.Pos_Balance, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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AAACFEGgAAACFEGgAAACFEGgAAACFEGgAAACFEGgAAACFEGgAAACFEGgAAACFEGgAAACF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\nAAAoxP8FfLMpzHwtwAsAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10ff57a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.No_Balance, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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oAAAAhRBoAAAAhRBoAAAAhRBoAAAAhRBoAAAAhRBoAAAAhRBoAAAAhRBoAAAAhRBoAAAA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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11c0617b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.Neg_Balance, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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CDQAAoBACDQAAoBACDQAAoBACDQAAoBACDQAAoBACDQAAoBACDQAAoBACDQAAoBACDQAA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\nAACgEAINAACgEAINAACgEAINAACgEP8HSUA/TyM88Y0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10ff67cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.campaign, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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AACiEQAMAACiEQAMAACiEQAMAACiEQAMAACiEQAMAACiEQAMAACiEQAMAACiEQAMAACiE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\nCvG/ASzXsCVRdZN8AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11b51f400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.marital, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11ba55160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_data = pd.crosstab(data.education, data.is_success)\n", "draw_data.div(draw_data.sum(1).astype(float), axis=0).plot(kind='bar', stacked=False, color=['deepskyblue','steelblue'],grid=False, figsize=(15, 5))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classifiers :\n", "\n", " Based on the values of different parameters we can conclude to the following classifiers for Binary Classification.\n", " \n", " 1. Gradient Boosting\n", " 2. AdaBoosting\n", " 3. Logistics Regression\n", " 4. Random Forest Classifier\n", " 5. Linear Discriminant Analysis\n", " 6. K Nearest Neighbour\n", " \n", " \n", " And performance metric using precision and recall calculation along with roc_auc_score & accuracy_score" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "classifiers = {'Gradient Boosting Classifier':GradientBoostingClassifier(),'Adaptive Boosting Classifier':AdaBoostClassifier(),'Linear Discriminant Analysis':LinearDiscriminantAnalysis(),'Logistic Regression':LogisticRegression(),'Random Forest Classifier': RandomForestClassifier(),'K Nearest Neighbour':KNeighborsClassifier(8)}#'Decision Tree Classifier':DecisionTreeClassifier(),'Gaussian Naive Bayes Classifier':GaussianNB(),'Support Vector Classifier':SVC(probability=True),}" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact',\n", " 'month', 'campaign', 'Adult', 'Middle_Aged', 'old', 'Neg_Balance',\n", " 'No_Balance', 'Pos_Balance', 'Not_Contacted', 'Contacted', 't_min',\n", " 't_e_min', 'e_min', 'pdays_not_contacted', 'months_passed'],\n", " dtype='object')\n" ] } ], "source": [ "data_y = pd.DataFrame(data['is_success'])\n", "data_X = data.drop(['is_success','balance','previous','pdays','age','duration'],axis=1)\n", "print(data_X.columns)\n", "log_cols = [\"Classifier\", \"Accuracy\",\"Precision Score\",\"Recall Score\",\"F1-Score\",\"roc-auc_Score\"]\n", "#metrics_cols = []\n", "log = pd.DataFrame(columns=log_cols)\n", "#metric = pd.DataFrame(columns=metrics_cols)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rs = StratifiedShuffleSplit(n_splits=2, test_size=0.2,random_state=0)\n", "rs.get_n_splits(data_X,data_y)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/mayurjain/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "/Users/mayurjain/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "/Users/mayurjain/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "/Users/mayurjain/anaconda/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:387: UserWarning: Variables are collinear.\n", " warnings.warn(\"Variables are collinear.\")\n", "/Users/mayurjain/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " import sys\n", "/Users/mayurjain/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", " import sys\n", "/Users/mayurjain/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " import sys\n" ] } ], "source": [ "for Name,classify in classifiers.items():\n", " for train_index, test_index in rs.split(data_X,data_y):\n", " #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n", " X,X_test = data_X.iloc[train_index], data_X.iloc[test_index]\n", " y,y_test = data_y.iloc[train_index], data_y.iloc[test_index]\n", " cls = classify\n", " cls =cls.fit(X,y)\n", " y_out = cls.predict(X_test)\n", " accuracy = m.accuracy_score(y_test,y_out)\n", " precision = m.precision_score(y_test,y_out,average='macro')\n", " recall = m.recall_score(y_test,y_out,average='macro')\n", " roc_auc = roc_auc_score(y_out,y_test)\n", " f1_score = m.f1_score(y_test,y_out,average='macro')\n", " log_entry = pd.DataFrame([[Name,accuracy,precision,recall,f1_score,roc_auc]], columns=log_cols)\n", " #metric_entry = pd.DataFrame([[precision,recall,f1_score,roc_auc]], columns=metrics_cols)\n", " log = log.append(log_entry)\n", " #metric = metric.append(metric_entry)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Classifier Accuracy Precision Score Recall Score \\\n", "0 Gradient Boosting Classifier 0.890634 0.737201 0.631822 \n", "0 Gradient Boosting Classifier 0.895278 0.760529 0.634452 \n", "0 Adaptive Boosting Classifier 0.888311 0.727402 0.622308 \n", "0 Adaptive Boosting Classifier 0.890744 0.740040 0.619995 \n", "0 Linear Discriminant Analysis 0.879686 0.706194 0.692858 \n", "0 Linear Discriminant Analysis 0.883114 0.714301 0.695209 \n", "0 Logistic Regression 0.888754 0.730773 0.612719 \n", "0 Logistic Regression 0.889638 0.736509 0.608710 \n", "0 Random Forest Classifier 0.879354 0.696065 0.641014 \n", "0 Random Forest Classifier 0.881566 0.700978 0.629967 \n", "0 K Nearest Neighbour 0.885105 0.715089 0.562686 \n", "0 K Nearest Neighbour 0.886321 0.725560 0.564604 \n", "\n", " F1-Score roc-auc_Score \n", "0 0.663033 0.737201 \n", "0 0.669548 0.760529 \n", "0 0.652172 0.727402 \n", "0 0.651823 0.740040 \n", "0 0.699173 0.706194 \n", "0 0.704061 0.714301 \n", "0 0.642977 0.730773 \n", "0 0.639433 0.736509 \n", "0 0.661609 0.696065 \n", "0 0.653961 0.700978 \n", "0 0.581003 0.715089 \n", "0 0.584010 0.725560 \n" ] }, { "data": { "image/png": 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OncLd3Z2SJUty4sQJqlevzoEDB6hYsSInT57k9u3bhIeHExcXR7du3Xj55ZcxmUwPDDb/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\nCcoYg9/vZ8GCBVRXV1uhrr+/n4ULF+LxeKx7wkS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"text/plain": [ "<matplotlib.figure.Figure at 0x11beb2f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(log)\n", "plt.xlabel('Accuracy')\n", "plt.title('Classifier Accuracy')\n", "sns.set_color_codes(\"muted\")\n", "sns.barplot(x='Accuracy', y='Classifier', data=log, color=\"g\") \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# rs = ShuffleSplit(n_splits=1, test_size=0.2,random_state=0)\n", "# rs.get_n_splits(data_X,data_y)\n", "# for Name,classify in classifiers.items():\n", "# for train_index, test_index in rs.split(data_X,data_y):\n", "# #print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n", "# X,X_test = data_X.iloc[train_index], data_X.iloc[test_index]\n", "# y,y_test = data_y.iloc[train_index], data_y.iloc[test_index]\n", "# cls = classify\n", "# cls =cls.fit(X,y)\n", "# y_out = cls.predict(X_test)\n", "# accuracy = accuracy_score(y_test,y_out)\n", "# precision = m.precision_score(y_test,y_out,average='macro')\n", "# recall = m.recall_score(y_test,y_out,average='macro')\n", "# f1_score = m.f1_score(y_test,y_out,average='macro')\n", "# roc_auc = roc_auc_score(y_out,y_test)\n", "# log_entry = pd.DataFrame([[Name,accuracy,precision,recall,f1_score,roc_auc]], columns=log_cols)\n", "# #metric_entry = pd.DataFrame([[precision,recall,f1_score,roc_auc]], columns=metrics_cols)\n", "# log = log.append(log_entry)\n", "# #metric = metric.append(metric_entry)\n", " \n", "# print(log)\n", "# plt.xlabel('Accuracy')\n", "# plt.title('Classifier Accuracy')\n", "# sns.set_color_codes(\"muted\")\n", "# sns.barplot(x='Accuracy', y='Classifier', data=log, color=\"g\") \n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1208fbdd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(log['Recall Score'], log['Precision Score'], color='navy',\n", " label='Precision-Recall')\n", "plt.xlabel('Recall')\n", "plt.ylabel('Precision')\n", "plt.ylim([0.0, 1.0])\n", "plt.xlim([0.0, 1.0])\n", "plt.title('Precision-Recall')\n", "plt.legend(loc=\"lower left\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "log.to_csv('/Users/mayurjain/Documents/Fragma ML TEST/August 13/marketing-data_Results.csv',sep=',',header='infer')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
mirjalil/DataScience	notebooks/deeplearning/08_efficient_convolution.ipynb	1	5857	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Efficient Convolution\n", "====" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\mathbf{x}\\in \\mathbb{R}^n$ and $\\mathbf{w}\\in \\mathbb{R}^m$\n", "\n", " * Convolution $$y[i] = \\sum_k x[i-k] w[k]$$\n", "\n", "\n", " * Output size: $m+n-1$\n", " * Order of complexity: $\\mathbf{O}(mn)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\left[y_1\\\\y_2..y_{m+n-1}\\begin{array}{c}\\end{array}\\right] = \\left[\\begin{array}{cccc}w_1&\\end{array}\\right] \\left[\\begin{array}{c}\\end{array}\\right]$$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ " * Linear and circular convolution are equivalent when $N\\ge m + n -1$\n", " * We are performing a N-point DFT: so assuming the signal is repeating with period of N\n", " * compute the convolution of this repeating signal $X$, with another repeating signal $W$\n", " * Original signal size: $n$ ($N = n$)\n", " * Non-zero elements: $m$\n", " * W is filled with $N - m$ zero elements\n", " * right at the edge, in linear convolution, signal is filled with zeros, but in circular convolution, some part is wrong.\n", "<img src=\"figs/2.png\"></img>\n", " * But if $N = n + m -1$, then\n", " * signal has $N - n$ zerofs filled\n", " * and at the edge, the same thing happens as in linear convolution\n", " \n", "<img src=\"figs/2.png\"></img>\n", " \n", " * Aliasing: when $N < m + n -1$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Long Singnal, Short Filter\n", "\n", " ### Overlap Add (OA)\n", " \n", " * Break it up to $L$ blocks\n", " * perform the convolution\n", " * Combine the outputs of each block\n", " \n", " <img src=\"figs/overlap-add.png\"></img>\n", " \n", " * In this case, DFT size is $N = L + m - 1$\n", " * Much smaller, and therefore more efficient\n", " * Since blocks are independent, it can also be performed in parallel\n", " \n", " \n", "\n", "### Overlap Save (OS)\n", "\n", " * Add a few zero in front\n", " * Process blocks of size $L$\n", " * DFT size is $N = L$ (same as block size)\n", " * as a result, we will have aliasing\n", " \n", " * Combine the result of each block\n", " * discard $m-1$ eleents due to aliasing\n", " \n", " \n", "### Other tricks\n", "\n", " * DFT of a signal is *Hermitian Symmetric\n", " Assume we are computing a $N$ point DFT\n", " * k=0 : DC value\n", " * k=1 : ..\n", " * ..\n", " * k = N-1 : ..\n", " \n", " $$Y[k] = Y[N-k]^* \\ \\ \\ (\\text{complex conjugate of }Y)$$\n", " \n", " * Advantages: \n", " * We only need to compute half of the values ($\\floor{\\frac{N}{2}} + 1$) , not all\n", " * We do not need to store the other half\n", " \n", " * If we have a 2d signal: $N\\times N$\n", " * We need to compute the lower triangular part of the $N\\times N$ matrix $N\\left(\\floor{\\frac{N}{2}}+1\\right)$\n", " \n", " * Complex multiplcation: needs 3 real multiplication (as opposed to a naive version which needs 4)\n", " $$(a + jb) (c+jd) = ac - bd + j(ad + bc)\\\\=u_c v_a + u_av_c + j(u_a v_c - u_b v_b)$$\n", " \n", " where $u_a = a, v_a = c, u_b = ..$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Winograd Convolution\n", "\n", " Minimal filtering algorithm \n", " \n", " \n", " * Is good for only small filters\n", " * Not using DFT\n", " \n", " * $m$ outputs with $r$-tap filter, the optimal solution needs $F(m,r)=m+r-1$ multiplcations\n", " \n", " * Original convolution, needs $(n+m-1)\\times m$\n", " * input size $n$, filter size $m$, $\\Longrightarrow$ outputsize: $n+m-1$\n", " * For each output, we need $m$ multiplcation\n", " * so the original method needs $(n+m-1)\\times m$ multiplcations\n", " * with this setting, the Winograd method needs $(n+m-1) + (m-1) = n+2m-2$ multiplcations\n", " \n", " * Example: output size $m=2$ and $3\\times 3$ filter size $r=3$\n", " \n", " $$F(2,3) = \\left[\\begin{array}{ccc}d_0 & d_1 & d_2\\\\d_1 & d_2 & d_3\\end{array}\\right] \\left[\\begin{array}{c}g_0\\\\g_1\\\\g_2\\end{array}\\right] = \\left[\\begin{array}{c}m_1+m_2+m_3\\\\m_2-m_3-m_4\\end{array}\\right]$$\n", " \n", " \n", " $$\\begin{array}{ccc}m_1 = ()g_0 & & m_2=(d_1+d_2)\\frac{}{2}\\\\m_4=..& & m_3=\\end{array}$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0rc4" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
phungkh/phys202-2015-work	assignments/assignment11/OptimizationEx01.ipynb	1	36527	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Optimization Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import scipy.optimize as opt" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Hat potential" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "The following potential is often used in Physics and other fields to describe symmetry breaking and is often known as the \"hat potential\":\n", "\n", "$$ V(x) = -a x^2 + b x^4 $$\n", "\n", "Write a function `hat(x,a,b)` that returns the value of this function:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [], "source": [ "def hat(x,a,b):\n", " return (-ax2 + bx*4)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "7204bd97cd003430289f171b6ba70d63", "grade": true, "grade_id": "optimizationex01a", "points": 2 } }, "outputs": [], "source": [ "assert hat(0.0, 1.0, 1.0)==0.0\n", "assert hat(0.0, 1.0, 1.0)==0.0\n", "assert hat(1.0, 10.0, 1.0)==-9.0" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Plot this function over the range $x\\in\\left[-3,3\\right]$ with $b=1.0$ and $a=5.0$:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "a = 5.0\n", "b = 1.0" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f6b5c952828>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6b5cd80128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(-3,3,1000)\n", "plt.plot(x, hat(x,a,b))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "bd49ce2f030e3366ee640213f26fdaa6", "grade": true, "grade_id": "optimizationex01b", "points": 2 } }, "outputs": [], "source": [ "assert True # leave this to grade the plot" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write code that finds the two local minima of this function for $b=1.0$ and $a=5.0$.\n", "\n", " Use `scipy.optimize.minimize` to find the minima. You will have to think carefully about how to get this function to find both minima.\n", "* Print the x values of the minima.\n", "* Plot the function as a blue line.\n", "* On the same axes, show the minima as red circles.\n", "* Customize your visualization to make it beatiful and effective." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " status: 0\n", " nfev: 18\n", " fun: -6.25\n", " message: 'Optimization terminated successfully.'\n", " hess_inv: array([[ 0.04999981]])\n", " njev: 6\n", " success: True\n", " x: array([-1.58113883])\n", " jac: array([ 2.38418579e-07]) status: 0\n", " nfev: 18\n", " fun: -6.249999999999999\n", " message: 'Optimization terminated successfully.'\n", " hess_inv: array([[ 0.05000113]])\n", " njev: 6\n", " success: True\n", " x: array([ 1.58113882])\n", " jac: array([ -1.19209290e-07])\n" ] } ], "source": [ "min1 = opt.minimize(hat, x0 =-1.7,args=(a,b))\n", "min2=opt.minimize(hat, x0 =1.7, args=(a,b))\n", "print(min1,min2)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Our minimas are x=-1.58113883 and x=1.58113882\n" ] } ], "source": [ "print('Our minimas are x=-1.58113883 and x=1.58113882')" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f6b5bddadd8>" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5dCEiIsVAwZUhdxxtbyIikjMKrmQouEREckTBlQwttisikiMKrmRoZqGISI4ouJKh4BIR\nyREFVzKWAJldBCIiIk2i4ErGe8CA0EWIiBQDBVcylgK9zGgbuhARyR9LliyhU6dOWrYpYQquBMQb\nSi4Hdg5di4jkj379+rFu3bom7Wa8cOFCTjjhBHbaaSd23HFHxo4dy8KFC3NQZfpkFFxmVmJmL5rZ\nXDObb2bXxPd3M7NnzGyhmT1tZl2TKTevlaPuQpH8MXcu/PWv0d5dKbB27VrGjx/PwoULqaio4IAD\nDuCEE04IXVZeyii43H0TcIS7DwOGAEeY2SjgIuAZd98dmBF/X+jeA3YNXYRIUaishNmzo1vNvbmq\nTZ0abQ55+umwzz5w332Jnn7AgAFcd911DBkyhE6dOnHWWWdRUVHBuHHj6NKlC6NHj2bNmjWUl5fT\nqlUrqqqqACgtLeXyyy9n1KhRdO7cmWOOOYZP4g0u999/f84880y6du1KmzZtmDJlCgsWLGB1zd2d\nBUigq9Ddq3dj2w5oDawGjgfujO+/Exif6XlSoBy1uESyb/VqGDoUxo6NbsOGRfdVmzsXbr452ijy\n009h40b47nejzSKrucONN0b7eI0bB6+/3qwSzIyHHnqIGTNmsGDBAh577DHGjRvHtddey4oVK6iq\nquKmm26q97n33nsvd9xxBytWrKCyspLrrruu3sc9//zz9O7dmx120PrddWUcXGbWyszmAhXAc+4+\nD+jp7hXxQyqAng0eoHCUo+ASyb6LLoq6/9ati26LF0f3VXv//fq3MolbNgBMmwaXXgovvhhtMnnI\nIbBoUbPKOO+88+jRowd9+vTh0EMPZeTIkQwdOpR27doxYcIE5syZU++WJmeeeSaDBg2ipKSEk08+\nmblz52517KVLl/KDH/yAG264oVk1FYskWlxVcVfhzsBhZnZEnZ87UAxTaspRcIlk37x5tbsHKyuj\n+6rtuy9s3lz7OR06QM8an59vuSVqkVXbtAnuv79ZZfSscbz27dvX+r6kpIT169fX+7xevXrVel7d\nx61cuZIxY8Zw7rnnMnHixGbVVCy2sZd187j7WjN7HPg6UGFmvdx9uZn1BlbUfbyZlQKlNe4qc/ey\npOoJQGNcIrlwwAHw6qtfdf2VlERdftUGDoTbb4+6ByEKrSefrL8VVs0s2gk5A0lMeV+9ejVjxoxh\n/PjxXHzxxRkfLw1akgUZBZeZdQe+cPc1ZtYeGA1cCTwCnAH8PP7vw3WfGxe2zeJSZhmwkxnbuVPP\naLGIJGL6dHjllegGMGIEXHVV7ceccgqMHx91D/bsuXVoXXhh9JwNG6LA6tABTj01J+U3FHCffvop\nxxxzDKNGjeLqq6/OSS35oCVZkGmLqzdwp5m1Iup2vMvdZ5jZHOABMzuLqAvt5AzPk/fc+cKMD4m2\nN1kcuh6RgtWhA8ycGY1lAfTvH7WY6iopgb596z/G1KnQo0c023DHHeHKK6PjZKDmeJaZbfm+vnGu\n+h73P//zP7zyyivMnz+fO+64Y8vP58+fz8476xLRmkxXdCfHjOeAn7nzbOhaRAqBmbneowqTmeHu\njV+ZXQ+tnJGscjRBQ0QkqxRcydJiuyIiWabgSlY5Ci4RkaxScCWrHE2JFxHJKgVXsspRi0tEJKs0\nqzBBZrQGNgCd3fk8dD0iaadZhYUrk1mFia2cIeDOl2YsBfoB74auR6QQNGUvKykuCq7klRONcym4\nRDLU0k/khc6MWcBl7gW1+lCTaYwreeVonEtEsmtXostvipKCK3m6lktEssaMdkAPYGnoWkJRcCWv\nHE2JF5Hs6Q8sdefL0IWEouBK3r+A3UIXISIFayDR+0zRUnAlbzEKLhHJnqIe3wIFVzasANqZ0TV0\nISJSkBRcoQsoNO44anWJSPaoqzB0AQVqMdEfl4hI0tTiCl1AgVKLS0SyRcEVuoACpeASkcTFY+dt\ngY9D1xKSgis7FFwikg27Au/FY+lFS8GVHQouEcmGgRR5NyEouLJlCdArXppFRCQpu6EFvBVc2eDO\nF8AHaM1CEUnWIGBR6CJCU3Blj7oLRSRpCi4UXNmk4BKRpCm4UHBlk4JLRBJjRntgJ6JhiKKm4Moe\nBZeIJGlXoLyYtzOppuDKHgWXiCRJ3YQxBVf2vAfsaqbfsYgkYhDRB+KipzfVLHFnPbAW6B26FhEp\nCGpxxRRc2aXuQhFJioIrpuDKLgWXiCRFwRVTcGWXgktEMmbGdkBf4P3QteQDBVd2vUv0KUlEJBMD\ngKXuVIYuJB8ouLJrIbB76CJEJPXUTViDgiu73gV2N8NCFyIiqabgqkHBlUXurAXWoynxIpIZBVcN\nCq7sU3ehiGRKwVWDgiv7FFwikikFVw0KruxTcIlIi5nRFuhHtIycoODKBQWXiGRiV2CZO5tCF5Iv\nFFzZp+ASkUzsQfQ+IjEFV/YtBgaY0SZ0ISKSSnsAC0IXkU8UXFnmzufAh0RXvouINNfuKLhqUXDl\nhroLRaSl1OKqQ8GVGwouEWkpBVcdCq7cUHCJSLOZ0QXYnmi4QWIKrtxYSPSpSUSkOfYAFrrjoQvJ\nJxkFl5ntYmbPmdk8M3vLzM6P7+9mZs+Y2UIze9rMuiZTbmqpxSUiLaFuwnpk2uLaDFzg7nsDBwHn\nmtlg4CLgGXffHZgRf1/MPgC6m9ExdCEikioKrnpkFFzuvtzd58ZfrwfeJtql83jgzvhhdwLjMzlP\n2rnzJdH1XNpUUkSaQ1Ph65HYGJeZDQCGAy8CPd29Iv5RBdAzqfOkmLoLRaS51OKqRyKrOZjZ9sBf\ngMnuvs7sq30T3d3NbKuBRTMrBUpr3FXm7mVJ1JOnNEFDRJrMjFbA14g2pC1YLcmCjIPLzNoShdZd\n7v5wfHeFmfVy9+Vm1htYUfd5cWHbLK7ALACODF2EiKTGLsBqd9aFLiSbWpIFmc4qNOA2YL6731jj\nR48AZ8RfnwE8XPe5RWg+sFfoIkQkNdRN2ABzb/nlAWY2CngeeAO2XGdwMfAS8ADRHjLlwMnuviaj\nSlMuvpBwGdDZnarQ9YhIfjPjPGAvd/4jdC35JqOuQnd/gYZbbUdncuxC485aM9YSNf/fD12PiOQ9\ntbgaoJUzcuttYHDoIkQkFQYTDTFIHQqu3NI4l4g01V4ouOql4MottbhEpFFmdAM6Eo2LSx0KrtxS\ncIlIUwwG5mtx3fopuHLrbWCwGdboI0WkmKmbcBsUXLlVfSF2j6BViEi+24vog67UQ8GVQ3Gz/200\nQUNEtk0trm1QcOWexrlEpDEKrm1QcOWegktEGmRGZ6AbWqigQQqu3JuPgktEGjYYeEdLwzVMwZV7\nGuMSkW1RN2EjFFy59wHQJV50V0SkLgVXIxRcORY3/99BrS4RqZ+CqxEKrjDeBPYJXYSI5CUtrtsI\nBVcYbwL7hi5CRPKLGR2BXsB7oWvJZwquMN5CLS4R2dqewLvufBG6kHym4ArjTWBfrVkoInXsS/T+\nINug4ApjOWBAz9CFiEheUXA1gYIrgHjNQnUXikhdQ4A3QheR7xRc4WiChojUpRZXEyi4wlFwicgW\nZuwEbId2PW6UgiscXcslIjXtC7ypXY8bp+AKZx6wl5n+H4gIoPGtJtObZiDufAp8DAwMXYuI5IV9\nUXA1iYIrLI1ziUi1IWhiRpMouMLSlHgRwYzWRGsUvhW6ljRQcIWlFpeIAAwClruzPnQhaaDgCusN\nou4BESluGt9qBgVXWAuAXeIVoUWkeGl8qxkUXAG5s5lo3x21ukSKm6bCN4OCK7y5wPDQRYhIUGpx\nNYOCK7w5wLDQRYhIGGbsAHQH3g1dS1oouMJTi0ukuA0D3nCnKnQhaaHgCu8NoqWf2oYuRESCGA68\nFrqINFFwBRZft/EBsEfoWkQkiP2IhgykiRRc+UHdhSLFSy2uZlJw5Yc5KLhEik58DeeuRJfFSBMp\nuPLDXDSzUKQYDQHmx9d0ShMpuPLDHGCYGRa6EBHJqeFofKvZFFx5wJ0VwEagf+haRCSn9kPjW82m\n4Mof6i4UKT5qcbWAgit/vAZ8PXQRIpIbZmxHtAeX1ihsJgVX/ngZ2D90ESKSM3sD77mzIXQhaaPg\nyh8vA/trgoZI0dD1Wy2k4MoT7nxENEFj19C1iEhOjEDB1SIKrvyi7kKR4nEA8GLoItJIwZVfFFwi\nRcCMEmAvNKOwRTIOLjO73cwqzOzNGvd1M7NnzGyhmT1tZl0zPU+RUHCJFIdhwDvubAxdSBol0eL6\nb2BsnfsuAp5x992BGfH30rhXgOFmtA5diIhk1QHAS6GLSKuMg8vdZwGr69x9PHBn/PWdwPhMz1MM\n3FkNVAB7hq5FRLJKwZWBbI1x9XT3ivjrCqBnls5TiNRdKFL4FFwZaJPtE7i7m5nXvd/MSoHSGneV\nuXtZtutJgerguiNwHSKSBWZ0A3oDb4euJR+0JAuyFVwVZtbL3ZebWW9gRd0HxIWVZen8afYyMCl0\nESKSNSOAV935MnQh+aAlWZCtrsJHgDPir88AHs7SeQrRHGBvM9qFLkREskLXb2Uoienw9wKzgT3M\n7AMzOxO4FhhtZguBI+PvpQnc+QxYiFaKFylUGt/KkLlvNfwkgZlxM7DInV+FrkVEkhOvRfoRsL87\nH4SuJ620ckZ+mg0cHLoIEUlcv/i/S4NWkXIKrvw0GzhYK8WLFJxDgNnuqKsrAwqu/FRO9P+mXyOP\nE5F0OQR4IXQRaafgykPxpzF1F4oUnkOA/w1dRNopuPKXgkukgJjRBRiEVoTPmIIrfym4RArLQUQX\nHleGLiTtFFz56zVgTzO2D12IiCRC41sJUXDlKXc+B+aiBXdFCsUoNL6VCAVXflN3oUgBMKMt0YfQ\nf4SupRAouPLbbKJPaSKSbkOB9+M99yRDCq789gLRhchZ335GRLJK41sJUnDlMXdWAkuA4aFrEZGM\naHwrQQqu/FdG7U3WRCRF4qXbDgVmha6lUCi48l8ZCi6RNBsMbHSnPHQhhULBlf+eB0ZpnEsktY4A\nngtdRCFRcOW5eJzrA7SxpEhaKbgSpuBKhzLUXSiSOma0Ivq3Wxa2ksKi4EqHMhRcImm0D7BGux0n\nS8GVDhrnEkmnUtRNmDgFVwq4swJYhsa5RNJG41tZoOBKj78DR4UuQkSaJh7fOhyNbyVOwZUeTwNj\nQhchIk02HFjhzoehCyk0Cq70KAMOMKNj6EJEpEmOAZ4KXUQhUnClhDvrgFeJuh5EJP8puLJEwZUu\n6i4USQEzOgP7ATND11KIFFzp8hTRpzgRyW+lwIvufBa6kEKk4EqXOUB3M/qFLkREtukYoh4SyQIF\nV4q4UwU8g7oLRfKdxreySMGVPhrnEsljZuwGdATeCF1LoVJwpc/TwNFa/kkkbx0DPO2Ohy6kUCm4\nUia+mPF9YGToWkSkXscCj4cuopApuNLpEeD40EWISG1mbA8cisa3skrBlU4KLpH8NAb4hztrQxdS\nyBRc6fQasL0Ze4QuRERqOQ54NHQRhU7BlULxoO+jRP9IRCQPmNEa+DcUXFmn4EqvR1BwieSTA4GP\n3CkPXUihU3Cl19+BYWbsGLoQEQGicWe1tnJAwZVS7mwiCq9jQ9ciUuzMMOAEop4QyTIFV7o9CJwY\nuggRYW+gA/By6EKKgYIr3R4FDjOja+hCRIrcycCftVpGbii4UsydT4HniLooRCSAuJvwZODPoWsp\nFgqu9HuA6B+NiISxL9AeeCl0IcVCwZV+jwKjzNghdCEiReok4AF1E+aOgivl3FlHNLtQ3YUiOaZu\nwjAUXIXhAeCU0EWIFKFhQDs0mzCnFFyF4a/AgWb0CV2ISJE5HbhL3YS5lbXgMrOxZvaOmb1rZj/O\n1nkE3NkAPAT8n9C1iBQLM9oCpwJ/DF1LsclKcJlZa+A3wFhgL2CSmQ3Oxrlkiz8CZ8R97iKSfccA\ni915N3QhxSZbLa4DgEXuXu7um4H70OSBbJsFdASGhy5EpEicAdwZuohilK3g6gt8UOP7pfF9kiXu\nVBG3ukLXIlLozOhGtGnkA6FrKUZtsnRcDVSGcRcw24wfuVMZuhipzYwSYEB86w50BjoBbYHN8W0D\nsDy+LQOWxR9KJL9MBJ5yZ3XoQopRtoJrGbBLje93IWp1bWFmpUBpjbvK3L0sS/UUBXcWmTEPmADc\nH7qeYmYAEW8JAAAOI0lEQVRGJ2AUMCK+DQd2IuqJeA9YCXwa3zbzVYBtD/QEegM7A53MmA+8BfwT\neB5YqFls4cTjyOcAU0PXUghakgXmnvzfv5m1ARYARwEfEi2FMsnd3078ZFKLGScB57rX+kOQHDBj\nH6Kx3DHAfsArwIvxf18D3nfny2YecweiCU5DgIOBw4muG3oaeJjoU//6pF6DNM6MkUTd8nuoNRxG\nVoILwMzGATcCrYHb3P2arJxIaomn6L4PjHZnXuh6Cp0ZuxFd/D0J6AL8BXgKmBlfppCNcw4AxgHj\ngZFEK6f8N/CEO5uzcU75ihl3AXPduT50LcUqa8El4ZhxFbCDO+eFrqUQmdGaaAPPc4m6AB8A7gVm\n5/oTeLylzTeB7wKDgLuBW9z5Vy7rKBZmdAcWAbu580noeoqVgqsAmbEz8AbQT91IyTFje6KxjfOJ\nusBvBh6Md6MOzow9gLOIQqwMuN6dfwQtqsCYMRXYy53vhK6lmGnJpwLkzlKiN67TApdSEMzoasZP\ngH8RXaP4TXdGunN3voQWgDsL3JlKNGvxeeAeM2aZcUTYygqDGW2A7wH/FbqWYqfgKlw3AD+Mu7Wk\nBczoGAfWIuBrwGHuTHTn1cClbZM76925CdgduBX4nRl/N+OQwKWl3TeBD915MXQhxU7BVaDceQGo\nIJoaL81gRhsz/h1YSLRJ4EHunOHOO4FLaxZ3vnDnbmAw0djXPWY8GncpSjPEU+CnAr8MXYsouArd\nL4CpWr+w6cwYDcwl6mb9ZtzCWhS4rIzEAXY7sAdRF/ILZvxKm482SynRNXaPBq5DUHAVukeJVmc4\nPHQh+c6MvmbcD/wOuBQoLbQuIXc+j6dw7w10AN4x4xwzvQ80wVTgOl23lR/0B1vA4n9kvwQuDl1L\nvjKjrRn/D3idqGtwb3f+WsgrU7izwp1ziC6UPh2YFV88LfUw4wCiLuO7Q9ciEQVX4bsL+JoZh4Uu\nJN+Y8XXgVaLtKQ5257JsXTScj9x5HTiUaBWI58y42oz2gcvKR1cCP8unGaTFTsFV4OLFdqcBP9NY\nVyRuZU0DngB+Dox1Z2HYqsJwp8qdW4mWlNoNeFPT579ixsFES27dHroW+YqCqzjcA+xI1LIoanGX\n2ItE12MNd+eeQu4WbCp3PnJnIjAZuMuMX5vRMXRdeeAqYLo7n4cuRL6i4CoC8cKulxG1uory/7kZ\nrc34MfAc0YoX/+bOh4HLyjvuPE40ntMFeN2MUYFLCsaMcUA/4I7ApUgdRfkmVqQeAr4Avh26kFwz\nY3eiHaKPAfZ35za1shrmzmp3Tgd+CDxgxg3FNvYVL1Z9PfBDLVycfxRcRSJ+oz4fuNaMzqHryQUz\nWplxHjCbaBHco90pD1tVerjzV6Kxr97AHDMOClxSLp1DtK/gY6ELka1pkd0iY8btwCfu/Ch0LdkU\nb/1xO9AeOKNYJ18kxYwTgd8Q/U6vLOQxn3gF+HlEH3TeDF2PbE0truJzMfCdQr1uxwyLl2t6mWhf\nrFEKrcy58yAwlOji5ZfMGBq4pGz6FXC3Qit/qcVVhMz4v8DZwEh3vghdT1LM6AP8AehF1MrSG0/C\n4ksqTgOuA/4T+HmB/Q2NJVr9fR93Pgtdj9RPLa7i9AdgNXBh6EKSELeyTiVaY/BlokVxFVpZ4I67\n80fg60Tr9802Y8+wVSUjHvv9LXCOQiu/qcVVpMzoR7RqxNHxCgqpZEYPok/Ig4laWa8ELqloxK2v\n7wE/BaYDN6V1Lb/4tdwNfObO2aHrkW1Ti6tIubOE6GLTP6d1lqEZJwFvAu8BX1do5Vbc+vov4CDg\nRODvZuwauKyWOh0YBkwJXYg0Ti2uImfGrcAOwMS0XNsUz/q6meiN5jvanj68eMPSC4AfA5cAf0jR\n39M+RBemH6ku5nRQi0smA4OItm3Ie2Z8i6iV9QEwTKGVH9z50p3riMa9zgGeMKNv2KoaZ8ZORNv/\nXKDQSg8FV5GLV7w+DjjXjFNC19MQM3qZcR9wDXCiOxe6szF0XVKbO/OAkcA/iC5a/na+Lu5sRgnR\nijJ/ineKlpRQcAnuLAOOBW4y4+jQ9dQUrzH4H0StrPeJWln/G7gs2QZ3NrtzFTCOqCX/dLzsVt4w\nYzvgQWAp0TqekiIKLgHAnTeAbwF/MsuPVeTNGE60XNOpwBHu/LiY9stKO3deBfYj2j5mthk/NaND\n4LKq1yG8D9gMnJbWmZDFTMElW7gzCxhPtK3FiaHqMKO3Gb8HngRuBQ53561Q9UjLufOFO78iWnXj\na8A8M04M1X1oRhfgcaA1cIoW0E0nBZfU4s5solXUbzBjWi63QTGjoxmXA28RXSC9hzu36xNx+rmz\nzJ1TgH8nmnX4z1xvWGnGQOAFYCHwrUJeb7HQKbhkK+7MIdpocTTwNzN2yeb5zOhixiXAv4guJP66\nO1PdWZPN80ruuTMDGAHcCNxmxlNmHJ7NFli8ssppRBuI3gqcV0jLVBUjBZfUy53lRFObXwBeM+Pc\neGwgMWb0N+NqYDFRYB3hziRtPVLY3Kly515gT+AvwO+Bf5jxTTPaJHkuM/Ygmu5+MdEqMb9Jy/Vl\n0jAFlzQonh32U+Ao4HjgbTO+2+AAu9kxmD0d3+qd4BF3B55oxuPAa0AH4EB3TnNnfpZeiuQhdyrd\n+R3Rh5ZfEG1cucSMX5ixV4NPbNrf2V5m/A74X2AmMDzNS5tJbVo5Q5rMjKOIVkc4iOj6l78BZe6s\njt9A/ge27JS7EZhg+AyizQgPAY4GjgD+CfwJeECzBKUms2jNSaKdujcS/Y09S7Su5oeOjaH+v7Nn\niVpwY4hmxw4iWsPyFndW5vRFSNYpuKTZ4k0aJxBdpzMSWLMfr3bsz/s7tGcjG2nPOjrxNoM3LGPn\nNkRdgS8QffJ9wp3VwYqXVIjHvIYS/Y2VEk2r9z14p93uLOzckc8wnLV0YRGD1i1kDwM+Ap4n+lA1\nQ5MvCpeCSzISzzrs/2dO/DPw9U2U0J6NdOQzurFq9oG8dFS8OodIi8VB1vsZjn5oPdsfuIEOOEZn\nPqUDG148mhljNZmneCi4JBkNdBXi/lS4oqTg6O9MUHBJkqI3lR/G312vNxPJCv2dFT0Fl4iIpIqm\nw4uISKoouEREJFUUXCIikioKLhERSRUFl4iIpIqCS0REUkXBJSIiqaLgEhGRVFFwiYhIqii4REQk\nVRRcIiKSKgouERFJlRYHl5mdZGbzzOxLM9uvzs8uNrN3zewdMxuTeZkiIiKRTFpcbxLtgvt8zTvN\nbC9gIrAXMBa4xcy2Oo+ZlWZw7ryg15Af9BryQ9pfQ9rrh+J5DS0OLnd/x90X1vOjE4B73X2zu5cD\ni4AD6nlco8WlQGnoAhJQGrqABJSGLiABpaELSEBp6AIyVBq6gASUhi4gAaWNPSAbY1x9gKU1vl8K\n9M3CeUREpAi12dYPzewZoFc9P7rE3R9txnm0W6WIiCRim8Hl7qNbcMxlwC41vt85vq+uNWY2rcb3\nZe5e1oLzhVQWuoAElIUuIAFloQtIQFnoAhJQFrqADJWFLiABZaELaK54TKu0xl1rGn2Oe2aNITN7\nDrjQ3V+Nv98L+BPRuFZf4FlgkGd6IhERETKbDj/BzD4ADgIeN7O/Abj7fOABYD7wN+D7Ci0REUlK\nxi0uERGRXMqLlTPM7IdmVmVm3ULX0lxm9lMze93M5prZDDPbpfFn5Rcz+6WZvR2/jofMrEvomppr\nWxfE5zMzGxtfqP+umf04dD3NZWa3m1mFmb0ZupaWMrNdzOy5+O/nLTM7P3RNzWVmJWb2Yvw+NN/M\nrgldU0uYWWszm2Nm25z8Fzy44jf60cD7oWtpoV+4+1B3HwY8DFwRuqAWeBrY292HAguBiwPX0xL1\nXhCfz8ysNfAbogv19wImmdngsFU1238T1Z9mm4EL3H1voqGPc9P2/8HdNwFHxO9DQ4AjzGxU4LJa\nYjLRMNM2uwKDBxdwAzA1dBEt5e7rany7PfBxqFpayt2fcfeq+NsXiWaCpso2LojPZwcAi9y93N03\nA/cRXcCfGu4+C1gduo5MuPtyd58bf70eeJvoetRUcfcN8ZfbAa2BVQHLaTYz2xn4BvAHwLb12KDB\nZWYnAEvd/Y2QdWTKzH5mZkuAM4BrQ9eToe8CT4Quokj0BT6o8b0u1g/MzAYAw4k+wKWKmbUys7lA\nBfBcPFEuTX4F/AioauyB27yOKwnbuIj5UqIuqZqL8G4zZUNp7EJsd78UuNTMLiL65Z+Z0wKboCkX\nk5vZpUClu/8pp8U1UYIXxOcLzYzKI2a2PfAgMDlueaVK3GsyLB6jfsrMStNybayZHQuscPc5TVmr\nMOvB1dBFzGa2D7Ar8LqZQdQ99aqZHeDuK7JdV3M040LsP5GnrZXGXoOZfYeomX5UTgpqgRZeEJ/P\n6l6svwu1l0uTHDGztsBfgLvd/eHQ9WTC3dea2ePACNJzQfLBwPFm9g2gBOhsZn9099Pre3CwrkJ3\nf8vde7r7ru6+K9E/2P3yLbQaY2Zfq/HtCcCcULW0lJmNJWqinxAP8qZdXrbc6/EK8DUzG2Bm2xHt\nqvBI4JqKjkWfnG8D5rv7jaHraQkz625mXeOv2xNNeEvNe5G7X+Luu8RZcArw94ZCC/Jjcka1tHab\nXGNmb8Z9y6XADwPX0xK/JppY8kw8FfWW0AU1V0MXxOczd/8C+AHwFNFMqvvd/e2wVTWPmd0LzAZ2\nN7MPzCzvusmb4BDg20Qz8ebEt7TNlOwN/D1+H3oReNTdZwSuKRPbzANdgCwiIqmSTy0uERGRRim4\nREQkVRRcIiKSKgouERFJFQWXiIikioJLRERSRcElIiKpouASEZFU+f9KE61nhfgZkgAAAABJRU5E\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6b5be10208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7,5))\n", "plt.plot(x,hat(x,a,b), color = 'b',label='hat potential')\n", "plt.box(False)\n", "plt.title('Hat Potential')\n", "plt.scatter(x=-1.58113883,y=hat(x=-1.58113883,a=5,b=1), color='r', label='min1')\n", "plt.scatter(x=1.58113883,y=hat(x=-1.58113883,a=5,b=1), color='r',label='min2')\n", "plt.legend()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "235361d4c954cf9fd6a8ecef309b3a44", "grade": true, "grade_id": "optimizationex01c", "points": 4 } }, "outputs": [], "source": [ "assert True # leave this for grading the plot" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "To check your numerical results, find the locations of the minima analytically. Show and describe the steps in your derivation using LaTeX equations. Evaluate the location of the minima using the above parameters." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "d7d37614ffa0d469a42ff3fd121335f2", "grade": true, "grade_id": "optimizationex01d", "points": 2, "solution": true } }, "source": [ "$$ V(x) = -5 x^2 + 1 x^4 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take the derivative" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\frac{dV}{dx} = -10x + 4x^3 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "set derivative to 0 and solve for x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ 0 = (-10+4x^2)x $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "critical points are $$x = 0 $$ and $$ x=\\sqrt\\frac{10}{4} $$ and $$ x=-\\sqrt\\frac{10}{4} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check concavity by taking the second derivative" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\frac{d^2V}{dx^2} = - 10 + 12 x^2 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At x = 0, concavity is negative so local maxima is at x=0." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At x= $$ \\sqrt\\frac{10}{4} $$ concavity is positive, so they are the local minimas." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
w0nk0/LSTMtest1	sampling with ordering test.ipynb	1	2380	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import random\n", "\n", "def w0nk0sample(a,diversity=0.4):\n", " randomized = np.array(a)\n", " winner = len(a)-1\n", " while random.random() < diversity:\n", " randomized[np.argmax(randomized)] = 0.25\n", " print randomized\n", " return np.argmax(randomized)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "vec = [x(1+x) for x in range(10)]\n", "import numpy as np\n", "v = np.array(vec)\n", "print v\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0 2 6 12 20 30 42 56 72 90]\n" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "print w0nk0sample(vec,0.7)\n", "print w0nk0sample(vec,0.7)\n", "print w0nk0sample(vec,0.7)\n", "print w0nk0sample(vec,0.7)\n", "print w0nk0sample(vec,0.7)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0 2 6 12 20 7 10 14 18 22]\n", "9\n", "[ 0 2 6 12 20 7 10 14 18 5]\n", "4\n", "[ 0 2 6 12 20 30 42 14 18 22]\n", "6\n", "[ 0 2 6 12 20 30 42 56 72 22]\n", "8\n", "[ 0 2 6 12 20 30 42 56 72 22]\n", "8\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "print w0nk0sample(vec,0.9999)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "9\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-2.0
mirjalil/DataScience	notebooks/statistics-probability/ipynb/24-bootstrapping.ipynb	2	5168	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Bootstrapping\n", "=========" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$X = \\theta + \\epsilon$$\n", "\n", "where \n", " * $\\theta \\in \\mathbb{R}$ and \n", " * $\\epsilon $ is the error with CDF $F_\\epsilon$\n", " * $\\mathbf{E}[\\epsilon] = 0$\n", " * density of $\\epsilon$ is symmetric" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall: \n", "\n", " * if $\\epsilon\\sim \\mathcal{N}$ (normal), then the \"best\" estimator for $\\theta$ is sample mean $\\bar{X}$.\n", "\n", " * However, if $\\epsilon$ is not normal, this may not be the case.\n", " \n", "For example, if $f_\\epsilon(x)=1/2 e^{-\|x\|}$ (\"double xponential\"), theb MLE is sample median." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is important because if $F_\\epsilon$ is not known, then it's hard to choose a good estimator.\n", "\n", " * The biggest problem with not knowing the $F_\\epsilon$ is its tails might be very heavy.\n", " \n", " (heavy tail refers to the speed at which the density goes to zero when its input goes to infinity)\n", " \n", " * This is a problem when the number of data points $n$ is not very large. Indeed, tjhen the CLT is NOT a good approximation, therefore the $\\bar{X}$ is NOT approximately normal, and the MLE for $\\theta$ is NOT $\\approx \\bar{X}$\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Question: what to do when $F_\\epsilon$ is not known and $n$ is not large? ($n=50$ is to low)\n", "\n", " * Answer: resample from the sample a number of times." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Definition:\n", "\n", "A Bootstrap sample $X^_i: i=1...n$ is a set of $n$ variables picked independently from the data points $X_i: i=1..n$\n", "\n", "(noice that the size of sample is the sample as the original)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IDEA: trimmed mean* $\\bar{X}_\\beta$ where $\\beta\\in(0,1)$:\n", " * throw away a proportion $\\beta$ of data points from the left & right tail of your data\n", " \n", " * This certainly avoids outliers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IDEA: Let $\\bar{\\epsilon}_\\beta = \\bar{X}_\\beta - \\theta$\n", "\n", "Consider the 0.025 & 0.975 quantiles $c_1$ and $c_2$ for $F_\\epsilon$.\n", "\n", "Then, we would be able to say $[\\bar{X}_\\beta-c_2, \\bar{X}_\\beta-c_1]$ is a $95\\%$ confidence interval for $\\theta$ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Problem: Since we don't have $c_1$ and $c_2$, let us estimate them.\n", "\n", " * Most obvious idea is to order the $n$ data points, and pick the ones corresponding to quantil $2.5\\%$ and $97.5\\%$. This is a bad idea, because not enough data. (becuase those quantiles will be extremely sensitive)\n", " \n", "Solution: let us use a large number $B$ of bootstrap samples:\n", " \n", " * these are $X^_{ij}$ where $i=1,..,n$ and $j=1,..,B$\n", " \n", " then for each $j$ we compute the sample quantiles\n", " \n", " * then, we average these sample quantiles over $j=1,..,B$\n", " \n", " * the resulting estimates are $\\hat{C}_1$ and $\\hat{C}_2$; we cold call them bootstapped sample quantiles.\n", " \n", " * finally, the bootstrapped $95\\%$ confidence interval for $\\theta$ is \n", " \n", " $$\\left[\\bar{X}_\\beta - \\hat{C}_2, \\bar{X}_\\beta - \\hat{C}_1\\right]$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remark:\n", "\n", "The textbook by stapelton has an excellent treatment of the extension of this idea to the case of linear regression with non-normal errors," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remark: \n", "\n", "The only good (not hard) alternative to the bootstrap for cofidence intervals is the ordinary $t$ method.\n", "\n", " * when tails are fat, this leads to big mistakes\n", " \n", " Using t-tables to build confidecen intervals when $n$ is not large, this only works well for NORMAL ERRORS\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-2.0
probml/pyprobml	notebooks/misc/text-autoencoders_aae_train.ipynb	1	39987	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "train_text_vae.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "metadata": { "id": "eUAv0Mzo0Lcm", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9c1b6474-aff7-4a58-b2bf-34481544f615" }, "source": [ "from google.colab import drive\n", "\n", "drive.mount(\"/content/drive\")" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "Mounted at /content/drive\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "CTs9XQvFCZa-", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2084e5fc-3005-48c5-dcd8-3dc716220d28" }, "source": [ "import torch\n", "from multiprocessing import cpu_count\n", "\n", "print(cpu_count())\n", "print(torch.cuda.is_available())" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "2\n", "True\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "OHQ1OBH6CbIV", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "747bae11-ea87-4107-b5e3-ff71da3d1267" }, "source": [ "!git clone https://github.com/shentianxiao/text-autoencoders.git" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "Cloning into 'text-autoencoders'...\n", "remote: Enumerating objects: 114, done.\u001b[K\n", "remote: Counting objects: 100% (31/31), done.\u001b[K\n", "remote: Compressing objects: 100% (27/27), done.\u001b[K\n", "remote: Total 114 (delta 11), reused 12 (delta 4), pack-reused 83\u001b[K\n", "Receiving objects: 100% (114/114), 270.78 KiB \| 19.34 MiB/s, done.\n", "Resolving deltas: 100% (56/56), done.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "7LeZIGyOCbQe", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d60b9fd8-fece-45ac-f9d6-d1c2f3cd5a3f" }, "source": [ "%cd text-autoencoders" ], "execution_count": 4, "outputs": [ { "output_type": "stream", "text": [ "/content/text-autoencoders\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "bzZ_0JsyCnAz" }, "source": [ "##DATA" ] }, { "cell_type": "code", "metadata": { "id": "GtRrInqSCoLc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "0f2bcb44-891e-4727-c054-1b073be02192" }, "source": [ "!bash download_data.sh" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ "--2021-07-25 06:11:06-- http://people.csail.mit.edu/tianxiao/data/yelp.zip\n", "Resolving people.csail.mit.edu (people.csail.mit.edu)... 128.30.2.133\n", "Connecting to people.csail.mit.edu (people.csail.mit.edu)\|128.30.2.133\|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 3676642 (3.5M) [application/zip]\n", "Saving to: ‘yelp.zip’\n", "\n", "yelp.zip 100%[===================>] 3.51M --.-KB/s in 0.1s \n", "\n", "2021-07-25 06:11:06 (33.5 MB/s) - ‘yelp.zip’ saved [3676642/3676642]\n", "\n", "Archive: yelp.zip\n", " creating: yelp/\n", " creating: yelp/tense/\n", " inflating: yelp/tense/valid.past \n", " inflating: yelp/tense/valid.present \n", " inflating: yelp/tense/test.past \n", " inflating: yelp/tense/test.present \n", " creating: yelp/sentiment/\n", " inflating: yelp/sentiment/100.neg \n", " inflating: yelp/sentiment/100.pos \n", " inflating: yelp/sentiment/1000.neg \n", " inflating: yelp/sentiment/1000.pos \n", " inflating: yelp/test.txt \n", " inflating: yelp/train.txt \n", " inflating: yelp/valid.txt \n", " creating: yelp/interpolate/\n", " inflating: yelp/interpolate/example.long \n", " inflating: yelp/interpolate/example.short \n", "--2021-07-25 06:11:07-- http://people.csail.mit.edu/tianxiao/data/yahoo.zip\n", "Resolving people.csail.mit.edu (people.csail.mit.edu)... 128.30.2.133\n", "Connecting to people.csail.mit.edu (people.csail.mit.edu)\|128.30.2.133\|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 11962156 (11M) [application/zip]\n", "Saving to: ‘yahoo.zip’\n", "\n", "yahoo.zip 100%[===================>] 11.41M 56.7MB/s in 0.2s \n", "\n", "2021-07-25 06:11:07 (56.7 MB/s) - ‘yahoo.zip’ saved [11962156/11962156]\n", "\n", "Archive: yahoo.zip\n", " creating: yahoo/\n", " inflating: yahoo/test.txt \n", " inflating: yahoo/train.txt \n", " inflating: yahoo/valid.txt \n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Jj1dupiX0z0b" }, "source": [ "## Training the AAE model for 30 epochs" ] }, { "cell_type": "code", "metadata": { "id": "nrcu6QBIhr-5", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "5ef8e81e-b5fb-4ffb-a9a2-339ba7354d62" }, "source": [ "NUM_EPOCHS = 30\n", "!python train.py --epochs $NUM_EPOCHS --train data/yelp/train.txt --valid data/yelp/valid.txt --model_type aae --lambda_adv 10 --noise 0.3,0,0,0 --save-dir checkpoints/yelp/daae" ], "execution_count": 6, "outputs": [ { "output_type": "stream", "text": [ "Namespace(batch_size=256, dim_d=512, dim_emb=512, dim_h=1024, dim_z=128, dropout=0.5, epochs=30, lambda_adv=10.0, lambda_kl=0, lambda_p=0, load_model='', log_interval=100, lr=0.0005, model_type='aae', nlayers=1, no_cuda=False, noise=[0.3, 0.0, 0.0, 0.0], save_dir='checkpoints/yelp/daae', seed=1111, train='data/yelp/train.txt', valid='data/yelp/valid.txt', vocab_size=10000)\n", "# train sents 200000, tokens 1821469\n", "# valid sents 10000, tokens 90833\n", "# vocab size 10005\n", "# model parameters: 34933782\n", "--------------------------------------------------------------------------------\n", "\| epoch 1 \| 100/ 790 batches \| rec 68.24, adv 0.83, \|lvar\| 83.43, loss_d 1.33, loss 76.56,\n", "\| epoch 1 \| 200/ 790 batches \| rec 59.54, adv 0.67, \|lvar\| 101.88, loss_d 1.42, loss 66.27,\n", "\| epoch 1 \| 300/ 790 batches \| rec 54.42, adv 0.65, \|lvar\| 70.78, loss_d 1.43, loss 60.89,\n", "\| epoch 1 \| 400/ 790 batches \| rec 58.19, adv 0.76, \|lvar\| 77.14, loss_d 1.41, loss 65.74,\n", "\| epoch 1 \| 500/ 790 batches \| rec 53.71, adv 0.71, \|lvar\| 99.88, loss_d 1.47, loss 60.79,\n", "\| epoch 1 \| 600/ 790 batches \| rec 52.89, adv 0.71, \|lvar\| 134.50, loss_d 1.45, loss 59.98,\n", "\| epoch 1 \| 700/ 790 batches \| rec 52.90, adv 0.73, \|lvar\| 191.29, loss_d 1.46, loss 60.23,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 1 \| time 107s \| valid rec 49.50, adv 0.70, \|lvar\| 197.75, loss_d 1.42, loss 56.52, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 2 \| 100/ 790 batches \| rec 52.23, adv 0.68, \|lvar\| 193.69, loss_d 1.43, loss 59.01,\n", "\| epoch 2 \| 200/ 790 batches \| rec 50.17, adv 0.66, \|lvar\| 243.93, loss_d 1.42, loss 56.81,\n", "\| epoch 2 \| 300/ 790 batches \| rec 46.20, adv 0.69, \|lvar\| 242.02, loss_d 1.40, loss 53.08,\n", "\| epoch 2 \| 400/ 790 batches \| rec 45.90, adv 0.70, \|lvar\| 249.27, loss_d 1.39, loss 52.85,\n", "\| epoch 2 \| 500/ 790 batches \| rec 48.70, adv 0.68, \|lvar\| 253.53, loss_d 1.39, loss 55.50,\n", "\| epoch 2 \| 600/ 790 batches \| rec 44.49, adv 0.70, \|lvar\| 252.18, loss_d 1.39, loss 51.49,\n", "\| epoch 2 \| 700/ 790 batches \| rec 45.74, adv 0.72, \|lvar\| 286.88, loss_d 1.40, loss 52.98,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 2 \| time 113s \| valid rec 41.76, adv 0.72, \|lvar\| 340.60, loss_d 1.38, loss 48.92, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 3 \| 100/ 790 batches \| rec 41.34, adv 0.71, \|lvar\| 320.15, loss_d 1.38, loss 48.47,\n", "\| epoch 3 \| 200/ 790 batches \| rec 43.73, adv 0.70, \|lvar\| 357.95, loss_d 1.39, loss 50.69,\n", "\| epoch 3 \| 300/ 790 batches \| rec 42.10, adv 0.72, \|lvar\| 385.49, loss_d 1.40, loss 49.30,\n", "\| epoch 3 \| 400/ 790 batches \| rec 42.10, adv 0.70, \|lvar\| 430.12, loss_d 1.38, loss 49.08,\n", "\| epoch 3 \| 500/ 790 batches \| rec 40.45, adv 0.71, \|lvar\| 441.09, loss_d 1.41, loss 47.57,\n", "\| epoch 3 \| 600/ 790 batches \| rec 41.37, adv 0.69, \|lvar\| 471.60, loss_d 1.39, loss 48.30,\n", "\| epoch 3 \| 700/ 790 batches \| rec 41.29, adv 0.69, \|lvar\| 519.58, loss_d 1.41, loss 48.23,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 3 \| time 116s \| valid rec 37.31, adv 0.68, \|lvar\| 574.89, loss_d 1.43, loss 44.14, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 4 \| 100/ 790 batches \| rec 37.80, adv 0.70, \|lvar\| 578.41, loss_d 1.41, loss 44.80,\n", "\| epoch 4 \| 200/ 790 batches \| rec 38.56, adv 0.70, \|lvar\| 592.78, loss_d 1.39, loss 45.55,\n", "\| epoch 4 \| 300/ 790 batches \| rec 39.70, adv 0.69, \|lvar\| 603.24, loss_d 1.39, loss 46.58,\n", "\| epoch 4 \| 400/ 790 batches \| rec 37.33, adv 0.69, \|lvar\| 626.04, loss_d 1.42, loss 44.22,\n", "\| epoch 4 \| 500/ 790 batches \| rec 34.17, adv 0.70, \|lvar\| 653.73, loss_d 1.39, loss 41.22,\n", "\| epoch 4 \| 600/ 790 batches \| rec 35.24, adv 0.69, \|lvar\| 665.88, loss_d 1.41, loss 42.18,\n", "\| epoch 4 \| 700/ 790 batches \| rec 33.41, adv 0.68, \|lvar\| 666.68, loss_d 1.41, loss 40.22,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 4 \| time 116s \| valid rec 30.44, adv 0.72, \|lvar\| 702.85, loss_d 1.37, loss 37.64, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 5 \| 100/ 790 batches \| rec 32.08, adv 0.70, \|lvar\| 706.27, loss_d 1.40, loss 39.03,\n", "\| epoch 5 \| 200/ 790 batches \| rec 36.59, adv 0.69, \|lvar\| 732.29, loss_d 1.40, loss 43.53,\n", "\| epoch 5 \| 300/ 790 batches \| rec 34.58, adv 0.69, \|lvar\| 745.95, loss_d 1.41, loss 41.47,\n", "\| epoch 5 \| 400/ 790 batches \| rec 31.86, adv 0.68, \|lvar\| 759.06, loss_d 1.40, loss 38.71,\n", "\| epoch 5 \| 500/ 790 batches \| rec 32.40, adv 0.69, \|lvar\| 785.60, loss_d 1.40, loss 39.30,\n", "\| epoch 5 \| 600/ 790 batches \| rec 29.13, adv 0.69, \|lvar\| 771.20, loss_d 1.39, loss 36.04,\n", "\| epoch 5 \| 700/ 790 batches \| rec 31.22, adv 0.70, \|lvar\| 766.68, loss_d 1.39, loss 38.23,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 5 \| time 117s \| valid rec 24.77, adv 0.63, \|lvar\| 787.74, loss_d 1.46, loss 31.08, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 6 \| 100/ 790 batches \| rec 28.84, adv 0.69, \|lvar\| 788.62, loss_d 1.40, loss 35.77,\n", "\| epoch 6 \| 200/ 790 batches \| rec 30.50, adv 0.69, \|lvar\| 812.93, loss_d 1.40, loss 37.43,\n", "\| epoch 6 \| 300/ 790 batches \| rec 31.98, adv 0.69, \|lvar\| 836.81, loss_d 1.39, loss 38.90,\n", "\| epoch 6 \| 400/ 790 batches \| rec 28.62, adv 0.70, \|lvar\| 829.66, loss_d 1.40, loss 35.58,\n", "\| epoch 6 \| 500/ 790 batches \| rec 29.83, adv 0.69, \|lvar\| 832.88, loss_d 1.39, loss 36.69,\n", "\| epoch 6 \| 600/ 790 batches \| rec 28.85, adv 0.70, \|lvar\| 825.33, loss_d 1.40, loss 35.82,\n", "\| epoch 6 \| 700/ 790 batches \| rec 28.46, adv 0.69, \|lvar\| 849.11, loss_d 1.40, loss 35.39,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 6 \| time 117s \| valid rec 21.09, adv 0.67, \|lvar\| 917.61, loss_d 1.42, loss 27.74, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 7 \| 100/ 790 batches \| rec 29.24, adv 0.68, \|lvar\| 865.57, loss_d 1.41, loss 36.06,\n", "\| epoch 7 \| 200/ 790 batches \| rec 27.08, adv 0.69, \|lvar\| 860.24, loss_d 1.40, loss 33.97,\n", "\| epoch 7 \| 300/ 790 batches \| rec 26.90, adv 0.69, \|lvar\| 875.02, loss_d 1.39, loss 33.78,\n", "\| epoch 7 \| 400/ 790 batches \| rec 27.22, adv 0.69, \|lvar\| 875.22, loss_d 1.40, loss 34.17,\n", "\| epoch 7 \| 500/ 790 batches \| rec 26.55, adv 0.69, \|lvar\| 897.40, loss_d 1.40, loss 33.46,\n", "\| epoch 7 \| 600/ 790 batches \| rec 26.24, adv 0.69, \|lvar\| 878.07, loss_d 1.40, loss 33.13,\n", "\| epoch 7 \| 700/ 790 batches \| rec 27.01, adv 0.70, \|lvar\| 901.25, loss_d 1.39, loss 34.01,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 7 \| time 117s \| valid rec 18.30, adv 0.61, \|lvar\| 899.35, loss_d 1.48, loss 24.40, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 8 \| 100/ 790 batches \| rec 24.28, adv 0.69, \|lvar\| 895.09, loss_d 1.40, loss 31.15,\n", "\| epoch 8 \| 200/ 790 batches \| rec 23.59, adv 0.70, \|lvar\| 905.66, loss_d 1.40, loss 30.55,\n", "\| epoch 8 \| 300/ 790 batches \| rec 26.14, adv 0.69, \|lvar\| 931.02, loss_d 1.40, loss 33.03,\n", "\| epoch 8 \| 400/ 790 batches \| rec 24.51, adv 0.69, \|lvar\| 911.00, loss_d 1.39, loss 31.40,\n", "\| epoch 8 \| 500/ 790 batches \| rec 25.17, adv 0.69, \|lvar\| 920.57, loss_d 1.40, loss 32.08,\n", "\| epoch 8 \| 600/ 790 batches \| rec 25.55, adv 0.69, \|lvar\| 930.32, loss_d 1.40, loss 32.47,\n", "\| epoch 8 \| 700/ 790 batches \| rec 25.72, adv 0.69, \|lvar\| 945.16, loss_d 1.39, loss 32.65,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 8 \| time 117s \| valid rec 15.14, adv 0.67, \|lvar\| 986.23, loss_d 1.42, loss 21.83, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 9 \| 100/ 790 batches \| rec 23.27, adv 0.69, \|lvar\| 939.97, loss_d 1.39, loss 30.18,\n", "\| epoch 9 \| 200/ 790 batches \| rec 22.89, adv 0.70, \|lvar\| 929.80, loss_d 1.39, loss 29.90,\n", "\| epoch 9 \| 300/ 790 batches \| rec 24.15, adv 0.70, \|lvar\| 922.12, loss_d 1.40, loss 31.13,\n", "\| epoch 9 \| 400/ 790 batches \| rec 22.27, adv 0.69, \|lvar\| 949.74, loss_d 1.39, loss 29.16,\n", "\| epoch 9 \| 500/ 790 batches \| rec 25.23, adv 0.70, \|lvar\| 964.55, loss_d 1.41, loss 32.22,\n", "\| epoch 9 \| 600/ 790 batches \| rec 23.71, adv 0.68, \|lvar\| 959.48, loss_d 1.40, loss 30.55,\n", "\| epoch 9 \| 700/ 790 batches \| rec 23.04, adv 0.69, \|lvar\| 959.72, loss_d 1.39, loss 29.96,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 9 \| time 117s \| valid rec 13.24, adv 0.68, \|lvar\| 1016.96, loss_d 1.40, loss 20.05, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 10 \| 100/ 790 batches \| rec 21.71, adv 0.69, \|lvar\| 953.79, loss_d 1.40, loss 28.66,\n", "\| epoch 10 \| 200/ 790 batches \| rec 22.27, adv 0.69, \|lvar\| 980.24, loss_d 1.39, loss 29.16,\n", "\| epoch 10 \| 300/ 790 batches \| rec 21.35, adv 0.69, \|lvar\| 979.13, loss_d 1.40, loss 28.29,\n", "\| epoch 10 \| 400/ 790 batches \| rec 23.09, adv 0.70, \|lvar\| 990.48, loss_d 1.40, loss 30.04,\n", "\| epoch 10 \| 500/ 790 batches \| rec 22.33, adv 0.69, \|lvar\| 993.04, loss_d 1.40, loss 29.24,\n", "\| epoch 10 \| 600/ 790 batches \| rec 21.74, adv 0.70, \|lvar\| 995.31, loss_d 1.40, loss 28.71,\n", "\| epoch 10 \| 700/ 790 batches \| rec 20.22, adv 0.69, \|lvar\| 995.65, loss_d 1.40, loss 27.14,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 10 \| time 117s \| valid rec 11.58, adv 0.71, \|lvar\| 1077.33, loss_d 1.36, loss 18.72, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 11 \| 100/ 790 batches \| rec 20.53, adv 0.69, \|lvar\| 1014.83, loss_d 1.40, loss 27.43,\n", "\| epoch 11 \| 200/ 790 batches \| rec 18.80, adv 0.70, \|lvar\| 982.87, loss_d 1.40, loss 25.76,\n", "\| epoch 11 \| 300/ 790 batches \| rec 20.57, adv 0.68, \|lvar\| 1018.79, loss_d 1.40, loss 27.41,\n", "\| epoch 11 \| 400/ 790 batches \| rec 23.10, adv 0.70, \|lvar\| 1030.56, loss_d 1.39, loss 30.07,\n", "\| epoch 11 \| 500/ 790 batches \| rec 21.04, adv 0.70, \|lvar\| 1023.06, loss_d 1.39, loss 27.99,\n", "\| epoch 11 \| 600/ 790 batches \| rec 21.03, adv 0.70, \|lvar\| 1015.36, loss_d 1.40, loss 28.00,\n", "\| epoch 11 \| 700/ 790 batches \| rec 21.34, adv 0.69, \|lvar\| 1015.47, loss_d 1.39, loss 28.21,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 11 \| time 117s \| valid rec 10.54, adv 0.67, \|lvar\| 1123.59, loss_d 1.41, loss 17.28, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 12 \| 100/ 790 batches \| rec 20.39, adv 0.69, \|lvar\| 1042.20, loss_d 1.40, loss 27.30,\n", "\| epoch 12 \| 200/ 790 batches \| rec 21.05, adv 0.68, \|lvar\| 1053.35, loss_d 1.39, loss 27.89,\n", "\| epoch 12 \| 300/ 790 batches \| rec 18.81, adv 0.70, \|lvar\| 1025.19, loss_d 1.40, loss 25.83,\n", "\| epoch 12 \| 400/ 790 batches \| rec 22.62, adv 0.69, \|lvar\| 1053.87, loss_d 1.40, loss 29.51,\n", "\| epoch 12 \| 500/ 790 batches \| rec 19.38, adv 0.69, \|lvar\| 1040.44, loss_d 1.39, loss 26.31,\n", "\| epoch 12 \| 600/ 790 batches \| rec 18.29, adv 0.70, \|lvar\| 1037.86, loss_d 1.40, loss 25.26,\n", "\| epoch 12 \| 700/ 790 batches \| rec 19.55, adv 0.69, \|lvar\| 1028.12, loss_d 1.40, loss 26.47,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 12 \| time 117s \| valid rec 9.68, adv 0.69, \|lvar\| 1126.53, loss_d 1.40, loss 16.60, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 13 \| 100/ 790 batches \| rec 18.88, adv 0.70, \|lvar\| 1052.31, loss_d 1.39, loss 25.83,\n", "\| epoch 13 \| 200/ 790 batches \| rec 18.34, adv 0.69, \|lvar\| 1047.16, loss_d 1.40, loss 25.22,\n", "\| epoch 13 \| 300/ 790 batches \| rec 18.19, adv 0.70, \|lvar\| 1037.11, loss_d 1.40, loss 25.21,\n", "\| epoch 13 \| 400/ 790 batches \| rec 21.49, adv 0.69, \|lvar\| 1056.77, loss_d 1.40, loss 28.35,\n", "\| epoch 13 \| 500/ 790 batches \| rec 18.94, adv 0.69, \|lvar\| 1045.14, loss_d 1.39, loss 25.84,\n", "\| epoch 13 \| 600/ 790 batches \| rec 17.89, adv 0.71, \|lvar\| 1050.69, loss_d 1.39, loss 24.96,\n", "\| epoch 13 \| 700/ 790 batches \| rec 20.22, adv 0.68, \|lvar\| 1074.84, loss_d 1.40, loss 27.06,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 13 \| time 117s \| valid rec 8.84, adv 0.71, \|lvar\| 1151.13, loss_d 1.40, loss 15.99, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 14 \| 100/ 790 batches \| rec 17.50, adv 0.69, \|lvar\| 1070.23, loss_d 1.39, loss 24.44,\n", "\| epoch 14 \| 200/ 790 batches \| rec 18.10, adv 0.70, \|lvar\| 1074.21, loss_d 1.39, loss 25.05,\n", "\| epoch 14 \| 300/ 790 batches \| rec 17.56, adv 0.70, \|lvar\| 1069.27, loss_d 1.40, loss 24.58,\n", "\| epoch 14 \| 400/ 790 batches \| rec 18.18, adv 0.69, \|lvar\| 1073.46, loss_d 1.39, loss 25.10,\n", "\| epoch 14 \| 500/ 790 batches \| rec 18.47, adv 0.70, \|lvar\| 1064.16, loss_d 1.40, loss 25.45,\n", "\| epoch 14 \| 600/ 790 batches \| rec 19.58, adv 0.69, \|lvar\| 1078.23, loss_d 1.39, loss 26.49,\n", "\| epoch 14 \| 700/ 790 batches \| rec 19.97, adv 0.70, \|lvar\| 1090.09, loss_d 1.40, loss 26.92,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 14 \| time 117s \| valid rec 8.96, adv 0.68, \|lvar\| 1164.92, loss_d 1.42, loss 15.80, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 15 \| 100/ 790 batches \| rec 19.31, adv 0.69, \|lvar\| 1099.62, loss_d 1.39, loss 26.17,\n", "\| epoch 15 \| 200/ 790 batches \| rec 17.16, adv 0.70, \|lvar\| 1105.94, loss_d 1.39, loss 24.17,\n", "\| epoch 15 \| 300/ 790 batches \| rec 18.28, adv 0.69, \|lvar\| 1110.65, loss_d 1.40, loss 25.17,\n", "\| epoch 15 \| 400/ 790 batches \| rec 17.68, adv 0.69, \|lvar\| 1098.25, loss_d 1.39, loss 24.61,\n", "\| epoch 15 \| 500/ 790 batches \| rec 16.87, adv 0.70, \|lvar\| 1094.54, loss_d 1.39, loss 23.87,\n", "\| epoch 15 \| 600/ 790 batches \| rec 18.12, adv 0.69, \|lvar\| 1097.11, loss_d 1.39, loss 25.02,\n", "\| epoch 15 \| 700/ 790 batches \| rec 17.94, adv 0.70, \|lvar\| 1090.42, loss_d 1.39, loss 24.92,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 15 \| time 117s \| valid rec 7.41, adv 0.66, \|lvar\| 1194.64, loss_d 1.42, loss 13.98, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 16 \| 100/ 790 batches \| rec 16.12, adv 0.70, \|lvar\| 1100.89, loss_d 1.39, loss 23.09,\n", "\| epoch 16 \| 200/ 790 batches \| rec 18.66, adv 0.69, \|lvar\| 1113.82, loss_d 1.40, loss 25.60,\n", "\| epoch 16 \| 300/ 790 batches \| rec 16.78, adv 0.69, \|lvar\| 1089.97, loss_d 1.39, loss 23.71,\n", "\| epoch 16 \| 400/ 790 batches \| rec 18.95, adv 0.69, \|lvar\| 1134.48, loss_d 1.39, loss 25.89,\n", "\| epoch 16 \| 500/ 790 batches \| rec 16.80, adv 0.69, \|lvar\| 1099.92, loss_d 1.38, loss 23.71,\n", "\| epoch 16 \| 600/ 790 batches \| rec 18.28, adv 0.70, \|lvar\| 1103.60, loss_d 1.39, loss 25.25,\n", "\| epoch 16 \| 700/ 790 batches \| rec 16.28, adv 0.69, \|lvar\| 1102.86, loss_d 1.40, loss 23.22,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 16 \| time 117s \| valid rec 7.39, adv 0.65, \|lvar\| 1206.63, loss_d 1.44, loss 13.85, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 17 \| 100/ 790 batches \| rec 16.41, adv 0.69, \|lvar\| 1103.86, loss_d 1.39, loss 23.35,\n", "\| epoch 17 \| 200/ 790 batches \| rec 16.01, adv 0.69, \|lvar\| 1119.21, loss_d 1.39, loss 22.95,\n", "\| epoch 17 \| 300/ 790 batches \| rec 18.12, adv 0.69, \|lvar\| 1122.81, loss_d 1.39, loss 25.00,\n", "\| epoch 17 \| 400/ 790 batches \| rec 17.01, adv 0.70, \|lvar\| 1114.16, loss_d 1.39, loss 24.03,\n", "\| epoch 17 \| 500/ 790 batches \| rec 17.23, adv 0.69, \|lvar\| 1110.68, loss_d 1.39, loss 24.11,\n", "\| epoch 17 \| 600/ 790 batches \| rec 16.85, adv 0.70, \|lvar\| 1114.49, loss_d 1.39, loss 23.84,\n", "\| epoch 17 \| 700/ 790 batches \| rec 16.66, adv 0.69, \|lvar\| 1123.05, loss_d 1.39, loss 23.58,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 17 \| time 116s \| valid rec 7.17, adv 0.70, \|lvar\| 1245.95, loss_d 1.37, loss 14.16,\n", "--------------------------------------------------------------------------------\n", "\| epoch 18 \| 100/ 790 batches \| rec 17.44, adv 0.71, \|lvar\| 1136.05, loss_d 1.40, loss 24.51,\n", "\| epoch 18 \| 200/ 790 batches \| rec 16.82, adv 0.70, \|lvar\| 1137.40, loss_d 1.39, loss 23.86,\n", "\| epoch 18 \| 300/ 790 batches \| rec 17.52, adv 0.68, \|lvar\| 1123.77, loss_d 1.39, loss 24.30,\n", "\| epoch 18 \| 400/ 790 batches \| rec 15.30, adv 0.70, \|lvar\| 1110.57, loss_d 1.38, loss 22.33,\n", "\| epoch 18 \| 500/ 790 batches \| rec 16.50, adv 0.69, \|lvar\| 1126.99, loss_d 1.39, loss 23.40,\n", "\| epoch 18 \| 600/ 790 batches \| rec 16.52, adv 0.69, \|lvar\| 1137.18, loss_d 1.39, loss 23.46,\n", "\| epoch 18 \| 700/ 790 batches \| rec 15.72, adv 0.70, \|lvar\| 1134.46, loss_d 1.39, loss 22.67,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 18 \| time 116s \| valid rec 6.59, adv 0.67, \|lvar\| 1230.58, loss_d 1.41, loss 13.27, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 19 \| 100/ 790 batches \| rec 16.26, adv 0.69, \|lvar\| 1145.20, loss_d 1.39, loss 23.21,\n", "\| epoch 19 \| 200/ 790 batches \| rec 16.57, adv 0.68, \|lvar\| 1154.73, loss_d 1.39, loss 23.40,\n", "\| epoch 19 \| 300/ 790 batches \| rec 15.64, adv 0.70, \|lvar\| 1129.59, loss_d 1.39, loss 22.60,\n", "\| epoch 19 \| 400/ 790 batches \| rec 16.91, adv 0.70, \|lvar\| 1148.80, loss_d 1.40, loss 23.87,\n", "\| epoch 19 \| 500/ 790 batches \| rec 16.47, adv 0.69, \|lvar\| 1134.61, loss_d 1.39, loss 23.41,\n", "\| epoch 19 \| 600/ 790 batches \| rec 15.78, adv 0.69, \|lvar\| 1128.19, loss_d 1.39, loss 22.71,\n", "\| epoch 19 \| 700/ 790 batches \| rec 15.99, adv 0.69, \|lvar\| 1157.17, loss_d 1.39, loss 22.92,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 19 \| time 116s \| valid rec 6.05, adv 0.66, \|lvar\| 1243.09, loss_d 1.42, loss 12.69, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 20 \| 100/ 790 batches \| rec 15.77, adv 0.69, \|lvar\| 1164.34, loss_d 1.39, loss 22.69,\n", "\| epoch 20 \| 200/ 790 batches \| rec 17.16, adv 0.69, \|lvar\| 1167.28, loss_d 1.39, loss 24.06,\n", "\| epoch 20 \| 300/ 790 batches \| rec 15.93, adv 0.70, \|lvar\| 1173.16, loss_d 1.39, loss 22.94,\n", "\| epoch 20 \| 400/ 790 batches \| rec 14.38, adv 0.69, \|lvar\| 1145.73, loss_d 1.39, loss 21.32,\n", "\| epoch 20 \| 500/ 790 batches \| rec 16.45, adv 0.69, \|lvar\| 1179.02, loss_d 1.39, loss 23.38,\n", "\| epoch 20 \| 600/ 790 batches \| rec 17.78, adv 0.70, \|lvar\| 1185.55, loss_d 1.39, loss 24.75,\n", "\| epoch 20 \| 700/ 790 batches \| rec 15.22, adv 0.69, \|lvar\| 1156.06, loss_d 1.39, loss 22.11,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 20 \| time 117s \| valid rec 5.65, adv 0.69, \|lvar\| 1285.47, loss_d 1.40, loss 12.56, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 21 \| 100/ 790 batches \| rec 14.77, adv 0.70, \|lvar\| 1155.83, loss_d 1.39, loss 21.72,\n", "\| epoch 21 \| 200/ 790 batches \| rec 15.24, adv 0.70, \|lvar\| 1183.53, loss_d 1.39, loss 22.20,\n", "\| epoch 21 \| 300/ 790 batches \| rec 14.14, adv 0.70, \|lvar\| 1156.57, loss_d 1.39, loss 21.12,\n", "\| epoch 21 \| 400/ 790 batches \| rec 16.11, adv 0.69, \|lvar\| 1182.08, loss_d 1.39, loss 23.04,\n", "\| epoch 21 \| 500/ 790 batches \| rec 16.90, adv 0.70, \|lvar\| 1166.74, loss_d 1.39, loss 23.87,\n", "\| epoch 21 \| 600/ 790 batches \| rec 17.07, adv 0.70, \|lvar\| 1183.29, loss_d 1.38, loss 24.04,\n", "\| epoch 21 \| 700/ 790 batches \| rec 15.80, adv 0.70, \|lvar\| 1164.53, loss_d 1.39, loss 22.81,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 21 \| time 117s \| valid rec 5.49, adv 0.72, \|lvar\| 1315.06, loss_d 1.37, loss 12.72,\n", "--------------------------------------------------------------------------------\n", "\| epoch 22 \| 100/ 790 batches \| rec 14.63, adv 0.69, \|lvar\| 1163.92, loss_d 1.38, loss 21.56,\n", "\| epoch 22 \| 200/ 790 batches \| rec 16.08, adv 0.70, \|lvar\| 1180.48, loss_d 1.39, loss 23.07,\n", "\| epoch 22 \| 300/ 790 batches \| rec 15.71, adv 0.69, \|lvar\| 1182.52, loss_d 1.39, loss 22.66,\n", "\| epoch 22 \| 400/ 790 batches \| rec 14.63, adv 0.70, \|lvar\| 1165.36, loss_d 1.39, loss 21.59,\n", "\| epoch 22 \| 500/ 790 batches \| rec 16.86, adv 0.69, \|lvar\| 1190.17, loss_d 1.39, loss 23.75,\n", "\| epoch 22 \| 600/ 790 batches \| rec 14.78, adv 0.70, \|lvar\| 1161.54, loss_d 1.38, loss 21.82,\n", "\| epoch 22 \| 700/ 790 batches \| rec 16.07, adv 0.69, \|lvar\| 1177.16, loss_d 1.39, loss 22.98,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 22 \| time 116s \| valid rec 5.53, adv 0.68, \|lvar\| 1285.32, loss_d 1.41, loss 12.30, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 23 \| 100/ 790 batches \| rec 14.93, adv 0.69, \|lvar\| 1165.13, loss_d 1.39, loss 21.88,\n", "\| epoch 23 \| 200/ 790 batches \| rec 14.49, adv 0.70, \|lvar\| 1178.22, loss_d 1.38, loss 21.46,\n", "\| epoch 23 \| 300/ 790 batches \| rec 14.83, adv 0.70, \|lvar\| 1178.25, loss_d 1.39, loss 21.79,\n", "\| epoch 23 \| 400/ 790 batches \| rec 15.21, adv 0.69, \|lvar\| 1193.13, loss_d 1.39, loss 22.16,\n", "\| epoch 23 \| 500/ 790 batches \| rec 16.65, adv 0.70, \|lvar\| 1210.04, loss_d 1.39, loss 23.61,\n", "\| epoch 23 \| 600/ 790 batches \| rec 16.17, adv 0.70, \|lvar\| 1184.62, loss_d 1.39, loss 23.16,\n", "\| epoch 23 \| 700/ 790 batches \| rec 14.67, adv 0.70, \|lvar\| 1192.78, loss_d 1.38, loss 21.62,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 23 \| time 116s \| valid rec 5.18, adv 0.64, \|lvar\| 1331.37, loss_d 1.43, loss 11.57, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 24 \| 100/ 790 batches \| rec 15.83, adv 0.71, \|lvar\| 1157.77, loss_d 1.38, loss 22.88,\n", "\| epoch 24 \| 200/ 790 batches \| rec 15.90, adv 0.69, \|lvar\| 1141.37, loss_d 1.38, loss 22.77,\n", "\| epoch 24 \| 300/ 790 batches \| rec 14.59, adv 0.70, \|lvar\| 1152.02, loss_d 1.38, loss 21.63,\n", "\| epoch 24 \| 400/ 790 batches \| rec 14.87, adv 0.71, \|lvar\| 1163.89, loss_d 1.38, loss 21.93,\n", "\| epoch 24 \| 500/ 790 batches \| rec 15.52, adv 0.70, \|lvar\| 1180.49, loss_d 1.38, loss 22.52,\n", "\| epoch 24 \| 600/ 790 batches \| rec 16.44, adv 0.70, \|lvar\| 1187.19, loss_d 1.38, loss 23.40,\n", "\| epoch 24 \| 700/ 790 batches \| rec 15.39, adv 0.70, \|lvar\| 1176.64, loss_d 1.39, loss 22.39,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 24 \| time 117s \| valid rec 5.18, adv 0.71, \|lvar\| 1317.07, loss_d 1.40, loss 12.29,\n", "--------------------------------------------------------------------------------\n", "\| epoch 25 \| 100/ 790 batches \| rec 16.09, adv 0.70, \|lvar\| 1182.39, loss_d 1.38, loss 23.04,\n", "\| epoch 25 \| 200/ 790 batches \| rec 15.28, adv 0.70, \|lvar\| 1198.63, loss_d 1.39, loss 22.28,\n", "\| epoch 25 \| 300/ 790 batches \| rec 15.05, adv 0.71, \|lvar\| 1207.02, loss_d 1.38, loss 22.10,\n", "\| epoch 25 \| 400/ 790 batches \| rec 14.33, adv 0.69, \|lvar\| 1186.76, loss_d 1.39, loss 21.28,\n", "\| epoch 25 \| 500/ 790 batches \| rec 15.29, adv 0.70, \|lvar\| 1200.70, loss_d 1.39, loss 22.31,\n", "\| epoch 25 \| 600/ 790 batches \| rec 14.54, adv 0.70, \|lvar\| 1191.40, loss_d 1.39, loss 21.50,\n", "\| epoch 25 \| 700/ 790 batches \| rec 14.91, adv 0.70, \|lvar\| 1207.75, loss_d 1.39, loss 21.89,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 25 \| time 117s \| valid rec 5.55, adv 0.70, \|lvar\| 1372.06, loss_d 1.39, loss 12.52,\n", "--------------------------------------------------------------------------------\n", "\| epoch 26 \| 100/ 790 batches \| rec 14.78, adv 0.70, \|lvar\| 1208.13, loss_d 1.39, loss 21.74,\n", "\| epoch 26 \| 200/ 790 batches \| rec 13.91, adv 0.70, \|lvar\| 1191.07, loss_d 1.39, loss 20.92,\n", "\| epoch 26 \| 300/ 790 batches \| rec 13.86, adv 0.70, \|lvar\| 1197.32, loss_d 1.39, loss 20.87,\n", "\| epoch 26 \| 400/ 790 batches \| rec 15.63, adv 0.69, \|lvar\| 1199.40, loss_d 1.38, loss 22.52,\n", "\| epoch 26 \| 500/ 790 batches \| rec 13.92, adv 0.71, \|lvar\| 1195.42, loss_d 1.38, loss 20.97,\n", "\| epoch 26 \| 600/ 790 batches \| rec 16.18, adv 0.70, \|lvar\| 1225.58, loss_d 1.38, loss 23.16,\n", "\| epoch 26 \| 700/ 790 batches \| rec 16.21, adv 0.70, \|lvar\| 1224.87, loss_d 1.38, loss 23.18,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 26 \| time 117s \| valid rec 4.73, adv 0.70, \|lvar\| 1364.54, loss_d 1.38, loss 11.74,\n", "--------------------------------------------------------------------------------\n", "\| epoch 27 \| 100/ 790 batches \| rec 14.74, adv 0.70, \|lvar\| 1206.85, loss_d 1.39, loss 21.75,\n", "\| epoch 27 \| 200/ 790 batches \| rec 13.49, adv 0.70, \|lvar\| 1193.34, loss_d 1.39, loss 20.53,\n", "\| epoch 27 \| 300/ 790 batches \| rec 14.35, adv 0.70, \|lvar\| 1198.20, loss_d 1.39, loss 21.34,\n", "\| epoch 27 \| 400/ 790 batches \| rec 16.27, adv 0.69, \|lvar\| 1236.38, loss_d 1.38, loss 23.18,\n", "\| epoch 27 \| 500/ 790 batches \| rec 14.39, adv 0.70, \|lvar\| 1214.22, loss_d 1.39, loss 21.42,\n", "\| epoch 27 \| 600/ 790 batches \| rec 16.36, adv 0.69, \|lvar\| 1229.03, loss_d 1.39, loss 23.29,\n", "\| epoch 27 \| 700/ 790 batches \| rec 14.41, adv 0.70, \|lvar\| 1226.28, loss_d 1.39, loss 21.41,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 27 \| time 117s \| valid rec 4.94, adv 0.66, \|lvar\| 1384.95, loss_d 1.43, loss 11.55, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 28 \| 100/ 790 batches \| rec 15.00, adv 0.70, \|lvar\| 1223.89, loss_d 1.39, loss 22.03,\n", "\| epoch 28 \| 200/ 790 batches \| rec 14.35, adv 0.70, \|lvar\| 1209.72, loss_d 1.38, loss 21.31,\n", "\| epoch 28 \| 300/ 790 batches \| rec 15.52, adv 0.70, \|lvar\| 1245.16, loss_d 1.39, loss 22.53,\n", "\| epoch 28 \| 400/ 790 batches \| rec 13.57, adv 0.69, \|lvar\| 1218.77, loss_d 1.39, loss 20.47,\n", "\| epoch 28 \| 500/ 790 batches \| rec 15.36, adv 0.70, \|lvar\| 1235.84, loss_d 1.38, loss 22.37,\n", "\| epoch 28 \| 600/ 790 batches \| rec 14.42, adv 0.70, \|lvar\| 1228.93, loss_d 1.39, loss 21.42,\n", "\| epoch 28 \| 700/ 790 batches \| rec 13.47, adv 0.70, \|lvar\| 1212.19, loss_d 1.39, loss 20.50,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 28 \| time 117s \| valid rec 4.68, adv 0.68, \|lvar\| 1429.76, loss_d 1.40, loss 11.46, \| saving model\n", "--------------------------------------------------------------------------------\n", "\| epoch 29 \| 100/ 790 batches \| rec 15.29, adv 0.70, \|lvar\| 1249.99, loss_d 1.39, loss 22.28,\n", "\| epoch 29 \| 200/ 790 batches \| rec 13.13, adv 0.70, \|lvar\| 1236.56, loss_d 1.39, loss 20.16,\n", "\| epoch 29 \| 300/ 790 batches \| rec 15.03, adv 0.70, \|lvar\| 1252.01, loss_d 1.39, loss 22.02,\n", "\| epoch 29 \| 400/ 790 batches \| rec 14.94, adv 0.69, \|lvar\| 1263.35, loss_d 1.38, loss 21.85,\n", "\| epoch 29 \| 500/ 790 batches \| rec 13.93, adv 0.70, \|lvar\| 1248.54, loss_d 1.38, loss 20.94,\n", "\| epoch 29 \| 600/ 790 batches \| rec 13.11, adv 0.70, \|lvar\| 1190.58, loss_d 1.39, loss 20.13,\n", "\| epoch 29 \| 700/ 790 batches \| rec 14.22, adv 0.70, \|lvar\| 1236.23, loss_d 1.39, loss 21.19,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 29 \| time 117s \| valid rec 4.56, adv 0.69, \|lvar\| 1405.63, loss_d 1.39, loss 11.47,\n", "--------------------------------------------------------------------------------\n", "\| epoch 30 \| 100/ 790 batches \| rec 12.55, adv 0.70, \|lvar\| 1231.61, loss_d 1.39, loss 19.60,\n", "\| epoch 30 \| 200/ 790 batches \| rec 15.15, adv 0.69, \|lvar\| 1261.56, loss_d 1.39, loss 22.04,\n", "\| epoch 30 \| 300/ 790 batches \| rec 14.61, adv 0.71, \|lvar\| 1262.45, loss_d 1.39, loss 21.69,\n", "\| epoch 30 \| 400/ 790 batches \| rec 15.11, adv 0.69, \|lvar\| 1252.17, loss_d 1.38, loss 22.04,\n", "\| epoch 30 \| 500/ 790 batches \| rec 15.16, adv 0.70, \|lvar\| 1267.43, loss_d 1.38, loss 22.19,\n", "\| epoch 30 \| 600/ 790 batches \| rec 14.69, adv 0.70, \|lvar\| 1245.33, loss_d 1.38, loss 21.71,\n", "\| epoch 30 \| 700/ 790 batches \| rec 13.59, adv 0.69, \|lvar\| 1231.06, loss_d 1.39, loss 20.53,\n", "--------------------------------------------------------------------------------\n", "\| end of epoch 30 \| time 117s \| valid rec 4.62, adv 0.70, \|lvar\| 1419.41, loss_d 1.39, loss 11.63,\n", "Done training\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MlK_CCuVuoLD", "outputId": "0ee9b4c5-40a6-45a3-976f-b248827895e6" }, "source": [ "!zip -r /content/text-autoencoders/checkpoints.zip /content/text-autoencoders/checkpoints/" ], "execution_count": 7, "outputs": [ { "output_type": "stream", "text": [ " adding: content/text-autoencoders/checkpoints/ (stored 0%)\n", " adding: content/text-autoencoders/checkpoints/yelp/ (stored 0%)\n", " adding: content/text-autoencoders/checkpoints/yelp/daae/ (stored 0%)\n", " adding: content/text-autoencoders/checkpoints/yelp/daae/model.pt (deflated 7%)\n", " adding: content/text-autoencoders/checkpoints/yelp/daae/vocab.txt (deflated 55%)\n", " adding: content/text-autoencoders/checkpoints/yelp/daae/log.txt (deflated 85%)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "mKQqSoV_vEDh" }, "source": [ "!cp /content/text-autoencoders/checkpoints.zip /content/drive/MyDrive/checkpoints" ], "execution_count": 9, "outputs": [] } ] }	mit
ES-DOC/esdoc-jupyterhub	notebooks/thu/cmip6/models/sandbox-3/seaice.ipynb	1	99801	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "MIP Era: CMIP6 \n", "Institute: THU \n", "Source ID: SANDBOX-3 \n", "Topic: Seaice \n", "Sub-Topics: Dynamics, Thermodynamics, Radiative Processes. \n", "Properties: 80 (63 required) \n", "Model descriptions: [Model description details](https://specializations.es-doc.org/cmip6/seaice?client=jupyter-notebook) \n", "Initialized From: -- \n", "\n", "Notebook Help: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "Notebook Initialised: 2018-02-15 16:54:40" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "IMPORTANT: to be executed each time you run the notebook " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'thu', 'sandbox-3', 'seaice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "Set document authors" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "Specify document contributors " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "Specify document publication status " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties --> Model](#1.-Key-Properties--->-Model) \n", "[2. Key Properties --> Variables](#2.-Key-Properties--->-Variables) \n", "[3. Key Properties --> Seawater Properties](#3.-Key-Properties--->-Seawater-Properties) \n", "[4. Key Properties --> Resolution](#4.-Key-Properties--->-Resolution) \n", "[5. Key Properties --> Tuning Applied](#5.-Key-Properties--->-Tuning-Applied) \n", "[6. Key Properties --> Key Parameter Values](#6.-Key-Properties--->-Key-Parameter-Values) \n", "[7. Key Properties --> Assumptions](#7.-Key-Properties--->-Assumptions) \n", "[8. Key Properties --> Conservation](#8.-Key-Properties--->-Conservation) \n", "[9. Grid --> Discretisation --> Horizontal](#9.-Grid--->-Discretisation--->-Horizontal) \n", "[10. Grid --> Discretisation --> Vertical](#10.-Grid--->-Discretisation--->-Vertical) \n", "[11. Grid --> Seaice Categories](#11.-Grid--->-Seaice-Categories) \n", "[12. Grid --> Snow On Seaice](#12.-Grid--->-Snow-On-Seaice) \n", "[13. Dynamics](#13.-Dynamics) \n", "[14. Thermodynamics --> Energy](#14.-Thermodynamics--->-Energy) \n", "[15. Thermodynamics --> Mass](#15.-Thermodynamics--->-Mass) \n", "[16. Thermodynamics --> Salt](#16.-Thermodynamics--->-Salt) \n", "[17. Thermodynamics --> Salt --> Mass Transport](#17.-Thermodynamics--->-Salt--->-Mass-Transport) \n", "[18. Thermodynamics --> Salt --> Thermodynamics](#18.-Thermodynamics--->-Salt--->-Thermodynamics) \n", "[19. Thermodynamics --> Ice Thickness Distribution](#19.-Thermodynamics--->-Ice-Thickness-Distribution) \n", "[20. Thermodynamics --> Ice Floe Size Distribution](#20.-Thermodynamics--->-Ice-Floe-Size-Distribution) \n", "[21. Thermodynamics --> Melt Ponds](#21.-Thermodynamics--->-Melt-Ponds) \n", "[22. Thermodynamics --> Snow Processes](#22.-Thermodynamics--->-Snow-Processes) \n", "[23. Radiative Processes](#23.-Radiative-Processes) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --> Model \n", "Name of seaice model used." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Overview of sea ice model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Name of sea ice model code (e.g. CICE 4.2, LIM 2.1, etc.)" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --> Variables \n", "List of prognostic variable in the sea ice model." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Prognostic\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### List of prognostic variables in the sea ice component." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea ice temperature\" \n", "# \"Sea ice concentration\" \n", "# \"Sea ice thickness\" \n", "# \"Sea ice volume per grid cell area\" \n", "# \"Sea ice u-velocity\" \n", "# \"Sea ice v-velocity\" \n", "# \"Sea ice enthalpy\" \n", "# \"Internal ice stress\" \n", "# \"Salinity\" \n", "# \"Snow temperature\" \n", "# \"Snow depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --> Seawater Properties \n", "Properties of seawater relevant to sea ice" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Ocean Freezing Point\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS-10\" \n", "# \"Constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Ocean Freezing Point Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant seawater freezing point, specify this value." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --> Resolution \n", "Resolution of the sea ice grid" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Name\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### This is a string usually used by the modelling group to describe the resolution of this grid e.g. N512L180, T512L70, ORCA025 etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Canonical Horizontal Resolution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Number Of Horizontal Gridpoints\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Total number of horizontal (XY) points (or degrees of freedom) on computational grid." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --> Tuning Applied \n", "Tuning applied to sea ice model component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Target\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Simulations\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Which simulations had tuning applied, e.g. all, not historical, only pi-control? " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Metrics Used\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List any observed metrics used in tuning model/parameters" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Variables\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Which variables were changed during the tuning process?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --> Key Parameter Values \n", "Values of key parameters" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Typical Parameters\n", "Is Required: FALSE    Type: ENUM    Cardinality: 0.N\n", "### What values were specificed for the following parameters if used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ice strength (P) in units of N m{-2}\" \n", "# \"Snow conductivity (ks) in units of W m{-1} K{-1} \" \n", "# \"Minimum thickness of ice created in leads (h0) in units of m\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Additional Parameters\n", "Is Required:* FALSE    Type: STRING    Cardinality: 0.N\n", "### If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --> Assumptions \n", "Assumptions made in the sea ice model" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.N\n", "### General overview description of any key* assumptions made in this model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.description') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. On Diagnostic Variables\n", "Is Required:* TRUE    Type: STRING    Cardinality: 1.N\n", "### Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Missing Processes\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.N\n", "### List any key* processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --> Conservation \n", "Conservation in the sea ice component" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "Is Required:* TRUE    Type: STRING    Cardinality: 1.1\n", "### Provide a general description of conservation methodology." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Properties\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Properties conserved in sea ice by the numerical schemes." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.properties') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Mass\" \n", "# \"Salt\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Budget\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Was Flux Correction Used\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does conservation involved flux correction?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Corrected Conserved Prognostic Variables\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### List any variables which are conserved by more* than the numerical scheme alone." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid --> Discretisation --> Horizontal \n", "Sea ice discretisation in the horizontal" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Grid\n", "Is Required:* TRUE    Type: ENUM    Cardinality: 1.1\n", "### Grid on which sea ice is horizontal discretised?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ocean grid\" \n", "# \"Atmosphere Grid\" \n", "# \"Own Grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Grid Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the type of sea ice grid?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Structured grid\" \n", "# \"Unstructured grid\" \n", "# \"Adaptive grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Scheme\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the advection scheme?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite differences\" \n", "# \"Finite elements\" \n", "# \"Finite volumes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Thermodynamics Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### What is the time step in the sea ice model thermodynamic component in seconds." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Dynamics Time Step\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### What is the time step in the sea ice model dynamic component in seconds." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional horizontal discretisation details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --> Discretisation --> Vertical \n", "Sea ice vertical properties" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Layering\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Zero-layer\" \n", "# \"Two-layers\" \n", "# \"Multi-layers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Number Of Layers\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### If using multi-layers specify how many." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional vertical grid details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --> Seaice Categories \n", "What method is used to represent sea ice categories ?" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Has Mulitple Categories\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Set to true if the sea ice model has multiple sea ice categories." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Number Of Categories\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### If using sea ice categories specify how many." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Category Limits\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### If using sea ice categories specify each of the category limits." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Ice Thickness Distribution Scheme\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the sea ice thickness distribution scheme" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Other\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.other') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Grid --> Snow On Seaice \n", "Snow on sea ice details" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Has Snow On Ice\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Is snow on ice represented in this model?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Number Of Snow Levels\n", "Is Required: TRUE    Type: INTEGER    Cardinality: 1.1\n", "### Number of vertical levels of snow on ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Snow Fraction\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe how the snow fraction on sea ice is determined" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Specify any additional details related to snow on ice." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamics \n", "Sea Ice Dynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Horizontal Transport\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of horizontal advection of sea ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Transport In Thickness Space\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of sea ice transport in thickness space (i.e. in thickness categories)?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Ice Strength Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Which method of sea ice strength formulation is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Hibler 1979\" \n", "# \"Rothrock 1975\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Redistribution\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Which processes can redistribute sea ice (including thickness)?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.redistribution') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rafting\" \n", "# \"Ridging\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Rheology\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Rheology, what is the ice deformation formulation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.rheology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Free-drift\" \n", "# \"Mohr-Coloumb\" \n", "# \"Visco-plastic\" \n", "# \"Elastic-visco-plastic\" \n", "# \"Elastic-anisotropic-plastic\" \n", "# \"Granular\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Thermodynamics --> Energy \n", "Processes related to energy in sea ice thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Enthalpy Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the energy formulation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice latent heat (Semtner 0-layer)\" \n", "# \"Pure ice latent and sensible heat\" \n", "# \"Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)\" \n", "# \"Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Thermal Conductivity\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What type of thermal conductivity is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice\" \n", "# \"Saline ice\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Heat Diffusion\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of heat diffusion?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Conduction fluxes\" \n", "# \"Conduction and radiation heat fluxes\" \n", "# \"Conduction, radiation and latent heat transport\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Basal Heat Flux\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method by which basal ocean heat flux is handled?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heat Reservoir\" \n", "# \"Thermal Fixed Salinity\" \n", "# \"Thermal Varying Salinity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Fixed Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Heat Content Of Precipitation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method by which the heat content of precipitation is handled." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.7. Precipitation Effects On Salinity\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Thermodynamics --> Mass \n", "Processes related to mass in sea ice thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. New Ice Formation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method by which new sea ice is formed in open water." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Ice Vertical Growth And Melt\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method that governs the vertical growth and melt of sea ice." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Ice Lateral Melting\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the method of sea ice lateral melting?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Floe-size dependent (Bitz et al 2001)\" \n", "# \"Virtual thin ice melting (for single-category)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Ice Surface Sublimation\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method that governs sea ice surface sublimation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Frazil Ice\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### Describe the method of frazil ice formation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Thermodynamics --> Salt \n", "Processes related to salt in sea ice thermodynamics." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Has Multiple Sea Ice Salinities\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Sea Ice Salinity Thermal Impacts\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Does sea ice salinity impact the thermal properties of sea ice?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Thermodynamics --> Salt --> Mass Transport \n", "Mass transport of salt" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Salinity Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is salinity determined in the mass transport of salt calculation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Constant Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant salinity value specify this value in PSU?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the salinity profile used." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Thermodynamics --> Salt --> Thermodynamics \n", "Salt thermodynamics" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Salinity Type\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is salinity determined in the thermodynamic calculation?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Constant Salinity Value\n", "Is Required: FALSE    Type: FLOAT    Cardinality: 0.1\n", "### If using a constant salinity value specify this value in PSU?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the salinity profile used." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Thermodynamics --> Ice Thickness Distribution \n", "Ice thickness distribution details." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Representation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is the sea ice thickness distribution represented?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Virtual (enhancement of thermal conductivity, thin ice melting)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Thermodynamics --> Ice Floe Size Distribution \n", "Ice floe-size distribution details." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Representation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### How is the sea ice floe-size represented?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Parameterised\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Additional Details\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Please provide further details on any parameterisation of floe-size." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Thermodynamics --> Melt Ponds \n", "Characteristics of melt ponds." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Are Included\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.1\n", "### Are melt ponds included in the sea ice model?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Formulation\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What method of melt pond formulation is used?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flocco and Feltham (2010)\" \n", "# \"Level-ice melt ponds\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.3. Impacts\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### What do melt ponds have an impact on?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Albedo\" \n", "# \"Freshwater\" \n", "# \"Heat\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Thermodynamics --> Snow Processes \n", "Thermodynamic processes in snow on sea ice" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Has Snow Aging\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.N\n", "### Set to True if the sea ice model has a snow aging scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Snow Aging Scheme\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the snow aging scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Has Snow Ice Formation\n", "Is Required: TRUE    Type: BOOLEAN    Cardinality: 1.N\n", "### Set to True if the sea ice model has snow ice formation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.4. Snow Ice Formation Scheme\n", "Is Required: FALSE    Type: STRING    Cardinality: 0.1\n", "### Describe the snow ice formation scheme." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Redistribution\n", "Is Required: TRUE    Type: STRING    Cardinality: 1.1\n", "### What is the impact of ridging on snow cover?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.6. Heat Diffusion\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### What is the heat diffusion through snow methodology in sea ice thermodynamics?" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Single-layered heat diffusion\" \n", "# \"Multi-layered heat diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiative Processes \n", "Sea Ice Radiative Processes" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Surface Albedo\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.1\n", "### Method used to handle surface albedo." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Parameterized\" \n", "# \"Multi-band albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Ice Radiation Transmission\n", "Is Required: TRUE    Type: ENUM    Cardinality: 1.N\n", "### Method by which solar radiation through sea ice is handled." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Exponential attenuation\" \n", "# \"Ice radiation transmission per category\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	gpl-3.0
alienmortar/GREAT2014	Untitled6.ipynb	1	1633397	null	mit
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The \"scores()\" method returns a list of centrality scores in order of the vertices. Thus, what you see below are the (normalized, see the respective argument) centrality scores for G.nodes()[0], G.nodes()[1], ... " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0.038890947542671417,\n", " 0.025204387816348928,\n", " 0.030099862584687796,\n", " 0.023008066197884643,\n", " 0.015014306929265246,\n", " 0.047450338825435673,\n", " 0.03877379800819393,\n", " 0.00018201165419890557,\n", " 0.0043027361546468185,\n", " 0.019820501996514393]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evzSciPy = networkit.centrality.SciPyEVZ(G, normalized=True)\n", "evzSciPy.run()\n", "evzSciPy.scores()[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now take a look at the 10 most central vertices according to the four heuristics. Here, the centrality algorithms offer the ranking() method that returns a list of (vertex, centrality) ordered by centrality." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(185, 0.37998920394223168),\n", " (146, 0.25589844844192655),\n", " (407, 0.25299992743917177),\n", " (144, 0.21952034822779035),\n", " (204, 0.18014173124747021),\n", " (230, 0.17037022146635697),\n", " (227, 0.15343976623211927),\n", " (226, 0.15343976623211925),\n", " (152, 0.15256017345576342),\n", " (425, 0.13620265458341316)]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evzSciPy.ranking()[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the EVZ using the C++ backend and also display the 10 most important vertices, just as above. This should hopefully look similar...\n", "\n", "Please note: The normalization argument may not be passed as a named argument to the C++-backed centrality measures. This is due to some limitation in the C++ wrapping code." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(185, 0.4555629557898871),\n", " (146, 0.280011306168103),\n", " (407, 0.26733105380714245),\n", " (144, 0.2382197258062029),\n", " (204, 0.17660126272043924),\n", " (227, 0.16982894291110087),\n", " (226, 0.16982894291110087),\n", " (230, 0.16960898722498557),\n", " (152, 0.14877108011932902),\n", " (425, 0.1398532276524453)]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evz = networkit.centrality.EigenvectorCentrality(G, True)\n", "evz.run()\n", "evz.ranking()[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's take a look at the PageRank. First, compute the PageRank using the C++ backend and display the 10 most important vertices. The second argument to the algorithm is the dampening factor, i.e. the probability that a random walk just stops at a vertex and instead teleports to some other vertex." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(185, 0.08967935808573076),\n", " (146, 0.036293663539769275),\n", " (144, 0.031391049006808995),\n", " (407, 0.02996667806766083),\n", " (227, 0.021190293055750178),\n", " (226, 0.021190293055750178),\n", " (425, 0.019907206754720692),\n", " (152, 0.017614662527267698),\n", " (230, 0.016888616220117537),\n", " (228, 0.01624170578197208)]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pageRank = networkit.centrality.PageRank(G, 0.95, True)\n", "pageRank.run()\n", "pageRank.ranking()[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Same in Python..." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(185, 0.054799391769678088),\n", " (146, 0.021805099201351385),\n", " (351, 0.020445516424587442),\n", " (407, 0.020286826697537379),\n", " (144, 0.017857147931302247),\n", " (227, 0.017629500247607024),\n", " (226, 0.017360862294165981),\n", " (154, 0.016264063508127456),\n", " (152, 0.013996872812656708),\n", " (425, 0.012831154843169931)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SciPyPageRank = networkit.centrality.SciPyPageRank(G, 0.95, normalized=True)\n", "SciPyPageRank.run()\n", "SciPyPageRank.ranking()[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything went well, these should look similar, too." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we take a look at the relative differences between the computed centralities for the vertices:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average relative difference: 0.22655120802802436\n", "Maximum relative difference: 4.118201311862968\n" ] } ], "source": [ "differences = [(max(x[0], x[1]) / min(x[0], x[1])) - 1 for x in zip(evz.scores(), evzSciPy.scores())]\n", "print(\"Average relative difference: {}\".format(sum(differences) / len(differences)))\n", "print(\"Maximum relative difference: {}\".format(max(differences)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.3.5" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
jrg365/gpytorch	examples/02_Scalable_Exact_GPs/Simple_GP_Regression_With_LOVE_Fast_Variances_and_Sampling.ipynb	1	19699	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# GP Regression with LOVE for Fast Predictive Variances and Sampling\n", "\n", "## Overview\n", "\n", "In this notebook, we demonstrate that LOVE (the method for fast variances and sampling introduced in this paper https://arxiv.org/abs/1803.06058) can significantly reduce the cost of computing predictive distributions. This can be especially useful in settings like small-scale Bayesian optimization, where predictions need to be made at enormous numbers of candidate points.\n", "\n", "In this notebook, we will train a KISS-GP model on the `skillcraft `UCI dataset, and then compare the time required to make predictions with each model.\n", "\n", "NOTE: The timing results reported in the paper compare the time required to compute (co)variances __only__. Because excluding the mean computations from the timing results requires hacking the internals of GPyTorch, the timing results presented in this notebook include the time required to compute predictive means, which are not accelerated by LOVE. Nevertheless, as we will see, LOVE achieves impressive speed-ups." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import math\n", "import torch\n", "import gpytorch\n", "import tqdm\n", "from matplotlib import pyplot as plt\n", "\n", "# Make plots inline\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading Data\n", "\n", "For this example notebook, we'll be using the `elevators` UCI dataset used in the paper. Running the next cell downloads a copy of the dataset that has already been scaled and normalized appropriately. For this notebook, we'll simply be splitting the data using the first 40% of the data as training and the last 60% as testing.\n", "\n", "Note: Running the next cell will attempt to download a small dataset file to the current directory." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "import urllib.request\n", "import os\n", "from scipy.io import loadmat\n", "from math import floor\n", "\n", "\n", "# this is for running the notebook in our testing framework\n", "smoke_test = ('CI' in os.environ)\n", "\n", "\n", "if not smoke_test and not os.path.isfile('../elevators.mat'):\n", " print('Downloading \\'elevators\\' UCI dataset...')\n", " urllib.request.urlretrieve('https://drive.google.com/uc?export=download&id=1jhWL3YUHvXIaftia4qeAyDwVxo6j1alk', '../elevators.mat')\n", "\n", "\n", "if smoke_test: # this is for running the notebook in our testing framework\n", " X, y = torch.randn(100, 3), torch.randn(100)\n", "else:\n", " data = torch.Tensor(loadmat('../elevators.mat')['data'])\n", " X = data[:, :-1]\n", " X = X - X.min(0)[0]\n", " X = 2 * (X / X.max(0)[0]) - 1\n", " y = data[:, -1]\n", "\n", "\n", "train_n = int(floor(0.8 * len(X)))\n", "train_x = X[:train_n, :].contiguous()\n", "train_y = y[:train_n].contiguous()\n", "\n", "test_x = X[train_n:, :].contiguous()\n", "test_y = y[train_n:].contiguous()\n", "\n", "if torch.cuda.is_available():\n", " train_x, train_y, test_x, test_y = train_x.cuda(), train_y.cuda(), test_x.cuda(), test_y.cuda()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LOVE can be used with any type of GP model, including exact GPs, multitask models and scalable approximations. Here we demonstrate LOVE in conjunction with KISS-GP, which has the amazing property of producing constant time variances.\n", "\n", "## The KISS-GP + LOVE GP Model\n", "\n", "We now define the GP model. For more details on the use of GP models, see our simpler examples. This model uses a `GridInterpolationKernel` (SKI) with an Deep RBF base kernel. The forward method passes the input data `x` through the neural network feature extractor defined above, scales the resulting features to be between 0 and 1, and then calls the kernel.\n", "\n", "The Deep RBF kernel (DKL) uses a neural network as an initial feature extractor. In this case, we use a fully connected network with the architecture `d -> 1000 -> 500 -> 50 -> 2`, as described in the original DKL paper. All of the code below uses standard PyTorch implementations of neural network layers." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class LargeFeatureExtractor(torch.nn.Sequential): \n", " def __init__(self, input_dim): \n", " super(LargeFeatureExtractor, self).__init__() \n", " self.add_module('linear1', torch.nn.Linear(input_dim, 1000))\n", " self.add_module('relu1', torch.nn.ReLU()) \n", " self.add_module('linear2', torch.nn.Linear(1000, 500)) \n", " self.add_module('relu2', torch.nn.ReLU()) \n", " self.add_module('linear3', torch.nn.Linear(500, 50)) \n", " self.add_module('relu3', torch.nn.ReLU()) \n", " self.add_module('linear4', torch.nn.Linear(50, 2)) \n", "\n", "\n", "class GPRegressionModel(gpytorch.models.ExactGP):\n", " def __init__(self, train_x, train_y, likelihood):\n", " super(GPRegressionModel, self).__init__(train_x, train_y, likelihood)\n", " \n", " self.mean_module = gpytorch.means.ConstantMean()\n", " self.covar_module = gpytorch.kernels.GridInterpolationKernel(\n", " gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel()),\n", " grid_size=100, num_dims=2,\n", " )\n", " \n", " # Also add the deep net\n", " self.feature_extractor = LargeFeatureExtractor(input_dim=train_x.size(-1))\n", "\n", " def forward(self, x):\n", " # We're first putting our data through a deep net (feature extractor)\n", " # We're also scaling the features so that they're nice values\n", " projected_x = self.feature_extractor(x)\n", " projected_x = projected_x - projected_x.min(0)[0]\n", " projected_x = 2 * (projected_x / projected_x.max(0)[0]) - 1\n", " \n", " # The rest of this looks like what we've seen\n", " mean_x = self.mean_module(projected_x)\n", " covar_x = self.covar_module(projected_x)\n", " return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)\n", "\n", " \n", "likelihood = gpytorch.likelihoods.GaussianLikelihood()\n", "model = GPRegressionModel(train_x, train_y, likelihood)\n", "\n", "if torch.cuda.is_available():\n", " model = model.cuda()\n", " likelihood = likelihood.cuda()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training the model\n", "\n", "The cell below trains the GP model, finding optimal hyperparameters using Type-II MLE. We run 20 iterations of training using the `Adam` optimizer built in to PyTorch. With a decent GPU, this should only take a few seconds." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7b200bd2e3d34c598b9c44c45cfbebb3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=20.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "CPU times: user 2.1 s, sys: 136 ms, total: 2.24 s\n", "Wall time: 2.23 s\n" ] } ], "source": [ "training_iterations = 1 if smoke_test else 20\n", "\n", "\n", "# Find optimal model hyperparameters\n", "model.train()\n", "likelihood.train()\n", "\n", "# Use the adam optimizer\n", "optimizer = torch.optim.Adam(model.parameters(), lr=0.1) # Includes GaussianLikelihood parameters\n", "\n", "# \"Loss\" for GPs - the marginal log likelihood\n", "mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n", "\n", "\n", "def train():\n", " iterator = tqdm.notebook.tqdm(range(training_iterations))\n", " for i in iterator:\n", " optimizer.zero_grad()\n", " output = model(train_x)\n", " loss = -mll(output, train_y)\n", " loss.backward()\n", " iterator.set_postfix(loss=loss.item())\n", " optimizer.step()\n", " \n", "%time train()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing predictive variances (KISS-GP or Exact GPs)\n", "\n", "### Using standard computaitons (without LOVE)\n", "\n", "The next cell gets the predictive covariance for the test set (and also technically gets the predictive mean, stored in `preds.mean`) using the standard SKI testing code, with no acceleration or precomputation. \n", "\n", "Note: Full predictive covariance matrices (and the computations needed to get them) can be quite memory intensive. Depending on the memory available on your GPU, you may need to reduce the size of the test set for the code below to run. If you run out of memory, try replacing `test_x` below with something like `test_x[:1000]` to use the first 1000 test points only, and then restart the notebook." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time to compute exact mean + covariances: 1.81s\n" ] } ], "source": [ "import time\n", "\n", "# Set into eval mode\n", "model.eval()\n", "likelihood.eval()\n", "\n", "with torch.no_grad():\n", " start_time = time.time()\n", " preds = likelihood(model(test_x))\n", " exact_covar = preds.covariance_matrix\n", " exact_covar_time = time.time() - start_time\n", " \n", "print(f\"Time to compute exact mean + covariances: {exact_covar_time:.2f}s\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using LOVE\n", "\n", "Next we compute predictive covariances (and the predictive means) for LOVE, but starting from scratch. That is, we don't yet have access to the precomputed cache discussed in the paper. This should still be faster than the full covariance computation code above.\n", "\n", "To use LOVE, use the context manager `with gpytorch.settings.fast_pred_var():`\n", "\n", "You can also set some of the LOVE settings with context managers as well. For example, `gpytorch.settings.max_root_decomposition_size(100)` affects the accuracy of the LOVE solves (larger is more accurate, but slower).\n", "\n", "In this simple example, we allow a rank 100 root decomposition, although increasing this to rank 20-40 should not affect the timing results substantially." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Clear the cache from the previous computations\n", "model.train()\n", "likelihood.train()\n", "\n", "# Set into eval mode\n", "model.eval()\n", "likelihood.eval()\n", "\n", "with torch.no_grad(), gpytorch.settings.fast_pred_var(), gpytorch.settings.max_root_decomposition_size(100):\n", " start_time = time.time()\n", " preds = model(test_x)\n", " fast_time_no_cache = time.time() - start_time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above cell additionally computed the caches required to get fast predictions. From this point onwards, unless we put the model back in training mode, predictions should be extremely fast. The cell below re-runs the above code, but takes full advantage of both the mean cache and the LOVE cache for variances." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "with torch.no_grad(), gpytorch.settings.fast_pred_var():\n", " start_time = time.time()\n", " preds = likelihood(model(test_x))\n", " fast_covar = preds.covariance_matrix\n", " fast_time_with_cache = time.time() - start_time" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time to compute mean + covariances (no cache) 0.32s\n", "Time to compute mean + variances (cache): 0.14s\n" ] } ], "source": [ "print('Time to compute mean + covariances (no cache) {:.2f}s'.format(fast_time_no_cache))\n", "print('Time to compute mean + variances (cache): {:.2f}s'.format(fast_time_with_cache))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compute Error between Exact and Fast Variances\n", "\n", "Finally, we compute the mean absolute error between the fast variances computed by LOVE (stored in fast_covar), and the exact variances computed previously. \n", "\n", "Note that these tests were run with a root decomposition of rank 10, which is about the minimum you would realistically ever run with. Despite this, the fast variance estimates are quite good. If more accuracy was needed, increasing `max_root_decomposition_size` would provide even better estimates." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MAE between exact covar matrix and fast covar matrix: 0.000657\n" ] } ], "source": [ "mae = ((exact_covar - fast_covar).abs() / exact_covar.abs()).mean()\n", "print(f\"MAE between exact covar matrix and fast covar matrix: {mae:.6f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing posterior samples (KISS-GP only)\n", "\n", "With KISS-GP models, LOVE can also be used to draw fast posterior samples. (The same does not apply to exact GP models.)\n", "\n", "### Drawing samples the standard way (without LOVE)\n", "\n", "We now draw samples from the posterior distribution. Without LOVE, we accomlish this by performing Cholesky on the posterior covariance matrix. This can be slow for large covariance matrices." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time to compute exact samples: 1.92s\n" ] } ], "source": [ "import time\n", "num_samples = 20 if smoke_test else 20000\n", "\n", "\n", "# Set into eval mode\n", "model.eval()\n", "likelihood.eval()\n", "\n", "with torch.no_grad():\n", " start_time = time.time()\n", " exact_samples = model(test_x).rsample(torch.Size([num_samples]))\n", " exact_sample_time = time.time() - start_time\n", " \n", "print(f\"Time to compute exact samples: {exact_sample_time:.2f}s\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using LOVE\n", "\n", "Next we compute posterior samples (and the predictive means) using LOVE.\n", "This requires the additional context manager `with gpytorch.settings.fast_pred_samples():`.\n", "\n", "Note that we also need the `with gpytorch.settings.fast_pred_var():` flag turned on. Both context managers respond to the `gpytorch.settings.max_root_decomposition_size(100)` setting." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time to compute LOVE samples (no cache) 0.74s\n", "Time to compute LOVE samples (cache) 0.02s\n" ] } ], "source": [ "# Clear the cache from the previous computations\n", "model.train()\n", "likelihood.train()\n", "\n", "# Set into eval mode\n", "model.eval()\n", "likelihood.eval()\n", "\n", "with torch.no_grad(), gpytorch.settings.fast_pred_var(), gpytorch.settings.max_root_decomposition_size(200):\n", " # NEW FLAG FOR SAMPLING\n", " with gpytorch.settings.fast_pred_samples():\n", " start_time = time.time()\n", " _ = model(test_x).rsample(torch.Size([num_samples]))\n", " fast_sample_time_no_cache = time.time() - start_time\n", " \n", "# Repeat the timing now that the cache is computed\n", "with torch.no_grad(), gpytorch.settings.fast_pred_var():\n", " with gpytorch.settings.fast_pred_samples():\n", " start_time = time.time()\n", " love_samples = model(test_x).rsample(torch.Size([num_samples]))\n", " fast_sample_time_cache = time.time() - start_time\n", " \n", "print('Time to compute LOVE samples (no cache) {:.2f}s'.format(fast_sample_time_no_cache))\n", "print('Time to compute LOVE samples (cache) {:.2f}s'.format(fast_sample_time_cache))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compute the empirical covariance matrices\n", "\n", "Let's see how well LOVE samples and exact samples recover the true covariance matrix." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Empirical covariance MAE (Exact samples): 0.0043566287495195866\n", "Empirical covariance MAE (LOVE samples): 0.0061592841520905495\n" ] } ], "source": [ "# Compute exact posterior covar\n", "with torch.no_grad():\n", " start_time = time.time()\n", " posterior = model(test_x)\n", " mean, covar = posterior.mean, posterior.covariance_matrix\n", "\n", "exact_empirical_covar = ((exact_samples - mean).t() @ (exact_samples - mean)) / num_samples\n", "love_empirical_covar = ((love_samples - mean).t() @ (love_samples - mean)) / num_samples\n", "\n", "exact_empirical_error = ((exact_empirical_covar - covar).abs()).mean()\n", "love_empirical_error = ((love_empirical_covar - covar).abs()).mean()\n", "\n", "print(f\"Empirical covariance MAE (Exact samples): {exact_empirical_error}\")\n", "print(f\"Empirical covariance MAE (LOVE samples): {love_empirical_error}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
seniosh/StatisticalMethods	notes/InferenceSandbox.ipynb	1	138378	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Inference Sandbox\n", "\n", "### In this notebook, we'll mock up some data from the linear model, as reviewed [here](./LMreview4.ipynb). Then it's your job to implement a Metropolis sampler and constrain the posterior distriubtion. The goal is to play with various strategies for accelerating the convergence and acceptance rate of the chain. Remember to check the convergence and stationarity of your chains, and compare them to the known analytic posterior for this problem!\n", "\n", "#### Generate a data set:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f32b88f85c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.stats\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (5.0, 5.0) \n", "\n", "# the model parameters\n", "a = np.pi\n", "b = 1.6818\n", "\n", "# my arbitrary constants\n", "mu_x = np.exp(1.0) # see definitions above\n", "tau_x = 1.0\n", "s = 1.0\n", "N = 50 # number of data points\n", "\n", "# get some x's and y's\n", "x = mu_x + tau_xnp.random.randn(N)\n", "y = a + bx + snp.random.randn(N)\n", "\n", "plt.plot(x, y, 'o');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Package up a log-posterior function." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def lnPost(params, x, y):\n", " # This is written for clarity rather than numerical efficiency. Feel free to tweak it.\n", " a = params[0]\n", " b = params[1]\n", " lnp = 0.0\n", " # Using informative priors to achieve faster convergence is cheating in this exercise!\n", " # But this is where you would add them.\n", " lnp += -0.5np.sum((a+bx - y)2)\n", " return lnp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Convenience functions encoding the exact posterior:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class ExactPosterior:\n", " def __init__(self, x, y, a0, b0):\n", " X = np.matrix(np.vstack([np.ones(len(x)), x]).T)\n", " Y = np.matrix(y).T\n", " self.invcov = X.T X\n", " self.covariance = np.linalg.inv(self.invcov)\n", " self.mean = self.covariance * X.T * Y\n", " self.a_array = np.arange(0.0, 6.0, 0.02)\n", " self.b_array = np.arange(0.0, 3.25, 0.02)\n", " self.P_of_a = np.array([self.marg_a(a) for a in self.a_array])\n", " self.P_of_b = np.array([self.marg_b(b) for b in self.b_array])\n", " self.P_of_ab = np.array([[self.lnpost(a,b) for a in self.a_array] for b in self.b_array])\n", " self.P_of_ab = np.exp(self.P_of_ab)\n", " self.renorm = 1.0/np.sum(self.P_of_ab)\n", " self.P_of_ab = self.P_of_ab * self.renorm\n", " self.levels = scipy.stats.chi2.cdf(np.arange(1,4)*2, 1) # confidence levels corresponding to contours below\n", " self.contourLevels = self.renormnp.exp(self.lnpost(a0,b0)-0.5scipy.stats.chi2.ppf(self.levels, 2))\n", " def lnpost(self, a, b): # the 2D posterior\n", " z = self.mean - np.matrix([[a],[b]])\n", " return -0.5 (z.T * self.invcov * z)[0,0]\n", " def marg_a(self, a): # marginal posterior of a\n", " return scipy.stats.norm.pdf(a, self.mean[0,0], np.sqrt(self.covariance[0,0]))\n", " def marg_b(self, b): # marginal posterior of b\n", " return scipy.stats.norm.pdf(b, self.mean[1,0], np.sqrt(self.covariance[1,1]))\n", "exact = ExactPosterior(x, y, a, b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Demo some plots of the exact posterior distribution" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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S61vfCqdFf/RHsSuRgUyeDJ/4RPjKAnV0Une7d4du4e67obU1djUymP37w/zG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\nRDJPQScimff/AbTgyowf6kNcAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f32b88f87f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(exact.a_array, exact.P_of_a);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Gt98OnUSKRuVXILFcx6+WtvtJCCq/Aolx5Ac6xU3CUPkVSMzlp50ekjWVX4HEWn6a9koI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\nKSSVn4gUkspPRApJ5ScihfT/AQlSCjZBIakWAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3291f9b358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(exact.b_array, exact.P_of_b);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3291fc1048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.contour(exact.a_array, exact.b_array, exact.P_of_ab, colors='blue', levels=exact.contourLevels);\n", "plt.plot(a, b, 'o', color='red');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ok, you're almost ready to go! A decidely minimal stub of a Metropolis loop appears below; of course, you don't need to stick exactly with this layout. Once again, after running a chain, be sure to\n", "1. visually inspect traces of each parameter to see whether they appear converged \n", "2. compare the marginal and joint posterior distributions to the exact solution to check whether they've converged to the correct distribution\n", "> Normally, you should always use quantitative tests of convergence in addition to visual inspection, as you saw on Tuesday. For this class (only), let's save some time by relying only on visual impressions and comparison to the exact posterior.\n", "\n", "(see the snippets farther down)\n", "\n", "### If you think you have a sampler that works well, use it to run some more chains from different starting points and compare them both visually and using the numerical convergence criteria covered in class.\n", "\n", "### Once you have a working sampler, the question is: how can we make it converge faster? Experiment! We'll compare notes in a bit." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Nsamples = 501*(2)\n", "samples = np.zeros((Nsamples, 2))\n", "# put any more global definitions here\n", "\n", "\n", "\n", "def proposal(a_try, b_try, temperature):\n", " a = a_try + temperaturenp.random.randn(1)\n", " b = b_try + temperature*np.random.randn(1)\n", " return a, b\n", "\n", "def we_accept_this_proposal(lnp_try, lnp_current):\n", " return np.exp(lnp_try - lnp_current) > np.random.uniform()\n", "\n", "temperature = 0.1\n", "a_current, b_current = proposal(0, 0, temperature)\n", "lnp_current = lnPost([a_current, b_current], x, y)\n", "\n", "for i in range(Nsamples):\n", " a_try, b_try = proposal(a_current, b_current, temperature) # propose new parameter value(s)\n", " lnp_try = lnPost([a_try,b_try], x, y) # calculate posterior density for the proposal\n", " if we_accept_this_proposal(lnp_try, lnp_current):\n", " lnp_current = lnp_try\n", " a_current, b_current = (a_try, b_try)\n", " else:\n", " pass\n", " samples[i, 0] = a_current\n", " samples[i, 1] = b_current\n", "\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IGVF/FfLmlQcLAe5pY2QviF3HsX+/9ywLXpGtYa+qdeMmvSA6n9dec/aLtMvI\n4Jf69SMvo21b9n/2bPsBSxRs7HxK7e7/nXc61x9JZzt1Kguq8VLG449bt5kDevzgRzgWMxzI3IqC\n0NLs3s18K938dQG5+8HBg/JgMhGzZUh2fFpaeOKoslgHF8rENGt2vqlugjagLozNm6d2nAyv2r0q\nVfzXJcO8gFIQwnG0CFIDKUO0VJiRuVepuCHZaVgBVpeuh1eeE+FWokgsBUFqjlWXrX7wQes2r8vT\nu/H+++7WXYIQibpwvGYNc+h3W24ZYJ1uRkY42EBmBrMzjU2fbk05FGk+RDMyrbVq55uc7D74i7gN\nNNFOuRMJss6ZX7vERPvVCUXzVokS7DeaBSK/Hb+dJt5tmViACfb16oXN1CLDh7P/YhYBQJ6jO4h7\n5uf3T50afp5klg67Z23uXPUFTQ4fVs9AIOYs5YiBU3aY22kOPgPYAOhFQy+mSRQx30/AqKm0uw8/\n/qjmihOJFcHrc6TaR6lO3Pny8Bx+X1TrkS1/HS3B2s8iL3bYPaN296NHD+s2FZeY8ePt69A0+6BN\nrjFW0RzbEeS4Mniw2nGySWyQ9w2QpzclCCdiZkQRzYhVq9ofN2VKeFYcqa9ckSKRna+C6oDw2Wfy\nwdwOmQAhkpmFY6fAQrPJWtSgyZbFNQvHQZvbuEbbLy++yP6bg03s0hPxZ9rt/trhZynpTZvCgqvs\nebULJGvaFChWzHt9biQn+3t+VbStTrEDXnCzCJlX4BNRcf3x+/5OnOj9meUrkbmh2ibzO8yf9SVL\ngLvucj+/dm3rNtWJlVdkFhy/yBYWSk9Xd60D1K6x7B3l772m2bvJiOkk3bBTFsgmLrFG1739lkjx\ncv+ISwcfa9BFjpOGQkz3E6k2IRZmvief9Ha8qsDvNjj/9599Wjov7N/POge/Uexe066Zze6vveZ8\nvPl6+e3Iopm+Ryboyp69XbuAmTOZACEzJargxy/03XflK0F6LTMoovlexipN08cf2++L5vXMDP6R\non82EHY3c1vmHrC/Nk4+tJkBu5iWzz/3Vo55iWo3ypVjbkI8z7fTCopeVpS1m8D5WZU2aGTuKAQR\na+KSrcJp8BAHt0gHOrtlPy8GRo5kAUeRMnw4ULq0f988P8sRm5e7dsJpoQUvdO3q7zwVOna0pha0\ne3b5ZCra2gpZ2iSOeXlvyvsZe4JaajxI/GZU4ekFVTLZ2OG2xHK8efJJIGfO2NQl+rzv2GFcyMRp\nQQ4vrgjeFmDnAAAgAElEQVR2/ajX/NwXAzVqxLsFRGYkEOHYawYBp8H4Yh+oVQP4groOjzzivF81\nVZkMcYU4L5g1T06Yr0NmDPoBrH6AduZ9VR9eO1SfC+4LLcO8YECs37khQ2JbX6y5/nr3Y9wCSLMS\nPIYgEo3fsGHBtCVarFoVv7FJNTjNi+LArt/PLOOvXZpXN/xkPSlRwl9dxMVNIMJxnz7ejnd6ATOr\n8BMUs2erHRfUdbALOOLwjtfPYg6xMH/9/rvRzzirPB9Okw67lbFUiIYGPNYDokpw7sVOZo4ZIKzs\n2CFPz5iZCOI99rIUdDRRzeVtxo9FTlwkhyA4gQjHTqlnZDi9xCqpoC4FYi2wePWdiyaiANyundGX\nMasIx044BXPFg4vhmmY1SDjOWpw6FV42/GLGbyxE0HhVuEWCXepG4tImLj7HToJfJPk/swIqieuB\n4IRj1fr8mrGigfm389UFZfsII35cXSJ19SC8Y5fOkMi8/PhjvFvgjJdYDjtUVqG82FDJsEJceigJ\nx5qmZdM0ba2maTODqNSvr+rFgDlwy44ZM6LbjsyM2S97+fLw52gn9c/qHD3qXSvptBojER1UJ60E\noUoky8BfyvgJKicufjRdQRWnaVp3ALUB5NN1/S7TPh0gdR4RG556Chg1Kt6tIAiCyFzUqpU5Uv1l\nNcgamfXQNA26rkfVOc1VONY07WoA4wG8DaC7ruutTftJOCaiSsGCFMRFEARBBA8Jx1mPWAjHKm4V\nQwH0AEBhO0RcIMGYIAiCIIhY4bgejqZprQAc1HV9raZpjeyP7Ct8bnThjyAyL9mykf8yQRAEQWR2\nkpKSkJSUFNM6Hd0qNE0bCOBBAGkAcgHID2CKruv/E47x7FZRvTrlFiTiS86cFMBCEARxKXL33cB7\n7wGVK5NbRVYk7m4Vuq6/put6SV3XywDoBGCeKBj7hfKqEvEmsy9XSxCEOg88EO8WEFmJJk2ASpUo\ngJGwx2ue40DmWH/+GUQpBOEfSvxuz333BVve5ZcHWx5x8fDKK0DJkpGXkz9/5GUQlw68/69ZM77t\nIDIvysKxrusLzGnc/EK+nkS8ySymtKefjncLrHTvHmx5fpeCvZTJlSveLYgNl10WTDnZHaNniMxG\nu3bxrf/TT+NbP5H5icsKeQRBMDKjcBz00sYJ1Mt4RlwyPdoULx67umTs2RN5GeQmlbUoVCi+9ZNr\nJ+EGDVuEZypVAq67Lt6tAEaPjncLIidHjni3wEqQwvHkycEL25cCl4p17e+/gymHhOP4M3iw+rHx\nnjDv3Bnf+onMDwnHhGdy5AB+/TXerQAef9z/ublzB9eOi40gtSr33htcWZcSsdRsxdPFaPz4YMqJ\nt7B1KfDMM+HPR49a93fooF7WnXcayyOIzMZF2aU0bx7vFgTHm29Gr2y/2paMDNIGBkVmvI6pqcGW\nlxl/Y2YnLY39z6rX7vnnY1tfVr1OWYkPPwx/lrlFFCigXlaJEkDhwv7a0aQJcNVV/s4lCFUuSuE4\nq0QuN2gAnDnjfEyfPv7Kfu454/dp06zHXHut/Fy3bAVZXTh+6KHYacvq1HHenxmvIxfMLiZ27ACa\nNo13K9ThmlC35yez8sgjsa0vM75Hlxpe74Efbf/rrwOzZ6sH+WZGtzUia3BRCsdZxcRWpQqQJ4/z\nMdmyAcnJzscUK2bdZr4Gsnpy5pSXlzs38xW1Y8uWrD0Y/fefUTh++OHo1TVzpvP+zHgdvWiAVPD7\nG4P0KS9TJnP2C4mJ4c9i+7hbBU+DZ57sxoJ33vF/bmZ6rkeMCH/+4Yf4tSMr0bKl93O83PPy5f09\nI3nysPdENZtLvLNiEFmXqAwXvXpFo1R1jh2Lb/2qiANjJPz1l3Wb2WVCJghXqyYvb9w4oGFD5zpV\nO7bMmMZL142ThTFjolfXFVe4t8XMRx9Fpy1m7PKNX311sPX4FZTsLBt+iWeE+ltvybfv3Rv+LLaP\nPxdt27L/Qacqu/tua11mSpTw72Kjes9VTesyBYCIU97y+vXDn++5R62+eBPNiZyKYPnjj/b7OneW\nb7dLy/fqq8bv1aqxCbiffoHHiowcqXa8eYzNm9d7ncSlSVRewRIlolGqOubOfuDA+LTDjY4dgylH\n1pGaB5NbbrEeM26cvDwVtwnZfjHAok0blqJp2DDnciJl0CDv5+g60Ldv+LvXgUhVG2E3+eCIwunr\nr4c/q6Z3sxO4/LJoEfsfdJolv8Jx0NrHaAvHMreN339n/+18JIsWlW/nbRWF4qpV/bfNiU6d5JPn\nadOsQrmqa4pqejjx3Vu82P64oUOdyxk+nP3ft8+6z89z1L6993MAdyHejKjE4L8BUH9W/WjCxefo\nttu8n+/VV9hsgShThv334/LABXtxwuOEuQ43N0aC4AQuHFeqFHSJzui6VRtQpIjxe2aNmFd9wd2E\nN9nA9tRT1jJUhZ68ed3rlA04L78c/nzHHUwDGXRwF4dPgPx0sBkZRg2C18HTTavOufNO5/3iu9K/\nv7c2AJFrVs33WFXL76YNN9O1q7fjOUFrz6ItHL/yinVbiRLMPURF2BIFZS40cW1cNH3kCxQwTs44\nsqA61UU7EhPVlBKi8C2bwHPcfK+5+0nx4sxdLVL8ptLbv9/b8U88Ef7sR6l0ww3ezxH55Rf2X9Vt\np2FDoHXryOrk99xPjncVzb/oJif27bQaHuGFwIXjzp2DN6V7mY1nywa8+KLasX5mkRMnAt26eT8v\nEtyEhIQEpg2oVSu8zazx0bRwcN9ff4U1WnaYBUbzqmkygVKclPD9P/3kXA8naD9XJyIVNFSzfNx+\nu3qZmsba5aVtCQlAixbqx5spX95angqqbeQaIr/+srJnTHViIiMI4fjnn43fmzQJf5YFMl51FUs5\nWLAg8MUXzmXz/d26AdWrAxMmhN0qgGA16WJZCQnW/uL99yO71qptfewxteNKlwa++85+vzgBq16d\n/ecaUk2Ta+jF/tJMrIJSRUHOz2QwX77I6uf36YUXrPuuvDL8ef169j8piSk+AG9ZoUStOL/u5rY/\n8IB7OSpZKkSLqDgm+Q1uJy5NAheONY0JyM2aWfd9/30wdcheZE7x4tYZotkMVKQIsGuXezCcjA4d\ngCFDgLJlvZ/rFxXf5FWrgCVLwt9lHS3vCMuXB2rXti8rPd16vujTt2CBfPATtUr8fFWB5Oab1Y5T\noW5d5/1ly0YmKNmZwv0QiaBeuTIT1vwOkOZ7GLQbQ6STENkzbB6Qr79evTy3iYRblhZZGeJvND9T\nkyYZv7tZ1Zo3Z+UNGRLuR/k9KVQouGwA11xj/M7rMm8z8+qrzn0vZ906ljFIxYqoImBx9yEnNzQu\nEAPAG2+wa9ioEfteqpRau4GwFS5Wy3eLkxI/VjA/71jlytb6ZeWI22TWopIl1esULXV2GvKvvlIv\nTxXxuWjTJvzZq/sLcekRFeGYazLN+F2m1PzicvObTNuYnm7t2AsXNnY8CQmsw3TCzXHfrVPyGxTo\npbP755/w5zx5jAtbyAY3VaFO5nMsaksbNHAfqJ06XRnlygH//mu/3+5+tWpl3eZWZ40a8mNUk9iL\nHa4T0c6OUKEC+z97tny/18BYcQK0ZUvYVG1G9Z46HadiXVIRBrl22oxMuBEFJDEgjeNlEQOO2Eaz\nP66XSHmnCc4//1iDmlSwyzhg7gc0jWlmndKvrV/PfEfN2mSZ9rVGDfa/bVt79wQeRKxyj80WGLEv\nGDcO+PxzJjjz5616daZ9798fmD+fTSzuuAO49VZjOTIXkZtuYrECXt9dnt1HdQLz6KPsv+gS52eS\n60c4btDAuu3yy62rnooCpMxa5iVlaiyyl8j6Kzsrn9kSShBmoiIcA6xjMvsUBeELBjibtU+ckL+I\nKp2I6GMnmktliOXJ/GoLFnQOMAkCp+sg69zthB0zoub4zz+Bgwet/spunR1vm6rv3tChzpr83bvl\n22UafP47s2dnAoo56ETT5Jpju9R2duUDzgJFtIVjfg/stO5ecuTecYfRX7JyZfvB78gRtTLN75yY\nhUNlsJRlYTGXaVeOTFMoCsylS1v3iwKz6iIWYnuyZ3f2A3fSVjk9K9dc40+raBfRz/1MzXWLk2tR\nOXDmjP1y8W5ZNBISmM91xYrG7dylxxwfouvOvqHTpgHffBP+/vDDYUHTzOWXh7XHN9wALFzIPvfr\nx5Q3U6ZYz5k7l7mcic+V27OaM2c4rsVOK2qexPFnTfz9Yn/u5vbG8WMBq1fP+F3X2bUyWyhF5ZPs\nGvTvD2zb5lwXD9KOhXAstp+/h3ZxNjKlCkGIBDZ884AT/hLkzWvtEP2ass0DYkIC61RkWqN8+dxf\nRLM5iA+aoqAlG+REs6vYJrv6nAJMIuXee50FflmbVDuookXDx15+OTOpme+dW1m8o+IZGcTgNNkk\nKTExOL9jLrzmyMFM219+adyvaUbTIkdVmBUHtKpV7bUQYnmqZl03xIHN7R7ceKN6ublyWa09kaSU\nGznSmmFA7A9UJqvm/kM8r1Mn9l+cjMuOk1GoEPOpdTIL+3VVccprXaqUvF233hr243TCq4Bhdp/g\nmCfJsnJFQcM8aV2+nAnSixcDn35q3FeunLWsxx+XZ5VJTZXf47Vr5e0GmFAZaRBa797AH39YXQXu\nuIP1GTlzGt/dnj2dy7vrLus282Qib16r772uM79enmGDj0O9esnd3kS3OY7XMbV+fXvLF382u3Sx\n7pNp2XPnDluvZDRtGl5Vz22y4eTfbvbzt0OcXHArkNnqwGUGWf9PECKBCcfcCV7UcJi1hk7Ch1tk\nPxB+qatXZzPWBQvY96lTw8domvsg8ttvxu/8eLHtAwYAx48bjxNfKDHThFt9Xle5UkmXZLdIR5s2\nzEdPZSC1yyLQvn34XvH/PEBF1XzIz9uxg/0XB6ItW+zPiyTAxM5PsEQJo6aAa5TE54Zz6pS3OjXN\nGDQlwlO5bd4cFt4ijZg+d85YN8dsMga8+dVddhk7XhS+3d7J+++33/fss9brImqiVfz9ZZYR/o5y\n4Y1fA3NbxO9mDTh3s5I9w1zwUp0ocW1rJIGRCxcGF49hxk6wa9Ei3J/y3/rkk+EJqlNmmzp12MJE\nt9xidaswT0Q5smutmrvZycUrSMR3yIvVR9QW87aaFSOaZlyMRDyneHGmROD+816yKzlNAlW1z5w1\na9j/zz6z7hOtCqrMmSPfLnsWeFyQORByy5bwu/XQQ871ie42/LqY6/r4Y6BHD+dyCAJQEI41Tcul\nadoKTdPWaZq2WdM06bpJefMCy5axQZFz003msuR1dO9uH9G+Zk34Qd+4kf0vV4513nzQM2uQeeS/\niPjdrKHkvpa8Q0xKYmUUKGDsKMRZuhg8kJDgnPsxe3ZvGTz8+mYDTOB78001zbE5/6TYefNj+TXh\n5knecZvLMneE/DyuWTDfj61bpc1Xiig2+4tyjf727fK2AezZBJgQyAN7zB2xpqmnqhLhAqXZT51r\nmKtUCV8Ps1DoRfNTrlx44sQnhhzZwGG+DuYAMZGPP2aTC5l2yg6ZhsmOvXuN/QH3L3dKCyW7F7fc\nYuwTnnyS/TcLM6LmzmwFcprY8Qj9pUvtjxEFzlq1mPuHqnYrEsSVMu0WcDFjNwF+4glgwwb2mV+P\n664D3n6bfbab8LlhJ6wFaVYPavEkwGgFEt9FMXhLlfz5w/2dTJvMadnSqhmuVCl8jeyulUxgt5tg\n5Msn1z57uQ+RBNTOm2e/z5z//aOPwgsxmSc+vL1PP+3e34i/za7tzZszqxFBuOEqHOu6ngKgsa7r\nNQFcB6CxpmnSDL116xpnmOY8vpomHww/+MA+alnTmPnOydSmEiDFhTsn+IspprAROxhRE27uZJy0\nbJpmDdBz8tsMaiAxm6rM5ZonCWKgjllzbNZ+m8sya7v5eVx7ae6sZOZUWbkqjB0brnPgwHCqPbFO\nfr1LlAjX4eYqIgsqNftxipYKu99kbgsANG5sf6wdCQlhVwBzUA1vw7FjTBCVRZcnJDCTuAw3lxZR\nyHroIW9p6gB7X0ynAbhcOblPea1a4fO4pjeod+a//9j/gwftjxFTTs2aZU2JFy24MiBfPu/55J0C\njEWhq2NH5g7j93raTfaC9jn95JNgyhEVEXxyDRgnB27jC38W9+xhbjV791rHM/77jx51nqQuXGjU\nxouaeFn8RuHCwM6d1u0qk3w3tz+/wnH27Nb+jZel61a3mEqV7C0V/Jn/6CP3dQHEBWCefjo62S+I\nSwcl45Gu61xnkQNANgBHxf1OKzdNnBh2ucid298yqBUqMHP0tm2sPDu+/JJFLsuYPdu9g+ZBOuJx\ntWoBhw6xz347C1m9qsFxkeC1vbydum7VYhQqZF+eTNDnWkI+WHrNcOAkrJmDeMS29uqlnt7LPNiI\nAaQ5csjNb6JlRKwbYH6Mdpg1Ik6aFRlt2zLTu50fKb9uBQsyQdROuPNqHuV+y+K1GT+eBS7JgtpU\nMFuUnJAF5QFWs6kX4Ut2Dnd3OXGC/Tc/YyKisGQ2XUcz8IgvGOGmOS1cOGwV4W4oMlM5wCbQ4sS2\nSBH1PPEy7FJEevUTLl/eOdg1qGWARR/x8+flx9x/P8s5bQf3s86fn1mGnBbzKFTI+R289VbjMyS6\nBG3bxlLUbdzIxiVuhZK9h/z9SEszrqQplq0agOyFH36Qr9pn1/87+UDXrOnNNUwM2i5a1DiJ9bMS\nIHFpoyQca5qWoGnaOgAHAMzXdX2zuN/Jh6dDBxZRrOvsZbTLOgC451qsUEGebmnYMLYW/IMP2vv+\nqfiQaRrTkJoDdfhA6SegsFo1ec5nEV13Fvo5zz1nzKJRogTw9dfO5YrYDdzmyY2uWzXHdpQubRX0\ne/cO+3/xa/bSS2oZAHib+QAjSz8nun8AVhcQVczCMW/zJ5+wP/F62QVaiXXaDa4AGxD37WOrqL32\nmlr7xOemSRM2WLRsaUy55gU/E1N+DapVsy5+U7p0WJgEmG+1G/v326eeE+GWGPMzy33CvQjHbqb+\n8+fDQi6fxJitAHbZGmIJdwcSf6ud9o//juLF2QTDnMeYk5QUTr0WBHa+5F7fzW3bwooO2b0NKnhX\n5qcqwy6vfXKydcIcJOJvr1yZvYPVqrHJ9a5d7udny2aMxXByLzTjRxl0zz1y6/Dff8uPX7TIPsWo\nygqLqvhxlyMubVQ1xxkX3CquBtBA07RG4v5p0/qib1/2l5SU5Fyhx05SdHGwo0wZ+5yeIiov+/Ll\n9h28qnDM/fYANsuXLc1qRhblbeaxx4xaiYQE56Ao1TyUvDPTNDYING7sLnB61ZDVrMmS8wPOC37w\ne9S/P+sc+WSKZ3soXtx6f/xoD83t0PVwJ/3UUyw9lIqWxUtwUPHizBdRfD6cyJs3PCiLC5v4WaBg\nwQK1d8SMqEF97z1mFhbh1+jdd50j1zlXXWX0vxe1/HXrWvNj8/K5oMTdQvh+lUmcOR6Alzl6NFuV\nLjExfB+5RtL8rqtqnlRyiT/4oFpZdvAJ8iuvsGwAZvcp8ypisXL7cMLrinNugdWtW9sLXF7hE9+9\ne+2PsRs7cud2bqcsW4MXxPPEd6VgQWfrhsrKg25t8hJ/4IbXtKb583sPcqWFPS5ekpKSQjJm3759\nY1KnJ12SrusnNE37CcANAJL49rZt++J//1MrwzyIuWVyiCQ47fPPwy4RAEtk7zX9mYg5yKJ2bWD1\nauM2v64X4izeqQwv5b/xhtryzdwUqmnWAcfumnBh0a09sgmFaOYzs2oV+28OCKpenfmmyVLs+dUc\nu2lSxd9uJwTL6lywQH3pXbfrt3EjqyPSFbtkif9VmDAhrB1OTLTPd/3MM94006VLs8Ut+vZlPpPz\n5zNXjU8+YZYo83Nz//1swOduAi++yIRxlYmRnUlclhWGu3E4BfQ6kScPC4g1p5YTuesu5g+pEgch\ng/eZ777L/vMAYoBNInv39lduNAl6OWY+kQ8CnsnGSdiU1ZWS4l62OfjMK/y+PvKIuhvE9u1GZQt/\ndu2C9CKlcmX3AFGv46LKRFtk5062RPWwYd7OI7IGjRo1QiOhw+zXr1/U63QdzjRNKwIgTdf145qm\n5QbQFIChZWPHQlk4rlgRWLGCBfV88YUxoTvAHnCuJYzUL9ecHF5mGuXpvO65x708s8ayX79wVLuX\nvIkyE0/58mEhxMkM7MW1wzzxuPZaazvdOq3UVO9+qmKZohbr8suZtstpgRWeVUJW5qZNzkKQeZ/s\nt4nHlCpln24IMGoc7eqVCc0NGgQnDKgucW4nmNqZfGUL18goUMDZ/CoTTt2W7waYG0NqKjtPDDpy\nm4Tw/XXqsD8uoMjuz/r1bFJhtqA4PUOXX85cN8yTnrp1Wd+kgllza4YH082fr1Yehz/PBw7YH9Ox\nY2ziGWSIAW1mZAGimQ2n51ZmEVARVvk9W7/ee3uKFVNfjVPEbIXkferRo+HnukMHe1cbr/BUdLHm\n7Nnw2FS6dOTL1ROEiIqurRiAeRd8jlcAmKnruiFTME8JpAJ/IcVALxHul1q6tPpgFAlt2qin7TG/\nfC1bhk3kr76q7gtqnhBw+CDOfZ4//ti9DU6YhYArr3TOMWxm8mT3vMNu7RE76mzZwtouO5yyGmTP\nLheeVLSHXONoXrzFKae0aNazGwjNAXI81V20crHa0bmzNb0bwLJryFZK8+N/LEN27e0yz4gULiwX\nImXXTdeZ1rp06XBQGocP9rJ7f911zhlEZPDng18fLpy0aMHcSjhnztiX8eST4awXMqpXj2wgt5sg\n16zp7LIEeM8yokpCgrNrWK5cwQY0B021ataFepwWiVElkvv877/h/sWtHKexh/cL2bOH35eJE/2l\nq/NLNATXSK1pBOGE6xCp6/pGAI7x/146L74SFO/gZeeuW8dyw/pZMtUv7dtbE9qbcUuHpPqyugUH\n9OnDAi9kvpJ+OhnVhSfM2kQvyehFxI7aq8aoWTNmWTAjLn5hRuZWcfvtRsG+YUPg11/t8ys7lQsw\noYMnyRcxBzPZLc5ih9Mz7uW9ypHDv+tEJJiF44MHI9MSOv1mWcoqv/7mdrRty0yz5slDgQLG/MZO\nGv1s2dRiJfxiJxyrXPegJkVmVHMPb97svMBIvOA59EXatw8vYgSE/e27do3t2KRCPAXFWExeVPA6\nESYIJwJZIc/Ly8E1Q7yTlvls1qgR+87n+++dO+1Dh7z7QZnhA4ibb2xiolUwfuQRppmJpunI6zWv\nVMmqqSpd2qg5fOklY/5JN15+meX6NOOUzUMmIM2da1wBz+/S5dz3X9PkkydeZ7ducuHNjTJl1LI8\nZFbM1z5S8zm3nqg+5+LESDVrgFN/xYMFoyVERgK/JrLsEnv3OufPNZcRNKrlVqni7nZiJl7C1+DB\nxsluoULs77vv7FcCNBPkgiV+8Xv9ePo9t3urcu+DCp50omtXuR/40KFqQfEEIRLIEODn5ePa06zi\nJ+QUrKFK4cLM3KoarCXCF7ro1o0NMEEzebL3bAZ//GG992YBMXt296BKUeOZL598KeRIB0i/PsBu\n94pPdIYM8Vc+YH8/oyEUmN0W+vVTW5XQDt7GoNxIeOYUr8KxpoXN+nv2+K/fnEowM/VPfPIuswQ5\n5dYl/KFpkT3XCxa4u7mokDu390VfRPw+wxMnhleyjJRrrmFpHFXxk6pP0+Tub5Hk7SYuXQIRju0W\nJnBCNdvBxUSNGkyTEInQc+ON4YUZVMieXc3c5MeFIlKBqEULFsgxbZrasW44XddPPnFerMYOt+cz\nM2oYnaha1bgASe/ekWU3CNqtgT9Tqpp+PjlJTw+34eqr/dfP73fHjszqsWmT8/GxvP9es7HIiLfm\n+FIiKDenXbuM6Q9jRdmywWUE8fKe7NihLhx37Wq/6idBREIgXbvXrBpr1jCBbdCgzOOvFAtmzJAv\nARpNjh7NfP5xnJ9/Vj82Up86v9p2u0F//Xrg+PH4ZQbwi6b5W7raqTwgGMHNTzniqo5B5PPl91t1\n4qeas5qwJyHBeTIU6z4zGkTiDxupq9KxY2rHXXmlcyaUSPAyeSpTRv1YWdA6QQRBIMLx5s3eTPKi\n72ZQg2pWIB5Cqlu2iayCSucay4lWZlgxLTMQtOaY9wdelxvOyGB9kFuKujfecJ4o8ZRdKpouUVsd\nS6KR6SJSImnTjz86n38xjBHmGJLMyMiR3lwfOCr3PlrPHUFEi0CEY75wgx8uJc0xQXghK7wbQQvH\n3ELw5JPezuPaRTeh1mlxDoCZadu1Y8LMtGnO6a6yotAWrawGjz/u/1w3l6ms7rJRqJA3V7h40b59\n9MrO6veQuPSIW0AeJzNE8xKXDi+/7O146tSdSUhgq70FBXdn8NqnBKWZypYtnE3h7ruDKTOzsGlT\ndFLMXXEFW3I9WmT1d/DQofhOpHr2BN5/P3rlq7yrN91EvsFE1iIQ4fi6RtsBeHf4O32aEnkT7lSs\n6B7c8tlnznmoASA52btrS1YfmKONpgEPPBDvVlxaZlu/gpZs6fUgOHgwOuVy4hGMFiSxXhDIjFte\nfYIgrAQiHF9Wyp9w7CbMEASgtnBHly7ux3hdBpvIGhQuHEyqxazA0qXRE3IzI6dOkXAXKc8/D1zv\nuIxXZKgoEIJYbZAgYknMFwEhiKxEPDXHl/J75eW3Hz4cPQEqmivd+eHmmy+t54IEY2/06mXdVqCA\n9xz2QfPii2yhGoLIKgSiOU7TXULECSKL0qABMG5c7Ot95BGWa/dSZMiQyHIVB8ml5K5BENEie3Za\nqIbIWgQiHGfTsmDYNkEokDMn8PDDsa+Xr4h4KdKtW7xbEIZ8zgmCIC49ApFqdZB6hSCIiw8SjgnC\nmfnz490CggieYBY/1WgEIQji4uP664GdO+PdCoIgCCKWBCIck+aYIIiLEbfV2wgi01B1EnIUagig\naGzrTUgFcpwBkMVz7hGEgKtbhaZpJTVNm69p2h+apm3SNO158zEZOgnHBEFcfGRPzEBiDurfiCxA\nh6Gcgx0AACAASURBVA44W3No7Ott8zDwaqHY10sQUUTF5zgVQDdd168FUBfAM5qmVREPIM0xcTFz\nLu1cvJtwSTHtz2nQPahrNx7YGLW2tJzQEreMvSVq5RNEkCzavSD2lV43IfZ1EkSUcRWOdV3/T9f1\ndRc+nwawBUBx8Zjlh371Vfnv//7u6zyCiBVr9q9Brrdjv4xj7rdzY+iyOGiBPKLrOr7d+G2gZd4z\n8R78fexv5eOvG3Ud9p5kSVTTM9IDa4eu6/hl+y9Yvle+7u3KfStxJPlIYPWpkNg/EYfOHIppnW6U\nHFoSmw9tjkrZq/at8jRRyoykpqcG+lw6sWzvMsu24ynHMeuvWTGp345vNnwDrd8llKCbyPJ4ylah\naVppALUArBC3T9/lLxHsjZ/diINnorz2aCZj3NpxSMtIi3czMg1nU8+6DhxaPw0/bvvR9ZjUdPt8\n25sObkJi/0RM2OhNyxGv5zMlLQUr/10Zl7q9oEPHfT/cF3i5Z1PPej7n098/Rfb+kYVRpGWkYduR\nbdh6eCsS3nLuHuuMqYMXZ78YUX1eSctIwzcbv/F1bqH3CuH1314PuEXA3pN78dys5wIvFwBuGnNT\nllei5BiQI+LnMhKGLBuCOyfcGbf6AdhOMAkis6IsHGuadhmAyQBeuKBBDpE2LxV9+/ZF3759kZSU\n5FjOqXOn8NaCt3A+/Xzou4wTKSdw+vxp6b6sxLYj23D07FEAQHJqMh6d8Sj+PPyn7/I+WPoBsr+l\n3tEOWz4MP//1s+/6IuXA6QOYvHmydN+sv2Yhz8A8eGXuK67lqPwG/kzJqP5JdaRlpOH5WRaXeUf+\nO/2f7b5bxt4SurfXDLsG245s81S2Gxqir2k5c/4Mth/d7vt8Hm/AtXs7ju2IaPLH+4Nev0mW+pLA\nJ1bZtGx46qenXI9/eNrDGLlipO3+lhNaotKHlZQH8+Mpx5WOCwLu3iOL8Ri9ejR+2/Gb4/nHU45j\n1b+rotK2eTvn+T635qia6DXX/n5f6sqEfSf3+ZoschIU1yE4kXICa/ev9V2PE2PXqSduH7RkEH7a\n9pPSscv2LMP4deN9torIKiQlJYVkzL59+8akTqW3RtO0RABTAHyt6/o0ywGNEWp0o0aNHMtauGsh\n+iT1wfr/1gOQCzQVR1ZEwfcK4vYvb1dpnjJ7T+7F30eN5trXf3sd7y5+1/G84ynHMXTZUEcNZ3pG\nOk6fP41Pf/8Ui3YtCm2v9GElPDTtIQBA629bA3AW4jhL9yzFsOXDLNtX/bsK6bq6ia7b7G7oPKWz\n63Gzt88OxHy5cNdCg6l50NJBaD+pfej7poObQoM812Z8sOyD0P7U9FR8s8GqGdt6ZKtr3Trc23/k\nrDcz+CPTH5Fuf2LmE1i6ZymW7WFmzN0nduOpH92FMy9oAawT/Mqvr6BvUt/Q91PnTqH0sNKh7zd8\ndgMqjKzgu3wuqJ08dxIAUG5EOaXnzVwGN/sOXc5cSXYc22E5bvjy4Rb/b37PxXv/6e+fAmDP0guz\nXjAc/8X6L/DJ75/YtmXO33MAALP/nu3YZm4idrNoBMmJcycAAAVzFQzVvfvEbgDAkz8+iW6z3VdP\nyZaQLXoNVKD5183x4i9Gbfv6A+vx7hLnPjjalB5W2qKoSU1PRUpaSuj7zZ/fjLpj6mLN/jVKE0qt\nnxaIsHn10Kvx0pyXfJ+vav0q+F5BXD/6et/1OJGcmqx8bM+5PdHq21aux+08thP1xtaz7aM5a/av\nibn7ExEsjRo1ynzCscZG6M8BbNZ13SqteYQPYjeNuQkADNq2OmPqoM13bfDX0b8AMJ8+lZcqJS0l\nNEiIrNq3Cn8c/CP0vd7n9VB+ZHnDMQMXD0Sv33ph6papSMtIQ6PxjQyaivX/rUeh9wqh+5zueGvB\nW4Zzx68bj+qfVAcADFg4APneyYenfnoKPef2NBx3+vxpDFoyKNSWVfvk2hsuNOu6jlvG3mIY7Fbs\nXQFd1/HbTmftkAgX5rng4kTzb5pj+9Ht2HlsZ0gAaT+pvWMmkobjG+LlOS9bttUbWy/0fe1/4cEh\nNT0V1T+pjjfmvWFb5mdrPsMDUx8IfW/3fTsAQKsKrXA4+TBOpJywnMOFerGtT//0tEUwkuE0cIha\nwYmbJlraCQBnUs+Ets3/Zz6e/flZJKcm4815b0rLnLF1BgYvHRz6furcKZQaWkp6rJfJyup/VyNH\n/xyW7e8vfR/vLn4Xzb5uhr0n9+LT1Z9i14ldof2RWDF0Xcemg5sAsN/OsbMUAOw6ir6HGXoGxqwZ\nE5oojVvHXLS2HN4CgFkX9pzYAwB4cfaLWLN/jaE8/oyLE1euQX55zssYsXIEVuw1eIFZnunz6ect\nE9/sCcw6UzwfC684c/4MXp37qu3vEpnz9xzP7jsq8Ek37z9af9saNUfVDLkdbDy40fVdX7kvPq46\nx84eg9ZPw+y/Z2P4iuFxaYMdKWkp2HViF9YfWG/YnmNADuR+O3fo+/K9y7Fi3wrUHl0bjcY3Uipb\n7As5K/etVM7wxJ/Lw8mHDdv3ndyHf47/E/oulrfv5L6QRQtAaDIo3vslu5fg31P/OtZ9Pv08Dpw+\nEDd/Zbf+r+yIsobvp86dCikrRGqPrq1kVSIIERXN8S0AHgDQWNO0tRf+mrudlJaRhmV7lkHrpyEl\nLQVaPw3pGekYs2aM4bg2E9uEPq/ctxLTt0437G8wrkHo89sL35Y69fea2wvXDLvGsv2mMTeh2ifV\nAABJ/yRhz8k9tu1t+31b/LDlByzYtQATNk4ICcii0L3uwDrDOY9MfwSbDm5CWkYa+i7oG9ouE9R7\nzu2JA2cOAGCD98JdC0P77v/hfvSa2ws5B+QEAKnGuO7nTGNh7iSdeHzm44bvaRlpmP6n8fp2ntIZ\n+d7JB4B1hmVHlMWAhQMAMCGHd6C6rmPosqGhDkvXdSzctRAfLPsAM7fONJS77ci2UKfOzYHHU46H\nfvPgZYOx/9R+aZu5efanbT/htx2/YcqWKQCAK/JegWuGXYNan9YyaHMA4Gwaq+PgmYPQ+mkYvHQw\nPvn9k5AAa8fRs0dx5eArAQBdf+wacqHYfGgzdh7biUFLBoWO7TSlk/TZM/s5f7TqI6zdvxYDFg0I\nbdP6aVi4ayGW7VmG1357DT1+7RFyE/n31L+2z+W3m74Nna/10/DEzCeQoWdg57GdmLl1puHYtf+t\nRWpGKpbuWYoVe1cYhL1z6ecw5+85WLZnWWg7t2K4MWXzFLT4pgX+Pvo3zqefN2hgZm2fhdqjawNg\nQXS7ju+yKybExD/YJKPKR1Wg9dOQc0BOPPnjkwCAFt+0sAz4d064E6WGlQpde2412XBgA3rM6RH6\nfi7dmlFkxMoRAIAXfjFOkrYe2Yqle5ai/4L+eHjaw8g5IKfFSvXVhq8AIPT81x9XH+8teQ8L/lng\nKNicTT2LZl83w/0/3G97zFsL3grdU4Bp1VTM5q/PY/7Coon6WMox3PjZjaHvBd4tgO//+N5w3oyt\nM/D2wrcBwCA0AcylgVvxIkXrp9n2T14tNm4s3r041F9GCh+TRIvVX0f+MhxjtmTsO7UP7y1+D+PW\njgv1ZbquWwQ63lcVyVMktK3OmDohCwXA7olMEKwxqkbIV3nS5kk4knwEWj8Nt395O+qNrYcyw8uE\njs32VtgicPXQq1H4/cIAWIyLWC//jfXH1UeJISXkF+QCz/38HK764CpP/spHzx7FiZQTtkGj5t+5\nbM8y2/cp4a0E23FCxtuL3pZORgDnCTtByFDJVrFY1/UEXddr6rpe68LfL+bjdh3fBa2fFhq031n0\nTuhB5bPvk+dOWoRfAJi7Y66t2WP1/tV47ufn0GpCK7wxn2kcRcESANb8x7RJZ1PPhoTaY2ePGY7p\nMKmD20/FFXmuAAA8NO2hkGl20NKwgDRj64zQ55dmh81cZ86HtYcADAIlwARzMw3HNwx9nrBxgsGs\nyLVxZsyBKWkZaXh/yfuGbfdNuS8k1HEtHGfBPwsMk5GpW6biu03fhXy7R6xgwsRPf/0UajvvnGb/\nPRvd53RHcmoyMvQMg4B613d3GcoFgK83fA0AOHWemSoLvVcITb5qEtpffIgh4UkILgy3+raV4fhv\nNn6D5NRk7Dy+0+IGwDt53tYev/YAEHZLMHfI/LvoVzpq9Si0+74dtH4arv34WpQdURYDFw+0tE/r\npxm0c6kZ1iDA+uPqW7at/nc16o2tF5o4tZzQ0tBGO7hLDsC01W0ntkXZEWVx13d3GX4X/y23jL0F\ndT+vi+z9sxusJqH2a/buAMdTjqPGqBqGbe0mtcMv239B+ZHlkXNAThQZFB7kzS4OpYeXDn3efWK3\ndICc+udUAGGNtWil+WW7sVuRvTdNvmTPxMerPsbgZYNDz6H5mRDfFdk1Hrx0MEauHIkv1n8BAFiw\na4GtRQcA1v3HJsaNvmiErj92NezbcGAD7v7ubgDAozMetS2D0yepj+H7TZ/dZDuoi3Ah4nz6ecfs\nEB0nd4TWT8Oxs8dQ8N2CuPu7u0N9J+fM+TP45/g/WH9gvaE/XbN/Da4afJVjOzYe2IjnZz0PrZ9m\n8Qf24oPdYVIHSz8NsHfsn+P/hJ7vn/6y+p/eOu5WR/c0mQaRIwqx7Se1DwUTTt4SFqBe/tVoEROt\nPZxXf3sVj854FOVGlAMAVP6oMrrM6ILZ22db3o2S+UsCAH79m2V2EifVhd8vjIS3ErD35F48Ov3R\nkMvGhgMbDGU8/fPTAJgCgfcjqemphj7CjPl5HLlypKubBb8n4vVQtWIVfr8wCr5XEEUHFzVMOG8s\nziZwZUeUNUw86o2th/k7w1YnswLi7UVvI0PPQLa3suGjlR/Z1qv10wzWmu//+N42O8Yv23+55P3Y\nCXc8Zatwgg+M3Iwuas44z856Vnpu06+aOvoNTd4y2dBBbj+6HV1/7IoPljJfVd655xmYB4n9E5Ga\nnorL37/cUMah5PBAvWjXIqSkpVgc+cUZ7JGzR7Dz2E4s2r3IcExyajLeXvg2hiwfEtrGNZci/53+\nzzXaXcbXG76WpuMBYDAN1R1TF62/bW0JZvt207cWtw472n7f1vB99JrRANg95Bq5k+dOYsuhLaHB\n4bJ3LkOHSR2ks/2yw8NmLi4U2wn6Zt5d/K5jBywKTjxtF8DuBx+QzSbj5NRkpKanWu7DD1t+ABAW\nUDlL9ixRauuB0wdCn3cd34X87+SXHicG8/28nWmK+XXhmIPuXvn1FYOQ9uX6Lw37xckld1FKSUsJ\naZlFzFkNFu9ebHhexHt48txJzNg6AxsObAjdBzt/Wq715AKjjGuGXYOmXzUNfW8wroFlIufGqN9H\nWbadSz8HrZ+GT1ezyWv5EeUtxwAwaFP5xEEcEFPSUgx9AhB29XKDvyecGqNqhCbO3236LrS9w6QO\nBg3+wTMHkdg/0VLeH4f+wIYDG7B2/1po/TR8se4Lab3ihPTaj691beeEjRNCfspmnp/1fEjzmKFn\nIC0jDXN3zEX32d1x4MwBwzN+JPkIbvvittD360Zdh5ErWVCjud/mfTInR/8cGLRkkGX787Oex6TN\nkyzPEP+NZYaXCVmw+IQKAPK/kx+95/cOfd9/ar/FPaDiyIqWyca+k/tQ6cNKANizweMgRI3i4eTD\neOXXV1B0UFFcluOy0Pav1n/l6Kd+Nu0sJm6aiG1HtmHBrgVo/k1zS/pH/q7e8fUdAIC/j/0dsqpy\nxq8bj3HrxqHCyAoWDT8Ai0WAt9ncR3Am/THJsm3FvhWG4ObT509D66cZgt/52Cm2QZZa8UTKCWls\nAGfeznkh17Zri7Ln9Z/j/2DpnqWG45p81QSPTX9MKrR/tOqjkHufnfzAES1wHSd3BGDs446nHMfM\nrTPR4psWBoGcIGQEJhxz3lrwFubvnI97q9xr2ScKNWZknQHHnDHgsRmPYdTqUQZXBhGzn7LZbNhg\nfAPkfju3pWMXO4g+SX0sPk0Am1WbBf9iHxSzHCfbpsKDUx8M+VtyZDPgFftWGATGFXtXuOaR5Kbi\n7rO7u/rM8UG9yVdNUPXjqgZf5ylbpkiF453Hd4Y+e03t1Ou3Xnj2Z+fOT4bo+mDW9gDMb9DMqNWj\npBorVUT/2r4L+loEXk6FkRVCGqS5O+ZKjzFrNd9f+r5UKJSh6zrOp59H/bH1pb757yx+x/Cduxlw\nRFNsgXcLhAbNk+dOotantVxdL7iAZIfow7lo9yKlrCQi3O/XiWMp6vdRtPzM2h6MH+X1xcIBTGZt\n4aTNk/DDlh9Ck40/Dv5hq7HK0DNC12fg4oFSFzQntzAZdsLEP8f/weGzYfeHtIw09Py1J5p+1RQL\ndrFFJK764CpsOLABx1OOo8igIpj/z3wk/ZNk+Y3cQsQZtTr87CanJiM1IxU95/Y0bAfCz86kzWEB\n7nDyYUOwaO8kJgSfSDmBlLQUHDxzEKfOn0L/hf1DxxQfUhwlhpRAcmoyPlz5IW7/8vZQzMqHKz8M\nCaBj144Nxbes3r8aU7ZMwZ3fWN0F3l/6vmXS9L9p/5NdRgOdpnQCYJ9ZY8+JPYb+mceTiK5Ib84P\nxyk8/dPTrnUC8sBVTofJcmupmFmEa9/d0uVl01hfcc/Ee0J9WcH3Coa05naMWDkCJ1JOGBRRU/+c\nivSMdIMLzth1Y0Mubma4MgMIW2WdXJvEid1X678KfS70XiHc9d1dAGDxLycIM4ELx+l6Om778jZD\nIBbH7A4hoqq1E7FL9WbWUNX8tKZSeWbXABmjV4+2+Lz6JahsHMOXD0fdz+satpkFmyPJR0Im5KHL\nh4YGQTvEQDMZMjOjGa9J3z/+/WPlY9tOZFpvP/di7o65uPnzm0PfvUaUq6atOn3+tGPmhsT+iaE0\nSydSToSulzjJcCJ7/+zIOSAnVu9frXS8G9wfOEPPcNQKc1QF089WO/t+x4porHQoBgg2/8YaitFh\ncgdsPrSZaV+/vM2yX+TXHczkvu3INuw8vhOPz3wc249ux8BFAyMKnDQj+qoCbFLJs4SI8GBkTuMv\nGiu5f2j9NJxLOxdyNXFCFK6/XP9lKC5DZN+pfbjxsxtthSeAuYQ9N+s5w7vJJ+h9k/qGBO37poRz\ncjtNkPwGVMp83wF7n+vKH1WWbufvohsyFy43uJIECFv7Gn/R2PGcR6Y/gp3HdmLan9MMFiERu/HY\nHA80fet0rNm/xtZC4gQPjDbHXIhc9UHYLcguJSN3vSMIO7RI03dpmqajbzCN8UO1otWUTfeZkTkP\nzAmZ2mToffRAVhbqWa8n3l/qzayd2Ynk2iRoCcoR49Fkx/M7pBaKeLLuyXXKE0o39D46rhp8lVTo\niSVHeh4JBSllJuye4a3PbkWlDyvhyzZf4n/T/oemZZuGhOcgaFWxVVTT0DUp28TWWhINetXvZbGU\ncArlKuTJwkAAb9z6htQ1MiiWPLrE17Ls9UrWw5JHl6DX3F5K6f+K5ytum5VD75O1V168lNE0Dbqu\nR3UhgPgt2xMQWVkwBuAoGAPeta92XGyCMeAcdONGZhCMAbXczLFGFjTrl/SMdIuZOh5kRsEYkPtV\nAyzoDQib9IMUjIHo52eOpWAMOKerJMHYO9EUjAH4EowBlv/fy5jolq6OIOzI8ppjgvBD4dyFA08v\n5QeuGcxM1LyqppJbBUFkFm4ofkOWX2aaiC2kOc66xEJzTMIxQRAEQRCXHLWL1cbvT9CkKqsRC+E4\n8IA8giAIgiCIzM7q/asd82UTly4kHBMEQRAEcUkiriJIEJy4CMdX5786HtUSBEEQBEGEMK8rQBBA\nnIRjp8VA3mzwpu2+i4Gnb1BL7h4UObJZF8HI7BTObZ9ZYEWXFTFsSXRQWdzCLy/UeSFqZRMEkXnp\neG3HeDchS5IZsukQmY9AhOOJzZ0XlPBCkTxFAisrM/JILftlskVuK+O8YIAqbzV6S+m4QU0HuR8k\nIRpWgP/VMGZv+Pbe8NLIfNGMrMz257ZHrexhzYe5HlM0b1HD9wevezBazSEIIkL+7a6WjqxlhZYR\n11WukPOKdxcjNxVXWzaeuLQIRNKoVOha2321rqoFAKhfSm0ln5zZcqLhNQ2DaFam46EaDyl3PlWL\nVHU9Jm9iXtdjXqnvvGTvhLYTMLn9ZHS/ubtSu8zs6WZd0jZPYh7b41+6+SXXMqsXrR76bBYk+TKm\nscRJkw0Aqx5fpVROYkIinrvpORTPV9x3W9pWaet6zK4Xd+HcG/arwR142bggxyu3eFvWOVJ2vbjL\n1zXQ4B6cnBkE/V8fdM9JnD9n/hi0xBvlLy/v67wNT20AAOzrvs933Zld63ntFfZjnBe8Wna2PLMF\nxfIVM2y7ofgN0mOvu/I6AJH1kfdUvsf3uUHh1t8Gjd2qhsSlTSDCcUKC/aClaRpmdp6JxbsXK5X1\nSK1HcG+VewEA91e/X7kNLcq3UNaSBknObDlRMn9JTO/kvnDCwNsHKi/68NQNTznuL1uoLDY9HfkC\nKB2rdcS9Ve911MiKbgA1rqzhWqZsIsTdZcoVKofZD8wObZ/QNrxM6/i7xwNgz8y8/83DmNZjUKZQ\nGbQo38K1zoG3DXTcb5fTUiXX5ZwH59hO2M68dsYyWNkJGTtf2IkRLUYgMVsiPr5TfalsERXNeakC\npQzuNO83eT90DWWD/BV5r5CW832777H+qfVK7bq+2PUY3Wo0ZnSa4XpsNi2bRZD6rPVn2NNtD7re\n0BVP1ZY/+0/UfsLwPaO3dSGX+qXq45aS6gsMyNyOmpRtIj029c3U0Oef7vvJtsxGpRu51ju61Wjp\n9oK5CjqeV6ZgGcf9kVC2kNpKjb8/bkx9VSBXAQDA/lP7Xe9/0kNJuLPCnZbtFQtXVGxlfNC0YLJG\nlcxf0vWYSe0nhT5XLsKWl173ZDjv+MouKy3njG41GjWuYn1zucvdFTCPX/+4dHudq+u4nhskeRLz\nYGrHqYZt0VgY6dgr9gvBmC1pBAEEJBw7dRy6rqNVxVbS2ezzNz1v2ZaYkBganGSa0TOvncHxV45b\ntn/W+jO82dDor3xXpbuw/DH52uqcpmXZOvEFchYIbXM7R+Sbtt9gd7fduKvSXY7H3VnhThTPVxzp\nGenS/QseXoBWFVvhtfqvAQCuLeqsqXi4xsMoXbC0dN+TtZ90b/gFVIQtURB4rNZjymWLNC7dGABQ\nplAZ3FHujpB24JqC14SO4QMBADQu0xiPXf8YErSE0ODLz5dxfbHrpdtzZssZ+pzROwPd63rXkF9f\n7HrUuqqW4RkBgNVPrJZqyWVa0UK5CqFE/hKh711v7Kpc//i7x4eE/yK55W5HTcs2xbLHrCsG5k3M\nix639EC3ut0AAPMemmc5xm5waH9t+5A2iv8GO3Rdx+O1H0frSq3tf8gF/j72t2Vb6YKlcXX+q/Fx\ny4/xSatPpOeZrS6832lTuU1IY1zssmJ4tNajANi7Oa3jNNt2TO04NfQ+9qjXI7R9VMtRONXrlOV4\ncZJ4Z4U7DedwMnpnIHtCdlf3sI7VjJpSXp+babzGVTUc74MZ8+Rv14u7bI/tUquLVHA1kyt7LsP3\n3NlzA2DCtVmwMQvcDUs3lN4TL30WwK7zq7e86ukcPzx303MAgD4N+6Bd1XaW/RUur4BWFVvZnr/5\n6c2h/jPl9RQkZkt0rVPWl3Ht8czOM6FpGna+sNOw//HaYWF325Fthusum+DaWWFKFShl2ea3z7/5\n6psN383PDQCkpKWgSpEqhm0Zegaal2/uq047nCad9UrWC7Qu4uLAVTLSNG2spmkHNE3baH8M0wCa\nzbVAeBb4RoM3LPv4C723214c7nEYaW+mQdM0VLmiCmZ0mmF44c++fhZnXjuDPIl5UCBXAeh9dEPH\nLwoenLyJeVHn6joYf/d4HHz5IACEtNKcQrkLoXeD3jj+KhO421RuozR7XvPEGgBApSKVbI95uObD\n6HoDE4LSMtIAhJctrnt1XcOxDa5pgJmdZxo6Bd6ZmLUN3ep2Q6dqnWzr7XBtB9t9TgF6K7usxMou\nK0MTBs4d5e5Az3o9pZo6fh7n3ir3QrawTPUrqxvq588F//7L/b/gxhI3ApBrXo/0PAK9j27p5BqX\nboy7K92NwnmMprhdL+5C/VL1sfCRhaHnRNM012WjX7/1dYMWiw98Q5oNwZGexhX1xEFMDLSU1XH0\nlaOO9YrwAapPwz4AmLDd69ZeePnml9H/tv74vt33lnM+afmJ5ZnKnzM/HqrxEACEtEqyAUoV8+8H\nwsKDilDFKXZZ2EzMtax2pmLOqV6n8FK9l3C612mk904P3dPTvU5jcvvJIc14y4ot8WitR6H30XFf\n9ftwd+W7bctsWrYpcicywU50c8iTmAeX5bjM9jw+UWlftb1lHxfYD/VwD/LZ+uzW0OfLclwGvY/u\nOsm+odgNaFjaasXgfcTGrtZumvfLm7pucnRNaV2pNX667ydHrTgAZEvIhln3z8Lw5sMBIPTuZU/I\nbnj3a15VE/0b9w99v6YAmwgnZkvEi3VeDG3/sfOPofsgUihXIYvgxNE0Dc3KNwMAwzET2k6QTloA\nNs7IOPjyQdv7xSfx7aq2w4jmIwz7xt09Dlue2YIHqj8gPbdeyXqockUVJCYwgThn9pyGvpy/32aS\nU5Mt24rmLYqDLx8M9UelC5Y2XFuR5256LlQnABw4ze7/49c/HtJKiwqtwz0OY1TLUWxyI+m7x9w1\nRloP577q90m3m/tkPgaLiFZEAHjn9nfwzu3vhPyAucuX0yTXjZEtRjruV3HXIi49VDTH4wA4TuMS\nNA2dq3dG0bxFcbQnEwJqF6sNABh711gARq3LHeXuABD2TS2RvwQK5ymMbAlh7XLrSq1DGpLpnaYj\nV/Zcjr6sHHF2/9GdHwEAHqr5EK7IewWWP7YcE9tNBAB8d+93aFWxFTpX64x+jfsBYNrjOiWYYHzi\n1ROO9RTOUxh6Hx3VilYLbfv6nq9DndczNz6DcXePw6v1mXaDL21aNG9RlMxfElM6TAmdJ/rYDRhR\nKgAAFQZJREFUputhzfLZ189C76MbBtGpHadiSLMhqFC4AgA2qy+RzzgxcNIG7+m2x1ZLc2OJG3Fj\niRsx58E5oW1tKrcBALzX9D1omobGZRpLz+NM7jDZsr9TtU4omKsgVnRZEdIg80748tyXAwCalW+G\nBC0Beh9d6pbBjzMz76F5mNZpmkG40vvoKFWgFBY9sgg3lTAGW4iarR3P7wh95hr7MgXLGDRS/Fpq\nmmZ4Ps3kzRG2cmToGWhWrpntsXZ0rtYZALMizOg0A30b9cWI5iNC12PQHYNQJE8RtL/WKJSlvpkq\nNaUe7nEYI+9kA8MVeZjrhDhoekXTNAxuOhgA85+/Is8VGN58ODJ6Z2DAbQOUyxEtHrrOBMJ8OfI5\nnnNZjsuQoCUgb468huc7b468yJaQLTQ5lg10/3b/F3MemGPY9t293yFvjryh6yJi9vEUKZq3aGgi\nIBPo7JC571QsXBGnep3Cn8/8GdrmNLEF2O/l787+l/aHti95dAk2dd2EakX/3969R0dV3XsA//7y\nJCQQSIhJgEggBGNEDM8AGohAwkMQKj5A5IJR5BFQLiKBRgW6aGGhVL1Qta32VrkVqliL9EoF7yLF\nwgKKF4SAXh6torxCESJiRK37/nEeOefMM5nJMAPfz1pnZXLmzMnJ7Nlnfmfv396nK2rm1uDEnBNm\n66LRa5eVbL/IdvYYGBdOJZ1K8KuRv8Kq4auwonQFjs0+hh0P7EBdZZ25v2Gdh5lBqfGeR0dF2+rX\nnql7cMf1d5h103phZh1w3D2zO1ISUlwuSL6o+AJfXvoSgP3iyTiHG+eEg+UHMaTTEFRPr8b4G8dj\neclyc9vBHQebj93leauFCmmJaS4t/bkpuebfeGmUFhw6PxeJsdpnz9OFoXEx0D+rP54Z+gwA7Xy6\n7i7tHGl8RwLAitIV5uOzX7u/rb0z/Wlu/7moLKrExR9fNNfVzK3B06VP287JL+zWemKqa6pxZ/6d\nWDl8pTnG5Lbc25DaPBVTe03F0YeP2nrvAHhthAG0C8Tf3fE7t885W5xbxNvr+Liu4zCk0xDbZ2b+\nLfMxrdc0PDHwCWycsBHLhiyDWqhsF7nuGgecuqR2Mb9XZ/aZCQAoKyjz+Toig8/gWCn1PgDPCTsO\nrRNa49jsY9h6/1ZsL9uO7pnagDxjVoMLCy5gya3aF2mn1p1sAYpTTko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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3291f5e278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = (12.0, 3.0)\n", "plt.plot(samples[:,0])\n", "plt.plot(samples[:,1]);" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f32920096d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = (5.0, 5.0)\n", "plt.plot(samples[:,0], samples[:,1]);" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3292003eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = (5.0, 5.0)\n", "plt.hist(samples[:,0], 20, normed=True, color='cyan');\n", "plt.plot(exact.a_array, exact.P_of_a, color='red');" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3291e129e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = (5.0, 5.0)\n", "plt.hist(samples[:,1], 20, normed=True, color='cyan');\n", "plt.plot(exact.b_array, exact.P_of_b, color='red');" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# If you know how to easily overlay the 2D sample and theoretical confidence regions, by all means do so." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
Blockchain-Research-Group/Tokens-Analysis	tokens.ipynb	1	23981	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tokens analysis\n", "\n", "Simple example of how to use Python to get data from the Ethereum blockchain.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from urllib2 import Request, urlopen, URLError\n", "import pandas as pd\n", "import numpy as np\n", "import json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get data from Etherscan" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Write your API key here\n", "APIKEY = \"\"\n", "\n", "# Make sure you have this file in the same directory\n", "tokens = pd.read_csv('tokens.csv', index_col=False, header=0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>blockHash</th>\n", " <th>blockNumber</th>\n", " <th>confirmations</th>\n", " <th>contractAddress</th>\n", " <th>cumulativeGasUsed</th>\n", " <th>from</th>\n", " <th>gas</th>\n", " <th>gasPrice</th>\n", " <th>gasUsed</th>\n", " <th>hash</th>\n", " <th>input</th>\n", " <th>isError</th>\n", " <th>nonce</th>\n", " <th>timeStamp</th>\n", " <th>to</th>\n", " <th>transactionIndex</th>\n", " <th>value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0x5fd4ccfbc4fd08790bec564d3941bdd39a59fc242fc7...</td>\n", " <td>2541610</td>\n", " <td>798691</td>\n", " <td></td>\n", " <td>419262</td>\n", " <td>0x5f23acdd1e87112b5fe143509d74ded22b6e59b3</td>\n", " <td>400000</td>\n", " <td>21800903077</td>\n", " <td>87290</td>\n", " <td>0x92089c7ccbcb0e190e4d87e3158c0572e066f22ab53a...</td>\n", " <td>0x7d124a0200000000000000000000000008e50ae3e83f...</td>\n", " <td>0</td>\n", " <td>15</td>\n", " <td>2016-10-31 14:38:13</td>\n", " <td>0xac709fcb44a43c35f0da4e3163b117a17f3770f5</td>\n", " <td>1</td>\n", " <td>0.000000e+00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0xacf96f6ef0fcd8878ed90705f3b35a01d36f41a73ffb...</td>\n", " <td>2541693</td>\n", " <td>798608</td>\n", " <td></td>\n", " <td>205240</td>\n", " <td>0xb6f2af0b3551161fe95f18219a8d402fc4e0233b</td>\n", " <td>100000</td>\n", " <td>21000000000</td>\n", " <td>100000</td>\n", " <td>0xf64a321a017ddf84a0daa9aecdf3353c0a5b1369822f...</td>\n", " <td>0x</td>\n", " <td>1</td>\n", " <td>27</td>\n", " <td>2016-10-31 14:58:24</td>\n", " <td>0xac709fcb44a43c35f0da4e3163b117a17f3770f5</td>\n", " <td>5</td>\n", " <td>1.000000e+17</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0x12aa0744a148380816466e7a8b7f7302bcd3971a998c...</td>\n", " <td>2541866</td>\n", " <td>798435</td>\n", " <td></td>\n", " <td>105000</td>\n", " <td>0x5d61433e4dbd2e6a44c62846a7ef3a1d4cd256b3</td>\n", " <td>21000</td>\n", " <td>21000000000</td>\n", " <td>21000</td>\n", " <td>0xdcdbed18849b1b7603c0fa5075a037cf48971ac9ce1c...</td>\n", " <td>0x</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2016-10-31 15:43:16</td>\n", " <td>0xac709fcb44a43c35f0da4e3163b117a17f3770f5</td>\n", " <td>4</td>\n", " <td>1.000000e+12</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0x9ea42e81a52662618b847314d253bbb3fc7bef219f0d...</td>\n", " <td>2542668</td>\n", " <td>797633</td>\n", " <td></td>\n", " <td>121000</td>\n", " <td>0xcecafbdbbb5d5baf57844a6611e36fc781aad017</td>\n", " <td>100000</td>\n", " <td>21000000000</td>\n", " <td>100000</td>\n", " <td>0x934743bcc99b9e211ff607407740e5eebd3255283a33...</td>\n", " <td>0x</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2016-10-31 18:52:16</td>\n", " <td>0xac709fcb44a43c35f0da4e3163b117a17f3770f5</td>\n", " <td>1</td>\n", " <td>6.000000e+18</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0x6d29e46ae11e4c5cef7f0ea2580993fd6451ce5a5ec7...</td>\n", " <td>2543686</td>\n", " <td>796615</td>\n", " <td></td>\n", " <td>358785</td>\n", " <td>0x6cfabd40891abe610efd0cc0cfb8a2f2209ea68d</td>\n", " <td>100000</td>\n", " <td>26000000000</td>\n", " <td>100000</td>\n", " <td>0xb369fac72065e37a74712f8c4375f5d1deea9b735c01...</td>\n", " <td>0x</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2016-10-31 22:58:32</td>\n", " <td>0xac709fcb44a43c35f0da4e3163b117a17f3770f5</td>\n", " <td>10</td>\n", " <td>4.000000e+18</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " blockHash blockNumber \\\n", "0 0x5fd4ccfbc4fd08790bec564d3941bdd39a59fc242fc7... 2541610 \n", "1 0xacf96f6ef0fcd8878ed90705f3b35a01d36f41a73ffb... 2541693 \n", "2 0x12aa0744a148380816466e7a8b7f7302bcd3971a998c... 2541866 \n", "3 0x9ea42e81a52662618b847314d253bbb3fc7bef219f0d... 2542668 \n", "4 0x6d29e46ae11e4c5cef7f0ea2580993fd6451ce5a5ec7... 2543686 \n", "\n", " confirmations contractAddress cumulativeGasUsed \\\n", "0 798691 419262 \n", "1 798608 205240 \n", "2 798435 105000 \n", "3 797633 121000 \n", "4 796615 358785 \n", "\n", " from gas gasPrice gasUsed \\\n", "0 0x5f23acdd1e87112b5fe143509d74ded22b6e59b3 400000 21800903077 87290 \n", "1 0xb6f2af0b3551161fe95f18219a8d402fc4e0233b 100000 21000000000 100000 \n", "2 0x5d61433e4dbd2e6a44c62846a7ef3a1d4cd256b3 21000 21000000000 21000 \n", "3 0xcecafbdbbb5d5baf57844a6611e36fc781aad017 100000 21000000000 100000 \n", "4 0x6cfabd40891abe610efd0cc0cfb8a2f2209ea68d 100000 26000000000 100000 \n", "\n", " hash \\\n", "0 0x92089c7ccbcb0e190e4d87e3158c0572e066f22ab53a... \n", "1 0xf64a321a017ddf84a0daa9aecdf3353c0a5b1369822f... \n", "2 0xdcdbed18849b1b7603c0fa5075a037cf48971ac9ce1c... \n", "3 0x934743bcc99b9e211ff607407740e5eebd3255283a33... \n", "4 0xb369fac72065e37a74712f8c4375f5d1deea9b735c01... \n", "\n", " input isError nonce \\\n", "0 0x7d124a0200000000000000000000000008e50ae3e83f... 0 15 \n", "1 0x 1 27 \n", "2 0x 1 0 \n", "3 0x 1 0 \n", "4 0x 1 0 \n", "\n", " timeStamp to \\\n", "0 2016-10-31 14:38:13 0xac709fcb44a43c35f0da4e3163b117a17f3770f5 \n", "1 2016-10-31 14:58:24 0xac709fcb44a43c35f0da4e3163b117a17f3770f5 \n", "2 2016-10-31 15:43:16 0xac709fcb44a43c35f0da4e3163b117a17f3770f5 \n", "3 2016-10-31 18:52:16 0xac709fcb44a43c35f0da4e3163b117a17f3770f5 \n", "4 2016-10-31 22:58:32 0xac709fcb44a43c35f0da4e3163b117a17f3770f5 \n", "\n", " transactionIndex value \n", "0 1 0.000000e+00 \n", "1 5 1.000000e+17 \n", "2 4 1.000000e+12 \n", "3 1 6.000000e+18 \n", "4 10 4.000000e+18 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenTxs = {}\n", "\n", "def getTxs(token):\n", " try:\n", " # Will look only for normal txs\n", " request = Request(\"http://api.etherscan.io/api?module=account&action=txlist&address={}&apikey={}\".format(token[\"Address\"], APIKEY)) \n", " response = urlopen(request)\n", " txs = response.read()\n", " tokenTxs[token[\"Project\"]] = pd.read_json(json.dumps(json.loads(txs)[\"result\"]), orient='records')\n", " except URLError, e:\n", " print 'API error. Got an error code:', e\n", "\n", "tokens.apply(getTxs, axis=1)\n", "\n", "# Sample data\n", "tokenTxs[\"Acade City\"].head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of user accounts (with duplicates): 17362\n", "Number of user accounts (after removing duplicates): 15286\n" ] } ], "source": [ "# Remove duplicates\n", "uniqueAddresses = np.array([])\n", "\n", "for txs in tokenTxs.itervalues():\n", " uniqueAddresses = np.concatenate([uniqueAddresses, np.unique(txs[\"from\"])])\n", " \n", "print \"Number of user accounts (with duplicates): {}\".format(len(uniqueAddresses))\n", "uniqueAddresses = np.unique(uniqueAddresses)\n", "print \"Number of user accounts (after removing duplicates): {}\".format(len(uniqueAddresses))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get data from local node" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Uncomment to install web3\n", "#!pip install web3" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from web3 import Web3, KeepAliveRPCProvider, IPCProvider\n", "\n", "# Note that you should create only one RPCProvider per\n", "# process, as it recycles underlying TCP/IP network connections between\n", "# your process and Ethereum node\n", "#web3 = Web3(KeepAliveRPCProvider(host='localhost', port='8545'))\n", "\n", "# or for an IPC based connection\n", "web3 = Web3(IPCProvider())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Augur</th>\n", " <th>ICONOMI</th>\n", " <th>Golem</th>\n", " <th>Digix</th>\n", " <th>Pluton (Plutus)</th>\n", " <th>SingularDTV</th>\n", " <th>First Blood</th>\n", " <th>VSice (VDlice)</th>\n", " <th>Hacker Gold (Ether.camp)</th>\n", " <th>Maker DAO</th>\n", " <th>Chrono Bank</th>\n", " <th>Unicorns (Ethereum Fundation)</th>\n", " <th>Xaurum</th>\n", " <th>Acade City</th>\n", " <th>Swarm City</th>\n", " <th>Bitpark Coin</th>\n", " <th>Round</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0x00004aba4ac63de11447e4e17aca83f0abb1fc33</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>0x000118f3bd5a727f663c85c671370760c7730927</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>0x0001aebe0b48bbf1cee8df2c0dfd7c2031543859</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>0x0001fe7648a2c144becdf9f17f0055315a519f86</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>134349877</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>0x000313efbb302549f83e35e50bf0a4e3f0a639af</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2262127659574400000</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Augur ICONOMI Golem \\\n", "0x00004aba4ac63de11447e4e17aca83f0abb1fc33 0 0 0 \n", "0x000118f3bd5a727f663c85c671370760c7730927 0 0 0 \n", "0x0001aebe0b48bbf1cee8df2c0dfd7c2031543859 0 0 0 \n", "0x0001fe7648a2c144becdf9f17f0055315a519f86 0 0 0 \n", "0x000313efbb302549f83e35e50bf0a4e3f0a639af 0 0 2262127659574400000 \n", "\n", " Digix Pluton (Plutus) SingularDTV \\\n", "0x00004aba4ac63de11447e4e17aca83f0abb1fc33 0 0 0 \n", "0x000118f3bd5a727f663c85c671370760c7730927 0 0 0 \n", "0x0001aebe0b48bbf1cee8df2c0dfd7c2031543859 0 0 0 \n", "0x0001fe7648a2c144becdf9f17f0055315a519f86 0 0 0 \n", "0x000313efbb302549f83e35e50bf0a4e3f0a639af 0 0 0 \n", "\n", " First Blood VSice (VDlice) \\\n", "0x00004aba4ac63de11447e4e17aca83f0abb1fc33 0 0 \n", "0x000118f3bd5a727f663c85c671370760c7730927 0 0 \n", "0x0001aebe0b48bbf1cee8df2c0dfd7c2031543859 0 0 \n", "0x0001fe7648a2c144becdf9f17f0055315a519f86 0 0 \n", "0x000313efbb302549f83e35e50bf0a4e3f0a639af 0 0 \n", "\n", " Hacker Gold (Ether.camp) Maker DAO \\\n", "0x00004aba4ac63de11447e4e17aca83f0abb1fc33 0 0 \n", "0x000118f3bd5a727f663c85c671370760c7730927 0 0 \n", "0x0001aebe0b48bbf1cee8df2c0dfd7c2031543859 0 0 \n", "0x0001fe7648a2c144becdf9f17f0055315a519f86 0 0 \n", "0x000313efbb302549f83e35e50bf0a4e3f0a639af 0 0 \n", "\n", " Chrono Bank \\\n", "0x00004aba4ac63de11447e4e17aca83f0abb1fc33 0 \n", "0x000118f3bd5a727f663c85c671370760c7730927 0 \n", "0x0001aebe0b48bbf1cee8df2c0dfd7c2031543859 0 \n", "0x0001fe7648a2c144becdf9f17f0055315a519f86 134349877 \n", "0x000313efbb302549f83e35e50bf0a4e3f0a639af 0 \n", "\n", " Unicorns (Ethereum Fundation) \\\n", "0x00004aba4ac63de11447e4e17aca83f0abb1fc33 0 \n", "0x000118f3bd5a727f663c85c671370760c7730927 0 \n", "0x0001aebe0b48bbf1cee8df2c0dfd7c2031543859 0 \n", "0x0001fe7648a2c144becdf9f17f0055315a519f86 0 \n", "0x000313efbb302549f83e35e50bf0a4e3f0a639af 0 \n", "\n", " Xaurum Acade City Swarm City \\\n", "0x00004aba4ac63de11447e4e17aca83f0abb1fc33 0 0 0 \n", "0x000118f3bd5a727f663c85c671370760c7730927 0 0 0 \n", "0x0001aebe0b48bbf1cee8df2c0dfd7c2031543859 0 0 0 \n", "0x0001fe7648a2c144becdf9f17f0055315a519f86 0 0 0 \n", "0x000313efbb302549f83e35e50bf0a4e3f0a639af 0 0 0 \n", "\n", " Bitpark Coin Round \n", "0x00004aba4ac63de11447e4e17aca83f0abb1fc33 0 0 \n", "0x000118f3bd5a727f663c85c671370760c7730927 0 0 \n", "0x0001aebe0b48bbf1cee8df2c0dfd7c2031543859 0 0 \n", "0x0001fe7648a2c144becdf9f17f0055315a519f86 0 0 \n", "0x000313efbb302549f83e35e50bf0a4e3f0a639af 0 0 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ERC20 standar\n", "abi = json.loads('[{\"constant\":false,\"inputs\":[{\"name\":\"_spender\",\"type\":\"address\"},{\"name\":\"_value\",\"type\":\"uint256\"}],\"name\":\"approve\",\"outputs\":[{\"name\":\"success\",\"type\":\"bool\"}],\"payable\":false,\"type\":\"function\"},{\"constant\":true,\"inputs\":[],\"name\":\"totalSupply\",\"outputs\":[{\"name\":\"\",\"type\":\"uint256\"}],\"payable\":false,\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"_from\",\"type\":\"address\"},{\"name\":\"_to\",\"type\":\"address\"},{\"name\":\"_value\",\"type\":\"uint256\"}],\"name\":\"transferFrom\",\"outputs\":[{\"name\":\"success\",\"type\":\"bool\"}],\"payable\":false,\"type\":\"function\"},{\"constant\":true,\"inputs\":[{\"name\":\"_owner\",\"type\":\"address\"}],\"name\":\"balanceOf\",\"outputs\":[{\"name\":\"balance\",\"type\":\"uint256\"}],\"payable\":false,\"type\":\"function\"},{\"constant\":false,\"inputs\":[{\"name\":\"_to\",\"type\":\"address\"},{\"name\":\"_value\",\"type\":\"uint256\"}],\"name\":\"transfer\",\"outputs\":[{\"name\":\"success\",\"type\":\"bool\"}],\"payable\":false,\"type\":\"function\"},{\"constant\":true,\"inputs\":[{\"name\":\"_owner\",\"type\":\"address\"},{\"name\":\"_spender\",\"type\":\"address\"}],\"name\":\"allowance\",\"outputs\":[{\"name\":\"remaining\",\"type\":\"uint256\"}],\"payable\":false,\"type\":\"function\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"name\":\"_from\",\"type\":\"address\"},{\"indexed\":true,\"name\":\"_to\",\"type\":\"address\"},{\"indexed\":false,\"name\":\"_value\",\"type\":\"uint256\"}],\"name\":\"Transfer\",\"type\":\"event\"},{\"anonymous\":false,\"inputs\":[{\"indexed\":true,\"name\":\"_owner\",\"type\":\"address\"},{\"indexed\":true,\"name\":\"_spender\",\"type\":\"address\"},{\"indexed\":false,\"name\":\"_value\",\"type\":\"uint256\"}],\"name\":\"Approval\",\"type\":\"event\"}]')\n", "\n", "balances = pd.DataFrame(\"\", index=uniqueAddresses, columns=tokens[\"Project\"].values)\n", "\n", "def setBalances(token):\n", " tokenContract = web3.eth.contract(\n", " abi = abi,\n", " address = token[\"Address\"]\n", " )\n", " \n", " for address in uniqueAddresses:\n", " try:\n", " balances.set_value(address, token[\"Project\"], tokenContract.call().balanceOf(address))\n", " except URLError, e:\n", " print 'Web3 error. Got an error code:', e\n", "\n", "tokens.apply(setBalances, axis=1)\n", "\n", "# Sample data\n", "balances.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Export" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Raw data\n", "for project, txs in tokenTxs.items():\n", " url = \"./datasets/{}.csv\".format(project)\n", " txs.to_csv(url, sep=',')\n", "\n", "# Balances\n", "balances.to_csv(\"./datasets/balances.csv\", sep=',')" ] } ], "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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
PyDataMallorca/WS_Introduction_to_data_science	ml_miguel/Crackeando el guess who.ipynb	2	7112	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cual es la mejor estrategia para adivinar?\n", "*Por Miguel Escalona" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "\n", "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ¡Adivina Quién es!\n", "\n", "El juego de adivina quién es, consiste en adivinar el personaje que tu oponente ha seleccionado antes de que él/ella adivine el tuyo.\n", "La dinámica del juego es:\n", " Cada jugador elige un personaje al azar \n", "* Por turnos, cada jugador realiza preguntas de sí o no, e intenta adivinar el personaje del oponente.\n", "* Las preguntas válidas están basadas en la apariencia de los personajes y deberían ser fáciles de responder.\n", "* Ejemplo de pregunta válida: ¿Tiene el cabello negro?\n", "* Ejemplo de pregunta no válida: ¿Luce como un ex-presidiario?\n", "\n", "A continuación, cargamos el tablero con los personajes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image('data/guess_who_board.jpg', width=700)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cargando los datos\n", "\n", "Para la carga de datos usaremos la función `read_csv` de pandas. Pandas cuenta con un amplio listado de funciones para la carga de datos. Mas informacion en la [documentación de la API.](http://pandas.pydata.org/pandas-docs/stable/io.html) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('data/guess_who.csv', index_col='observacion')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ¿Cuántos personajes tenemos con cada caracteristica?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Separamos los tipos de variables\n", "categorical_var = 'color de cabello'\n", "binary_vars = list(set(df.keys()) - set([categorical_var, 'NOMBRE']))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Para las variables booleanas calculamos la suma\n", "df[binary_vars].sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Para las variables categoricas, observamos la frecuencia de cada categoría\n", "df[categorical_var].value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "labels = df['NOMBRE']\n", "del df['NOMBRE'] \n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Codificación de variables categóricas" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.feature_extraction import DictVectorizer\n", "vectorizer = DictVectorizer(sparse=False)\n", "ab=vectorizer.fit_transform(df.to_dict('records'))\n", "dft = pd.DataFrame(ab, columns=vectorizer.get_feature_names())\n", "dft.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Entrenando un arbol de decisión" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "\n", "classifier = DecisionTreeClassifier(criterion='entropy', splitter='random', random_state=42)\n", "classifier.fit(dft, labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Obtención de los pesos de cada feature" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "classifier.feature_importances_" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feat = pd.DataFrame(index=dft.keys(), data=classifier.feature_importances_, columns=['score'])\n", "feat = feat.sort_values(by='score', ascending=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feat.plot(kind='bar',rot=85,figsize=(10,4),)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Bonus: Visualizando el arbol, requiere graphviz\n", "```\n", "conda install graphviz\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.tree import export_graphviz\n", "dotfile = open('guess_who_tree.dot', 'w')\n", "export_graphviz(\n", " classifier, \n", " out_file = dotfile, \n", " filled=True, \n", " feature_names = dft.columns, \n", " class_names=list(labels), \n", " rotate=True, \n", " max_depth=1, \n", " rounded=True,\n", ")\n", "dotfile.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!dot -Tpng guess_who_tree.dot -o guess_who_tree.png " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image('guess_who_tree.png', width=1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
NathanYee/ThinkBayes2	code/.ipynb_checkpoints/chap07mine-checkpoint.ipynb	1	116253	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Think Bayes: Chapter 7\n", "\n", "This notebook presents code and exercises from Think Bayes, second edition.\n", "\n", "Copyright 2016 Allen B. Downey\n", "\n", "MIT License: https://opensource.org/licenses/MIT" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function, division\n", "\n", "% matplotlib inline\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "import math\n", "import numpy as np\n", "\n", "from thinkbayes2 import Pmf, Cdf, Suite, Joint\n", "import thinkplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Warm-up exercises" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise: Suppose that goal scoring in hockey is well modeled by a \n", "Poisson process, and that the long-run goal-scoring rate of the\n", "Boston Bruins against the Vancouver Canucks is 2.9 goals per game.\n", "In their next game, what is the probability\n", "that the Bruins score exactly 3 goals? Plot the PMF of `k`, the number\n", "of goals they score in a game." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise: Assuming again that the goal scoring rate is 2.9, what is the probability of scoring a total of 9 goals in three games? Answer this question two ways:\n", "\n", "1. Compute the distribution of goals scored in one game and then add it to itself twice to find the distribution of goals scored in 3 games.\n", "\n", "2. Use the Poisson PMF with parameter $\\lambda t$, where $\\lambda$ is the rate in goals per game and $t$ is the duration in games." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Exercise: Suppose that the long-run goal-scoring rate of the\n", "Canucks against the Bruins is 2.6 goals per game. Plot the distribution\n", "of `t`, the time until the Canucks score their first goal.\n", "In their next game, what is the probability that the Canucks score\n", "during the first period (that is, the first third of the game)?\n", "\n", "Hint: `thinkbayes2` provides `MakeExponentialPmf` and `EvalExponentialCdf`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Exercise: Assuming again that the goal scoring rate is 2.8, what is the probability that the Canucks get shut out (that is, don't score for an entire game)? Answer this question two ways, using the CDF of the exponential distribution and the PMF of the Poisson distribution." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## The Boston Bruins problem\n", "\n", "The `Hockey` suite contains hypotheses about the goal scoring rate for one team against the other. The prior is Gaussian, with mean and variance based on previous games in the league.\n", "\n", "The Likelihood function takes as data the number of goals scored in a game." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from thinkbayes2 import MakeNormalPmf\n", "from thinkbayes2 import EvalPoissonPmf\n", "\n", "class Hockey(Suite):\n", " \"\"\"Represents hypotheses about the scoring rate for a team.\"\"\"\n", "\n", " def __init__(self, label=None):\n", " \"\"\"Initializes the Hockey object.\n", "\n", " label: string\n", " \"\"\"\n", " mu = 2.8\n", " sigma = 0.3\n", "\n", " pmf = MakeNormalPmf(mu, sigma, num_sigmas=4, n=101)\n", " Suite.__init__(self, pmf, label=label)\n", " \n", " def Likelihood(self, data, hypo):\n", " \"\"\"Computes the likelihood of the data under the hypothesis.\n", "\n", " Evaluates the Poisson PMF for lambda and k.\n", "\n", " hypo: goal scoring rate in goals per game\n", " data: goals scored in one game\n", " \"\"\"\n", " lam = hypo\n", " k = data\n", " like = EvalPoissonPmf(k, lam)\n", " return like" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can initialize a suite for each team:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "suite1 = Hockey('bruins')\n", "suite2 = Hockey('canucks')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's what the priors look like:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ALcDT+GYP3uzMEmxMo3bg6Am2HS7wl6+/wFZmPFXXXdAbj9PtdfBECau3Hay+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\nLJf3D5ylTFu8y85SGjlLKKb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"text/plain": [ "<matplotlib.figure.Figure at 0x7f009a60d210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "thinkplot.PrePlot(num=2)\n", "thinkplot.Pdf(suite1)\n", "thinkplot.Pdf(suite2)\n", "thinkplot.Config(xlabel='Goals per game',\n", " ylabel='Probability')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can update each suite with the scores from the first 4 games." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2.8814477910015515, 2.6145205761109502)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4iCDSLS4G2dPT59adcSL5T/qZHmv0SAHo6VMUVjQPs8J7hIcFkz3b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\ndh3WfVL8Ea1QNCOmqKaV8voOAIIDbVz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"text/plain": [ "<matplotlib.figure.Figure at 0x7f009a5f8f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "suite1.UpdateSet([0, 2, 8, 4])\n", "suite2.UpdateSet([1, 3, 1, 0])\n", "\n", "thinkplot.PrePlot(num=2)\n", "thinkplot.Pdf(suite1)\n", "thinkplot.Pdf(suite2)\n", "thinkplot.Config(xlabel='Goals per game',\n", " ylabel='Probability')\n", "\n", "suite1.Mean(), suite2.Mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To predict the number of goals scored in the next game we can compute, for each hypothetical value of $\\lambda$, a Poisson distribution of goals scored, then make a weighted mixture of Poissons:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from thinkbayes2 import MakeMixture\n", "from thinkbayes2 import MakePoissonPmf\n", "\n", "def MakeGoalPmf(suite, high=10):\n", " \"\"\"Makes the distribution of goals scored, given distribution of lam.\n", "\n", " suite: distribution of goal-scoring rate\n", " high: upper bound\n", "\n", " returns: Pmf of goals per game\n", " \"\"\"\n", " metapmf = Pmf()\n", "\n", " for lam, prob in suite.Items():\n", " pmf = MakePoissonPmf(lam, high)\n", " metapmf.Set(pmf, prob)\n", "\n", " mix = MakeMixture(metapmf, label=suite.label)\n", " return mix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's what the results look like." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2.8792178420902261, 2.6134252104851328)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0097906550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "goal_dist1 = MakeGoalPmf(suite1)\n", "goal_dist2 = MakeGoalPmf(suite2)\n", "\n", "thinkplot.PrePlot(num=2)\n", "thinkplot.Pmf(goal_dist1)\n", "thinkplot.Pmf(goal_dist2)\n", "thinkplot.Config(xlabel='Goals',\n", " ylabel='Probability',\n", " xlim=[-0.7, 11.5])\n", "\n", "goal_dist1.Mean(), goal_dist2.Mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can compute the probability that the Bruins win, lose, or tie in regulation time." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prob win, loss, tie: 0.457999782312 0.370290326041 0.171709891647\n" ] } ], "source": [ "diff = goal_dist1 - goal_dist2\n", "p_win = diff.ProbGreater(0)\n", "p_loss = diff.ProbLess(0)\n", "p_tie = diff.Prob(0)\n", "\n", "print('Prob win, loss, tie:', p_win, p_loss, p_tie)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the game goes into overtime, we have to compute the distribution of `t`, the time until the first goal, for each team. For each hypothetical value of $\\lambda$, the distribution of `t` is exponential, so the predictive distribution is a mixture of exponentials." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from thinkbayes2 import MakeExponentialPmf\n", "\n", "def MakeGoalTimePmf(suite):\n", " \"\"\"Makes the distribution of time til first goal.\n", "\n", " suite: distribution of goal-scoring rate\n", "\n", " returns: Pmf of goals per game\n", " \"\"\"\n", " metapmf = Pmf()\n", "\n", " for lam, prob in suite.Items():\n", " pmf = MakeExponentialPmf(lam, high=2.5, n=1001)\n", " metapmf.Set(pmf, prob)\n", "\n", " mix = MakeMixture(metapmf, label=suite.label)\n", " return mix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's what the predictive distributions for `t` look like." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.34678105634353618, 0.38140698509499399)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f009786ee10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "time_dist1 = MakeGoalTimePmf(suite1) \n", "time_dist2 = MakeGoalTimePmf(suite2)\n", " \n", "thinkplot.PrePlot(num=2)\n", "thinkplot.Pmf(time_dist1)\n", "thinkplot.Pmf(time_dist2) \n", "thinkplot.Config(xlabel='Games until goal',\n", " ylabel='Probability')\n", "\n", "time_dist1.Mean(), time_dist2.Mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In overtime the first team to score wins, so the probability of winning is the probability of generating a smaller value of `t`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p_win_in_overtime 0.524145104619\n" ] } ], "source": [ "p_win_in_overtime = time_dist1.ProbLess(time_dist2)\n", "p_adjust = time_dist1.ProbEqual(time_dist2)\n", "p_win_in_overtime += p_adjust / 2\n", "print('p_win_in_overtime', p_win_in_overtime)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can compute the overall chance that the Bruins win, either in regulation or overtime." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p_win_overall 0.548000681433\n" ] } ], "source": [ "p_win_overall = p_win + p_tie * p_win_in_overtime\n", "print('p_win_overall', p_win_overall)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise: To make the model of overtime more correct, we could update both suites with 0 goals in one game, before computing the predictive distribution of `t`. Make this change and see what effect it has on the results." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Solution goes here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise: In the final match of the 2014 FIFA World Cup, Germany defeated Argentina 1-0. What is the probability that Germany had the better team? What is the probability that Germany would win a rematch?\n", "\n", "For a prior distribution on the goal-scoring rate for each team, use a gamma distribution with parameter 1.3." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.3103599490022571" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Sz9stAv7Z20Tkeefc5nra1AB/8R7/CXjWWyDsauAZL1gBMuJe09Dy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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f00973ff150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from thinkbayes2 import MakeGammaPmf\n", "\n", "xs = np.linspace(0, 8, 101)\n", "pmf = MakeGammaPmf(xs, 1.3)\n", "thinkplot.Pdf(pmf)\n", "thinkplot.Config(xlabel='Goals per game')\n", "pmf.Mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise: In the 2014 FIFA World Cup, Germany played Brazil in a semifinal match. Germany scored after 11 minutes and again at the 23 minute mark. At that point in the match, how many goals would you expect Germany to score after 90 minutes? What was the probability that they would score 5 more goals (as, in fact, they did)?\n", "\n", "Note: for this one you will need a new suite that provides a Likelihood function that takes as data the time between goals, rather than the number of goals in a game. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Exercise: Which is a better way to break a tie: overtime or penalty shots?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Exercise: Suppose that you are an ecologist sampling the insect population in a new environment. You deploy 100 traps in a test area and come back the next day to check on them. You find that 37 traps have been triggered, trapping an insect inside. Once a trap triggers, it cannot trap another insect until it has been reset.\n", "If you reset the traps and come back in two days, how many traps do you expect to find triggered? Compute a posterior predictive distribution for the number of traps." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
ledeprogram/algorithms	class1/homework/zhao_shengying_1_3.ipynb	1	1703	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "The following code will print the prime numbers between 1 and 100. Modify the code so it prints every other prime number from 1 to 100" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "5\n", "11\n", "17\n", "23\n", "31\n", "41\n", "47\n", "59\n", "67\n", "73\n", "83\n", "97\n" ] } ], "source": [ "n = 0\n", "for num in range(1,101): #range(1,101):from 1 to 101 not including 101\n", " prime = True # prime is a variable that contains boolean value/true or false, boolean flag to check the number for being prime\n", " for i in range(2,num): #range(2,num): from 2 to num not including 101\n", " if (num%i==0): \n", " prime = False \n", " if prime: \n", " n=n+1\n", " if (n%2==0):\n", " print(num)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
GPflow/GPflowOpt	doc/source/notebooks/constrained_bo.ipynb	1	164922	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bayesian Optimization with black-box constraints\n", "Joachim van der Herten" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "This notebook demonstrates the optimization of an analytical function using the well known Expected Improvement (EI) function. The problem is constrained by a black-box constraint function. The feasible regions are learnt jointly with the optimal regions by considering a second acquisition function known as the Probability of Feasibility (PoF), following the approach of Gardner et al. (2014)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "import gpflow\n", "import gpflowopt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Constrained problem\n", "\n", "First we set up an objective function (the townsend function) and a constraint function. We further assume both functions are black-box. We also define the optimization domain (2 continuous parameters)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 504x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Objective & constraint\n", "def townsend(X):\n", " return -(np.cos((X[:,0]-0.1)X[:,1])2 + X[:,0] np.sin(3X[:,0]+X[:,1]))[:,None]\n", "\n", "def constraint(X):\n", " return -(-np.cos(1.5X[:,0]+np.pi)np.cos(1.5X[:,1])+np.sin(1.5X[:,0]+np.pi)np.sin(1.5X[:,1]))[:,None]\n", "\n", "# Setup input domain\n", "domain = gpflowopt.domain.ContinuousParameter('x1', -2.25, 2.5) + \\\n", " gpflowopt.domain.ContinuousParameter('x2', -2.5, 1.75)\n", "\n", "# Plot\n", "def plotfx(): \n", " X = gpflowopt.design.FactorialDesign(101, domain).generate()\n", " Zo = townsend(X)\n", " Zc = constraint(X)\n", " mask = Zc>=0\n", " Zc[mask] = np.nan\n", " Zc[np.logical_not(mask)] = 1\n", " Z = Zo Zc\n", " shape = (101, 101)\n", "\n", " f, axes = plt.subplots(1, 1, figsize=(7, 5))\n", " axes.contourf(X[:,0].reshape(shape), X[:,1].reshape(shape), Z.reshape(shape))\n", " axes.set_xlabel('x1')\n", " axes.set_ylabel('x2')\n", " axes.set_xlim([domain.lower[0], domain.upper[0]])\n", " axes.set_ylim([domain.lower[1], domain.upper[1]])\n", " return axes\n", "\n", "plotfx();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modeling and joint acquisition function\n", "\n", "We proceed by assigning the objective and constraint function a GP prior. Both functions are evaluated on a space-filling set of points (here, a Latin Hypercube design). Two GPR models are created.\n", "The EI is based on the model of the objective function (townsend), whereas PoF is based on the model of the constraint function. We then define the joint criterioin as the product of the EI and PoF." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Initial evaluations\n", "design = gpflowopt.design.LatinHyperCube(11, domain)\n", "X = design.generate()\n", "Yo = townsend(X)\n", "Yc = constraint(X)\n", "\n", "# Models\n", "objective_model = gpflow.gpr.GPR(X, Yo, gpflow.kernels.Matern52(2, ARD=True))\n", "objective_model.likelihood.variance = 0.01\n", "constraint_model = gpflow.gpr.GPR(np.copy(X), Yc, gpflow.kernels.Matern52(2, ARD=True))\n", "constraint_model.kern.lengthscales.transform = gpflow.transforms.Log1pe(1e-3)\n", "constraint_model.likelihood.variance = 0.01\n", "constraint_model.likelihood.variance.prior = gpflow.priors.Gamma(1./4.,1.0)\n", "\n", "# Setup\n", "ei = gpflowopt.acquisition.ExpectedImprovement(objective_model)\n", "pof = gpflowopt.acquisition.ProbabilityOfFeasibility(constraint_model)\n", "joint = ei * pof" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initial belief\n", "\n", "We can now inspect our belief about the optimization problem by plotting the models, the EI, PoF and joint mappings. Both models clearly are not very accurate yet. More specifically, the constraint model does not correctly capture the feasibility yet." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From c:\\users\\icouckuy\\documents\\projecten\\gpflowopt\\gpflowopt\\acquisition\\acquisition.py:362: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "keep_dims is deprecated, use keepdims instead\n", "name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n", "[0.18194828 0.14835351]\n", "name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n", "[0.63086542]\n", "name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:Ga([0.25],[1.])\n", "[0.16823107]\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 5 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot():\n", " Xeval = gpflowopt.design.FactorialDesign(101, domain).generate()\n", " Yevala,_ = joint.operands[0].models[0].predict_f(Xeval)\n", " Yevalb,_ = joint.operands[1].models[0].predict_f(Xeval)\n", " Yevalc = np.maximum(ei.evaluate(Xeval), 0)\n", " Yevald = pof.evaluate(Xeval)\n", " Yevale = np.maximum(joint.evaluate(Xeval), 0)\n", " shape = (101, 101)\n", " plots = [('Objective model', Yevala), ('Constraint model', Yevalb), \n", " ('EI', Yevalc), ('PoF', Yevald), \n", " ('EI * PoF', Yevale)]\n", "\n", " plt.figure(figsize=(10,10))\n", " for i, plot in enumerate(plots):\n", " if i == 4:\n", " ax = plt.subplot2grid((3, 4), (2, 1), colspan=2)\n", " else:\n", " ax = plt.subplot2grid((3, 2), (int(i/2), i % 2))\n", " \n", " ax.contourf(Xeval[:,0].reshape(shape), Xeval[:,1].reshape(shape), plot[1].reshape(shape))\n", " ax.scatter(joint.data[0][:,0], joint.data[0][:,1], c='w')\n", " ax.set_title(plot[0])\n", " ax.set_xlabel('x1')\n", " ax.set_ylabel('x2')\n", " ax.set_xlim([domain.lower[0], domain.upper[0]])\n", " ax.set_ylim([domain.lower[1], domain.upper[1]])\n", " plt.tight_layout()\n", " \n", "# Plot representing the model belief, and the belief mapped to EI and PoF\n", "plot()\n", "print(constraint_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running Bayesian Optimizer\n", "\n", "Running the Bayesian optimization is the next step. For this, we must set up an appropriate strategy to optimize the joint acquisition function. Sometimes this can be a bit challenging as often large non-varying areas may occur. A typical strategy is to apply a Monte Carlo optimization step first, then optimize the point with the best value (several variations exist). This approach is followed here. We then run the Bayesian Optimization and allow it to select up to 50 additional decisions. \n", "\n", "The joint acquisition function assures the feasibility (w.r.t the constraint) is taken into account while selecting decisions for optimality." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iter # 0 - MLL [-15.4, -16.4] - fmin [-1.21] - constraints [-0.916]\n", "iter # 1 - MLL [-17.3, -17.7] - fmin [-1.21] - constraints [-0.916]\n", "iter # 2 - MLL [-14.4, -12.7] - fmin [-1.21] - constraints [-0.916]\n", "iter # 3 - MLL [-11.2, -13.4] - fmin [-1.54] - constraints [-0.778]\n", "iter # 4 - MLL [-15.2, -14.5] - fmin [-1.54] - constraints [-0.778]\n", "iter # 5 - MLL [-15.8, -14.6] - fmin [-1.63] - constraints [-0.615]\n", "iter # 6 - MLL [-17.4, -14.8] - fmin [-1.63] - constraints [-0.615]\n", "iter # 7 - MLL [-18.4, -14.4] - fmin [-1.63] - constraints [-0.615]\n", "iter # 8 - MLL [-19.3, -15.1] - fmin [-1.63] - constraints [-0.615]\n", "iter # 9 - MLL [-21.0, -15.2] - fmin [-1.63] - constraints [-0.615]\n", "iter # 10 - MLL [-22.4, -15.1] - fmin [-1.63] - constraints [-0.615]\n", "iter # 11 - MLL [-23.5, -15.2] - fmin [-1.63] - constraints [-0.615]\n", "iter # 12 - MLL [-22.8, -14.3] - fmin [-1.63] - constraints [-0.615]\n", "iter # 13 - MLL [-23.6, -14.9] - fmin [-1.88] - constraints [-0.921]\n", "iter # 14 - MLL [-24.7, -15.7] - fmin [-1.88] - constraints [-0.921]\n", "iter # 15 - MLL [-25.8, -16.2] - fmin [-1.88] - constraints [-0.921]\n", "iter # 16 - MLL [-25.5, -15.3] - fmin [-1.88] - constraints [-0.921]\n", "iter # 17 - MLL [-26.4, -15.1] - fmin [-2.3] - constraints [-0.889]\n", "iter # 18 - MLL [-26.5, -15.2] - fmin [-2.65] - constraints [-0.567]\n", "iter # 19 - MLL [-25.5, -15.3] - fmin [-2.85] - constraints [-0.393]\n", "iter # 20 - MLL [-24.7, -15.4] - fmin [-2.96] - constraints [-0.299]\n", "iter # 21 - MLL [-26.2, -18.3] - fmin [-2.96] - constraints [-0.299]\n", "iter # 22 - MLL [-26.5, -17.1] - fmin [-3.17] - constraints [-0.493]\n", "iter # 23 - MLL [-29.2, -18.2] - fmin [-3.17] - constraints [-0.493]\n", "iter # 24 - MLL [-26.6, -15.9] - fmin [-3.18] - constraints [-0.64]\n", "iter # 25 - MLL [-27.7, -16.5] - fmin [-3.18] - constraints [-0.64]\n", "iter # 26 - MLL [-29.1, -18.1] - fmin [-3.18] - constraints [-0.64]\n", "iter # 27 - MLL [-29.9, -18.7] - fmin [-3.18] - constraints [-0.64]\n", "iter # 28 - MLL [-31.0, -18.6] - fmin [-3.18] - constraints [-0.64]\n", "iter # 29 - MLL [-32.3, -19.6] - fmin [-3.18] - constraints [-0.64]\n", "iter # 30 - MLL [-34.1, -18.8] - fmin [-3.18] - constraints [-0.64]\n", "iter # 31 - MLL [-28.8, -14.1] - fmin [-3.2] - constraints [-0.571]\n", "iter # 32 - MLL [-29.7, -14.2] - fmin [-3.2] - constraints [-0.571]\n", "iter # 33 - MLL [-26.8, -10.7] - fmin [-3.2] - constraints [-0.571]\n", "iter # 34 - MLL [-27.2, -11.0] - fmin [-3.2] - constraints [-0.571]\n", "iter # 35 - MLL [-27.2, -11.9] - fmin [-3.2] - constraints [-0.571]\n", "iter # 36 - MLL [-24.9, -9.19] - fmin [-3.2] - constraints [-0.571]\n", "iter # 37 - MLL [-19.0, -3.94] - fmin [-3.2] - constraints [-0.571]\n", "iter # 38 - MLL [-18.5, -3.12] - fmin [-3.2] - constraints [-0.571]\n", "iter # 39 - MLL [-19.8, -3.26] - fmin [-3.2] - constraints [-0.571]\n", "iter # 40 - MLL [-21.5, -2.7] - fmin [-3.2] - constraints [-0.571]\n", "iter # 41 - MLL [-22.1, -1.64] - fmin [-3.2] - constraints [-0.571]\n", "iter # 42 - MLL [-22.6, -0.981] - fmin [-3.2] - constraints [-0.571]\n", "iter # 43 - MLL [-26.5, -4.06] - fmin [-3.2] - constraints [-0.571]\n", "iter # 44 - MLL [-26.5, -3.61] - fmin [-3.2] - constraints [-0.571]\n", "iter # 45 - MLL [-25.5, -1.62] - fmin [-3.2] - constraints [-0.571]\n", "iter # 46 - MLL [-26.3, -1.42] - fmin [-3.2] - constraints [-0.571]\n", "iter # 47 - MLL [-27.8, 0.0245] - fmin [-3.2] - constraints [-0.571]\n", "iter # 48 - MLL [-28.4, 0.894] - fmin [-3.2] - constraints [-0.571]\n", "iter # 49 - MLL [-25.9, 3.7] - fmin [-3.2] - constraints [-0.571]\n", " constraints: array([-0.57055951])\n", " fun: array([-3.19946737])\n", " message: 'OK'\n", " nfev: 50\n", " success: True\n", " x: array([[-2.25 , -1.29638397]])\n" ] } ], "source": [ "# First setup the optimization strategy for the acquisition function\n", "# Combining MC step followed by L-BFGS-B\n", "acquisition_opt = gpflowopt.optim.StagedOptimizer([gpflowopt.optim.MCOptimizer(domain, 200), \n", " gpflowopt.optim.SciPyOptimizer(domain)])\n", "\n", "# Then run the BayesianOptimizer for 50 iterations\n", "optimizer = gpflowopt.BayesianOptimizer(domain, joint, optimizer=acquisition_opt, verbose=True)\n", "result = optimizer.optimize([townsend, constraint], n_iter=50)\n", " \n", "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results\n", "\n", "If we now plot the belief, we clearly see the constraint model has improved significantly. More specifically, its PoF mapping is an accurate representation of the true constraint function. By multiplying the EI by the PoF, the search is restricted to the feasible regions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n", "[0.44676004 0.4866224 ]\n", "name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n", "[8.70831234]\n", "name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:Ga([0.25],[1.])\n", "[1.96119947e-06]\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 5 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting belief again\n", "print(constraint_model)\n", "plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we inspect the sampling distribution, we can see that the amount of samples in the infeasible regions is limited. The optimization has focussed on the feasible areas. In addition, it has been active mostly in two optimal regions." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x1b941465dd8>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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p1/PK5iDeVyS3lK9aj5bWzheHltY2lK9a71KLiOy17qUbsHbdFty1sBK79xhfrKSnjW2iKCLBaMVTNoiMqqwKLQc4a9Jo9M/Pw+6GJpSvWn9gO1EQrXvpBpROuANr121BVVWVocds8NFRLrp28wWWHMeNx0ex+zR98PFTFAQiwkdHed3dI5fj7pHL3W4GUVwcdEPphKHoAQxG8gMGI6UDhqJHsGokP2DVSEHHUPQYBiP5AYORgoqh6EEMRvIDVo0URAxFj2J3KvkFg5GChKHocQxG8gMGIwUFQ9EH4lWNyc5RJLIau1MpCFwNRRGZICLVIrJVRG6M8vXpIlInIm+F3y51o51ewaqR/IDBSH7m2jJvItIDwB8AjANQA+ANEVmhqv/ssutyVb3C8QZ6VCQYrVoNh8gOkWDkajjkN25WiqcC2Kqq21S1FcAyAFNcbI+v3D1yObtOXfabn5Wi6t7ZeGvhHFTdOxu/+Vmp203yHFaN5LqKCmDIEBQDxUZ2dzMUjwTQ8bERNeFtXf1MRN4WkadFJOYq2iIyU0Q2icimuro6q9vqSW9PvtXtJqSt3/ysFBf86CT07JEBEUHPHhm44EcnMRij4L1Gck1FBTBzJrBjh+GXuBmKEmVb19XJVwIYoqojALwC4NFYB1PVxapaoqolffv2tbCZ3vb25FsZji449/SREOn8IywiOPf0kS61yPsYjOS4uXOB5mZTL3EzFGsAdKz8BgCo7biDqn6hqt+EP30IBsvfdMRgdFaPjGh/08XeTiGsGslRO3eafombofgGgGEiMlREsgBMBbCi4w4ickSHT88CsMXB9vmOmaoxlcdGEfBte/RHrsXaTp0xGMkRgwaZfolroaiq+wFcAeBlhMLuSVV9T0RuFZGzwrvNEpH3RGQzgFkAprvTWn9h1Wi/p1/fjK7PIlVVPP36Zpda5D8MRrLdggVAbq6pl/AhwwE3YuXNUbenWim6/ZDh/OpWAEBDUZZrbfjNz0px7ukj0SND8G17KBB/95d1rrXHzzh1g2xTUQHMnYuSHTuwSTXh/Q2GYhqIFox+DsVIIHbkZjiSNRiMZCcRqVLVkkT7cZm3NBCkEarRAjHedvIPDsIhL2AophG/B2Oi4GMwBgODkdzEUEwzb0++1ZcjT40GHoMxGBiM5BaGYhraNusat5tgKwYjESWLoUhERBTGUCRHlBUXoXLeDLy5cA4q581AWbG9i5mzWvQ/dqGSGxiKacrJLtSy4iLMmzoOhQW9kCGCwoJemDd1nO3BSP7HYCSnMRTT2LZZ1yQdjk1Htxved9ak0cjJyuy0LScrE7MmjU7q3EaxWgwGTtUgJzEUyfaqsX9+nqntRNEwGMkJPd1uAHlDJBiPKr/H8mPvbmhCYUGvqNvtll/dytVuHFZWXIRZk0ajf34edjc0oXzVelRWVVty7EgwcvUbsgsrRerEjqqxfNV6tLS2ddrW0tqG8lXrLT8Xucup+8esGskuDEXqxupgrKyqxvxla1Bb34h2VdTWN2L+sjWmqgdWe/7g5P1j3mskO7D7lKKyuju1sqrasi60MaXDcdklZ+Dwvr3wWV0jHnrkr1i7LvajNtmF6hw37h+PnH0fu1PJMqwUKa54VaOZEahWaCjKwpjS4bhuThn69+uNjAxB/369cd2cMowpHe5oWyi6WPeJ7b5/zIqRrMJQpIRSmbphtcsuOQPZ2Z2757KzM3HZJWe41CLqyM37x+xOJSswFMmweMHo1Io1h/ftPoo13nZylhX3j1PFYKRU8J4imRLtXmNkxGFkgEVkxCEAyy+Gu7+MPr3js7pG08eyc+pAOrPy/nGyOHWDksVKkZLSsWp0csRh+ar12Levc/fcvn1teOiRv5o6DpeeSw+sGskshiIlLRKMTo44rKyqxi1PrsHuPXvR3q7YvWcv7lpYGXf0aTRuLT1HzuO9RjIjmN2nVVXAkCHAggXAtGlutybQ4nVP2TXisLKqGhufeCelY3DpufTDqRtkRHArxR07gJkzgYoKt1uSluwecZjqvEO3pg6Qu1g1UiLBDUUAaG4G5s51uxVpx6kRh6kEI5eeS28MRorF1VAUkQkiUi0iW0XkxihfP0hEloe//ncRGWL6JDt3WtBSMqOwoJdjow+TDUYvTB2wk9MPdfYjBiNFI6rqzolFegD4AMA4ADUA3gBwoar+s8M+vwYwQlUvF5GpAH6qqhckOnaJiG6KfDJ4MLB9u9XNJ4OcuvDEe3Ziui3x1nWKDBCqgoMU+lbjvcbgE5EqVS1JtJ+bleKpALaq6jZVbQWwDMCULvtMAfBo+OOnAYwRETF8htzc0GAbco1TF5uGoqyo4ZdugQhwZG0yWDVShJujT48EsKvD5zUAfhBrH1XdLyJ7ARwG4POuBxORmQBmAsAgIFQhcvSpJ0SC0YkLTzqGYFd+HVkbrdp38v+TE/4JcLdSjFbxde3LNbJPaKPqYlUtUdWSvsXFoS5TBqKn8GLjDD+OrI3V/R2vW9wurBrTm5uhWANgYIfPBwCojbWPiPQE0BtAvSOts1pFRWjuZEZG6H2aThVhMNrPbyNrEwWfW8HIcExPbobiGwCGichQEckCMBXAii77rABwcfjjcwG8qm6NDEpFRUVozuSOHYBq2s+hZDDaK4gja90IRoBVYzpybfQpAIjIRAALAfQA8LCqLhCRWwFsUtUVIpIN4DEAJyNUIU5V1W2JjltSUqKbNm1KtJtzhgwJBWFXaTwylhcbijATeG7dM+Yfcv5ndPSpq8u8qeqLAF7ssu3mDh/vA3Ce0+2yXKy5kpxDSWRKfnUrB1ORrYK9oo1XDBpkbnsa4F/elCy3ulIpPTAUnbBgQWjOZEecQ4nNi65iOKY5vwQcu/vTB0PRCdOmAYsXh+4hioTeL17MKSNhDEYyi1M1yC6uDrSxi+cG2pBhvPCkl1TDzcn7i01Htx/4uONDtskf/LDMG1E3rBrJq/I++u5yeVT5PS62hOzEUCTPYTCSUW7ekzyq/B6GYwAxFMmTOAgnPQRhegWDMVgYiuRpDEZKxAsjWFk1BgdDkTyPVSN5Rcf7itEwGP2PoUi+wWCkWLxQLUYwGP2NoUi+wqqR/IDdqf7FUCRfYjCSHzAY/YeT98n3OOHf/6zo/nRrIr9RnPDvLk7ep7TBqpEAb91XjIZVoz8wFCkQGIzkB7zX6H1xQ1FEeonI0VG2j7CvSUTJ4SAcSlVZcREq583AmwvnoHLeDJQVF9lyHgajgyoqgCFDUAwUG9k9ZiiKyPkA3gfwFxF5T0RO6fDlJam1ksg+DEb/8cLKNmXFRZg3dRwKC3ohQwSFBb0wb+o4W4LxkKF7MWLlzYl3pNRUVAAzZwI7dhh+SbxK8SYAxap6EoBLADwmIueEvybJt5LIfqwa01Mq9xVnTRqNnKzMTttysjIxa9LoVJsV04iVNzMc7TR3LtDcbOol8UKxh6p+CgCq+r8AzgQwV0RmAQjekFUKJAYjGdU/P8/w9kQr25jFYLTJzp2mXxLvf7ap4/3EcECWApgC4HjTZyJyCatGf3C7C3V3Q5Op7VZj1WiDQYNMvyReKP4KQIaIHBfZoKpNACYAuNT0mYhcxmCkeMpXrUdLa1unbS2tbShftd7RdjAYLbRgAZCba+olMUNRVTer6ocAnhSRGyQkB8C9AH6dWkuJ3MFgpFgqq6oxf9ka1NY3ol0VtfWNmL9sDSqrqh1vC6tGi0ybBixeDAwebPglCVe0EZGDAdyB0HDWPAAVAO5QVfNLOjiEK9qQEVwJx5tSnYTvVDdsMqvaRBwydK/hfd+efGvS56HvWLmiTRuAFgA5ALIBfJxqIIpIgYisEZEPw+/zY+z3rYi8FX5bkco5ibpi1Uh2OuuYY/Ha9Eux9cqr8dr0S3HWMccmdRxWjM4yEopvIBSKpwAYDeBCEXk6xfPeCGCtqg4DsDb8eTQtqnpS+O2sFM9J1A2D0XvcHnBjhbOOORa3jRmPAb16I0MEA3r1xm1jxicdjOQcI6E4Q1VvVtU2Vd2tqlMAPJ/ieacAeDT88aMAzk7xeEREnnHtqNHIzew85zE3MxPXjrJvziNZI2Eoqmq3m3Oq+liK5+3XYQ7kpwAOj7FftohsEpGNIhI3OEVkZnjfTXV1dSk2j8g5Ti0t5id+rxYL83qZ2p4Iu1CdY9uC4CLyioi8G+VtionDDArfGP05gIXR1mGNUNXFqlqiqiV9+/ZNuf2UPtzsQnVyaTG/8XMw1jY1mtpuBIPRGbaFoqqOVdUTorw9D2CPiBwBAOH3n8U4Rm34/TYA6wCcbFd7Kb25NcHfjaXFgswrQXr3hvVobus857G5rQ13b0htziOnatjPrUdHrQBwcfjjixHlHqWI5IvIQeGP+wA4HcA/HWshpSWng9HM0mLpyCshZ9aKD97HTWtXo6ZxL9pVUdO4FzetXY0VH7yf8rHHD6rGtZsvsKCVFE1Pl857O0KLAswAsBPAeQAgIiUALlfVSwEMB/CgiLQjFN63qypDkWy3edFVjs1h3N3QhMKC7veZnFpazA8airIMzV10OkDzPsqIO1dxxQfvWxKCsUSC8e6Ry207RzpypVJU1S9UdYyqDgu/rw9v3xQORKjqBlU9UVVHht//yY22UnpyqjvVK0uLeZ1fK0YnsGq0llvdp0S+YHcwemlpMa+LF4zpEprjB0X/ubh28wUMR4skXObNj7jMG9mBy8J5R6Q71e0wTHapNzPLvHUUKxQ7YndqdFYu80ZE4Oo3XtJQlOV6IHoVK8bUMBSJTOCzGckP2J2aPIYiURIYjOQHDEbz3JqSQeR7kWBMp3uNZcVFmDVpNPrn52F3QxPKV63noCCP49QNc1gpEqUoXapGLknnb6wajWEoElkgHYKRS9L5H+81JsZQJLJI0AfhcEm6YLgofwM27xzodjM8i6FIZLGgBmOspee4JJ0/MRijYygS2SCIVWM6LEmXbs+23LxzIMOxC4YikY2CFIxBX5IuHQYSXZS/Iep2BuN3OCWDyGZBmrpRWVUdmBDsKt5AIqv/zV993Dvppd7sEgnGkYN2udwSd7FSJHJIkKrGIOJAopB0rxoZikQOYjB6FwcSfSed7zUyFIkcFsRBOEGQDgOJzErHYGQoErmEwegtQR9IlKx0qxo50IaIKCzIA4lStXnnwLQYhMNKkYiIDEmHipGVIpGLNi+6KhBTNcgZJx96OsqOuBD5mYehoe0LVH66FG9++brbzQoUVopELuO9RTKirPBEnDdwJgqy+kIkAwVZfXHewJk4+dDT3W5aoAQyFD98O/j93hQsHJFKicwePg5ZGdmdtmVlZKPsiAsNH+OxhlEptyPoXaiBDEUAKBswC2UDZrndDCJTGIwUS/+c3lG352ce5nBLgh2MroSiiJwnIu+JSLuIlMTZb4KIVIvIVhG5MZlzMRjJbxiM6eGrj6OHXCy7W6IvC9fQ9oUVzTEtqFM13KoU3wVwDoDXYu0gIj0A/AFAGYDjAFwoIsclczIGI/kNu1Opq0Vb1qBlf2unba3t+1D56VJTx7GiC7WjoAWjK6GoqltUNdFkoFMBbFXVbaraCmAZgCnJnpPdqeRHDEaKqKx9B7dsfh71rXVQbUd9ax2e2rXYE6NPg1Q1enlKxpEAOo6YqQHwg1g7i8hMADMBILtH7AV8ywbMQmVNuUVNJLJfkJ6yQamprH0HlbXvYPwgby4wEIQJ/rZViiLyioi8G+XNaLUnUbZprJ1VdbGqlqhqSVZGTtwDl064A6UT7jDYDCJvYNVIVrG6C7Ujv1eMtoWiqo5V1ROivD1v8BA1ADp+dwcAqLWibTnvfgIADEbyHd5rDBazg238ws/dqV6ekvEGgGEiMlREsgBMBbDCqoN3DEaGI/kNg9Eb8j5y5xK6emdRysews1qM8GMwujUl46ciUgPghwBeEJGXw9sLReRFAFDV/QCuAPAygC0AnlTV94wcf8ixR+CFnYuwZOMtKD272FCbGIzkN+lQNZYVF6Fy3gy8uXAOKufNQFlx6mFA33EiGK/dfAGu3XyB7eexilujT59V1QGqepCq9lPVn4S316rqxA77vaiqx6jq0aq6wOjxM7N6IiND0G9AAWbfeWHMYIxUixEMRvKjoAZjWXER5k1rw6J3AAAegUlEQVQdh8KCXsgQQWFBL8ybOo7BaDEnghGAb4LRy92nlsjOzcL0GyfH/Hq0YGQ4kt8EMRhnTRqNnKzMTttysjIxa9Jol1pkj2TvK1rRhWq3roHrh6ox8KEIAH0L8+N+vWswAqwayX+C1p3aPz/61KpY2yl5dlSL8Y7p5WBMi1Csq21IuE+sYGQ4kt8EJRh3NzSZ2p6OrKwWH2sY5VhXKuDdYAx8KO5rbsWS21ca2jdaMAKsGsl/glA1lq9aj5bWtk7bWlrbUL5qvUstso+XpmZYEYxGj+HF7tRAhmJb6360tyv21NRj0fVLse65qpSPyaqR/MjPwVhZVY35y9agtr4R7aqorW/E/GVrUFnlzdVc3GLHvcVUqsZkXuelYBTVmIvE+FbvrH466vDkv8ktJxwZ9+vrXroh6WOTSRUVwNy5wM6dwKBBwIIFwLRpbrfKl7hMnH2ajm5P+RiHDI3+FAyj7Fz67aL8DQn3sarr9e6Ryy05TlciUqWqMZ/KdGC/IIZiSUmJvvDcaiy5fWXSVSKD0QMqKoCZM4Hm5u+25eYCixczGJPEYLSHFaEIpBaMXl0PNRl2BGPah+KmTZuwr7k1pe7TRMEIMBxtNWQIsGNH9+2DBwPbtzvdmsBgMFrPC6EIBCsYV+8swtuTb7XseEZDMZD3FCMSzVEkj9u509x2MiQIg3CCKtUBN36Yu2jGiJU3O37OQIcikHiOYjyxRqOSQwYNMredTGEwWsetNVDTwYiVNzsajoH/n2xq+Dql1zMYXbRgQegeYke5uaHtZAlWjd7DajE6p4Ix8KF4cO8cw4uCJ4NTNWw0bVpoUM3gwYBI6D0H2diCwegtDEb3BD4Ue/bsgWsX/iKlYDRSLTIYbTJtWmhQTXt76D0DkTzKa12oDMbkeOt/0SY9evaI+7QMI4wGI8ORiKxgxSo3DEbz0iIUAWtGohq9v8hgJD+K1YXadHR7zDeyF4OxMycG3aRNKALGRqKWnl2MJRtvMf2Q4m7HYdVIPhQJRqPBx5DszGtdqBFBCkbA3kE33vwftEmip2WUnl2M2XdeiH4DCmI+pNjsaFQGI/nN5kVXYdusa5J6LQPSelYtFh7EYLQjHNMmFI08LWP6jZORnZvVaZsV3a5cQYT8KNlgjEjXcLSjWmQwxmZ1MAY+FFWNPy0jVvdq1+3JzF0cOfs+hiP5TqrBCKRn9chgdJaVVWOgQ3FfcyvuvPLPmH7aLYbWP43VvRptu5lgzK9uPfAxg5H8ZtusaywJRyB9q0erWBmMXg/HZNZxtSIYAxuKyTxLccntK7GvubXTNjMPKTaKVSP5kVXBCKRHONo16MbKBxJ7PRiTkWowBvIpGak8T7H07GJMv3Ey+hbmo662IeHjp4w8SSOioSir2zauJEJ+dFT5PZYez6ujNq1gV/in+kSNjrz8dI1kg/urj3t3+kMurR8dlepDhs0wE4pA9GAEGI7kP1YHIxDccGQwJi+VarZjMHo6FEXkPAC3ABgO4FRV3RRjv+0AmgB8C2C/kX8QkDgUzVaDiaRaLUYwGMmPGI7G+CEYAW+GYyrVYsTHs6/19PMU3wVwDoDXDOx7pqqeZDQQEzEyF9FOHQfddMX7jORHVt5rjAjiPUc/3GMEgnWfMZk/GFwJRVXdoqqu/Dlix1xEs1M0EgUjw5H8xo5gBIIXjgzG5KRSvZoNRq/3USiA1SJSJSIz4+0oIjNFZJOIbGptb4m5n9G5iG5jMJLfWDl1oysGY2IMxtjMBKNtoSgir4jIu1Heppg4zOmq+n0AZQD+XUT+JdaOqrpYVUtUtSQrIyfmAc3MRTTDymqxrLgIlfNmoF0VtfWNKbWLyGmsGhPzUzB6KRyduN9pWyiq6lhVPSHK2/MmjlEbfv8ZgGcBnJpqu5yai5issuIizJs6DoUFvZAhgsKCXmhpbXO7WUSmsGpMLO+jDNtWvgly1Th+ULWt4ejZ7lMROVhE8iIfAxiP0ACdlKx7rgqLrl+KPTX1aG83vgScEVZUi7MmjUZOVmbn42Zlora+kV2q5DusGhPzU9XoJXYFo1tTMn4K4H4AfQF8CeAtVf2JiBQC+G9VnSgiRyFUHQJATwBPqOoCI8d3cp5iV6nOW3xz4RxkiHTbr10V37v/XgD2XWiI7GTH1A0gONM3OGUjOUbD+p2z/q93p2So6rOqOkBVD1LVfqr6k/D2WlWdGP54m6qODL8dbzQQ3ZZqtbi7oSnqfrsbmg788h9Vfo9tFxgiu7A7NT47u1ODzOqQDsafWC6x6oHEHYOxfNX6bvcQW1rbUL5qfbfXMRjJb+y61xiUYAS8/YQNIHpldvKhp+Om4b/HXSOW4qbhv8fJh55u2fmMsPI+I0MxSXYtAlBZVY35y9agtr7xwOjT+cvWoLIq9B8elK4iSm8MxvjsqBrtCsaTDz0d5w2ciYKsvhDJQEFWX5w3cKbjwQhErxrLCk/ES2OuRnFxsaGLM9c+TdKSjbeg34CCbtv31NRj+mm3mL63CMRfAq6ryAWA9xfJz7hEXGJWh71V9xgjAXTT8N+jIKtvt6/Xt9bhti1XWHKuZKzeWYSywhNxy8gpyOmZhZKSEmzatKn7gI0ugvXT4yC/LAJA5GV2dKcGqWIErK8arX5YcX7mYVG/Hmu7U8YPqsbs4eOQ09N4sQEwFJNmxyIA8Sb0d8VBN0SxBS0YAWsrYCu7UhvavjC13UlH5PQy/ZpAhmJ7TmbinVKUaBEAs6NQU8VgJOqMwRifVcFY+elStLbv67SttX0fKj9dasnxU5FMMAcyFAHz8wXNsmsRgGSqxQhWjUTBZ9fUjWSs3lmEN798HU/tWoz61jqotqO+tQ5P7VqMN7983e3mRQ3sRAI50Cav9wAt/uGVAJyv2DpKNpjNDLgBov9FzAE45EcceGOOFdVwqgNvvDaZv6uTDz0dZUdciPGjyjjQBggFk91Vo9XMVIuxsGokP+Ifc+ZYEfhBn9z/5pev47YtV6CqqspQN17gQzHCjWB0qkqN94vBYCS/sXpEahDvLXYU5ErYDWn13fRbxWgVBiP5EatG41INxqBXi2akVSgC/glGs12oiX4p2J1KfmRVMAa9WgSCWTFelL+h25vdetp+Bg9qOeFIVwfg2CXvo4yEv/xHld/Dv8DJVyI/r/yjLjEj14BYvvq4t+VP1EhGouDr+vXHGkZZev7g/WlhkB8qRisG3ETDqpH8iH/MGePXijHZStDqKtKf3z2L+HFkaiJmfiEYjOQ3qQzCSYcu1Ihkg9Gte4tWBZoVx0nrUIywKxitOK5d1WIEq0byI1aNifmlYrT6PmGqVaM/vmsOCFLVmMwvA4OR/MauZzOSc+wcOJPssRmKXQQlGJPBYCQ/MhOM6dSFCiT3B7JTXahOjCRN5hwMxSisqBqtDNdkulCT7Tphdyr5kZ8qxvzq1m5vdvJLN6pdzAZjen+3Ekg2HL1SbabyyzBi5c0WtoTIfl7tTjUSgE4FpFc4USUme75ALgie22+gnnqy9U98NjK30a5ANLtIeESy3UUd5yu9PfnWpI5B5JZ4vR1WV0552Zm4dPRwDMg/BNJluemMtuSvr+2ZCdeuNkVNzEqXnsavGzk920y14+CMb0ztb44CPXZAcxdBMjrPuTxpcE2VqpYkOgJDMQWRkHSqMmQwEpkTLRytDsWrxo7AiGGDkZVzCCScij33WXfvcn+2Ne1tP8j4tT7joG8N79sry9yjmQ7r8bWp/c1QVXz5ZRs+r38TOKTzNctoKAa2+zTZADEjSCNWExmx8mZ2qZLvROtOtXqwzYD8Q2wLxMjxrDhmxjfGK8/2b3qkfD43iAgOPTQT+HZw0sdwJRRF5C4ReV9E3haRZ0Xk0Bj7TRCRahHZKiI3Ot1OM8aUDseyRy/Hqy9ej2WPXo4xpcPdbtIBVk7kZTCS39h9r1EEtgViR3Ye2yl2VokRof+L5Lue3aoU1wA4QVVHAPgAwG+67iAiPQD8AUAZgOMAXCgix5k5iRPVIhAKxOvmlKF/v97IyBD079cb180pszwY3bgJHysYGY7kN1YHY1lxESrnzcARBb0wrLAPeudmW3r8aGIF4+OP/Dcm//hHuGHWry0716/OuwiNe/fik527cPYPx0TdZ/qkc7H5H+8kdfwd2z9ByffPSbjP8mUvJnX8ZLkSiqq6WlX3hz/dCGBAlN1OBbBVVbepaiuAZQCmmD2XE8F42SVnIDs7s9O27OxMXHbJGbaf2yg7hmUzGMlvrKoay4qLMG/qOBQW9IIAyOrZA4UFecjLy0m9kQlEC8bljy3BH5c8jjvK/yvua810of7xqcfQq3f8OYs9M4xXr8lUiTt21OLJ5WkQil38K4DKKNuPBLCrw+c14W1RichMEdkkIpv2t9hfond0eN9eprb7TbzJvAxG8qPNi65K6fWzJo1GTlbnP4QzMjLQt88hCV+bl5eNo4b2xTHD+uOooX2Rl2e+wuwYjPNvuh67du7AlZdejD//94Nobv4av712Di6Y/BOcWzYWr65+CQDwya6d+OW5U3D+uHE4f9w4vPXGGwCAuj17MP3ss3HemLH46RmlqNq4EQAwfsRpaPiiHgDw7bf7cdOv5uCnp4/FVRfPREtzS7c2vfbK/8PZZ56LiaPOwuW/uAJff9X9OvzmP/6JH5xyHs484yI8+ODyA9t3bP8E4348HaNOuwCjTrsAG//2FgDg5v9YhA2vv4nTTj0f95c/FnM/K9kWiiLyioi8G+VtSod95gLYD6Ai2iGibIs5fEpVF6tqiaqW9Mw5uNPX7K4WP6trNLU9Fal0oaZSLSYKRoYj+U0qwdg/Py/q9p494w9QycvLRv9+vZGZ2RMigszMnujfr3dSwRgx77Y7cXi/fnh42V/wy0v/DYt/vwg/GDUay1e+jIeX/QX33HYrmpu/RkGfPnjo8eV4cs0a3PXgg7h97m8BAC8+8yxGlZbiqbWv4OlX1+LYE07odo6PP/wI5148Dc++/goOzsvDsj892unr9Z/Xo/zO/8ITq/6MFzeswIiTT8RD9z/c7Tj/NvNm3H3PDfifvz7WaXvfwwuw8sUHsWHjcvz58Ttx7TV3AABu/b+zMer0k7Hxf5/ElbMuirmflWx7nqKqjo33dRG5GMAkAGM0+ryQGgADO3w+AECtdS20zkOP/BXXzSnr1IW6b18bHnrkry62ynpeed4a+VxFBTB3LrBzJzBoELBgATBtmitNiQTjyNn3mXrd7oYmFBZ07wnavz/+VIa+ffKQkdH5j9NQhZmHpiZzUxt67muPOl1jw2vrsG7Ny1jy0B8BAN988w0+/eQTHN6vPxbcfBPe3/IuevTogR3bPgIAHH/SSMy76mrsb9uPH5dNiBqK/Y8sxPdPOwUAMPn8c1Dx4MO45MrLD3z9H2+8hQ/f34pzxlwAAGhta0XxqSd3OsbevU3Y+2UTfvQvoVkRF/58Ela/vB4A0Na2H1fP+R3efrsaPXr0wNYPd0T9NxvdLxWuPGRYRCYAuAHAGaraHGO3NwAME5GhAD4BMBXAz5M9Z0NRlm0DVdau2wIgdG/x8L698FldIx565K8HtntJKg8hJUpZRQUwcybQHP6137Ej9DngWjACoXA0E4zlq9Zj3tRxnbpQ29vbUff5V3FfF6uSTFRhxjxetGBU4L4H/oShR3+v0+Y/3HcXDuvTB89Uvor9md/ilMFDAAAlP/whHnnuWbz2yiu46YorMf3Xv8JZ55/f6bXSdVWCLp+qKn505un4/aMLo7bzsB5f40vV7scJ+3354zi832H4+xtPob29HQW9T01pv1S4dU/x9wDyAKwRkbdE5AEAEJFCEXkRAMIDca4A8DKALQCeVNX3XGpvQmvXbcHUix/AjyfeiakXP+DJQLSCW89bo4CYO/e7QIxobg5td9nmRVcZ7lKtrKrG/GVrsHvPXqiGKpjdexrR1NT9XltHsSrJRBWmGaPOKMUTS/6ESAfclndDo0O/ampC38P7ISMjA6ueehrffhs6Z+2uXSjo0wfn/uIX+OnPL8SWd7qPJv205hO89b9VAIAX//Icvn9a5zD6/iknYdPGKmz/aDsAoKW5Bds+/LjTPoce2gu9eh+CDa//AwCwfNkLB762t/Er9O/fBxkZGXiiYtWBtuXlHYyvmpoT7mclt0affk9VB6rqSeG3y8Pba1V1Yof9XlTVY1T1aFVdkOp5nZqi4XV2LRDMe4uU0M6d5rZ7WGVVNaZe/AD2fLYX2z6uSxiIAFD3eRPa2zv31IQqzKak29F1NOrls67C/v37cc5PzsTZ487A/feE7rtNvWg6nv/Lk/j52ROxY9tHyMnNBQC8seFvOG/MWJw/dhxeeeEFTLv0sm7nOKpoGJ5f+hR+evpY7G34Ehf86y87ff2wvofhngfvxBXTr8L4U/8PppSei60ffNTtOA8uvhVXzfkdzjzjIuRkf3cfdea/nY+Kx1ei9F9+ga0f7sDBB4dG8Z5w4jD06NkDPzjlPNxf/ljM/awUyGXeSkpKdMXqV1G+aj0qq6o7fS0IC+5aEe6pdqEmurfIZeEoqiFDQl2mXQ0eDGzf7nRrojLTjZpf3Yp5N56BwkLjK6jk5WWjb5889OzZA/v3f4u6z5tM30/syuxScEaWfDOy1JvRJd6cmLTf0Ycf7AF6/2unbWm/zFthQS/MmzoOZcVFnbYHoVq0IthTrRYTdaOyaqSoFiwAwhXKAbm5oe0eYbQLNdnfw6amfdj2cR0++HB3uMJMLRCBYKx24xWBDUUAyMnKxKxJo91uRmAZub/IYKROpk0DFi8OVYYiofeLF7s6yCaaVOcxkn8FOhSB6POJglAtWsGph48yGKmTadNCXaXt7aH3HgvECAZjegp8KO5uSP4GNiVmdDQqu1PJj2IFoxfHJrAL1RqBDsWW1jaUr1rvdjM8zalqMYLBSH5jZqqGX5hZAzXdBDYUa+sbMX/Zmm6jTyPYhWods3MXWTWSHwUtGCm6QIbiP3ftQdn8P8UMROrMimoxmUn9DEbymyBWjdRZIEOR/INVI/nRupducLsJcf3hvrvwyIOxHyO19uVKfFTNoiEahiJZJpUl4BiM5DfrXrohqXDMW/kMjjrzFBxz7JE46sxTkLfyGcvaZHSwzaurK/HRBx9adt4gcWVBcPIeqxYKT+VJGpFg5Go45Cf9+xn/YzBv5TPo/9vrkLEvtCRcZu0n6P/b6wAATZPjP4U+kQf+uAjPPfcU+h95JPIPOwzHnTACTy99HE898Rja2towaPBQ/G7h/Xj/vffwP2tW442//w0PLVyIe//03/j7+vX4y2OPo62tDQOHDsFt99+Pgw86KKX2+BUrRR8K+iAhVo3kN0XHHGFov773/u5AIEZk7GtB33t/l9L53333bbzw4vN49rnVWPjgw3h3c+jhu2MnTMTylS/jmZdexVHfG4Znlj2Bk0tOwZnjxuPqm2/GU2tfwcAhQzB24kQsffklPP3qWhw1bBiefWJpSu3xM1aKdIAXqsWIEStvZsVIvhIJxuoPPo25T89Poz8SNtZ2o6o2/R3jxk5ATk4uMrMzcObYnwAAPqx+H/fffQeaGvei+euvMeqMM6O+fuv77+P+O+5A095GNH/9NU4/szSl9vgZQzEgyoqLMGvSaPTPz8Puhqaoi6E7yapgBNidSv5SdMwRMYNx/xGFyKz9JOr2lEV5VuFvr5mNRQ8twbHHHY/nnlqGNzZuiPrS386eg0VLHkHR8cfj+WXL8caG6PulA3afBkBZcRHmTR2HwoJeyBCJuRi6EU5P5jeC3ankN0XHHBG1S7Xu6t+gPbvz447as3NQd/VvUjpfySk/wCtrXsK+fS34+quvsG7tagDA119/jb6HH462tjaseu67AT0HH3wImr/67oHIzV9/hT6H90NbWxteeMa6gT8RX3x7sOXHtIv3roAUV7T7ibMmje70BHDAG4uhW/lAYk7dID/qGoxNk8/B7v+8C22FR0JF0FZ4JHb/510pD7I5/vgRmDjxLJw9ZRzmXD4D3z/lBwCAK665Hj+fMhGX/eJ8HHX09w7sP2Hy2VjyX/+F88eOw67t2/Hv19+AaRMn4t/OvwBDv/e9WKdJC4F8nmJuv4E6bOrVCffz4vqFiUQLxTcXzkFGlK6TdlWcPGdhUuex4t5iRKrdqF2xO5W8ZMuWLRg+fHjC/eLda7SS0WcrJnqmopXPUwScfaYin6eYJmKNOo216HlQF0Nn1Uh+ZHSEqle0f9PD0uP5pQuVoRgA5avWo6W1rdM2Ly2GbmU3akcMRvKbWPca/aqxNdvtJlgubUPRb12n8eYmVlZVY/6yNaitb0S7asLF0I2wesANg5GCzOxtqCAFoxlOVIuh/4vkbwtySkZAVFZVp+0C6JzTSG7Kzs7GF198gcMOOwwS5d5+LPGmblByVBVfftkG9NjRaftjDaMAPGnoGAxFH3BrBRurJvNHWDF3kchrBgwYgJqaGtTV1SV9jN17rPu9aM80Fsxq4OovPRP//u/t2ZZwn452ATg44xtTrzFOgR47oLmLkOwTI10JRRG5C8BkAK0APgJwiap+GWW/7QCaAHwLYL+RkUNERE7KzMzE0KFDUzrG8OFA6YQ7LGmP0T+ijfzBa/SP2PGDTPZStQMX5du3QEDHQAxVica5dU9xDYATVHUEgA8AxJu5eqaqnuSFQBxTOhzLHr0cr754PZY9ejnGlCYehp3K6wBnqsSy4iJUzpuBNxfOQeW8GUlN+jfKlXuLFRXAkCFARkbofUWFLW0gMqXLz+W6iwbY+kiqrr/nZx1zrG3nMsJsWDnFlUpRVVd3+HQjgHOdPH8yg2zGlA7HdXPKkJ0dmiTfv19vXDenDACwdt0Wy1/nlMhqOJHJ/5HVcIDQfUqru1AB+7pRoy4LV1EBzJwJNDeHPt+xI/Q5AEybZnkbiAyJ93Npg2i/57eNGQ8AWPHB+7adN5FIMNpVNSYTvF4YffqvACpjfE0BrBaRKhGx7yfGgMsuOeNAsEVkZ2fiskvOsOV1gDNVoldXw0lFp6px7tzvLjwRzc2h7URucfjnMtrveW5mJq4dZc3v+eqdqfUuealqtG1FGxF5BUD/KF+aq6rPh/eZC6AEwDkapSEiUqiqtSJyOEJdrleq6msxzjcTQCQ4iwBYORSzT3Fx8eBYX6yqqqqK9bXi4uLiZF7nFIfb1wfA5xYfM65iIPa/D3D9+58Cx7+XAeXK99Hpn0sHfs/98PM4WFX7JtrJtWXeRORiAJcDGKOqzQb2vwXAV6p6t91ti3LuTV64p+l3/D5ah99La/D7aI0gfR9d6T4VkQkAbgBwVqxAFJGDRSQv8jGA8QDeda6VRESUbty6p/h7AHkA1ojIWyLyABDqLhWRF8P79AOwXkQ2A/hfAC+o6kvuNJeIiNKBW6NPoz6bRFVrAUwMf7wNwEgn2xXHYrcbEBD8PlqH30tr8PtojcB8HwP56CgiIqJkeGFKBhERkScwFImIiMIYigaJyF0i8r6IvC0iz4rIoW63yY9E5DwReU9E2kUkEEO4nSQiE0SkWkS2isiNbrfHr0TkYRH5TEQ4oj0FIjJQRP5HRLaEf69nu92mVDEUjTOzXivF9i6AcwBEXYSBYhORHgD+AKAMwHEALhSR49xtlW8tATDB7UYEwH4A16jqcACnAfh3v/9MMhQNUtXVqro//OlGAAPcbI9fqeoWVU3PBz+m7lQAW1V1m6q2AlgGYIrLbfKl8MpY9W63w+9U9VNV/Uf44yYAWwAc6W6rUsNQTE689VqJ7HIkQo+ji6iBzy9AFBwiMgTAyQD+7m5LUsOHDHdgYr3W/QD4/KEYjHwfKSnRnpvKOVXkOhE5BMBfAMxR1Ua325MKhmIHqjo23tfD67VOQmi9Vl6MYkj0faSk1QAY2OHzAQBqXWoLEQBARDIRCsQKVX3G7fakit2nBhlZr5XIZm8AGCYiQ0UkC8BUACtcbhOlMRERAH8CsEVV73W7PVZgKBoXdb1WMkdEfioiNQB+COAFEXnZ7Tb5RXig1xUAXkZoQMOTqvqeu63yJxFZCuBvAIpEpEZEZrjdJp86HcBFAH4cvi6+JSIT3W5UKrjMGxERURgrRSIiojCGIhERURhDkYiIKIyhSEREFMZQJCIiCmMoEgWYiLwkIl+KyCq320LkBwxFomC7C6F5ZERkAEORKABE5JTwsz6zReTg8LPtTlDVtQCa3G4fkV9w7VOiAFDVN0RkBYD/BJAD4HFV5QN0iUxiKBIFx60IrY+6D8Asl9tC5EvsPiUKjgIAhyC0Rm+2y20h8iWGIlFwLAbwHwg96/MOl9tC5EvsPiUKABH5JYD9qvqEiPQAsEFEfgxgPoBjARwSfjrJDFXlk0mIYuBTMoiIiMLYfUpERBTGUCQiIgpjKBIREYUxFImIiMIYikRERGEMRSIiojCGIhERUdj/Bxb9+GLcqnmzAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 504x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot function, overlayed by the constraint. Also plot the samples\n", "axes = plotfx()\n", "valid = joint.feasible_data_index()\n", "axes.scatter(joint.data[0][valid,0], joint.data[0][valid,1], label='feasible data', c='w')\n", "axes.scatter(joint.data[0][np.logical_not(valid),0], joint.data[0][np.logical_not(valid),1], label='data', c='r');\n", "axes.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the evolution of the best value over the number of iterations clearly shows a very good solution is already found after only a few evaluations." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 504x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axes = plt.subplots(1, 1, figsize=(7, 5))\n", "f = joint.data[1][:,0]\n", "f[joint.data[1][:,1] > 0] = np.inf\n", "axes.plot(np.arange(0, joint.data[0].shape[0]), np.minimum.accumulate(f))\n", "axes.set_ylabel('fmin')\n", "axes.set_xlabel('Number of evaluated points');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 1 }	apache-2.0
CompPhysics/ComputationalPhysics2	doc/Projects/2018/Project1/ipynb/Project1.ipynb	1	23667	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- dom:TITLE: Project 1, deadline March 23 -->\n", "# Project 1, deadline March 23 \n", "<!-- dom:AUTHOR: [Computational Physics I FYS4411/FYS9411](http://www.uio.no/studier/emner/matnat/fys/FYS4411/index-eng.html) at Department of Physics, University of Oslo, Norway -->\n", "<!-- Author: --> \n", "[Computational Physics I FYS4411/FYS9411](http://www.uio.no/studier/emner/matnat/fys/FYS4411/index-eng.html), Department of Physics, University of Oslo, Norway\n", "\n", "Date: Feb 28, 2018\n", "\n", "Copyright 1999-2018, [Computational Physics I FYS4411/FYS9411](http://www.uio.no/studier/emner/matnat/fys/FYS4411/index-eng.html). Released under CC Attribution-NonCommercial 4.0 license\n", "\n", "\n", "\n", "## Introduction\n", "\n", "\n", "\n", "\n", " The spectacular demonstration of Bose-Einstein condensation (BEC) in gases of\n", " alkali atoms $^{87}$Rb, $^{23}$Na, $^7$Li confined in magnetic\n", " traps has led to an explosion of interest in\n", " confined Bose systems. Of interest is the fraction of condensed atoms, the\n", " nature of the condensate, the excitations above the condensate, the atomic\n", " density in the trap as a function of Temperature and the critical temperature of BEC,\n", " $T_c$. \n", "\n", " A key feature of the trapped alkali and atomic hydrogen systems is that they are\n", " dilute. The characteristic dimensions of a typical trap for $^{87}$Rb is\n", " $a_{h0}=\\left( {\\hbar}/{m\\omega_\\perp}\\right)^\\frac{1}{2}=1-2 \\times 10^4$\n", " \\AA\\ . The interaction between $^{87}$Rb atoms can be well represented\n", " by its s-wave scattering length, $a_{Rb}$. This scattering length lies in the\n", " range $85 < a_{Rb} < 140 a_0$ where $a_0 = 0.5292$ \\AA\\ is the Bohr radius.\n", " The definite value $a_{Rb} = 100 a_0$ is usually selected and\n", " for calculations the definite ratio of atom size to trap size \n", " $a_{Rb}/a_{h0} = 4.33 \\times 10^{-3}$ \n", " is usually chosen. A typical $^{87}$Rb atom\n", " density in the trap is $n \\simeq 10^{12}- 10^{14}$ atoms per cubic cm, giving an\n", " inter-atom spacing $\\ell \\simeq 10^4$ \\AA. Thus the effective atom size is small\n", " compared to both the trap size and the inter-atom spacing, the condition\n", " for diluteness ($na^3_{Rb} \\simeq 10^{-6}$ where $n = N/V$ is the number\n", " density). \n", "\n", "Many theoretical studies of Bose-Einstein condensates (BEC) in gases\n", "of alkali atoms confined in magnetic or optical traps have been\n", "conducted in the framework of the Gross-Pitaevskii (GP) equation. The\n", "key point for the validity of this description is the dilute condition\n", "of these systems, that is, the average distance between the atoms is\n", "much larger than the range of the inter-atomic interaction. In this\n", "situation the physics is dominated by two-body collisions, well\n", "described in terms of the $s$-wave scattering length $a$. The crucial\n", "parameter defining the condition for diluteness is the gas parameter\n", "$x(\\mathbf{r})= n(\\mathbf{r}) a^3$, where $n(\\mathbf{r})$ is the local density\n", "of the system. For low values of the average gas parameter $x_{av}\\le 10^{-3}$, the mean field Gross-Pitaevskii equation does an excellent\n", "job. However,\n", "in recent experiments, the local gas parameter may well exceed this\n", "value due to the possibility of tuning the scattering length in the\n", "presence of a so-called Feshbach resonance.\n", "\n", "\n", "\n", "Thus, improved many-body methods like Monte Carlo calculations may be\n", "needed. The aim of this project is to use the Variational Monte Carlo\n", "(VMC) method and evaluate the ground state energy of a trapped, hard\n", "sphere Bose gas for different numbers of particles with a specific\n", "trial wave function.\n", "\n", " This trial wave function is used to study the sensitivity of\n", " condensate and non-condensate properties to the hard sphere radius\n", " and the number of particles. The trap we will use is a spherical (S)\n", " or an elliptical (E) harmonic trap in one, two and finally three\n", " dimensions, with the latter given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"trap_eqn\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " V_{ext}(\\mathbf{r}) = \n", " \\Bigg\\{\n", " \\begin{array}{ll}\n", "\t \\frac{1}{2}m\\omega_{ho}^2r^2 & (S)\\\\\n", " \\strut\n", "\t \\frac{1}{2}m[\\omega_{ho}^2(x^2+y^2) + \\omega_z^2z^2] & (E)\n", "\\label{trap_eqn} \\tag{1}\n", " \\end{array}\n", " \\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where (S) stands for symmetric and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto1\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " H = \\sum_i^N \\left(\\frac{-\\hbar^2}{2m}{\\bigtriangledown }_{i}^2 +V_{ext}({\\mathbf{r}}_i)\\right) +\n", "\t \\sum_{i<j}^{N} V_{int}({\\mathbf{r}}_i,{\\mathbf{r}}_j),\n", "\\label{_auto1} \\tag{2}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "as the two-body Hamiltonian of the system. Here $\\omega_{ho}^2$\n", " defines the trap potential strength. In the case of the elliptical\n", " trap, $V_{ext}(x,y,z)$, $\\omega_{ho}=\\omega_{\\perp}$ is the trap\n", " frequency in the perpendicular or $xy$ plane and $\\omega_z$ the\n", " frequency in the $z$ direction. The mean square vibrational\n", " amplitude of a single boson at $T=0K$ in the trap ([trap_eqn](#trap_eqn)) is\n", " $\\langle x^2\\rangle=(\\hbar/2m\\omega_{ho})$ so that $a_{ho} \\equiv\n", " (\\hbar/m\\omega_{ho})^{\\frac{1}{2}}$ defines the characteristic length\n", " of the trap. The ratio of the frequencies is denoted\n", " $\\lambda=\\omega_z/\\omega_{\\perp}$ leading to a ratio of the trap\n", " lengths $(a_{\\perp}/a_z)=(\\omega_z/\\omega_{\\perp})^{\\frac{1}{2}} =\n", " \\sqrt{\\lambda}$.\n", "\n", " We will represent the inter-boson interaction by a pairwise,\n", " repulsive potential" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto2\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " V_{int}(\|\\mathbf{r}_i-\\mathbf{r}_j\|) = \\Bigg\\{\n", " \\begin{array}{ll}\n", "\t \\infty & {\|\\mathbf{r}_i-\\mathbf{r}_j\|} \\leq {a}\\\\\n", "\t 0 & {\|\\mathbf{r}_i-_r\\mathbf{r}_j\|} > {a}\n", " \\end{array}\n", "\\label{_auto2} \\tag{3}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $a$ is the so-called hard-core diameter of the bosons.\n", " Clearly, $V_{int}(\|\\mathbf{r}_i-\\mathbf{r}_j\|)$ is zero if the bosons are\n", " separated by a distance $\|\\mathbf{r}_i-\\mathbf{r}_j\|$ greater than $a$ but\n", " infinite if they attempt to come within a distance $\|\\mathbf{r}_i-\\mathbf{r}_j\| \\leq a$.\n", "\n", " Our trial wave function for the ground state with $N$ atoms is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"eq:trialwf\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " \\Psi_T(\\mathbf{r})=\\Psi_T(\\mathbf{r}_1, \\mathbf{r}_2, \\dots \\mathbf{r}_N,\\alpha,\\beta)=\\prod_i g(\\alpha,\\beta,\\mathbf{r}_i)\\prod_{i<j}f(a,\|\\mathbf{r}_i-\\mathbf{r}_j\|),\n", "\\label{eq:trialwf} \\tag{4}\n", " \\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\alpha$ and $\\beta$ are variational parameters. The\n", " single-particle wave function is proportional to the harmonic\n", " oscillator function for the ground state, i.e.," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto3\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " g(\\alpha,\\beta,\\mathbf{r}_i)= \\exp{[-\\alpha(x_i^2+y_i^2+\\beta z_i^2)]}.\n", "\\label{_auto3} \\tag{5}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For spherical traps we have $\\beta = 1$ and for non-interacting\n", " bosons ($a=0$) we have $\\alpha = 1/2a_{ho}^2$. The correlation wave\n", " function is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto4\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " f(a,\|\\mathbf{r}_i-\\mathbf{r}_j\|)=\\Bigg\\{\n", " \\begin{array}{ll}\n", "\t 0 & {\|\\mathbf{r}_i-\\mathbf{r}_j\|} \\leq {a}\\\\\n", "\t (1-\\frac{a}{\|\\mathbf{r}_i-\\mathbf{r}_j\|}) & {\|\\mathbf{r}_i-\\mathbf{r}_j\|} > {a}.\n", " \\end{array}\n", "\\label{_auto4} \\tag{6}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Project 1 a): Local energy\n", "\n", "Find the analytic expressions for the local energy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"eq:locale\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " E_L(\\mathbf{r})=\\frac{1}{\\Psi_T(\\mathbf{r})}H\\Psi_T(\\mathbf{r}),\n", "\\label{eq:locale} \\tag{7}\n", " \\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "for the above \n", " trial wave function of Eq. ([eq:trialwf](#eq:trialwf)). \n", "Find first the local energy the case with only the harmonic oscillator potential, that is we set $a=0$.\n", "Use first that $\\beta =1$ and find the relevant local energies in one, two and three dimensions for one and\n", "$N$ particles with the same mass. \n", "\n", " Compute also the analytic expression for the drift force to be used in importance sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- Equation labels as ordinary links -->\n", "<div id=\"_auto5\"></div>\n", "\n", "$$\n", "\\begin{equation}\n", " F = \\frac{2\\nabla \\Psi_T}{\\Psi_T}.\n", "\\label{_auto5} \\tag{8}\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find first the equivalent expressions for the just the harmonic oscillator part in one, two and three dimensions\n", "with $\\beta=1$. \n", "\n", "Next, we will find the local energy for the full problem in three dimensions.\n", "The tricky part is to find an analytic expressions for the derivative of the trial wave function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{1}{\\Psi_T(\\mathbf{r})}\\sum_i^{N}\\nabla_i^2\\Psi_T(\\mathbf{r}),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with the above \n", "trial wave function of Eq. ([eq:trialwf](#eq:trialwf)).\n", "We rewrite (and we can use the same general expressions for projects 2 and 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Psi_T(\\mathbf{r})=\\Psi_T(\\mathbf{r}_1, \\mathbf{r}_2, \\dots \\mathbf{r}_N,\\alpha,\\beta)=\\prod_i g(\\alpha,\\beta,\\mathbf{r}_i)\\prod_{i<j}f(a,\|\\mathbf{r}_i-\\mathbf{r}_j\|),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\Psi_T(\\mathbf{r})=\\prod_i g(\\alpha,\\beta,\\mathbf{r}_i)\\exp{\\left(\\sum_{i<j}u(r_{ij})\\right)}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have defined $r_{ij}=\|\\mathbf{r}_i-\\mathbf{r}_j\|$\n", "and" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "f(r_{ij})= \\exp{\\left(\\sum_{i<j}u(r_{ij})\\right)},\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "with $u(r_{ij})=\\ln{f(r_{ij})}$.\n", "We have also" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "g(\\alpha,\\beta,\\mathbf{r}_i) = \\exp{-\\alpha(x_i^2+y_i^2+\\beta z_i^2)}= \\phi(\\mathbf{r}_i).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show that the first derivative for particle $k$ is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\nabla_k\\Psi_T(\\mathbf{r}) = \\nabla_k\\phi(\\mathbf{r}_k)\\left[\\prod_{i\\ne k}\\phi(\\mathbf{r}_i)\\right]\\exp{\\left(\\sum_{i<j}u(r_{ij})\\right)}+ \n", "\\prod_i\\phi(\\mathbf{r}_i)\\exp{\\left(\\sum_{i<j}u(r_{ij})\\right)}\\sum_{j\\ne k}\\nabla_k u(r_{ij}),\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and find the final expression for our specific trial function.\n", "The expression for the second derivative is (show this)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\frac{1}{\\Psi_T(\\mathbf{r})}\\nabla_k^2\\Psi_T(\\mathbf{r})=\n", " \\frac{\\nabla_k^2\\phi(\\mathbf{r}_k)}{\\phi(\\mathbf{r}_k)}+\n", "\\frac{\\nabla_k\\phi(\\mathbf{r}_k)}{\\phi(\\mathbf{r}_k)}\\left(\\sum_{j\\ne k}\\frac{(\\mathbf{r}_k-\\mathbf{r}_j)}{r_{kj}}u'(r_{ij})\\right)+\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\sum_{ij\\ne k}\\frac{(\\mathbf{r}_k-\\mathbf{r}_i)(\\mathbf{r}_k-\\mathbf{r}_j)}{r_{ki}r_{kj}}u'(r_{ki})u'(r_{kj})+\n", "\\sum_{j\\ne k}\\left( u''(r_{kj})+\\frac{2}{r_{kj}}u'(r_{kj})\\right).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use this expression to find the final second derivative entering the definition of the local energy. \n", "You need to get the analytic expression for this expression using the harmonic oscillator wave functions\n", "and the correlation term defined in the project.\n", "\n", "\n", "### Project 1 b): Developing the code\n", "\n", "Write a Variational Monte Carlo program which uses standard\n", " Metropolis sampling and compute the ground state energy of a\n", " spherical harmonic oscillator ($\\beta = 1$) with no interaction and\n", " one dimension. Use natural units and make an analysis of your\n", " calculations using both the analytic expression for the local\n", " energy and a numerical calculation of the kinetic energy using\n", " numerical derivation. Compare the CPU time difference. The only\n", " variational parameter is $\\alpha$. Perform these calculations for\n", " $N=1$, $N=10$, $100$ and $500$ atoms. Compare your results with the\n", " exact answer. Extend then your results to two and three dimensions\n", " and compare with the analytical results.\n", "\n", "### Project 1 c): Adding importance sampling\n", "\n", "We repeat part b), but now we replace the brute force Metropolis algorithm with\n", "importance sampling based on the Fokker-Planck and the Langevin equations. \n", "Discuss your results and comment on eventual differences between importance sampling and brute force sampling.\n", "Run the calculations for the one, two and three-dimensional systems only and without the repulsive potential. \n", "Study the dependence of the results as a function of the time step $\\delta t$. \n", "Compare the results with those obtained under b) and comment eventual differences.\n", "\n", "### Project 1 d): A better statistical analysis\n", "\n", "In performing the Monte Carlo analysis we will use the \n", " blocking and bootstrap techniques to make the statistical analysis of the\n", " numerical data. Present your results with a proper evaluation of the\n", " statistical errors. Repeat the calculations from exercise c) and\n", " include a proper error analysis. Limit yourself to the\n", " three-dimensional case only.\n", "\n", "### Project 1 e): The repulsive interaction\n", "\n", " * [e)] We turn now to the elliptic trap with a repulsive\n", "\n", " interaction. We fix, as in Refs. [1,2] below,\n", " $a/a_{ho}=0.0043$. Introduce lengths in units of $a_{ho}$,\n", " $r\\rightarrow r/a_{ho}$ and energy in units of $\\hbar\\omega_{ho}$.\n", " Show then that the original Hamiltonian can be rewritten as" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "H=\\sum_{i=1}^N\\frac{1}{2}\\left(-\\nabla^2_i+x_i^2+y_i^2+\\gamma^2z_i^2\\right)+\\sum_{i<j}V_{int}(\|\\mathbf{r}_i-\\mathbf{r}_j\|).\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the expression for $\\gamma$? Choose the initial value for\n", " $\\beta=\\gamma = 2.82843$ and set up a VMC program which computes the\n", " ground state energy using the trial wave function of\n", " Eq. ([eq:trialwf](#eq:trialwf)) using only $\\alpha$ as variational\n", " parameter. Use standard Metropolis sampling and vary the parameter\n", " $\\alpha$ in order to find a minimum. Perform the calculations for\n", " $N=10,50$ and $N=100$ and compare your results to those from the\n", " ideal case in the previous exercises. In actual calculations\n", " employing the Metropolis algorithm, all moves are recast into the\n", " chosen simulation cell with periodic boundary conditions. To carry\n", " out consistently the Metropolis moves, it has to be assumed that the\n", " correlation function has a range shorter than $L/2$. Then, to decide\n", " if a move of a single particle is accepted or not, only the set of\n", " particles contained in a sphere of radius $L/2$ centered at the\n", " referred particle have to be considered. Benchmark your results with\n", " those of Refs. [1,2].\n", "\n", "### Project 1 f): Finding the best parameter\n", "\n", "Repeat the previous calculations by varying the energy using the\n", "conjugate gradient method or similar methods to obtain the best possible parameter\n", "$\\alpha$. \n", "\n", "### Project 1 g): Onebody densities\n", "\n", "With the optimal parameters for the ground state wave function, compute again the onebody density with and without the Jastrow\n", "factor. How important are the correlations induced by the Jastrow factor?\n", "\n", "\n", "\n", "\n", "# Literature\n", "\n", " 1. J. L. DuBois and H. R. Glyde, H. R., Bose-Einstein condensation in trapped bosons: A variational Monte Carlo analysis, Phys. Rev. A 63, 023602 (2001).\n", "\n", " 2. J. K. Nilsen, J. Mur-Petit, M. Guilleumas, M. Hjorth-Jensen, and A. Polls, Vortices in atomic Bose-Einstein condensates in the large-gas-parameter region, Phys. Rev. A 71, 053610 (2005).\n", "\n", "## Introduction to numerical projects\n", "\n", "Here follows a brief recipe and recommendation on how to write a report for each\n", "project.\n", "\n", " * Give a short description of the nature of the problem and the eventual numerical methods you have used.\n", "\n", " * Describe the algorithm you have used and/or developed. Here you may find it convenient to use pseudocoding. In many cases you can describe the algorithm in the program itself.\n", "\n", " * Include the source code of your program. Comment your program properly.\n", "\n", " * If possible, try to find analytic solutions, or known limits in order to test your program when developing the code.\n", "\n", " * Include your results either in figure form or in a table. Remember to label your results. All tables and figures should have relevant captions and labels on the axes.\n", "\n", " * Try to evaluate the reliabilty and numerical stability/precision of your results. If possible, include a qualitative and/or quantitative discussion of the numerical stability, eventual loss of precision etc.\n", "\n", " * Try to give an interpretation of you results in your answers to the problems.\n", "\n", " * Critique: if possible include your comments and reflections about the exercise, whether you felt you learnt something, ideas for improvements and other thoughts you've made when solving the exercise. We wish to keep this course at the interactive level and your comments can help us improve it.\n", "\n", " * Try to establish a practice where you log your work at the computerlab. You may find such a logbook very handy at later stages in your work, especially when you don't properly remember what a previous test version of your program did. Here you could also record the time spent on solving the exercise, various algorithms you may have tested or other topics which you feel worthy of mentioning.\n", "\n", "## Format for electronic delivery of report and programs\n", "\n", "The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report:\n", "\n", " * Use Devilry to hand in your projects, log in at <http://devilry.ifi.uio.no> with your normal UiO username and password.\n", "\n", " * Upload only the report file! For the source code file(s) you have developed please provide us with your link to your github domain. The report file should include all of your discussions and a list of the codes you have developed. The full version of the codes should be in your github repository.\n", "\n", " * In your github repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters.\n", "\n", " * Still in your github make a folder where you place your codes. \n", "\n", " * In this and all later projects, you should include tests (for example unit tests) of your code(s).\n", "\n", " * Comments from us on your projects, approval or not, corrections to be made etc can be found under your Devilry domain and are only visible to you and the teachers of the course.\n", "\n", "Finally, \n", "we encourage you to work two and two together. Optimal working groups consist of \n", "2-3 students. You can then hand in a common report." ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 2 }	cc0-1.0
mtchem/Twitter-Politics	EDA.ipynb	1	263074	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# imports\n", "import pandas as pd\n", "import numpy as np\n", "import pickle\n", "import re\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The following data was generated using code that can be found on GitHub \n", "##### https://github.com/mtchem/Twitter-Politics/blob/master/data_wrangle/Data_Wrangle.ipynb " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# load federal document data from pickle file\n", "fed_reg_data = r'data/fed_reg_data.pickle'\n", "fed_data = pd.read_pickle(fed_reg_data)\n", "# load twitter data from csv\n", "twitter_file_path = r'data/twitter_01_20_17_to_3-2-18.pickle'\n", "twitter_data = pd.read_pickle(twitter_file_path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "281" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(fed_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*\n", "## In order to explore the twitter and executive document data I will look at the following:\n", "1. Determine the most used hashtags\n", "2. Determine who President Trump tweeted at(@) the most\n", "3. Create a word frequency plot for the most used words in the twitter data and the presidental documents\n", "4. Find words that both data sets have in common, and determine those words document frequency (what percentage of documents those words appear in)\n", "" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[nltk_data] Downloading package stopwords to /home/aregel/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n" ] } ], "source": [ "# imports\n", "import nltk\n", "nltk.download('stopwords')\n", "from nltk.corpus import stopwords\n", "import itertools\n", "from collections import Counter\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "plt.style.use('ggplot')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the most used hashtags and @ tags" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('#MAGA', 25), ('#USAðŸ‡ºðŸ‡¸', 25), ('#MAGAðŸ‡ºðŸ‡¸', 14), ('#FakeNews', 11), ('#TaxReform', 10), ('#HurricaneHarvey', 10), ('#UNGA', 9), ('#AmericaFirstðŸ‡ºðŸ‡¸', 8), ('#MAGA!', 7), ('#APEC2017', 7), ('#WeeklyAddressðŸ‡ºðŸ‡¸', 7), ('#WEF18', 6), ('#LESM', 6), ('#PuertoRico', 6), ('#MakeAmericaGreatAgain', 5), ('#MakeAmericaGreatAgainðŸ‡ºðŸ‡¸', 5), ('#Harvey', 5), ('#USA', 5), ('#ICYMI-', 5), ('#Fake', 4)]\n" ] } ], "source": [ "# find the most used hashtags\n", "hashtag_freq = Counter(list(itertools.chain((twitter_data.hash_tags))))\n", "hashtag_top20 = hashtag_freq.most_common(20)\n", "# find the most used @ tags\n", "at_tag_freq = Counter(list(itertools.chain((twitter_data['@_tags']))))\n", "at_tags_top20 = at_tag_freq.most_common(20)\n", "\n", "print(hashtag_top20)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# frequency plot for the most used hashtags\n", "df = pd.DataFrame(hashtag_top20, columns=['Hashtag', 'frequency'])\n", "df.plot(kind='bar', x='Hashtag',legend=None,fontsize = 15, figsize = (15,5))\n", "plt.ylabel('Frequency',fontsize = 18)\n", "plt.xlabel('Hashtag', fontsize=18)\n", "plt.title('Most Common Hashtags', fontsize = 15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# frequency plot for the most used @ tags\n", "df = pd.DataFrame(at_tags_top20, columns=['@ Tag', 'frequency'])\n", "df.plot(kind='bar', x='@ Tag',legend=None, figsize = (15,5))\n", "plt.ylabel('Frequency',fontsize = 18)\n", "plt.xlabel('@ Tags', fontsize=18)\n", "plt.title('Most Common @ Tags', fontsize = 15)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Top used words for the twitter data and the federal document data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define a list of words that have no meaning, such as 'a', 'the', and punctuation" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# use nltk's list of stopwords\n", "stop_words = set(stopwords.words('english'))\n", "# add puncuation to stopwords\n", "stop_words.update(['.', ',','get','going','one', 'amp','like' '\"','...',\"''\", \"'\",\"n't\", '?', '!', ':', ';', '#','@', '(', ')', 'https', '``',\"'s\", 'rt' ]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Make a list of hashtags and @entites used in the twitter data" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# combine the hashtags and @ tags, flatten the list of lists, keep the unique items\n", "stop_twitter = set(list(itertools.chain((twitter_data.hash_tags + twitter_data['@_tags']))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The federal document data also has some words that need to be removed. The words Federal Registry and the date are on the top of every page so they should be removed. Also, words like 'shall', 'order', and 'act' are used quite a bit but don't convay much meaning, so I'm going to remove those words as well." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "stop_fed_docs = ['united', 'states', '1','2','3','4','5','6','7','8','9','10', '11','12',\n", " '13','14','15','16','17','18','19','20','21','22','23','24','25','26',\n", " '27','28','29','30','31','2016', '2015','2014','federal','shall', '4790',\n", " 'national', '2017', 'order','president', 'presidential', 'sep',\n", " 'register','po','verdate', 'jkt','00000','frm','fmt','sfmt','vol',\n", " 'section','donald','act','america', 'executive','secretary', 'law', \n", " 'proclamation','81','day','including', 'code', '4705','authority', 'agencies', \n", " '241001','americans','238001','year', 'amp','government','agency','hereby',\n", " 'people','public','person','state','american','two','nation', '82', 'sec',\n", " 'laws', 'policy','set','fr','appropriate','doc','new','filed','u.s.c',\n", " 'department','ii','also','office','country','within','memorandum', \n", " 'director', 'us', 'sunday','monday', 'tuesday','wednesday','thursday', \n", " 'friday', 'saturday','title','upon','constitution','support', 'vested',\n", " 'part', 'month', 'subheading', 'foreign','general','january',\n", " 'february', 'march', 'april','may','june','july','august', 'september',\n", " 'october', 'november', 'december', 'council','provide','consistent','pursuant',\n", " 'thereof','00001','documents','11:15', 'area','management',\n", " 'following','house','white','week','therefore','amended', 'continue',\n", " 'chapter','must','years', '00002', 'use','make','date','one',\n", " 'many','12', 'commission','provisions', 'every','u.s.','functions',\n", " 'made','hand','necessary', 'witness','time','otherwise', 'proclaim',\n", " 'follows','thousand','efforts','jan', 'trump','j.',\n", " 'applicable', '4717','whereof','hereunto', 'subject', 'report',\n", " '3—', '3295–f7–p']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create functions that removes the stop words for each of the datasets" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def remove_from_fed_data(token_lst):\n", " # remove stopwords and one letter words\n", " filtered_lst = [word for word in token_lst if word.lower() not in stop_fed_docs and len(word) > 1 \n", " and word.lower() not in stop_words]\n", " return filtered_lst " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def remove_from_twitter_data(token_lst):\n", " # remove stopwords and one letter words\n", " filtered_lst = [word for word in token_lst if word.lower() not in stop_words and len(word) > 1 \n", " and word.lower() not in stop_twitter]\n", " return filtered_lst " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Remove all of the stop words from the tokenized twitter and document data" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# apply the remove_stopwords function to all of the tokenized twitter text\n", "twitter_words = twitter_data.text_tokenized.apply(lambda x: remove_from_twitter_data(x))\n", "# apply the remove_stopwords function to all of the tokenized document text\n", "document_words = fed_data.token_text.apply(lambda x: remove_from_fed_data(x))\n", "\n", "# flatten each the word lists into one list\n", "all_twitter_words = list(itertools.chain(twitter_words))\n", "all_document_words =list(itertools.chain(document_words))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Count how many times each word is used for both datasets" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# create a dictionary using the Counter method, where the key is a word and the value is the number of time it was used\n", "twitter_freq = Counter(all_twitter_words)\n", "doc_freq = Counter(all_document_words)\n", "# determine the top 30 words used in the twitter data\n", "top_30_tweet = twitter_freq.most_common(30)\n", "top_30_fed = doc_freq.most_common(30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the most used words for the twitter data and the federal document data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# frequency plot for the most used Federal Data\n", "df = pd.DataFrame(top_30_fed, columns=['Federal Data', 'frequency'])\n", "df.plot(kind='bar', x='Federal Data',legend=None, figsize = (15,5))\n", "plt.ylabel('Frequency',fontsize = 18)\n", "plt.xlabel('Words', fontsize=18)\n", "plt.title('Most Used Words that Occured in the Federal Data', fontsize = 15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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TNFeVUg4D3lBKuand5vnUJraz8QNqTc/jqbWV/U6nNtNtWD3ZW+NzOoMTqM3rPlvqBPA3AG+krya1HQn1q9SRSX9O7ef3SmqN1c9m2Pd5wBNKKV+hDuJx/mBt3Qy+Tq0x/i51KolnAvcY2OY31Odrn1LK74Eb+5L0VTRNc1Yp5XXA20spW1Ofp79QE4QXA//bNE1/X9CDqH2s/q99Tf9IbZJ4ZtM0x1Nfn+dQJxl/C3AxNQm8XdM072yaZnkp5fvAm0op11MT/texeg3udN5LHbH1m6WU91Frq7ekvidPa5rm08zitRuDWb8G03g1cGIp5RbqqL/XUfuQPgH4t6Zpfj7H8p1C/QxezcqpNU6lfi809I3uO43zgCeVUk6ivgd+1jTNtaWUy4C9Sym/oCZU51BH+z2lPZ53UD8bd6BN1Jum+c8hj2FpqdNkrEcd7fmR1IsZP6I2H+35OrB/KeVs4NfU0XQH+4f+ivq+36+U8mfgz23S+nXgXaWUV1O/h57EylGmJS1U8z36jTdv3ibnRt+opDNss9rIlNS+Uz+mnqAspyY8S/rWv6tdfx21udlJwEP71m9EHc7+SurJ3cfWUIadqYnNtdSTuHOptXKlb5snUq/4/5Z6gn0+NTno32ZDak3ZBW3ZLwe+AjxhYJsPUEePvJo6vcIrWMOopH2P/1Z7TE8dWP6IdvlPp3ncmp7TfdvH336Kx96lPY4/UU8oD6SepJ/cd0yHt8/J9e1rejzwd2s4lgdSk/I/0jdyJVOMFDv4XqIm0x+lJlK/o05T8kQGRqukJow/b497jc8xte/Vye1763rqxYb9mGL0UWoSdkq7Xe99uEPf+rtS+3Nd3W7zQ/pGqqQmsie3x38+teb4ZFYflfTMacq6rH0OrqC+by+iTqfyt7N97WZ4HgbLscrz37d8tddqim1Wew2Y5aisfZ/Pr1A/n3+kfj7fC2w2i9dz2lFJ+16jBjhu4DP6J+DcgW2nGpV0F2rN7PX0jeZJTVx/Qv2uaIA7Nyu/m94KXNg+H5dRE/jH9e3zcuDNs/w+OKPdf0NtMn4l9YLFC2hH5+3bdlPg4+378Spqy41/ah97j77t9qN+h90I3NAu24BaY35l+zp8hpoYrvJ8ePPmbWHdStOMotm6JEmSJGmhso+hJEmSJE04E0NJkiRJmnAmhpIkSZI04UwMJUmSJGnCmRhKkiRJ0oRb7PMYOuSqJEmSpElX1rTBYk8MufTSS9f6MUuXLmXFihUdlMZ4xls4sYxnPONNTrzFfGzGM57x5i/eYj62hRRv2bJls9rOpqSSJEmSNOFMDCVJkiRpwpkYSpIkSdKEMzGUJEmSpAlnYihJkiRJE87EUJIkSZImnImhJEmSJE04E0NJkiRJmnAmhpIkSZI04UwMJUmSJGnCmRhKkiRJ0oRbMt8FmC83P/9J0667YobHrX/4caMvjCRJkiTNI2sMJUmSJGnCmRhKkiRJ0oQzMZQkSZKkCWdiKEmSJEkTzsRQkiRJkiaciaEkSZIkTTgTQ0mSJEmacCaGkiRJkjThTAwlSZIkacKZGEqSJEnShDMxlCRJkqQJZ2IoSZIkSRPOxFCSJEmSJpyJoSRJkiRNOBNDSZIkSZpwJoaSJEmSNOFMDCVJkiRpwpkYSpIkSdKEMzGUJEmSpAlnYihJkiRJE87EUJIkSZImnImhJEmSJE04E0NJkiRJmnAmhpIkSZI04UwMJUmSJGnCLZnP4BFxEXAdcDNwU2buGBGbA58BtgMuAiIzr46IAhwK7A5cD+ybmWfPR7klSZIkaTFZF2oMd8vMHTJzx/b+QcCJmbk9cGJ7H+DxwPbt7QDgg2MvqSRJkiQtQutCYjhoT+DI9v8jgSf3LT8qM5vMPAO4Q0RsNR8FlCRJkqTFZF6bkgIN8LWIaIAPZeZhwJaZeRlAZl4WEVu0224NXNz32OXtssv6dxgRB1BrFMlMli5dOmXgK4Ys8HT7m4slS5Z0sl/jLb54i/nYjGc8481fvMV8bMYznvHmL95iPrbFGG++E8MHZ+albfL39Yg4b4ZtyxTLmsEFbXJ5WG/9ihUrRlDMlUa9P6jJZhf7Nd7ii7eYj814xjPe/MVbzMdmPOMZb/7iLeZjW0jxli1bNqvt5rUpaWZe2v69EvgCsBNwRa+JaPv3ynbz5cC2fQ/fBrh0fKWVJEmSpMVp3hLDiLhdRGzS+x94LPAT4Dhgn3azfYBj2/+PA54TESUidgF+32tyKkmSJEka3nzWGG4JnBYRPwS+B3w5M78CvB14TET8AnhMex/gBOBC4ALgcOCF4y+yJEmSJC0+89bHMDMvBO4/xfKrgEdNsbwBXjSGokmSJEnSRFkXp6uQJEmSJI2RiaEkSZIkTTgTQ0mSJEmacCaGkiRJkjThTAwlSZIkacKZGEqSJEnShDMxlCRJkqQJZ2IoSZIkSRPOxFCSJEmSJpyJoSRJkiRNOBNDSZIkSZpwJoaSJEmSNOFMDCVJkiRpwpkYSpIkSdKEMzGUJEmSpAlnYihJkiRJE87EUJIkSZImnImhJEmSJE04E0NJkiRJmnAmhpIkSZI04UwMJUmSJGnCmRhKkiRJ0oQzMZQkSZKkCWdiKEmSJEkTzsRQkiRJkiaciaEkSZIkTTgTQ0mSJEmacCaGkiRJkjThTAwlSZIkacKZGEqSJEnShDMxlCRJkqQJZ2IoSZIkSRPOxFCSJEmSJpyJoSRJkiRNOBNDSZIkSZpwJoaSJEmSNOGWzHcBImJ94Ezgksx8YkTcDTga2Bw4G3h2Zv4lIjYEjgIeCFwFPC0zL5qnYkuSJEnSorEu1Bi+DPhZ3/13AIdk5vbA1cD+7fL9gasz8x7AIe12kiRJkqQ5mtfEMCK2AZ4AfLi9X4BHAse0mxwJPLn9f8/2Pu36R7XbS5IkSZLmYL5rDP8LeDVwS3v/jsA1mXlTe385sHX7/9bAxQDt+t+320uSJEmS5mDe+hhGxBOBKzPzrIh4RLt4qhrAZhbr+vd7AHAAQGaydOnSKeNfsbYFbk23v7lYsmRJJ/s13uKLt5iPzXjGM978xVvMx2Y84xlv/uIt5mNbjPHmc/CZBwNPiojdgY2ATak1iHeIiCVtreA2wKXt9suBbYHlEbEE2Az43eBOM/Mw4LD2brNixYqRFnrU+4OabHaxX+MtvniL+diMZzzjzV+8xXxsxjOe8eYv3mI+toUUb9myZbPabt6akmbmazNzm8zcDtgb+GZmPhM4CXhqu9k+wLHt/8e192nXfzMzV6sxlCRJkiStnfnuYziV1wCviIgLqH0Ij2iXHwHcsV3+CuCgeSqfJEmSJC0qa92UNCJ+Rh1F9KjM/O0oCpGZJwMnt/9fCOw0xTY3AHuNIp4kSZIkaaVh+hiuB7wLeGtEfImaJH7VZp2SJEmStDCtdVPSzLwn8DDgU8DjgC8Dv46IN0TEXUdcPkmSJElSx4bqY5iZp2XmfsBWwAuoI4f+B/DLiPhaVBuMsJySJEmSpI7MabqKzPwDcDhweETcB/h36gijjwJ+FxEfA/4rMy+Za0ElSZIkSd2Y86ikEbFeROwBvJWVg8OcCpxNHT30/HYye0mSJEnSOmjoGsOI2B54LnVuwTsDK4BDgcMy8+ftNvcEPgO8Gzh+zqWVJEmSJI3cMNNVPAfYH3hIu+hk4F+Bz2fmjf3bZub5EfFf1OamkiRJkqR10DA1hh8DrqTWAh6emResYfufAUcPEUeSJEmSNAbDJIZPA76QmTfNZuPM/C7w3SHiSJIkSZLGYK0Tw8z8bBcFkSRJkiTNj7UelTQi/jMizplh/Q8i4rVzK5YkSZIkaVyGma7in6kDzkznJCCGKo0kSZIkaeyGSQzvRh1QZjrnA389XHEkSZIkSeM2TGJYgDvMsH4zYP3hiiNJkiRJGrdhEsNzgT1mWL8HtdZQkiRJkrQADDNdxUeAD0bEEcCrMvN3ABGxOfBOYFfgpaMroiRJkiSpS8NMV/GhiNgN2A/YJyKWAw2wLbUG8pjMfP9oiylJkiRJ6sowTUnJzL2BZwFfBf4M3Aj8H/CMzHREUkmSJElaQIZpSgpAZn4K+NQIyyJJkiRJmgdD1RhKkiRJkhaPoWoMI2Jj4GnA9sAdqVNY9Gsy88A5lk2SJEmSNAZrnRhGxI7A8cCdWD0h7GkAE0NJkiRJWgCGaUp6CHBb4JnAnYENprjdZlQFlCRJkiR1a5impDsCb8vMo0ddGEmSJEnS+A1TY3gd8NtRF0SSJEmSND+GSQy/ADx21AWRJEmSJM2PYRLDVwNbR8QhEXHXURdIkiRJkjRew/QxXNH+fSDw0oi4hToKab8mMzecU8kkSZIkSWMxTGL4GVZPBCVJkiRJC9RaJ4aZ+awuCiJJkiRJmh/D9DGUJEmSJC0iwzQlJSLWA/amjk66JXBQZv4wIu4A7A6cnJmXjq6YkiRJkqSurHWNYUTcFjgJ+AQQ1OTwju3qPwDvBV4wqgJKkiRJkro1TFPSg4FdgL2A7YDSW5GZNwGfB/5xBGWTJEmSJI3BMInhXsBhmfk54OYp1v+CmjBKkiRJkhaAYRLDrYEfzrD+j8CmwxVHkiRJkjRuwySGvwO2mmH9fYDLhiuOJEmSJGnchhmV9JvAfhHx7sEVEXFX4LnAp9a0k4jYCDgF2LAtxzGZ+fqIuBtwNLA5cDbw7Mz8S0RsCBwFPBC4CnhaZl40RPklSZIkSX2GqTF8A3UU0u8BBwAN8JiIeBM1kbsReOss9vNn4JGZeX9gB+AfI2IX4B3AIZm5PXA1sH+7/f7A1Zl5D+CQdjtJkiRJ0hytdWKYmT8HHkMdjfQt7d/XAP8GXA48JjN/M4v9NJn5h/buBu2tAR4JHNMuPxJ4cvv/nu192vWPiohbR0SVJEmSJA1nqAnuM/N7wH0jYgfg3tTk8BfAmZnZzHY/EbE+cBZwD+D9wC+Ba9ppLwCWUwe7of17cRv/poj4PbXmcsUwxyBJkiRJqoZKDHsy8xzgnDk8/mZgh4i4A/AFapI5qJdoTlU7uFoSGhEHUJu4kpksXbp0ythXDFNgmHZ/c7FkyZJO9mu8xRdvMR+b8YxnvPmLt5iPzXjGM978xVvMx7YY480pMRyVzLwmIk4GdgHuEBFL2lrDbYBL282WA9sCyyNiCbAZdYTUwX0dBhzW3m1WrBhtheKo9wc12exiv8ZbfPEW87EZz3jGm794i/nYjGc8481fvMV8bAsp3rJly2a13VonhhFxI1PU1A1oMnPDNeznTsCNbVJ4W+DR1AFlTgKeSh2ZdB/g2PYhx7X3T2/Xf3Ntmq1KkiRJkqY2TI3hZ1g9MVwC3J06lcSP29uabAUc2fYzXA/IzDw+Is4Fjo6INwM/AI5otz8C+HhEXECtKdx7iLJLkiRJkgasdWKYmc+abl1EPJTaV/CAWeznR8ADplh+IbDTFMtvAPZaq8JKkiRJktZomHkMp5WZpwIfA945yv1KkiRJkroz0sSw9XNgxw72K0mSJEnqQBeJ4UOBGzrYryRJkiSpA8OMSvqMaVZtTh1ZdA/go3MplCRJkiRpfIYZlfQT1FFJp5pw/mbgSOBf51IoSZIkSdL4DJMYPmaKZQ11CokLM/PauRVJkiRJkjROw0xXcWIXBZEkSZIkzY8uBp+RJEmSJC0gwww+c9gQcZrMPHCIx0mSJEmSOjZMH8PnUfsUwuoD0My03MRQkiRJktZBwzQlXQacAxwPPAxYCtwJeDjwZeAHwFbABn2324yisJIkSZKk0RumxvDtwIrM3HNg+anAqRHxNeAdmbnvXAsnSZIkSereMDWGTwSOm2H9se02kiRJkqQFYJjEcCNqc9LpbN1uI0mSJElaAIZJDL8DvCQidh1cEREPBl7SbiNJkiRJWgCG6WP4Clb2JzwDOI866ui9gV2A64BXjqyEkiRJkqROrXWNYWb+BNgR+BywA7Af8Nz2/88BO2bmj0dZSEmSJElSd4apMSQzfwlERKwP3Jk6b+FlmXnzKAsnSZIkSereUIlhT5sIXjKiskiSJEmS5sFQiWFE3B54KfBYYEtgv8w8IyKWAgcAx2Tmz0dXTEmSJElSV9a6j2FE3BH4PvBG6tQUfwNsDJCZK4DnUZNDSZIkSdICMEyN4ZupCeEGVAclAAAgAElEQVSDgF8BVw6s/yLw6DmWS5IkSZI0JsPMY7gH8IHM/D51mopBvwK2nVOpJEmSJEljM0xieCfgFzOsv4m2aakkSZIkad03TGJ4BfDXM6x/APCb4YojSZIkSRq3YRLDE4D9I2LLwRURsSPwHOC4uRZMkiRJkjQewww+80ZgT+AH1IFmGuBZEbEfsBe1RvHtIyvhInHz85807borZnjc+oebY0uSJEnq1lrXGGbmpcCu1MTwQKAA+wLPAE4CHpqZV42wjJIkSZKkDg01wX1m/gp4QkT8FXAvanJ4QWYOTl2heWINpSRJkqTZWqvEMCJuD7wX+FpmHpOZVwOnd1IySZIkSdJYrFVT0sz8A/BsYLNuiiNJkiRJGrdhRiU9F7jrqAsiSZIkSZofwySG7wL+JSLuPurCSJIkSZLGb5jBZ/4aWA78JCKOA34BXD+wTZOZb5tr4SRJkiRJ3RsmMXxz3/97TbNNA5gYSpIkSdICMExiuP3ISyFJkiRJmjezSgwjYifqPIW/y8xfdlwmSZIkSdIYzbbG8HTqNBWfglvnMzwMeHNmnjtM4IjYFjgKuDNwC3BYZh4aEZsDnwG2Ay4CIjOvjogCHArsTu3TuG9mnj1MbEmSJEnSSrMdlbQM3N8Q2Jua1A3rJuCVmXlvYBfgRRFxH+Ag4MTM3B44sb0P8HhqM9btgQOAD84htiRJkiSpNcx0FSORmZf1avwy8zrgZ8DWwJ7Ake1mRwJPbv/fEzgqM5vMPAO4Q0RsNeZiS5IkSdKiM2+JYb+I2A54APBdYMvMvAxq8ghs0W62NXBx38OWt8skSZIkSXMwzKikI9X2V/wc8PLMvDYiptt0sDkr1GkxBvd3ALWpKZnJ0qVLp9zZFUOVlmn3tyaLPd5MlixZ0sl+JzHeYj424xnPePMXbzEfm/GMZ7z5i7eYj20xxlubxHD3iOj1KdyYmpTtFRE7TLFtk5mHrGmHEbEBNSn8ZGZ+vl18RURslZmXtU1Fr2yXLwe27Xv4NsClg/vMzMOoA+MANCtWrFhTMdbKqPc3CfGWLl061uNYzPEW87EZz3jGm794i/nYjGc8481fvMV8bAsp3rJly2a13dokhs9ob/0OnGbbBpgxMWxHGT0C+Flmvrdv1XHAPsDb27/H9i1/cUQcDewM/L7X5FSSJEmSNLzZJoa7dRD7wdQpMH4cEee0y15HTQgzIvYHfgPs1a47gTpVxQXU6Sr266BMGtLNz3/StOtmata6/uHHjb4wkiRJktbKrBLDzPzWqANn5mlM3W8Q4FFTbN8ALxp1ObQwjTsRNfGVJEnSYrZOjEoqSZIkSZo/JoaSJEmSNOHmfboKSauymawkSZLGzRpDSZIkSZpwJoaSJEmSNOFMDCVJkiRpwtnHUNJY2adRkiRp3WONoSRJkiRNOBNDSZIkSZpwNiWVtKjZdFWSJGnNTAwlaYRMRCVJ0kJkU1JJkiRJmnAmhpIkSZI04UwMJUmSJGnCmRhKkiRJ0oQzMZQkSZKkCeeopJK0gDkKqiRJGgVrDCVJkiRpwpkYSpIkSdKEMzGUJEmSpAlnYihJkiRJE87BZyRJs+ZgN5IkLU4mhpKkdda4E1ETX0nSpLIpqSRJkiRNOBNDSZIkSZpwJoaSJEmSNOHsYyhJ0jwZpk+j/RklSV2wxlCSJEmSJpyJoSRJkiRNOJuSSpI0AZyKQ5I0ExNDSZI0ciaikrSw2JRUkiRJkiacNYaSJGnBs4ZSkubGGkNJkiRJmnAmhpIkSZI04WxKKkmStJZsuippsTExlCRJWseNOxFd7PEkrW7eEsOI+AjwRODKzLxvu2xz4DPAdsBFQGTm1RFRgEOB3YHrgX0z8+z5KLckSZIkLTbzWWP4MeB/gKP6lh0EnJiZb4+Ig9r7rwEeD2zf3nYGPtj+lSRJktaKNZTS6uYtMczMUyJiu4HFewKPaP8/EjiZmhjuCRyVmQ1wRkTcISK2yszLxlRcSZIkaSgmoloI1rVRSbfsJXvt3y3a5VsDF/dtt7xdJkmSJEmao4Uy+EyZYlkz1YYRcQBwAEBmsnTp0il3ONPVmZlMt781MZ7x1sVYxjOe8RZevMV8bMYznvFGFO8puw5Vli2/8J2h4k1nyZIlQx+D8cYfb11LDK/oNRGNiK2AK9vly4Ft+7bbBrh0qh1k5mHAYe3dZsWKFSMt4Kj3ZzzjLcRYxjOe8eYv3mI+NuMZz3gLL95MzWRn0sUItl3Em8nSpUvH+hoNG2/ZsmWz2m5dSwyPA/YB3t7+PbZv+Ysj4mjqoDO/t3+hJEmSpC5N0tQt8zldxaepA80sjYjlwOupCWFGxP7Ab4C92s1PoE5VcQF1uor9xl5gSZIkSVqk5nNU0qdPs+pRU2zbAC/qtkSSJEmSNJnWtVFJJUmSJEljZmIoSZIkSRPOxFCSJEmSJpyJoSRJkiRNOBNDSZIkSZpwJoaSJEmSNOFMDCVJkiRpwpkYSpIkSdKEMzGUJEmSpAlnYihJkiRJE87EUJIkSZImnImhJEmSJE04E0NJkiRJmnAmhpIkSZI04UwMJUmSJGnCmRhKkiRJ0oQzMZQkSZKkCWdiKEmSJEkTzsRQkiRJkiaciaEkSZIkTTgTQ0mSJEmacCaGkiRJkjThTAwlSZIkacKZGEqSJEnShDMxlCRJkqQJZ2IoSZIkSRPOxFCSJEmSJpyJoSRJkiRNOBNDSZIkSZpwJoaSJEmSNOFMDCVJkiRpwpkYSpIkSdKEMzGUJEmSpAlnYihJkiRJE87EUJIkSZImnImhJEmSJE04E0NJkiRJmnBL5rsAayMi/hE4FFgf+HBmvn2eiyRJkiRJC96CqTGMiPWB9wOPB+4DPD0i7jO/pZIkSZKkhW/BJIbATsAFmXlhZv4FOBrYc57LJEmSJEkL3kJKDLcGLu67v7xdJkmSJEmag9I0zXyXYVYiYi/gcZn5vPb+s4GdMvMlA9sdABwAkJkPHHtBJUmSJGndUta0wUKqMVwObNt3fxvg0sGNMvOwzNwxM3ekPgFrfYuIs4Z9rPGMt1hiGc94xpuceIv52IxnPOPNX7zFfGwLMN4aLaRRSb8PbB8RdwMuAfYGnjG/RZIkSZKkhW/B1Bhm5k3Ai4GvAj+ri/Kn81sqSZIkSVr4FlKNIZl5AnDCGEIdNoYYxjPeuh7LeMYz3uTEW8zHZjzjGW/+4i3mY1t08RbM4DOSJEmSpG4smKakkiRJkqRumBhKkiRJ0oQzMZTWUkRsPsWyu81HWRaDqZ47n09NJSJeFhGbRkSJiCMi4uyIeOx8l0sCiIgHz2aZpIUtItaf7zJ0ZUENPtOliNgrMz+7pmUdxL1dZv6xyxi9OMCfMvOWiPgb4F7A/2XmjV3GHNOxnQl8FPhUZl7ddTzgSxHx+My8to1/HyCB+3YRLCL+e4rFvwfOzMxjO4i3EbA/8LfARr3lmfncUcdqfQ74+4FlxwAPHHWgiLhPZp47sOwRmXnyqGP17f9zwEeon7dbuorTxtoIeCHwEKABTgM+mJk3dBl3jJ6bmYdGxOOAOwH7UT/7X+sqYES8G/joYhwFu/0t+CCwZWbeNyLuBzwpM9/cUby7A8sz888R8QjgfsBRmXlNF/HmwftY/btsqmUjERF/nZkXdrHvdUVE/BWwPav+Fp3SQZwHA+dk5h8j4lnU1+zQzPz1qGMNxB3XedI7gTcDfwK+AtwfeHlmfqKjeCdm5qPWtGwBuyAijqH+Npy7xq3noE1Cv5qZj+4yTo+J4UqvBQaTwKmWjURE7Ap8GLg9cJeIuD9wYGa+sIt4wCnAQ9sv2ROBM4GnAc8cdaB5OLa9qSeI3+9LEr+WmV2NrPRWanL4BOCewFF08Dz22YiayPfei/8M/BTYPyJ2y8yXjzjex4HzgMcBb6Qe289GHIOIuBc1+dwsIv6pb9Wm9J0EjFhGxMeBd7Yx3gnsCDyoo3hQT7z3A/47Ij4LfCwzz+so1lHAddSTUYCnU1/PvboI1r5u7wC2YOUEuk1mbtpFPFZO0Ls79Qf5hxExq0l75+A84LCIWEL9bvl0Zv6+45i3ioizM7OTxAI4HHgV8CGAzPxRRHyKegLZhc8BO0bEPYAjgOOAT1Ffz5GJiB9TL4xMKTPvN+J4DwJ2Be4UEa/oW7Up0GXNwsciYmvqPM+nAKdm5o+7Cjbuz3tEPA94GbANcA6wC3A68MgOwn0QuH97vvJq6vvzKODhHcSaj/Okx2bmqyPiKcBy6m/CScBIE8P24uTGwNL2fLP3/bwpsGyUsQbiPhg4GLgrNbfpvTf/uqOQ96Oee344ItajXvw9uldhMEqZeXNEXB8Rm43jt2fiE8OIeDz1R2nrgZqZTYGbOgx9CPXE+ziA9gTnYR3GK5l5fUTsD7wvM98ZET/oKNZYjy0zLwD+LSL+A3gi9QN6S0R8hHrF73cjjvfliNiAWkuxCfDkzPzFKGMMuAfwyHYuTyLig23sxwBdnATcIzP3iog9M/PI9kTxqx3EuSf19boDsEff8uuA53cQD2Bn6onNd6iv3SeBTpt6ZeY3gG9ExGbURO3rEXEx9aT8EyOutb9nZt6/7/5JEfHDEe5/0DuBPTJz5BcOpnFWRHwNuBvw2ojYBOi0FjYzP0z98b8nNcH/UUR8Gzg8M0/qMnYbv6ukEGDjzPxeRPQv6/J375bMvKk9Of2vzHxfR79DT2z/vqj9+/H27zOB6zuIdxvqCf4S6vdKz7XAUzuIB0BmPiwibgP8A/AI4MsRcfvMXK27w4iM+/P+MuqxnZGZu7UXE9/QUaybMrOJiD2p5w1HRMQ+HcWC8Z8DbtD+3Z16cet3A5/7UTkQeDk1CTyLlYnhtcD7uwjYOgL41zbmzR3GASAzr6P+hh/evm6fBg5paxHf1J6XjtINwI8j4uvArTXMmfnSEccxMQQupdaePYn6huq5jvom60xmXjzwwezyzVzaq5rPpDYThA5f/zEfG20TqP2oX3qfo57wPwT4JrDDiGK8j1WvQm8KXAi8JCI6+YC2tgZuR20+Svv/svYq0p87iNdLVK6JiPsClwPbjTpI2wz22Ih4UGaePur9T+NGalOa21JrDH/VdfNOgIi4I/As4NnAD1j5/tyHekI3Kj+IiF0y84w27s7At0e4/0FXjPEkEep31w7Ahe2FrjtSP/edapvy3Ku9rQB+CLwiIg7MzL1HGGfcTf5XtM07mzb+U4HLOooFcGNEPJ36vu9dDNpghu2H0mv+FxEPzsz+Cz8HtUn9G0cc71vAtyLiY103PewXEQ8BHtre7gAcD5zaYchxf95vyMwbIoKI2DAzz2sv0HThuoh4LfV7+mHtZ37k781+Yz5POi4izqP+/r0wIu5ETTZGKjMPBQ6NiJdk5vvW+IDR+X1m/t+4grXvjydQf3+2A95D/V1/KHW+9b8Zccgvt7fOTXximJk/BH4YEZ/qsr/dFC5umxI07RW/l9JBc70+L6M2jf1CZv40Iv6a2oygC2M9tog4C7iGesXooMzsJUvfjdF2/D9z4P5ZU241eu8EzomIk6lX3x4GvLU9ifxGB/EOa5uA/Dv1aubtgf/sIE7PBRHxOuqX663fSR31afw+cCz1KvQdgQ9FxFMzs7Or+hHxeeoJ/sepV9t7J96faZs+j9LOwHMi4jft/bsAP+s1rRt1EzrgzIj4DPBF4NaLFJn5+RHH6e33lojYDnhWRDTAaZn5hS5i9UTEe6kXDk8E3pqZ32tXvSMizh9xuLE1+W+9iDpZ8r0i4hLgV9QT467sB7wAeEtm/irqIFOd9HFq3S4iHpKZp8Gtzfdu12G8DSPiMFb/Luui6SPAt6jvkbcBJ2TmX7oI0tfUf6yfd2B5RNyhjff1iLiaejG/C08DngHsn5mXR8RdgHd1FAvGeJ7UNnX8EvVc4tpe00Rgzy7itS6PiE0y87qI+Hdqn803Z+bZHcU7KSLeBXyeVd+bXcX7BfUc+l2Z+Z2+5cd0UfPbtt66DSsTzvO7ylkmPjHss11EvA24D6t2cu6qffILgEOptUHLqU0DXzTjI+ZmeWY+qXen7bDeVQ3XuI9tr+k64GfmP021fBiZeeSo9rWWcY+IiBOAnaiJ4esys/fj+KoO4n24/fcUoKv3f79jqVe5v0H3TUD2z8xeMnY5sGdEPLurYO0P8jnTvQ8zc8cRh/zHEe9vTTalNs3rHxm0of44j1xEfIDatPrT7aIDI+LRmdnl98tPgH/PzKmaIO404ljjbPLf+x14dHuRab22eVSXHtPfsqJNDv/UYbz9gY+0zbihXkDsahAtqP3A/5fad6zz5mzUi1sPpl4sfGlE3AKcnpn/MeI4/U39x/Z5z8yntP8eHBEnAZtRB07pItblwHv77v+G2sewK2M7T2ovqL0nMx/Ut+yP9DVJ7MB/ZOZn21rtxwHvpvbj3LmjeL399v+mNnTTHxXgfpn5h6lWdNF6LOpgXUcCF1HPA7eNiH2yg4GYTAxX+ijwemq7792oVzY7G9QgM1fQ7YAlg8bZSf2WzFzl2Norw1d1ESwzL4w6EMzgKJojbS7UE2Pq5BwR92qbzvT6GF3c/r1zRNy5qythEfFW4J3ZjhTY1l68MjP/vYt41H5Or+lo36voJYURsQUr3yvf6jDeLVH7MXfyXpwi3q/b12tbVq2x6OS9kpmdN+Mc8HDgvtkOLBURR9JNP9tbZeZHImLriNiBVZ/TU3L0AwGMtcl/WxvzHNoarl6ztg6bxe9DPRnut+8Uy0YiM8+iDiiyKTXp7nrghpsy84Mdx7hVZl4TERdSP+/bUAfA6aJp7lg/5xGxaWZeG6tODdX7nN8eGNm4ARFxHTMPVNTVQFpjPU8CvhYR/wx8PrsbmK9f78LIE6gjYx8bEQd3FSwzd+tq39O4KSJexPhGb38PdQCh8+HWEaU/TQejt5sYrnTbzDwxIkrbR+DgiDiVmiyOzBT91FbR1Q9yjreT+uB0DvemXkntajqH/6WOgrUb9UrtU4HvzfiguRlXJ+dXAAdQvxD63zOFbq+EPT4zX9e7k5lXR8Tu1KalXTg+InbPzBM62v+tImIP6lXhZcCV1OT+Z9Qv966M7Qc5It5EPdH+JSvfM529V2L8U5ucT20e2+vHtS3wo45iARARb6eOPncuKz/vDfUC26iNs8k/1L4wZ1BPujvra9v2K3wGcLeIOK5v1SZ0cCIcq44M2r8cgMx871TrR+BLEfFC4Aus2pxtpAOg9UTEL6mfidOoNZX7ddWctI13JPCygYuG7+ng8/4p6gBCZ1E/a/0X6RtG2JIlMzcBiIg3UluRfLyN90xWHUho1MZ6nkQ9n7gdNaG5ge5HkL4kIj4EPJra7H5DOpw7vW0V8Hpq7TnUC75v7PBi0FhGb++zQS8pBMjMn0cdBHHkTAxXuqFt9vWLiHgxcAl1SOZRG3WfolmJ8XZSH/d0Drtm5v0i4keZ+YaIeA8dNW1pjaWTc2Ye0P67O6vOTXcqtUlGV9aP2tH/zwARcVtgww7jvQx4XUT8BfgL3f5gvZk65Pk3MvMBEbEbdaTQLo3zBzmAu3d5cjhgXFObfIn63t+M2meyd+FnJ+oIs116CnW01y4GelpF2yzolL77XTb5B9goM6dMokbsO9RBbZZSL3T1XEc3iX2XJ/Qz6Y1i2d/Ef6SJzIDtcwyDZ/W5X/bNOdleNHzAqINk5hPbv3cb9b5n8LjM7G/m+MGI+C61X14Xxnqe1EuAxyioXRve3dZsb0UHXV/6fITa7L83ms+zqS0BR9adaMC4Rm/vOTMijmDVEZY7GefCxHCll1NrnV4KvIla+zTyoYoH+6m1TVyaMfTtGEsndZiX6Rx6fVSuj4hl1CvQXf6gjLuT85HUoZ5706k8nfoj0slY09TBIE6MiI9ST2qe25ahE2P+wboxM6+KiPUiYr3MPCki3tFlwDEf30+oF36uHFO8cf04vruDfc7WhdTmeZ0nhlOJiAMy87COdv/xiHg+9UJhZzVcbSucX9PtfKH98bqa0mBNcceZyADcI+r0RVtm5n2jjs79pMzsah7K9SLirzLzaoC2qefIzyP7uk9MqaPf2psj4pnA0dTfvafTYYugeThP6tXwbs+qrTu6aPkA9SJQr+vGXdplXc3fC/WC6D/33X9DRJzTYbyxjN7e51+ofVBfSr24fArwgS4CmRi2MvP7ABHRjKM9fUTsSL2asQm1X8k1wHPbPhFd6LyT+hTNZMc1ncPxbV+ZdwFnt2X48MwPmZNxd3Ie69x0WQe8+DHwKOoX0Jsys7MrYVEnKH8mcLfMfFNEbAtslStHfxylayLi9tQv1U9GxJWs/ILvREScmJmPWtOyEXkbdcqKn7Dqif6Tpn/InIxrapNb+4FGxJbUJvEA38vMrpPg66mjAp/Iqs9plzV5/Trr606toX8X8G+s2vS4kxquiNgFeB9wb+rcf+sDf+yqOVvfxa1VdNjUmfZzMDiIXVeDmBxOrYX5UBvnR+3Fma4Sw/cA34k6VxvUSdLf0lEcqM/hjtTpYQp1UvHvUlvPjNozqH1dD6W+Z77dLhup+TpPiojnUVvnbAOcQ205czrdnbd8mZXNgDeiXqw/n+66bfwpVh2B+MGsrDTowlSjt4960KdbtS1W3kvfAEldMTFstR3+j6C+uHeJiPsDB2bmCzsK+RHghZl5ahv/IdREcdTDyQNj66Q+L9M5ZOab2n8/FxHHU5tHdTbIwDx0ch733HS0TWXHNSfQB6j9mx5Jra3/A3Ui3H+Y6UFD+iH1RP9fqcnoZtTP/Mi1/e82Bpa2PyC9E/xNqX0cu3Ak8A467jPWZ6xTm0TtJPYu4GTq8/m+iHhVZh4z4wPn5rj21rmI6HJamKm8glrru2JM8f6H2l/zs9QT/udQR5ntyvF9/29EbRbc1XQHRMTrqX3470Ptv/l4av+/rhLDjTPze7HqXHg3dRSLzDwq6hQ7j6R+/v4pM8/tIM5uABFxNHBAtgPltUn3/xt1vDbmRXQ7fUPPfE179TLqb+oZmblbRNwL6KxmPTP/rv9+Wwt8YFfxqDVqR7Z9DQt1gKJ9Rx1koP9yrxLp/e3fzqbCidUHPQS6mTnBxHCl/6L2kzkO6vyG0cFcJH2u6yWFbbzToo6O1YlxdFIfbCbbtVg5t9JU67qcW4kYwyioba1dQ03ge3PTNdQvhpH/GEfEaZn5kFh9lLauO6nvnJl/H+2w/G2/ldt0FGu3tk/OLbTNYyOiq8FLDqQ2UV9G/fHvJYbXsvKHZNRWZOZ/r3mz0cjxT23yb8A/9GoJo07S/A2gs8RwzN9r/cPHb0QdgKPLAQ1+Sr1QMjaZeUFErJ+ZNwMfjYjO+ohm5uf670fEp+lm7teepwL3B36Qmfu1tdtdtl5ZERF3p/2+joinUvtydqJtEvgH+i6URMRdsk7t0IV7Zd/o6Zn5k6ijA49c+13yfDqeT3fc50l9bsjMGyKCdgyB8yLinuMKnplnR0QXF3t7+z+HlSMQ0xvUpwO9riH3pCbavc/CHnQzIFnPuAY9NDHsl5kXD1x56/LJ/17UEZs+Tf1Sfxpwcq9tfQdt6MfWST0itqc2aet6Tsje3EpbUGtAv9ne341ao9DVXGrjGgX1iR3sc1qZ+ZD277g7qd8YEeuz8uTmToy4tisi/oU6gM/dBxLBTeio9jUzDwUOjYiXZOb7uogxhbOizsd6HGPo/xrjn9pkvYGmo1fR4Uh3MNbvMzKzf2AWIuLddFtbeTO1mexJjKeZ7PXtRZ9zIuKd1CSmywnnB21PHdW2KzdknaLmpvYE9Uq6vWDyIuAw4F4RcQnwK7od6K3XPBDgtnTfPPBnEfFhar/3BngW3V0oGed8umP9Xmktb7vcfBH4ekRcTbe15/01a+tRp1X4bQdxnpWZnxiI19kIxL3+yxHxNeDve+ODRJ2K47OjjDVgLIMegolhv4sjYlegaX+4Xkq3V2p7V70Gp8PYlW76q42zk/pY5oTs9QVtm4/eJzMva+9vRXc1MjCmUVDbARvGKurIvD/KzK6GzJ7Kf1OHd98iIt5CTbRHnVh8ito09m3AQX3Lrxv1QBuDMvN97XfLdqx6JbqL5mW9EQJ36Vu2mKY2+UpEfJWVE9zvTfdNnsc6x+2Ajek2sfhiexuXZ1NPEl9Mvfq9LfDPMz5iDvpaP/Sm+Lkc6HLO1O+3J9+HU6/s/4Fup066hPr+PAnYnNoaYR86mjd1HpoH7kdtIviy9v4pdDci98Y5pvl0W+OeO/sp7b8HtxeCNgO+0lU86kXX3kWEm4AvAZ+bfvOh9S4sTXVBu8vpoe5C7aPd8xe6HXxmbIMemhiu9AJqp+OtgeXUkaJe1FWweeinNs5O6mOZE7LPdr2ksHUF8DcdxYLxj4I6Nu3V7h923DxoMOYnI+IsVg528+TMHOlFmbbP6e/pfmqK1UTEx4G7Uzv898+DN/LEcB6+V8Y6tUlmvqptQv5g6nvlfzOz68RmbN9nfc3HoQ7Mcic6OsmH8TZra1sFvCUznwXcQIf9m3rmofXDJtQBWU6mnnRvmpldzrN5LHANddC1zmp/pjOG5oE3tC10Tsi+Odw6Mrb5dFvjPk/qjWWxfWZ+tG2ZszW1lrkLJwCvY9ULogcx4nE0MvND7b/fyMxVWv+0/fK68nFqy78vUL+zn0KHo7czxkEPTQy59Qfr2ZnZZROMwZjjnoxznJ3UxzUnZM/JfbUIDbUWoctJocc9Cuq4bQX8NOpccbf2ecoRj2wZdajznitZWQtERGzedU3eGO1IrdHudHJ7mH7wklH3f+0zlqlNpuj/2ruy/vyoIyz///buPEzOqsrj+DcMZoYlikhkVAwQRBAQF3YEJzjiIEIEwd+ICwquDGgUBcWNZcANXB5hhCECsjoeGIMoBglDEiCAIIssguMIggs4BBgQREKg54/7Fv120R0SqXtvuur3eZ5+KrV03dudrrfqvPfcc+4Djo6IHOW7SyS/Wh8AABVtSURBVB7P2unji4E/RkTPj9OSIiLUFYh2DMXIKsg9ERGPS5osaWKv97cviaTpDL/PzouIHy3p8c/QKaSKmceSVnqvl3RJk1aew1oRsVOm536KUumBrfGmk95nJwLrNvsLj+j1e1GjZD9dKPw5Sakw0uakvXGnkGoXnEE6yZbDGaRCQTdRphDasUB3m5PRbuuJiDhK0mxSb3BIdTuuyzFWM16xk74ODHnyDevNpCX9Uko348y+SV3S6RHxLtJZzHZPyNeRoSdkR0QcIGl3ht/8T4yIWRnHK1oFtYJSPcCuYfhD/hTg/ubfqwF30iersKTX+d+TsShES9HiJVGotcnT7X+V9DxSE/UcgeFoPW73zjBOyfTxTmreLYxsOj2BfA29AX4DLJB0HiNPOmUpwS7pS6QCEWc2N82Q9JqIOCTHeBFxsaT5zZg7kDKRNiZlI+VwuaSXtwu0ZFYqPbDjUGBL0gosEXG9pHVyDFRhdbnYcaWxO2mrwbUAEfEHSTl/5nsi4ocZnx94sqPAtsDkrhMXzyZlXWTTpHHm6l89QsnFJAeGwxZIOg74HiPfsHL9p5duxllik/pmktZunncmqdrdx3s8xlguJ71RDZF3TwcA3XvGlKqg5ipJXlREzG/+H9ePiIskrUyGA2w0zaCbVKHzOik8kt4IvL7X41W0BvCLZgU2a2/BCsVLSrc2GWsO90qalunph0hpQ2sz3OJnJplaC5XQSr1/SXcwqlTGPpc/NF8rMPqeoF7bGXhlNIXXJJ0KXAdkCQyVel2uQuoPdymtCrqZbAe8R9LtpGNLZ5Ur199mkfTAlsUR8UBXplMWKttPF8ofVxZFxJCkzuJA7qJPhzaFg7r7v/a6HsNEUpukFRl5THmQVK+gXxRbTHJgOGzb5rKzWtLZrJ6raEPpZpwlNqmfQNpXMZXh8vztjf+5miYX7W1Wcs9YDZLeD3yA9HeyHmkfwgmkVaEctoiID3WuRMRsSf+6pG8YZw6rOHaW4iWjpHZ25E6/GlPXPuNeOpO0qlaqN2R2Gq7SO1WFqvTCcEW/wlYjpRpDKriR0w2k9MpNSHua/0/SFRGR6739jZmedyyl0wNvkvR20l7m9Umra7nam5TspwvljyuhVAl/teY9fl9SIJrLPsCGpKC38/MN0eNCfRExH5gv6Ts1CvYVVGwxyYHhsB8xcu/KEPCgpFdG6o/Sa+1mnJDS6N6TYZyO7JvUI/VP+6ak4yNivxxjjKF0b7Nie8Yq2Z+UvvNTgIj4laSce0QXSvosI0uS35txvKKaN64iShUvebrUzj5zT0QUaXBfUJUqvZLmAG+Nke1N/iMi/inTkF8ArpU0j/Te/loyrRYCRMTHACStSvpgfAopjTxLQaYKH4SLpAe2fJj0/v4o6W/2J6SgLYeS/XSh/HHlUdLnogdJ+ww/HxFzMo73iuiqYpvZtyWVPLaUVmwxyYHhsM1IH/jPI72BvAm4GvigpLMjoqf7LqJcM86OYpvUCweFUL63Wck9YzU8GhGLOuk7klYkb9nnvUi58519oZdQoXpoLl2rahNJZ1AfzrSqVqR4CVRrbVJDqZSoYqJeld7JnQ9uzTzuz3zS6U2kFKz7SfuWPxkRd+carCkisj3p88QdzdiX5hqvgtKvhY2arxWbrzcD08mTbpm9n26X0r/LNUl7i68l/V1elGmcjislbRQRv8g8TscahY8tpY22mJSldocDw2HPIzWrfAierOB0DukM4zX0eEO+pDVJZzNfGBFvlLQRsE1EnNTLcVpKb1IvabZG9jb7Z9JeiJ6S9EPSm8YkCu0Zq2S+pE8DK0nakZRylu0scbNCMeNpHzhOda+qSdqNtCLbU02gdn6pQC0qtDappEhK1IB4vP330uxlznnSqVMldDplqoSuBHwNuCbXCZnKSr8WzqRc6mqJfrptRX+XEfFZSZ8D3tCMfZykAE6KiF9nGHI74N0F978+UfjYUtotpDhkPVJ6/APAbqT09Z5yYDisu1nlY8DaEfGIpEfH+J5n4jukN63PNNf/m1T4JldgWHqTeklDpP6M25F+rhMZ2eC7V45pnv/LpBdkR+e2fvEp4L2kvQ8fJAXZPW/HIekbEfHRVsA9Qh8F2iNExLmSPvX0j1zm560RqBVpbVJZ6ZSofvYZ4LKmciekE68fyDVY6SqhEXF0juddjpR+LRRLXY0C/XS7FD+uNMVn7gbuJmWUPBc4R9KciDi4x8MVa6PSKHpsqaC9Hez3OQdyYDjsLNLS9w+a67sC320qN+VYCl8jIkLSIQARsVjS40/3Tc9A6U3qJe0YEZ+kdaZN0uHAJ3s5SGevmKRnde8bU2rs3ReaCn4zybsxHVJFNkgBd99SasjesQIpZT3XmczSgVqNYiKllU6J6lsRcYGkVzN84u5jEbEw13gVqoT2u9KvhezplpKeHREPKvXVLdlPt+jvUtJHSKmHC0kneg+KiMeaTJNfAT0NDEvvf+06tkwg87GlgmLbwRwYNpryxD9meNXpQxHxs+buHI3vH1bqvdXJZ9+atDScRT9WaypdWa9WJb/SJO1C2uC/NukYkaXSZERc01w+GWQ3G8ZfHBE9T4+oaNfWvxeTerm9OdNYqzJyn2HW1ewo1NqkstIpUf1uW4Z7cUEq/JZL6Sqh/a70a6FEuuVZpGNmp69uR9Zq6pT/Xa4BvKX7s2CTabLLGN8zbii1G9kJmBoRR0iaImnLyNdupLRi28EcGLY0H1SvKTTcgaRCN1MlLSBVD+ynnisllK6sV6WSXwXfIPXGubFE5dWmYuB00vHoeuAeSfMj4sAlfuM4ERH7FBxuxZKr2Srf2qSG0ilRfUvlG84XrRI6AEq/FrKnW0bELk1Q8Q+F90oX/V1GxOeXcF/OlNlS2u1GjgD+BPwn+dqNFNGqNL4isI+k28h8IsGBYT2/IG10/jPpD/hc0j5DW0qlK+tVrORX2m+Bm0oEhY3nNKk87wNOiYhDu1ZkxzVJawHHAq8hHeAvA2ZExO96OEat1ezSrU2K68dsi4pKN5zv9yqhRVV4LRRJt2z23s0i/Z0U4eNKz5VuN1JK8dVcB4b1nEbqJ/OF5vpepD1Xb602I7PkYODHzSbu9r6Or2Uab0VJLwDEcDGmfnIKabW589p+Z3Pbjj0co9ZqdunWJjb+lWw43+9VQvtdyXTLKyVtERFXZ3huy690u5EiapxAcGBYzwYR8YrW9bmSfl5tNmbDjgIeAv6O1HcvtyNIjYsXRMTVkqaSNsP3i8kRcUrr+nckfbSXA1RczS7a2sTGvS8C10maS5mG8/1eJbTflUy33IHUt/oOUuEu7yUeX0q3G+lbDgzruU7S1hFxJYCkreijAiY2rq0eEW8oNVhEnA2c3bp+G7BHqfELWCjpnQxXu9sLuLfifHqpSGsT6w8R8d1mT/EWpA/eWRvO2/hWYrVE0roRcTv9Xbm971VoN9K3JgwNOeunBkm3ABsAnc3OU0gNLJ/AZ6msoqZAxMURcWGh8V4KHA+sGRGbSNoUmB4RR5YYPzdJU4DjgG1IaS6XAx/p86bwZqNqXt/r0Dox3cv2A2bLQtI1EbGZpP+KiH4qmjUQmjYjY+qzwoBFeMWwHle6s+XV/sDBkh4FHiNTu4qWmcBBwL8DRMQNks4C+iIwJLX+eHdE3A9PvpEdA+xbdVY9UKq1ifUHSScDmwI3k6/9gNmyWEHSocBLJT2lEnbGvfXWG502I532Ih252430LQeGlbgilS2vImJSE7ysT9pnmNvKEXFVp4BJo58KRWzaCQohncGU9KqaE+qhoq1NbNzbOiI2qj0Js5a3AbuRPg9PqjwXW0YRsS6ApBVIPcfX7fQxBF5QdXLjlANDMxuhaRsxA1iL1Fdwa1L6Y640m4WS1mO4mtiewF2ZxqphBUnP7Vox7Jdjb+nWJja+XVGi/YDZ0oqIXwJflnRDRMyuPR/7q/0bfdjHsIZ++XBiZr0zg3QwvTIidpC0IXB4xvH2B04ENpT0e+B20pm/fvFV4HJJ55CCX5Eqv/aD0q1NbHw7lRQc3k3+9gNmT6udPirpZd33+1g2bvRrH8PiVqg9ATNb7vwlIv4CIOlvI+JWUqGknmvSPzaPiNcDk4ENI2K7fkq1jojTSFVW/wjcA7wlIk6vO6ueOQr4MynleFLry2w0JwPvIu2x35XUvHnXqjOyQdc5Zm0O7Ae8qPn6EOC05/GjL/sY1uAVQzPr9jtJqwHnAnMk3Q/8IcdAEfGEpAPSP+PhHGMsD5rUuX5Mnyva2sTGvTsj4rzakzDriIjDASRdCLw6Iv7UXD+MVhslW+65j2GPODA0sxEiYvfmn4c1jaifA1yQccg5kj4BfI/UWLgzD5eZXv5dJOkNpVqb2Lh3a1Nx+IeMTD12VVKrbQqwqHV9Eamtio0D7mPYOw4MzWxMETG/wDD7ktI//qXrdpeZXv6Vbm1i49tKpICwvcrsdhW2PDgduErSLNLf5O7AaXWnZMui2fZya+15jHducG9mVUlaiRQUbkd6Q74UOCEiHqk6MVsqo7U2KXRCwcysZyRtRnofArgkIq6rOR+zGrxiaGa1nQo8SNojALBXc5vG/A5bLlRobWLjmKSXAscDa0bEJpI2BaZHxJGVp2YG6Rh2F81nY0lTIuLOulMyK8tVSc2stg0i4n0RMbf5+gCZqqBaz3Vam9wRETsArwIW1p2SLcdmAoeQ0o6JiBtIDcbNqpL0YVLl6DnAj4Dzm0uzgeLA0Mxqu07S1p0rkrYCFlScjy29Yq1NrC+sHBFXdd22uMpMzEaaQTpJuXFEbBoRL3d/TRtETiU1s9q2AvaW1EnZmQLcIulG3Px6eVestYn1hYWS1mO419iepNQ9s9p+CzxQexJmtTkwNLPadqo9AfvrVGhtYuPb/sCJwIaSfg/cDryj7pTMALgNmCfpfEa2UvlavSmZlefA0Myqiog7as/BnjlXIrWxSDqwdfXHwFzSVpaHgT0Af/i22u5sviY2X2YDyYGhmZmZ5TSpudyAVKzoB6Sel+8CLqk1KbOOiDi89hzMlgfuY2hmZmbZSboQ2CMi/tRcnwScHRFOJ7eqJE0GDgY2ZmRP1tdVm5RZBa5KamZmZiVMARa1ri8C1qkzFbMRzgRuBdYFDgd+A1xdc0JmNTiV1MzMzEo4HbhK0ixSZdLdgVPrTskMgOdFxEmSZjT7pedL8r5pGzgODM3MzCy7iDhK0mxg++amfSLiuppzMms81lzeJelNpLY7a1Wcj1kV3mNoZmZmZgNL0i7ApcCLgWOBZwOHR8R5VSdmVpgDQzMzMzOzFkkTI2LR0z/SrH+4+IyZmZmZDSxJ8ySt07q+BS4+YwPIewzNzMzMbJB9EbhA0jeBFwE7A/vUnZJZeU4lNTMzM7OBJmkaMAdYCLwqIu6uOyOz8pxKamZmZmYDS9LnSEVnXgscBsxrqpOaDRSnkpqZmZnZIFsD2DIiHgGukHQB8G3g/LrTMivLqaRmZmZmNvAkTQKGIuKh2nMxq8GBoZmZmZkNLEmbAKcDqwMTgHuAvSPi5qoTMyvMewzNzMzMbJCdCBwYEWtHxBTg48DMynMyK86BoZmZmZkNslUiYm7nSkTMA1apNx2zOlx8xszMzMwG2W1NZdLTm+vvBG6vOB+zKrxiaGZmZmYDR1InELwUmAx8H5hFqlLqBvc2cLxiaGZmZmaDaDNJawPvBnYgFZ7pVGWcUG1WZpU4MDQzMzOzQXQCcAEwFfhZ6/ZOgDi1xqTManG7CjMzMzMbWJKOj4j9as/DrDYHhmZmZmZmZgPOxWfMzMzMzMwGnANDMzMzMzOzAefA0MzMbDkl6QxJi2vPw8zM+p+rkpqZ2UCRtBMwGzgyIj7Xdd82wOXAIuC5EfHnrvt/AuwIPD8iFhaaspmZWXZeMTQzs0FzGbCY1Les27TmvonAtu07JK3Y3HaTg0IzM+s3DgzNzGygRMRDwNXAlpJW7rp7GjAHuKv5d9sWwKrAvF7MQ9JKkv6mF89lZmb2TDmV1MzMBtFcYBvgNaRAsL0ieCTwIE9dUZzW+l6a73klcDiwPbAy8GvgZODrEfFE63FnAG8DXgh8BdgZWAOYAvxO0krNuG8HVgNuAD4z2sQlvRw4DNi6eY77gFuAoyNi9jL+HszMzACvGJqZ2WDqBHfTWrd1VgTnN19bSFqldf80YKi5D0lbkfYjvhb4FnAQaaXxGOCUUcacAFwEPB84Avg00NnDGMCBwE+BTzTPey7wivYTSJoMXEwKaGcC+wFfB+4FtlzaH97MzKybVwzNzGwQLSAVmGmvCk4DHgZ+BjwAPIsUgF3YWk28ISLuax7/zeYxW0bETQCSjgPOAfaWdHJEzG89/wrAtRHxnvZEJO0M7AKcFBHva92+ADgbeLz18O1Jq4R7RMT3/+qf3szMrItXDM3MbOBExCOk1bnNW6uC04AFEbE4Im4B/pfhFcXOauJcAEkvIK3QzeoEhc3zDgFfbK7uPsrQx4xy227N5dFdczyHlJra9kBzubOkSUv4Ec3MzJaJA0MzMxtUc0krftu1VgTbK3yXMLyiOK25nNdcTm0ubx7leW/uekzbr0a5bSqpEur/jHLfLV3XLwbOAt4L3CtpgaRDJW04yveamZktNQeGZmY2qNr7DNv7Czvmk1YUV20e8wQpWIS0X3AsQ2Pc/nhEPDrK7Ut6rhH3RcRQRLwD2BT4LKnwzMHATZL2W8LzmJmZLZH3GJqZ2aC6AvgLaVXwQeARUhuLjvmk98lppL2G10fE/c19nRTPjUd53o2ay9uWch6/Bl4HvAT4Zdd9o64ERsSNwI3AVyStDlwFfAk4finHNDMzG8ErhmZmNpCa1bsrgM1IxV+uiIhFrYfcRKr2eRCwCq3+hRFxFykY203Syzq3S5oAHNJcnbWUU/lBc3lQ+0ZJewLrdd22ejNG++e4D/gNsKqkiUs5ppmZ2QheMTQzs0E2l7RiuC1waPuOiBiSdCnDxWHmdn3vR5rbLpP0LeCPwHRgR+C0roqkY4qI8yXNBt4raQ3gQmB94P2k4PRlrYfvCxwgaRZpT+LiZv7/CJzVFdiamZktNa8YmpnZIGsHe6MFcp3bHgcubd8RET8lpZguAA4AvgqsRVr523cZ57En8A1S0/qvkgLV3YCfdz3uYtI+x12BrzRfGwIfB/ZZxjHNzMyeNGFoaKw98mZmZmZmZjYIvGJoZmZmZmY24BwYmpmZmZmZDTgHhmZmZmZmZgPOgaGZmZmZmdmAc2BoZmZmZmY24BwYmpmZmZmZDTgHhmZmZmZmZgPOgaGZmZmZmdmAc2BoZmZmZmY24BwYmpmZmZmZDbj/B4Tb+f6QU+k4AAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 1080x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# frequency plot for the most used words in the twitter data\n", "df = pd.DataFrame(top_30_tweet, columns=['Twitter Data', 'frequency'])\n", "df.plot(kind='bar', x='Twitter Data',legend=None, figsize = (15,5))\n", "plt.ylabel('Frequency',fontsize = 18)\n", "plt.xlabel('Words', fontsize=18)\n", "plt.title('Most Used Words that Occured in the Twitter Data', fontsize = 15)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Determine all of the words that are used in both datasets" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# find the unique words in each dataset\n", "joint_words = list((set(all_document_words)).intersection(all_twitter_words))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create a dictionary with the unique joint words as keys" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# make array of zeros\n", "values = np.zeros(len(joint_words))\n", "# create dictionary\n", "joint_words_dict = dict(zip(joint_words, values))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create dictionaries for both datasets with document frequency for each joint word " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# create a dictionary with a word as key, and a value = number of documents that contain the word for Twitter\n", "twitter_document_freq = joint_words_dict.copy()\n", "for word in joint_words:\n", " for lst in twitter_data.text_tokenized:\n", " if word in lst:\n", " twitter_document_freq[word]= twitter_document_freq[word] + 1\n", " \n", "# create a dictionary with a word as key, and a value = number of documents that contain the word for Fed Data\n", "fed_document_freq = joint_words_dict.copy()\n", "for word in joint_words:\n", " for lst in fed_data.token_text:\n", " if word in lst:\n", " fed_document_freq[word]= fed_document_freq[word] + 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create dataframe with the word and the document percentage for each data set" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame([fed_document_freq, twitter_document_freq]).T" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "df.columns = ['Fed', 'Tweet']\n", "df['% Fed'] = (df.Fed/len(df.Fed))100\n", "df['% Tweet'] = (df.Tweet/len(df.Tweet))100" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "top_joint_fed = df[['% Fed','% Tweet']].sort_values(by='% Fed', ascending=False)[0:50] \n", "top_joint_tweet = df[['% Fed','% Tweet']].sort_values(by='% Tweet', ascending=False)[0:50] " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "top_joint_fed.plot.bar(figsize=(14,5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "top_joint_tweet.plot.bar(figsize=(14,5))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "df['diff %'] = df['% Fed'] - df['% Tweet']" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "top_same = df[df['diff %'] == 0].sort_values(by='% Fed', ascending=False)[0:50]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "top_same[['% Fed', '% Tweet']].plot.bar(figsize=(14,5))\n", "plt.show()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
arasdar/DL	udacity-dl/CNN/cnn_bp-learning-curves.ipynb	1	164071	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Image Classification\n", "In this project, you'll classify images from the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html). The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.\n", "## Get the Data\n", "Run the following cell to download the [CIFAR-10 dataset for python](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All files found!\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "from urllib.request import urlretrieve\n", "from os.path import isfile, isdir\n", "from tqdm import tqdm\n", "import problem_unittests as tests\n", "import tarfile\n", "\n", "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n", "\n", "class DLProgress(tqdm):\n", " last_block = 0\n", "\n", " def hook(self, block_num=1, block_size=1, total_size=None):\n", " self.total = total_size\n", " self.update((block_num - self.last_block) * block_size)\n", " self.last_block = block_num\n", "\n", "if not isfile('cifar-10-python.tar.gz'):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n", " urlretrieve(\n", " 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n", " 'cifar-10-python.tar.gz',\n", " pbar.hook)\n", "\n", "if not isdir(cifar10_dataset_folder_path):\n", " with tarfile.open('cifar-10-python.tar.gz') as tar:\n", " tar.extractall()\n", " tar.close()\n", "\n", "\n", "tests.test_folder_path(cifar10_dataset_folder_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the Data\n", "The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named `data_batch_1`, `data_batch_2`, etc.. Each batch contains the labels and images that are one of the following:\n", "* airplane 1\n", "* automobile 2\n", "* bird 3\n", "* cat 4\n", "* deer 5\n", "* dog 6\n", "* frog 7\n", "* horse 8\n", "* ship 9\n", "* truck 10\n", "\n", "* Total 10 classes (Aras changed above/this section a bit)\n", "\n", "Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the `batch_id` and `sample_id`. The `batch_id` is the id for a batch (1-5). The `sample_id` is the id for a image and label pair in the batch.\n", "\n", "Ask yourself \"What are all possible labels?\", \"What is the range of values for the image data?\", \"Are the labels in order or random?\". Answers to questions like these will help you preprocess the data and end up with better predictions." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Stats of batch 1:\n", "Samples: 10000\n", "Label Counts: {0: 1005, 1: 974, 2: 1032, 3: 1016, 4: 999, 5: 937, 6: 1030, 7: 1001, 8: 1025, 9: 981}\n", "First 20 Labels: [6, 9, 9, 4, 1, 1, 2, 7, 8, 3, 4, 7, 7, 2, 9, 9, 9, 3, 2, 6]\n", "\n", "Example of Image 5:\n", "Image - Min Value: 0 Max Value: 252\n", "Image - Shape: (32, 32, 3)\n", "Label - Label Id: 1 Name: automobile\n" ] }, { "data": { "image/png": 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QmKAHgMIEPQAUJugBoDBBDwCFCXoAKEzQA0Bh/xfkBwlHN40TWAAAAABJRU5E\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11bb93358>" ] }, "metadata": { "image/png": { "height": 250, "width": 253 } }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import helper\n", "import numpy as np\n", "\n", "# Explore the dataset\n", "batch_id = 1\n", "sample_id = 5\n", "helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implement Preprocess Functions\n", "### Normalize\n", "In the cell below, implement the `normalize` function to take in image data, `x`, and return it as a normalized Numpy array. The values should be in the range of 0 to 1, inclusive. The return object should be the same shape as `x`." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def normalize(x):\n", " \"\"\"\n", " Normalize a list of sample image data in the range of 0 to 1\n", " : x: List of image data. The image shape is (32, 32, 3)\n", " : return: Numpy array of normalize data\n", " \"\"\"\n", " # TODO: Implement Function\n", " ## image data shape = [t, i,j,k], t= num_img_per_batch (basically the list of images), i,j,k=height,width, and depth/channel\n", " return x/255\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_normalize(normalize)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### One-hot encode\n", "Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the `one_hot_encode` function. The input, `x`, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to `one_hot_encode`. Make sure to save the map of encodings outside the function.\n", "\n", "Hint: Don't reinvent the wheel." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "# import helper ## I did this because sklearn.preprocessing was defined in there\n", "from sklearn import preprocessing ## from sklearn lib import preprocessing lib/sublib/functionality/class\n", "\n", "def one_hot_encode(x):\n", " \"\"\"\n", " One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n", " : x: List of sample Labels\n", " : return: Numpy array of one-hot encoded labels\n", " \"\"\"\n", " # TODO: Implement Function\n", "\n", " ## This was in the helper.py which belongs to the generic helper functions\n", " # def display_image_predictions(features, labels, predictions):\n", " # n_classes = 10\n", " # label_names = _load_label_names()\n", " # label_binarizer = LabelBinarizer()\n", " # label_binarizer.fit(range(n_classes))\n", " # label_ids = label_binarizer.inverse_transform(np.array(labels))\n", " label_binarizer = preprocessing.LabelBinarizer() ## instantiate and initialized the one-hot encoder from class to one-hot\n", " n_class = 10 ## total num_classes\n", " label_binarizer.fit(range(n_class)) ## fit the one-vec to the range of number of classes, 10 in this case (dataset)\n", " return label_binarizer.transform(x) ## transform the class labels to one-hot vec\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_one_hot_encode(one_hot_encode)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocess all the data and save it\n", "Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Preprocess Training, Validation, and Testing Data\n", "helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementation of CNN with backprop in NumPy" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "def get_im2col_indices(x_shape, field_height, field_width, padding=1, stride=1):\n", " # First figure out what the size of the output should be\n", " N, C, H, W = x_shape\n", " assert (H + 2 * padding - field_height) % stride == 0\n", " assert (W + 2 * padding - field_height) % stride == 0\n", " out_height = int((H + 2 * padding - field_height) / stride + 1)\n", " out_width = int((W + 2 * padding - field_width) / stride + 1)\n", "\n", " i0 = np.repeat(np.arange(field_height), field_width)\n", " i0 = np.tile(i0, C)\n", " i1 = stride * np.repeat(np.arange(out_height), out_width)\n", " j0 = np.tile(np.arange(field_width), field_height * C)\n", " j1 = stride * np.tile(np.arange(out_width), out_height)\n", " i = i0.reshape(-1, 1) + i1.reshape(1, -1)\n", " j = j0.reshape(-1, 1) + j1.reshape(1, -1)\n", "\n", " k = np.repeat(np.arange(C), field_height * field_width).reshape(-1, 1)\n", "\n", " return (k.astype(int), i.astype(int), j.astype(int))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "def im2col_indices(x, field_height, field_width, padding=1, stride=1):\n", " \"\"\" An implementation of im2col based on some fancy indexing \"\"\"\n", " # Zero-pad the input\n", " p = padding\n", " x_padded = np.pad(x, ((0, 0), (0, 0), (p, p), (p, p)), mode='constant')\n", "\n", " k, i, j = get_im2col_indices(x.shape, field_height, field_width, padding, stride)\n", "\n", " cols = x_padded[:, k, i, j]\n", " C = x.shape[1]\n", " cols = cols.transpose(1, 2, 0).reshape(field_height * field_width * C, -1)\n", " return cols" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "def col2im_indices(cols, x_shape, field_height=3, field_width=3, padding=1,\n", " stride=1):\n", " \"\"\" An implementation of col2im based on fancy indexing and np.add.at \"\"\"\n", " N, C, H, W = x_shape\n", " H_padded, W_padded = H + 2 * padding, W + 2 * padding\n", " x_padded = np.zeros((N, C, H_padded, W_padded), dtype=cols.dtype)\n", " k, i, j = get_im2col_indices(x_shape, field_height, field_width, padding, stride)\n", " cols_reshaped = cols.reshape(C * field_height * field_width, -1, N)\n", " cols_reshaped = cols_reshaped.transpose(2, 0, 1)\n", " np.add.at(x_padded, (slice(None), k, i, j), cols_reshaped)\n", " if padding == 0:\n", " return x_padded\n", " return x_padded[:, :, padding:-padding, padding:-padding]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "def conv_forward(X, W, b, stride=1, padding=1):\n", " cache = W, b, stride, padding\n", " n_filters, d_filter, h_filter, w_filter = W.shape\n", " n_x, d_x, h_x, w_x = X.shape\n", " h_out = (h_x - h_filter + 2 * padding) / stride + 1\n", " w_out = (w_x - w_filter + 2 * padding) / stride + 1\n", "\n", " if not h_out.is_integer() or not w_out.is_integer():\n", " raise Exception('Invalid output dimension!')\n", "\n", " h_out, w_out = int(h_out), int(w_out)\n", "\n", " X_col = im2col_indices(X, h_filter, w_filter, padding=padding, stride=stride)\n", " W_col = W.reshape(n_filters, -1)\n", "\n", " out = W_col @ X_col + b\n", " out = out.reshape(n_filters, h_out, w_out, n_x)\n", " out = out.transpose(3, 0, 1, 2)\n", "\n", " cache = (X, W, b, stride, padding, X_col)\n", "\n", " return out, cache" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "def conv_backward(dout, cache):\n", " X, W, b, stride, padding, X_col = cache\n", " n_filter, d_filter, h_filter, w_filter = W.shape\n", "\n", " db = np.sum(dout, axis=(0, 2, 3))\n", " db = db.reshape(n_filter, -1)\n", "\n", " dout_reshaped = dout.transpose(1, 2, 3, 0).reshape(n_filter, -1)\n", " dW = dout_reshaped @ X_col.T\n", " dW = dW.reshape(W.shape)\n", "\n", " W_reshape = W.reshape(n_filter, -1)\n", " dX_col = W_reshape.T @ dout_reshaped\n", " dX = col2im_indices(dX_col, X.shape, h_filter, w_filter, padding=padding, stride=stride)\n", "\n", " return dX, dW, db" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Now it is time to calculate the error using cross entropy\n", "def cross_entropy(y_pred, y_train):\n", " m = y_pred.shape[0]\n", "\n", " prob = softmax(y_pred)\n", " log_like = -np.log(prob[range(m), y_train])\n", "\n", " data_loss = np.sum(log_like) / m\n", " # reg_loss = regularization(model, reg_type='l2', lam=lam)\n", "\n", " return data_loss # + reg_loss\n", "\n", "def dcross_entropy(y_pred, y_train):\n", " m = y_pred.shape[0]\n", "\n", " grad_y = softmax(y_pred)\n", " grad_y[range(m), y_train] -= 1.\n", " grad_y /= m\n", "\n", " return grad_y" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Softmax and sidmoid are equally based on Bayesian NBC/ Naiive Bayesian Classifer as a probability-based classifier\n", "def softmax(X):\n", " eX = np.exp((X.T - np.max(X, axis=1)).T)\n", " return (eX.T / eX.sum(axis=1)).T\n", "\n", "def dsoftmax(X, sX): # derivative of the softmax which is the same as sigmoid as softmax is sigmoid and bayesian function for probabilistic classfication\n", " # X is the input to the softmax and sX is the sX=softmax(X)\n", " grad = np.zeros(shape=(len(sX[0]), len(X[0])))\n", " \n", " # Start filling up the gradient\n", " for i in range(len(sX[0])): # mat_1xn, n=num_claess, 10 in this case\n", " for j in range(len(X[0])):\n", " if i==j: \n", " grad[i, j] = (sX[0, i] * (1-sX[0, i]))\n", " else: \n", " grad[i, j] = (-sX[0, i]* sX[0, j])\n", " # return the gradient as the derivative of softmax/bwd softmax layer\n", " return grad\n", "\n", "def sigmoid(X):\n", " return 1. / (1 + np.exp(-X))\n", "\n", "def dsigmoid(X):\n", " return sigmoid(X) * (1-sigmoid(X))" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "def squared_loss(y_pred, y_train):\n", " m = y_pred.shape[0]\n", " data_loss = (0.5/m) * np.sum(y_pred - y_train)*2 # This is now convex error surface x^2 \n", " return data_loss #+ reg_loss\n", "\n", "def dsquared_loss(y_pred, y_train):\n", " m = y_pred.shape[0]\n", " grad_y = (y_pred - y_train)/m # f(x)-y is the convex surface for descending/minimizing\n", " return grad_y" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.utils import shuffle as sklearn_shuffle\n", "\n", "def get_minibatch(X, y, minibatch_size, shuffle=True):\n", " minibatches = []\n", "\n", " if shuffle:\n", " X, y = sklearn_shuffle(X, y)\n", "\n", " for i in range(0, X.shape[0], minibatch_size):\n", " X_mini = X[i:i + minibatch_size]\n", " y_mini = y[i:i + minibatch_size]\n", "\n", " minibatches.append((X_mini, y_mini))\n", "\n", " return minibatches" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import pickle\n", "import problem_unittests as tests\n", "import helper\n", "\n", "# Load the Preprocessed Validation data\n", "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# This is where the CNN imllementation in NumPy starts!" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x114084128>" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LYg1SqqXYY3bgkL/hVCXQKEmSJien3DXj4/7tV5KGBmPPzcas/zErBB5nSUpt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\nNvlrA+M1mjOBKqkaaIIxWI4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"text/plain": [ "<matplotlib.figure.Figure at 0x11bb90080>" ] }, "metadata": { "image/png": { "height": 250, "width": 253 } }, "output_type": "display_data" } ], "source": [ "# Displaying an image using matplotlib\n", "# importing the library/package\n", "import matplotlib.pyplot as plot\n", "\n", "# Using plot with imshow to show the image (N=5000, H=32, W=32, C=3)\n", "plot.imshow(valid_features[0, :, :, :])" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# # Training cycle\n", "# for epoch in range(num_):\n", "# # Loop over all batches\n", "# n_batches = 5\n", "# for batch_i in range(1, n_batches + 1):\n", "# for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n", "# train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n", "# print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')\n", "# print_stats(sess, batch_features, batch_labels, cost, accuracy)\n" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 Error: 0.45008646682\n", "Epoch: 1 Error: 0.449997899769\n", "Epoch: 2 Error: 0.449996336761\n", "Epoch: 3 Error: 0.449994805444\n", "Epoch: 4 Error: 0.449993304455\n", "Epoch: 5 Error: 0.449991833198\n", "Epoch: 6 Error: 0.449990391089\n", "Epoch: 7 Error: 0.449988977555\n", "Epoch: 8 Error: 0.449987592032\n", "Epoch: 9 Error: 0.449986233971\n", "Epoch: 10 Error: 0.449984902832\n", "Epoch: 11 Error: 0.449983598083\n", "Epoch: 12 Error: 0.449982319207\n", "Epoch: 13 Error: 0.449981065693\n", "Epoch: 14 Error: 0.449979837043\n", "Epoch: 15 Error: 0.449978632767\n", "Epoch: 16 Error: 0.449977452386\n", "Epoch: 17 Error: 0.449976295428\n", "Epoch: 18 Error: 0.449975161432\n", "Epoch: 19 Error: 0.449974049946\n", "Epoch: 20 Error: 0.449972960526\n", "Epoch: 21 Error: 0.449971892738\n", "Epoch: 22 Error: 0.449970846154\n", "Epoch: 23 Error: 0.449969820358\n", "Epoch: 24 Error: 0.449968814939\n", "Epoch: 25 Error: 0.449967829495\n", "Epoch: 26 Error: 0.449966863632\n", "Epoch: 27 Error: 0.449965916964\n", "Epoch: 28 Error: 0.449964989113\n", "Epoch: 29 Error: 0.449964079705\n", "Epoch: 30 Error: 0.449963188379\n", "Epoch: 31 Error: 0.449962314776\n", "Epoch: 32 Error: 0.449961458547\n", "Epoch: 33 Error: 0.449960619348\n", "Epoch: 34 Error: 0.449959796844\n", "Epoch: 35 Error: 0.449958990703\n", "Epoch: 36 Error: 0.449958200603\n", "Epoch: 37 Error: 0.449957426227\n", "Epoch: 38 Error: 0.449956667263\n", "Epoch: 39 Error: 0.449955923408\n", "Epoch: 40 Error: 0.449955194361\n", "Epoch: 41 Error: 0.449954479831\n", "Epoch: 42 Error: 0.44995377953\n", "Epoch: 43 Error: 0.449953093176\n", "Epoch: 44 Error: 0.449952420493\n", "Epoch: 45 Error: 0.44995176121\n", "Epoch: 46 Error: 0.449951115063\n", "Epoch: 47 Error: 0.449950481791\n", "Epoch: 48 Error: 0.449949861139\n", "Epoch: 49 Error: 0.449949252857\n", "Epoch: 50 Error: 0.449948656699\n", "Epoch: 51 Error: 0.449948072426\n", "Epoch: 52 Error: 0.449947499802\n", "Epoch: 53 Error: 0.449946938596\n", "Epoch: 54 Error: 0.44994638858\n", "Epoch: 55 Error: 0.449945849535\n", "Epoch: 56 Error: 0.449945321241\n", "Epoch: 57 Error: 0.449944803485\n", "Epoch: 58 Error: 0.449944296058\n", "Epoch: 59 Error: 0.449943798756\n", "Epoch: 60 Error: 0.449943311377\n", "Epoch: 61 Error: 0.449942833723\n", "Epoch: 62 Error: 0.449942365603\n", "Epoch: 63 Error: 0.449941906826\n", "Epoch: 64 Error: 0.449941457207\n", "Epoch: 65 Error: 0.449941016563\n", "Epoch: 66 Error: 0.449940584718\n", "Epoch: 67 Error: 0.449940161494\n", "Epoch: 68 Error: 0.449939746722\n", "Epoch: 69 Error: 0.449939340232\n", "Epoch: 70 Error: 0.449938941861\n", "Epoch: 71 Error: 0.449938551447\n", "Epoch: 72 Error: 0.449938168831\n", "Epoch: 73 Error: 0.449937793858\n", "Epoch: 74 Error: 0.449937426377\n", "Epoch: 75 Error: 0.449937066237\n", "Epoch: 76 Error: 0.449936713293\n", "Epoch: 77 Error: 0.449936367402\n", "Epoch: 78 Error: 0.449936028423\n", "Epoch: 79 Error: 0.449935696218\n", "Epoch: 80 Error: 0.449935370652\n", "Epoch: 81 Error: 0.449935051594\n", "Epoch: 82 Error: 0.449934738913\n", "Epoch: 83 Error: 0.449934432483\n", "Epoch: 84 Error: 0.449934132178\n", "Epoch: 85 Error: 0.449933837877\n", "Epoch: 86 Error: 0.44993354946\n", "Epoch: 87 Error: 0.449933266809\n", "Epoch: 88 Error: 0.44993298981\n", "Epoch: 89 Error: 0.44993271835\n", "Epoch: 90 Error: 0.449932452318\n", "Epoch: 91 Error: 0.449932191607\n", "Epoch: 92 Error: 0.449931936109\n", "Epoch: 93 Error: 0.449931685721\n", "Epoch: 94 Error: 0.44993144034\n", "Epoch: 95 Error: 0.449931199868\n", "Epoch: 96 Error: 0.449930964206\n", "Epoch: 97 Error: 0.449930733257\n", "Epoch: 98 Error: 0.449930506928\n", "Epoch: 99 Error: 0.449930285127\n", "Epoch: 100 Error: 0.449930067762\n", "Epoch: 101 Error: 0.449929854746\n", "Epoch: 102 Error: 0.449929645991\n", "Epoch: 103 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0.449921491966\n", "Epoch: 182 Error: 0.44992145049\n", "Epoch: 183 Error: 0.449921409841\n", "Epoch: 184 Error: 0.449921370004\n", "Epoch: 185 Error: 0.449921330962\n", "Epoch: 186 Error: 0.449921292699\n", "Epoch: 187 Error: 0.4499212552\n", "Epoch: 188 Error: 0.449921218449\n", "Epoch: 189 Error: 0.449921182432\n", "Epoch: 190 Error: 0.449921147134\n", "Epoch: 191 Error: 0.449921112539\n", "Epoch: 192 Error: 0.449921078635\n", "Epoch: 193 Error: 0.449921045408\n", "Epoch: 194 Error: 0.449921012843\n", "Epoch: 195 Error: 0.449920980928\n", "Epoch: 196 Error: 0.44992094965\n", "Epoch: 197 Error: 0.449920918995\n", "Epoch: 198 Error: 0.449920888952\n", "Epoch: 199 Error: 0.449920859508\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11587bfd0>]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0UwgdV2mrY0ckSZJUGA0TeABSSv8Ejq7iuunA+4Y7am2nos59ZOGoljpX0DFw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\nRLKWAo+IiIiIiGQtBR4REREREcla/x8GKLw9A8iArgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x115643828>" ] }, "metadata": { "image/png": { "height": 258, "width": 414 } }, "output_type": "display_data" } ], "source": [ "# # input and output dataset\n", "X=valid_features.transpose(0, 3, 1, 2) # NCHW == mat_txn\n", "Y=valid_labels #NH= num_classes=10 = mat_txn\n", "#for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n", "# train_features, train_labels = helper.load_preprocess_training_batch(batch_id=, batch_size=)\n", "\n", "\n", "\n", "# Initilizting the parameters\n", "# Convolutional layer\n", "# Suppose we have 20 of 3x3 filter: 20x1x3x3. W_col will be 20x9 matrix\n", "# Let this be 3x3 convolution with stride = 1 and padding = 1\n", "h_filter=3 \n", "w_filter=3 \n", "c_filter=3\n", "padding=1 \n", "stride=1\n", "num_filters = 20\n", "w1 = np.random.normal(loc=0.0, scale=1.0, size=(num_filters, c_filter, h_filter, w_filter))# NCHW 20x9 x 9x500 = 20x500\n", "w1 = w1/(c_filter h_filter* w_filter) # taking average from them or average running for initialization.\n", "b1 = np.zeros(shape=(num_filters, 1), dtype=float)\n", "\n", "# FC layer to the output layer -- This is really hard to have a final size for the FC to the output layer\n", "# num_classes = y[0, 1] # txn\n", "w2 = np.random.normal(loc=0.0, scale=1.0, size=Y[0:1].shape) # This will be resized though\n", "b2 = np.zeros(shape=Y[0:1].shape) # number of output nodes/units/neurons are equal to the number of classes\n", "\n", "# Initializing hyper parameters\n", "num_epochs = 200\n", "## minibatch_size = 512 # This will eventually used for stochstic or random minibatch from the whole batch\n", "batch_size = X.shape[0]//1 #NCHW, N= number of samples or t\n", "error_list = [] # to display the plot or plot the error curve/ learning rate\n", "\n", "# Training loops for epochs and updating params\n", "for epoch in range(num_epochs): # start=0, stop=num_epochs, step=1\n", "\n", " # Initializing/reseting the gradients\n", " dw1 = np.zeros(shape=w1.shape)\n", " db1 = np.zeros(shape=b1.shape)\n", " dw2 = np.zeros(shape=w2.shape)\n", " db2 = np.zeros(shape=b2.shape)\n", " err = 0\n", " \n", " # # Shuffling the entire batch for a minibatch\n", " # # Stochastic part for randomizing/shuffling through the dataset in every single epoch\n", " # minibatches = get_minibatch(X=X, y=Y, minibatch_size=batch_size, shuffle=True)\n", " # X_mini, Y_mini = minibatches[0]\n", " \n", " \n", " # The loop for learning the gradients\n", " for t in range(batch_size): # start=0, stop=mini_batch_size/batch_size, step=1\n", " \n", " # input and output each sample in the batch/minibatch for updating the gradients/d_params/delta_params\n", " x= X[t:t+1] # mat_nxcxhxw\n", " y= Y[t:t+1] # mat_txm\n", " # print(\"inputs:\", x.shape, y.shape)\n", " \n", " # Forward pass\n", " # start with the convolution layer forward\n", " h1_in, h1_cache = conv_forward(X=x, W=w1, b=b1, stride=1, padding=1)\n", " h1_out = h1_in * 1 # activation func. = LU\n", " #h1_out = np.maximum(h1_in, 0) # ReLU for avoiding the very high ERROR in classification\n", " # print(\"Convolution layer:\", h1_out)\n", "\n", " # Connect the flattened layer to the output layer/visible layer FC layer\n", " h1_fc = h1_out.reshape(1, -1)\n", " # initializing w2 knowing the size/given the size of fc layer\n", " if t==0: w2 = (1/h1_fc.shape[1]) * np.resize(a=w2, new_shape=(h1_fc.shape[1], y.shape[1])) # mat_hxm # initialization\n", " out = h1_fc @ w2 + b2\n", " y_prob = softmax(X=out) # can also be sigmoid/logistic function/Bayesina/ NBC\n", " # print(\"Output layer: \", out, y_prob, y)\n", "\n", " # Mean Square Error: Calculate the error one by one sample from the batch -- Euclidean distance\n", " err += 0.5 * (1/ batch_size) * np.sum((y_prob - y)*2) # convex surface ax2+b\n", " dy = (1/ batch_size) (y_prob - y) # convex surface this way # ignoring the constant coefficies\n", " # print(\"error:\", dy, err)\n", " \n", " # # Mean Cross Entropy Error: np.log is np.log(exp(x))=x equals to ln in math\n", " # err += (1/batch_size) * -(np.sum(y* np.log(y_prob))) \n", " # dy = (1/batch_size) * -(y/ y_prob) # y_prop= 0-1, log(y_prob)==-inf-0\n", " # # print(\"Error:\", dy, err)\n", "\n", " # Backward pass\n", " # output layer gradient\n", " dout = dy @ dsoftmax(X=out, sX=y_prob).T\n", " if t==0: dw2 = np.resize(a=dw2, new_shape=w2.shape)\n", " dw2 += h1_fc.T @ dout # mat_hx1 @ mat_1xm = mat_hxm\n", " db2 += dout # mat_1xm\n", " dh1_fc = dout @ w2.T # mat_1xm @ mat_mxh\n", "\n", " # convolution layer back\n", " dh1_out = dh1_fc.reshape(h1_out.shape)\n", " # dh1[h1_out<=0] = 0 #drelu\n", " dh1 = dh1_out * 1 # derivative of the LU in bwd pass/prop\n", " dX_conv, dW_conv, db_conv = conv_backward(cache=h1_cache, dout=dh1)\n", " dw1 += dW_conv\n", " db1 += db_conv\n", "\n", " # Updating the params in the model/cnn in ech epoch \n", " w1 -= dw1\n", " b1 -= db1\n", " w2 -= dw2\n", " b2 -= db2\n", "\n", " # displaying the total error and accuracy\n", " print(\"Epoch:\", epoch, \"Error:\", err)\n", " error_list.append(err)\n", "\n", "# Ploting the error list for the learning rate\n", "\n", "plot.plot(error_list)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "error_list_MCE = error_list" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11bb93780>]" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Q7AnxeoolU1+imGtwJXBxo0namXl9RBwFfAY4EZgOPAx8oryn/pvx54DjgEMp9oboBp4G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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x115649358>" ] }, "metadata": { "image/png": { "height": 250, "width": 392 } }, "output_type": "display_data" } ], "source": [ "plot.plot(error_list_MCE)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": true }, "outputs": [], "source": [ "error_list_MSE = error_list" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x115a9ef98>]" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0UwgdV2mrY0ckSZJUGA0TeABSSv8Ejq7iuunA+4Y7am2nos59ZOGoljpX0DFw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\njV08fbCBpw428NSBBk62Dg5AvTHHH44284ejzXx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"text/plain": [ "<matplotlib.figure.Figure at 0x11519fcf8>" ] }, "metadata": { "image/png": { "height": 258, "width": 414 } }, "output_type": "display_data" } ], "source": [ "plot.plot(error_list_MSE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 1 }	unlicense
Zhenxingzhang/AnalyticsVidhya	LoanPrediction/Loan_Prediction_Explore.ipynb	1	47369	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "# training_data[training_data.apply(lambda x: x['Credit_History'] == 0 and x['Loan_Status'] == 'Y',axis = 1)]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "training_data = pd.read_csv('Data/train_u6lujuX_CVtuZ9i.csv')\n", "testing_data = pd.read_csv('Data/test_Y3wMUE5_7gLdaTN.csv')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Loan_ID</th>\n", " <th>Gender</th>\n", " <th>Married</th>\n", " <th>Dependents</th>\n", " <th>Education</th>\n", " <th>Self_Employed</th>\n", " <th>ApplicantIncome</th>\n", " <th>CoapplicantIncome</th>\n", " <th>LoanAmount</th>\n", " <th>Loan_Amount_Term</th>\n", " <th>Credit_History</th>\n", " <th>Property_Area</th>\n", " <th>Loan_Status</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>LP001002</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>5849</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>LP001003</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>1</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>4583</td>\n", " <td>1508.0</td>\n", " <td>128.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>LP001005</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>Yes</td>\n", " 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<td>LP002925</td>\n", " <td>NaN</td>\n", " <td>No</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>4750</td>\n", " <td>0.0</td>\n", " <td>94.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Semiurban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>589</th>\n", " <td>LP002926</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>2</td>\n", " <td>Graduate</td>\n", " <td>Yes</td>\n", " <td>2726</td>\n", " <td>0.0</td>\n", " <td>106.0</td>\n", " <td>360.0</td>\n", " <td>0.0</td>\n", " <td>Semiurban</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <th>590</th>\n", " <td>LP002928</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>3000</td>\n", " <td>3416.0</td>\n", " <td>56.0</td>\n", " <td>180.0</td>\n", " <td>1.0</td>\n", " <td>Semiurban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>591</th>\n", " <td>LP002931</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>2</td>\n", " <td>Graduate</td>\n", " <td>Yes</td>\n", " <td>6000</td>\n", " <td>0.0</td>\n", " <td>205.0</td>\n", " <td>240.0</td>\n", " <td>1.0</td>\n", " <td>Semiurban</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <th>592</th>\n", " <td>LP002933</td>\n", " <td>NaN</td>\n", " <td>No</td>\n", " <td>3+</td>\n", " <td>Graduate</td>\n", " <td>Yes</td>\n", " <td>9357</td>\n", " <td>0.0</td>\n", " <td>292.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Semiurban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>593</th>\n", " <td>LP002936</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>3859</td>\n", " <td>3300.0</td>\n", " <td>142.0</td>\n", " <td>180.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>594</th>\n", " <td>LP002938</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>Yes</td>\n", " <td>16120</td>\n", " <td>0.0</td>\n", " <td>260.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>595</th>\n", " <td>LP002940</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>0</td>\n", " <td>Not Graduate</td>\n", " <td>No</td>\n", " <td>3833</td>\n", " <td>0.0</td>\n", " <td>110.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>596</th>\n", " <td>LP002941</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>2</td>\n", " <td>Not Graduate</td>\n", " <td>Yes</td>\n", " <td>6383</td>\n", " <td>1000.0</td>\n", " <td>187.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <th>597</th>\n", " <td>LP002943</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>NaN</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>2987</td>\n", " <td>0.0</td>\n", " <td>88.0</td>\n", " <td>360.0</td>\n", " <td>0.0</td>\n", " <td>Semiurban</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <th>598</th>\n", " <td>LP002945</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>Yes</td>\n", " <td>9963</td>\n", " <td>0.0</td>\n", " <td>180.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>599</th>\n", " <td>LP002948</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>2</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>5780</td>\n", " <td>0.0</td>\n", " <td>192.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>600</th>\n", " <td>LP002949</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>3+</td>\n", " <td>Graduate</td>\n", " <td>NaN</td>\n", " <td>416</td>\n", " <td>41667.0</td>\n", " <td>350.0</td>\n", " <td>180.0</td>\n", " <td>NaN</td>\n", " <td>Urban</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <th>601</th>\n", " <td>LP002950</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>0</td>\n", " <td>Not Graduate</td>\n", " <td>NaN</td>\n", " <td>2894</td>\n", " <td>2792.0</td>\n", " <td>155.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>602</th>\n", " <td>LP002953</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>3+</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>5703</td>\n", " <td>0.0</td>\n", " <td>128.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>603</th>\n", " <td>LP002958</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>3676</td>\n", " <td>4301.0</td>\n", " <td>172.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>604</th>\n", " <td>LP002959</td>\n", " <td>Female</td>\n", " <td>Yes</td>\n", " <td>1</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>12000</td>\n", " <td>0.0</td>\n", " <td>496.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Semiurban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>605</th>\n", " <td>LP002960</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>0</td>\n", " <td>Not Graduate</td>\n", " <td>No</td>\n", " <td>2400</td>\n", " <td>3800.0</td>\n", " <td>NaN</td>\n", " <td>180.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <th>606</th>\n", " <td>LP002961</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>1</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>3400</td>\n", " <td>2500.0</td>\n", " <td>173.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Semiurban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>607</th>\n", " <td>LP002964</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>2</td>\n", " <td>Not Graduate</td>\n", " <td>No</td>\n", " <td>3987</td>\n", " <td>1411.0</td>\n", " <td>157.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>608</th>\n", " <td>LP002974</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>3232</td>\n", " <td>1950.0</td>\n", " <td>108.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>609</th>\n", " <td>LP002978</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>2900</td>\n", " <td>0.0</td>\n", " <td>71.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>610</th>\n", " <td>LP002979</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>3+</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>4106</td>\n", " <td>0.0</td>\n", " <td>40.0</td>\n", " <td>180.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>611</th>\n", " <td>LP002983</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>1</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>8072</td>\n", " <td>240.0</td>\n", " <td>253.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>612</th>\n", " <td>LP002984</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>2</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>7583</td>\n", " <td>0.0</td>\n", " <td>187.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>613</th>\n", " <td>LP002990</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>Yes</td>\n", " <td>4583</td>\n", " <td>0.0</td>\n", " <td>133.0</td>\n", " <td>360.0</td>\n", " <td>0.0</td>\n", " <td>Semiurban</td>\n", " <td>N</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>614 rows × 13 columns</p>\n", "</div>" ], "text/plain": [ " Loan_ID Gender Married Dependents Education Self_Employed \\\n", "0 LP001002 Male No 0 Graduate No \n", "1 LP001003 Male Yes 1 Graduate No \n", "2 LP001005 Male Yes 0 Graduate Yes \n", "3 LP001006 Male Yes 0 Not Graduate No \n", "4 LP001008 Male No 0 Graduate No \n", "5 LP001011 Male Yes 2 Graduate Yes \n", "6 LP001013 Male Yes 0 Not Graduate No \n", "7 LP001014 Male Yes 3+ Graduate No \n", "8 LP001018 Male Yes 2 Graduate No \n", "9 LP001020 Male Yes 1 Graduate No \n", "10 LP001024 Male Yes 2 Graduate No \n", "11 LP001027 Male Yes 2 Graduate NaN \n", "12 LP001028 Male Yes 2 Graduate No \n", "13 LP001029 Male No 0 Graduate No \n", "14 LP001030 Male Yes 2 Graduate No \n", "15 LP001032 Male No 0 Graduate No \n", "16 LP001034 Male No 1 Not Graduate No \n", "17 LP001036 Female No 0 Graduate No \n", "18 LP001038 Male Yes 0 Not Graduate No \n", "19 LP001041 Male Yes 0 Graduate NaN \n", "20 LP001043 Male Yes 0 Not Graduate No \n", "21 LP001046 Male Yes 1 Graduate No \n", "22 LP001047 Male Yes 0 Not Graduate No \n", "23 LP001050 NaN Yes 2 Not Graduate No \n", "24 LP001052 Male Yes 1 Graduate NaN \n", "25 LP001066 Male Yes 0 Graduate Yes \n", "26 LP001068 Male Yes 0 Graduate No \n", "27 LP001073 Male Yes 2 Not Graduate No \n", "28 LP001086 Male No 0 Not Graduate No \n", "29 LP001087 Female No 2 Graduate NaN \n", ".. ... ... ... ... ... ... \n", "584 LP002911 Male Yes 1 Graduate No \n", "585 LP002912 Male Yes 1 Graduate No \n", "586 LP002916 Male Yes 0 Graduate No \n", "587 LP002917 Female No 0 Not Graduate No \n", "588 LP002925 NaN No 0 Graduate No \n", "589 LP002926 Male Yes 2 Graduate Yes \n", "590 LP002928 Male Yes 0 Graduate No \n", "591 LP002931 Male Yes 2 Graduate Yes \n", "592 LP002933 NaN No 3+ Graduate Yes \n", "593 LP002936 Male Yes 0 Graduate No \n", "594 LP002938 Male Yes 0 Graduate Yes \n", "595 LP002940 Male No 0 Not Graduate No \n", "596 LP002941 Male Yes 2 Not Graduate Yes \n", "597 LP002943 Male No NaN Graduate No \n", "598 LP002945 Male Yes 0 Graduate Yes \n", "599 LP002948 Male Yes 2 Graduate No \n", "600 LP002949 Female No 3+ Graduate NaN \n", "601 LP002950 Male Yes 0 Not Graduate NaN \n", "602 LP002953 Male Yes 3+ Graduate No \n", "603 LP002958 Male No 0 Graduate No \n", "604 LP002959 Female Yes 1 Graduate No \n", "605 LP002960 Male Yes 0 Not Graduate No \n", "606 LP002961 Male Yes 1 Graduate No \n", "607 LP002964 Male Yes 2 Not Graduate No \n", "608 LP002974 Male Yes 0 Graduate No \n", "609 LP002978 Female No 0 Graduate No \n", "610 LP002979 Male Yes 3+ Graduate No \n", "611 LP002983 Male Yes 1 Graduate No \n", "612 LP002984 Male Yes 2 Graduate No \n", "613 LP002990 Female No 0 Graduate Yes \n", "\n", " ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term \\\n", "0 5849 0.0 NaN 360.0 \n", "1 4583 1508.0 128.0 360.0 \n", "2 3000 0.0 66.0 360.0 \n", "3 2583 2358.0 120.0 360.0 \n", "4 6000 0.0 141.0 360.0 \n", "5 5417 4196.0 267.0 360.0 \n", "6 2333 1516.0 95.0 360.0 \n", "7 3036 2504.0 158.0 360.0 \n", "8 4006 1526.0 168.0 360.0 \n", "9 12841 10968.0 349.0 360.0 \n", "10 3200 700.0 70.0 360.0 \n", "11 2500 1840.0 109.0 360.0 \n", "12 3073 8106.0 200.0 360.0 \n", "13 1853 2840.0 114.0 360.0 \n", "14 1299 1086.0 17.0 120.0 \n", "15 4950 0.0 125.0 360.0 \n", "16 3596 0.0 100.0 240.0 \n", "17 3510 0.0 76.0 360.0 \n", "18 4887 0.0 133.0 360.0 \n", "19 2600 3500.0 115.0 NaN \n", "20 7660 0.0 104.0 360.0 \n", "21 5955 5625.0 315.0 360.0 \n", "22 2600 1911.0 116.0 360.0 \n", "23 3365 1917.0 112.0 360.0 \n", "24 3717 2925.0 151.0 360.0 \n", "25 9560 0.0 191.0 360.0 \n", "26 2799 2253.0 122.0 360.0 \n", "27 4226 1040.0 110.0 360.0 \n", "28 1442 0.0 35.0 360.0 \n", "29 3750 2083.0 120.0 360.0 \n", ".. ... ... ... ... \n", "584 2787 1917.0 146.0 360.0 \n", "585 4283 3000.0 172.0 84.0 \n", "586 2297 1522.0 104.0 360.0 \n", "587 2165 0.0 70.0 360.0 \n", "588 4750 0.0 94.0 360.0 \n", "589 2726 0.0 106.0 360.0 \n", "590 3000 3416.0 56.0 180.0 \n", "591 6000 0.0 205.0 240.0 \n", "592 9357 0.0 292.0 360.0 \n", "593 3859 3300.0 142.0 180.0 \n", "594 16120 0.0 260.0 360.0 \n", "595 3833 0.0 110.0 360.0 \n", "596 6383 1000.0 187.0 360.0 \n", "597 2987 0.0 88.0 360.0 \n", "598 9963 0.0 180.0 360.0 \n", "599 5780 0.0 192.0 360.0 \n", "600 416 41667.0 350.0 180.0 \n", "601 2894 2792.0 155.0 360.0 \n", "602 5703 0.0 128.0 360.0 \n", "603 3676 4301.0 172.0 360.0 \n", "604 12000 0.0 496.0 360.0 \n", "605 2400 3800.0 NaN 180.0 \n", "606 3400 2500.0 173.0 360.0 \n", "607 3987 1411.0 157.0 360.0 \n", "608 3232 1950.0 108.0 360.0 \n", "609 2900 0.0 71.0 360.0 \n", "610 4106 0.0 40.0 180.0 \n", "611 8072 240.0 253.0 360.0 \n", "612 7583 0.0 187.0 360.0 \n", "613 4583 0.0 133.0 360.0 \n", "\n", " Credit_History Property_Area Loan_Status \n", "0 1.0 Urban Y \n", "1 1.0 Rural N \n", "2 1.0 Urban Y \n", "3 1.0 Urban Y \n", "4 1.0 Urban Y \n", "5 1.0 Urban Y \n", "6 1.0 Urban Y \n", "7 0.0 Semiurban N \n", "8 1.0 Urban Y \n", "9 1.0 Semiurban N \n", "10 1.0 Urban Y \n", "11 1.0 Urban Y \n", "12 1.0 Urban Y \n", "13 1.0 Rural N \n", "14 1.0 Urban Y \n", "15 1.0 Urban Y \n", "16 NaN Urban Y \n", "17 0.0 Urban N \n", "18 1.0 Rural N \n", "19 1.0 Urban Y \n", "20 0.0 Urban N \n", "21 1.0 Urban Y \n", "22 0.0 Semiurban N \n", "23 0.0 Rural N \n", "24 NaN Semiurban N \n", "25 1.0 Semiurban Y \n", "26 1.0 Semiurban Y \n", "27 1.0 Urban Y \n", "28 1.0 Urban N \n", "29 1.0 Semiurban Y \n", ".. ... ... ... \n", "584 0.0 Rural N \n", "585 1.0 Rural N \n", "586 1.0 Urban Y \n", "587 1.0 Semiurban Y \n", "588 1.0 Semiurban Y \n", "589 0.0 Semiurban N \n", "590 1.0 Semiurban Y \n", "591 1.0 Semiurban N \n", "592 1.0 Semiurban Y \n", "593 1.0 Rural Y \n", "594 1.0 Urban Y \n", "595 1.0 Rural Y \n", "596 1.0 Rural N \n", "597 0.0 Semiurban N \n", "598 1.0 Rural Y \n", "599 1.0 Urban Y \n", "600 NaN Urban N \n", "601 1.0 Rural Y \n", "602 1.0 Urban Y \n", "603 1.0 Rural Y \n", "604 1.0 Semiurban Y \n", "605 1.0 Urban N \n", "606 1.0 Semiurban Y \n", "607 1.0 Rural Y \n", "608 1.0 Rural Y \n", "609 1.0 Rural Y \n", "610 1.0 Rural Y \n", "611 1.0 Urban Y \n", "612 1.0 Urban Y \n", "613 0.0 Semiurban N \n", "\n", "[614 rows x 13 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "training_data" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Loan_ID 0\n", "Gender 13\n", "Married 3\n", "Dependents 15\n", "Education 0\n", "Self_Employed 32\n", "ApplicantIncome 0\n", "CoapplicantIncome 0\n", "LoanAmount 22\n", "Loan_Amount_Term 14\n", "Credit_History 50\n", "Property_Area 0\n", "Loan_Status 0\n", "dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "training_data.isnull().sum()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
gorayni/fingerprint	1. Hopfield/Hopfield.ipynb	1	33416	{ "metadata": { "name": "", "signature": "sha256:bf68234cbf66acad03af5da62ce27e3b201582025f39bdd3f2bb07aae8afa585" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Hopefield Networks\n", "==================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hopfield networks were introduced in [1982](http://cns.upf.edu/jclub/hopfield82.pdf) by [John Hopfield](http://genomics.princeton.edu/hopfield/Index.html) and they represent the return of Neural Networks to the Artificial Intelligence field. I will briefly explore its continuous version as a mean to understand Boltzmann Machines.\n", "\n", "The Hopfield nets are mainly used as associative memories and for solving optimization problems. The associative memory links concepts by association, for example when you hear or see an image of the Eiffel Tower you might recall that it is in Paris.\n", "\n", "The inputs and outputs of the Hopfield network are binary, so we can train it to remember B&W images such as a collection of letters. For instance, we can teach them the next set of letters:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<table border=\"0\" cellpadding=\"5\" cellspacing=\"3\" style=\"background-color: white; margin-left: auto; margin-right: auto; text-align: left;\">\n", " <tbody>\n", "<tr>\n", " <td><a href=\"letterC.png\" imageanchor=\"1\" style=\"margin-left: 1em; margin-right: 1em;\"><img border=\"0\" height=\"120\" src=\"letterC.png\" width=\"120\" /></a></td>\n", "<td><a href=\"letterD.png\" imageanchor=\"1\" style=\"margin-left: 1em; margin-right: 1em;\"><img border=\"0\" height=\"120\" src=\"letterD.png\" width=\"120\" /></a></td>\n", "<td><a href=\"letterJ.png\" imageanchor=\"1\" style=\"margin-left: 1em; margin-right: 1em;\"><img border=\"0\" height=\"120\" src=\"letterJ.png\" width=\"120\" /></a></td>\n", "<td><a href=\"letterM.png\" imageanchor=\"1\" style=\"margin-left: 1em; margin-right: 1em;\"><img border=\"0\" height=\"120\" src=\"letterM.png\" width=\"120\" /></a></td>\n", " </tr>\n", "</tbody>\n", " </table>\n", "<div style=\"text-align: center;\">\n", "<b>Fig. 1</b>. A set of 4 letters to be stored in a Hopfield Network. Based on <a href=\"http://www.inference.phy.cam.ac.uk/itila/\">MacKay</a>.</div>\n", "<div style=\"text-align: center;\">\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So how does the Hopfield network recalls a pattern? Well, the recalling process is iterative and in each cycle all the neurons in the net are fired. This process ends when the state of the neurons doesn't change, as seen in the next figure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<table border=\"0\" cellpadding=\"5\" cellspacing=\"3\" style=\"background-color: white; margin-left: auto; margin-right: auto; text-align: left;\">\n", " <tbody>\n", " <tr>\n", " <td>\n", " <a href=\"recallingProcess1.png\" imageanchor=\"1\" style=\"margin-left: 1em; margin-right: 1em;\">\n", " <img border=\"0\" height=\"120\" src=\"recallingProcess1.png\" width=\"120\" />\n", " </a>\n", " <br />\n", " <div style=\"text-align: center;\">\n", " (a) Input pattern\n", " </div>\n", " </td>\n", " <td>\n", " <a href=\"recallingProcess2.png\" imageanchor=\"1\" style=\"margin-left: 1em; margin-right: 1em;\">\n", " <img border=\"0\" height=\"120\" src=\"recallingProcess2.png\" width=\"120\" />\n", " </a>\n", " <br />\n", " <div style=\"text-align: center;\">\n", " (b) Iteration 1\n", " </div>\n", " </td>\n", " <td>\n", " <a href=\"recallingProcess3.png\" imageanchor=\"1\" style=\"margin-left: 1em; margin-right: 1em;\">\n", " <img border=\"0\" height=\"120\" src=\"recallingProcess3.png\" width=\"120\" />\n", " </a>\n", " <br />\n", " <div style=\"text-align: center;\">\n", " (c) Iteration 2\n", " </div>\n", " </td>\n", " <td>\n", " <a href=\"recallingProcess4.png\" imageanchor=\"1\" style=\"margin-left: 1em; margin-right: 1em;\">\n", " <img border=\"0\" height=\"120\" src=\"recallingProcess4.png\" width=\"120\" />\n", " </a>\n", " <br />\n", " <div style=\"text-align: center;\">\n", " (d) Iteration 3\n", " </div>\n", " </td>\n", " <td>\n", " <a href=\"recallingProcess5.png\" imageanchor=\"1\" style=\"margin-left: 1em; margin-right: 1em;\">\n", " <img border=\"0\" height=\"120\" src=\"recallingProcess5.png\" width=\"120\" />\n", " </a>\n", " <br />\n", " <div style=\"text-align: center;\">\n", " (e) Final iteration\n", " </div>\n", " </td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<div style=\"text-align: center;\">\n", " <b>\n", " Fig. 2\n", " </b>\n", " . Recalling process of an input pattern.\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We shall setup the notebook with the required libraries:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.lines as mlines\n", "import matplotlib.patches as mpatches" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A simple representation for each letter is an array, so the patterns can be stored in a matrix:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "patterns = np.array([\n", " [-1,-1,-1,-1,1,1,-1,1,1,-1,1,-1,1,1,-1,1,-1,1,1,-1,1,-1,-1,-1,1.], # Letter D\n", " [-1,-1,-1,-1,-1,1,1,1,-1,1,1,1,1,-1,1,-1,1,1,-1,1,-1,-1,-1,1,1.], # Letter J\n", " [1,-1,-1,-1,-1,-1,1,1,1,1,-1,1,1,1,1,-1,1,1,1,1,1,-1,-1,-1,-1.], # Letter C\n", " [-1,1,1,1,-1,-1,-1,1,-1,-1,-1,1,-1,1,-1,-1,1,1,1,-1,-1,1,1,1,-1.],]) # Letter M " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can plot any letter with the following function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_letter(letter): \n", " black = '#000000'; white='#FFFFFF'; gray='#AAAAAA'\n", " squareSide = 1\n", " \n", " fig = plt.figure(figsize=(0.35,0.35))\n", " ax = plt.axes([0,0,6.5,6.5])\n", " \n", " ax.set_ylim([0,5])\n", " ax.set_xlim([0,5])\n", " plt.gca().invert_yaxis()\n", "\n", " # Plotting squares\n", " for i in xrange(25):\n", " coords = np.array([i%5squareSide, (i/5)squareSide])\n", " color = black if letter[i]< 0 else white\n", " square = mpatches.Rectangle(coords, squareSide, squareSide, \n", " color = color, linewidth=0.5)\n", " ax.add_patch(square)\n", " \n", " # Plotting grid\n", " for i in xrange(6):\n", " x = (5-i%5)squareSide\n", " s1, s2 = np.array([[x, x], [0, 5squareSide]])\n", " ax.add_line(mlines.Line2D(s1, s2, lw=1, color = gray ))\n", " ax.add_line(mlines.Line2D(s2, s1, lw=1, color = gray ))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Architecture\n", "\n", "A Hopfield net is a set of neurons that are:\n", "\n", "* Bidirectionally connected between each other with symmetric weights, i.e. the weights between all neurons $i$ and $j$ are $w_{ij}=w_{ji}$.\n", "* Not self-connected, this means that $w_{ii}=0$.\n", "\n", "Both properties are illustrated in Fig. 3, where a Hopfield network consisting of 5 neurons is shown." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"text-align: center;\"><img border=\"0\" height=\"294\" src=\"hopfieldArchitecture.png\" width=\"300\" /></div>\n", "<div style=\"text-align: center;\"><b>Fig. 3</b>. Hopfield network architecture.</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One property that the diagram fails to capture it is the recurrency of the network. The Hopfield networks are recurrent because the inputs of each neuron are the outputs of the others, i.e. it posses feedback loops as seen in Fig. 4. This last property is better understood by the recalling process.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"text-align: center;\"><img border=\"0\" height=\"259\" src=\"recurrentHopfield.png\" width=\"600\" /></div>\n", "<div style=\"text-align: center;\"><b>Fig. 4</b>. A Hopfield network consisting of 5 neurons with feedback loops. Based on <a href=\"http://books.google.com.mx/books?id=M5abQgAACAAJ&dq=haykin+neural+networks&hl=en&sa=X&ei=Is4BUrvEJ-bkyQHbiYGQBA&redir_esc=y\">Haykin</a>.</div>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Learning Rule\n", "\n", "In order to learn a set of patterns $\\textbf{x}^{(n)}$, such as the four letters presented above, the weights of the net must be set using the Hebb Rule:\n", "\n", "$$w_{ij}=\\eta\\sum_n{x_{i}^{(n)}x_{j}^{(n)}}$$\n", "where $\\eta$ is a constant that might be set to $\\eta=1/N$ for $N$ patterns. In the case of the continuous Hopfield network, this constant prevents the weight from growing with $N$. The idea of the Hebbian rule is that two neurons that are repeatedly fired at the same time must have a stronger association.\n", "\n", "So the learning function is as follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def train(patterns):\n", " n, m = patterns.shape\n", " eta = 1./n\n", "\n", " weights = np.zeros((m,m))\n", " for i in xrange(m-1):\n", " for j in xrange(i+1,m):\n", " weights[i,j] = eta*np.dot(patterns[:,i], patterns[:,j])\n", " weights[j,i] = weights[i,j] \n", " \n", " return weights" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Activity Rule\n", "\n", "The activation $a_{i}$ of the neuron $i$ is given by\n", "\n", "$$a_{i}=\\eta\\sum_j{w_{ij}x_{j}}$$\n", "\n", "and the update of the state $x_{i}$ for the continuous case is given by\n", "\n", "$$x_{i}=\\tanh{(a_{i})}$$\n", "\n", "The order of the neuron updates can be performed synchronously or asynchronously. In the former kind the activation and update of all neurons are done at the same time. In the latter kind the neurons are activated and updates sequentially and its order is randomly permuted beforehand.\n", "\n", "The recall function can be stated as:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def recall(weights, states, maxNumItr):\n", " EPS = np.finfo(np.float).eps\n", " \n", " activations = np.zeros(states.shape)\n", " states_are_unstable = True; itr=0\n", " while states_are_unstable and itr<maxNumItr:\n", " prevStates = states.copy()\n", " for i in np.random.permutation(states.size): # asynchronous activation\n", " activations[i] = np.dot(weights[i,:], states)\n", " states[i]=np.tanh(activations[i])\n", " \n", " states_are_unstable = not np.allclose(states, prevStates, EPS)\n", " itr=itr+1\n", " \n", " return states" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Learning Rule\n", "\n", "In order to learn a set of patterns $\\textbf{x}^{(n)}$, such as the four letters presented above, the weights of the net must be set using the Hebb Rule:\n", "\n", "$$w_{ij}=\\eta\\sum_n{x_{i}^{(n)}x_{j}^{(n)}}$$\n", "where $\\eta$ is a constant that might be set to $\\eta=1/N$ for $N$ patterns. In the case of the continuous Hopfield network, this constant prevents the weight from growing with $N$. The idea of the Hebbian rule is that two neurons that are repeatedly fired at the same time must have a stronger association.\n", "\n", "#Activity Rule\n", "\n", "The activation $a_{i}$ of the neuron $i$ is given by\n", "\n", "$$a_{i}=\\eta\\sum_j{w_{ij}x_{j}}$$\n", "\n", "and the update of the state $x_{i}$ for the continuous case is given by\n", "\n", "$$x_{i}=\\tanh{(a_{i})}$$\n", "\n", "The order of the neuron updates can be performed synchronously or asynchronously. In the former kind the activation and update of all neurons are done at the same time. In the latter kind the neurons are activated and updates sequentially and its order is randomly permuted beforehand.\n", "\n", "#Convergence\n", "\n", "After seeing the recalling process example one might be wondering many interesting questions like does the Hopfield network will always stabilize? The answer is yes, but the net might not recall the correct pattern. This could happen if the net is overloaded or the weights were altered.\n", "\n", "The convergence to a stable state is guaranteed for networks that have the two main characteristics of its architecture and that follows an asynchronous update procedure.\n", "\n", "Please note that in this post I didn't provide any mathematical proof for the convergence neither I talked about attractors, nonlinear dynamical systems, and Lyapunov functions. Maybe I will talk about them in a future post, in the meantime please see the references section.\n", "\n", "# Example\n", "\n", "Now lets code the example of Fig. 2, first we have to train the weights with the memory patterns:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "weights = train(patterns)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets define and plot the example input pattern:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "states = np.array([1,1,1,1,-1,1,-1,1,1,-1,1,1,-1,1,1,1,1,1,1,-1,-1,1,1,1,1.])\n", "plot_letter(states)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x108d53d90>" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, lets just randomly recall the input pattern for four iterations, please note that it approximately takes twenty five iterations to stabilize numerically:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.random.seed(10)\n", "\n", "for i in range(4):\n", " states = recall(weights, states, 1)\n", " plot_letter(states)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x108e56990>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109065850>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x1092d8b50>" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References\n", "\n", "I heavily used the book [Information Theory, Inference and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/) by [David MacKay](http://www.inference.phy.cam.ac.uk/mackay/) for writing this post. The example presented in this post is discussed in depth in the same book. Another good source for understanding the training and update procedures is presented in this page. By the way, David MacKay is one of Hopfield's PhD alumni.\n", "\n", "I also found useful these books [Neural Networks and Learning Machines](http://www.amazon.com/Neural-Networks-Learning-Machines-Edition/dp/0131471392), [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage), and [Neural Networks - A Systematic Introduction](http://page.mi.fu-berlin.de/rojas/neural/neuron.pdf). Another resources on the web are [Wikipedia](https://en.wikipedia.org/wiki/Hopfield_network) and [Scholarpedia](http://www.scholarpedia.org/article/Hopfield_network)." ] } ], "metadata": {} } ] }	mit
bosscha/alma-calibrator	notebooks/2mass/check_the_environment.ipynb	1	208352	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append('../alma-calibrator/src/utils/')\n", "\n", "from galenv import \n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "ga = Galenv()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Query from name: PKS J0334-4008\n", "RA, Dec (deg) : 53.55689 -40.14039\n", "Redshift : 1.445\n", "Velocity : 433200.0\n", "Angular-diameter distance : 1788.9300167266858 Mpc\n", "Angular radius for 1.0 Mpc seen from distance 1788.9300167266858 Mpc : 0.03202796027645615 deg\n", "Searching objects in cone section... \n", "theta = 0.03202796027645615 deg; v = [432200.0, 434200.0]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x432 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Query from name: PKS J0334-4008\n", "RA, Dec (deg) : 53.55689 -40.14039\n", "Searching objects in cone (only).. \n", "theta = 0.008 deg\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<i>Table masked=True length=3</i>\n", "<table id=\"table139854729877488\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>No.</th><th>Object Name</th><th>RA(deg)</th><th>DEC(deg)</th><th>Type</th><th>Velocity</th><th>Redshift</th><th>Redshift Flag</th><th>Magnitude and Filter</th><th>Distance (arcmin)</th><th>References</th><th>Notes</th><th>Photometry Points</th><th>Positions</th><th>Redshift Points</th><th>Diameter Points</th><th>Associations</th></tr></thead>\n", "<thead><tr><th></th><th></th><th>degrees</th><th>degrees</th><th></th><th>km / s</th><th></th><th></th><th></th><th>arcm</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n", "<thead><tr><th>int32</th><th>bytes30</th><th>float64</th><th>float64</th><th>object</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th></tr></thead>\n", "<tr><td>1</td><td>GALEXASC J033412.25-400804.8</td><td>53.55095</td><td>-40.13529</td><td>G</td><td>--</td><td>--</td><td></td><td>18.64</td><td>0.41</td><td>0</td><td>0</td><td>5</td><td>2</td><td>0</td><td>0</td><td>0</td></tr>\n", "<tr><td>2</td><td>[HB89] 0332-403</td><td>53.55689</td><td>-40.14039</td><td>QSO</td><td>433200.0</td><td>1.445</td><td></td><td>18.5</td><td>0.0</td><td>171</td><td>7</td><td>100</td><td>30</td><td>6</td><td>0</td><td>0</td></tr>\n", "<tr><td>3</td><td>MRSS 301-045883</td><td>53.56289</td><td>-40.14486</td><td>G</td><td>--</td><td>--</td><td></td><td>19.1r</td><td>0.384</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "</table>" ], "text/plain": [ "<Table masked=True length=3>\n", " No. Object Name RA(deg) ... Diameter Points Associations\n", " degrees ... \n", "int32 bytes30 float64 ... int32 int32 \n", "----- ---------------------------- ---------- ... --------------- ------------\n", " 1 GALEXASC J033412.25-400804.8 53.55095 ... 0 0\n", " 2 [HB89] 0332-403 53.55689 ... 0 0\n", " 3 MRSS 301-045883 53.56289 ... 0 0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ga.conedv_byname(\"PKS J0334-4008\", 1., 1000., show=True, savefig=True, imgname=\"PKSJ0334-4008_env.png\")\n", "ga.cone_byname(\"PKS J0334-4008\", 0.008, show=True, savefig=True, imgname=\"PKSJ0334-4008_cone.png\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Query from name: PKS J0423-0120\n", "RA, Dec (deg) : 65.81584 -1.34252\n", "Redshift : 0.916087\n", "Velocity : 274636.0\n", "Angular-diameter distance : 1660.6063087722853 Mpc\n", "Angular radius for 1.0 Mpc seen from distance 1660.6063087722853 Mpc : 0.03450292776223527 deg\n", "Searching objects in cone section... \n", "theta = 0.03450292776223527 deg; v = [273636.0, 275636.0]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x432 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<i>Table masked=True length=1</i>\n", "<table id=\"table139854810427744\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>No.</th><th>Object Name</th><th>RA(deg)</th><th>DEC(deg)</th><th>Type</th><th>Velocity</th><th>Redshift</th><th>Redshift Flag</th><th>Magnitude and Filter</th><th>Distance (arcmin)</th><th>References</th><th>Notes</th><th>Photometry Points</th><th>Positions</th><th>Redshift Points</th><th>Diameter Points</th><th>Associations</th></tr></thead>\n", "<thead><tr><th></th><th></th><th>degrees</th><th>degrees</th><th></th><th>km / s</th><th></th><th></th><th></th><th>arcm</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n", "<thead><tr><th>int32</th><th>bytes30</th><th>float64</th><th>float64</th><th>object</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th></tr></thead>\n", "<tr><td>25</td><td>[HB89] 0420-014</td><td>65.81584</td><td>-1.34252</td><td>QSO</td><td>274636.0</td><td>0.916087</td><td></td><td>18.1b</td><td>0.001</td><td>612</td><td>12</td><td>172</td><td>36</td><td>15</td><td>0</td><td>0</td></tr>\n", "</table>" ], "text/plain": [ "<Table masked=True length=1>\n", " No. Object Name RA(deg) ... Diameter Points Associations\n", " degrees ... \n", "int32 bytes30 float64 ... int32 int32 \n", "----- --------------- ---------- ... --------------- ------------\n", " 25 [HB89] 0420-014 65.81584 ... 0 0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ga.conedv_byname(\"PKS J0423-0120\", 1., 1000., show=True, savefig=True, imgname=\"PKS J0423-0120_env.png\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Query from name: NGC 1052\n", "RA, Dec (deg) : 40.26999 -8.25576\n", "Redshift : 0.005037\n", "Velocity : 1510.0\n", "Angular-diameter distance : 22.15427839862817 Mpc\n", "Angular radius for 0.2 Mpc seen from distance 22.15427839862817 Mpc : 0.5172434730858142 deg\n", "Searching objects in cone section... \n", "theta = 0.5172434730858142 deg; v = [1010.0, 2010.0]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x432 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<i>Table masked=True length=30</i>\n", "<table id=\"table139854810427912\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>No.</th><th>Object Name</th><th>RA(deg)</th><th>DEC(deg)</th><th>Type</th><th>Velocity</th><th>Redshift</th><th>Redshift Flag</th><th>Magnitude and Filter</th><th>Distance (arcmin)</th><th>References</th><th>Notes</th><th>Photometry Points</th><th>Positions</th><th>Redshift Points</th><th>Diameter Points</th><th>Associations</th></tr></thead>\n", "<thead><tr><th></th><th></th><th>degrees</th><th>degrees</th><th></th><th>km / s</th><th></th><th></th><th></th><th>arcm</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n", "<thead><tr><th>int32</th><th>bytes30</th><th>float64</th><th>float64</th><th>object</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th></tr></thead>\n", "<tr><td>716</td><td>NGC 1035</td><td>39.87121</td><td>-8.13294</td><td>G</td><td>1241.0</td><td>0.00414</td><td></td><td>12.89</td><td>24.803</td><td>185</td><td>4</td><td>51</td><td>13</td><td>13</td><td>9</td><td>0</td></tr>\n", "<tr><td>727</td><td>SDSS J023929.29-080812.4</td><td>39.87205</td><td>-8.13678</td><td>G</td><td>1299.0</td><td>0.004333</td><td>SPEC</td><td></td><td>24.688</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td></tr>\n", "<tr><td>742</td><td>2MFGC 02101</td><td>39.87467</td><td>-8.13919</td><td>PofG</td><td>1392.0</td><td>0.004643</td><td></td><td>13.5K</td><td>24.497</td><td>8</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n", "<tr><td>753</td><td>HDCE 0160</td><td>39.87583</td><td>-7.94389</td><td>GGroup</td><td>1432.0</td><td>0.004777</td><td></td><td></td><td>29.973</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n", "<tr><td>3644</td><td>NGC 1042</td><td>40.09986</td><td>-8.43354</td><td>G</td><td>1371.0</td><td>0.004573</td><td></td><td>11.5B</td><td>14.69</td><td>204</td><td>10</td><td>83</td><td>14</td><td>16</td><td>15</td><td>0</td></tr>\n", "<tr><td>3738</td><td>SDSS J024025.32-082617.2</td><td>40.10551</td><td>-8.43813</td><td></td><td>1245.0</td><td>0.004153</td><td>SPEC</td><td></td><td>14.665</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td></tr>\n", "<tr><td>4235</td><td>NGC 1047</td><td>40.13683</td><td>-8.14767</td><td>G</td><td>1340.0</td><td>0.00447</td><td></td><td>14.3g</td><td>10.227</td><td>54</td><td>2</td><td>56</td><td>11</td><td>5</td><td>14</td><td>0</td></tr>\n", "<tr><td>5874</td><td>SDSS J024059.55-081845.1</td><td>40.24813</td><td>-8.31253</td><td>Cl</td><td>1659.0</td><td>0.005534</td><td></td><td>21.7g</td><td>3.645</td><td>1</td><td>0</td><td>5</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>5981</td><td>NGC 1052:[PBF2005] GC29</td><td>40.25417</td><td>-8.26667</td><td>Cl</td><td>1273.0</td><td>0.004246</td><td></td><td></td><td>1.145</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n", "<tr><td>6385</td><td>NGC 1052:[PBF2005] GC74</td><td>40.27375</td><td>-8.21453</td><td>Cl</td><td>1675.0</td><td>0.005587</td><td></td><td></td><td>2.484</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "<tr><td>6387</td><td>CXO J024105.71-081455.1</td><td>40.27382</td><td>-8.24865</td><td>Cl</td><td>1649.0</td><td>0.0055</td><td></td><td></td><td>0.483</td><td>2</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n", "<tr><td>6448</td><td>NGC 1052:[PBF2005] GC12</td><td>40.27667</td><td>-8.20942</td><td>Cl</td><td>1610.0</td><td>0.00537</td><td></td><td></td><td>2.809</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "<tr><td>6479</td><td>SDSS J024106.82-081347.6</td><td>40.27843</td><td>-8.2299</td><td>Cl</td><td>1415.0</td><td>0.00472</td><td></td><td>22.6g</td><td>1.631</td><td>1</td><td>0</td><td>5</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>6704</td><td>NGC 0988 GROUP</td><td>40.28894</td><td>-8.11943</td><td>GGroup</td><td>1477.0</td><td>0.004927</td><td></td><td></td><td>8.257</td><td>18</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n", "<tr><td>7637</td><td>LDCE 0182</td><td>40.34375</td><td>-8.20639</td><td>GGroup</td><td>1442.0</td><td>0.00481</td><td></td><td></td><td>5.287</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n", "<tr><td>7807</td><td>LEDA 3097911</td><td>40.35638</td><td>-8.12686</td><td>G</td><td>1412.0</td><td>0.00471</td><td></td><td>13.5K</td><td>9.281</td><td>9</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n", "<tr><td>8409</td><td>2MASX J02413514-0810243</td><td>40.3961</td><td>-8.17348</td><td>G</td><td>1530.0</td><td>0.005105</td><td></td><td>15.8g</td><td>8.969</td><td>19</td><td>0</td><td>38</td><td>5</td><td>5</td><td>6</td><td>0</td></tr>\n", "<tr><td>9347</td><td>SDSS J024149.94-075530.0</td><td>40.45813</td><td>-7.92502</td><td>G</td><td>1372.0</td><td>0.004575</td><td></td><td>15.5g</td><td>22.775</td><td>21</td><td>0</td><td>43</td><td>5</td><td>2</td><td>10</td><td>0</td></tr>\n", "<tr><td>9848</td><td>[TSK2008] 0889</td><td>40.49543</td><td>-7.9678</td><td>GGroup</td><td>1421.0</td><td>0.00474</td><td></td><td></td><td>21.859</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n", "</table>" ], "text/plain": [ "<Table masked=True length=30>\n", " No. Object Name RA(deg) ... Diameter Points Associations\n", " degrees ... \n", "int32 bytes30 float64 ... int32 int32 \n", "----- ------------------------ ---------- ... --------------- ------------\n", " 716 NGC 1035 39.87121 ... 9 0\n", " 727 SDSS J023929.29-080812.4 39.87205 ... 0 0\n", " 742 2MFGC 02101 39.87467 ... 0 0\n", " 753 HDCE 0160 39.87583 ... 0 0\n", " 3644 NGC 1042 40.09986 ... 15 0\n", " 3738 SDSS J024025.32-082617.2 40.10551 ... 0 0\n", " 4235 NGC 1047 40.13683 ... 14 0\n", " 5874 SDSS J024059.55-081845.1 40.24813 ... 4 0\n", " 5981 NGC 1052:[PBF2005] GC29 40.25417 ... 0 0\n", " ... ... ... ... ... ...\n", " 6385 NGC 1052:[PBF2005] GC74 40.27375 ... 0 0\n", " 6387 CXO J024105.71-081455.1 40.27382 ... 0 1\n", " 6448 NGC 1052:[PBF2005] GC12 40.27667 ... 0 0\n", " 6479 SDSS J024106.82-081347.6 40.27843 ... 4 0\n", " 6704 NGC 0988 GROUP 40.28894 ... 0 0\n", " 7637 LDCE 0182 40.34375 ... 0 0\n", " 7807 LEDA 3097911 40.35638 ... 0 0\n", " 8409 2MASX J02413514-0810243 40.3961 ... 6 0\n", " 9347 SDSS J024149.94-075530.0 40.45813 ... 10 0\n", " 9848 [TSK2008] 0889 40.49543 ... 0 0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ga.conedv_byname(\"NGC 1052\", 0.2, 500., show=True, savefig=True, imgname=\"NGC1052.png\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Query from name: NGC 1052\n", "RA, Dec (deg) : 40.26999 -8.25576\n", "Searching objects in cone (only).. \n", "theta = 0.2 deg\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 540x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<i>Table masked=True length=2015</i>\n", "<table id=\"table139854728641168\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>No.</th><th>Object Name</th><th>RA(deg)</th><th>DEC(deg)</th><th>Type</th><th>Velocity</th><th>Redshift</th><th>Redshift Flag</th><th>Magnitude and Filter</th><th>Distance (arcmin)</th><th>References</th><th>Notes</th><th>Photometry Points</th><th>Positions</th><th>Redshift Points</th><th>Diameter Points</th><th>Associations</th></tr></thead>\n", "<thead><tr><th></th><th></th><th>degrees</th><th>degrees</th><th></th><th>km / s</th><th></th><th></th><th></th><th>arcm</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead>\n", "<thead><tr><th>int32</th><th>bytes30</th><th>float64</th><th>float64</th><th>object</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th></tr></thead>\n", "<tr><td>1</td><td>SDSS J024017.17-081441.3</td><td>40.07157</td><td>-8.24482</td><td></td><td>--</td><td>--</td><td></td><td>21.8g</td><td>11.801</td><td>2</td><td>0</td><td>5</td><td>1</td><td>0</td><td>4</td><td>1</td></tr>\n", "<tr><td>2</td><td>2XMM J024017.2-081434</td><td>40.07187</td><td>-8.24282</td><td>XrayS</td><td>--</td><td>--</td><td></td><td></td><td>11.79</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n", "<tr><td>3</td><td>SDSS J024017.63-081256.3</td><td>40.07348</td><td>-8.21565</td><td></td><td>--</td><td>--</td><td></td><td>22.4g</td><td>11.915</td><td>0</td><td>0</td><td>5</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>4</td><td>GALEXMSC J024017.82-081517.3</td><td>40.07427</td><td>-8.25483</td><td>UvS</td><td>--</td><td>--</td><td></td><td></td><td>11.622</td><td>0</td><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n", "<tr><td>5</td><td>SDSS J024018.24-081723.6</td><td>40.07602</td><td>-8.28991</td><td>G</td><td>--</td><td>--</td><td></td><td>22.5g</td><td>11.698</td><td>0</td><td>0</td><td>15</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>6</td><td>SDSS J024018.49-081219.0</td><td>40.07708</td><td>-8.2053</td><td>G</td><td>--</td><td>--</td><td></td><td>22.6g</td><td>11.849</td><td>0</td><td>0</td><td>15</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>7</td><td>SDSS J024018.63-081257.2</td><td>40.07766</td><td>-8.2159</td><td>G</td><td>--</td><td>--</td><td></td><td>22.3g</td><td>11.669</td><td>0</td><td>0</td><td>15</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>8</td><td>SDSS J024018.88-081855.0</td><td>40.07868</td><td>-8.31528</td><td>G</td><td>--</td><td>--</td><td></td><td>23.2g</td><td>11.907</td><td>0</td><td>0</td><td>19</td><td>1</td><td>0</td><td>3</td><td>0</td></tr>\n", "<tr><td>9</td><td>GALEXMSC J024018.98-081532.9</td><td>40.07909</td><td>-8.25917</td><td>UvS</td><td>--</td><td>--</td><td></td><td></td><td>11.337</td><td>0</td><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n", "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n", "<tr><td>2006</td><td>SDSS J024151.32-081413.3</td><td>40.46384</td><td>-8.23703</td><td>G</td><td>--</td><td>--</td><td></td><td>22.5g</td><td>11.565</td><td>1</td><td>0</td><td>15</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>2007</td><td>SDSS J024151.50-081545.7</td><td>40.46459</td><td>-8.2627</td><td></td><td>--</td><td>--</td><td></td><td>24.0g</td><td>11.562</td><td>0</td><td>0</td><td>5</td><td>1</td><td>0</td><td>3</td><td>0</td></tr>\n", "<tr><td>2008</td><td>SDSS J024151.63-081718.0</td><td>40.46514</td><td>-8.28836</td><td></td><td>--</td><td>--</td><td></td><td>25.5g</td><td>11.751</td><td>0</td><td>0</td><td>5</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>2009</td><td>SDSS J024151.63-081417.1</td><td>40.46514</td><td>-8.2381</td><td></td><td>--</td><td>--</td><td></td><td>24.5g</td><td>11.636</td><td>0</td><td>0</td><td>5</td><td>1</td><td>0</td><td>3</td><td>0</td></tr>\n", "<tr><td>2010</td><td>GALEXMSC J024151.80-081352.3</td><td>40.46587</td><td>-8.23121</td><td>UvS</td><td>--</td><td>--</td><td></td><td></td><td>11.724</td><td>0</td><td>0</td><td>3</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n", "<tr><td>2011</td><td>GALEXMSC J024152.17-081410.6</td><td>40.46739</td><td>-8.23628</td><td>UvS</td><td>--</td><td>--</td><td></td><td></td><td>11.779</td><td>0</td><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n", "<tr><td>2012</td><td>SDSS J024152.41-081519.0</td><td>40.46839</td><td>-8.25531</td><td>G</td><td>--</td><td>--</td><td></td><td>20.1g</td><td>11.78</td><td>0</td><td>0</td><td>19</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>2013</td><td>SDSS J024152.48-081516.3</td><td>40.46868</td><td>-8.25454</td><td></td><td>--</td><td>--</td><td></td><td>22.4g</td><td>11.798</td><td>0</td><td>0</td><td>5</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>2014</td><td>SDSS J024152.72-081349.0</td><td>40.46969</td><td>-8.23029</td><td>*</td><td>--</td><td>--</td><td></td><td>21.1g</td><td>11.956</td><td>0</td><td>0</td><td>5</td><td>1</td><td>0</td><td>4</td><td>0</td></tr>\n", "<tr><td>2015</td><td>SDSS J024153.29-081518.2</td><td>40.47209</td><td>-8.25508</td><td>G</td><td>--</td><td>--</td><td></td><td>17.9r</td><td>12.0</td><td>1</td><td>0</td><td>23</td><td>2</td><td>0</td><td>4</td><td>0</td></tr>\n", "</table>" ], "text/plain": [ "<Table masked=True length=2015>\n", " No. Object Name RA(deg) ... Diameter Points Associations\n", " degrees ... \n", "int32 bytes30 float64 ... int32 int32 \n", "----- ---------------------------- ---------- ... --------------- ------------\n", " 1 SDSS J024017.17-081441.3 40.07157 ... 4 1\n", " 2 2XMM J024017.2-081434 40.07187 ... 0 1\n", " 3 SDSS J024017.63-081256.3 40.07348 ... 4 0\n", " 4 GALEXMSC J024017.82-081517.3 40.07427 ... 0 0\n", " 5 SDSS J024018.24-081723.6 40.07602 ... 4 0\n", " 6 SDSS J024018.49-081219.0 40.07708 ... 4 0\n", " 7 SDSS J024018.63-081257.2 40.07766 ... 4 0\n", " 8 SDSS J024018.88-081855.0 40.07868 ... 3 0\n", " 9 GALEXMSC J024018.98-081532.9 40.07909 ... 0 0\n", " ... ... ... ... ... ...\n", " 2006 SDSS J024151.32-081413.3 40.46384 ... 4 0\n", " 2007 SDSS J024151.50-081545.7 40.46459 ... 3 0\n", " 2008 SDSS J024151.63-081718.0 40.46514 ... 4 0\n", " 2009 SDSS J024151.63-081417.1 40.46514 ... 3 0\n", " 2010 GALEXMSC J024151.80-081352.3 40.46587 ... 0 0\n", " 2011 GALEXMSC J024152.17-081410.6 40.46739 ... 0 0\n", " 2012 SDSS J024152.41-081519.0 40.46839 ... 4 0\n", " 2013 SDSS J024152.48-081516.3 40.46868 ... 4 0\n", " 2014 SDSS J024152.72-081349.0 40.46969 ... 4 0\n", " 2015 SDSS J024153.29-081518.2 40.47209 ... 4 0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ga.cone_byname(\"NGC 1052\", 0.2, show=True, savefig=True, imgname=\"NGC1052_cone.png\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-2.0
hdesmond/StatisticalMethods	examples/SDSScatalog/CorrFunc.ipynb	1	109241	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# \"Spatial Clustering\" - the Galaxy Correlation Function\n", "\n", "\n", "* The degree to which objects positions are correlated with each other - \"clustered\" - is of great interest in astronomy. \n", "\n", "\n", "* We expect galaxies to appear in groups and clusters, as they fall together under gravity: the statistics of galaxy clustering should contain information about galaxy evolution during hierarchical structure formation.\n", "\n", "\n", "* Let's try and measure a clustering signal in our SDSS photometric object catalog." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import SDSS\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import copy" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IOError", "evalue": "File downloads/SDSSobjects.csv does not exist", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-3-3ac8e020f7e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# We want to select galaxies, and then are only interested in their positions on the sky.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"downloads/SDSSobjects.csv\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ra'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'dec'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'u'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'g'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'i'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'size'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# Filter out objects with bad magnitude or size measurements:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/lib/python2.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)\u001b[0m\n\u001b[0;32m 472\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 473\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 474\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 475\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/lib/python2.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 250\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 251\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 252\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/lib/python2.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, kwds)\u001b[0m\n\u001b[0;32m 564\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 565\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 566\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 567\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 568\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_options_with_defaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/usr/lib/python2.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 703\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[1;33m*\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1073\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1074\u001b[0m \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:3173)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas/parser.c:5912)\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mIOError\u001b[0m: File downloads/SDSSobjects.csv does not exist" ] } ], "source": [ "# We want to select galaxies, and then are only interested in their positions on the sky.\n", "\n", "data = pd.read_csv(\"downloads/SDSSobjects.csv\",usecols=['ra','dec','u','g',\\\n", " 'r','i','size'])\n", "\n", "# Filter out objects with bad magnitude or size measurements:\n", "data = data[(data['u'] > 0) & (data['g'] > 0) & (data['r'] > 0) & (data['i'] > 0) & (data['size'] > 0)]\n", "\n", "# Make size cuts, to exclude stars and nearby galaxies, and magnitude cuts, to get good galaxy detections:\n", "data = data[(data['size'] > 0.8) & (data['size'] < 10.0) & (data['i'] > 17) & (data['i'] < 22)]\n", "\n", "# Drop the things we're not so interested in:\n", "del data['u'], data['g'], data['r'], data['i'],data['size']\n", "\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "987 galaxy-like objects in (ra,dec) range ( 185.000415714 : 185.199844645 , 15.000287913 : 15.1996295363 )\n" ] } ], "source": [ "Ngals = len(data)\n", "ramin,ramax = np.min(data['ra']),np.max(data['ra'])\n", "decmin,decmax = np.min(data['dec']),np.max(data['dec'])\n", "print Ngals,\"galaxy-like objects in (ra,dec) range (\",ramin,\":\",ramax,\",\",decmin,\":\",decmax,\")\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Correlation Function\n", "\n", "\n", " The 2-point correlation function $\\xi(\\theta)$ is defined as \"the probability of finding two galaxies separated by an angular distance $\\theta$ with respect to that expected for a random distribution\" (Peebles 1980), and is an excellent summary statistic for quantifying the clustering of galaxies.\n", "\n", "\n", "* The simplest possible _estimator_ for this excess probability is just \n", "$\\hat{\\xi}(\\theta) = \\frac{DD - RR}{RR}$, \n", "where $DD(\\theta) = N_{\\rm pairs}(\\theta) / N_D(N_D-1)/2$. Here, $N_D$ is the total number of galaxies in the dataset, and $N_{\\rm pairs}(\\theta)$ is the number of galaxy pairs with separation lying in a bin centered on $\\theta$. $RR(\\theta)$ is the same quantity computed in a \"random catalog,\" covering the same field of view but with uniformly randomly distributed positions.\n", "\n", "\n", "* We'll use Mike Jarvis' `TreeCorr` code [(Jarvis et al 2004)](http://arxiv.org/abs/astro-ph/0307393) to compute this correlation function estimator efficiently. You can read more about better estimators starting from [the TreeCorr wiki](https://github.com/rmjarvis/TreeCorr/wiki/Guide-to-using-TreeCorr-in-Python#using-random-catalogs)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# !pip install --upgrade TreeCorr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Catalogs \n", "\n", "First we'll need a random catalog. Let's make it the same size as the data one." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "random = pd.DataFrame({'ra' : ramin + (ramax-ramin)np.random.rand(Ngals), 'dec' : decmin + (decmax-decmin)np.random.rand(Ngals)})" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "987 <class 'pandas.core.frame.DataFrame'>\n" ] } ], "source": [ "print len(random), type(random)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's plot both catalogs, and compare." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x102a00a10>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ISFHQh+Fqek6qbZL9f7E4Off8Fb/rasjrayfraxps6u3LAJP4GaNYHPJDG0mqjGkSNHRZ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EAAAb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\n3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"text/plain": [ "<matplotlib.figure.Figure at 0x10460e190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=1, ncols=2)\n", "fig.set_size_inches(15, 6)\n", "plt.subplots_adjust(wspace=0.2)\n", " \n", "random.plot(kind='scatter', x='ra', y='dec', ax=ax[0], title='Random')\n", "ax[0].set_xlabel('RA / deg')\n", "ax[0].set_ylabel('Dec. / deg')\n", "\n", "data.plot(kind='scatter', x='ra', y='dec', ax=ax[1], title='Data')\n", "ax[1].set_xlabel('RA / deg')\n", "ax[1].set_ylabel('Dec. / deg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimating $\\xi(\\theta)$" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import treecorr\n", "\n", "random_cat = treecorr.Catalog(ra=random['ra'], dec=random['dec'], ra_units='deg', dec_units='deg')\n", "data_cat = treecorr.Catalog(ra=data['ra'], dec=data['dec'], ra_units='deg', dec_units='deg')\n", "\n", "# Set up some correlation function estimator objects:\n", "\n", "sep_units='arcmin'\n", "min_sep=0.5\n", "max_sep=10.0\n", "N = 7\n", "bin_size = np.log10(1.0max_sep/min_sep)/(1.0N)\n", "\n", "dd = treecorr.NNCorrelation(bin_size=bin_size, min_sep=min_sep, max_sep=max_sep, sep_units=sep_units, bin_slop=0.05/bin_size)\n", "rr = treecorr.NNCorrelation(bin_size=bin_size, min_sep=min_sep, max_sep=max_sep, sep_units=sep_units, bin_slop=0.05/bin_size)\n", "\n", "# Process the data:\n", "dd.process(data_cat)\n", "rr.process(random_cat)\n", "\n", "# Combine into a correlation function and its variance:\n", "xi, varxi = dd.calculateXi(rr)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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KWFEexouKkDlZ7e6l3Af4DDA9hHBSCGGRSWCM8UJgmUG3MLvRwKx+js/sfmxx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\nadMAmDx5Mj3RLhssHQWMY8k2WFoHmMzCN1g6OqU0fhHfe1V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"text/plain": [ "<matplotlib.figure.Figure at 0x1075ea5d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,8))\n", "plt.rc('xtick', labelsize=16) \n", "plt.rc('ytick', labelsize=16)\n", "plt.errorbar(np.exp(dd.logr),xi,np.sqrt(varxi),c='blue',linewidth=2)\n", "# plt.xscale('log')\n", "plt.xlabel('$\\\\theta / {\\\\rm arcmin}$',fontsize=20)\n", "plt.ylabel('$\\\\xi(\\\\theta)$',fontsize=20)\n", "plt.ylim([-0.1,0.2])\n", "plt.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Q: Are galaxies uniformly randomly distributed?\n", "\n", "Discuss the clustering signal (or lack thereof) in the above plot with your neighbor. What would you want to do better, in a second pass at this?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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 }	gpl-2.0
flo-compbio/gopca	docs/source/notebooks/Demo_DMAP.ipynb	1	1057314	null	gpl-3.0
ledeprogram/algorithms	class4/homework/ronga_paul_4_1.ipynb	1	233565	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as mticker\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pg8000\n", "conn = pg8000.connect(host='training.c1erymiua9dx.us-east-1.rds.amazonaws.com', database=\"training\", port=5432, user='dot_student', password='qgis')\n", "cursor = conn.cursor()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gid unique_key agency agency nam complaint descriptor location t incident z incident a street nam cross stre cross st_1 intersecti intersec_1 address ty city landmark facility t status due date resolution resoluti_1 community borough x coordina y coordina park facil park borou school nam school num school reg school cod school pho school add school cit school sta school zip school not school or vehicle ty taxi compa taxi pick bridge hig bridge h_1 road ramp bridge h_2 garage lot ferry dire ferry term latitude longitude location geom created_date closed_date\n" ] } ], "source": [ "# Getting the column names\n", "statement = \"select * from INFORMATION_SCHEMA.COLUMNS where table_name = 'dot_311'\"\n", "cursor.execute(statement)\n", "columns = []\n", "for row in cursor.fetchall():\n", " columns.append(row[3])\n", "print(columns)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "statement = \"SELECT unique_key, complaint, created_date, closed_date FROM dot_311\"\n", "cursor.execute(statement)\n", "complaints = []\n", "for row in cursor:\n", " complaints.append(row)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for complaint in complaints:\n", " try:\n", " complaint.append(complaint[3] - complaint[2])\n", " except:\n", " complaint.append(None)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.DataFrame(complaints)\n", "df.columns = ['key', 'complaint', 'created_date', 'closed_date', 'time']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>complaint</th>\n", " <th>created_date</th>\n", " <th>closed_date</th>\n", " <th>time</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>5851</td>\n", " <td>5851</td>\n", " <td>5851</td>\n", " <td>5650</td>\n", " <td>5650</td>\n", " </tr>\n", " <tr>\n", " <th>unique</th>\n", " <td>5851</td>\n", " <td>20</td>\n", " <td>4753</td>\n", " <td>3950</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>top</th>\n", " <td>32607302</td>\n", " <td>Street Light Condition</td>\n", " <td>2016-02-03 07:00:00</td>\n", " <td>2016-02-03 22:00:00</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>freq</th>\n", " <td>1</td>\n", " <td>2040</td>\n", " <td>29</td>\n", " <td>122</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>first</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2016-02-01 00:06:44</td>\n", " <td>2016-01-15 00:05:00</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>last</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2016-02-07 00:00:00</td>\n", " <td>2016-05-02 11:02:00</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>6 days 15:05:05.589380</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>14 days 12:05:38.260805</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>-19 days +09:29:00</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0 days 01:14:00</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0 days 21:48:15</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>4 days 00:30:48.500000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>89 days 18:54:00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " key complaint created_date \\\n", "count 5851 5851 5851 \n", "unique 5851 20 4753 \n", "top 32607302 Street Light Condition 2016-02-03 07:00:00 \n", "freq 1 2040 29 \n", "first NaN NaN 2016-02-01 00:06:44 \n", "last NaN NaN 2016-02-07 00:00:00 \n", "mean NaN NaN NaN \n", "std NaN NaN NaN \n", "min NaN NaN NaN \n", "25% NaN NaN NaN \n", "50% NaN NaN NaN \n", "75% NaN NaN NaN \n", "max NaN NaN NaN \n", "\n", " closed_date time \n", "count 5650 5650 \n", "unique 3950 NaN \n", "top 2016-02-03 22:00:00 NaN \n", "freq 122 NaN \n", "first 2016-01-15 00:05:00 NaN \n", "last 2016-05-02 11:02:00 NaN \n", "mean NaN 6 days 15:05:05.589380 \n", "std NaN 14 days 12:05:38.260805 \n", "min NaN -19 days +09:29:00 \n", "25% NaN 0 days 01:14:00 \n", "50% NaN 0 days 21:48:15 \n", "75% NaN 4 days 00:30:48.500000 \n", "max NaN 89 days 18:54:00 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "201 values out of 5851 weren't time deltas.\n" ] } ], "source": [ "from datetime import timedelta\n", "from math import nan\n", "\n", "no_delta_count = 0\n", "\n", "def delta_to_seconds(delta):\n", " if not isinstance(delta, timedelta):\n", " global no_delta_count\n", " no_delta_count = no_delta_count + 1\n", " return nan\n", " else:\n", " return timedelta.total_seconds(delta)\n", "\n", " \n", "df['time_seconds'] = df['time'].apply(delta_to_seconds)\n", "\n", "print(no_delta_count, \"values out of\", len(df), \"weren't time deltas.\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1129d3630>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1129a6390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['time_seconds'].hist(bins=100, figsize=(30, 6))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x115ebfa20>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x115ede860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[['time_seconds']].boxplot(return_type='axes')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>complaint</th>\n", " <th>created_date</th>\n", " <th>closed_date</th>\n", " <th>time</th>\n", " <th>time_seconds</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1743</th>\n", " <td>32591202</td>\n", " <td>Street Light Condition</td>\n", " <td>2016-02-02 16:08:00</td>\n", " <td>2016-05-02 11:02:00</td>\n", " <td>89 days 18:54:00</td>\n", " <td>7757640.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " key complaint created_date \\\n", "1743 32591202 Street Light Condition 2016-02-02 16:08:00 \n", "\n", " closed_date time time_seconds \n", "1743 2016-05-02 11:02:00 89 days 18:54:00 7757640.0 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The record time is 89 days\n", "df.iloc[df[['time_seconds']].idxmax()]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>key</th>\n", " <th>complaint</th>\n", " <th>created_date</th>\n", " <th>closed_date</th>\n", " <th>time</th>\n", " <th>time_seconds</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1625</th>\n", " <td>32585502</td>\n", " <td>Street Light Condition</td>\n", " <td>2016-02-02 14:36:00</td>\n", " <td>2016-01-15 00:05:00</td>\n", " <td>-19 days +09:29:00</td>\n", " <td>-1607460.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " key complaint created_date \\\n", "1625 32585502 Street Light Condition 2016-02-02 14:36:00 \n", "\n", " closed_date time time_seconds \n", "1625 2016-01-15 00:05:00 -19 days +09:29:00 -1607460.0 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# And one request was recorded as closed 19 days before* being created\n", "df.iloc[df[['time_seconds']].idxmin()]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(4547, 6)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We'll now study the appropriate times\n", "df_positive_time = df[df['time_seconds'] > 0]\n", "df_positive_time.shape" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1168fa2e8>]], dtype=object)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1164a2ba8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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PAv4ZmA0cTfGprjMruu83AHM5tIgsB/44LV8HXJ+Wz0o/fJPTC/rPHDxj2wCc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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1168dee48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_positive_time.boxplot(return_type='axes')\n", "df_positive_time.hist()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_less_11_days = df_positive_time[df_positive_time['time_seconds'] < 1000000]\n", "df_more_11_days = df[df['time_seconds'] > 1000000]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x116b6b828>]], dtype=object)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116afc6a0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116b63a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_more_11_days.boxplot(return_type='axes')\n", "df_more_11_days.hist()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x116dcdef0>]], dtype=object)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116d532e8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116db5a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_less_11_days.boxplot(return_type='axes')\n", "df_less_11_days.hist()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The interquartile is 343008.5 seconds.\n", "Which is: 3 days 23:16:48.500000\n", "The standard deviation is 14 days 12:05:38.260805\n" ] } ], "source": [ "df_no_nan = df[df['time_seconds'] == df['time_seconds']]\n", "iqr = df_no_nan['time_seconds'].quantile(q=0.75) - df_no_nan['time_seconds'].quantile(q=0.25)\n", "print(\"The interquartile is\", iqr, \"seconds.\")\n", "print(\"Which is:\", df['time'].quantile(q=0.75) - df['time'].quantile(q=0.25))\n", "print(\"The standard deviation is\", df['time'].std())" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def set_timeframe(time_seconds):\n", " if time_seconds < 0:\n", " return 'Negative'\n", " elif time_seconds == 0:\n", " return 'Zero'\n", " elif time_seconds < 604800:\n", " return 'Less than a week'\n", " elif time_seconds >= 604800:\n", " return 'More than a week'\n", " else:\n", " return 'Undefined'\n", " \n", "df['timeframe'] = df['time_seconds'].apply(set_timeframe)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Less than a week 3499\n", "More than a week 1048\n", "Negative 582\n", "Zero 521\n", "Undefined 201\n", "Name: timeframe, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['timeframe'].value_counts()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Top complaints per timeframe:\n" ] }, { "data": { "text/plain": [ "timeframe complaint \n", "Less than a week Street Condition 1640\n", " Traffic Signal Condition 762\n", " Street Light Condition 739\n", " Sidewalk Condition 86\n", " Highway Condition 67\n", " Street Sign - Damaged 42\n", " Street Sign - Missing 33\n", " DOT Literature Request 31\n", " Street Sign - Dangling 30\n", " Curb Condition 17\n", " Bridge Condition 14\n", " Ferry Inquiry 13\n", " Broken Muni Meter 12\n", " Bus Stop Shelter Placement 6\n", " Ferry Complaint 4\n", " Public Toilet 2\n", " Highway Sign - Damaged 1\n", "More than a week Broken Muni Meter 513\n", " Street Light Condition 244\n", " Street Condition 67\n", " Traffic Signal Condition 45\n", " Broken Parking Meter 32\n", " Street Sign - Damaged 29\n", " Street Sign - Dangling 24\n", " DOT Literature Request 22\n", " Sidewalk Condition 19\n", " Highway Condition 18\n", " Street Sign - Missing 17\n", " Bridge Condition 4\n", " Curb Condition 3\n", " Ferry Complaint 3\n", " Highway Sign - Damaged 3\n", " Municipal Parking Facility 3\n", " Bus Stop Shelter Placement 1\n", " Parking Card 1\n", "Negative Street Light Condition 582\n", "Undefined Street Light Condition 89\n", " Street Condition 75\n", " Street Sign - Missing 17\n", " Street Sign - Damaged 12\n", " Highway Condition 5\n", " Curb Condition 1\n", " Sidewalk Condition 1\n", " Street Sign - Dangling 1\n", "Zero Street Light Condition 386\n", " Street Condition 134\n", " Traffic Signal Condition 1\n", "Name: complaint, dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Top complaints per timeframe:\")\n", "df.groupby('timeframe')['complaint'].value_counts()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "timeframe\n", "Less than a week [[Axes(0.125,0.125;0.775x0.775)]]\n", "More than a week [[Axes(0.125,0.125;0.775x0.775)]]\n", "Negative [[Axes(0.125,0.125;0.775x0.775)]]\n", "Undefined [[Axes(0.125,0.125;0.775x0.775)]]\n", "Zero [[Axes(0.125,0.125;0.775x0.775)]]\n", "dtype: object" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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h6FSStOsy51Sam135zekG4OBe+aBW91PGxsYYGxtj5cqV7LfffoyNjd03/WLq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\nJ0mSJEmaY+ZzcCppO5hzKkmSpC7s2vUAdqB1wIv6yvu1up8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"text/plain": [ "<matplotlib.figure.Figure at 0x117347a20>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1168b39b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116dcd390>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1172fb898>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116dc0a58>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1170a5358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's have a closer look at these four groups\n", "\n", "df.groupby('timeframe').boxplot(figsize=(\u001d", "15, 10), return_type='axes')\n", "df.groupby('timeframe').hist()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AV9LrWRGxCSAingIOTOmzgfW5ZTamtNnAhlz6hpQ2ZpmIeAnYLmn/RnlJOgDYGhE7cnkd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\nrLO9Q9JkSYeVpwiDlTXYefbT399Pf39/43VfXx99fX2DJY2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"text/plain": [ "<matplotlib.figure.Figure at 0x1158b63c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Fun with small multiples!\n", "\n", "category = 'timeframe'\n", "categories = df[category].unique().tolist()\n", "\n", "nrows = 2; ncols = 2\n", "num_plots = nrows * ncols # number of subplots\n", "\n", "fig = plt.figure(figsize=(10, 6))\n", "\n", "axes = [plt.subplot(nrows,ncols,i) for i in range(1,num_plots+1)]\n", "\n", "plt.tight_layout(pad=0, w_pad=3, h_pad=1)\n", "plt.subplots_adjust(hspace=.5)\n", "\n", "\n", "for i in range(num_plots):\n", " ax = axes[i]\n", " ax.spines['top'].set_visible(False)\n", " ax.spines['bottom'].set_visible(False)\n", " ax.spines['left'].set_visible(False)\n", " ax.spines['right'].set_visible(False)\n", " \n", " dfx = df[df[category]==categories[i]]#.head(5)\n", " \n", "# BAR CHARTS\n", " x = dfx['complaint']\n", " y = dfx['time_seconds']\n", " ax.set_title(categories[i])\n", " ax.bar(left=range(0, len(dfx)), height=dfx['time_seconds'], linewidth=0) \n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
cvxgrp/cvxpylayers	examples/tf/convex_approximate_dynamic_programming.ipynb	1	23389	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convex approximate dynamic programming\n", "\n", "We consider a stochastic control problem of the form\n", "\\begin{equation}\n", "\\begin{array}{ll}\n", "\\mbox{minimize} & \\underset{T \\to \\infty}\\lim {\\mathbb E} \\left[\\frac{1}{T} \\sum_{t=0}^{T-1} \\\|{x_t}\\\|_2^2 + \\\|{\\phi(x_t)}\\\|_2^2\\right]\\\\[.2cm]\n", "\\mbox{subject to} & x_{t+1} = Ax_t + B\\phi(x_t) + \\omega_t,\n", "\\end{array}\n", "\\label{eq:adp}\n", "\\end{equation}\n", "where $x_t\\in\\mathbf{R}^n$ is the state, $\\phi:\\mathbf{R}^n \\to \\mathcal U \\subseteq \\mathbf{R}^m$ is\n", "the policy, $\\mathcal U$ is a convex set representing the allowed set of controls,\n", "and $\\omega_t\\in\\Omega$ is a (random, i.i.d.) disturbance.\n", "Here the variable is the policy $\\phi$, and the expectation is taken over\n", "disturbances and the initial state $x_0$. If $\\mathcal U$ is not an affine\n", "set, then this problem is in general very difficult to solve.\n", "\n", "A common heuristic for solving stochastic control problems is\n", "approximate dynamic programming (ADP), which parametrizes $\\phi$\n", "and replaces the minimization over functions $\\phi$ with a minimization over parameters.\n", "In this example, we take $\\mathcal U$ to be the unit ball and we represent $\\phi$\n", "as a particular quadratic control-Lyapunov policy.\n", "Evaluating $\\phi$ corresponds to solving the SOCP\n", "\\begin{equation}\n", "\\begin{array}{ll}\n", "\\mbox{minimize} & u^T P u + x_t^T Q u + q^T u \\\\\n", "\\mbox{subject to} & \\\|{u}\\\|_2 \\leq 1,\n", "\\end{array}\n", "\\label{eq:policy}\n", "\\end{equation}\n", "with variable $u$ and parameters $P$, $Q$, $q$, and $x_t$. We can run\n", "stochastic gradient descent (SGD) on $P$, $Q$, and $q$ to\n", "approximately solve the original problem, which requires requires differentiating\n", "through the quadratic policy. Note that if $u$ were unconstrained, the original problem\n", "could be solved exactly, via linear quadratic regulator (LQR) theory." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import cvxpy as cp\n", "import numpy as np\n", "import tensorflow as tf\n", "from scipy.linalg import solve_discrete_are\n", "from scipy.linalg import sqrtm\n", "\n", "\n", "from cvxpylayers.tensorflow.cvxpylayer import CvxpyLayer\n", "\n", "\n", "# Generate data\n", "tf.random.set_seed(1)\n", "np.random.seed(1)\n", "\n", "n = 2\n", "m = 3\n", "\n", "A = np.eye(n) + 1e-2 * np.random.randn(n, n)\n", "B = 1e-2 / 3 * np.random.randn(n, m)\n", "Q = np.eye(n)\n", "R = np.eye(m)\n", "\n", "# Compute LQR control policy\n", "P_lqr = solve_discrete_are(A, B, Q, R)\n", "P = R + B.T@P_lqr@B\n", "P_sqrt_lqr = sqrtm(P)\n", "\n", "# Construct CVXPY problem and layer\n", "x_cvxpy = cp.Parameter((n, 1))\n", "P_sqrt_cvxpy = cp.Parameter((m, m))\n", "P_21_cvxpy = cp.Parameter((n, m))\n", "q_cvxpy = cp.Parameter((m, 1))\n", "\n", "u_cvxpy = cp.Variable((m, 1))\n", "y_cvxpy = cp.Variable((n, 1))\n", "\n", "objective = .5 * cp.sum_squares(P_sqrt_cvxpy @ u_cvxpy) + x_cvxpy.T @ y_cvxpy + q_cvxpy.T @ u_cvxpy\n", "problem = cp.Problem(cp.Minimize(objective), [cp.norm(u_cvxpy) <= 1, y_cvxpy == P_21_cvxpy @ u_cvxpy])\n", "assert problem.is_dpp()\n", "policy = CvxpyLayer(\n", " problem, [x_cvxpy, P_sqrt_cvxpy, P_21_cvxpy, q_cvxpy], [u_cvxpy])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we train the policy and plot the estimated average cost for each iteration of\n", "SGD for a numerical example, with $x \\in\n", "\\mathbf{R}^2$ and $u \\in \\mathbf{R}^3$, a time horizon of $T=25$, and a batch\n", "size of $8$. We initialize our policy's parameters with the LQR solution,\n", "ignoring the constraint on $u$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(iter 0) loss: 1.18634 \n", "(iter 1) loss: 1.17733 \n", "(iter 2) loss: 1.16115 \n", "(iter 3) loss: 1.13998 \n", "(iter 4) loss: 1.11706 \n", "(iter 5) loss: 1.09234 \n", "(iter 6) loss: 1.06502 \n", "(iter 7) loss: 1.03633 \n", "(iter 8) loss: 1.00895 \n", "(iter 9) loss: 0.982198 \n", "(iter 10) loss: 0.954998 \n", "(iter 11) loss: 0.926645 \n", "(iter 12) loss: 0.900704 \n", "(iter 13) loss: 0.874988 \n", "(iter 14) loss: 0.851813 \n", "(iter 15) loss: 0.829279 \n", "(iter 16) loss: 0.809518 \n", "(iter 17) loss: 0.79314 \n", "(iter 18) loss: 0.77915 \n", "(iter 19) loss: 0.767274 \n", "(iter 20) loss: 0.756987 \n", "(iter 21) loss: 0.74633 \n", "(iter 22) loss: 0.735943 \n", "(iter 23) loss: 0.725892 \n", "(iter 24) loss: 0.716452 \n", "(iter 25) loss: 0.70797 \n", "(iter 26) loss: 0.700616 \n", "(iter 27) loss: 0.694235 \n", "(iter 28) loss: 0.688617 \n", "(iter 29) loss: 0.683476 \n", "(iter 30) loss: 0.678953 \n", "(iter 31) loss: 0.67472 \n", "(iter 32) loss: 0.670976 \n", "(iter 33) loss: 0.667654 \n", "(iter 34) loss: 0.664699 \n", "(iter 35) loss: 0.662062 \n", "(iter 36) loss: 0.659702 \n", "(iter 37) loss: 0.657582 \n", "(iter 38) loss: 0.655674 \n", "(iter 39) loss: 0.653949 \n", "(iter 40) loss: 0.652387 \n", "(iter 41) loss: 0.650967 \n", "(iter 42) loss: 0.649673 \n", "(iter 43) loss: 0.648491 \n", "(iter 44) loss: 0.647407 \n", "(iter 45) loss: 0.646412 \n", "(iter 46) loss: 0.645496 \n", "(iter 47) loss: 0.64465 \n", "(iter 48) loss: 0.643867 \n", "(iter 49) loss: 0.64314 \n", "(iter 50) loss: 0.642464 \n", "(iter 51) loss: 0.641835 \n", "(iter 52) loss: 0.641246 \n", "(iter 53) loss: 0.640696 \n", "(iter 54) loss: 0.640179 \n", "(iter 55) loss: 0.639693 \n", "(iter 56) loss: 0.639236 \n", "(iter 57) loss: 0.638805 \n", "(iter 58) loss: 0.638397 \n", "(iter 59) loss: 0.63801 \n", "(iter 60) loss: 0.637644 \n", "(iter 61) loss: 0.637296 \n", "(iter 62) loss: 0.636964 \n", "(iter 63) loss: 0.636648 \n", "(iter 64) loss: 0.636346 \n", "(iter 65) loss: 0.636058 \n", "(iter 66) loss: 0.635782 \n", "(iter 67) loss: 0.635517 \n", "(iter 68) loss: 0.635262 \n", "(iter 69) loss: 0.635018 \n", "(iter 70) loss: 0.634782 \n", "(iter 71) loss: 0.634555 \n", "(iter 72) loss: 0.634336 \n", "(iter 73) loss: 0.634125 \n", "(iter 74) loss: 0.63392 \n", "(iter 75) loss: 0.633722 \n", "(iter 76) loss: 0.633531 \n", "(iter 77) loss: 0.633345 \n", "(iter 78) loss: 0.633164 \n", "(iter 79) loss: 0.632989 \n", "(iter 80) loss: 0.632819 \n", "(iter 81) loss: 0.632653 \n", "(iter 82) loss: 0.632492 \n", "(iter 83) loss: 0.632335 \n", "(iter 84) loss: 0.632182 \n", "(iter 85) loss: 0.632033 \n", "(iter 86) loss: 0.631887 \n", "(iter 87) loss: 0.631745 \n", "(iter 88) loss: 0.631606 \n", "(iter 89) loss: 0.631471 \n", "(iter 90) loss: 0.631339 \n", "(iter 91) loss: 0.631209 \n", "(iter 92) loss: 0.631082 \n", "(iter 93) loss: 0.630958 \n", "(iter 94) loss: 0.630837 \n", "(iter 95) loss: 0.630718 \n", "(iter 96) loss: 0.630601 \n", "(iter 97) loss: 0.630487 \n", "(iter 98) loss: 0.630375 \n", "(iter 99) loss: 0.630265 \n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "\n", "def train(iters):\n", " # Initialize with LQR control lyapunov function\n", " P_sqrt = tf.Variable(P_sqrt_lqr)\n", " P_21 = tf.Variable(A.T @ P_lqr @ B)\n", " q = tf.Variable(tf.zeros((m, 1), dtype=tf.float64))\n", " variables = [P_sqrt, P_21, q]\n", " A_tf, B_tf, Q_tf, R_tf = map(tf.constant, [A, B, Q, R])\n", "\n", " def g(x, u):\n", " return tf.squeeze(tf.transpose(x) @ Q_tf @ x + tf.transpose(u) @ R_tf @ u)\n", "\n", " def evaluate(x0, P_sqrt, P_21, q, T):\n", " x = x0\n", " cost = 0.\n", " for _ in range(T):\n", " u, = policy(x, P_sqrt, P_21, q)\n", " cost += g(x, u) / T\n", " x = A_tf @ x + B_tf @ u + .2 * tf.random.normal((n, 1), dtype=tf.float64)\n", " return cost\n", "\n", " def eval_loss(N=8, T=25):\n", " return sum([evaluate(tf.zeros((n, 1), dtype=tf.float64), P_sqrt, P_21, q, T=T)\n", " for _ in range(N)]) / N\n", "\n", " results = []\n", " optimizer = tf.keras.optimizers.SGD(learning_rate=0.02, momentum=0.9)\n", " for i in range(iters):\n", " tf.random.set_seed(1)\n", " np.random.seed(1)\n", " with tf.GradientTape() as tape:\n", " loss = eval_loss()\n", " gradients = tape.gradient(loss, variables)\n", " optimizer.apply_gradients(zip(gradients, variables))\n", " results.append(loss.numpy())\n", " print(\"(iter %d) loss: %g \" % (i, results[-1]))\n", " return results\n", "\n", "\n", "results = train(iters=100)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(results)\n", "plt.xlabel('iteration')\n", "plt.ylabel('average cost')\n", "plt.show()" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }	apache-2.0
anandha2017/udacity	nd101 Deep Learning Nanodegree Foundation/DockerImages/12_tensorflow/notebooks/09 Mini-batch Quiz 4.ipynb	1	6049	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def batches(batch_size, features, labels):\n", " \"\"\"\n", " Create batches of features and labels\n", " :param batch_size: The batch size\n", " :param features: List of features\n", " :param labels: List of labels\n", " :return: Batches of (Features, Labels)\n", " \"\"\"\n", " assert len(features) == len(labels)\n", " outout_batches = []\n", " \n", " sample_size = len(features)\n", " for start_i in range(0, sample_size, batch_size):\n", " end_i = start_i + batch_size\n", " batch = [features[start_i:end_i], labels[start_i:end_i]]\n", " outout_batches.append(batch)\n", " \n", " return outout_batches" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "import tensorflow as tf\n", "import numpy as np\n", "#from helper import batches" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "learning_rate = 0.001\n", "n_input = 784 # MNIST data input (img shape: 28*28)\n", "n_classes = 10 # MNIST total classes (0-9 digits)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting mnist/train-images-idx3-ubyte.gz\n", "Extracting mnist/train-labels-idx1-ubyte.gz\n", "Extracting mnist/t10k-images-idx3-ubyte.gz\n", "Extracting mnist/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Import MNIST data\n", "mnist = input_data.read_data_sets('mnist', one_hot=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The features are already scaled and the data is shuffled\n", "train_features = mnist.train.images\n", "test_features = mnist.test.images\n", "\n", "train_labels = mnist.train.labels.astype(np.float32)\n", "test_labels = mnist.test.labels.astype(np.float32)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Features and Labels\n", "features = tf.placeholder(tf.float32, [None, n_input])\n", "labels = tf.placeholder(tf.float32, [None, n_classes])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Weights & bias\n", "weights = tf.Variable(tf.random_normal([n_input, n_classes]))\n", "bias = tf.Variable(tf.random_normal([n_classes]))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Logits - xW + b\n", "logits = tf.add(tf.matmul(features, weights), bias)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Define loss and optimizer\n", "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Calculate accuracy\n", "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Set batch size\n", "batch_size = 128\n", "assert batch_size is not None, 'You must set the batch size'" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Accuracy: 0.13099999725818634\n" ] } ], "source": [ "with tf.Session() as sess:\n", " sess.run(init)\n", " \n", " # TODO: Train optimizer on all batches\n", " # for batch_features, batch_labels in ______\n", " for batch_features, batch_labels in batches(batch_size, train_features, train_labels):\n", " sess.run(optimizer, feed_dict={features: batch_features, labels: batch_labels})\n", "\n", " # Calculate accuracy for test dataset\n", " test_accuracy = sess.run(\n", " accuracy,\n", " feed_dict={features: test_features, labels: test_labels})\n", "\n", "print('Test Accuracy: {}'.format(test_accuracy))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
tensorflow/docs-l10n	site/zh-cn/tutorials/distribute/custom_training.ipynb	1	27197	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "MhoQ0WE77laV" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "_ckMIh7O7s6D" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "jYysdyb-CaWM" }, "source": [ "# 使用 tf.distribute.Strategy 进行自定义训练" ] }, { "cell_type": "markdown", "metadata": { "id": "S5Uhzt6vVIB2" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td> <a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/distribute/custom_training\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看</a> </td>\n", " <td> <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/distribute/custom_training.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 上运行</a> </td>\n", " <td> <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/distribute/custom_training.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 上查看源代码</a> </td>\n", " <td> <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/distribute/custom_training.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载该 notebook</a> </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "FbVhjPpzn6BM" }, "source": [ "本教程演示了如何使用 [`tf.distribute.Strategy`](https://tensorflow.google.cn/guide/distribute_strategy) 进行自定义训练循环。我们将在 Fashion-MNIST 数据集上训练一个简单的 CNN 模型。Fashion-MNIST 数据集包含了 60000 个大小为 28 x 28 的训练图像和 10000 个大小为 28 x 28 的测试图像。\n", "\n", "我们用自定义训练循环来训练我们的模型是因为它们在训练的过程中为我们提供了灵活性和在训练过程中更好的控制。而且，使它们调试模型和训练循环的时候更容易。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dzLKpmZICaWN" }, "outputs": [], "source": [ "# Import TensorFlow\n", "import tensorflow as tf\n", "\n", "# Helper libraries\n", "import numpy as np\n", "import os\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": { "id": "MM6W__qraV55" }, "source": [ "## 下载流行的 MNIST 数据集" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7MqDQO0KCaWS" }, "outputs": [], "source": [ "fashion_mnist = tf.keras.datasets.fashion_mnist\n", "\n", "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n", "\n", "# Adding a dimension to the array -> new shape == (28, 28, 1)\n", "# We are doing this because the first layer in our model is a convolutional\n", "# layer and it requires a 4D input (batch_size, height, width, channels).\n", "# batch_size dimension will be added later on.\n", "train_images = train_images[..., None]\n", "test_images = test_images[..., None]\n", "\n", "# Getting the images in [0, 1] range.\n", "train_images = train_images / np.float32(255)\n", "test_images = test_images / np.float32(255)" ] }, { "cell_type": "markdown", "metadata": { "id": "4AXoHhrsbdF3" }, "source": [ "## 创建一个分发变量和图形的策略" ] }, { "cell_type": "markdown", "metadata": { "id": "5mVuLZhbem8d" }, "source": [ "`tf.distribute.MirroredStrategy` 策略是如何运作的？\n", "\n", "- 所有变量和模型图都复制在副本上。\n", "- 输入都均匀分布在副本中。\n", "- 每个副本在收到输入后计算输入的损失和梯度。\n", "- 通过求和，每一个副本上的梯度都能同步。\n", "- 同步后，每个副本上的复制的变量都可以同样更新。\n", "\n", "注意：您可以将下面的所有代码放在一个单独单元内。我们将它分成几个代码单元用于说明目的。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "F2VeZUWUj5S4" }, "outputs": [], "source": [ "# If the list of devices is not specified in the\n", "# `tf.distribute.MirroredStrategy` constructor, it will be auto-detected.\n", "strategy = tf.distribute.MirroredStrategy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZngeM_2o0_JO" }, "outputs": [], "source": [ "print ('Number of devices: {}'.format(strategy.num_replicas_in_sync))" ] }, { "cell_type": "markdown", "metadata": { "id": "k53F5I_IiGyI" }, "source": [ "## 设置输入流水线" ] }, { "cell_type": "markdown", "metadata": { "id": "0Qb6nDgxiN_n" }, "source": [ "将图形和变量导出成平台不可识别的 SavedModel 格式。在你的模型保存后，你可以在有或没有范围的情况下载入它。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jwJtsCQhHK-E" }, "outputs": [], "source": [ "BUFFER_SIZE = len(train_images)\n", "\n", "BATCH_SIZE_PER_REPLICA = 64\n", "GLOBAL_BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync\n", "\n", "EPOCHS = 10" ] }, { "cell_type": "markdown", "metadata": { "id": "J7fj3GskHC8g" }, "source": [ "创建数据集并分发它们：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WYrMNNDhAvVl" }, "outputs": [], "source": [ "train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels)).shuffle(BUFFER_SIZE).batch(GLOBAL_BATCH_SIZE) \n", "test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(GLOBAL_BATCH_SIZE) \n", "\n", "train_dist_dataset = strategy.experimental_distribute_dataset(train_dataset)\n", "test_dist_dataset = strategy.experimental_distribute_dataset(test_dataset)" ] }, { "cell_type": "markdown", "metadata": { "id": "bAXAo_wWbWSb" }, "source": [ "## 创建模型\n", "\n", "使用 `tf.keras.Sequential` 创建一个模型。你也可以使用模型子类化 API 来完成这个。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9ODch-OFCaW4" }, "outputs": [], "source": [ "def create_model():\n", " model = tf.keras.Sequential([\n", " tf.keras.layers.Conv2D(32, 3, activation='relu'),\n", " tf.keras.layers.MaxPooling2D(),\n", " tf.keras.layers.Conv2D(64, 3, activation='relu'),\n", " tf.keras.layers.MaxPooling2D(),\n", " tf.keras.layers.Flatten(),\n", " tf.keras.layers.Dense(64, activation='relu'),\n", " tf.keras.layers.Dense(10)\n", " ])\n", "\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9iagoTBfijUz" }, "outputs": [], "source": [ "# Create a checkpoint directory to store the checkpoints.\n", "checkpoint_dir = './training_checkpoints'\n", "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")" ] }, { "cell_type": "markdown", "metadata": { "id": "0-VVTqDEICrl" }, "source": [ "## 定义损失函数\n", "\n", "通常，在具有 1 个 GPU/CPU 的单台机器上，损失会除以输入批次中的样本数量。\n", "\n", "因此，使用 `tf.distribute.Strategy` 时应如何计算损失？\n", "\n", "- 例如，假设有 4 个 GPU，批次大小为 64。一个批次的输入会分布在各个副本（4 个 GPU）上，每个副本获得一个大小为 16 的输入。\n", "\n", "- 每个副本上的模型都会使用其各自的输入进行前向传递，并计算损失。现在，不将损失除以其相应输入中的样本数 (BATCH_SIZE_PER_REPLICA = 16)，而应将损失除以 GLOBAL_BATCH_SIZE (64)。" ] }, { "cell_type": "markdown", "metadata": { "id": "OCIcsaeoIHJX" }, "source": [ "为什么这样做？\n", "\n", "- 之所以需要这样做，是因为在每个副本上计算完梯度后，会通过对梯度求和在副本之间同步梯度。" ] }, { "cell_type": "markdown", "metadata": { "id": "e-wlFFZbP33n" }, "source": [ "如何在 TensorFlow 中执行此操作？\n", "\n", "- 如果您正在编写自定义训练循环（如本教程中所述），则应将每个样本的损失相加，然后将总和除以 GLOBAL_BATCH_SIZE: `scale_loss = tf.reduce_sum(loss) * (1. / GLOBAL_BATCH_SIZE)`，或者您可以使用 `tf.nn.compute_average_loss`，它会将每个样本的损失、可选样本权重和 GLOBAL_BATCH_SIZE 作为参数，并返回经过缩放的损失。\n", "\n", "- 如果在模型中使用正则化损失，则需要按副本数缩放损失值。您可以使用 `tf.nn.scale_regularization_loss` 函数进行此操作。\n", "\n", "- 不建议使用 `tf.reduce_mean`。这样做会将损失除以实际的每个副本批次大小，该大小可能会随着步骤的不同而发生变化。\n", "\n", "- 这种缩减和缩放会在 Keras `model.compile` 和 <br> `model.fit` 中自动完成。\n", "\n", "- 如果使用 `tf.keras.losses` 类（如下面的示例所示），则需要将损失缩减显式地指定为 `NONE` 或 `SUM`。与 `tf.distribute.Strategy` 一起使用时，不允许使用 `AUTO` 和 `SUM_OVER_BATCH_SIZE`。不允许使用 `AUTO`，因为用户应明确考虑他们想要的缩减量，以确保在分布式情况下缩减量正确。不允许使用 `SUM_OVER_BATCH_SIZE`，因为当前它只能按副本批次大小进行划分，而将按副本数量划分划留给用户，这可能很容易遗漏。因此，我们转而要求用户自己显式地执行缩减操作。\n", "\n", "- 如果 `labels` 为多维，则对每个样本中的元素数量的 `per_example_loss` 求平均值。例如，如果 `predictions` 的形状为 `(batch_size, H, W, n_classes)`，而 `labels` 为 `(batch_size, H, W)`，则需要更新 `per_example_loss`，例如：`per_example_loss /= tf.cast(tf.reduce_prod(tf.shape(labels)[1:]), tf.float32)`\n", "\n", " 小心：验证损失的形状。`tf.losses`/`tf.keras.losses` 中的损失函数通常会返回输入最后一个维度的平均值。损失类封装这些函数。在创建损失类的实例时传递 `reduction=Reduction.NONE`，表示“无额外缩减”。对于样本输入形状为 `[batch, W, H, n_classes]` 的类别损失，会缩减 `n_classes` 维度。对于类似 `losses.mean_squared_error` 或 `losses.binary_crossentropy` 的逐点损失，应包含一个虚拟轴，使 `[batch, W, H, 1]` 缩减为 `[batch, W, H]`。如果没有虚拟轴，`则 [batch, W, H]` 将被错误地缩减为 `[batch, W]`。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "R144Wci782ix" }, "outputs": [], "source": [ "with strategy.scope():\n", " # Set reduction to `none` so we can do the reduction afterwards and divide by\n", " # global batch size.\n", " loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n", " from_logits=True,\n", " reduction=tf.keras.losses.Reduction.NONE)\n", " def compute_loss(labels, predictions):\n", " per_example_loss = loss_object(labels, predictions)\n", " return tf.nn.compute_average_loss(per_example_loss, global_batch_size=GLOBAL_BATCH_SIZE)" ] }, { "cell_type": "markdown", "metadata": { "id": "w8y54-o9T2Ni" }, "source": [ "## 定义衡量指标以跟踪损失和准确性\n", "\n", "这些指标可以跟踪测试的损失，训练和测试的准确性。您可以使用`.result()`随时获取累积的统计信息。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zt3AHb46Tr3w" }, "outputs": [], "source": [ "with strategy.scope():\n", " test_loss = tf.keras.metrics.Mean(name='test_loss')\n", "\n", " train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(\n", " name='train_accuracy')\n", " test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(\n", " name='test_accuracy')" ] }, { "cell_type": "markdown", "metadata": { "id": "iuKuNXPORfqJ" }, "source": [ "## 训练循环" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OrMmakq5EqeQ" }, "outputs": [], "source": [ "# model, optimizer, and checkpoint must be created under `strategy.scope`.\n", "with strategy.scope():\n", " model = create_model()\n", "\n", " optimizer = tf.keras.optimizers.Adam()\n", "\n", " checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3UX43wUu04EL" }, "outputs": [], "source": [ "def train_step(inputs):\n", " images, labels = inputs\n", "\n", " with tf.GradientTape() as tape:\n", " predictions = model(images, training=True)\n", " loss = compute_loss(labels, predictions)\n", "\n", " gradients = tape.gradient(loss, model.trainable_variables)\n", " optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n", "\n", " train_accuracy.update_state(labels, predictions)\n", " return loss \n", "\n", "def test_step(inputs):\n", " images, labels = inputs\n", "\n", " predictions = model(images, training=False)\n", " t_loss = loss_object(labels, predictions)\n", "\n", " test_loss.update_state(t_loss)\n", " test_accuracy.update_state(labels, predictions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gX975dMSNw0e" }, "outputs": [], "source": [ "# `run` replicates the provided computation and runs it\n", "# with the distributed input.\n", "@tf.function\n", "def distributed_train_step(dataset_inputs):\n", " per_replica_losses = strategy.run(train_step, args=(dataset_inputs,))\n", " return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses,\n", " axis=None)\n", "\n", "@tf.function\n", "def distributed_test_step(dataset_inputs):\n", " return strategy.run(test_step, args=(dataset_inputs,))\n", "\n", "for epoch in range(EPOCHS):\n", " # TRAIN LOOP\n", " total_loss = 0.0\n", " num_batches = 0\n", " for x in train_dist_dataset:\n", " total_loss += distributed_train_step(x)\n", " num_batches += 1\n", " train_loss = total_loss / num_batches\n", "\n", " # TEST LOOP\n", " for x in test_dist_dataset:\n", " distributed_test_step(x)\n", "\n", " if epoch % 2 == 0:\n", " checkpoint.save(checkpoint_prefix)\n", "\n", " template = (\"Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, \"\n", " \"Test Accuracy: {}\")\n", " print (template.format(epoch+1, train_loss,\n", " train_accuracy.result()100, test_loss.result(),\n", " test_accuracy.result()100))\n", "\n", " test_loss.reset_states()\n", " train_accuracy.reset_states()\n", " test_accuracy.reset_states()" ] }, { "cell_type": "markdown", "metadata": { "id": "Z1YvXqOpwy08" }, "source": [ "以上示例中需要注意的事项：\n", "\n", "- 我们使用`for x in ...`迭代构造`train_dist_dataset`和`test_dist_dataset`。\n", "- 缩放损失是`distributed_train_step`的返回值。这个值会在各个副本使用`tf.distribute.Strategy.reduce`的时候合并，然后通过`tf.distribute.Strategy.reduce`叠加各个返回值来跨批次。\n", "- 在执行`tf.distribute.Strategy.experimental_run_v2`时，`tf.keras.Metrics`应在`train_step`和`test_step`中更新。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "-q5qp31IQD8t" }, "source": [ "## 恢复最新的检查点并进行测试" ] }, { "cell_type": "markdown", "metadata": { "id": "WNW2P00bkMGJ" }, "source": [ "使用 `tf.distribute.Strategy` 设置了检查点的模型可以使用或不使用策略进行恢复。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pg3B-Cw_cn3a" }, "outputs": [], "source": [ "eval_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(\n", " name='eval_accuracy')\n", "\n", "new_model = create_model()\n", "new_optimizer = tf.keras.optimizers.Adam()\n", "\n", "test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(GLOBAL_BATCH_SIZE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7qYii7KUYiSM" }, "outputs": [], "source": [ "@tf.function\n", "def eval_step(images, labels):\n", " predictions = new_model(images, training=False)\n", " eval_accuracy(labels, predictions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LeZ6eeWRoUNq" }, "outputs": [], "source": [ "checkpoint = tf.train.Checkpoint(optimizer=new_optimizer, model=new_model)\n", "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))\n", "\n", "for images, labels in test_dataset:\n", " eval_step(images, labels)\n", "\n", "print ('Accuracy after restoring the saved model without strategy: {}'.format(\n", " eval_accuracy.result()100))" ] }, { "cell_type": "markdown", "metadata": { "id": "EbcI87EEzhzg" }, "source": [ "## 迭代一个数据集的替代方法\n", "\n", "### 使用迭代器\n", "\n", "如果你想要迭代一个已经给定步骤数量而不需要整个遍历的数据集，你可以创建一个迭代器并在迭代器上调用`iter`和显式调用`next`。您可以选择在 tf.function 内部和外部迭代数据集。这是一个小片段，演示了使用迭代器在 tf.function 外部迭代数据集。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7c73wGC00CzN" }, "outputs": [], "source": [ "for _ in range(EPOCHS):\n", " total_loss = 0.0\n", " num_batches = 0\n", " train_iter = iter(train_dist_dataset)\n", "\n", " for _ in range(10):\n", " total_loss += distributed_train_step(next(train_iter))\n", " num_batches += 1\n", " average_train_loss = total_loss / num_batches\n", "\n", " template = (\"Epoch {}, Loss: {}, Accuracy: {}\")\n", " print (template.format(epoch+1, average_train_loss, train_accuracy.result()100))\n", " train_accuracy.reset_states()" ] }, { "cell_type": "markdown", "metadata": { "id": "GxVp48Oy0m6y" }, "source": [ "### 在 tf.function 中迭代\n", "\n", "您还可以使用`for x in ...`构造在 tf.function 内部迭代整个输入`train_dist_dataset`，或者像上面那样创建迭代器。下面的例子演示了在 tf.function 中包装一个 epoch 并在功能内迭代`train_dist_dataset`。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-REzmcXv00qm" }, "outputs": [], "source": [ "@tf.function\n", "def distributed_train_epoch(dataset):\n", " total_loss = 0.0\n", " num_batches = 0\n", " for x in dataset:\n", " per_replica_losses = strategy.run(train_step, args=(x,))\n", " total_loss += strategy.reduce(\n", " tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None)\n", " num_batches += 1\n", " return total_loss / tf.cast(num_batches, dtype=tf.float32)\n", "\n", "for epoch in range(EPOCHS):\n", " train_loss = distributed_train_epoch(train_dist_dataset)\n", "\n", " template = (\"Epoch {}, Loss: {}, Accuracy: {}\")\n", " print (template.format(epoch+1, train_loss, train_accuracy.result()100))\n", "\n", " train_accuracy.reset_states()" ] }, { "cell_type": "markdown", "metadata": { "id": "MuZGXiyC7ABR" }, "source": [ "### 跟踪副本中的训练的损失\n", "\n", "注意：作为通用的规则，您应该使用`tf.keras.Metrics`来跟踪每个样本的值以避免它们在副本中合并。\n", "\n", "我们不* 建议使用`tf.metrics.Mean` 来跟踪不同副本的训练损失，因为在执行过程中会进行损失缩放计算。\n", "\n", "例如，如果您运行具有以下特点的训练作业：\n", "\n", "- 两个副本\n", "- 在每个副本上处理两个例子\n", "- 产生的损失值：每个副本为[2,3]和[4,5]\n", "- 全局批次大小 = 4\n", "\n", "通过损失缩放，您可以通过添加损失值来计算每个副本上的每个样本的损失值，然后除以全局批量大小。在这种情况下：`（2 + 3）/ 4 = 1.25`和`（4 + 5）/ 4 = 2.25`。\n", "\n", "如果您使用 `tf.metrics.Mean` 来跟踪两个副本的损失，结果会有所不同。在这个例子中，你最终得到一个`total`为 3.50 和`count`为 2 的结果，当调用`result（）`时，你将得到`total` /`count` = 1.75。使用`tf.keras.Metrics`计算损失时会通过一个等于同步副本数量的额外因子来缩放。" ] }, { "cell_type": "markdown", "metadata": { "id": "xisYJaV9KZTN" }, "source": [ "### 例子和教程\n", "\n", "以下是一些使用自定义训练循环来分发策略的示例：\n", "\n", "1. [分布式训练指南](../../guide/distributed_training)\n", "2. [DenseNet](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/models/densenet/distributed_train.py) 使用 `MirroredStrategy`的例子。\n", "3. [BERT](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_classifier.py) 使用 `MirroredStrategy` 和`TPUStrategy`来训练的例子。此示例对于了解如何在分发训练过程中如何载入一个检测点和定期生成检查点特别有帮助。\n", "4. [NCF](https://github.com/tensorflow/models/blob/master/official/recommendation/ncf_keras_main.py) 使用 `MirroredStrategy` 来启用 `keras_use_ctl` 标记。\n", "5. [NMT](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/models/nmt_with_attention/distributed_train.py) 使用 `MirroredStrategy`来训练的例子。\n", "\n", "更多的例子列在 [分发策略指南](../../guide/distribute_strategy.ipynb#examples_and_tutorials)。" ] }, { "cell_type": "markdown", "metadata": { "id": "6hEJNsokjOKs" }, "source": [ "## 下一步\n", "\n", "- 在您的模型上尝试新的 `tf.distribute.Strategy` API。\n", "- 访问指南中的[性能部分](../../guide/function.ipynb)，了解有关其他策略和[工具](../../guide/profiler.md)的更多信息，您可以使用它们来优化 TensorFlow 模型的性能。" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "custom_training.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
to266/hyperspy	hyperspy/tests/drawing/test_plot_image.ipynb	1	374681	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Testing (and demonstrating) `plot_images()`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# %hyperspy -r inline\n", "import numpy as np\n", "import hyperspy.api as hs\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`plot_images()` is used to plot several images in the same figure. It supports many configurations and has many options available to customize the resulting output. The function returns a list of `matplotlib` axes, which can be used to further customize the figure. Some examples are given below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Default usage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A common usage for `plot_images()` is to view the different slices of a multidimensional image (a hyperimage):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/fjd29/Anaconda/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py:1057: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal\n", " if aspect == 'normal':\n", "/home/fjd29/Anaconda/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py:1062: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal\n", " elif aspect in ('equal', 'auto'):\n" ] }, { "data": { "text/plain": [ "[<matplotlib.axes._subplots.AxesSubplot at 0x7fac2cf12290>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd9367e90>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd928b510>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd91a0850>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd903d990>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd8f5b090>]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T4HvJmV7hC+zKGc3DgnkzHPNNRIQFhNT0RpK8zaTQELXvN5BKE8m8yjQKKKeB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\njXuMlsjORyV1VnvR5jpjTNdGz7IP8HUrbe5Ap5FPQYsr1ACXt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"text/plain": [ "<matplotlib.figure.Figure at 0x7fabda0f0f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import scipy.ndimage\n", "image = hs.signals.Image(np.random.random((2, 3, 512, 512)))\n", "for i in range(2):\n", " for j in range(3):\n", " image.data[i,j,:] = scipy.misc.ascent()(i+0.5+j)\n", " \n", "axes = image.axes_manager\n", "axes[2].name = \"x\"\n", "axes[3].name = \"y\"\n", "axes[2].units = \"nm\"\n", "axes[3].units = \"nm\"\n", " \n", "image.metadata.General.title = 'multi-dimensional Lena'\n", "hs.plot.plot_images(image, tight_layout=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Specified labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, `plot_images()` will attempt to auto-label the images based on the `Signal` titles. The labels (and title) can be customized with the `label` and `suptitle` arguments. In this example, the axes labels and ticks are also disabled with `axes_decor`:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.axes._subplots.AxesSubplot at 0x7fabd8f797d0>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd81a9b10>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd80dca50>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd06aaa10>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd0557290>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd0424b50>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5zWYogpaAB19rlNwW6CqBnCQ0lRTi62gnXFBIhHy8dHL9whg3HNNF7Rx4FU32VWDm8c3k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\njgO4FX7b9IQEZWMhPb6cWNw0buoNaRoP0bR11p67axdfA6KKW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"text/plain": [ "<matplotlib.figure.Figure at 0x7fabd9258350>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import scipy.ndimage\n", "image = hs.signals.Image(np.random.random((2, 3, 512, 512)))\n", "for i in range(2):\n", " for j in range(3):\n", " image.data[i,j,:] = scipy.misc.ascent()(i+0.5+j)\n", " \n", "axes = image.axes_manager\n", "axes[2].name = \"x\"\n", "axes[3].name = \"y\"\n", "axes[2].units = \"nm\"\n", "axes[3].units = \"nm\"\n", " \n", "image.metadata.General.title = 'multi-dimensional Lena'\n", "hs.plot.plot_images(image, suptitle='Custom figure title', \n", " label=['Image 1', 'Image 2', 'Image 3', 'Image 4', 'Image 5', 'Image 6'],\n", " axes_decor=None, tight_layout=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List of images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`plot_images()` can also be used to easily plot a list of `Images`, comparing different `Signals`, including RGB images. This example also demonstrates how to wrap labels using `labelwrap` (for preventing overlap) and using a single `colorbar` for all the `Images`, as opposed to multiple individual ones:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.axes._subplots.AxesSubplot at 0x7fabd01dfc50>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd0086c50>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabcb15e410>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabd07171d0>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabcafd5110>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabcae6d950>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabcad82b50>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabcaca1850>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabcab2f850>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Kram3B9JTIGtq3WecDpBRZdvBwpnxNjZRbPIk8nDpBY4ezQTSS3FcqL3TIieU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mTjyUO598DrMSMqF30mq4hRrOReGclBOt5UztiAJmydV0MajSSQRSXZVLCsyCx7K8mivP9BNsgK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\nfa9a7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"text/plain": [ "<matplotlib.figure.Figure at 0x7fabd01bab10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import scipy.ndimage\n", "\n", "# load red channel of raccoon as an image\n", "image0 = hs.signals.Image(scipy.misc.ascent()[:,:,0])\n", "image0.metadata.General.title = 'Rocky Raccoon - R'\n", "axes0 = image0.axes_manager\n", "axes0[0].name = \"x\"\n", "axes0[1].name = \"y\"\n", "axes0[0].units = \"mm\"\n", "axes0[1].units = \"mm\"\n", "\n", "# load lena into 2x3 hyperimage\n", "image1 = hs.signals.Image(np.random.random((2, 3, 512, 512)))\n", "image1.metadata.General.title = 'multi-dimensional Lena'\n", "for i in range(2):\n", " for j in range(3):\n", " image1.data[i,j,:] = scipy.misc.ascent()(i+0.5+j)\n", "axes1 = image1.axes_manager\n", "axes1[2].name = \"x\"\n", "axes1[3].name = \"y\"\n", "axes1[2].units = \"nm\"\n", "axes1[3].units = \"nm\"\n", "\n", "# load green channel of raccoon as an image\n", "image2 = hs.signals.Image(scipy.misc.ascent()[:,:,1])\n", "image2.metadata.General.title = 'Rocky Raccoon - G'\n", "axes2 = image2.axes_manager\n", "axes2[0].name = \"x\"\n", "axes2[1].name = \"y\"\n", "axes2[0].units = \"mm\"\n", "axes2[1].units = \"mm\"\n", "\n", "# load rgb image\n", "rgb = hs.signals.Spectrum(scipy.misc.ascent())\n", "rgb.change_dtype(\"rgb8\")\n", "rgb.metadata.General.title = 'RGB'\n", "axesRGB = rgb.axes_manager\n", "axesRGB[0].name = \"x\"\n", "axesRGB[1].name = \"y\"\n", "axesRGB[0].units = \"nm\"\n", "axesRGB[1].units = \"nm\"\n", "\n", "\n", "hs.plot.plot_images([image0, image1, image2, rgb], tight_layout=True,\n", " #colorbar='single', \n", " labelwrap=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Real-world use" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another example for this function is plotting EDS line intensities. Using a spectrum image with EDS data, one can use the following commands to get a representative figure of the line intensities. This example also demonstrates changing the colormap (with `cmap`), adding scalebars to the plots (with `scalebar`), and changing the padding between the images. The `padding` is specified as a dictionary, which is used to call `matplotlib.figure.Figure.subplots_adjust()` (see [documentation](http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure.subplots_adjust)).\n", "Note, this padding can also be changed interactively by clicking on the `subplots_adjust` button (<img src=\"plot_images_subplots.png\" style=\"display:inline-block;vertical-align:bottom\">) in the GUI (button may be different when using different graphical backends)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sample and the data used are described in \n", "P. Burdet, et al., Acta Materialia, 61, p. 3090-3098 (2013) (see http://infoscience.epfl.ch/record/185861/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Further information is available in the Hyperspy EDS tutorial: \n", "\n", " http://nbviewer.ipython.org/github/hyperspy/hyperspy-demos/blob/master/electron_microscopy/EDS/Hyperpsy_EDS_TEM_tutorial_CAM_2015.ipynb" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('core_shell.hdf5', <httplib.HTTPMessage instance at 0x7fabd021ce60>)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from urllib import urlretrieve\n", "url = 'http://cook.msm.cam.ac.uk//~hyperspy//EDS_tutorial//'\n", "urlretrieve(url + 'core_shell.hdf5', 'core_shell.hdf5')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/fjd29/Anaconda/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py:1653: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n", " warnings.warn(\"This figure includes Axes that are not \"\n" ] }, { "data": { "text/plain": [ "[<matplotlib.axes._subplots.AxesSubplot at 0x7fabc953df90>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x7fabcab2f990>]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AYNJnbK1vjJCDS0ScqC/qe7KEnGSsy0Tns0OJIt5S8/Dw8PDYWeD31CIgK7sa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\nM0SLu0/UK8zDhwRlVa25Pmfss1xwgAk4VpVslRx6owm+Xn0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"text/plain": [ "<matplotlib.figure.Figure at 0x7fabc96a9950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "si_EDS = hs.load(\"core_shell.hdf5\")\n", "im = si_EDS.get_lines_intensity()\n", "hs.plot.plot_images(\n", " im, tight_layout=True, cmap='RdYlBu_r', axes_decor='off',\n", " colorbar='single', scalebar='all', \n", " scalebar_color='black', suptitle_fontsize=16,\n", " padding={'top':0.8, 'bottom':0.10, 'left':0.05,\n", " 'right':0.85, 'wspace':0.20, 'hspace':0.10}) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
shimanluck/AY250-HW	hw_3/homework.ipynb	1	138274	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Interaction with the World Homework (#3)\n", "Python Computing for Data Science (c) J Bloom, UC Berkeley, 2016" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1) Monty: The Python Siri\n", "\n", "Let's make a Siri-like program with the following properties:\n", " - record your voice command\n", " - use a webservice to parse that sound file into text\n", " - based on what the text, take three different types of actions:\n", " - send an email to yourself\n", " - do some math\n", " - tell a joke\n", "\n", "So for example, if you say \"Monty: email me with subject hello and body goodbye\", it will email you with the appropriate subject and body. If you say \"Monty: tell me a joke\" then it will go to the web and find a joke and print it for you. If you say, \"Monty: calculate two times three\" it should response with printing the number 6.\n", "\n", "Hint: you can use speed-to-text apps like Houndify to return the text (but not do the actions). You'll need to sign up for a free API and then follow documentation instructions for using the service within Python. " ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pyaudio\n", "import wave\n", "import houndify\n", "import speech_recognition as sr\n", "import sys\n", "from bs4 import BeautifulSoup\n", "try:\n", " # For Python 3.0 and later\n", " from urllib.request import urlopen\n", "except ImportError:\n", " # Fall back to Python 2's urllib2\n", " from urllib2 import urlopen\n", " \n", "import smtplib\n", "from email.mime.text import MIMEText\n", "from email.message import EmailMessage\n", "from email.headerregistry import Address\n", "from email.utils import make_msgid" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# listen and record command\n", "def record(WAVE_OUTPUT_FILENAME):\n", " chunk = 1024\n", " FORMAT = pyaudio.paInt16\n", " CHANNELS = 1\n", " RATE = 8000\n", " RECORD_SECONDS = 7\n", " p = pyaudio.PyAudio()\n", " stream = p.open(format = FORMAT,\n", " channels = CHANNELS,\n", " rate = RATE,\n", " input = True,\n", " frames_per_buffer = chunk)\n", " all = []\n", " for i in range(0, int(RATE / chunk * RECORD_SECONDS)):\n", " data = stream.read(chunk)\n", " all.append(data)\n", " print(\"* done recording\")\n", " stream.close()\n", " p.terminate()\n", " \n", " data = b\"\".join(all)\n", " wf = wave.open(WAVE_OUTPUT_FILENAME, \"wb\")\n", " wf.setnchannels(CHANNELS)\n", " wf.setsampwidth(p.get_sample_size(FORMAT))\n", " wf.setframerate(RATE)\n", " wf.writeframes(data)\n", " wf.close()" ] }, { "cell_type": "code", "execution_count": 328, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def speechtotext(audiofile):\n", " \"\"\"\n", " using HOUNDIFY to parse audiofile into text\n", " \"\"\"\n", " CLIENT_ID = \"rXaxIajhCsCO5JSgTvSEBg==\"\n", " CLIENT_KEY = \"MyIuxruWQNgEjaWvWCvyYBbOQELBmZKCWDHpgIFrCgI5m3d-H1UVgbM5-RPyQ1czKsIJ1JGxaPzUmU5wnlvgSQ==\"\n", "\n", " client = houndify.StreamingHoundClient(CLIENT_ID, CLIENT_KEY, \"test user\", sampleRate=8000)\n", "\n", " !houndify/sample_wave.py $CLIENT_ID $CLIENT_KEY $audiofile > result.txt\n", " \n", " # parse and obtain the last line \n", " with open(\"result.txt\") as f:\n", " lst_line = None\n", " for line in f:\n", " if 'Partial transcript: ' in line:\n", " lst_line = line\n", " lst_line = lst_line.split(': ')[1]\n", " lst_line = lst_line.strip('\\n')\n", " print(lst_line, \"\\n\")\n", " return(lst_line)" ] }, { "cell_type": "code", "execution_count": 245, "metadata": { "collapsed": false }, "outputs": [], "source": [ "email = speechtotext(\"email.wav\")" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def tell_joke(url = \"http://www.laughfactory.com/jokes/food-jokes\"): \n", " response = urlopen(url)\n", " html = response.read()\n", " response.close()\n", " soup = BeautifulSoup(html,\"html.parser\")\n", " forms = soup.findAll(\"div\", class_=\"joke-text\")\n", " joke = forms[-1].get_text()\n", " print((\" \").join(joke.split()))" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: Have you ever had Ethiopian food? A: Neither have they.\n" ] } ], "source": [ "tell_joke()" ] }, { "cell_type": "code", "execution_count": 355, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def send_email(text):\n", " text = text.split(\" \")\n", " subject = \"subject\"\n", " body = \"body\"\n", " for i in range(len(text)):\n", " if b[i] == 'subject':\n", " start = i+1\n", " if b[i] == 'body':\n", " subject = (\" \").join(b[start:i-1])\n", " body = (\" \").join(b[i+1:])\n", "\n", " from_email = \"[email protected]\"\n", " to_email = \"[email protected]\"\n", " msg = EmailMessage()\n", " msg['Subject'] = subject\n", " msg['From'] = from_email\n", " msg['To'] = to_email\n", " msg.set_content(body)\n", "\n", " mailServer = smtplib.SMTP(\"smtp.gmail.com\", 587)\n", " mailServer.starttls()\n", " mailServer.login(from_email, \"hahahahahaha\")\n", " mailServer.sendmail(from_email, to_email, msg.as_string())\n", " mailServer.close()" ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": false }, "outputs": [], "source": [ "send_email(email)" ] }, { "cell_type": "code", "execution_count": 271, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# reference: https://github.com/akshaynagpal/w2n/blob/master/word2number/w2n.py\n", "# transfered strings into numerical number\n", "american_number_system = {\n", " 'zero': 0,\n", " 'one': 1,\n", " 'two': 2,\n", " 'three': 3,\n", " 'four': 4,\n", " 'five': 5,\n", " 'six': 6,\n", " 'seven': 7,\n", " 'eight': 8,\n", " 'nine': 9,\n", " 'ten': 10,\n", " 'eleven': 11,\n", " 'twelve': 12,\n", " 'thirteen': 13,\n", " 'fourteen': 14,\n", " 'fifteen': 15,\n", " 'sixteen': 16,\n", " 'seventeen': 17,\n", " 'eighteen': 18,\n", " 'nineteen': 19,\n", " 'twenty': 20,\n", " 'thirty': 30,\n", " 'forty': 40,\n", " 'fifty': 50,\n", " 'sixty': 60,\n", " 'seventy': 70,\n", " 'eighty': 80,\n", " 'ninety': 90,\n", " 'hundred': 100,\n", " 'thousand': 1000,\n", " 'million': 1000000,\n", " 'billion': 1000000000\n", "}\n", "\n", "def word_to_num(number_sentence):\n", " number_sentence = number_sentence.lower()\n", " split_words = number_sentence.split() # split sentence into words\n", " clean_numbers = [] # removing and, & etc.\n", " for word in split_words:\n", " if word in american_number_system:\n", " clean_numbers.append(word)\n", "\n", " # Error message if the user enters invalid input! \n", " if len(clean_numbers) == 0:\n", " return \"Error: Please enter a valid number word (eg. two million twenty three thousand and forty nine) \"\n", "\n", " billion_index = clean_numbers.index('billion') if 'billion' in clean_numbers else -1\n", " million_index = clean_numbers.index('million') if 'million' in clean_numbers else -1\n", " thousand_index = clean_numbers.index('thousand') if 'thousand' in clean_numbers else -1\n", "\n", " total_sum = 0\n", " if billion_index>-1 :\n", " billion_multiplier = number_formation(clean_numbers[0:billion_index])\n", " # print \"billion_multiplier\",str(billion_multiplier)\n", " total_sum += billion_multiplier * 1000000000\n", "\n", " if million_index>-1 :\n", " if billion_index>-1:\n", " million_multiplier = number_formation(clean_numbers[billion_index+1:million_index])\n", " else:\n", " million_multiplier = number_formation(clean_numbers[0:million_index])\n", " total_sum += million_multiplier * 1000000\n", " # print \"million_multiplier\",str(million_multiplier)\n", "\n", " if thousand_index > -1:\n", " if million_index > -1:\n", " thousand_multiplier = number_formation(clean_numbers[million_index+1:thousand_index])\n", " elif billion_index>-1 and million_index==-1:\n", " thousand_multiplier = number_formation(clean_numbers[billion_index+1:thousand_index])\n", " else:\n", " thousand_multiplier = number_formation(clean_numbers[0:thousand_index])\n", " total_sum += thousand_multiplier * 1000\n", " # print \"thousand_multiplier\",str(thousand_multiplier)\n", "\n", " if thousand_index>-1 and thousand_index != len(clean_numbers)-1:\n", " hundreds = number_formation(clean_numbers[thousand_index+1:])\n", " elif million_index>-1 and million_index != len(clean_numbers)-1:\n", " hundreds = number_formation(clean_numbers[million_index+1:])\n", " elif billion_index>-1 and billion_index != len(clean_numbers)-1:\n", " hundreds = number_formation(clean_numbers[billion_index+1:])\n", " elif thousand_index==-1 and million_index==-1 and billion_index==-1:\n", " hundreds = number_formation(clean_numbers)\n", " else:\n", " hundreds = 0\n", " total_sum += hundreds\n", " # print \"hundreds\",str(hundreds)\n", " return total_sum\n", "\n", "\n", "def number_formation(number_words):\n", " numbers = []\n", " for number_word in number_words:\n", " numbers.append(american_number_system[number_word])\n", " if len(numbers)==4:\n", " return (numbers[0]numbers[1])+numbers[2]+numbers[3]\n", " elif len(numbers)==3:\n", " return numbers[0]numbers[1] + numbers[2]\n", " elif len(numbers)==2:\n", " if 100 in numbers:\n", " return numbers[0]numbers[1]\n", " else:\n", " return numbers[0]+numbers[1]\n", " else:\n", " return numbers[0]" ] }, { "cell_type": "code", "execution_count": 354, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def do_math(text):\n", " text = text.split(\" \")\n", " start = 0\n", " nums = []\n", " operations = {'plus':1, 'minus':2, 'times':3, 'devided':4}\n", " flag = 0\n", " for i in range(len(text)):\n", " if flag == 0:\n", " if text[i] == 'calculate':\n", " start = i+1\n", " flag = 1\n", " continue\n", " if text[i] not in american_number_system:\n", " if text[i] != 'and':\n", " nums.append(word_to_num((\" \").join(text[start:i])))\n", " start = i+1\n", " operation = text[i]\n", "\n", " nums.append(word_to_num((\" \").join(text[start:])))\n", " if operation == 'plus' or operation == 'add':\n", " return (nums[0] + nums[1])\n", " elif operation == 'minus':\n", " return (nums[0] - nums[1])\n", " elif operation == 'multiple' or operation == 'times':\n", " return (nums[0] nums[1])\n", " else:\n", " return (nums[0] / nums[1])" ] }, { "cell_type": "code", "execution_count": 347, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# main function\n", "def main():\n", " print(\"What do you want to do?\")\n", " record(\"result.wav\")\n", " text = speechtotext(\"result.wav\")\n", " \n", " if 'email' in text:\n", " print(\"Sending the email...\\n\")\n", " send_email(text)\n", " elif 'joke' in text:\n", " print(\"You want a joke? Sure!\\n\")\n", " tell_joke()\n", " else:\n", " print(\"Let's do math!\\n\")\n", " result = do_math(text)\n", " print(result)" ] }, { "cell_type": "code", "execution_count": 356, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "What do you want to do?\n", "* done recording\n", "tell me a joke \n", "\n", "You want a joke? Sure!\n", "\n", "Q: Have you ever had Ethiopian food? A: Neither have they.\n" ] } ], "source": [ "main()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2) Write a program that identifies musical notes from sound (AIFF) files. \n", "\n", " - Run it on the supplied sound files (12) and report your program’s results. \n", " - Use the labeled sounds (4) to make sure it works correctly. The provided sound files contain 1-3 simultaneous notes from different organs.\n", " - Save copies of any example plots to illustrate how your program works.\n", " \n", " https://piazza.com/berkeley/fall2016/ay250/resources -> hw3_sound_files.zip" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hints: You’ll want to decompose the sound into a frequency power spectrum. Use a Fast Fourier Transform. Be care about “unpacking” the string hexcode into python data structures. The sound files use 32 bit data. Play around with what happens when you convert the string data to other integer sizes, or signed vs unsigned integers. Also, beware of harmonics." ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# import all packages\n", "import aifc\n", "import numpy as np\n", "import scipy as sp\n", "from scipy.signal.spectral import lombscargle\n", "import pandas as pd\n", "import struct\n", "import matplotlib.pyplot as plt\n", "import peakutils\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# get standard freq for each notes\n", "notes_freq = pd.read_csv('notes_freq.csv')\n", "notes_name = notes_freq.columns.values[1:]\n", "notes_freq = (np.asarray(notes_freq)[:,1:])" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def find_notes(file, notes_freq, notes_name, use_bin = True):\n", " audio = aifc.open(file, 'r')\n", " channel_num = audio.getnchannels()\n", " # 1 for mono, 2 for stereo\n", " rate = audio.getframerate()\n", " # the sampling rate (number of audio frames per second).\n", " period = 1./rate\n", " frame_num = audio.getnframes()\n", " data = audio.readframes(frame_num)\n", "\n", " # unpack the string hexcode into python data structures.\n", " unpacked_data = struct.unpack(\"<%uh\" % (frame_num * channel_num), data)\n", "\n", " #extract one channel\n", " list_data = np.array(unpacked_data[::channel_num])\n", "\n", "\n", " #using lombscargle to compare periods\n", "\n", " timesteps = period * np.arange(len(list_data))\n", " freq = np.logspace(1, np.log(8000)/np.log(10), 500)\n", " freq = np.hstack((freq, notes_freq.ravel()))\n", " freq.sort()\n", " ang_freq = freq * 2 * np.pi\n", " lombs = lombscargle(timesteps.astype('float64'), list_data.astype('float64'), ang_freq)\n", " plt.plot(ang_freq/2/np.pi, lombs)\n", " plt.xlabel('frequency [Hz]')\n", " plt.ylabel('power')\n", "\n", " \n", " def find_nearest(array,value):\n", " idx = np.unravel_index((np.abs(array-value)).argmin(), np.shape(array))\n", " return idx\n", " \n", " if (use_bin):\n", " binned = find_bin(notes_freq, freq, lombs)\n", " indexes = peakutils.indexes(binned, thres=0.8, min_dist=0)\n", " level = 1\n", " for i in indexes:\n", " idx = np.unravel_index(i, np.shape(notes_freq))\n", " print (\"The\", level, \"th donimant note is: \", notes_name[idx[1]]+str(idx[0]))\n", " level += 1\n", " else:\n", " indexes = peakutils.indexes(lombs, thres=0.8, min_dist=50)\n", " level = 1\n", " for i in indexes:\n", " idx = find_nearest(notes_freq, i)\n", " print (\"The\", level, \"th donimant note is: \", notes_name[idx[1]]+str(idx[0]))\n", " level += 1\n", " return (lombs)" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def find_bin(notes_freq, freq, lombs):\n", " \"\"\"\n", " note_freq: standard notes freq to be compared\n", " freq: freqs we used in test\n", " lombs: power obtained from lombscargle\n", " \"\"\"\n", " C0 = 16.35/2\n", " B8 = 7902\n", " shape = np.shape(notes_freq)\n", " bins = np.ravel(notes_freq)\n", "\n", " \n", " mids = np.hstack((0, np.exp((np.log(bins[:-1]) + np.log(bins[1:])) / 2), 16000))\n", " lower = mids[:-1]\n", " upper = mids[1:]\n", "\n", " binned = np.zeros(lower.size) \n", " for i, (l, u) in enumerate(zip(lower, upper)):\n", " idx = (freq >= l) & (freq < u)\n", " if sum(idx)>0:\n", " binned[i] = np.mean(lombs[idx])\n", " else:\n", " binned[i] = 0\n", " return(binned)" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The 1 th donimant note is: D4\n", "The 2 th donimant note is: G4\n", "The 1 th donimant note is: F4\n", "The 2 th donimant note is: C6\n", "The 1 th donimant note is: A4\n", "The 1 th donimant note is: C4\n", "The 1 th donimant note is: D4\n", "The 1 th donimant note is: G6\n", "The 1 th donimant note is: D6\n", "The 2 th donimant note is: A6\n", "The 1 th donimant note is: C6\n", "The 1 th donimant note is: G4\n", "The 1 th donimant note is: G4\n", "The 1 th donimant note is: E6\n", "The 1 th donimant note is: C2\n" ] }, { "data": { "image/png": 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7sTtOnIjGGKJTVx/3y4XDOA73PpyMMTx9cRSP9N9Izf9WLyQlkVGYTPTKe8nY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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10807b518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "file1 = 'sound_files/1.aif'\n", "result1 = find_notes(file1, notes_freq, notes_name)\n", "file2 = 'sound_files/2.aif'\n", "result2 = find_notes(file2, notes_freq, notes_name)\n", "file3 = 'sound_files/3.aif'\n", "result3 = find_notes(file3, notes_freq, notes_name)\n", "file4 = 'sound_files/4.aif'\n", "result4 = find_notes(file4, notes_freq, notes_name)\n", "file5 = 'sound_files/5.aif'\n", "result5 = find_notes(file5, notes_freq, notes_name)\n", "file6 = 'sound_files/6.aif'\n", "result6 = find_notes(file6, notes_freq, notes_name)\n", "file7 = 'sound_files/7.aif'\n", "result7 = find_notes(file7, notes_freq, notes_name)\n", "file8 = 'sound_files/8.aif'\n", "result8 = find_notes(file8, notes_freq, notes_name)\n", "file9 = 'sound_files/9.aif'\n", "result9 = find_notes(file9, notes_freq, notes_name)\n", "file10 = 'sound_files/10.aif'\n", "result10 = find_notes(file10, notes_freq, notes_name)\n", "file11 = 'sound_files/11.aif'\n", "result11 = find_notes(file11, notes_freq, notes_name)\n", "file12 = 'sound_files/12.aif'\n", "result12 = find_notes(file12, notes_freq, notes_name)" ] }, { "cell_type": "code", "execution_count": 221, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The 1 th donimant note is: A4\n" ] }, { "data": { "image/png": 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TEYw992RKql1EjdcPMnmy/XeONuhsZs60zinV+Aijri57Z+Tuu2e2suLSVlu3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\nulG5htUC5rTnzLHX77xjN+p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9BLuASspWL9UzB8BqAE+r6islaOcNAC4HEOzkKzUbHQrg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\nSCIoGIQQQhLx/wGHua3O3OAshQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1221fa0b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fileA4 = 'sound_files/A4_PopOrgan.aif'\n", "A4=find_notes(fileA4, notes_freq, notes_name)" ] }, { "cell_type": "code", "execution_count": 222, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The 1 th donimant note is: A4\n" ] }, { "data": { "image/png": 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wsBCFhYVpW34gg0Es/jNy3b95eakLDMwMsqOszCrZvDzvSKLS0uAGg4ayXSgYwhvJDz30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\n/wGva9tqY9Um4gAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x123932668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fileA4C4 = 'sound_files/C4A4_PopOrgan.aif'\n", "a4c4=find_notes(fileA4C4, notes_freq, notes_name)\n" ] }, { "cell_type": "code", "execution_count": 223, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The 1 th donimant note is: F4\n" ] }, { "data": { "image/png": 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KdXEVjFcBXE9EZxTu/xnAz40xNX3oVtth6NIg+SALh2FM+iEpQAVDqS6ugnEngAYAPyvc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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x122123da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fileF3 = 'sound_files/F3_PopOrgan.aif'\n", "f3=find_notes(fileF3, notes_freq, notes_name)" ] }, { "cell_type": "code", "execution_count": 224, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The 1 th donimant note is: F4\n", "The 2 th donimant note is: F5\n", "The 3 th donimant note is: C6\n" ] }, { "data": { "image/png": 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gPych4Sh1GUrcqGAEUGmCocTPU0/xRIBJIL+bX1hKhEIFQ4mbYS0Y+XSrrZQchgpGcvzq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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11fc32630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fileF4 = 'sound_files/F4_CathedralOrgan.aif'\n", "f4=find_notes(fileF4, notes_freq, notes_name)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "I discussed this Homework with Jing Dai, Kevin Li, Ying Cao" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [py3]", "language": "python", "name": "Python [py3]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
tensorflow/docs-l10n	site/ko/tutorials/keras/regression.ipynb	1	25937	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "FhGuhbZ6M5tl" }, "source": [ "##### Copyright 2018 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "AwOEIRJC6Une" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "KyPEtTqk6VdG" }, "outputs": [], "source": [ "#@title MIT License\n", "#\n", "# Copyright (c) 2017 François Chollet\n", "#\n", "# Permission is hereby granted, free of charge, to any person obtaining a\n", "# copy of this software and associated documentation files (the \"Software\"),\n", "# to deal in the Software without restriction, including without limitation\n", "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n", "# and/or sell copies of the Software, and to permit persons to whom the\n", "# Software is furnished to do so, subject to the following conditions:\n", "#\n", "# The above copyright notice and this permission notice shall be included in\n", "# all copies or substantial portions of the Software.\n", "#\n", "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n", "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n", "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n", "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n", "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n", "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n", "# DEALINGS IN THE SOFTWARE." ] }, { "cell_type": "markdown", "metadata": { "id": "EIdT9iu_Z4Rb" }, "source": [ "# 자동차 연비 예측하기: 회귀" ] }, { "cell_type": "markdown", "metadata": { "id": "bBIlTPscrIT9" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/regression\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />TensorFlow.org에서 보기</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ko/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ko/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />깃허브(GitHub) 소스 보기</a>\n", " </td>\n", " <td>\n", " <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ko/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "YYwLLNVaaJU9" }, "source": [ "Note: 이 문서는 텐서플로 커뮤니티에서 번역했습니다. 커뮤니티 번역 활동의 특성상 정확한 번역과 최신 내용을 반영하기 위해 노력함에도\n", "불구하고 [공식 영문 문서](https://www.tensorflow.org/?hl=en)의 내용과 일치하지 않을 수 있습니다.\n", "이 번역에 개선할 부분이 있다면\n", "[tensorflow/docs-l10n](https://github.com/tensorflow/docs-l10n/) 깃헙 저장소로 풀 리퀘스트를 보내주시기 바랍니다.\n", "문서 번역이나 리뷰에 참여하려면\n", "[[email protected]](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-ko)로\n", "메일을 보내주시기 바랍니다." ] }, { "cell_type": "markdown", "metadata": { "id": "AHp3M9ZmrIxj" }, "source": [ "회귀(regression)는 가격이나 확률 같이 연속된 출력 값을 예측하는 것이 목적입니다. 이와는 달리 분류(classification)는 여러개의 클래스 중 하나의 클래스를 선택하는 것이 목적입니다(예를 들어, 사진에 사과 또는 오렌지가 포함되어 있을 때 어떤 과일인지 인식하는 것).\n", "\n", "이 노트북은 [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) 데이터셋을 사용하여 1970년대 후반과 1980년대 초반의 자동차 연비를 예측하는 모델을 만듭니다. 이 기간에 출시된 자동차 정보를 모델에 제공하겠습니다. 이 정보에는 실린더 수, 배기량, 마력(horsepower), 공차 중량 같은 속성이 포함됩니다.\n", "\n", "이 예제는 `tf.keras` API를 사용합니다. 자세한 내용은 [케라스 가이드](https://www.tensorflow.org/guide/keras)를 참고하세요." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "moB4tpEHxKB3" }, "outputs": [], "source": [ "# 산점도 행렬을 그리기 위해 seaborn 패키지를 설치합니다\n", "!pip install seaborn" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1rRo8oNqZ-Rj" }, "outputs": [], "source": [ "import pathlib\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": { "id": "F_72b0LCNbjx" }, "source": [ "## Auto MPG 데이터셋\n", "\n", "이 데이터셋은 [UCI 머신 러닝 저장소](https://archive.ics.uci.edu/ml/)에서 다운로드할 수 있습니다." ] }, { "cell_type": "markdown", "metadata": { "id": "gFh9ne3FZ-On" }, "source": [ "### 데이터 구하기\n", "먼저 데이터셋을 다운로드합니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "p9kxxgzvzlyz" }, "outputs": [], "source": [ "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n", "dataset_path" ] }, { "cell_type": "markdown", "metadata": { "id": "nslsRLh7Zss4" }, "source": [ "판다스를 사용하여 데이터를 읽습니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CiX2FI4gZtTt" }, "outputs": [], "source": [ "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n", " 'Acceleration', 'Model Year', 'Origin']\n", "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n", " na_values = \"?\", comment='\\t',\n", " sep=\" \", skipinitialspace=True)\n", "\n", "dataset = raw_dataset.copy()\n", "dataset.tail()" ] }, { "cell_type": "markdown", "metadata": { "id": "3MWuJTKEDM-f" }, "source": [ "### 데이터 정제하기\n", "\n", "이 데이터셋은 일부 데이터가 누락되어 있습니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JEJHhN65a2VV" }, "outputs": [], "source": [ "dataset.isna().sum()" ] }, { "cell_type": "markdown", "metadata": { "id": "9UPN0KBHa_WI" }, "source": [ "문제를 간단하게 만들기 위해서 누락된 행을 삭제하겠습니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4ZUDosChC1UN" }, "outputs": [], "source": [ "dataset = dataset.dropna()" ] }, { "cell_type": "markdown", "metadata": { "id": "8XKitwaH4v8h" }, "source": [ "`\"Origin\"` 열은 수치형이 아니고 범주형이므로 원-핫 인코딩(one-hot encoding)으로 변환하겠습니다:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gWNTD2QjBWFJ" }, "outputs": [], "source": [ "origin = dataset.pop('Origin')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ulXz4J7PAUzk" }, "outputs": [], "source": [ "dataset['USA'] = (origin == 1)1.0\n", "dataset['Europe'] = (origin == 2)1.0\n", "dataset['Japan'] = (origin == 3)1.0\n", "dataset.tail()" ] }, { "cell_type": "markdown", "metadata": { "id": "Cuym4yvk76vU" }, "source": [ "### 데이터셋을 훈련 세트와 테스트 세트로 분할하기\n", "\n", "이제 데이터를 훈련 세트와 테스트 세트로 분할합니다.\n", "\n", "테스트 세트는 모델을 최종적으로 평가할 때 사용합니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qn-IGhUE7_1H" }, "outputs": [], "source": [ "train_dataset = dataset.sample(frac=0.8,random_state=0)\n", "test_dataset = dataset.drop(train_dataset.index)" ] }, { "cell_type": "markdown", "metadata": { "id": "J4ubs136WLNp" }, "source": [ "### 데이터 조사하기\n", "\n", "훈련 세트에서 몇 개의 열을 선택해 산점도 행렬을 만들어 살펴 보겠습니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oRKO_x8gWKv-" }, "outputs": [], "source": [ "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")" ] }, { "cell_type": "markdown", "metadata": { "id": "gavKO_6DWRMP" }, "source": [ "전반적인 통계도 확인해 보죠:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yi2FzC3T21jR" }, "outputs": [], "source": [ "train_stats = train_dataset.describe()\n", "train_stats.pop(\"MPG\")\n", "train_stats = train_stats.transpose()\n", "train_stats" ] }, { "cell_type": "markdown", "metadata": { "id": "Db7Auq1yXUvh" }, "source": [ "### 특성과 레이블 분리하기\n", "\n", "특성에서 타깃 값 또는 \"레이블\"을 분리합니다. 이 레이블을 예측하기 위해 모델을 훈련시킬 것입니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "t2sluJdCW7jN" }, "outputs": [], "source": [ "train_labels = train_dataset.pop('MPG')\n", "test_labels = test_dataset.pop('MPG')" ] }, { "cell_type": "markdown", "metadata": { "id": "mRklxK5s388r" }, "source": [ "### 데이터 정규화\n", "\n", "위 `train_stats` 통계를 다시 살펴보고 각 특성의 범위가 얼마나 다른지 확인해 보죠." ] }, { "cell_type": "markdown", "metadata": { "id": "-ywmerQ6dSox" }, "source": [ "특성의 스케일과 범위가 다르면 정규화(normalization)하는 것이 권장됩니다. 특성을 정규화하지 않아도 모델이 수렴할 수 있지만, 훈련시키기 어렵고 입력 단위에 의존적인 모델이 만들어집니다.\n", "\n", "노트: 의도적으로 훈련 세트만 사용하여 통계치를 생성했습니다. 이 통계는 테스트 세트를 정규화할 때에도 사용됩니다. 이는 테스트 세트를 모델이 훈련에 사용했던 것과 동일한 분포로 투영하기 위해서입니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JlC5ooJrgjQF" }, "outputs": [], "source": [ "def norm(x):\n", " return (x - train_stats['mean']) / train_stats['std']\n", "normed_train_data = norm(train_dataset)\n", "normed_test_data = norm(test_dataset)" ] }, { "cell_type": "markdown", "metadata": { "id": "BuiClDk45eS4" }, "source": [ "정규화된 데이터를 사용하여 모델을 훈련합니다.\n", "\n", "주의: 여기에서 입력 데이터를 정규화하기 위해 사용한 통계치(평균과 표준편차)는 원-핫 인코딩과 마찬가지로 모델에 주입되는 모든 데이터에 적용되어야 합니다. 여기에는 테스트 세트는 물론 모델이 실전에 투입되어 얻은 라이브 데이터도 포함됩니다." ] }, { "cell_type": "markdown", "metadata": { "id": "SmjdzxKzEu1-" }, "source": [ "## 모델" ] }, { "cell_type": "markdown", "metadata": { "id": "6SWtkIjhrZwa" }, "source": [ "### 모델 만들기\n", "\n", "모델을 구성해 보죠. 여기에서는 두 개의 완전 연결(densely connected) 은닉층으로 `Sequential` 모델을 만들겠습니다. 출력 층은 하나의 연속적인 값을 반환합니다. 나중에 두 번째 모델을 만들기 쉽도록 `build_model` 함수로 모델 구성 단계를 감싸겠습니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "c26juK7ZG8j-" }, "outputs": [], "source": [ "def build_model():\n", " model = keras.Sequential([\n", " layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n", " layers.Dense(64, activation='relu'),\n", " layers.Dense(1)\n", " ])\n", "\n", " optimizer = tf.keras.optimizers.RMSprop(0.001)\n", "\n", " model.compile(loss='mse',\n", " optimizer=optimizer,\n", " metrics=['mae', 'mse'])\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cGbPb-PHGbhs" }, "outputs": [], "source": [ "model = build_model()" ] }, { "cell_type": "markdown", "metadata": { "id": "Sj49Og4YGULr" }, "source": [ "### 모델 확인\n", "\n", "`.summary` 메서드를 사용해 모델에 대한 간단한 정보를 출력합니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ReAD0n6MsFK-" }, "outputs": [], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "Vt6W50qGsJAL" }, "source": [ "모델을 한번 실행해 보죠. 훈련 세트에서 `10` 샘플을 하나의 배치로 만들어 `model.predict` 메서드를 호출해 보겠습니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-d-gBaVtGTSC" }, "outputs": [], "source": [ "example_batch = normed_train_data[:10]\n", "example_result = model.predict(example_batch)\n", "example_result" ] }, { "cell_type": "markdown", "metadata": { "id": "QlM8KrSOsaYo" }, "source": [ "제대로 작동하는 것 같네요. 결괏값의 크기와 타입이 기대했던 대로입니다." ] }, { "cell_type": "markdown", "metadata": { "id": "0-qWCsh6DlyH" }, "source": [ "### 모델 훈련\n", "\n", "이 모델을 1,000번의 에포크(epoch) 동안 훈련합니다. 훈련 정확도와 검증 정확도는 `history` 객체에 기록됩니다." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sD7qHCmNIOY0" }, "outputs": [], "source": [ "# 에포크가 끝날 때마다 점(.)을 출력해 훈련 진행 과정을 표시합니다\n", "class PrintDot(keras.callbacks.Callback):\n", " def on_epoch_end(self, epoch, logs):\n", " if epoch % 100 == 0: print('')\n", " print('.', end='')\n", "\n", "EPOCHS = 1000\n", "\n", "history = model.fit(\n", " normed_train_data, train_labels,\n", " epochs=EPOCHS, validation_split = 0.2, verbose=0,\n", " callbacks=[PrintDot()])" ] }, { "cell_type": "markdown", "metadata": { "id": "tQm3pc0FYPQB" }, "source": [ "`history` 객체에 저장된 통계치를 사용해 모델의 훈련 과정을 시각화해 보죠." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4Xj91b-dymEy" }, "outputs": [], "source": [ "hist = pd.DataFrame(history.history)\n", "hist['epoch'] = history.epoch\n", "hist.tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "B6XriGbVPh2t" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "def plot_history(history):\n", " hist = pd.DataFrame(history.history)\n", " hist['epoch'] = history.epoch\n", "\n", " plt.figure(figsize=(8,12))\n", "\n", " plt.subplot(2,1,1)\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Mean Abs Error [MPG]')\n", " plt.plot(hist['epoch'], hist['mae'],\n", " label='Train Error')\n", " plt.plot(hist['epoch'], hist['val_mae'],\n", " label = 'Val Error')\n", " plt.ylim([0,5])\n", " plt.legend()\n", "\n", " plt.subplot(2,1,2)\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Mean Square Error [$MPG^2$]')\n", " plt.plot(hist['epoch'], hist['mse'],\n", " label='Train Error')\n", " plt.plot(hist['epoch'], hist['val_mse'],\n", " label = 'Val Error')\n", " plt.ylim([0,20])\n", " plt.legend()\n", " plt.show()\n", "\n", "plot_history(history)" ] }, { "cell_type": "markdown", "metadata": { "id": "AqsuANc11FYv" }, "source": [ "이 그래프를 보면 수 백번 에포크를 진행한 이후에는 모델이 거의 향상되지 않는 것 같습니다. `model.fit` 메서드를 수정하여 검증 점수가 향상되지 않으면 자동으로 훈련을 멈추도록 만들어 보죠. 에포크마다 훈련 상태를 점검하기 위해 EarlyStopping 콜백(callback)을 사용하겠습니다. 지정된 에포크 횟수 동안 성능 향상이 없으면 자동으로 훈련이 멈춥니다.\n", "\n", "이 콜백에 대해 더 자세한 내용은 [여기](https://www.tensorflow.org/versions/master/api_docs/python/tf/keras/callbacks/EarlyStopping)를 참고하세요." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fdMZuhUgzMZ4" }, "outputs": [], "source": [ "model = build_model()\n", "\n", "# patience 매개변수는 성능 향상을 체크할 에포크 횟수입니다\n", "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n", "\n", "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n", " validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n", "\n", "plot_history(history)" ] }, { "cell_type": "markdown", "metadata": { "id": "3St8-DmrX8P4" }, "source": [ "이 그래프를 보면 검증 세트의 평균 오차가 약 +/- 2 MPG입니다. 좋은 결과인가요? 이에 대한 평가는 여러분에게 맡기겠습니다.\n", "\n", "모델을 훈련할 때 사용하지 않았던 테스트 세트에서 모델의 성능을 확인해 보죠. 이를 통해 모델이 실전에 투입되었을 때 모델의 성능을 짐작할 수 있습니다:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jl_yNr5n1kms" }, "outputs": [], "source": [ "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n", "\n", "print(\"테스트 세트의 평균 절대 오차: {:5.2f} MPG\".format(mae))" ] }, { "cell_type": "markdown", "metadata": { "id": "ft603OzXuEZC" }, "source": [ "## 예측\n", "\n", "마지막으로 테스트 세트에 있는 샘플을 사용해 MPG 값을 예측해 보겠습니다:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Xe7RXH3N3CWU" }, "outputs": [], "source": [ "test_predictions = model.predict(normed_test_data).flatten()\n", "\n", "plt.scatter(test_labels, test_predictions)\n", "plt.xlabel('True Values [MPG]')\n", "plt.ylabel('Predictions [MPG]')\n", "plt.axis('equal')\n", "plt.axis('square')\n", "plt.xlim([0,plt.xlim()[1]])\n", "plt.ylim([0,plt.ylim()[1]])\n", "_ = plt.plot([-100, 100], [-100, 100])\n" ] }, { "cell_type": "markdown", "metadata": { "id": "mU1jBsRLaCeY" }, "source": [ "모델이 꽤 잘 예측한 것 같습니다. 오차의 분포를 살펴 보죠." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "f-OHX4DiXd8x" }, "outputs": [], "source": [ "error = test_predictions - test_labels\n", "plt.hist(error, bins = 25)\n", "plt.xlabel(\"Prediction Error [MPG]\")\n", "_ = plt.ylabel(\"Count\")" ] }, { "cell_type": "markdown", "metadata": { "id": "3PkzkjFkaCed" }, "source": [ "가우시안 분포가 아니지만 아마도 훈련 샘플의 수가 매우 작기 때문일 것입니다." ] }, { "cell_type": "markdown", "metadata": { "id": "vgGQuV-yqYZH" }, "source": [ "## 결론\n", "\n", "이 노트북은 회귀 문제를 위한 기법을 소개합니다.\n", "\n", " 평균 제곱 오차(MSE)는 회귀 문제에서 자주 사용하는 손실 함수입니다(분류 문제에서 사용하는 손실 함수와 다릅니다).\n", "* 비슷하게 회귀에서 사용되는 평가 지표도 분류와 다릅니다. 많이 사용하는 회귀 지표는 평균 절댓값 오차(MAE)입니다.\n", "* 수치 입력 데이터의 특성이 여러 가지 범위를 가질 때 동일한 범위가 되도록 각 특성의 스케일을 독립적으로 조정해야 합니다.\n", "* 훈련 데이터가 많지 않다면 과대적합을 피하기 위해 은닉층의 개수가 적은 소규모 네트워크를 선택하는 방법이 좋습니다.\n", "* 조기 종료(Early stopping)은 과대적합을 방지하기 위한 좋은 방법입니다." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "regression.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
UltronAI/Deep-Learning	CS231n/reference/cnn_assignments-master/assignment3/q2.ipynb	2	741481	{ "metadata": { "name": "", "signature": "sha256:d8738737574bae9ae5c18694770c433a1ad6131ea9ec66c5cbd11d1eece1e0d9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TinyImageNet and Ensembles\n", "So far, we have only worked with the CIFAR-10 dataset. In this exercise we will introduce the TinyImageNet dataset. You will combine several pretrained models into an ensemble, and show that the ensemble performs better than any individual model." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# A bit of setup\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from time import time\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "# for auto-reloading extenrnal modules\n", "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", "%load_ext autoreload\n", "%autoreload 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introducing TinyImageNet\n", "\n", "The TinyImageNet dataset is a subset of the ILSVRC-2012 classification dataset. It consists of 200 object classes, and for each object class it provides 500 training images, 50 validation images, and 50 test images. All images have been downsampled to 64x64 pixels. We have provided the labels for all training and validation images, but have withheld the labels for the test images.\n", "\n", "We have further split the full TinyImageNet dataset into two equal pieces, each with 100 object classes. We refer to these datasets as TinyImageNet-100-A and TinyImageNet-100-B.\n", "\n", "To download the data, go into the `cs231n/datasets` directory and run the script `get_tiny_imagenet_splits.sh`. Then run the following code to load the TinyImageNet-100-A dataset into memory.\n", "\n", "NOTE: The full TinyImageNet dataset will take up about 490MB of disk space, and loading the full TinyImageNet-100-A dataset into memory will use about 2.8GB of memory." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from cs231n.data_utils import load_tiny_imagenet\n", "\n", "tiny_imagenet_a = 'cs231n/datasets/tiny-imagenet-100-A'\n", " \n", "class_names, X_train, y_train, X_val, y_val, X_test, y_test = load_tiny_imagenet(tiny_imagenet_a)\n", "\n", "# Zero-mean the data\n", "mean_img = np.mean(X_train, axis=0)\n", "X_train -= mean_img\n", "X_val -= mean_img\n", "X_test -= mean_img" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "loading training data for synset 20 / 100\n", "loading training data for synset 40 / 100" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "loading training data for synset 60 / 100" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "loading training data for synset 80 / 100" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "loading training data for synset 100 / 100" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# TinyImageNet-100-A classes\n", "Since ImageNet is based on the WordNet ontology, each class in ImageNet (and TinyImageNet) actually has several different names. For example \"pop bottle\" and \"soda bottle\" are both valid names for the same class. Run the following to see a list of all classes in TinyImageNet-100-A:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for names in class_names:\n", " print ' '.join('\"%s\"' % name for name in names)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\"Egyptian cat\"\n", "\"reel\"\n", "\"volleyball\"\n", "\"rocking chair\" \"rocker\"\n", "\"lemon\"\n", "\"bullfrog\" \"Rana catesbeiana\"\n", "\"basketball\"\n", "\"cliff\" \"drop\" \"drop-off\"\n", "\"espresso\"\n", "\"plunger\" \"plumber's helper\"\n", "\"parking meter\"\n", "\"German shepherd\" \"German shepherd dog\" \"German police dog\" \"alsatian\"\n", "\"dining table\" \"board\"\n", "\"monarch\" \"monarch butterfly\" \"milkweed butterfly\" \"Danaus plexippus\"\n", "\"brown bear\" \"bruin\" \"Ursus arctos\"\n", "\"school bus\"\n", "\"pizza\" \"pizza pie\"\n", "\"guinea pig\" \"Cavia cobaya\"\n", "\"umbrella\"\n", "\"organ\" \"pipe organ\"\n", "\"oboe\" \"hautboy\" \"hautbois\"\n", "\"maypole\"\n", "\"goldfish\" \"Carassius auratus\"\n", "\"potpie\"\n", "\"hourglass\"\n", "\"seashore\" \"coast\" \"seacoast\" \"sea-coast\"\n", "\"computer keyboard\" \"keypad\"\n", "\"Arabian camel\" \"dromedary\" \"Camelus dromedarius\"\n", "\"ice cream\" \"icecream\"\n", "\"nail\"\n", "\"space heater\"\n", "\"cardigan\"\n", "\"baboon\"\n", "\"snail\"\n", "\"coral reef\"\n", "\"albatross\" \"mollymawk\"\n", "\"spider web\" \"spider's web\"\n", "\"sea cucumber\" \"holothurian\"\n", "\"backpack\" \"back pack\" \"knapsack\" \"packsack\" \"rucksack\" \"haversack\"\n", "\"Labrador retriever\"\n", "\"pretzel\"\n", "\"king penguin\" \"Aptenodytes patagonica\"\n", "\"sulphur butterfly\" \"sulfur butterfly\"\n", "\"tarantula\"\n", "\"lesser panda\" \"red panda\" \"panda\" \"bear cat\" \"cat bear\" \"Ailurus fulgens\"\n", "\"pop bottle\" \"soda bottle\"\n", "\"banana\"\n", "\"sock\"\n", "\"cockroach\" \"roach\"\n", "\"projectile\" \"missile\"\n", "\"beer bottle\"\n", "\"mantis\" \"mantid\"\n", "\"freight car\"\n", "\"guacamole\"\n", "\"remote control\" \"remote\"\n", "\"European fire salamander\" \"Salamandra salamandra\"\n", "\"lakeside\" \"lakeshore\"\n", "\"chimpanzee\" \"chimp\" \"Pan troglodytes\"\n", "\"pay-phone\" \"pay-station\"\n", "\"fur coat\"\n", "\"alp\"\n", "\"lampshade\" \"lamp shade\"\n", "\"torch\"\n", "\"abacus\"\n", "\"moving van\"\n", "\"barrel\" \"cask\"\n", "\"tabby\" \"tabby cat\"\n", "\"goose\"\n", "\"koala\" \"koala bear\" \"kangaroo bear\" \"native bear\" \"Phascolarctos cinereus\"\n", "\"bullet train\" \"bullet\"\n", "\"CD player\"\n", "\"teapot\"\n", "\"birdhouse\"\n", "\"gazelle\"\n", "\"academic gown\" \"academic robe\" \"judge's robe\"\n", "\"tractor\"\n", "\"ladybug\" \"ladybeetle\" \"lady beetle\" \"ladybird\" \"ladybird beetle\"\n", "\"miniskirt\" \"mini\"\n", "\"golden retriever\"\n", "\"triumphal arch\"\n", "\"cannon\"\n", "\"neck brace\"\n", "\"sombrero\"\n", "\"gasmask\" \"respirator\" \"gas helmet\"\n", "\"candle\" \"taper\" \"wax light\"\n", "\"desk\"\n", "\"frying pan\" \"frypan\" \"skillet\"\n", "\"bee\"\n", "\"dam\" \"dike\" \"dyke\"\n", "\"spiny lobster\" \"langouste\" \"rock lobster\" \"crawfish\" \"crayfish\" \"sea crawfish\"\n", "\"police van\" \"police wagon\" \"paddy wagon\" \"patrol wagon\" \"wagon\" \"black Maria\"\n", "\"iPod\"\n", "\"punching bag\" \"punch bag\" \"punching ball\" \"punchball\"\n", "\"beacon\" \"lighthouse\" \"beacon light\" \"pharos\"\n", "\"jellyfish\"\n", "\"wok\"\n", "\"potter's wheel\"\n", "\"sandal\"\n", "\"pill bottle\"\n", "\"butcher shop\" \"meat market\"\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualize Examples\n", "Run the following to visualize some example images from random classses in TinyImageNet-100-A. It selects classes and images randomly, so you can run it several times to see different images." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Visualize some examples of the training data\n", "classes_to_show = 7\n", "examples_per_class = 5\n", "\n", "class_idxs = np.random.choice(len(class_names), size=classes_to_show, replace=False)\n", "for i, class_idx in enumerate(class_idxs):\n", " train_idxs, = np.nonzero(y_train == class_idx)\n", " train_idxs = np.random.choice(train_idxs, size=examples_per_class, replace=False)\n", " for j, train_idx in enumerate(train_idxs):\n", " img = X_train[train_idx] + mean_img\n", " img = img.transpose(1, 2, 0).astype('uint8')\n", " plt.subplot(examples_per_class, classes_to_show, 1 + i + classes_to_show * j)\n", " if j == 0:\n", " plt.title(class_names[class_idx][0])\n", " plt.imshow(img)\n", " plt.gca().axis('off')\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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KC0xNHMF1bUaHR1m3bpjDh3XA9yPjB6lWlxnbuIHLL7mYcrlMs1kHFBs39mur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\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tfkvjY2DoDFSiXm+wY3WHZ/EBwyBL2Dx6ZGsBzMEgMdV3NwtwDjrf4O/HcYxr2UMjaWtS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kkGWZ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\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1TFOnVutQLpdJB3kv7/rpnwWg2/T5uXe9h9e87rVommBldRUhTFqtDpbl0O4GuE6O6ak5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rsAtJ7ON6CcK2WI98Tk4fZWKoQmtuhjE7pCgiemtLlCtFvKHUILcAVatIoWjhxxb2ap/2aoSK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\nT8tgMODjH/8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"text": [ "<matplotlib.figure.Figure at 0x10f36c850>" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test human performance\n", "Run the following to test your own classification performance on the TinyImageNet-100-A dataset.\n", "\n", "You can run several times in 'training' mode to get familiar with the task; once you are ready to test yourself, switch the mode to `'val'`.\n", "\n", "You won't be penalized if you don't correctly classify all the images, but you should still try your best." ] }, { "cell_type": "code", "collapsed": false, "input": [ "mode = 'val'\n", "\n", "name_to_label = {n.lower(): i for i, ns in enumerate(class_names) for n in ns}\n", "\n", "if mode == 'train':\n", " X, y = X_train, y_train\n", "elif mode == 'val':\n", " X, y = X_val, y_val\n", " \n", "num_correct = 0\n", "num_images = 10\n", "for i in xrange(num_images):\n", " idx = np.random.randint(X.shape[0])\n", " img = (X[idx] + mean_img).transpose(1, 2, 0).astype('uint8')\n", " plt.imshow(img)\n", " plt.gca().axis('off')\n", " plt.gcf().set_size_inches((2, 2))\n", " plt.show()\n", " got_name = False\n", " while not got_name:\n", " name = raw_input('Guess the class for the above image (%d / %d) : ' % (i + 1, num_images))\n", " name = name.lower()\n", " got_name = name in name_to_label\n", " if not got_name:\n", " print 'That is not a valid class name; try again'\n", " guess = name_to_label[name]\n", " if guess == y[idx]:\n", " num_correct += 1\n", " print 'Correct!'\n", " else:\n", " print 'Incorrect; it was actually %r' % class_names[y[idx]]\n", "\n", "acc = float(num_correct) / num_images\n", "print 'You got %d / %d correct for an accuracy of %f' % (num_correct, num_images, acc)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Kq+Q4PmgvVI6VlY/TThbEKi3M0lUQRdZFzPFDB2V523zMBqpaVWH0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\nk0jLkgyaRFqW/wuzWBX8KhsMqwAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x11d700e10>" ] }, { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "Guess the class for the above image (1 / 10) : sock\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Correct!\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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KC/wIym5TCdgVklW2sJBFXT3TILPSQiwv1xiQ0XanOjtgIkc0ic2kkctzt/l9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\n/+/x+xdoOu/cStKbnef7dl6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"text": [ "<matplotlib.figure.Figure at 0x10fb367d0>" ] }, { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "Guess the class for the above image (2 / 10) : crayfish\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Incorrect; it was actually ['sea cucumber', 'holothurian']\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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v+HNPAQBkxQRuKatA1ybyHaPs+LqmBl5bT6f23bNaibwqO/COxx/EQECei0T4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\n", "text": [ "<matplotlib.figure.Figure at 0x11d4e7950>" ] }, { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "Guess the class for the above image (3 / 10) : tractor\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Correct!\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ovzJUCoLnB++V/8uyZQxabTcwSFT2w5Ek3J5C6Vi66hFu3/pdH5CUUWXquZ5K/2P2oGWj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\n0rGEkyaUjiWcNKF0LP8HvlDe+Wkmw2gAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x11d6551d0>" ] }, { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "Guess the class for the above image (4 / 10) : cockroach\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Correct!\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x11d6c4410>" ] }, { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "Guess the class for the above image (5 / 10) : dining table\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Correct!\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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qlEk6y+X4+86eletVdZpKGitXhmkrZe1lVoXYgDEa2/B4Gy1qxEkT7gbBWTfldhhbA+O1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\nsrb8H47+RQyNN8v5AAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x11d821b10>" ] }, { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "Guess the class for the above image (6 / 10) : wax light\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Incorrect; it was actually ['espresso']\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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8KudUXU3wLRnpvXv2KQNaiuXuuOMOAMAX/+LLeOjfidF208f/CACQ4Cy7kcni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\nO2lcKVncSeNKyeJOGldKlv8LLwhCO85IjeMAAAAASUVORK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x10fbbc8d0>" ] }, { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "Guess the class for the above image (7 / 10) : Egyptian cat\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Incorrect; it was actually ['tabby', 'tabby cat']\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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TiegRxOZoVq8aKBAbLEQcY9WAYploUCjwrPlDFM5PD+5BaxNloF8/9mMAZyIGm5saYbUx\nXkdlRvSeohFzeCSKMoeH0UShfHiYMlQqmUMqQzX63/6NIbJffegaAIAJdqTn+EODRO411dHAuW59\nAekMx6gUiXAqyi8YbMDcPMesGInSbifX1VhJY3jkHc695s9vCx1pdFowLapxz2unwzIUCmF0nKpz\nXS0NcD6vBFJHY7j8yncBOBM3PBOnGXztupVIzBOFrDbOc2iMSODzO9HSyrEaGqhG+n1EkxMnTqBc\nphzQ38sYlnCIqqXJZML/+OzfAQDefJNGQZuV6HXw8BFk0uT/yQzlqokpGt2y2TzmU/xbUcZOpajO\n10RqEI9T5mqsbeP9/Jzb9MQsWlvoLB3qo/xQLhHFJqMz8PvF/C9jJuYoT5SqJfj8VMeHhwcBAO56\nImttyIwrLqaWlZMY8huuuREAsLx7CZ59hnFMN7+XgfMTYxwzEunC3Jy4Cgxcz7KZA5Sto5gvnOR6\nthIRl/q//JcPLN+yaU0VAC688EL85Kc/AQB89rN/CwB44MH/BQD43Oc+gVP9DJvs7GoDAKzdQMun\nP+DC0RMMm7z5fWQpuRKtxunMLEQuRT5PyFWhCpFIBENDVGWb6vny0knaWyYmJpDLSaS3uN4NkiWQ\nzRfgcXMzZ3Jc3EKZEB/wh1GSpSoU+Z3Lz01UG25ELMp/d7Ux+PzQ/kEAwAvP7MToMF92fCoj9+NL\nSeeyZ2w4Rb68SB1V946lLYgleNC8ftpp3nj7lwCAv37fX8Fa5SFY0kT1uq2RqnQxN4+mRkYCZNNc\nK5XbNDsL2G1k/VUJ+YhnuE7OUALeOgk8kyS7dXXf1C3COp0bWlRB+FiUqvQqRz0q3Px4/RT9Pld+\ngJbTG+5eCYOR8D03T7YUnRG112GBt5Us4ODgM/wuShU1HA7DbOGeV4HX5iIt0Cf643A7KXjPZwlH\nyaSksFRbYbM6/uR3Xi+F1ko5hYJYiafn6FFubOQ8x6ajcHkocKsMzQJoBEtlJlEpE732HN0OALAH\neN+r/1sY+TzHN8vYHjeNixvWbsLXv8ZMzqHBQQBAPE52WDEvRyBMYTcYJAsrTxOhVjdfpKFjLkfL\nrkqFMVjsyJZ5n5So8atX0uyQOtmLdJ7s3uPkmE4J7axk3IielNyyAQrJ6+ia+/+QjjQ6LZgWVab5\n5s/eVwWAa6+7GmnhryrHemxsTPu/SsdIiaCphMOAP4L5eZ6ghnqqtHY7T8bxYz1YupSyT89Jem5f\neXk/ACCbzmF0lELnwACDwV0OntIrr3y3lqw2OUFke+YZopjb7caoxNMsXUoZYXae6mu5XEahQDSp\nqaPgXZHMR6/PiXCEckNnF41mq1fSJB8M+lEUGagiQpHTSTmmmCuiJLnmBhFMqxWO6fHa8exzFGhP\n9tBP1xam6n7JJZf80RpRtjGKPyuZTGpKRiZDJAxLomE4XIPJSSL16BiR1Cr+tMbGRjgcRKi+0/Sg\nX3nFTbpMo9O5oUVFmkf/8MEqAJTLVXjEC5uW1Fm3WyT6RBpNDTR7+308wcoY9dyzf9DSRl99hQ5A\nFUy+ft1mDPQTrUZHqGUMTvCa2pp67NnD09ncxBM5PkYT+X33fVyL/hs4TfU9HqdKmi8VtfQS5SLw\neHj6guEgRkd5P7udqBWwE01MJqBUJkqarVSdXW6e/EIxi9kE5Yh8nijS2blE1sCNj911B/8tZUiS\nc0Q/j88Kg5EoNDo6yGcJ0rM9Pj6u5V37fFxHFRMdCkXgDfCZZ6IxmR+vbW5t0d7D6dM0RahqE+l0\nGqPjfL73vIeaqs/RpSONTueGFhVpnnj9s1WA9V8aG0UmsdHmcPwYT/Lp3iEMD4lGFKSm0nOSEWdu\nZwQ+D/lxXBBnepIGPI87iL7TvG5wgJ9dq3iiBgYG0N5OmWRkhDLNjTfQ0NV/+jS8Ek/79tu0Aan4\n5YbGOq2Oznf+kek03/72NwEAY6PTiNTwlKpsgkKUNhKX2woYxGhWkfQPA1GlXM3AYOR3brdTvuPv\nDYaqFiek7DQGcS6ef8Ea3Hb7zQCAcUnSK8aIXlarVUPEvj6i5dETnHcoFNLkHYgTNSOJglarFVdc\ncQUAwC7yiyq6kMmmtZCRRIJy3H13f+8vb9z7yoN/UwX44lQujlkEtlOnuGmcThemp6hWd3SQ9dSL\nH6VcMmDjhs0AgH17WclqWippHTvag4JUdCiJj2UyyjEtFguy4tu69gbGzPT3099TE6nDrl0M6ayr\n44ZUgngoHMDY2Ij8jevy6o6XAQDvvvpKTVBUhYgMaXqkfS43LFbOoSwpJSpzsVBMoSwpJC4X2Vos\nzucNhn1aFS9vkIdJwnFQ1xDQwl7Hxnkorr+MvrlVq1ZhvVSwUPOdk/IgFrtZM2xWKmc2JwAYzQZM\nSYrrHXd8mM8sWZUPP/wwyhJmoOKLPnvPz3T2pNO5oUU17r29k7s/Hi+cgWYpcVYboFDn83ngdxCa\n3U6etkiArGx2dhb79tDzqnxJp3oIw/fd9wl897vfBXCm5p7FyN93tndiTnK462taAQCjgzzdpaIJ\npSxPXk4+02ne32p1wO8Tb7qDwvitt3wAAHDje27CE088DgBoaeSY5bhN7p9HtkhkM0g4qsttle+c\nKJSkNqCZ7FOxDwPMCEXq5N48v6VKTuZpx8FDNI7edDPRUtW5OXb0JAb6iT5ON9Fv8/kbZWyvZt4w\nytu12iSg3WRA5zLOvW+IqFwSRLz5lmtx6BDRXJV3OxvpSKPTgmlRkSaX4glrbVyiqYaJBNVbm3gb\nc6kygpJr3NbOU6BQqZQzID3Hk7fzjZ0AgHpxQP7kRz9FWzNjhHe8yjiadWsYzVYbqUdbM9XatxVS\nSRDhqZOnEZRSZypAvLWV12bTaczEaNyrk1hmi4lo9MaON7CkhfPT6sWYeKKLlSKMEtxervA7ZTQr\no4xqSRn3uNwBcQtksmXNHeAW4dwrFSxsDhOKRavMgWvnEZnIZDKhKohdLFDs6O+jbGO1mWCRe3eL\ngXJggCaGqrGiyZSn++jR9geIzna7FQcPUzG46iqWjDsb6Uij04JpUZGmro7mco/HgpGRHgBnIvkD\nUsa1vr5Rq+bkk9p505JW+spLf9DGam9lOobi1/lMFXtPHQQALF9KLWZcXBNmk0mrOhWVsUxm3reY\ny8NklfotMva0mNaNRoMWSlEqU2VW6qrX4wSk7sv8HDU4t5mo4gt4UVtH2cIoFTDKIELNziVRlZSZ\nSoV/i83QxVFX3w5UqFlZTJyfKDwYGhyHWaL5egVFEla+Lp/Ph2CQrgKHaDpziZw8J+Dx82/DQ9SU\nLBaOE6oJoH+AskxXJ10wKnIwFp/F7bdTfjt27Bj+HC3qplE5StPRcSxfwZfulHq+k5NkA/0Dp9DV\nxe/eOUCL7pEj4h1ftQptbW0AgFiML0p5pE+dOoVsjlbe1jZJh5E8Za/bjgulJFr/wGmZjArSzsMq\nQu7sbFzmxEU1mYHe04TtUJD3UbV1DdUiolPclB+49VYAwIG9rPRQLhe1okvrNq6X+1ItX9LWhPEJ\n3ufgEb6wgGQ8JmIzcLr474L45GJxWo/HJoaxeTPNDSMjFHqtfh606YkEurpUOg3nnsueKehULphk\nTDkWonKnZvNw2MnqpiY5p1CYbHF6ehoemcv5m5jZeTbS2ZNOC6ZFRZpinnzHCAuOHeEJDgS405X1\nMVvOwGzi3g2HuOtranjKX3ttO/btp/DX0UEhdNlyGgAf3fZP+NKXPw/gjJd67WpC7ooVK/Av//I9\nAIBVLNAQI6bdboShylPZ3KJyv8mCcpkUaqR8mstJNJoTv5EBZYQFfcoSPbh2GXOmu7s7MTBM42FI\nfGo1G2igPN7bg03riaSHDtB6my+pOJ4w0hnx/stnUAThI4enEJHA9Ykxznd4iD629vZ2nBTPflFK\n1zY0UpmITs5h2Ur68oq5rHxHS3tsdholCTXNpbge/TGO6fDYtbJpu3aS7bfdwJDS/0w60ui0YFpU\npPF7yW+HhoawYgX9NMroFg5SSK5WMvjB974NAJiUqLzUPPntze+7EQcOMEamsYGn7gffZ52aXDaK\nhx5ioeaJCcoKiRFav3bs+D2sZp4kq4Wf8QSNe55AEDHxrWSlTJgSziuVImoiNKtPjYspXgTirVs2\nYeMGlk8VYMTKVfTjVFHClvVEwNMDRNTbbqPc891//C72vkkZ7e6PfAIA8JsnGCdTLhkQCVJmik/z\n2ZWQbDUCu3dSEVD1ejxOhSZRrSpGz3EqGG7xPzS2NGFIvP8KSUeHqAy4PHZYJbXYVOVzJeJci51v\n7seRd7iOKlrxbKQjjU4LpkVFmoqBp2b5qgAmJsgnHVLbZfValoofHKjg8stZGPrjd9GJdu+99wIA\nfvrjh/H000x0v/9rX+XvJf7jB//4GFKzRJE7P8QMh4suodT/7X+4H9t+y7p6pbIUaRYV2GAyoEUq\nZ83Oi/vCzTn19/XC6+b4qkx+Rvoa7N61Dx4pEL1FCln7XdJyqL0ZkRrxLFcpg725g4ay1qZlWNJO\nlN27n4bGGj+1vWhsBnMz1AB9knGgtLCLt27C62+weIHFJZ1gJMWnra0FExNUp9esZvzvhKQIOZx2\nzTiaiHLsxiZW5ypVypiUji6FAp+rLtwGAEg1FDQNNWamJnc2WlQv9513rqsCwM9+9mtc+S5C+dXv\nZoCPUvnq69owNkLWoQouVqWK5UfvuAcbN/AFKdXyga+z+catt96uea5//etfAwA+9lFWDU8lMxiV\nxfnVrxnIXpQ2P/d84tP43//6CABoDTIuvJAb+OWXXtRsMEFpt7NhjRQSSidgFitsczPhuxLnpnnn\nwF60L+FGdHq5acKiymYLaS1EISovxSLPct4F52NCWv7U1JBNvXOImaHx+SiiM9wIDZJ77rUzHKJU\nKmn+K6dLSrho7YhiWLqcc1bee7Vp3B4PIpKpqgLPhkcGAQCt7e1ao5FZ6aF0++13615unc4NLSrS\nfOHz11cBYG42Cb8EQLdJRYNqhZxx/77DmJY6LOkUBdn33sjgo9d27MJ3vs1gqFrJzHRKTd5/+Pz9\n+I7Uo3FI4cW/+ThLxIZraxGTjEfJeUM0QaH34JETqJMgdatYU+eFTTU1NMAm3uaRQRoFfeLvQamA\nfI6nuaWJcwnayWby+Sz6h3h9UdTxtg76xe6660786tFtAIB4nL8vlRSrtGE+Kc3MxBQcCEsxJUMR\naUmfVH0TzCaynSNHjuDCC2m8HBe25HISGSenp2Aw/GkoqOo24/L4EJJMU5VTXxSTcEtLi9ZfQYWO\nbty8VUcanc4NLSrSfOIjd7CHZaWCjiU0cKnoNxUd1tPTi2PHqJI2NPAEr1pNwfG5557Gnr3sv/Tp\n++7hmJ+8W8axYmiYpvqdO1mL5sCbVM+PHD2GFWtozh+WvgBuKRM2PpVAuI73cbmpxgcCPN3lUgFV\nSTdxSMpvuUgkmJ4cxcQI1fBlS/ksHheR4+TJ46iR7ilJic1Rpzs6HcOPt1Eov/097wMAhEMqXiiH\nYIBqdG8fn0Wp/w6XXevL0NjI+UYTFH47Ojrw+9+z1s2NNzKMNS/BNqFgBGOSnuIUpWNWKmZEIrVw\nOohIFll/JfyOjU5gRmQ8FVj+yc98Rkcanc4NLSrSfOC6z1UBwOfzoquLsR1NzTw1u3a/DgDweG14\n+RXW2gtHaPC77npqQY/8248ghwUTU9SUtlxIj/Z9n/k46ht4uvftY8xv9BR5fjqTRUxkmP0HGOmX\nzPAkOtxBHDrGggPvv4V5p0oLKxRzKOfpLXZL55SKmPwjAS+iU5QfnJJ419KWkPsfR07K1P3oR18F\nADz4LQakG2DFP//oZwCAQwco9/z85+yct3zpBkSjlIGaJTZobJT3mEnEYBfHqtKM0iUa6ex2u5YO\nUyhQJvnyl74CADCbregfICKGQtSUZmUt3nxrFyak8pby8Y8M0xDo9Hg1J7LiAm8fOaQjjU7nhhYV\naW7761urAMMZNm1iVF1WCkyr0zMzM40TJ3jylQ1B2Qsef/xJvOc6Os22bfsNAHZ4A4BioYSaGtov\nlMFw6xWUhYaG+5BKUh4oFqV9colQsGbVSlilIlWddHzrO0VH4vTkFBIx2i+CEtEWkr5NhWIS6zew\nBO3IEK80wKamAAAcZElEQVR/5MlVMqcM6ut5/mwW/r4mJFWzMhNYv5J2kxuvYTbB755kkYDxoRhW\nLKdrYmpGHKNSvt5sL8EoiXfK4DcVJZKOjQ9hSQdtRck05ZCr3n0ZAKBUqWD9elY2lWgLzMxwrcdG\nY9i+/QUAQLMg/myK952dH0MwTHlzz17W7enrKf2XSLOoFmGV2WexWDSDkVU6kahNY7FYtFxnldes\nUjCuvfZa7NlDQVjF1ahWfqlkWssHb2qSooOSGjIbj6Eom0Q1BHM5uVG8XjfMMq+2NgqkX/wcveUf\nv/seNEiYZ1Fq/p63mV1vt29/CkVhVcUqWV05Rzhvagnj0i3cQJs2cS7//P2vAgCc1hIO7GNHl8//\n7WcBAM+A7Hjd2pVa3E1enj0ppc9q6v0IesmuR8YGZT3p6e9ob8fYGFmqQVr3vPYa+0ucPHkSf/8V\nPldFrNMtEvo6PjaDd7+boZwnTjDQasNGsvv9bycRjZI1qlz3s5HOnnRaMC0q0igjkdvthmKDNht3\nv0Iaq9WspacoZFJFHAcG9ml1/RXC+P1EjpMnerBhA9NghoYYDjk9zZMSjoQ07zQkejAp/Q9jNgOs\nZikWmeLffvFvLAm7YuVSLX2jUOD8TvWzVs7KNd249nqe0kceeRgAcOAtuiju/NiHsP8Neq5nhjjf\nh39ID/w3vv5lfP8hGijrQvxutXio44kUIDVulsvfBkYNsnbAvr0sINnUQjaalOrrQV8DqlJSNxAg\nGsXES+6wWfHTn7BY5MqVVPsb6qUjTVMnClJeVnV7eeFFxiJdf/31OHaCJosTJ4/iz5GONDotmBYV\naRS5XC7NgGYy/6lsNT8/f6aVnnwqZ9p1112H++//OgBg5Qo2AVVB6B/+8Ie1UrD//u9MYmtt4alr\naKhFfIbyRmKWampW1dAr5JGTznATkzyB522gkP7mzh1Ys2qV/I5o195J+eB03zEcOkovdVsnnZPP\n/vZbAIBM7B3c+dEPAgAmRymPvfMmPdTrurvRuKQNAHDkdabhdHfw9xe9768xc1IKSYrw/9jjfJad\nu3fB7+eazYgnvE2qa/T1nEBTE5UAVbgyHCYCd7a34dTpQQDA47+l8uCw0bm5fMVa3HUPIwiefpZd\n60ZENmpeEtbSjZVMeTbSkUanBdOiIo1KzKpWqxqKZOWUK5SYm0to36lqBUoreuKJJ9AtvSdVf8uf\n/OR/AwAOHTqE559nY/iuLkbN2S0qc6CqJbQpQ5WK+TUYK1r0YCxGdfOppymPNNTXaKEJVVBmqC/y\nBNscFmzaIqkyUsUh3UN55I4PbMXFm9sAAOu+9EkAwI+/8lUAwOf+9j6tcedwP+N6J0X+ePHFp5GS\nmnnRuBTS9vF+G8+/UHuGugYi09e/SYPh3336E+jrZ8Re2E9XyGAfo+6mJyfQ0CRpwwXKkcMjfM7O\njha8KTE6x44yBffSK7YCAP592zaE64hIq1cT1c9Gi2qnueeuu6sA7S8qkLxY4iIpNXtoaEArB6K8\nzWrzDA4O45KLLwNwphTYyAg31KqVq7WHe/hhViVvaeEmfde7LtdeejIVl0+O6Q94NMFbvRRlXXU6\n7ShJWdlly1X5NL7gQMgOo4lzvnArU0us0xSW163eiB1/oGngogvYIdjVTjaHqVnkhNXZJVD8wX/4\nEgDAYKrA7qE9qLVDCjtJW8J8wQAYaJcJBGkGGDlNAfWNN17Tqj0ckwZiMPI9zqcySGX5PD4//VoZ\nSW/p7F6Bg5IeZDDzoBbKXIPauiAmReW+46MfAgB85m8e0i3COp0bWtxwT5FabTabhiwOSUxTRQ9d\nLpemhisPuEKaUCiElVLO9MknyUJMEhjd29uL7S/SsmqUx6iPkD3lM0X4pK2xXwLSpcIajAYTHBI+\nqXK5zSaiWCaX1gT1nMzvkksvAwC8c/AtrBNk2/82VeH3XUyVfyY1iqtvYNX16TGy3wNPPgUAiASa\n8JS0dE6KlRpGdbr9aO+QxqwzRNuZGQruK1ZswrSEaw73DQIAAl6iycfvvgMrLr8EAPDei8heAuJn\nKhUrWom0oRF6u8M1qgB3DPm8FIWSgpeq+rrTaccN19G73S8e97ORjjQ6LZgWFWkUmni9Xk3YDUco\n26h8bbPZrAmrHg9VZlVEMJlMajEeKp1XxYqUimXYJPpM+aPWrqGsceCd46iI8evW2xgF2NlB2eHQ\n4bc1maahgfKD6kzidoXgFHdD3wBlp+M99Is1NUdQllxuZZ7f9uyTAIArLrkWTzxDAb2S5ylPxYgK\nzXUJnBygar9pPVFzZIxCrDfgxZz0QmioVzIKheUdL21HpKYNABCPSe0b6f4y2G9GXy9TZX4nzeY/\nciN7IxQLBS0qTwn/kRDnVDFWcN4m+rpGx4lsGzYSPZ0eJ2ZlLqdPcw5nIx1pdFowLSrSqGj4gYEB\nrbCgkm2U5tLe3qaVaFUGQPVdbW2tNpbygKv4kUq5iiGJpLNJuftSgY8T9DdiPsNTc/sH7gQAuD08\nfd///v9CIsFotZLE5XZ10Ck5NjaKlLRDrglTba1AatAU0vj9CzzVmSy1PIeL9zjUPwynNI0/up8n\n2GGg8W1iLon6dmpGeYndbWxtAwAkUnOw2ziH4QODAACToNnEaBTRcarKNisRuCZC1XtkeBARKSj9\nc6kG9q1vMl76q/d/DRNT/F1zI6+/7lpxUvaewnSMCG808b4q4+GC7gtxTPpuKdnybLSom+aMLWZO\nYwkqX2fpUtpWjh07ol2ngoDU/8fGxtAkOUqTk2fYGQCMT01o3WstFsLwiy8xsCuZncMVlzIHqnMJ\nWcIvH6Va/qlPfRaTEkz1wx/+kPcZJQts71yBipT+mJzihqwaxL5jqKIiz+B1iyor2Zv7j/XDaeWm\nNkkXlyVyX0vZjnGxyzilqNHW8xiKOjJ0HDnxaqviRuqFeVwu1IoA29fLuczO8bv3XHcjjhwle/JI\neZYZafNosdjQ2clw1D3v0OZ0qpcs9p0D+1EGn2/dOtqcrHaOubSrHS+8SLuXKpx5NtLZk04LpsVN\nlrvjo1WAKrdCiOUriDDqRI2ODmt9DNTn4cNs8DkzE8d11zJw6aKLqGI+8ACT5a695jot+zIphY7M\n4Jjtra2YT5IFReNELyVoJmZn8PrrRKT14iX/g5Rf279/v1ZWrCRGr2KJBrwKsjBbpMSqFIacrXJs\nryeEukgb5zzG68tZ6e9tcaKtgQFTn7qXrPKl52k+8DoNOHqQnuUBEWztkkg3MxFDpUL2XswTHcJN\nfIY1a9biiiuvAwA8/TuaHZKSCVpb34DfSEHJpStpTVf9ombi02iQvuH+UFD+xnV65eUdKKpYJelR\nceJUUTfu6XRuaFFlGiX8plIpTZ0elL5GygWQz+c1IVfJNLdKpal//ucfa6r5fx7z4MGDWuKX10uf\nyahUnDJYrJiXpqGKZ49OTGpjXHr5FTIvGvXuuecuAMDXvvr/aF3qHvkFVehShSgWCjvRP0BVeUwq\nSjgDPK3JWB7xccoNbU0MEN8qMpXVYMWJIzT/f+3rXwUAzM5QxrGZjQhLy8Hu5US9k0dp5l+zYQOq\nklSnfGVVmwSDT0zihRdfAgBMSbN5uwjL5apJi0GqSKPWkMTxpHMJWCxEk7gIwG+/Q0NltVJEuUQE\nXda9FH+OdKTRacG0qDLNFz//hSpALUg5LF1SLFkhjdVq1hLElNyzbRvTWKtVA65+N1Nt7XYa9x59\nlNFy259/AeedxwDqQ1Kk2e7kiZqcGtN6Q1akDotb0msrlRKmpqlaRoSv5yXWJp1KomMJkeLOj9Fp\nd801lwEAUukYXvkDtYu3dlEmcoo7oqYmrBkh/VI4wCef87NJ2KXSRT5Lg2NVUoXtVgds4sqIi8e9\nThyRXZ3tWsPTNauoie2UgG+X04/xMV7fvZSaWHOTlH8dHIJPKnbNJnnNnASPw5CHLyTtqHvpFVe4\ncfxEHzZuYGUvpYk9/vTRv3xvhG9+48EqJzGjBY1fJj4TFWjl83lw/DhDKjs7ac9QQvL27S/id//B\ncMQf/vBHAM601Hn019u0Sp+qWcfRXgYkBQM+zEgQlto0qh7wfDKNVqn6EIvyRavcZ7vFjrjUOXZK\nBkBTC1lnNh1HSyttIx/8EPOlVrfRlhNPTOPV18gupsWHpOoJZ7NZBKWSaTEvuVRlsTr3juKyS2lD\nUVVIi9J4rFTOIigCrD/AFz0+IQUbLW6sXU3rtwHckE5R9VPJDCwSvO9w8Rl+/woDrjZfsAaJ5IzM\ni2v3xJO0PXV2NGB6SjI6GygCPPH8CV0Q1unc0KIKwj09FBzL5TLCYcKusv6qEl0nTpzQfE/K56TS\nXWKxmGYd3r6dquXWrRQwk8mkdl2d5GYbxEM9l4yjrPUoIGrlcyqTsRYJqUFjkSJBRjk7mXQKLslq\nhLTnGR6QMrO+M3WKv/xFlnv75E1XAgCqyKO5hmiwcSURYMtFDOquq6/Hk//BlJWnniIaVav0BdXW\nNuDgIaJstUK0U22jU+kY6qT82ZU3kEXv+D1zlowGK5Ii6HtcZMMGYXMWK1CR/gq7djN1ZtkyNjPb\nt28v1m4kq3vmGbLapkay/dbWZhw9ourhSDHjs5CONDotmBYVaVSczPLly7WgZSXbqLoqDQ0NaG2l\nbKACmru7aZSy253YvYun5be//S0AVsAC6IZQ6ruK+Gtf0izjDMIuFaJmBVVUstxMYgYmyWN2uYgO\nJpFpgj6/VgHcKEhVVl1RSinMS2pNRjIX9+xgtYrO7iasXcWKXSeOq+arfL7rb3ovrCY+c3cXhezx\nCcotkXAXBgcoY3zoIzT8vb5zBwDgZM8BvHOQIZn9owz+bgxQwDWaHPB7CUkOB80OCQkXdTqdMErM\nkUL3srR/vuiii/D4f1DJ8Hr5nJdIybnHtr2Ia65hzZujR2k+OBvpSKPTgmlxk+VUOda5KFZKzRnl\n+W6QlA0AmJ1VscE8LT3HVWHlIlZK/bh+aVJ6+iTTSa+56gr86EfUqHIF8vdgI/m6y1FELsfTZZNW\ng9kkEcPp9GrtBw0Gnra4RAra7UW4VBtkKVBjNhONShUzilLssWykq2D3INFrIJuDtYHo8d1/YkWI\nviM7AAAH9r2Dm66myf/yCyhXjY7xfj2np1Abpoa0czdL5g9J2+dYwYiKOEGLRcpl5qQgYsiDjMQE\nHzxNZGtukdjiqg37d7OKxvr1dEqWpAPfo795TIt8XL6MxsQXn3kLAHDxBRfh7V1EtjVr6PU/G+lI\no9OCaVHtNN/5zreqADA8PKyFRqhTXiexMqlUSiu/qmSgTJInOZ8vaq6CZ5+mvUZ1P/vVr36lIc3u\n3WzEsWRNm3bvAanRUhWnn0uMg/PzKa2fZiZN9PGKiyOXy2jZCKr8qioXa7YYkU4ntTEAYG07NaWp\nyX5ceglP5+WXEFFHhlkCd3z4GLq7mFB/1VXXAACee4ZFpXsHppFIcv1NThoaId1jCjAhI3PJSLn6\ntpAU7K6WkUoRnZdJOq/dQaZx/NhhrFvPTAjVu3LHK7yfxWzGxg00iL7+OhFm7UpG8qVSWTjsLvk3\nhbafP7H7L181YnycBjaDwQSPhzCv8rQnJD4mn8tolmCleqvPVCqlvcQuWRz14g4dOoB3vYvFj159\nlbk8+/bRj+L3uxGP8sXW1IW1sQAgHkugUWJ0lPdYpbBUKhUtn1x9quD4XK6gqf8SxYrBYW7MufQM\nnn2eL+aRJxm386VPsg1Obd0SZLNc+0MHTsozkF0EA7UIhLkuuTLvNyEsOldIw+aQ9ZB87WiUsUjL\nli2DX3ooqJSgQcnUbG5uRX0dzRnbpFTu+vVkRV1dS/Hor/i3yy7j2tUEaXhMJlN49VUa+trbKbCf\njXT2pNOCaVGRZlRKgXk8HrjECKXa9EmcOdLlKlJSFlWdeBXmuWLFMk1FNxgI8QHxq3z57/8e77+F\nRQprasnCslKr5YMf/DCOi2dZ+aVsUjI1Hp/FmMTtRMIUNHNS+8zhcGil0dT8VEZoJnuGrfl8vEaV\nJWttbMfoGE0Kfqkh86mPM2nu+MlTWLuKwnyhTEHd45X+VFOzmIlR5c5Ix5q8MASXww6Y+LdkikbM\nlcu7tbnVSkEmlSy3di19UGtWrcT3f8AONKo8nHLLPPbYY/jwh+8AAK2JmnLFWHI5eL1kT+rzbKQj\njU4LpkUVhP/lJw9XAdAhKa34lMGpto6nfGhoSOvB5xADXFAag1qtVpSkhr8S/BQKjYyMIC1/U/Gu\nn/nc5wAwZWb5cprLh4fp5HtnP6MBfX4vuiWGtqeHqv2ZlscVbe7KcZiRPkzVchkeSaFVHntjVT4N\nJRSydHROTw4CAC7czJiU915/FQrSTyqTysr1FLJHxiYxIc3pM6IWG6TloDvggUWE24wgocVYkrkV\ntRgi5eRdvZLC76OPPqo9u7HK59m3j7HCV199tbZmHR1EbhWz7XDa8LTktCul5YnnB3SHpU7nhhZV\npklIqfl8roSSmLIrEsuiNKZSqQSTiRtaaSeJBFHJ7/ehrZ1u+uEBmtKPHKHK7fG6MCFZBZusdA7e\ncsstAICnnnpKiwLs6GCcyZwYEGemYxr6WCzUWBTCKJkKAGyicocFVSqViibnKDnLJOEW0WgUPult\ntXod52IQ7WtkLImWBqKjzc7r+3poqExnSpqxzWaVNSgS2eZnc3CUpLemrM+MCifxehH00+m5pI2a\nzje+wYoSm8+7AMl5jtErYSQrRa2Ox+fh8XDMmBSkVOg+ONgPsxQFUL1Hz0aLumlOS3Edu92KcJiq\nXU5KTipGEPCHtE4iqi2gCpgqFApasLndRcGtuYXq5OjoCAIBQvRrr1HdvfhyeoP37NmHqSl6pzs7\nyIo6pPjgbCKhwW9V1GklDAJnGnKFpKCQ2typdPJMp948P9O5kozdhoSowxMTtPZOG/jd3GwGV0oM\nkcNGQbi2kS86mkginRZrdoTP0tXA7xxuK9LSiyEW57PU1PJZvF6vplD84he/AACtSeqxo8exRPqM\nt7fzwMxEOaeODr9Waq5ZSrK5ZO1feuVlrF1DG5Mq5ng20tmTTgumxe2NcO/fVQFWgXCLEKmEzsZG\nKT6YSsAu4Z4maSE4OEhWFAkHtCoOdXVEqslJsqRiPqcZ/pQBLtKwRK6tw4OS6qLYlKr339fXh+NH\n6b9Swe6KLVosFvgl+Uzljit1fHZ2FpWqVFiQYPVCVXoPzMURCVHAlyYuMIkQaqiUEZLQ0498iAa/\neIzI0XvqmOaF90ifKFUzuFTOoigsXZWiLQnqzc0lEQ7wfq0tfObpabKbcCiCeGxemzNwRuWuVEta\ng1SXoPkLLzLW5/wLNsItDdIa5N3cee+/6IKwTueGFlWmicYkxsPlhFQiRTxG/nrGoGZB1UOECTp5\nyhukXNjcfAwO8Ucp4VNF/OXyWS2OZm6OY87GebLsVjvuuotpKf/6ryz3+vLLLwMALrvkUq3a48GD\n9A+pwHav26WVrlWB4iXJPTebzVry2twc71u2cpxQKIiMBNnMSjm0WknLcTvsyMoYv5Sq6x/7CBGn\nf6BXQ8t4nIZCl5tzqQkHMDrGOSiBfXyCKBsIhJCRyD1VGaws6SpTpSkEAiH8MeULqmp7URP2cyIv\nKRNGpVJBVtT+OfH9nY10pNFpwbSoSJPPc+fa7U5YxWhlkDgQiEm+VDqTQpIvSC8AKWzocnoAA0/z\n/BxPstXGfe5yuRAKkq/bRJNQjsH5uRT8EuW2dSsrRZ0+RUPe0NCgFkWoPOhm0aUNBgNKZTGyyTMo\nTatQKMAmKbN+KaY4OS8d8XJGWKUIgaXKz4KkuA7OjMIio4VClJPuf/ABAMBdH/sgUlmiiRH8XTbN\nMROJBFqb2/idzM/k4jNlM3lA4pJU1TClnabTaU0eczipzrvcROvp6BSmJFFPqfEV9aRGKzJpotBE\nQVJezkKLumlKkrFXLlc0v41BPotiIa7mi1pglgpVUNZih8OBclltKKq5Ffm9w+HSqks4HIT2KWlr\nGI/HNU95bYSLOSq2md7eU0hJI69N4v2NRrlIyWQS5SJftgrhUKwrlcwgJVXMVbkUp4NsIzYzCaNB\nBYBR6G1ubpVr88hlZH7SdzwiLYreeOs1jAzTltLSSH+UUQK9Is4IRqX8mUHWKibu9WQyqbFpq9hW\nlJW7f3AA6cz8n8xdhZ7YXVaN/SpTgjJzpFMZjcWaLH9+W+jsSacF0+KGe4q1s1SuaoKbgIrWzLNi\nNcEgbCwnxrKZKNVHGCqwKstxmQigfELlEuB0qGAt/s1kVGXDnDDKjf44VgYgehlEdVZ/U8WUSoUi\nZqVg0ZkmoNJJxmhCTgTFTIEnMisn02K1oL6OSFEpq3rJnNvs/CzcbmETDrKXmJSZdcbKaGjm7zKC\nRl/4n18AADz/zPMo5IkK61bTt7bzMAV3A6xIzvPehQLRWfnmbBYjKqLu+6X9kFI6DMYK0knxaguS\nivyMYokB6wDgkIC1s5GONDotmBYVaZS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"text": [ "<matplotlib.figure.Figure at 0x11d425f10>" ] }, { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "Guess the class for the above image (8 / 10) : spiny lobster\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Incorrect; it was actually ['bullfrog', 'Rana catesbeiana']\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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uv0UvBfvnONh5wRkIIcMOUxNJHpwakXHC0JDTCQJ62WGDcECJy4tEUnJNITqm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\nVhDFAAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x10f3a2cd0>" ] }, { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "Guess the class for the above image (9 / 10) : teapot\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Correct!\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10fbe7f50>" ] }, { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "Guess the class for the above image (10 / 10) : seashore\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Correct!\n", "You got 6 / 10 correct for an accuracy of 0.600000\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Download pretrained models\n", "We have provided 10 pretrained ConvNets for the TinyImageNet-100-A dataset. Each of these models is a five-layer ConvNet with the architecture\n", "\n", "[conv - relu - pool] x 3 - affine - relu - affine - softmax\n", "\n", "All convolutional layers are 3x3 with stride 1 and all pooling layers are 2x2 with stride 2. The first two convolutional layers have 32 filters each, and the third convolutional layer has 64 filters. The hidden affine layer has 512 neurons. You can run the forward and backward pass for these five layer convnets using the function `five_layer_convnet` in the file `cs231n/classifiers/convnet.py`.\n", "\n", "Each of these models was trained for 25 epochs over the TinyImageNet-100-A training data with a batch size of 50 and with dropout on the hidden affine layer. Each model was trained using slightly different values for the learning rate, regularization, and dropout probability.\n", "\n", "To download the pretrained models, go into the `cs231n/datasets` directory and run the `get_pretrained_models.sh` script. Once you have done so, run the following to load the pretrained models into memory.\n", "\n", "NOTE: The pretrained models will take about 245MB of disk space." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from cs231n.data_utils import load_models\n", "\n", "models_dir = 'cs231n/datasets/tiny-100-A-pretrained'\n", "\n", "# models is a dictionary mappping model names to models.\n", "# Like the previous assignment, each model is a dictionary mapping parameter\n", "# names to parameter values.\n", "models = load_models(models_dir)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Run models on the validation set\n", "To benchmark the performance of each model on its own, we will use each model to make predictions on the validation set." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from cs231n.classifiers.convnet import five_layer_convnet\n", "\n", "# Dictionary mapping model names to their predicted class probabilities on the\n", "# validation set. model_to_probs[model_name] is an array of shape (N_val, 100)\n", "# where model_to_probs[model_name][i, j] = p indicates that models[model_name]\n", "# predicts that X_val[i] has class i with probability p.\n", "model_to_probs = {}\n", "\n", "################################################################################\n", "# Use each model to predict classification probabilities for all images #\n", "# in the validation set. Store the predicted probabilities in the #\n", "# model_to_probs dictionary as above. To compute forward passes and compute #\n", "# probabilities, use the function five_layer_convnet in the file #\n", "# cs231n/classifiers/convnet.py. #\n", "# #\n", "# HINT: Trying to predict on the entire validation set all at once will use a #\n", "# ton of memory, so you should break the validation set into batches and run #\n", "# each batch through each model separately. #\n", "################################################################################\n", "\n", "# Who cares, I have a lot of memory...\n", "for key, value in models.iteritems(): \n", " p = five_layer_convnet(X_val, value, y=None, reg=0.0, dropout=1.0, extract_features=False, compute_dX=False, return_probs=True)\n", " model_to_probs[key] = p\n", "\n", "# Compute and print the accuracy for each model.\n", "for model_name, probs in model_to_probs.iteritems():\n", " acc = np.mean(np.argmax(probs, axis=1) == y_val)\n", " print '%s got accuracy %f' % (model_name, acc)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "model9 got accuracy 0.358400\n", "model8 got accuracy 0.357000\n", "model3 got accuracy 0.370600" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "model2 got accuracy 0.371000\n", "model1 got accuracy 0.371800" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "model7 got accuracy 0.359800\n", "model6 got accuracy 0.363400" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "model5 got accuracy 0.368600\n", "model4 got accuracy 0.369200\n", "model10 got accuracy 0.357000\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Use a model ensemble\n", "A simple way to implement an ensemble of models is to average the predicted probabilites for each model in the ensemble.\n", "\n", "More concretely, suppose we have models $k$ models $m_1,\\ldots,m_k$ and we want to combine them into an ensemble. If $p(x=y_i \\mid m_j)$ is the probability that the input $x$ is classified as $y_i$ under model $m_j$, then the enemble predicts\n", "\n", "$$p(x=y_i \\mid \\{m_1,\\ldots,m_k\\}) = \\frac1k\\sum_{j=1}^kp(x=y_i\\mid m_j)$$\n", "\n", "In the cell below, implement this simple ensemble method by filling in the `compute_ensemble_preds` function. The ensemble of all 10 models should perform much better than the best individual model." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def compute_ensemble_preds(probs_list):\n", " \"\"\"\n", " Use the predicted class probabilities from different models to implement\n", " the ensembling method described above.\n", " \n", " Inputs:\n", " - probs_list: A list of numpy arrays, where each gives the predicted class\n", " probabilities under some model. In other words,\n", " probs_list[j][i, c] = p means that the jth model in the ensemble thinks\n", " that the ith data point has class c with probability p.\n", " \n", " Returns:\n", " An array y_pred_ensemble of ensembled predictions, such that\n", " y_pred_ensemble[i] = c means that ensemble predicts that the ith data point\n", " is predicted to have class c.\n", " \"\"\"\n", " \n", " mean_probs = np.mean(probs_list[:], axis = 0)\n", " y_pred_ensemble = np.argmax(mean_probs, axis=1)\n", "\n", " return y_pred_ensemble\n", " \n", "# Combine all models into an ensemble and make predictions on the validation set.\n", "# This should be significantly better than the best individual model.\n", "print np.mean(compute_ensemble_preds(model_to_probs.values()) == y_val)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.4244\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ensemble size vs Performance\n", "Using our 10 pretrained models, we can form many different ensembles of different sizes. More precisely, if we have $n$ models and we want to form an ensemble of $k$ models, then there are $\\binom{n}{k}$ possible ensembles that we can form, where\n", "\n", "$$\\binom{n}{k} = \\frac{n!}{(n-k)!k!}$$\n", "\n", "We can use these different possible ensembles to study the effect of ensemble size on ensemble performance.\n", "\n", "In the cell below, compute the validation set accuracy of all possible ensembles of our 10 pretrained models. Produce a scatter plot with \"ensemble size\" on the horizontal axis and \"validation set accuracy\" on the vertical axis. Your plot should have a total of\n", "\n", "$$\\sum_{k=1}^{10} \\binom{10}{k}$$\n", "\n", "points corresponding to all possible ensembles of the 10 pretrained models.\n", "\n", "You should be able to compute the validation set predictions of these ensembles without computing any more forward passes through any of the networks." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from itertools import combinations;\n", "\n", "# Generate data\n", "models = model_to_probs.values()\n", "ensemble_sizes = []\n", "validation_accuracies = []\n", "\n", "for l in xrange(10):\n", " k = l+1\n", " for comb in combinations(models, k):\n", " ensemble_sizes.append(k)\n", " accuracy = np.mean(compute_ensemble_preds(comb) == y_val)\n", " validation_accuracies.append(accuracy)\n", " \n", "plt.scatter(ensemble_sizes, validation_accuracies)\n", "plt.title('combination accuarcies of ensembles vs. size')\n", "plt.xlabel('ensemble size')\n", "plt.ylabel('validation set accuracy')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "<matplotlib.text.Text at 0x10f320e10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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sHnzwgyQ/L/Pm/QsPPPC7LMMSkTraFkpkQniSYs7rgjCXq74ER/63KAorOevn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\nHn74cUKC1p5ouyCzeERERNLaW0/amcD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"text": [ "<matplotlib.figure.Figure at 0x11d479e10>" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
keras-team/keras-io	examples/vision/ipynb/randaugment.ipynb	1	18233	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "# RandAugment for Image Classification for Improved Robustness\n", "\n", "Author: [Sayak Paul](https://twitter.com/RisingSayak)<br>\n", "Date created: 2021/03/13<br>\n", "Last modified: 2021/03/17<br>\n", "Description: RandAugment for training an image classification model with improved robustness." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "Data augmentation is a very useful technique that can help to improve the translational\n", "invariance of convolutional neural networks (CNN). RandAugment is a stochastic data\n", "augmentation routine for vision data and was proposed in\n", "[RandAugment: Practical automated data augmentation with a reduced search space](https://arxiv.org/abs/1909.13719).\n", "It is composed of strong augmentation transforms like color jitters, Gaussian blurs,\n", "saturations, etc. along with more traditional augmentation transforms such as\n", "random crops.\n", "\n", "RandAugment has two parameters:\n", "\n", "* `n` that denotes the number of randomly selected augmentation transforms to apply\n", "sequentially\n", "* `m` strength of all the augmentation transforms\n", "\n", "These parameters are tuned for a given dataset and a network architecture. The authors of\n", "RandAugment also provide pseudocode of RandAugment in the original paper (Figure 2).\n", "\n", "Recently, it has been a key component of works like\n", "[Noisy Student Training](https://arxiv.org/abs/1911.04252) and\n", "[Unsupervised Data Augmentation for Consistency Training](https://arxiv.org/abs/1904.12848).\n", "It has been also central to the\n", "success of [EfficientNets](https://arxiv.org/abs/1905.11946).\n", "\n", "This example requires TensorFlow 2.4 or higher, as well as\n", "[`imgaug`](https://imgaug.readthedocs.io/),\n", "which can be installed using the following command:\n", "\n", "```python\n", "pip install imgaug\n", "```" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Imports & setup" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from tensorflow.keras import layers\n", "import tensorflow_datasets as tfds\n", "from imgaug import augmenters as iaa\n", "import imgaug as ia\n", "\n", "tfds.disable_progress_bar()\n", "tf.random.set_seed(42)\n", "ia.seed(42)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Load the CIFAR10 dataset\n", "\n", "For this example, we will be using the\n", "[CIFAR10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html)." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n", "print(f\"Total training examples: {len(x_train)}\")\n", "print(f\"Total test examples: {len(x_test)}\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Define hyperparameters" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "AUTO = tf.data.AUTOTUNE\n", "BATCH_SIZE = 128\n", "EPOCHS = 1\n", "IMAGE_SIZE = 72" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Initialize `RandAugment` object\n", "\n", "Now, we will initialize a `RandAugment` object from the `imgaug.augmenters` module with\n", "the parameters suggested by the RandAugment authors." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "rand_aug = iaa.RandAugment(n=3, m=7)\n", "\n", "\n", "def augment(images):\n", " # Input to `augment()` is a TensorFlow tensor which\n", " # is not supported by `imgaug`. This is why we first\n", " # convert it to its `numpy` variant.\n", " images = tf.cast(images, tf.uint8)\n", " return rand_aug(images=images.numpy())\n", "" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Create TensorFlow `Dataset` objects\n", "\n", "Because `RandAugment` can only process NumPy arrays, it\n", "cannot be applied directly as part of the `Dataset` object (which expects TensorFlow\n", "tensors). To make `RandAugment` part of the dataset, we need to wrap it in a\n", "[`tf.py_function`](https://www.tensorflow.org/api_docs/python/tf/py_function).\n", "\n", "A `tf.py_function` is a TensorFlow operation (which, like any other TensorFlow operation,\n", "takes TF tensors as arguments and returns TensorFlow tensors) that is capable of running\n", "arbitrary Python code. Naturally, this Python code can only be executed on CPU (whereas\n", "the rest of the TensorFlow graph can be accelerated on GPU), which in some cases can\n", "cause significant slowdowns -- however, in this case, the `Dataset` pipeline will run\n", "asynchronously together with the model, and doing preprocessing on CPU will remain\n", "performant." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "train_ds_rand = (\n", " tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", " .shuffle(BATCH_SIZE * 100)\n", " .batch(BATCH_SIZE)\n", " .map(\n", " lambda x, y: (tf.image.resize(x, (IMAGE_SIZE, IMAGE_SIZE)), y),\n", " num_parallel_calls=AUTO,\n", " )\n", " .map(\n", " lambda x, y: (tf.py_function(augment, [x], [tf.float32])[0], y),\n", " num_parallel_calls=AUTO,\n", " )\n", " .prefetch(AUTO)\n", ")\n", "\n", "test_ds = (\n", " tf.data.Dataset.from_tensor_slices((x_test, y_test))\n", " .batch(BATCH_SIZE)\n", " .map(\n", " lambda x, y: (tf.image.resize(x, (IMAGE_SIZE, IMAGE_SIZE)), y),\n", " num_parallel_calls=AUTO,\n", " )\n", " .prefetch(AUTO)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "Note about using `tf.py_function`:\n", "\n", "* As our `augment()` function is not a native TensorFlow operation chances are likely\n", "that it can turn into an expensive operation. This is why it is much better to apply it\n", "_after_ batching our dataset.\n", "* `tf.py_function` is [not compatible](https://github.com/tensorflow/tensorflow/issues/38762)\n", "with TPUs. So, if you have distributed TensorFlow training pipelines that use TPUs\n", "you cannot use `tf.py_function`. In that case, consider switching to a multi-GPU environment,\n", "or rewriting the contents of the function in pure TensorFlow." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "For comparison purposes, let's also define a simple augmentation pipeline consisting of\n", "random flips, random rotations, and random zoomings." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "simple_aug = tf.keras.Sequential(\n", " [\n", " layers.Resizing(IMAGE_SIZE, IMAGE_SIZE),\n", " layers.RandomFlip(\"horizontal\"),\n", " layers.RandomRotation(factor=0.02),\n", " layers.RandomZoom(\n", " height_factor=0.2, width_factor=0.2\n", " ),\n", " ]\n", ")\n", "\n", "# Now, map the augmentation pipeline to our training dataset\n", "train_ds_simple = (\n", " tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", " .shuffle(BATCH_SIZE * 100)\n", " .batch(BATCH_SIZE)\n", " .map(lambda x, y: (simple_aug(x), y), num_parallel_calls=AUTO)\n", " .prefetch(AUTO)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Visualize the dataset augmented with RandAugment" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "sample_images, _ = next(iter(train_ds_rand))\n", "plt.figure(figsize=(10, 10))\n", "for i, image in enumerate(sample_images[:9]):\n", " ax = plt.subplot(3, 3, i + 1)\n", " plt.imshow(image.numpy().astype(\"int\"))\n", " plt.axis(\"off\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "You are encouraged to run the above code block a couple of times to see different\n", "variations." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Visualize the dataset augmented with `simple_aug`" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "sample_images, _ = next(iter(train_ds_simple))\n", "plt.figure(figsize=(10, 10))\n", "for i, image in enumerate(sample_images[:9]):\n", " ax = plt.subplot(3, 3, i + 1)\n", " plt.imshow(image.numpy().astype(\"int\"))\n", " plt.axis(\"off\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Define a model building utility function\n", "\n", "Now, we define a CNN model that is based on the\n", "[ResNet50V2 architecture](https://arxiv.org/abs/1603.05027). Also,\n", "notice that the network already has a rescaling layer inside it. This eliminates the need\n", "to do any separate preprocessing on our dataset and is specifically very useful for\n", "deployment purposes." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "\n", "def get_training_model():\n", " resnet50_v2 = tf.keras.applications.ResNet50V2(\n", " weights=None,\n", " include_top=True,\n", " input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3),\n", " classes=10,\n", " )\n", " model = tf.keras.Sequential(\n", " [\n", " layers.Input((IMAGE_SIZE, IMAGE_SIZE, 3)),\n", " layers.Rescaling(scale=1.0 / 127.5, offset=-1),\n", " resnet50_v2,\n", " ]\n", " )\n", " return model\n", "\n", "\n", "get_training_model().summary()\n", "" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "We will train this network on two different versions of our dataset:\n", "\n", "* One augmented with RandAugment.\n", "* Another one augmented with `simple_aug`.\n", "\n", "Since RandAugment is known to enhance the robustness of models to common perturbations\n", "and corruptions, we will also evaluate our models on the CIFAR-10-C dataset, proposed in\n", "[Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261)\n", "by Hendrycks et al. The CIFAR-10-C dataset\n", "consists of 19 different image corruptions and perturbations (for example speckle noise,\n", "fog, Gaussian blur, etc.) that too at varying severity levels. For this example we will\n", "be using the following configuration:\n", "[`cifar10_corrupted/saturate_5`](https://www.tensorflow.org/datasets/catalog/cifar10_corrupted#cifar10_corruptedsaturate_5).\n", "The images from this configuration look like so:\n", "\n", "![](https://storage.googleapis.com/tfds-data/visualization/fig/cifar10_corrupted-saturate_5-1.0.0.png)\n", "\n", "In the interest of reproducibility, we serialize the initial random weights of our shallow\n", "network." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "initial_model = get_training_model()\n", "initial_model.save_weights(\"initial_weights.h5\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Train model with RandAugment" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "rand_aug_model = get_training_model()\n", "rand_aug_model.load_weights(\"initial_weights.h5\")\n", "rand_aug_model.compile(\n", " loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"]\n", ")\n", "rand_aug_model.fit(train_ds_rand, validation_data=test_ds, epochs=EPOCHS)\n", "_, test_acc = rand_aug_model.evaluate(test_ds)\n", "print(\"Test accuracy: {:.2f}%\".format(test_acc * 100))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Train model with `simple_aug`" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "simple_aug_model = get_training_model()\n", "simple_aug_model.load_weights(\"initial_weights.h5\")\n", "simple_aug_model.compile(\n", " loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"]\n", ")\n", "simple_aug_model.fit(train_ds_simple, validation_data=test_ds, epochs=EPOCHS)\n", "_, test_acc = simple_aug_model.evaluate(test_ds)\n", "print(\"Test accuracy: {:.2f}%\".format(test_acc * 100))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "## Load the CIFAR-10-C dataset and evaluate performance" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab_type": "code" }, "outputs": [], "source": [ "# Load and prepare the CIFAR-10-C dataset\n", "# (If it's not already downloaded, it takes ~10 minutes of time to download)\n", "cifar_10_c = tfds.load(\"cifar10_corrupted/saturate_5\", split=\"test\", as_supervised=True)\n", "cifar_10_c = cifar_10_c.batch(BATCH_SIZE).map(\n", " lambda x, y: (tf.image.resize(x, (IMAGE_SIZE, IMAGE_SIZE)), y),\n", " num_parallel_calls=AUTO,\n", ")\n", "\n", "# Evaluate `rand_aug_model`\n", "_, test_acc = rand_aug_model.evaluate(cifar_10_c, verbose=0)\n", "print(\n", " \"Accuracy with RandAugment on CIFAR-10-C (saturate_5): {:.2f}%\".format(\n", " test_acc * 100\n", " )\n", ")\n", "\n", "# Evaluate `simple_aug_model`\n", "_, test_acc = simple_aug_model.evaluate(cifar_10_c, verbose=0)\n", "print(\n", " \"Accuracy with simple_aug on CIFAR-10-C (saturate_5): {:.2f}%\".format(\n", " test_acc * 100\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "For the purpose of this example, we trained the models for only a single epoch. On the\n", "CIFAR-10-C dataset, the model with RandAugment can perform better with a higher accuracy\n", "(for example, 76.64% in one experiment) compared with the model trained with `simple_aug`\n", "(e.g., 64.80%). RandAugment can also help stabilize the training. You can explore this\n", "[notebook](https://nbviewer.jupyter.org/github/sayakpaul/Keras-Examples-RandAugment/blob/main/RandAugment.ipynb) to check some of the results.\n", "\n", "In the notebook, you may notice that, at the expense of increased training time with RandAugment,\n", "we are able to carve out far better performance on the CIFAR-10-C dataset. You can\n", "experiment on the other corruption and perturbation settings that come with the\n", "run the same CIFAR-10-C dataset and see if RandAugment helps.\n", "\n", "You can also experiment with the different values of `n` and `m` in the `RandAugment`\n", "object. In the [original paper](https://arxiv.org/abs/1909.13719), the authors show\n", "the impact of the individual augmentation transforms for a particular task and a range of\n", "ablation studies. You are welcome to check them out.\n", "\n", "RandAugment has shown great progress in improving the robustness of deep models for\n", "computer vision as shown in works like [Noisy Student Training](https://arxiv.org/abs/1911.04252) and\n", "[FixMatch](https://arxiv.org/abs/2001.07685). This makes RandAugment quite a useful\n", "recipe for training different vision models.\n", "\n", "You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/randaugment) ", "and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/randaugment).", ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "randaugment", "private_outputs": false, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
phoebe-project/phoebe2-docs	2.3/tutorials/ltte.ipynb	1	24074	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Rømer and Light Travel Time Effects (ltte)\n", "============================\n", "\n", "Setup\n", "-----------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#!pip install -I \"phoebe>=2.3,<2.4\"" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "As always, let's do imports and initialize a logger and a new Bundle." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import phoebe\n", "from phoebe import u # units\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "logger = phoebe.logger('error')\n", "\n", "b = phoebe.default_binary()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Now let's add a light curve dataset to see how ltte affects the timings of eclipses." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<ParameterSet: 73 parameters \| contexts: dataset, constraint, figure, compute>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.add_dataset('lc', times=phoebe.linspace(-0.05, 0.05, 51), dataset='lc01')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Relevant Parameters\n", "------------------------\n", "\n", "The 'ltte' parameter in context='compute' defines whether light travel time effects are taken into account or not." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameter: ltte@phoebe01@compute\n", " Qualifier: ltte\n", " Description: Correct for light travel time effects\n", " Value: False\n", " Constrained by: \n", " Constrains: None\n", " Related to: None\n", "\n" ] } ], "source": [ "print(b['ltte@compute'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparing with and without ltte\n", "--------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to have a binary system with any noticeable ltte effects, we'll set a somewhat extreme mass-ratio and semi-major axis." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "b['sma@binary'] = 100" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "b['q'] = 0.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll just ignore the fact that this will be a completely unphysical system since we'll leave the radii and temperatures alone despite somewhat ridiculous masses - but since the masses and radii disagree so much, we'll have to abandon atmospheres and use blackbody." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "b.set_value_all('atm', 'blackbody')\n", "b.set_value_all('ld_mode', 'manual')\n", "b.set_value_all('ld_func', 'logarithmic')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 51/51 [00:00<00:00, 138.19it/s]\n" ] }, { "data": { "text/plain": [ "<ParameterSet: 3 parameters \| qualifiers: times, comments, fluxes>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.run_compute(irrad_method='none', ltte=False, model='ltte_off')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 51/51 [00:00<00:00, 126.95it/s]\n" ] }, { "data": { "text/plain": [ "<ParameterSet: 3 parameters \| qualifiers: times, comments, fluxes>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.run_compute(irrad_method='none', ltte=True, model='ltte_on')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "afig, mplfig = b.plot(show=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
McIntyre-Lab/papers	fear_ase_2016/sas_programs/sas_enrichment.ipynb	2	2089137	null	lgpl-3.0
edjuaro/cuzcatlan	tests/benchmarking_CCALnoir-v2-Copy1.ipynb	1	649486	{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "if (!(\"Notification\" in window)) {\n", " alert(\"This browser does not support desktop notifications, so the %%notify magic will not work.\");\n", "} else if (Notification.permission !== 'granted' && Notification.permission !== 'denied') {\n", " Notification.requestPermission(function (permission) {\n", " if(!('permission' in Notification)) {\n", " Notification.permission = permission;\n", " }\n", " })\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.stats import kendalltau as kTau\n", "import matplotlib.pyplot as plt\n", "\n", "# from sklearn.externals.joblib import Memory\n", "# memory = Memory(cachedir='/tmp',verbose=0)\n", "\n", "import jupyternotify\n", "ip = get_ipython()\n", "ip.register_magics(jupyternotify.JupyterNotifyMagics)\n", "\n", "# %autonotify -a 30" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# This is probably unnecesary ¯\\_(ツ)_/¯\n", "def ODF2DF(GP_ODF):\n", " GP_ODF = GP_ODF[['Rank','Feature']]\n", " GP_ODF.sort_values('Rank', inplace=true)\n", " GP_ODF.set_index('Rank', inplace=True)\n", " return GP_ODF" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* GCT file: [all_aml_test.gct](https://software.broadinstitute.org/cancer/software/genepattern/data/all_aml/all_aml_test.gct).\n", "* CLS file: [all_aml_test.cls](https://software.broadinstitute.org/cancer/software/genepattern/data/all_aml/all_aml_test.cls).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using CMS: Gold Standard" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "genepattern": { "server": "https://genepattern.broadinstitute.org/gp", "type": "auth" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a7040e54b0314237a1784306b5517dcc", "version_major": 2, "version_minor": 0 }, "text/html": [ "<p>Failed to display Jupyter Widget of type <code>GPAuthWidget</code>.</p>\n", "<p>\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "GPAuthWidget()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Requires GenePattern Notebook: pip install genepattern-notebook\n", "import gp\n", "import genepattern\n", "\n", "# Username and password removed for security reasons.\n", "genepattern.GPAuthWidget(genepattern.register_session(\"https://genepattern.broadinstitute.org/gp\", \"\", \"\"))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "genepattern": { "type": "task" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "202a3196aee547efa60f910f5cd2807f", "version_major": 2, "version_minor": 0 }, "text/html": [ "<p>Failed to display Jupyter Widget of type <code>GPTaskWidget</code>.</p>\n", "<p>\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "GPTaskWidget(lsid='urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00044')" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "comparativemarkerselection_task = gp.GPTask(genepattern.get_session(0), 'urn:lsid:broad.mit.edu:cancer.software.genepattern.module.analysis:00044')\n", "comparativemarkerselection_job_spec = comparativemarkerselection_task.make_job_spec()\n", "comparativemarkerselection_job_spec.set_parameter(\"input.file\", \"https://software.broadinstitute.org/cancer/software/genepattern/data/all_aml/all_aml_test.gct\")\n", "comparativemarkerselection_job_spec.set_parameter(\"cls.file\", \"https://software.broadinstitute.org/cancer/software/genepattern/data/all_aml/all_aml_test.cls\")\n", "comparativemarkerselection_job_spec.set_parameter(\"confounding.variable.cls.file\", \"\")\n", "comparativemarkerselection_job_spec.set_parameter(\"test.direction\", \"2\")\n", "comparativemarkerselection_job_spec.set_parameter(\"test.statistic\", \"0\")\n", "comparativemarkerselection_job_spec.set_parameter(\"min.std\", \"\")\n", "comparativemarkerselection_job_spec.set_parameter(\"number.of.permutations\", \"10000\")\n", "comparativemarkerselection_job_spec.set_parameter(\"log.transformed.data\", \"false\")\n", "comparativemarkerselection_job_spec.set_parameter(\"complete\", \"false\")\n", "comparativemarkerselection_job_spec.set_parameter(\"balanced\", \"false\")\n", "comparativemarkerselection_job_spec.set_parameter(\"random.seed\", \"779948241\")\n", "comparativemarkerselection_job_spec.set_parameter(\"smooth.p.values\", \"true\")\n", "comparativemarkerselection_job_spec.set_parameter(\"phenotype.test\", \"one versus all\")\n", "comparativemarkerselection_job_spec.set_parameter(\"output.filename\", \"<input.file_basename>.comp.marker.odf\")\n", "genepattern.GPTaskWidget(comparativemarkerselection_task)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "genepattern": { "type": "job" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d118278bce59474aa0ddd4cd69759959", "version_major": 2, "version_minor": 0 }, "text/html": [ "<p>Failed to display Jupyter Widget of type <code>GPJobWidget</code>.</p>\n", "<p>\n", " If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n", " Widgets Documentation</a> for setup instructions.\n", "</p>\n", "<p>\n", " If you're reading this message in another frontend (for example, a static\n", " rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n", " it may mean that your frontend doesn't currently support widgets.\n", "</p>\n" ], "text/plain": [ "GPJobWidget(job_number=1587350)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "job1587350 = gp.GPJob(genepattern.get_session(0), 1587350)\n", "genepattern.GPJobWidget(job1587350)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Rank</th>\n", " <th>Feature</th>\n", " <th>Description</th>\n", " <th>Score</th>\n", " <th>Feature P</th>\n", " <th>Feature P Low</th>\n", " <th>Feature P High</th>\n", " <th>FDR(BH)</th>\n", " <th>Q Value</th>\n", " <th>Bonferroni</th>\n", " <th>maxT</th>\n", " <th>FWER</th>\n", " <th>Fold Change</th>\n", " <th>ALL Mean</th>\n", " <th>ALL Std</th>\n", " <th>AML Mean</th>\n", " <th>AML Std</th>\n", " <th>k</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>M89957_at</td>\n", " <td>IGB Immunoglobulin-associated beta (B29)</td>\n", " <td>8.335325</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>-5.856628</td>\n", " <td>2011.333333</td>\n", " <td>1200.105468</td>\n", " <td>-343.428571</td>\n", " <td>396.421049</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>J05243_at</td>\n", " <td>SPTAN1 Spectrin, alpha, non-erythrocytic 1 (al...</td>\n", " <td>7.305599</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0001</td>\n", " <td>0.0001</td>\n", " <td>3.773193</td>\n", " <td>718.523810</td>\n", " <td>299.612186</td>\n", " <td>190.428571</td>\n", " <td>115.366783</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>M11722_at</td>\n", " <td>Terminal transferase mRNA</td>\n", " <td>7.287828</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0001</td>\n", " <td>0.0001</td>\n", " <td>70.797073</td>\n", " <td>5067.047619</td>\n", " <td>3134.301939</td>\n", " <td>71.571429</td>\n", " <td>169.242073</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>M31523_at</td>\n", " <td>TCF3 Transcription factor 3 (E2A immunoglobuli...</td>\n", " <td>7.285013</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0001</td>\n", " <td>0.0001</td>\n", " <td>4.714167</td>\n", " <td>1488.666667</td>\n", " <td>720.431352</td>\n", " <td>315.785714</td>\n", " <td>129.907765</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>M84371_rna1_s_at</td>\n", " <td>CD19 gene</td>\n", " <td>7.249615</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0001</td>\n", " <td>0.0001</td>\n", " <td>3.475639</td>\n", " <td>1929.476190</td>\n", " <td>807.017820</td>\n", " <td>555.142857</td>\n", " <td>262.578005</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>7</td>\n", " <td>D88270_at</td>\n", " <td>GB DEF = (lambda) DNA for immunoglobin light c...</td>\n", " <td>6.464445</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0012</td>\n", " <td>0.0012</td>\n", " <td>59.118573</td>\n", " <td>4024.285714</td>\n", " <td>2802.251811</td>\n", " <td>68.071429</td>\n", " <td>92.032991</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>8</td>\n", " <td>U05259_rna1_at</td>\n", " <td>MB-1 gene</td>\n", " <td>6.425300</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0012</td>\n", " <td>0.0012</td>\n", " <td>10.178065</td>\n", " <td>5177.000000</td>\n", " <td>3281.358271</td>\n", " <td>508.642857</td>\n", " <td>460.668011</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>9</td>\n", " <td>M92287_at</td>\n", " <td>CCND3 Cyclin D3</td>\n", " <td>6.389370</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0014</td>\n", " <td>0.0014</td>\n", " <td>5.087627</td>\n", " <td>4570.142857</td>\n", " <td>2573.249391</td>\n", " <td>898.285714</td>\n", " <td>457.413200</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>11</td>\n", " <td>U29175_at</td>\n", " <td>Transcriptional activator hSNF2b</td>\n", " <td>6.106756</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0031</td>\n", " <td>0.0031</td>\n", " <td>2.449352</td>\n", " <td>1188.285714</td>\n", " <td>459.793121</td>\n", " <td>485.142857</td>\n", " <td>211.345818</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>14</td>\n", " <td>Z49194_at</td>\n", " <td>OBF-1 mRNA for octamer binding factor 1</td>\n", " <td>5.713933</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0076</td>\n", " <td>0.0076</td>\n", " <td>-11.227687</td>\n", " <td>440.285714</td>\n", " <td>375.058414</td>\n", " <td>-39.214286</td>\n", " <td>69.363234</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>15</td>\n", " <td>X59417_at</td>\n", " <td>PROTEASOME IOTA CHAIN</td>\n", " <td>5.650425</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0101</td>\n", " <td>0.0101</td>\n", " <td>3.601700</td>\n", " <td>3933.571429</td>\n", " <td>2206.576434</td>\n", " <td>1092.142857</td>\n", " <td>542.501451</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>16</td>\n", " <td>X66401_cds1_at</td>\n", " <td>LMP2 gene extracted from H.sapiens genes TAP1,...</td>\n", " <td>5.499817</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0158</td>\n", " <td>0.0158</td>\n", " <td>2.684612</td>\n", " <td>1610.000000</td>\n", " <td>739.640994</td>\n", " <td>599.714286</td>\n", " <td>328.174304</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>17</td>\n", " <td>M31211_s_at</td>\n", " <td>MYL1 Myosin light chain (alkali)</td>\n", " <td>5.460969</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0177</td>\n", " <td>0.0177</td>\n", " <td>2.829403</td>\n", " <td>462.809524</td>\n", " <td>185.501110</td>\n", " <td>163.571429</td>\n", " <td>138.187005</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>18</td>\n", " <td>U51240_at</td>\n", " <td>KIAA0085 gene, partial cds</td>\n", " <td>5.455191</td>\n", " <td>0.0004</td>\n", " <td>0.00003</td>\n", " <td>0.000640</td>\n", " <td>0.016576</td>\n", " <td>0.011780</td>\n", " <td>1.0</td>\n", " <td>0.0181</td>\n", " <td>0.0181</td>\n", " <td>2.327868</td>\n", " <td>7474.619048</td>\n", " <td>3016.387516</td>\n", " <td>3210.928571</td>\n", " <td>1576.865170</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>X58529_at</td>\n", " <td>IGHM Immunoglobulin mu</td>\n", " <td>5.425563</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0200</td>\n", " <td>0.0202</td>\n", " <td>6.656980</td>\n", " <td>5432.571429</td>\n", " <td>3517.526483</td>\n", " <td>816.071429</td>\n", " <td>1373.782956</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>22</td>\n", " <td>M33680_at</td>\n", " <td>26-kDa cell surface protein TAPA-1 mRNA</td>\n", " <td>5.295728</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0294</td>\n", " <td>0.0297</td>\n", " <td>2.622641</td>\n", " <td>6025.142857</td>\n", " <td>2583.860006</td>\n", " <td>2297.357143</td>\n", " <td>1576.775854</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>24</td>\n", " <td>Y08612_at</td>\n", " <td>RABAPTIN-5 protein</td>\n", " <td>5.255236</td>\n", " <td>0.0004</td>\n", " <td>0.00003</td>\n", " <td>0.000640</td>\n", " <td>0.016576</td>\n", " <td>0.011780</td>\n", " <td>1.0</td>\n", " <td>0.0338</td>\n", " <td>0.0341</td>\n", " <td>2.087321</td>\n", " <td>367.666667</td>\n", " <td>144.434183</td>\n", " <td>176.142857</td>\n", " <td>68.463193</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>26</td>\n", " <td>X69111_at</td>\n", " <td>ID3 Inhibitor of DNA binding 3, dominant negat...</td>\n", " <td>5.225453</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0365</td>\n", " <td>0.0369</td>\n", " <td>3.951904</td>\n", " <td>1889.857143</td>\n", " <td>1210.983744</td>\n", " <td>478.214286</td>\n", " <td>209.906638</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>27</td>\n", " <td>X67951_at</td>\n", " <td>PAGA Proliferation-associated gene A (natural ...</td>\n", " <td>5.180374</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0401</td>\n", " <td>0.0405</td>\n", " <td>4.097582</td>\n", " <td>4673.000000</td>\n", " <td>3066.699594</td>\n", " <td>1140.428571</td>\n", " <td>490.242125</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>29</td>\n", " <td>U88964_at</td>\n", " <td>HEM45 mRNA</td>\n", " <td>5.140885</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0459</td>\n", " <td>0.0464</td>\n", " <td>2.165290</td>\n", " <td>1648.095238</td>\n", " <td>673.612270</td>\n", " <td>761.142857</td>\n", " <td>337.973112</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>31</td>\n", " <td>U07139_at</td>\n", " <td>CAB3b mRNA for calcium channel beta3 subunit</td>\n", " <td>5.117449</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0500</td>\n", " <td>0.0506</td>\n", " <td>-0.808652</td>\n", " <td>110.380952</td>\n", " <td>181.315878</td>\n", " <td>-136.500000</td>\n", " <td>103.278377</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>33</td>\n", " <td>HG1612-HT1612_at</td>\n", " <td>Macmarcks</td>\n", " <td>5.094685</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0522</td>\n", " <td>0.0528</td>\n", " <td>2.753363</td>\n", " <td>3635.619048</td>\n", " <td>1668.185705</td>\n", " <td>1320.428571</td>\n", " <td>1017.789741</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>36</td>\n", " <td>X97267_rna1_s_at</td>\n", " <td>LPAP gene</td>\n", " <td>4.964845</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0736</td>\n", " <td>0.0745</td>\n", " <td>4.712416</td>\n", " <td>2786.047619</td>\n", " <td>1849.861656</td>\n", " <td>591.214286</td>\n", " <td>674.313004</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>37</td>\n", " <td>X82240_rna1_at</td>\n", " <td>TCL1 gene (T cell leukemia) extracted from H.s...</td>\n", " <td>4.954403</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0759</td>\n", " <td>0.0768</td>\n", " <td>-120.650362</td>\n", " <td>6342.761905</td>\n", " <td>5910.101453</td>\n", " <td>-52.571429</td>\n", " <td>203.690834</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>39</td>\n", " <td>J03473_at</td>\n", " <td>ADPRT ADP-ribosyltransferase (NAD+; poly (ADP-...</td>\n", " <td>4.945180</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0781</td>\n", " <td>0.0790</td>\n", " <td>2.254494</td>\n", " <td>1430.476190</td>\n", " <td>704.483614</td>\n", " <td>634.500000</td>\n", " <td>178.462558</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>41</td>\n", " <td>S50223_at</td>\n", " <td>HKR-T1</td>\n", " <td>4.931526</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0799</td>\n", " <td>0.0807</td>\n", " <td>-6.278867</td>\n", " <td>205.857143</td>\n", " <td>206.483725</td>\n", " <td>-32.785714</td>\n", " <td>66.032834</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>42</td>\n", " <td>U94855_at</td>\n", " <td>Translation initiation factor 3 47 kDa subunit...</td>\n", " <td>4.922441</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0818</td>\n", " <td>0.0826</td>\n", " <td>1.890199</td>\n", " <td>3802.000000</td>\n", " <td>1518.813386</td>\n", " <td>2011.428571</td>\n", " <td>560.895399</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>43</td>\n", " <td>D30742_at</td>\n", " <td>CAMK4 Calcium/calmodulin-dependent protein kin...</td>\n", " <td>4.913935</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0844</td>\n", " <td>0.0852</td>\n", " <td>2.348602</td>\n", " <td>238.047619</td>\n", " <td>83.182015</td>\n", " <td>101.357143</td>\n", " <td>78.867569</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>44</td>\n", " <td>M74719_at</td>\n", " <td>SEF2-1A protein (SEF2-1A) mRNA, 5' end</td>\n", " <td>4.899401</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0878</td>\n", " <td>0.0886</td>\n", " <td>4.561467</td>\n", " <td>1978.047619</td>\n", " <td>1374.866265</td>\n", " <td>433.642857</td>\n", " <td>361.866658</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>46</td>\n", " <td>M28170_at</td>\n", " <td>CD19 CD19 antigen</td>\n", " <td>4.820329</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.1081</td>\n", " <td>0.1092</td>\n", " <td>-9.601329</td>\n", " <td>206.428571</td>\n", " <td>183.235251</td>\n", " <td>-21.500000</td>\n", " <td>94.438217</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>7099</th>\n", " <td>78</td>\n", " <td>M14328_s_at</td>\n", " <td>ENO1 Enolase 1, (alpha)</td>\n", " <td>-4.278580</td>\n", " <td>0.0004</td>\n", " <td>0.00003</td>\n", " <td>0.000640</td>\n", " <td>0.016576</td>\n", " <td>0.011780</td>\n", " <td>1.0</td>\n", " <td>0.3826</td>\n", " <td>0.3866</td>\n", " <td>1.775402</td>\n", " <td>6287.428571</td>\n", " <td>2476.978292</td>\n", " <td>11162.714286</td>\n", " <td>3753.265069</td>\n", " <td>9999</td>\n", " </tr>\n", " <tr>\n", " <th>7100</th>\n", " <td>77</td>\n", " <td>M62762_at</td>\n", " <td>ATP6C Vacuolar H+ ATPase proton channel subunit</td>\n", " <td>-4.284413</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.3787</td>\n", " <td>0.3824</td>\n", " <td>1.920622</td>\n", " <td>1415.761905</td>\n", " <td>576.413992</td>\n", " <td>2719.142857</td>\n", " <td>1036.411699</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7101</th>\n", " <td>76</td>\n", " <td>M93056_at</td>\n", " <td>LEUKOCYTE ELASTASE INHIBITOR</td>\n", " <td>-4.300358</td>\n", " <td>0.0004</td>\n", " <td>0.00003</td>\n", " <td>0.000640</td>\n", " <td>0.016576</td>\n", " <td>0.011780</td>\n", " <td>1.0</td>\n", " <td>0.3650</td>\n", " <td>0.3689</td>\n", " <td>3.942776</td>\n", " <td>480.571429</td>\n", " <td>678.199570</td>\n", " <td>1894.785714</td>\n", " <td>1098.838000</td>\n", " <td>9999</td>\n", " </tr>\n", " <tr>\n", " <th>7102</th>\n", " <td>71</td>\n", " <td>D11327_s_at</td>\n", " <td>PTPN7 Protein tyrosine phosphatase, non-recept...</td>\n", " <td>-4.349638</td>\n", " <td>0.0004</td>\n", " <td>0.00003</td>\n", " <td>0.000640</td>\n", " <td>0.016576</td>\n", " <td>0.011780</td>\n", " <td>1.0</td>\n", " <td>0.3307</td>\n", " <td>0.3340</td>\n", " <td>3.344542</td>\n", " <td>146.571429</td>\n", " <td>153.909575</td>\n", " <td>490.214286</td>\n", " <td>267.568303</td>\n", " <td>9999</td>\n", " </tr>\n", " <tr>\n", " <th>7103</th>\n", " <td>69</td>\n", " <td>U10868_at</td>\n", " <td>ALDH7 Aldehyde dehydrogenase 7</td>\n", " <td>-4.383202</td>\n", " <td>0.0004</td>\n", " <td>0.00003</td>\n", " <td>0.000640</td>\n", " <td>0.016576</td>\n", " <td>0.011780</td>\n", " <td>1.0</td>\n", " <td>0.3078</td>\n", " <td>0.3110</td>\n", " <td>2.036434</td>\n", " <td>588.142857</td>\n", " <td>301.177736</td>\n", " <td>1197.714286</td>\n", " <td>458.578309</td>\n", " <td>9999</td>\n", " </tr>\n", " <tr>\n", " <th>7104</th>\n", " <td>68</td>\n", " <td>M63138_at</td>\n", " <td>CTSD Cathepsin D (lysosomal aspartyl protease)</td>\n", " <td>-4.399921</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.2974</td>\n", " <td>0.3007</td>\n", " <td>3.692145</td>\n", " <td>1521.047619</td>\n", " <td>584.929011</td>\n", " <td>5615.928571</td>\n", " <td>3449.347284</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7105</th>\n", " <td>67</td>\n", " <td>M19507_at</td>\n", " <td>MPO Myeloperoxidase</td>\n", " <td>-4.409131</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.2910</td>\n", " <td>0.2943</td>\n", " <td>20.551466</td>\n", " <td>560.238095</td>\n", " <td>785.642661</td>\n", " <td>11513.714286</td>\n", " <td>9273.129821</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7106</th>\n", " <td>65</td>\n", " <td>Y07604_at</td>\n", " <td>Nucleoside-diphosphate kinase</td>\n", " <td>-4.453201</td>\n", " <td>0.0004</td>\n", " <td>0.00003</td>\n", " <td>0.000640</td>\n", " <td>0.016576</td>\n", " <td>0.011780</td>\n", " <td>1.0</td>\n", " <td>0.2640</td>\n", " <td>0.2668</td>\n", " <td>2.003732</td>\n", " <td>854.904762</td>\n", " <td>483.931700</td>\n", " <td>1713.000000</td>\n", " <td>603.071497</td>\n", " <td>9999</td>\n", " </tr>\n", " <tr>\n", " <th>7107</th>\n", " <td>57</td>\n", " <td>J02783_at</td>\n", " <td>P4HB Procollagen-proline, 2-oxoglutarate 4-dio...</td>\n", " <td>-4.496895</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.2406</td>\n", " <td>0.2430</td>\n", " <td>3.170091</td>\n", " <td>495.952381</td>\n", " <td>419.798223</td>\n", " <td>1572.214286</td>\n", " <td>827.312905</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7108</th>\n", " <td>56</td>\n", " <td>X16546_at</td>\n", " <td>RNS2 Ribonuclease 2 (eosinophil-derived neurot...</td>\n", " <td>-4.529169</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.2239</td>\n", " <td>0.2262</td>\n", " <td>3.452787</td>\n", " <td>281.904762</td>\n", " <td>166.651704</td>\n", " <td>973.357143</td>\n", " <td>554.782509</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7109</th>\n", " <td>52</td>\n", " <td>M27891_at</td>\n", " <td>CST3 Cystatin C (amyloid angiopathy and cerebr...</td>\n", " <td>-4.656582</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.1617</td>\n", " <td>0.1634</td>\n", " <td>42.876270</td>\n", " <td>243.809524</td>\n", " <td>477.041048</td>\n", " <td>10453.642857</td>\n", " <td>8194.555440</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7110</th>\n", " <td>49</td>\n", " <td>U05572_s_at</td>\n", " <td>MANB Mannosidase alpha-B (lysosomal)</td>\n", " <td>-4.711378</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.1424</td>\n", " <td>0.1436</td>\n", " <td>-4.822661</td>\n", " <td>-95.190476</td>\n", " <td>286.420429</td>\n", " <td>459.071429</td>\n", " <td>372.918317</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7111</th>\n", " <td>48</td>\n", " <td>X05908_at</td>\n", " <td>ANX1 Annexin I (lipocortin I)</td>\n", " <td>-4.775031</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.1194</td>\n", " <td>0.1205</td>\n", " <td>12.963248</td>\n", " <td>132.809524</td>\n", " <td>344.828163</td>\n", " <td>1721.642857</td>\n", " <td>1212.737057</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7112</th>\n", " <td>47</td>\n", " <td>M32304_s_at</td>\n", " <td>TIMP2 Tissue inhibitor of metalloproteinase 2</td>\n", " <td>-4.781974</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.1167</td>\n", " <td>0.1178</td>\n", " <td>2.571388</td>\n", " <td>506.952381</td>\n", " <td>263.555208</td>\n", " <td>1303.571429</td>\n", " <td>584.990429</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7113</th>\n", " <td>45</td>\n", " <td>M23197_at</td>\n", " <td>CD33 CD33 antigen (differentiation antigen)</td>\n", " <td>-4.821914</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.1080</td>\n", " <td>0.1091</td>\n", " <td>5.357715</td>\n", " <td>178.380952</td>\n", " <td>88.155814</td>\n", " <td>955.714286</td>\n", " <td>598.876824</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7114</th>\n", " <td>40</td>\n", " <td>U59878_at</td>\n", " <td>Low-Mr GTP-binding protein (RAB32) mRNA, parti...</td>\n", " <td>-4.937669</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0792</td>\n", " <td>0.0800</td>\n", " <td>3.902114</td>\n", " <td>189.238095</td>\n", " <td>88.497969</td>\n", " <td>738.428571</td>\n", " <td>409.843444</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7115</th>\n", " <td>38</td>\n", " <td>L09717_at</td>\n", " <td>LAMP2 Lysosome-associated membrane protein 2 {...</td>\n", " <td>-4.947793</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0775</td>\n", " <td>0.0784</td>\n", " <td>4.013771</td>\n", " <td>44.952381</td>\n", " <td>60.780323</td>\n", " <td>180.428571</td>\n", " <td>89.628905</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7116</th>\n", " <td>35</td>\n", " <td>L41559_at</td>\n", " <td>PCBD 6-pyruvoyl-tetrahydropterin synthase/dime...</td>\n", " <td>-5.010096</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0640</td>\n", " <td>0.0648</td>\n", " <td>135.362069</td>\n", " <td>1.380952</td>\n", " <td>94.627943</td>\n", " <td>186.928571</td>\n", " <td>115.032079</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7117</th>\n", " <td>34</td>\n", " <td>M22960_at</td>\n", " <td>PPGB Protective protein for beta-galactosidase...</td>\n", " <td>-5.085313</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0534</td>\n", " <td>0.0541</td>\n", " <td>4.102370</td>\n", " <td>415.857143</td>\n", " <td>269.889475</td>\n", " <td>1706.000000</td>\n", " <td>923.325428</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7118</th>\n", " <td>32</td>\n", " <td>X61587_at</td>\n", " <td>ARHG Ras homolog gene family, member G (rho G)</td>\n", " <td>-5.094950</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0521</td>\n", " <td>0.0527</td>\n", " <td>5.924811</td>\n", " <td>358.142857</td>\n", " <td>569.906684</td>\n", " <td>2121.928571</td>\n", " <td>1208.829724</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7119</th>\n", " <td>30</td>\n", " <td>M84526_at</td>\n", " <td>DF D component of complement (adipsin)</td>\n", " <td>-5.124765</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0486</td>\n", " <td>0.0492</td>\n", " <td>-68.529247</td>\n", " <td>-93.619048</td>\n", " <td>168.850371</td>\n", " <td>6415.642857</td>\n", " <td>4750.496000</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7120</th>\n", " <td>28</td>\n", " <td>M14636_at</td>\n", " <td>PYGL Glycogen phosphorylase L (liver form)</td>\n", " <td>-5.167731</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0417</td>\n", " <td>0.0422</td>\n", " <td>6.423604</td>\n", " <td>45.190476</td>\n", " <td>60.271568</td>\n", " <td>290.285714</td>\n", " <td>170.499460</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7121</th>\n", " <td>25</td>\n", " <td>X12447_at</td>\n", " <td>ALDOA Aldolase A</td>\n", " <td>-5.241978</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0347</td>\n", " <td>0.0351</td>\n", " <td>1.932889</td>\n", " <td>5173.380952</td>\n", " <td>2364.026110</td>\n", " <td>9999.571429</td>\n", " <td>2853.315419</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7122</th>\n", " <td>23</td>\n", " <td>Z29067_at</td>\n", " <td>Nek3 mRNA for protein kinase</td>\n", " <td>-5.279477</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0307</td>\n", " <td>0.0310</td>\n", " <td>11.653409</td>\n", " <td>12.571429</td>\n", " <td>75.233351</td>\n", " <td>146.500000</td>\n", " <td>72.359944</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7123</th>\n", " <td>21</td>\n", " <td>L09209_s_at</td>\n", " <td>APLP2 Amyloid beta (A4) precursor-like protein 2</td>\n", " <td>-5.420151</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0203</td>\n", " <td>0.0205</td>\n", " <td>4.849143</td>\n", " <td>580.809524</td>\n", " <td>438.118320</td>\n", " <td>2816.428571</td>\n", " <td>1501.269602</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7124</th>\n", " <td>19</td>\n", " <td>U60319_at</td>\n", " <td>HLA-H MHC protein HLA-H (hereditary haemochrom...</td>\n", " <td>-5.431076</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0197</td>\n", " <td>0.0198</td>\n", " <td>-1.185282</td>\n", " <td>-45.619048</td>\n", " <td>47.170410</td>\n", " <td>54.071429</td>\n", " <td>56.864839</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7125</th>\n", " <td>13</td>\n", " <td>X17042_at</td>\n", " <td>PRG1 Proteoglycan 1, secretory granule</td>\n", " <td>-5.778204</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0066</td>\n", " <td>0.0066</td>\n", " <td>3.457207</td>\n", " <td>1739.285714</td>\n", " <td>1722.343901</td>\n", " <td>6013.071429</td>\n", " <td>2383.543370</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7126</th>\n", " <td>12</td>\n", " <td>X95735_at</td>\n", " <td>Zyxin</td>\n", " <td>-6.016839</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0037</td>\n", " <td>0.0037</td>\n", " <td>8.504233</td>\n", " <td>410.619048</td>\n", " <td>668.831031</td>\n", " <td>3492.000000</td>\n", " <td>1836.736738</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7127</th>\n", " <td>10</td>\n", " <td>M63959_at</td>\n", " <td>LRPAP1 Low density lipoprotein-related protein...</td>\n", " <td>-6.259319</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0019</td>\n", " <td>0.0019</td>\n", " <td>2.296480</td>\n", " <td>656.190476</td>\n", " <td>269.201898</td>\n", " <td>1506.928571</td>\n", " <td>458.594586</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7128</th>\n", " <td>6</td>\n", " <td>U46499_at</td>\n", " <td>GLUTATHIONE S-TRANSFERASE, MICROSOMAL</td>\n", " <td>-6.799713</td>\n", " <td>0.0002</td>\n", " <td>0.00000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0005</td>\n", " <td>0.0005</td>\n", " <td>29.698819</td>\n", " <td>48.380952</td>\n", " <td>56.772772</td>\n", " <td>1436.857143</td>\n", " <td>762.625108</td>\n", " <td>10000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>7129 rows × 18 columns</p>\n", "</div>" ], "text/plain": [ "<gp.data.ODF at 0x113a5c438>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The code below will only run if pandas is installed: http://pandas.pydata.org\n", "from gp.data import ODF\n", "all_aml_test_comp_marker_odf_1587350 = ODF(job1587350.get_file(\"all_aml_test.comp.marker.odf\"))\n", "all_aml_test_comp_marker_odf_1587350" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Rank</th>\n", " <th>Feature</th>\n", " <th>Description</th>\n", " <th>Score</th>\n", " <th>Feature P</th>\n", " <th>Feature P Low</th>\n", " <th>Feature P High</th>\n", " <th>FDR(BH)</th>\n", " <th>Q Value</th>\n", " <th>Bonferroni</th>\n", " <th>maxT</th>\n", " <th>FWER</th>\n", " <th>Fold Change</th>\n", " <th>ALL Mean</th>\n", " <th>ALL Std</th>\n", " <th>AML Mean</th>\n", " <th>AML Std</th>\n", " <th>k</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>M89957_at</td>\n", " <td>IGB Immunoglobulin-associated beta (B29)</td>\n", " <td>8.335325</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>-5.856628</td>\n", " <td>2011.333333</td>\n", " <td>1200.105468</td>\n", " <td>-343.428571</td>\n", " <td>396.421049</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>J05243_at</td>\n", " <td>SPTAN1 Spectrin, alpha, non-erythrocytic 1 (al...</td>\n", " <td>7.305599</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0001</td>\n", " <td>0.0001</td>\n", " <td>3.773193</td>\n", " <td>718.523810</td>\n", " <td>299.612186</td>\n", " <td>190.428571</td>\n", " <td>115.366783</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>M11722_at</td>\n", " <td>Terminal transferase mRNA</td>\n", " <td>7.287828</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0001</td>\n", " <td>0.0001</td>\n", " <td>70.797073</td>\n", " <td>5067.047619</td>\n", " <td>3134.301939</td>\n", " <td>71.571429</td>\n", " <td>169.242073</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>M31523_at</td>\n", " <td>TCF3 Transcription factor 3 (E2A immunoglobuli...</td>\n", " <td>7.285013</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0001</td>\n", " <td>0.0001</td>\n", " <td>4.714167</td>\n", " <td>1488.666667</td>\n", " <td>720.431352</td>\n", " <td>315.785714</td>\n", " <td>129.907765</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>M84371_rna1_s_at</td>\n", " <td>CD19 gene</td>\n", " <td>7.249615</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0001</td>\n", " <td>0.0001</td>\n", " <td>3.475639</td>\n", " <td>1929.476190</td>\n", " <td>807.017820</td>\n", " <td>555.142857</td>\n", " <td>262.578005</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7128</th>\n", " <td>6</td>\n", " <td>U46499_at</td>\n", " <td>GLUTATHIONE S-TRANSFERASE, MICROSOMAL</td>\n", " <td>-6.799713</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0005</td>\n", " <td>0.0005</td>\n", " <td>29.698819</td>\n", " <td>48.380952</td>\n", " <td>56.772772</td>\n", " <td>1436.857143</td>\n", " <td>762.625108</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>7</td>\n", " <td>D88270_at</td>\n", " <td>GB DEF = (lambda) DNA for immunoglobin light c...</td>\n", " <td>6.464445</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0012</td>\n", " <td>0.0012</td>\n", " <td>59.118573</td>\n", " <td>4024.285714</td>\n", " <td>2802.251811</td>\n", " <td>68.071429</td>\n", " <td>92.032991</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>8</td>\n", " <td>U05259_rna1_at</td>\n", " <td>MB-1 gene</td>\n", " <td>6.425300</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0012</td>\n", " <td>0.0012</td>\n", " <td>10.178065</td>\n", " <td>5177.000000</td>\n", " <td>3281.358271</td>\n", " <td>508.642857</td>\n", " <td>460.668011</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>9</td>\n", " <td>M92287_at</td>\n", " <td>CCND3 Cyclin D3</td>\n", " <td>6.389370</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0014</td>\n", " <td>0.0014</td>\n", " <td>5.087627</td>\n", " <td>4570.142857</td>\n", " <td>2573.249391</td>\n", " <td>898.285714</td>\n", " <td>457.413200</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7127</th>\n", " <td>10</td>\n", " <td>M63959_at</td>\n", " <td>LRPAP1 Low density lipoprotein-related protein...</td>\n", " <td>-6.259319</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0019</td>\n", " <td>0.0019</td>\n", " <td>2.296480</td>\n", " <td>656.190476</td>\n", " <td>269.201898</td>\n", " <td>1506.928571</td>\n", " <td>458.594586</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>11</td>\n", " <td>U29175_at</td>\n", " <td>Transcriptional activator hSNF2b</td>\n", " <td>6.106756</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0031</td>\n", " <td>0.0031</td>\n", " <td>2.449352</td>\n", " <td>1188.285714</td>\n", " <td>459.793121</td>\n", " <td>485.142857</td>\n", " <td>211.345818</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7126</th>\n", " <td>12</td>\n", " <td>X95735_at</td>\n", " <td>Zyxin</td>\n", " <td>-6.016839</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0037</td>\n", " <td>0.0037</td>\n", " <td>8.504233</td>\n", " <td>410.619048</td>\n", " <td>668.831031</td>\n", " <td>3492.000000</td>\n", " <td>1836.736738</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>7125</th>\n", " <td>13</td>\n", " <td>X17042_at</td>\n", " <td>PRG1 Proteoglycan 1, secretory granule</td>\n", " <td>-5.778204</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0066</td>\n", " <td>0.0066</td>\n", " <td>3.457207</td>\n", " <td>1739.285714</td>\n", " <td>1722.343901</td>\n", " <td>6013.071429</td>\n", " <td>2383.543370</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>14</td>\n", " <td>Z49194_at</td>\n", " <td>OBF-1 mRNA for octamer binding factor 1</td>\n", " <td>5.713933</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0076</td>\n", " <td>0.0076</td>\n", " <td>-11.227687</td>\n", " <td>440.285714</td>\n", " <td>375.058414</td>\n", " <td>-39.214286</td>\n", " <td>69.363234</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>15</td>\n", " <td>X59417_at</td>\n", " <td>PROTEASOME IOTA CHAIN</td>\n", " <td>5.650425</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0101</td>\n", " <td>0.0101</td>\n", " <td>3.601700</td>\n", " <td>3933.571429</td>\n", " <td>2206.576434</td>\n", " <td>1092.142857</td>\n", " <td>542.501451</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>16</td>\n", " <td>X66401_cds1_at</td>\n", " <td>LMP2 gene extracted from H.sapiens genes TAP1,...</td>\n", " <td>5.499817</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0158</td>\n", " <td>0.0158</td>\n", " <td>2.684612</td>\n", " <td>1610.000000</td>\n", " <td>739.640994</td>\n", " <td>599.714286</td>\n", " <td>328.174304</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>17</td>\n", " <td>M31211_s_at</td>\n", " <td>MYL1 Myosin light chain (alkali)</td>\n", " <td>5.460969</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0177</td>\n", " <td>0.0177</td>\n", " <td>2.829403</td>\n", " <td>462.809524</td>\n", " <td>185.501110</td>\n", " <td>163.571429</td>\n", " <td>138.187005</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>18</td>\n", " <td>U51240_at</td>\n", " <td>KIAA0085 gene, partial cds</td>\n", " <td>5.455191</td>\n", " <td>0.000400</td>\n", " <td>0.000030</td>\n", " <td>0.000640</td>\n", " <td>0.016576</td>\n", " <td>0.011780</td>\n", " <td>1.0</td>\n", " <td>0.0181</td>\n", " <td>0.0181</td>\n", " <td>2.327868</td>\n", " <td>7474.619048</td>\n", " <td>3016.387516</td>\n", " <td>3210.928571</td>\n", " <td>1576.865170</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7124</th>\n", " <td>19</td>\n", " <td>U60319_at</td>\n", " <td>HLA-H MHC protein HLA-H (hereditary haemochrom...</td>\n", " <td>-5.431076</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0197</td>\n", " <td>0.0198</td>\n", " <td>-1.185282</td>\n", " <td>-45.619048</td>\n", " <td>47.170410</td>\n", " <td>54.071429</td>\n", " <td>56.864839</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>20</td>\n", " <td>X58529_at</td>\n", " <td>IGHM Immunoglobulin mu</td>\n", " <td>5.425563</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0200</td>\n", " <td>0.0202</td>\n", " <td>6.656980</td>\n", " <td>5432.571429</td>\n", " <td>3517.526483</td>\n", " <td>816.071429</td>\n", " <td>1373.782956</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7123</th>\n", " <td>21</td>\n", " <td>L09209_s_at</td>\n", " <td>APLP2 Amyloid beta (A4) precursor-like protein 2</td>\n", " <td>-5.420151</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0203</td>\n", " <td>0.0205</td>\n", " <td>4.849143</td>\n", " <td>580.809524</td>\n", " <td>438.118320</td>\n", " <td>2816.428571</td>\n", " <td>1501.269602</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>22</td>\n", " <td>M33680_at</td>\n", " <td>26-kDa cell surface protein TAPA-1 mRNA</td>\n", " <td>5.295728</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0294</td>\n", " <td>0.0297</td>\n", " <td>2.622641</td>\n", " <td>6025.142857</td>\n", " <td>2583.860006</td>\n", " <td>2297.357143</td>\n", " <td>1576.775854</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7122</th>\n", " <td>23</td>\n", " <td>Z29067_at</td>\n", " <td>Nek3 mRNA for protein kinase</td>\n", " <td>-5.279477</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0307</td>\n", " <td>0.0310</td>\n", " <td>11.653409</td>\n", " <td>12.571429</td>\n", " <td>75.233351</td>\n", " <td>146.500000</td>\n", " <td>72.359944</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>24</td>\n", " <td>Y08612_at</td>\n", " <td>RABAPTIN-5 protein</td>\n", " <td>5.255236</td>\n", " <td>0.000400</td>\n", " <td>0.000030</td>\n", " <td>0.000640</td>\n", " <td>0.016576</td>\n", " <td>0.011780</td>\n", " <td>1.0</td>\n", " <td>0.0338</td>\n", " <td>0.0341</td>\n", " <td>2.087321</td>\n", " <td>367.666667</td>\n", " <td>144.434183</td>\n", " <td>176.142857</td>\n", " <td>68.463193</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7121</th>\n", " <td>25</td>\n", " <td>X12447_at</td>\n", " <td>ALDOA Aldolase A</td>\n", " <td>-5.241978</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0347</td>\n", " <td>0.0351</td>\n", " <td>1.932889</td>\n", " <td>5173.380952</td>\n", " <td>2364.026110</td>\n", " <td>9999.571429</td>\n", " <td>2853.315419</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>26</td>\n", " <td>X69111_at</td>\n", " <td>ID3 Inhibitor of DNA binding 3, dominant negat...</td>\n", " <td>5.225453</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0365</td>\n", " <td>0.0369</td>\n", " <td>3.951904</td>\n", " <td>1889.857143</td>\n", " <td>1210.983744</td>\n", " <td>478.214286</td>\n", " <td>209.906638</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>27</td>\n", " <td>X67951_at</td>\n", " <td>PAGA Proliferation-associated gene A (natural ...</td>\n", " <td>5.180374</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0401</td>\n", " <td>0.0405</td>\n", " <td>4.097582</td>\n", " <td>4673.000000</td>\n", " <td>3066.699594</td>\n", " <td>1140.428571</td>\n", " <td>490.242125</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7120</th>\n", " <td>28</td>\n", " <td>M14636_at</td>\n", " <td>PYGL Glycogen phosphorylase L (liver form)</td>\n", " <td>-5.167731</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0417</td>\n", " <td>0.0422</td>\n", " <td>6.423604</td>\n", " <td>45.190476</td>\n", " <td>60.271568</td>\n", " <td>290.285714</td>\n", " <td>170.499460</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>29</td>\n", " <td>U88964_at</td>\n", " <td>HEM45 mRNA</td>\n", " <td>5.140885</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0459</td>\n", " <td>0.0464</td>\n", " <td>2.165290</td>\n", " <td>1648.095238</td>\n", " <td>673.612270</td>\n", " <td>761.142857</td>\n", " <td>337.973112</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7119</th>\n", " <td>30</td>\n", " <td>M84526_at</td>\n", " <td>DF D component of complement (adipsin)</td>\n", " <td>-5.124765</td>\n", " <td>0.000200</td>\n", " <td>0.000000</td>\n", " <td>0.000299</td>\n", " <td>0.011590</td>\n", " <td>0.010518</td>\n", " <td>1.0</td>\n", " <td>0.0486</td>\n", " <td>0.0492</td>\n", " <td>-68.529247</td>\n", " <td>-93.619048</td>\n", " <td>168.850371</td>\n", " <td>6415.642857</td>\n", " <td>4750.496000</td>\n", " <td>10000</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>3482</th>\n", " <td>7100</td>\n", " <td>S73205_at</td>\n", " <td>GB DEF = Insulin activator factor [human, panc...</td>\n", " <td>-0.008349</td>\n", " <td>0.985003</td>\n", " <td>0.982491</td>\n", " <td>0.987261</td>\n", " <td>0.994733</td>\n", " <td>0.667916</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.013672</td>\n", " <td>12.190476</td>\n", " <td>40.156717</td>\n", " <td>12.357143</td>\n", " <td>67.112543</td>\n", " <td>5075</td>\n", " </tr>\n", " <tr>\n", " <th>3453</th>\n", " <td>7101</td>\n", " <td>X59766_at</td>\n", " <td>AZGP1 Zinc-alpha-2-glycoprotein 1</td>\n", " <td>0.008136</td>\n", " <td>0.992202</td>\n", " <td>0.990346</td>\n", " <td>0.993802</td>\n", " <td>0.996736</td>\n", " <td>0.669301</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.021277</td>\n", " <td>13.714286</td>\n", " <td>90.495935</td>\n", " <td>13.428571</td>\n", " <td>108.646013</td>\n", " <td>4961</td>\n", " </tr>\n", " <tr>\n", " <th>3454</th>\n", " <td>7102</td>\n", " <td>U47621_at</td>\n", " <td>Nucleolar autoantigen No55 mRNA</td>\n", " <td>0.007673</td>\n", " <td>0.969806</td>\n", " <td>0.966324</td>\n", " <td>0.973035</td>\n", " <td>0.990775</td>\n", " <td>0.665306</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>2.333333</td>\n", " <td>1.333333</td>\n", " <td>280.266718</td>\n", " <td>0.571429</td>\n", " <td>292.715854</td>\n", " <td>4849</td>\n", " </tr>\n", " <tr>\n", " <th>3481</th>\n", " <td>7103</td>\n", " <td>HG3976-HT4246_at</td>\n", " <td>Pou-Domain Dna Binding Factor Pit1, Pituitary-...</td>\n", " <td>-0.007589</td>\n", " <td>0.921816</td>\n", " <td>0.916429</td>\n", " <td>0.926954</td>\n", " <td>0.973996</td>\n", " <td>0.653992</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.002226</td>\n", " <td>96.285714</td>\n", " <td>107.844398</td>\n", " <td>96.500000</td>\n", " <td>58.372478</td>\n", " <td>5391</td>\n", " </tr>\n", " <tr>\n", " <th>3480</th>\n", " <td>7104</td>\n", " <td>U65002_at</td>\n", " <td>Zinc finger protein PLAG1 mRNA</td>\n", " <td>-0.006979</td>\n", " <td>0.991602</td>\n", " <td>0.989682</td>\n", " <td>0.993266</td>\n", " <td>0.996695</td>\n", " <td>0.669233</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>0.995086</td>\n", " <td>-19.380952</td>\n", " <td>53.325863</td>\n", " <td>-19.285714</td>\n", " <td>26.675914</td>\n", " <td>4958</td>\n", " </tr>\n", " <tr>\n", " <th>3479</th>\n", " <td>7105</td>\n", " <td>X81372_at</td>\n", " <td>Biphenyl hydrolase-related protein</td>\n", " <td>-0.006885</td>\n", " <td>0.974005</td>\n", " <td>0.970758</td>\n", " <td>0.976999</td>\n", " <td>0.991813</td>\n", " <td>0.665955</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.003117</td>\n", " <td>106.952381</td>\n", " <td>92.746146</td>\n", " <td>107.285714</td>\n", " <td>164.569568</td>\n", " <td>5130</td>\n", " </tr>\n", " <tr>\n", " <th>3478</th>\n", " <td>7106</td>\n", " <td>U58032_at</td>\n", " <td>GB DEF = Myotubularin related protein 1 (MTMR1...</td>\n", " <td>-0.006750</td>\n", " <td>0.991602</td>\n", " <td>0.989682</td>\n", " <td>0.993266</td>\n", " <td>0.996695</td>\n", " <td>0.669233</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.003073</td>\n", " <td>77.476190</td>\n", " <td>81.879557</td>\n", " <td>77.714286</td>\n", " <td>113.787541</td>\n", " <td>5042</td>\n", " </tr>\n", " <tr>\n", " <th>3477</th>\n", " <td>7107</td>\n", " <td>M74826_at</td>\n", " <td>GAD2 Glutamate decarboxylase 2</td>\n", " <td>-0.005795</td>\n", " <td>0.982204</td>\n", " <td>0.979483</td>\n", " <td>0.984670</td>\n", " <td>0.993976</td>\n", " <td>0.667407</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.001053</td>\n", " <td>158.333333</td>\n", " <td>94.794163</td>\n", " <td>158.500000</td>\n", " <td>74.758843</td>\n", " <td>5089</td>\n", " </tr>\n", " <tr>\n", " <th>3455</th>\n", " <td>7108</td>\n", " <td>U81787_at</td>\n", " <td>Wnt10B mRNA</td>\n", " <td>0.005695</td>\n", " <td>0.987403</td>\n", " <td>0.985086</td>\n", " <td>0.989464</td>\n", " <td>0.995643</td>\n", " <td>0.668526</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.000978</td>\n", " <td>195.047619</td>\n", " <td>89.307601</td>\n", " <td>194.857143</td>\n", " <td>101.691734</td>\n", " <td>4937</td>\n", " </tr>\n", " <tr>\n", " <th>3456</th>\n", " <td>7109</td>\n", " <td>X16281_at</td>\n", " <td>ZNF44 Zinc finger protein 44 (KOX 7)</td>\n", " <td>0.005660</td>\n", " <td>0.991202</td>\n", " <td>0.989240</td>\n", " <td>0.992908</td>\n", " <td>0.996695</td>\n", " <td>0.669233</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>0.997725</td>\n", " <td>-41.761905</td>\n", " <td>49.761335</td>\n", " <td>-41.857143</td>\n", " <td>48.095829</td>\n", " <td>5044</td>\n", " </tr>\n", " <tr>\n", " <th>3476</th>\n", " <td>7110</td>\n", " <td>Z46629_at</td>\n", " <td>SOX9 SRY (sex-determining region Y)-box 9 (cam...</td>\n", " <td>-0.005463</td>\n", " <td>0.993001</td>\n", " <td>0.991235</td>\n", " <td>0.994512</td>\n", " <td>0.996917</td>\n", " <td>0.669429</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.011628</td>\n", " <td>8.190476</td>\n", " <td>52.335092</td>\n", " <td>8.285714</td>\n", " <td>49.278531</td>\n", " <td>4965</td>\n", " </tr>\n", " <tr>\n", " <th>3457</th>\n", " <td>7111</td>\n", " <td>X63563_at</td>\n", " <td>RPS13 RNA polymerase II polypeptide B (140 kD)</td>\n", " <td>0.004951</td>\n", " <td>0.991202</td>\n", " <td>0.989240</td>\n", " <td>0.992908</td>\n", " <td>0.996695</td>\n", " <td>0.669233</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.001141</td>\n", " <td>313.428571</td>\n", " <td>195.114216</td>\n", " <td>313.071429</td>\n", " <td>217.898870</td>\n", " <td>4956</td>\n", " </tr>\n", " <tr>\n", " <th>3475</th>\n", " <td>7112</td>\n", " <td>L75847_at</td>\n", " <td>ZNF45 Zinc finger protein 45 (a Kruppel-associ...</td>\n", " <td>-0.004928</td>\n", " <td>0.997600</td>\n", " <td>0.996506</td>\n", " <td>0.998439</td>\n", " <td>0.999283</td>\n", " <td>0.670971</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.003914</td>\n", " <td>24.333333</td>\n", " <td>38.497186</td>\n", " <td>24.428571</td>\n", " <td>65.123214</td>\n", " <td>5012</td>\n", " </tr>\n", " <tr>\n", " <th>3474</th>\n", " <td>7113</td>\n", " <td>L77559_at</td>\n", " <td>GB DEF = DGS-B partial mRNA</td>\n", " <td>-0.004650</td>\n", " <td>1.000000</td>\n", " <td>0.999701</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>0.671452</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>0.998031</td>\n", " <td>-60.476190</td>\n", " <td>77.445219</td>\n", " <td>-60.357143</td>\n", " <td>71.950418</td>\n", " <td>5000</td>\n", " </tr>\n", " <tr>\n", " <th>3458</th>\n", " <td>7114</td>\n", " <td>U12595_at</td>\n", " <td>Tumor necrosis factor type 1 receptor associat...</td>\n", " <td>0.003895</td>\n", " <td>0.981604</td>\n", " <td>0.978840</td>\n", " <td>0.984113</td>\n", " <td>0.993976</td>\n", " <td>0.667407</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.004320</td>\n", " <td>71.952381</td>\n", " <td>155.581964</td>\n", " <td>71.642857</td>\n", " <td>268.831834</td>\n", " <td>4908</td>\n", " </tr>\n", " <tr>\n", " <th>3459</th>\n", " <td>7115</td>\n", " <td>X60487_at</td>\n", " <td>GB DEF = H4/h gene for H4 histone</td>\n", " <td>0.003891</td>\n", " <td>0.992601</td>\n", " <td>0.990790</td>\n", " <td>0.994157</td>\n", " <td>0.996796</td>\n", " <td>0.669301</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.001554</td>\n", " <td>61.380952</td>\n", " <td>45.892784</td>\n", " <td>61.285714</td>\n", " <td>83.561519</td>\n", " <td>5037</td>\n", " </tr>\n", " <tr>\n", " <th>3460</th>\n", " <td>7116</td>\n", " <td>U57911_at</td>\n", " <td>Fetal brain (239FB) mRNA, from the WAGR region</td>\n", " <td>0.003789</td>\n", " <td>0.983003</td>\n", " <td>0.980340</td>\n", " <td>0.985412</td>\n", " <td>0.994141</td>\n", " <td>0.667518</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.004274</td>\n", " <td>11.190476</td>\n", " <td>33.853536</td>\n", " <td>11.142857</td>\n", " <td>38.036131</td>\n", " <td>5085</td>\n", " </tr>\n", " <tr>\n", " <th>3461</th>\n", " <td>7117</td>\n", " <td>M18700_s_at</td>\n", " <td>ELASTASE IIIA PRECURSOR</td>\n", " <td>0.003198</td>\n", " <td>0.990802</td>\n", " <td>0.988799</td>\n", " <td>0.992549</td>\n", " <td>0.996695</td>\n", " <td>0.669233</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.000846</td>\n", " <td>309.761905</td>\n", " <td>173.297693</td>\n", " <td>309.500000</td>\n", " <td>271.842927</td>\n", " <td>5046</td>\n", " </tr>\n", " <tr>\n", " <th>3473</th>\n", " <td>7118</td>\n", " <td>U67171_at</td>\n", " <td>GB DEF = Selenoprotein W (selW) mRNA</td>\n", " <td>-0.002712</td>\n", " <td>0.988002</td>\n", " <td>0.985738</td>\n", " <td>0.990012</td>\n", " <td>0.995745</td>\n", " <td>0.668636</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.000810</td>\n", " <td>499.523810</td>\n", " <td>323.232829</td>\n", " <td>499.928571</td>\n", " <td>492.047913</td>\n", " <td>5060</td>\n", " </tr>\n", " <tr>\n", " <th>3462</th>\n", " <td>7119</td>\n", " <td>U52828_s_at</td>\n", " <td>Delta-catenin mRNA, partial cds</td>\n", " <td>0.002499</td>\n", " <td>0.993801</td>\n", " <td>0.992130</td>\n", " <td>0.995216</td>\n", " <td>0.997158</td>\n", " <td>0.669544</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.000605</td>\n", " <td>118.142857</td>\n", " <td>98.639893</td>\n", " <td>118.071429</td>\n", " <td>70.389536</td>\n", " <td>5031</td>\n", " </tr>\n", " <tr>\n", " <th>3472</th>\n", " <td>7120</td>\n", " <td>D31833_s_at</td>\n", " <td>AVPR1B Arginine vasopressin receptor 1B</td>\n", " <td>-0.002246</td>\n", " <td>0.988202</td>\n", " <td>0.985955</td>\n", " <td>0.990194</td>\n", " <td>0.995745</td>\n", " <td>0.668636</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>0.999245</td>\n", " <td>-126.095238</td>\n", " <td>111.217762</td>\n", " <td>-126.000000</td>\n", " <td>130.072169</td>\n", " <td>5059</td>\n", " </tr>\n", " <tr>\n", " <th>3471</th>\n", " <td>7121</td>\n", " <td>X12662_rna1_at</td>\n", " <td>Arginase gene exon 1 and flanking regions (EC ...</td>\n", " <td>-0.001772</td>\n", " <td>0.990002</td>\n", " <td>0.987921</td>\n", " <td>0.991828</td>\n", " <td>0.996431</td>\n", " <td>0.669198</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.001314</td>\n", " <td>36.238095</td>\n", " <td>79.984314</td>\n", " <td>36.285714</td>\n", " <td>76.460577</td>\n", " <td>5050</td>\n", " </tr>\n", " <tr>\n", " <th>3470</th>\n", " <td>7122</td>\n", " <td>J04156_at</td>\n", " <td>IL7 Interleukin 7</td>\n", " <td>-0.001696</td>\n", " <td>0.996601</td>\n", " <td>0.995326</td>\n", " <td>0.997620</td>\n", " <td>0.998822</td>\n", " <td>0.670756</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>0.998705</td>\n", " <td>-18.380952</td>\n", " <td>33.893180</td>\n", " <td>-18.357143</td>\n", " <td>44.663885</td>\n", " <td>4983</td>\n", " </tr>\n", " <tr>\n", " <th>3463</th>\n", " <td>7123</td>\n", " <td>U04898_at</td>\n", " <td>RORA RAR-related orphan receptor A</td>\n", " <td>0.001460</td>\n", " <td>0.990602</td>\n", " <td>0.988579</td>\n", " <td>0.992369</td>\n", " <td>0.996695</td>\n", " <td>0.669233</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>0.997653</td>\n", " <td>-20.238095</td>\n", " <td>84.060041</td>\n", " <td>-20.285714</td>\n", " <td>100.862762</td>\n", " <td>5047</td>\n", " </tr>\n", " <tr>\n", " <th>3469</th>\n", " <td>7124</td>\n", " <td>U79295_at</td>\n", " <td>Clone 23961 mRNA sequence</td>\n", " <td>-0.001441</td>\n", " <td>0.994401</td>\n", " <td>0.992806</td>\n", " <td>0.995740</td>\n", " <td>0.997620</td>\n", " <td>0.669854</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.000956</td>\n", " <td>49.809524</td>\n", " <td>64.448909</td>\n", " <td>49.857143</td>\n", " <td>111.892708</td>\n", " <td>4972</td>\n", " </tr>\n", " <tr>\n", " <th>3464</th>\n", " <td>7125</td>\n", " <td>U82303_at</td>\n", " <td>GB DEF = Unknown protein mRNA, partial cds</td>\n", " <td>0.001299</td>\n", " <td>0.985803</td>\n", " <td>0.983354</td>\n", " <td>0.987997</td>\n", " <td>0.994733</td>\n", " <td>0.667916</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.000227</td>\n", " <td>314.857143</td>\n", " <td>169.164797</td>\n", " <td>314.785714</td>\n", " <td>152.441756</td>\n", " <td>5071</td>\n", " </tr>\n", " <tr>\n", " <th>3465</th>\n", " <td>7126</td>\n", " <td>HG721-HT4827_s_at</td>\n", " <td>Placental Protein 14, Endometrial Alpha 2 Glob...</td>\n", " <td>0.000854</td>\n", " <td>0.990002</td>\n", " <td>0.987921</td>\n", " <td>0.991828</td>\n", " <td>0.996431</td>\n", " <td>0.669198</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.000220</td>\n", " <td>216.190476</td>\n", " <td>113.471414</td>\n", " <td>216.142857</td>\n", " <td>186.935142</td>\n", " <td>5050</td>\n", " </tr>\n", " <tr>\n", " <th>3468</th>\n", " <td>7127</td>\n", " <td>U38291_rna1_at</td>\n", " <td>Microtubule-associated protein 1a (MAP1A) geno...</td>\n", " <td>-0.000607</td>\n", " <td>0.999400</td>\n", " <td>0.998774</td>\n", " <td>0.999765</td>\n", " <td>0.999681</td>\n", " <td>0.671238</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.000130</td>\n", " <td>182.476190</td>\n", " <td>114.806628</td>\n", " <td>182.500000</td>\n", " <td>113.017187</td>\n", " <td>4997</td>\n", " </tr>\n", " <tr>\n", " <th>3466</th>\n", " <td>7128</td>\n", " <td>U72511_at</td>\n", " <td>B-cell receptor associated protein (hBAP) mRNA...</td>\n", " <td>0.000284</td>\n", " <td>0.989802</td>\n", " <td>0.987702</td>\n", " <td>0.991647</td>\n", " <td>0.996431</td>\n", " <td>0.669198</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.000028</td>\n", " <td>1681.619048</td>\n", " <td>561.958760</td>\n", " <td>1681.571429</td>\n", " <td>428.851187</td>\n", " <td>4949</td>\n", " </tr>\n", " <tr>\n", " <th>3467</th>\n", " <td>7129</td>\n", " <td>U52077_s_at</td>\n", " <td>GB DEF = Mariner1 transposase gene, complete c...</td>\n", " <td>0.000000</td>\n", " <td>0.983603</td>\n", " <td>0.980984</td>\n", " <td>0.985967</td>\n", " <td>0.994202</td>\n", " <td>0.667559</td>\n", " <td>1.0</td>\n", " <td>1.0000</td>\n", " <td>1.0000</td>\n", " <td>1.000000</td>\n", " <td>200.428571</td>\n", " <td>97.493370</td>\n", " <td>200.428571</td>\n", " <td>135.909310</td>\n", " <td>5082</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>7129 rows × 18 columns</p>\n", "</div>" ], "text/plain": [ " Rank Feature \\\n", "0 1 M89957_at \n", "1 2 J05243_at \n", "2 3 M11722_at \n", "3 4 M31523_at \n", "4 5 M84371_rna1_s_at \n", "7128 6 U46499_at \n", "5 7 D88270_at \n", "6 8 U05259_rna1_at \n", "7 9 M92287_at \n", "7127 10 M63959_at \n", "8 11 U29175_at \n", "7126 12 X95735_at \n", "7125 13 X17042_at \n", "9 14 Z49194_at \n", "10 15 X59417_at \n", "11 16 X66401_cds1_at \n", "12 17 M31211_s_at \n", "13 18 U51240_at \n", "7124 19 U60319_at \n", "14 20 X58529_at \n", "7123 21 L09209_s_at \n", "15 22 M33680_at \n", "7122 23 Z29067_at \n", "16 24 Y08612_at \n", "7121 25 X12447_at \n", "17 26 X69111_at \n", "18 27 X67951_at \n", "7120 28 M14636_at \n", "19 29 U88964_at \n", "7119 30 M84526_at \n", "... ... ... \n", "3482 7100 S73205_at \n", "3453 7101 X59766_at \n", "3454 7102 U47621_at \n", "3481 7103 HG3976-HT4246_at \n", "3480 7104 U65002_at \n", "3479 7105 X81372_at \n", "3478 7106 U58032_at \n", "3477 7107 M74826_at \n", "3455 7108 U81787_at \n", "3456 7109 X16281_at \n", "3476 7110 Z46629_at \n", "3457 7111 X63563_at \n", "3475 7112 L75847_at \n", "3474 7113 L77559_at \n", "3458 7114 U12595_at \n", "3459 7115 X60487_at \n", "3460 7116 U57911_at \n", "3461 7117 M18700_s_at \n", "3473 7118 U67171_at \n", "3462 7119 U52828_s_at \n", "3472 7120 D31833_s_at \n", "3471 7121 X12662_rna1_at \n", "3470 7122 J04156_at \n", "3463 7123 U04898_at \n", "3469 7124 U79295_at \n", "3464 7125 U82303_at \n", "3465 7126 HG721-HT4827_s_at \n", "3468 7127 U38291_rna1_at \n", "3466 7128 U72511_at \n", "3467 7129 U52077_s_at \n", "\n", " Description Score Feature P \\\n", "0 IGB Immunoglobulin-associated beta (B29) 8.335325 0.000200 \n", "1 SPTAN1 Spectrin, alpha, non-erythrocytic 1 (al... 7.305599 0.000200 \n", "2 Terminal transferase mRNA 7.287828 0.000200 \n", "3 TCF3 Transcription factor 3 (E2A immunoglobuli... 7.285013 0.000200 \n", "4 CD19 gene 7.249615 0.000200 \n", "7128 GLUTATHIONE S-TRANSFERASE, MICROSOMAL -6.799713 0.000200 \n", "5 GB DEF = (lambda) DNA for immunoglobin light c... 6.464445 0.000200 \n", "6 MB-1 gene 6.425300 0.000200 \n", "7 CCND3 Cyclin D3 6.389370 0.000200 \n", "7127 LRPAP1 Low density lipoprotein-related protein... -6.259319 0.000200 \n", "8 Transcriptional activator hSNF2b 6.106756 0.000200 \n", "7126 Zyxin -6.016839 0.000200 \n", "7125 PRG1 Proteoglycan 1, secretory granule -5.778204 0.000200 \n", "9 OBF-1 mRNA for octamer binding factor 1 5.713933 0.000200 \n", "10 PROTEASOME IOTA CHAIN 5.650425 0.000200 \n", "11 LMP2 gene extracted from H.sapiens genes TAP1,... 5.499817 0.000200 \n", "12 MYL1 Myosin light chain (alkali) 5.460969 0.000200 \n", "13 KIAA0085 gene, partial cds 5.455191 0.000400 \n", "7124 HLA-H MHC protein HLA-H (hereditary haemochrom... -5.431076 0.000200 \n", "14 IGHM Immunoglobulin mu 5.425563 0.000200 \n", "7123 APLP2 Amyloid beta (A4) precursor-like protein 2 -5.420151 0.000200 \n", "15 26-kDa cell surface protein TAPA-1 mRNA 5.295728 0.000200 \n", "7122 Nek3 mRNA for protein kinase -5.279477 0.000200 \n", "16 RABAPTIN-5 protein 5.255236 0.000400 \n", "7121 ALDOA Aldolase A -5.241978 0.000200 \n", "17 ID3 Inhibitor of DNA binding 3, dominant negat... 5.225453 0.000200 \n", "18 PAGA Proliferation-associated gene A (natural ... 5.180374 0.000200 \n", "7120 PYGL Glycogen phosphorylase L (liver form) -5.167731 0.000200 \n", "19 HEM45 mRNA 5.140885 0.000200 \n", "7119 DF D component of complement (adipsin) -5.124765 0.000200 \n", "... ... ... ... \n", "3482 GB DEF = Insulin activator factor [human, panc... -0.008349 0.985003 \n", "3453 AZGP1 Zinc-alpha-2-glycoprotein 1 0.008136 0.992202 \n", "3454 Nucleolar autoantigen No55 mRNA 0.007673 0.969806 \n", "3481 Pou-Domain Dna Binding Factor Pit1, Pituitary-... -0.007589 0.921816 \n", "3480 Zinc finger protein PLAG1 mRNA -0.006979 0.991602 \n", "3479 Biphenyl hydrolase-related protein -0.006885 0.974005 \n", "3478 GB DEF = Myotubularin related protein 1 (MTMR1... -0.006750 0.991602 \n", "3477 GAD2 Glutamate decarboxylase 2 -0.005795 0.982204 \n", "3455 Wnt10B mRNA 0.005695 0.987403 \n", "3456 ZNF44 Zinc finger protein 44 (KOX 7) 0.005660 0.991202 \n", "3476 SOX9 SRY (sex-determining region Y)-box 9 (cam... -0.005463 0.993001 \n", "3457 RPS13 RNA polymerase II polypeptide B (140 kD) 0.004951 0.991202 \n", "3475 ZNF45 Zinc finger protein 45 (a Kruppel-associ... -0.004928 0.997600 \n", "3474 GB DEF = DGS-B partial mRNA -0.004650 1.000000 \n", "3458 Tumor necrosis factor type 1 receptor associat... 0.003895 0.981604 \n", "3459 GB DEF = H4/h gene for H4 histone 0.003891 0.992601 \n", "3460 Fetal brain (239FB) mRNA, from the WAGR region 0.003789 0.983003 \n", "3461 ELASTASE IIIA PRECURSOR 0.003198 0.990802 \n", "3473 GB DEF = Selenoprotein W (selW) mRNA -0.002712 0.988002 \n", "3462 Delta-catenin mRNA, partial cds 0.002499 0.993801 \n", "3472 AVPR1B Arginine vasopressin receptor 1B -0.002246 0.988202 \n", "3471 Arginase gene exon 1 and flanking regions (EC ... -0.001772 0.990002 \n", "3470 IL7 Interleukin 7 -0.001696 0.996601 \n", "3463 RORA RAR-related orphan receptor A 0.001460 0.990602 \n", "3469 Clone 23961 mRNA sequence -0.001441 0.994401 \n", "3464 GB DEF = Unknown protein mRNA, partial cds 0.001299 0.985803 \n", "3465 Placental Protein 14, Endometrial Alpha 2 Glob... 0.000854 0.990002 \n", "3468 Microtubule-associated protein 1a (MAP1A) geno... -0.000607 0.999400 \n", "3466 B-cell receptor associated protein (hBAP) mRNA... 0.000284 0.989802 \n", "3467 GB DEF = Mariner1 transposase gene, complete c... 0.000000 0.983603 \n", "\n", " Feature P Low Feature P High FDR(BH) Q Value Bonferroni maxT \\\n", "0 0.000000 0.000299 0.011590 0.010518 1.0 0.0000 \n", "1 0.000000 0.000299 0.011590 0.010518 1.0 0.0001 \n", "2 0.000000 0.000299 0.011590 0.010518 1.0 0.0001 \n", "3 0.000000 0.000299 0.011590 0.010518 1.0 0.0001 \n", "4 0.000000 0.000299 0.011590 0.010518 1.0 0.0001 \n", "7128 0.000000 0.000299 0.011590 0.010518 1.0 0.0005 \n", "5 0.000000 0.000299 0.011590 0.010518 1.0 0.0012 \n", "6 0.000000 0.000299 0.011590 0.010518 1.0 0.0012 \n", "7 0.000000 0.000299 0.011590 0.010518 1.0 0.0014 \n", "7127 0.000000 0.000299 0.011590 0.010518 1.0 0.0019 \n", "8 0.000000 0.000299 0.011590 0.010518 1.0 0.0031 \n", "7126 0.000000 0.000299 0.011590 0.010518 1.0 0.0037 \n", "7125 0.000000 0.000299 0.011590 0.010518 1.0 0.0066 \n", "9 0.000000 0.000299 0.011590 0.010518 1.0 0.0076 \n", "10 0.000000 0.000299 0.011590 0.010518 1.0 0.0101 \n", "11 0.000000 0.000299 0.011590 0.010518 1.0 0.0158 \n", "12 0.000000 0.000299 0.011590 0.010518 1.0 0.0177 \n", "13 0.000030 0.000640 0.016576 0.011780 1.0 0.0181 \n", "7124 0.000000 0.000299 0.011590 0.010518 1.0 0.0197 \n", "14 0.000000 0.000299 0.011590 0.010518 1.0 0.0200 \n", "7123 0.000000 0.000299 0.011590 0.010518 1.0 0.0203 \n", "15 0.000000 0.000299 0.011590 0.010518 1.0 0.0294 \n", "7122 0.000000 0.000299 0.011590 0.010518 1.0 0.0307 \n", "16 0.000030 0.000640 0.016576 0.011780 1.0 0.0338 \n", "7121 0.000000 0.000299 0.011590 0.010518 1.0 0.0347 \n", "17 0.000000 0.000299 0.011590 0.010518 1.0 0.0365 \n", "18 0.000000 0.000299 0.011590 0.010518 1.0 0.0401 \n", "7120 0.000000 0.000299 0.011590 0.010518 1.0 0.0417 \n", "19 0.000000 0.000299 0.011590 0.010518 1.0 0.0459 \n", "7119 0.000000 0.000299 0.011590 0.010518 1.0 0.0486 \n", "... ... ... ... ... ... ... \n", "3482 0.982491 0.987261 0.994733 0.667916 1.0 1.0000 \n", "3453 0.990346 0.993802 0.996736 0.669301 1.0 1.0000 \n", "3454 0.966324 0.973035 0.990775 0.665306 1.0 1.0000 \n", "3481 0.916429 0.926954 0.973996 0.653992 1.0 1.0000 \n", "3480 0.989682 0.993266 0.996695 0.669233 1.0 1.0000 \n", "3479 0.970758 0.976999 0.991813 0.665955 1.0 1.0000 \n", "3478 0.989682 0.993266 0.996695 0.669233 1.0 1.0000 \n", "3477 0.979483 0.984670 0.993976 0.667407 1.0 1.0000 \n", "3455 0.985086 0.989464 0.995643 0.668526 1.0 1.0000 \n", "3456 0.989240 0.992908 0.996695 0.669233 1.0 1.0000 \n", "3476 0.991235 0.994512 0.996917 0.669429 1.0 1.0000 \n", "3457 0.989240 0.992908 0.996695 0.669233 1.0 1.0000 \n", "3475 0.996506 0.998439 0.999283 0.670971 1.0 1.0000 \n", "3474 0.999701 1.000000 1.000000 0.671452 1.0 1.0000 \n", "3458 0.978840 0.984113 0.993976 0.667407 1.0 1.0000 \n", "3459 0.990790 0.994157 0.996796 0.669301 1.0 1.0000 \n", "3460 0.980340 0.985412 0.994141 0.667518 1.0 1.0000 \n", "3461 0.988799 0.992549 0.996695 0.669233 1.0 1.0000 \n", "3473 0.985738 0.990012 0.995745 0.668636 1.0 1.0000 \n", "3462 0.992130 0.995216 0.997158 0.669544 1.0 1.0000 \n", "3472 0.985955 0.990194 0.995745 0.668636 1.0 1.0000 \n", "3471 0.987921 0.991828 0.996431 0.669198 1.0 1.0000 \n", "3470 0.995326 0.997620 0.998822 0.670756 1.0 1.0000 \n", "3463 0.988579 0.992369 0.996695 0.669233 1.0 1.0000 \n", "3469 0.992806 0.995740 0.997620 0.669854 1.0 1.0000 \n", "3464 0.983354 0.987997 0.994733 0.667916 1.0 1.0000 \n", "3465 0.987921 0.991828 0.996431 0.669198 1.0 1.0000 \n", "3468 0.998774 0.999765 0.999681 0.671238 1.0 1.0000 \n", "3466 0.987702 0.991647 0.996431 0.669198 1.0 1.0000 \n", "3467 0.980984 0.985967 0.994202 0.667559 1.0 1.0000 \n", "\n", " FWER Fold Change ALL Mean ALL Std AML Mean AML Std \\\n", "0 0.0000 -5.856628 2011.333333 1200.105468 -343.428571 396.421049 \n", "1 0.0001 3.773193 718.523810 299.612186 190.428571 115.366783 \n", "2 0.0001 70.797073 5067.047619 3134.301939 71.571429 169.242073 \n", "3 0.0001 4.714167 1488.666667 720.431352 315.785714 129.907765 \n", "4 0.0001 3.475639 1929.476190 807.017820 555.142857 262.578005 \n", "7128 0.0005 29.698819 48.380952 56.772772 1436.857143 762.625108 \n", "5 0.0012 59.118573 4024.285714 2802.251811 68.071429 92.032991 \n", "6 0.0012 10.178065 5177.000000 3281.358271 508.642857 460.668011 \n", "7 0.0014 5.087627 4570.142857 2573.249391 898.285714 457.413200 \n", "7127 0.0019 2.296480 656.190476 269.201898 1506.928571 458.594586 \n", "8 0.0031 2.449352 1188.285714 459.793121 485.142857 211.345818 \n", "7126 0.0037 8.504233 410.619048 668.831031 3492.000000 1836.736738 \n", "7125 0.0066 3.457207 1739.285714 1722.343901 6013.071429 2383.543370 \n", "9 0.0076 -11.227687 440.285714 375.058414 -39.214286 69.363234 \n", "10 0.0101 3.601700 3933.571429 2206.576434 1092.142857 542.501451 \n", "11 0.0158 2.684612 1610.000000 739.640994 599.714286 328.174304 \n", "12 0.0177 2.829403 462.809524 185.501110 163.571429 138.187005 \n", "13 0.0181 2.327868 7474.619048 3016.387516 3210.928571 1576.865170 \n", "7124 0.0198 -1.185282 -45.619048 47.170410 54.071429 56.864839 \n", "14 0.0202 6.656980 5432.571429 3517.526483 816.071429 1373.782956 \n", "7123 0.0205 4.849143 580.809524 438.118320 2816.428571 1501.269602 \n", "15 0.0297 2.622641 6025.142857 2583.860006 2297.357143 1576.775854 \n", "7122 0.0310 11.653409 12.571429 75.233351 146.500000 72.359944 \n", "16 0.0341 2.087321 367.666667 144.434183 176.142857 68.463193 \n", "7121 0.0351 1.932889 5173.380952 2364.026110 9999.571429 2853.315419 \n", "17 0.0369 3.951904 1889.857143 1210.983744 478.214286 209.906638 \n", "18 0.0405 4.097582 4673.000000 3066.699594 1140.428571 490.242125 \n", "7120 0.0422 6.423604 45.190476 60.271568 290.285714 170.499460 \n", "19 0.0464 2.165290 1648.095238 673.612270 761.142857 337.973112 \n", "7119 0.0492 -68.529247 -93.619048 168.850371 6415.642857 4750.496000 \n", "... ... ... ... ... ... ... \n", "3482 1.0000 1.013672 12.190476 40.156717 12.357143 67.112543 \n", "3453 1.0000 1.021277 13.714286 90.495935 13.428571 108.646013 \n", "3454 1.0000 2.333333 1.333333 280.266718 0.571429 292.715854 \n", "3481 1.0000 1.002226 96.285714 107.844398 96.500000 58.372478 \n", "3480 1.0000 0.995086 -19.380952 53.325863 -19.285714 26.675914 \n", "3479 1.0000 1.003117 106.952381 92.746146 107.285714 164.569568 \n", "3478 1.0000 1.003073 77.476190 81.879557 77.714286 113.787541 \n", "3477 1.0000 1.001053 158.333333 94.794163 158.500000 74.758843 \n", "3455 1.0000 1.000978 195.047619 89.307601 194.857143 101.691734 \n", "3456 1.0000 0.997725 -41.761905 49.761335 -41.857143 48.095829 \n", "3476 1.0000 1.011628 8.190476 52.335092 8.285714 49.278531 \n", "3457 1.0000 1.001141 313.428571 195.114216 313.071429 217.898870 \n", "3475 1.0000 1.003914 24.333333 38.497186 24.428571 65.123214 \n", "3474 1.0000 0.998031 -60.476190 77.445219 -60.357143 71.950418 \n", "3458 1.0000 1.004320 71.952381 155.581964 71.642857 268.831834 \n", "3459 1.0000 1.001554 61.380952 45.892784 61.285714 83.561519 \n", "3460 1.0000 1.004274 11.190476 33.853536 11.142857 38.036131 \n", "3461 1.0000 1.000846 309.761905 173.297693 309.500000 271.842927 \n", "3473 1.0000 1.000810 499.523810 323.232829 499.928571 492.047913 \n", "3462 1.0000 1.000605 118.142857 98.639893 118.071429 70.389536 \n", "3472 1.0000 0.999245 -126.095238 111.217762 -126.000000 130.072169 \n", "3471 1.0000 1.001314 36.238095 79.984314 36.285714 76.460577 \n", "3470 1.0000 0.998705 -18.380952 33.893180 -18.357143 44.663885 \n", "3463 1.0000 0.997653 -20.238095 84.060041 -20.285714 100.862762 \n", "3469 1.0000 1.000956 49.809524 64.448909 49.857143 111.892708 \n", "3464 1.0000 1.000227 314.857143 169.164797 314.785714 152.441756 \n", "3465 1.0000 1.000220 216.190476 113.471414 216.142857 186.935142 \n", "3468 1.0000 1.000130 182.476190 114.806628 182.500000 113.017187 \n", "3466 1.0000 1.000028 1681.619048 561.958760 1681.571429 428.851187 \n", "3467 1.0000 1.000000 200.428571 97.493370 200.428571 135.909310 \n", "\n", " k \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "7128 10000 \n", "5 0 \n", "6 0 \n", "7 0 \n", "7127 10000 \n", "8 0 \n", "7126 10000 \n", "7125 10000 \n", "9 0 \n", "10 0 \n", "11 0 \n", "12 0 \n", "13 1 \n", "7124 10000 \n", "14 0 \n", "7123 10000 \n", "15 0 \n", "7122 10000 \n", "16 1 \n", "7121 10000 \n", "17 0 \n", "18 0 \n", "7120 10000 \n", "19 0 \n", "7119 10000 \n", "... ... \n", "3482 5075 \n", "3453 4961 \n", "3454 4849 \n", "3481 5391 \n", "3480 4958 \n", "3479 5130 \n", "3478 5042 \n", "3477 5089 \n", "3455 4937 \n", "3456 5044 \n", "3476 4965 \n", "3457 4956 \n", "3475 5012 \n", "3474 5000 \n", "3458 4908 \n", "3459 5037 \n", "3460 5085 \n", "3461 5046 \n", "3473 5060 \n", "3462 5031 \n", "3472 5059 \n", "3471 5050 \n", "3470 4983 \n", "3463 5047 \n", "3469 4972 \n", "3464 5071 \n", "3465 5050 \n", "3468 4997 \n", "3466 4949 \n", "3467 5082 \n", "\n", "[7129 rows x 18 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cms_scores = all_aml_test_comp_marker_odf_1587350.dataframe\n", "cms_scores.sort_values(by='Rank',inplace=True)\n", "cms_scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using CCALnoir" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import cuzcatlan as cusca\n", "import pandas as pd\n", "import numpy as np\n", "from cuzcatlan import differential_gene_expression\n", "import urllib.request\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 99.9 ms, sys: 21 ms, total: 121 ms\n", "Wall time: 2.11 s\n" ] } ], "source": [ "%%time\n", "TOP = 500\n", "permuations=1000\n", "\n", "RUN = True\n", "\n", "data_url = \"https://software.broadinstitute.org/cancer/software/genepattern/data/all_aml/all_aml_test.gct\"\n", "pheno_url = \"https://software.broadinstitute.org/cancer/software/genepattern/data/all_aml/all_aml_test.cls\"\n", "\n", "data_df = pd.read_table(data_url, header=2, index_col=0)\n", "data_df.drop('Description', axis=1, inplace=True)\n", "url_file, __ = urllib.request.urlretrieve(pheno_url)\n", "temp = open(url_file)\n", "temp.readline()\n", "temp.readline()\n", "classes = [int(i) for i in temp.readline().strip('\\n').split(' ')]\n", "classes = pd.Series(classes, index=data_df.columns)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dropping 0 axis-1 slices ...\n", "Computing match score with <function custom_pearson_corr at 0x113e54e18> (1 process) ...\n", "Computing MoEs with 30 samplings ...\n", "Computing p-values and FDRs with 10 permutations ...\n", "\t1/10 ...\n", "\t2/10 ...\n", "\t3/10 ...\n", "\t4/10 ...\n", "\t5/10 ...\n", "\t6/10 ...\n", "\t7/10 ...\n", "\t8/10 ...\n", "\t9/10 ...\n", "\t10/10 ...\n", "\t10/10 - done.\n" ] }, { "data": { "image/png": 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Q39vw862PAbD8yu2P8cSM9scAqJ9rf4yxeL0Ann5qbMYZC0+Pwfv/xPT2xwCYtGT7Yyyx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\nsXv7nZx+ePt9AGy2d/t9bLhW/3Vmhx8eM8+WrMuz4lRiQOsNq7q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was already sent\n", " if (document.getElementById(\"5f4b5784-e6d9-482f-9757-70665b3fec92\") === null) {\n", " var notificationPayload = {\"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"body\": \"Cell Execution Has Finished!!\"};\n", " if (Notification.permission !== 'denied') {\n", " if (Notification.permission !== 'granted') { \n", " Notification.requestPermission(function (permission) {\n", " if(!('permission' in Notification)) {\n", " Notification.permission = permission\n", " }\n", " if (Notification.permission === 'granted') {\n", " var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n", " appendUniqueDiv()\n", " }\n", " })\n", " } else if (Notification.permission === 'granted') {\n", " var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n", " appendUniqueDiv()\n", " }\n", " }\n", " }\n", " }\n", " }\n", ")\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%notify\n", "%%time\n", "raw_scores = differential_gene_expression(phenotypes=pheno_url, gene_expression=data_url, \n", " output_filename='DE_test', ranking_method=cusca.custom_pearson_corr,\n", " number_of_permutations=10)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Name Score 0.95 MoE p-value FDR \\\n", "0 U46499_at 8.245435e-01 NaN 0.000014 0.002381 \n", "1 X95735_at 7.756710e-01 NaN 0.000014 0.002381 \n", "2 M89957_at -7.756137e-01 NaN 0.000014 0.002273 \n", "3 M63959_at 7.696607e-01 NaN 0.000014 0.002381 \n", "4 L09209_s_at 7.475753e-01 NaN 0.000014 0.002381 \n", "5 M84526_at 7.400529e-01 NaN 0.000014 0.002381 \n", "6 J05243_at -7.371616e-01 NaN 0.000014 0.002273 \n", "7 X17042_at 7.316505e-01 NaN 0.000014 0.002381 \n", "8 M84371_rna1_s_at -7.298155e-01 NaN 0.000014 0.002273 \n", "9 M14636_at 7.268337e-01 NaN 0.000014 0.002381 \n", "10 M22960_at 7.260735e-01 NaN 0.000014 0.002381 \n", "11 M31523_at -7.221959e-01 0.0228747 0.000014 0.002273 \n", "12 U59878_at 7.209896e-01 NaN 0.000014 0.002381 \n", "13 M11722_at -7.181291e-01 NaN 0.000014 0.002273 \n", "14 M23197_at 7.162585e-01 NaN 0.000014 0.002381 \n", "15 X61587_at 7.114731e-01 NaN 0.000014 0.002381 \n", "16 M27891_at 7.067257e-01 NaN 0.000014 0.002381 \n", "17 X05908_at 7.046788e-01 NaN 0.000014 0.002381 \n", "18 U60319_at 7.007208e-01 NaN 0.000014 0.002381 \n", "19 M32304_s_at 6.908595e-01 NaN 0.000014 0.002381 \n", "20 X12447_at 6.880660e-01 NaN 0.000014 0.002381 \n", "21 M19507_at 6.865670e-01 NaN 0.000014 0.002381 \n", "22 X16546_at 6.844507e-01 0.0291901 0.000014 0.002381 \n", "23 M63138_at 6.825158e-01 NaN 0.000014 0.002381 \n", "24 L09717_at 6.809467e-01 NaN 0.000014 0.002381 \n", "25 U29175_at -6.807426e-01 0.0185103 0.000014 0.002273 \n", "26 U05259_rna1_at -6.755212e-01 NaN 0.000014 0.002273 \n", "27 M92287_at -6.752193e-01 NaN 0.000014 0.002273 \n", "28 D88270_at -6.749073e-01 0.0228069 0.000014 0.002273 \n", "29 Z29067_at 6.737274e-01 NaN 0.000014 0.002381 \n", "... ... ... ... ... ... \n", "7099 X59766_at -1.470199e-03 NaN 0.484949 0.940479 \n", "7100 M29540_at 1.457900e-03 NaN 0.478440 0.935234 \n", "7101 U47621_at -1.347656e-03 NaN 0.484584 0.940027 \n", "7102 X81372_at 1.334454e-03 NaN 0.478594 0.935280 \n", "7103 U58032_at 1.254863e-03 NaN 0.478777 0.935342 \n", "7104 HG3976-HT4246_at 1.180234e-03 NaN 0.478889 0.935342 \n", "7105 U65002_at 1.073424e-03 NaN 0.479099 0.935413 \n", "7106 U81787_at -1.018220e-03 NaN 0.483967 0.939085 \n", "7107 X16281_at -9.783761e-04 NaN 0.483841 0.939085 \n", "7108 M74826_at 9.615364e-04 NaN 0.479366 0.935413 \n", "7109 L75847_at 9.480223e-04 NaN 0.479408 0.935413 \n", "7110 Z46629_at 9.393421e-04 NaN 0.479450 0.935413 \n", "7111 X63563_at -8.815716e-04 NaN 0.483658 0.938998 \n", "7112 L77559_at 7.973411e-04 NaN 0.479801 0.935841 \n", "7113 X60487_at -7.571721e-04 NaN 0.483392 0.938736 \n", "7114 U12595_at -7.518560e-04 NaN 0.483378 0.938736 \n", "7115 U57911_at -6.756185e-04 NaN 0.483252 0.938736 \n", "7116 M18700_s_at -6.074864e-04 NaN 0.483083 0.938736 \n", "7117 U67171_at 5.126211e-04 NaN 0.480418 0.936557 \n", "7118 U52828_s_at -4.067761e-04 NaN 0.482410 0.937851 \n", "7119 D31833_s_at 4.037733e-04 NaN 0.480586 0.936557 \n", "7120 J04156_at 3.120324e-04 NaN 0.480741 0.936557 \n", "7121 X12662_rna1_at 3.056031e-04 NaN 0.480755 0.936557 \n", "7122 U79295_at 2.783558e-04 NaN 0.480825 0.936557 \n", "7123 U04898_at -2.638654e-04 NaN 0.482045 0.937398 \n", "7124 U82303_at -2.213848e-04 NaN 0.481989 0.937398 \n", "7125 HG721-HT4827_s_at -1.635845e-04 NaN 0.481793 0.937398 \n", "7126 U38291_rna1_at 1.052758e-04 NaN 0.481119 0.936875 \n", "7127 U72511_at -4.677248e-05 NaN 0.481554 0.937210 \n", "7128 U52077_s_at -3.737679e-17 NaN 0.481526 0.937210 \n", "\n", " abs_score Feature Rank \n", "0 8.245435e-01 U46499_at 1 \n", "1 7.756710e-01 X95735_at 2 \n", "2 7.756137e-01 M89957_at 3 \n", "3 7.696607e-01 M63959_at 4 \n", "4 7.475753e-01 L09209_s_at 5 \n", "5 7.400529e-01 M84526_at 6 \n", "6 7.371616e-01 J05243_at 7 \n", "7 7.316505e-01 X17042_at 8 \n", "8 7.298155e-01 M84371_rna1_s_at 9 \n", "9 7.268337e-01 M14636_at 10 \n", "10 7.260735e-01 M22960_at 11 \n", "11 7.221959e-01 M31523_at 12 \n", "12 7.209896e-01 U59878_at 13 \n", "13 7.181291e-01 M11722_at 14 \n", "14 7.162585e-01 M23197_at 15 \n", "15 7.114731e-01 X61587_at 16 \n", "16 7.067257e-01 M27891_at 17 \n", "17 7.046788e-01 X05908_at 18 \n", "18 7.007208e-01 U60319_at 19 \n", "19 6.908595e-01 M32304_s_at 20 \n", "20 6.880660e-01 X12447_at 21 \n", "21 6.865670e-01 M19507_at 22 \n", "22 6.844507e-01 X16546_at 23 \n", "23 6.825158e-01 M63138_at 24 \n", "24 6.809467e-01 L09717_at 25 \n", "25 6.807426e-01 U29175_at 26 \n", "26 6.755212e-01 U05259_rna1_at 27 \n", "27 6.752193e-01 M92287_at 28 \n", "28 6.749073e-01 D88270_at 29 \n", "29 6.737274e-01 Z29067_at 30 \n", "... ... ... ... \n", "7099 1.470199e-03 X59766_at 7100 \n", "7100 1.457900e-03 M29540_at 7101 \n", "7101 1.347656e-03 U47621_at 7102 \n", "7102 1.334454e-03 X81372_at 7103 \n", "7103 1.254863e-03 U58032_at 7104 \n", "7104 1.180234e-03 HG3976-HT4246_at 7105 \n", "7105 1.073424e-03 U65002_at 7106 \n", "7106 1.018220e-03 U81787_at 7107 \n", "7107 9.783761e-04 X16281_at 7108 \n", "7108 9.615364e-04 M74826_at 7109 \n", "7109 9.480223e-04 L75847_at 7110 \n", "7110 9.393421e-04 Z46629_at 7111 \n", "7111 8.815716e-04 X63563_at 7112 \n", "7112 7.973411e-04 L77559_at 7113 \n", "7113 7.571721e-04 X60487_at 7114 \n", "7114 7.518560e-04 U12595_at 7115 \n", "7115 6.756185e-04 U57911_at 7116 \n", "7116 6.074864e-04 M18700_s_at 7117 \n", "7117 5.126211e-04 U67171_at 7118 \n", "7118 4.067761e-04 U52828_s_at 7119 \n", "7119 4.037733e-04 D31833_s_at 7120 \n", "7120 3.120324e-04 J04156_at 7121 \n", "7121 3.056031e-04 X12662_rna1_at 7122 \n", "7122 2.783558e-04 U79295_at 7123 \n", "7123 2.638654e-04 U04898_at 7124 \n", "7124 2.213848e-04 U82303_at 7125 \n", "7125 1.635845e-04 HG721-HT4827_s_at 7126 \n", "7126 1.052758e-04 U38291_rna1_at 7127 \n", "7127 4.677248e-05 U72511_at 7128 \n", "7128 3.737679e-17 U52077_s_at 7129 \n", "\n", "[7129 rows x 8 columns]\n" ] } ], "source": [ "ccal_scores = raw_scores.copy()\n", "ccal_scores['abs_score'] = abs(ccal_scores['Score'])\n", "ccal_scores['Feature'] = ccal_scores.index\n", "ccal_scores.sort_values('abs_score', ascending=False, inplace=True)\n", "ccal_scores.reset_index(inplace=True)\n", "ccal_scores['Rank'] = ccal_scores.index +1\n", "print(ccal_scores)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dropping 0 axis-1 slices ...\n", "Computing match score with <function compute_information_coefficient at 0x114d3cc80> (1 process) ...\n", "Computing MoEs with 30 samplings ...\n", "Computing p-values and FDRs with 10 permutations ...\n", "\t1/10 ...\n", "\t2/10 ...\n", "\t3/10 ...\n", "\t4/10 ...\n", "\t5/10 ...\n", "\t6/10 ...\n", "\t7/10 ...\n", "\t8/10 ...\n", "\t9/10 ...\n", "\t10/10 ...\n", "\t10/10 - done.\n" ] }, { "data": { "image/png": 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46Mv2ibYXKJafPsO/BoznLjoqGoMBHBwfjmw257VI4O8bYPrixMrKKlWjfiVl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\nkMiI2+zfeRiAG9dCCA0Oo3zlMnTs7sOcn6ZhNBp597UPib4XY5lPw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"text/plain": [ "<matplotlib.figure.Figure at 0x114786128>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "$(document).ready(\n", " function() {\n", " function appendUniqueDiv(){\n", " // append a div with our uuid so we can check that it's already\n", " // been sent and avoid duplicates on page reload\n", " var notifiedDiv = document.createElement(\"div\")\n", " notifiedDiv.id = \"6b4102b3-6adb-40f0-9128-bd2e8b32beec\"\n", " element.append(notifiedDiv)\n", " }\n", "\n", " // only send notifications if the pageload is complete; this will\n", " // help stop extra notifications when a saved notebook is loaded,\n", " // which during testing gives us state \"interactive\", not \"complete\"\n", " if (document.readyState === 'complete') {\n", " // check for the div that signifies that the notification\n", " // was already sent\n", " if (document.getElementById(\"6b4102b3-6adb-40f0-9128-bd2e8b32beec\") === null) {\n", " var notificationPayload = {\"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"body\": \"Cell Execution Has Finished!!\"};\n", " if (Notification.permission !== 'denied') {\n", " if (Notification.permission !== 'granted') { \n", " Notification.requestPermission(function (permission) {\n", " if(!('permission' in Notification)) {\n", " Notification.permission = permission\n", " }\n", " if (Notification.permission === 'granted') {\n", " var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n", " appendUniqueDiv()\n", " }\n", " })\n", " } else if (Notification.permission === 'granted') {\n", " var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n", " appendUniqueDiv()\n", " }\n", " }\n", " }\n", " }\n", " }\n", ")\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.14 s, sys: 188 ms, total: 3.33 s\n", "Wall time: 1min 40s\n" ] } ], "source": [ "%%time\n", "%%notify\n", "raw_ic_scores = differential_gene_expression(phenotypes=pheno_url, gene_expression=data_url, \n", " output_filename='DE_test', ranking_method=cusca.compute_information_coefficient,\n", " number_of_permutations=10)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Name Score 0.95 MoE p-value FDR abs_score \\\n", "0 U46499_at 0.753714 NaN 0.000014 0.002778 0.753714 \n", "1 M84526_at 0.649866 NaN 0.000014 0.002778 0.649866 \n", "2 M89957_at -0.645258 NaN 0.000014 0.002941 0.645258 \n", "3 M27891_at 0.634431 NaN 0.000014 0.002778 0.634431 \n", "4 U59878_at 0.613581 NaN 0.000014 0.002778 0.613581 \n", "5 X95735_at 0.608938 NaN 0.000014 0.002778 0.608938 \n", "6 M11722_at -0.607143 NaN 0.000014 0.002941 0.607143 \n", "7 M84371_rna1_s_at -0.601197 NaN 0.000014 0.002941 0.601197 \n", "8 M31523_at -0.596433 0.0230147 0.000014 0.002941 0.596433 \n", "9 M22960_at 0.591253 NaN 0.000014 0.002778 0.591253 \n", "10 M63959_at 0.583427 NaN 0.000014 0.002778 0.583427 \n", "11 M19507_at 0.579449 NaN 0.000014 0.002778 0.579449 \n", "12 L09209_s_at 0.577150 NaN 0.000014 0.002778 0.577150 \n", "13 X16546_at 0.569908 0.0278228 0.000014 0.002778 0.569908 \n", "14 M63138_at 0.569470 NaN 0.000014 0.002778 0.569470 \n", "15 J05243_at -0.566304 NaN 0.000014 0.002941 0.566304 \n", "16 M11147_at 0.564826 NaN 0.000014 0.002778 0.564826 \n", "17 X17042_at 0.562581 NaN 0.000014 0.002778 0.562581 \n", "18 M92357_at 0.561667 NaN 0.000014 0.002778 0.561667 \n", "19 M23197_at 0.556172 NaN 0.000014 0.002778 0.556172 \n", "20 X05908_at 0.554616 NaN 0.000014 0.002778 0.554616 \n", "21 M14636_at 0.546838 NaN 0.000014 0.002778 0.546838 \n", "22 M92287_at -0.542418 NaN 0.000014 0.002941 0.542418 \n", "23 M96326_rna1_at 0.534105 NaN 0.000014 0.002778 0.534105 \n", "24 D88270_at -0.532985 0.02201 0.000014 0.002941 0.532985 \n", "25 M32304_s_at 0.531376 NaN 0.000014 0.002778 0.531376 \n", "26 U05259_rna1_at -0.529745 NaN 0.000014 0.002941 0.529745 \n", "27 X12447_at 0.527874 NaN 0.000014 0.002778 0.527874 \n", "28 U60319_at 0.519148 NaN 0.000014 0.002778 0.519148 \n", "29 Z49194_at -0.518524 NaN 0.000014 0.002941 0.518524 \n", "... ... ... ... ... ... ... \n", "7099 U78107_at -0.048218 NaN 0.485159 0.940631 0.048218 \n", "7100 X70476_at -0.048158 NaN 0.485159 0.940631 0.048158 \n", "7101 X57351_s_at -0.047757 NaN 0.485033 0.940631 0.047757 \n", "7102 U57911_at -0.047616 NaN 0.484949 0.940631 0.047616 \n", "7103 D42087_at -0.047045 NaN 0.484710 0.940631 0.047045 \n", "7104 U07681_at 0.046782 NaN 0.478230 0.934164 0.046782 \n", "7105 U00943_at -0.046711 NaN 0.484598 0.940631 0.046711 \n", "7106 U24389_s_at 0.046648 NaN 0.478286 0.934164 0.046648 \n", "7107 D67029_at 0.046491 NaN 0.478342 0.934164 0.046491 \n", "7108 X64269_at 0.046176 NaN 0.478468 0.934164 0.046176 \n", "7109 U34880_at 0.045413 NaN 0.478679 0.934164 0.045413 \n", "7110 HG2264-HT2360_at 0.045404 NaN 0.478679 0.934164 0.045404 \n", "7111 U79254_at 0.044575 NaN 0.479015 0.934565 0.044575 \n", "7112 M94345_at -0.044011 NaN 0.483771 0.939472 0.044011 \n", "7113 U65093_at 0.043777 NaN 0.479240 0.934747 0.043777 \n", "7114 U96131_at -0.043237 NaN 0.483630 0.939455 0.043237 \n", "7115 U44975_at -0.043093 NaN 0.483588 0.939455 0.043093 \n", "7116 X03794_s_at 0.042390 NaN 0.479576 0.935148 0.042390 \n", "7117 HG4593-HT4998_at -0.042009 NaN 0.483280 0.939286 0.042009 \n", "7118 X66114_rna1_at -0.041481 NaN 0.483209 0.939286 0.041481 \n", "7119 S69369_at -0.041401 NaN 0.483195 0.939286 0.041401 \n", "7120 U18291_at 0.040940 NaN 0.479899 0.935521 0.040940 \n", "7121 U79734_at 0.039445 NaN 0.480081 0.935621 0.039445 \n", "7122 L40411_at -0.038328 NaN 0.482648 0.938827 0.038328 \n", "7123 U18919_at 0.036377 NaN 0.480446 0.935874 0.036377 \n", "7124 U05659_at 0.036083 NaN 0.480474 0.935874 0.036083 \n", "7125 D86971_at -0.034714 NaN 0.482087 0.937991 0.034714 \n", "7126 L05515_at 0.026648 NaN 0.481161 0.936957 0.026648 \n", "7127 M14565_at -0.024418 NaN 0.481540 0.937183 0.024418 \n", "7128 D87685_at 0.022770 NaN 0.481302 0.936974 0.022770 \n", "\n", " Feature Rank \n", "0 U46499_at 1 \n", "1 M84526_at 2 \n", "2 M89957_at 3 \n", "3 M27891_at 4 \n", "4 U59878_at 5 \n", "5 X95735_at 6 \n", "6 M11722_at 7 \n", "7 M84371_rna1_s_at 8 \n", "8 M31523_at 9 \n", "9 M22960_at 10 \n", "10 M63959_at 11 \n", "11 M19507_at 12 \n", "12 L09209_s_at 13 \n", "13 X16546_at 14 \n", "14 M63138_at 15 \n", "15 J05243_at 16 \n", "16 M11147_at 17 \n", "17 X17042_at 18 \n", "18 M92357_at 19 \n", "19 M23197_at 20 \n", "20 X05908_at 21 \n", "21 M14636_at 22 \n", "22 M92287_at 23 \n", "23 M96326_rna1_at 24 \n", "24 D88270_at 25 \n", "25 M32304_s_at 26 \n", "26 U05259_rna1_at 27 \n", "27 X12447_at 28 \n", "28 U60319_at 29 \n", "29 Z49194_at 30 \n", "... ... ... \n", "7099 U78107_at 7100 \n", "7100 X70476_at 7101 \n", "7101 X57351_s_at 7102 \n", "7102 U57911_at 7103 \n", "7103 D42087_at 7104 \n", "7104 U07681_at 7105 \n", "7105 U00943_at 7106 \n", "7106 U24389_s_at 7107 \n", "7107 D67029_at 7108 \n", "7108 X64269_at 7109 \n", "7109 U34880_at 7110 \n", "7110 HG2264-HT2360_at 7111 \n", "7111 U79254_at 7112 \n", "7112 M94345_at 7113 \n", "7113 U65093_at 7114 \n", "7114 U96131_at 7115 \n", "7115 U44975_at 7116 \n", "7116 X03794_s_at 7117 \n", "7117 HG4593-HT4998_at 7118 \n", "7118 X66114_rna1_at 7119 \n", "7119 S69369_at 7120 \n", "7120 U18291_at 7121 \n", "7121 U79734_at 7122 \n", "7122 L40411_at 7123 \n", "7123 U18919_at 7124 \n", "7124 U05659_at 7125 \n", "7125 D86971_at 7126 \n", "7126 L05515_at 7127 \n", "7127 M14565_at 7128 \n", "7128 D87685_at 7129 \n", "\n", "[7129 rows x 8 columns]\n" ] } ], "source": [ "ccal_ic_scores = raw_ic_scores.copy()\n", "ccal_ic_scores['abs_score'] = abs(ccal_ic_scores['Score'])\n", "ccal_ic_scores['Feature'] = ccal_ic_scores.index\n", "ccal_ic_scores.sort_values('abs_score', ascending=False, inplace=True)\n", "ccal_ic_scores.reset_index(inplace=True)\n", "ccal_ic_scores['Rank'] = ccal_ic_scores.index +1\n", "print(ccal_ic_scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CMS vs CCAL_correlation" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# @memory.cache\n", "def custom_metric(list_1, list_2):\n", " temp = list_1 - list_2\n", " temp.fillna(len(temp), inplace=True)\n", " # Metric is 0 if perfect overlap, 1 if list are reversed. It can be larger than one!\n", " return sum(abs(temp))/ np.floor(list_1.shape[0]*2/2)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# @memory.cache\n", "def map_df1_to_df2(df_1, df_2):\n", " to_return = df_1.copy()\n", " df_2_copy = df_2.copy()\n", " \n", " to_return.sort_values(by='Rank', inplace=True)\n", " to_return.set_index('Feature', inplace=True)\n", " df_2_copy.sort_values(by='Rank', inplace=True)\n", " df_2_copy.set_index('Feature', inplace=True)\n", " \n", " df_2_copy.rename(columns={'Rank': 'new_Rank'}, inplace=True)\n", " to_return_2 = to_return.join(df_2_copy)\n", " \n", " return to_return_2" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "def compute_overlap(reference_df, new_df, col='index'):\n", " if col == 'index':\n", " common = (list(set(reference_df.index) & set(new_df.index)))\n", " else:\n", " common = (list(set(reference_df[col]) & set(new_df[col])))\n", "\n", " overlap = 100len(common)/len(reference_df)\n", " return overlap" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# @memory.cache\n", "def compare_ranks(df_a, df_b, number_of_genes=5, verbose=False):\n", " # Not ssuming both df's are ranked already!\n", " subset_a = df_a.head(number_of_genes)[['Feature', 'Rank']]\n", " subset_b = df_b.head(number_of_genes)[['Feature', 'Rank']]\n", "\n", " a_in_b = map_df1_to_df2(subset_a, df_b[['Feature','Rank']])\n", " b_in_a = map_df1_to_df2(subset_b, df_a[['Feature','Rank']]) \n", "\n", " metric_1 = custom_metric(a_in_b['Rank'], a_in_b['new_Rank'])\n", " metric_2 = custom_metric(b_in_a['Rank'], b_in_a['new_Rank'])\n", " \n", " overlap = compute_overlap(subset_a, subset_b, col='Feature')\n", " \n", " if verbose:\n", " print(a_in_b) \n", " print(b_in_a)\n", " \n", " return ((metric_1 + metric_2)/2, overlap)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "# @memory.cache\n", "def compare_multiple_ranks(df_a, df_b, max_number_of_genes=10, verbose=False):\n", "\n", " # This is the largest subset we will consider\n", " subset_a = df_a.head(max_number_of_genes)[['Feature', 'Rank']]\n", " subset_b = df_b.head(max_number_of_genes)[['Feature', 'Rank']]\n", " \n", " df_a_to_use = df_a[['Feature','Rank']]\n", " df_b_to_use = df_b[['Feature','Rank']]\n", " \n", " indexes = []\n", " metrics = []\n", " overlap = []\n", " for i in range(max_number_of_genes, 0, -1):\n", " \n", " if i == max_number_of_genes:\n", " subset_a_to_use = subset_a\n", " subset_b_to_use = subset_b\n", " else:\n", " subset_a_to_use = subset_a_to_use.drop(subset_a_to_use.index[i])\n", " subset_b_to_use = subset_b_to_use.drop(subset_b_to_use.index[i])\n", "\n", " a_in_b = map_df1_to_df2(subset_a_to_use, df_b_to_use)\n", " b_in_a = map_df1_to_df2(subset_b_to_use, df_a_to_use)\n", " \n", " overlap.append(compute_overlap(subset_a_to_use, subset_b_to_use, col='Feature'))\n", "\n", " metric_1 = custom_metric(a_in_b['Rank'], a_in_b['new_Rank'])\n", " metric_2 = custom_metric(b_in_a['Rank'], b_in_a['new_Rank'])\n", " \n", " indexes.append(i)\n", "# print(i, metric_1, metric_2)\n", " metrics.append((metric_1 + metric_2)/2)\n", " \n", " if verbose:\n", " print('Depreciated!')\n", " \n", " return indexes, metrics, overlap" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5, 4, 3, 2, 1]\n", "[2.875, 3.0625, 4.375, 5.5, inf]\n", "[20.0, 25.0, 33.333333333333336, 0.0, 0.0]\n", "CPU times: user 51.7 ms, sys: 2.46 ms, total: 54.2 ms\n", "Wall time: 52.9 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/edjuaro/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in double_scalars\n", " \n" ] } ], "source": [ "%%time\n", "ixs, mets, over = compare_multiple_ranks(cms_scores, ccal_scores, max_number_of_genes=5, verbose=False)\n", "print(ixs)\n", "print(mets)\n", "print(over)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Rank new_Rank\n", "Feature \n", "M89957_at 1 3\n", "J05243_at 2 16\n", "M11722_at 3 7\n", "M31523_at 4 9\n", "M84371_rna1_s_at 5 8\n", "U46499_at 6 1\n", "D88270_at 7 25\n", "U05259_rna1_at 8 27\n", "M92287_at 9 23\n", "M63959_at 10 11\n", " Rank new_Rank\n", "Feature \n", "U46499_at 1 6\n", "M84526_at 2 30\n", "M89957_at 3 1\n", "M27891_at 4 52\n", "U59878_at 5 40\n", "X95735_at 6 12\n", "M11722_at 7 3\n", "M84371_rna1_s_at 8 5\n", "M31523_at 9 4\n", "M22960_at 10 34\n", "\n", "Metric = 2.45 Overlap= 50.0\n", "CPU times: user 15.4 ms, sys: 2.4 ms, total: 17.8 ms\n", "Wall time: 15.8 ms\n" ] } ], "source": [ "%%time\n", "m1, ov = compare_ranks(cms_scores, ccal_ic_scores, number_of_genes=10, verbose=True)\n", "print(\"\\nMetric =\",m1, \"Overlap=\", ov)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CMS vs CCAL_ic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CCAL_correlation vs CCAL_ic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting trends" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CMS vs CCAL_ic" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4.07 s, sys: 52 ms, total: 4.13 s\n", "Wall time: 4.13 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/edjuaro/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in double_scalars\n", " \n" ] } ], "source": [ "%%time\n", "plt.clf()\n", "fig, axs = plt.subplots(2,1,dpi=150)\n", "\n", "# for i in range(int(len(scores)/2)):\n", "# for i in range(1000):\n", "# if i ==0:\n", "# continue\n", "# metric = compare_ranks(cms_scores, ccal_ic_scores, number_of_genes=i)\n", "# fig.gca().scatter(i,metric,color='k')\n", "# fig.gca().set_ylim(-0.1,8)\n", "\n", "ixs, mets,over = compare_multiple_ranks(cms_scores, ccal_ic_scores, max_number_of_genes=500, verbose=False)\n", "axs[0].scatter(ixs,mets,color='k')\n", "axs[0].set_ylim(-0.1,8)\n", "axs[0].set_ylabel('Custom metric')\n", "axs[1].scatter(ixs,over,color='k')\n", "axs[1].set_ylabel('% Overlap')\n", "axs[1].set_xlabel('Top n genes')\n", "axs[0].set_title(\"CMS vs CCAL_IC\")\n", "fig" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "image/png": 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+c6toIiIiIqotVicp69atg52dnTFJWbduHQRBgCiKFdZjklKzlEpljdYjIiIi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\nSVLdunXpv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"text/plain": [ "<matplotlib.figure.Figure at 0x11411ba20>" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CMS vs CCAL_correlation" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 812 ms, sys: 13.6 ms, total: 826 ms\n", "Wall time: 823 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/edjuaro/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in double_scalars\n", " \n" ] } ], "source": [ "%%time\n", "plt.close('all')\n", "plt.clf()\n", "fig2, axs2 = plt.subplots(2,1,dpi=150)\n", "\n", "# for i in range(int(len(scores)/2)):\n", "# for i in range(1000):\n", "# if i ==0:\n", "# continue\n", "# metric = compare_ranks(cms_scores, ccal_scores, number_of_genes=i)\n", "# fig2.gca().scatter(i,metric,color='k')\n", "ixs, mets, over = compare_multiple_ranks(cms_scores, ccal_scores, max_number_of_genes=100, verbose=False)\n", "axs2[0].scatter(ixs,mets,color='k')\n", "axs2[0].set_ylim(-0.1,8)\n", "axs2[0].set_ylabel('Custom metric')\n", "axs2[1].scatter(ixs,over,color='k')\n", "axs2[1].set_ylabel('% Overlap')\n", "axs2[1].set_xlabel('Top n genes')\n", "axs2[0].set_title(\"CMS vs CCAL_PC\")" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "image/png": 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FheVaxmw2a+nSpTp37py2b98uFxeXEhwhAAAAkDvDQsro0aNtKpeYmGhUl8iF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"text/plain": [ "<matplotlib.figure.Figure at 0x1143a5278>" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CCAL_correlation vs CCAL_ic" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 977 ms, sys: 14.6 ms, total: 992 ms\n", "Wall time: 991 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/edjuaro/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in double_scalars\n", " \n" ] } ], "source": [ "%%time\n", "fig3, axs3 = plt.subplots(2,1,dpi=150)\n", "# for i in range(int(len(scores)/2)):\n", "# for i in range(1000):\n", "# if i ==0:\n", "# continue\n", "# metric = compare_ranks(ccal_ic_scores, ccal_scores, number_of_genes=i)\n", "# fig2.gca().scatter(i,metric,color='k')\n", "# fig2.gca().set_ylim(-0.1,8)\n", "ixs, mets, over = compare_multiple_ranks(ccal_ic_scores, ccal_scores, max_number_of_genes=100, verbose=False)\n", "axs3[0].scatter(ixs,mets,color='k')\n", "axs3[0].set_ylim(-0.1,8)\n", "axs3[0].set_ylabel('Custom metric')\n", "axs3[1].scatter(ixs,over,color='k')\n", "axs3[1].set_ylabel('% Overlap')\n", "axs3[1].set_xlabel('Top n genes')" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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fP4WGhio+Pt6oEvAnYWFhJZ4YoXfv3vLy8irTGJeilHXSAAAAAEAyKLwcOXJE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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10cc31908>" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
danresende/deep-learning	sentiment_network/Sentiment Classification - Project 5 Solution.ipynb	2	432409	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n", "\n", "by Andrew Trask\n", "\n", "- Twitter: @iamtrask\n", "- Blog: http://iamtrask.github.io" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### What You Should Already Know\n", "\n", "- neural networks, forward and back-propagation\n", "- stochastic gradient descent\n", "- mean squared error\n", "- and train/test splits\n", "\n", "### Where to Get Help if You Need it\n", "- Re-watch previous Udacity Lectures\n", "- Leverage the recommended Course Reading Material - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) (40% Off: traskud17)\n", "- Shoot me a tweet @iamtrask\n", "\n", "\n", "### Tutorial Outline:\n", "\n", "- Intro: The Importance of \"Framing a Problem\"\n", "\n", "\n", "- Curate a Dataset\n", "- Developing a \"Predictive Theory\"\n", "- PROJECT 1: Quick Theory Validation\n", "\n", "\n", "- Transforming Text to Numbers\n", "- PROJECT 2: Creating the Input/Output Data\n", "\n", "\n", "- Putting it all together in a Neural Network\n", "- PROJECT 3: Building our Neural Network\n", "\n", "\n", "- Understanding Neural Noise\n", "- PROJECT 4: Making Learning Faster by Reducing Noise\n", "\n", "\n", "- Analyzing Inefficiencies in our Network\n", "- PROJECT 5: Making our Network Train and Run Faster\n", "\n", "\n", "- Further Noise Reduction\n", "- PROJECT 6: Reducing Noise by Strategically Reducing the Vocabulary\n", "\n", "\n", "- Analysis: What's going on in the weights?" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "nbpresent": { "id": "56bb3cba-260c-4ebe-9ed6-b995b4c72aa3" } }, "source": [ "# Lesson: Curate a Dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "eba2b193-0419-431e-8db9-60f34dd3fe83" } }, "outputs": [], "source": [ "def pretty_print_review_and_label(i):\n", " print(labels[i] + \"\\t:\\t\" + reviews[i][:80] + \"...\")\n", "\n", "g = open('reviews.txt','r') # What we know!\n", "reviews = list(map(lambda x:x[:-1],g.readlines()))\n", "g.close()\n", "\n", "g = open('labels.txt','r') # What we WANT to know!\n", "labels = list(map(lambda x:x[:-1].upper(),g.readlines()))\n", "g.close()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "25000" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(reviews)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "bb95574b-21a0-4213-ae50-34363cf4f87f" } }, "outputs": [ { "data": { "text/plain": [ "'bromwell high is a cartoon comedy . it ran at the same time as some other programs about school life such as teachers . my years in the teaching profession lead me to believe that bromwell high s satire is much closer to reality than is teachers . the scramble to survive financially the insightful students who can see right through their pathetic teachers pomp the pettiness of the whole situation all remind me of the schools i knew and their students . when i saw the episode in which a student repeatedly tried to burn down the school i immediately recalled . . . . . . . . . at . . . . . . . . . . high . a classic line inspector i m here to sack one of your teachers . student welcome to bromwell high . i expect that many adults of my age think that bromwell high is far fetched . what a pity that it isn t '" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews[0]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "e0408810-c424-4ed4-afb9-1735e9ddbd0a" } }, "outputs": [ { "data": { "text/plain": [ "'POSITIVE'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[0]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Lesson: Develop a Predictive Theory" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "e67a709f-234f-4493-bae6-4fb192141ee0" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "labels.txt \t : \t reviews.txt\n", "\n", "NEGATIVE\t:\tthis movie is terrible but it has some good effects . ...\n", "POSITIVE\t:\tadrian pasdar is excellent is this film . he makes a fascinating woman . ...\n", "NEGATIVE\t:\tcomment this movie is impossible . is terrible very improbable bad interpretat...\n", "POSITIVE\t:\texcellent episode movie ala pulp fiction . days suicides . it doesnt get more...\n", "NEGATIVE\t:\tif you haven t seen this it s terrible . it is pure trash . i saw this about ...\n", "POSITIVE\t:\tthis schiffer guy is a real genius the movie is of excellent quality and both e...\n" ] } ], "source": [ "print(\"labels.txt \\t : \\t reviews.txt\\n\")\n", "pretty_print_review_and_label(2137)\n", "pretty_print_review_and_label(12816)\n", "pretty_print_review_and_label(6267)\n", "pretty_print_review_and_label(21934)\n", "pretty_print_review_and_label(5297)\n", "pretty_print_review_and_label(4998)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 1: Quick Theory Validation" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from collections import Counter\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "positive_counts = Counter()\n", "negative_counts = Counter()\n", "total_counts = Counter()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "for i in range(len(reviews)):\n", " if(labels[i] == 'POSITIVE'):\n", " for word in reviews[i].split(\" \"):\n", " positive_counts[word] += 1\n", " total_counts[word] += 1\n", " else:\n", " for word in reviews[i].split(\" \"):\n", " negative_counts[word] += 1\n", " total_counts[word] += 1" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[('', 550468),\n", " ('the', 173324),\n", " ('.', 159654),\n", " ('and', 89722),\n", " ('a', 83688),\n", " ('of', 76855),\n", " 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" ('following', 306),\n", " ('members', 305),\n", " ('german', 305),\n", " ('e', 305),\n", " ('reasons', 305),\n", " ('follows', 305),\n", " ('themes', 305),\n", " ('touch', 304),\n", " ('genius', 304),\n", " ('free', 304),\n", " ('edge', 304),\n", " ('cute', 304),\n", " ('outside', 303),\n", " ('ok', 302),\n", " ('admit', 302),\n", " ('younger', 302),\n", " ('reviews', 302),\n", " ('odd', 301),\n", " ('fighting', 301),\n", " ('master', 301),\n", " ('break', 300),\n", " ('thanks', 300),\n", " ('recent', 300),\n", " ('comment', 300),\n", " ('apart', 299),\n", " ('lovely', 298),\n", " ('begin', 298),\n", " ('emotions', 298),\n", " ('doctor', 297),\n", " ('italian', 297),\n", " ('party', 297),\n", " ('la', 296),\n", " ('missed', 296),\n", " ...]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "positive_counts.most_common()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "pos_neg_ratios = Counter()\n", "\n", "for term,cnt in list(total_counts.most_common()):\n", " if(cnt > 100):\n", " pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n", " pos_neg_ratios[term] = pos_neg_ratio\n", "\n", "for word,ratio in pos_neg_ratios.most_common():\n", " if(ratio > 1):\n", " pos_neg_ratios[word] = np.log(ratio)\n", " else:\n", " pos_neg_ratios[word] = -np.log((1 / (ratio+0.01)))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[('edie', 4.6913478822291435),\n", " ('paulie', 4.0775374439057197),\n", " ('felix', 3.1527360223636558),\n", " ('polanski', 2.8233610476132043),\n", " ('matthau', 2.8067217286092401),\n", " ('victoria', 2.6810215287142909),\n", " ('mildred', 2.6026896854443837),\n", " ('gandhi', 2.5389738710582761),\n", " ('flawless', 2.451005098112319),\n", " ('superbly', 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0.5323318289869543),\n", " ('loves', 0.53062825106217038),\n", " ('olivier', 0.53062825106217038),\n", " ('revolution', 0.53062825106217038),\n", " ('roman', 0.53062825106217038),\n", " ('century', 0.53002783074992665),\n", " ('musical', 0.52966871156747064),\n", " ('heroic', 0.52925932545482868),\n", " ('ironically', 0.52806743020049673),\n", " ('approach', 0.52806743020049673),\n", " ('temple', 0.52806743020049673),\n", " ('moves', 0.5279372642387119),\n", " ('gift', 0.52702030968597136),\n", " ('julie', 0.52609309589677911),\n", " ('tells', 0.52415107836314001),\n", " ('radio', 0.52394671172868779),\n", " ('uncle', 0.52354439617376536),\n", " ('union', 0.52324814376454787),\n", " ('deep', 0.52309571635780505),\n", " ('reminds', 0.52157841554225237),\n", " ('famous', 0.52118841080153722),\n", " ('jazz', 0.52053443789295151),\n", " ('dennis', 0.51987545928590861),\n", " ('epic', 0.51919387343650736),\n", " ('adult', 0.519167695083386),\n", " ('shows', 0.51915322220375304),\n", " 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0.51082562376599072),\n", " ('provided', 0.51082562376599072),\n", " ('carter', 0.51082562376599072),\n", " ('ring', 0.51082562376599072),\n", " ('aging', 0.51082562376599072),\n", " ('countryside', 0.51082562376599072),\n", " ('alan', 0.51082562376599072),\n", " ('visit', 0.51082562376599072),\n", " ('begins', 0.51015650363396647),\n", " ('success', 0.50900578704900468),\n", " ('japan', 0.50900578704900468),\n", " ('accurate', 0.50895471583017893),\n", " ('proud', 0.50800474742434931),\n", " ('daily', 0.5075946031845443),\n", " ('atmospheric', 0.50724780241810674),\n", " ('karloff', 0.50724780241810674),\n", " ('recently', 0.50714914903668207),\n", " ('fu', 0.50704490092608467),\n", " ('horrors', 0.50656122497953315),\n", " ('finding', 0.50637127341661037),\n", " ('lust', 0.5059356384717989),\n", " ('hitchcock', 0.50574947073413001),\n", " ('among', 0.50334004951332734),\n", " ('viewing', 0.50302139827440906),\n", " ('shining', 0.50262885656181222),\n", " ('investigation', 0.50262885656181222),\n", " ('duo', 0.5020919437972361),\n", " ('cameron', 0.5020919437972361),\n", " ('finds', 0.50128303100539795),\n", " ('contemporary', 0.50077528791248915),\n", " ('genuine', 0.50046283673044401),\n", " ('frightening', 0.49995595152908684),\n", " ('plays', 0.49975983848890226),\n", " ('age', 0.49941323171424595),\n", " ('position', 0.49899116611898781),\n", " ('continues', 0.49863035067217237),\n", " ('roles', 0.49839716550752178),\n", " ('james', 0.49837216269470402),\n", " ('individuals', 0.49824684155913052),\n", " ('brought', 0.49783842823917956),\n", " ('hilarious', 0.49714551986191058),\n", " ('brutal', 0.49681488669639234),\n", " ('appropriate', 0.49643688631389105),\n", " ('dance', 0.49581998314812048),\n", " ('league', 0.49578774640145024),\n", " ('helping', 0.49578774640145024),\n", " ('answers', 0.49578774640145024),\n", " ('stunts', 0.49561620510246196),\n", " ('traveling', 0.49532143723002542),\n", " ('thoroughly', 0.49414593456733524),\n", " ('depicted', 0.49317068852726992),\n", " ('honor', 0.49247648509779424),\n", " ('combination', 0.49247648509779424),\n", " ('differences', 0.49247648509779424),\n", " ('fully', 0.49213349075383811),\n", " ('tracy', 0.49159426183810306),\n", " ('battles', 0.49140753790888908),\n", " ('possibility', 0.49112055268665822),\n", " ('romance', 0.4901589869574316),\n", " ('initially', 0.49002249613622745),\n", " ('happy', 0.4898997500608791),\n", " ('crime', 0.48977221456815834),\n", " ('singing', 0.4893852925281213),\n", " ('especially', 0.48901267837860624),\n", " ('shakespeare', 0.48754793889664511),\n", " ('hugh', 0.48729512635579658),\n", " ('detail', 0.48609484250827351),\n", " ('guide', 0.48550781578170082),\n", " ('companion', 0.48550781578170082),\n", " ('julia', 0.48550781578170082),\n", " ('san', 0.48550781578170082),\n", " ('desperation', 0.48550781578170082),\n", " ('strongly', 0.48460242866688824),\n", " ('necessary', 0.48302334245403883),\n", " ('humanity', 0.48265474679929443),\n", " ('drama', 0.48221998493060503),\n", " ('warming', 0.48183808689273838),\n", " ('intrigue', 0.48183808689273838),\n", " ('nonetheless', 0.48183808689273838),\n", " ('cuba', 0.48183808689273838),\n", " ('planned', 0.47957308026188628),\n", " ('pictures', 0.47929937011921681),\n", " ('broadcast', 0.47849024312305422),\n", " ('nine', 0.47803580094299974),\n", " ('settings', 0.47743860773325364),\n", " ('history', 0.47732966933780852),\n", " ('ordinary', 0.47725880012690741),\n", " ('trade', 0.47692407209030935),\n", " ('primary', 0.47608267532211779),\n", " ('official', 0.47608267532211779),\n", " ('episode', 0.47529620261150429),\n", " ('role', 0.47520268270188676),\n", " ('spirit', 0.47477690799839323),\n", " ('grey', 0.47409361449726067),\n", " ('ways', 0.47323464982718205),\n", " ('cup', 0.47260441094579297),\n", " ('piano', 0.47260441094579297),\n", " ('familiar', 0.47241617565111949),\n", " ('sinister', 0.47198579044972683),\n", " ('reveal', 0.47171449364936496),\n", " ('max', 0.47150852042515579),\n", " ('dated', 0.47121648567094482),\n", " ('discovery', 0.47000362924573563),\n", " ('vicious', 0.47000362924573563),\n", " ('losing', 0.47000362924573563),\n", " ('genuinely', 0.46871413841586385),\n", " ('hatred', 0.46734051182625186),\n", " ('mistaken', 0.46702300110759781),\n", " ('dream', 0.46608972992459924),\n", " ('challenge', 0.46608972992459924),\n", " ('crisis', 0.46575733836428446),\n", " ('photographed', 0.46488852857896512),\n", " ('machines', 0.46430560813109778),\n", " ('critics', 0.46430560813109778),\n", " ('bird', 0.46430560813109778),\n", " ('born', 0.46411383518967209),\n", " ('detective', 0.4636633473511525),\n", " ('higher', 0.46328467899699055),\n", " ('remains', 0.46262352194811296),\n", " ('inevitable', 0.46262352194811296),\n", " ('soviet', 0.4618180446592961),\n", " ('ryan', 0.46134556650262099),\n", " ('african', 0.46112595521371813),\n", " ('smaller', 0.46081520319132935),\n", " ('techniques', 0.46052488529119184),\n", " ('information', 0.46034171833399862),\n", " ('deserved', 0.45999798712841444),\n", " ('cynical', 0.45953232937844013),\n", " ('lynch', 0.45953232937844013),\n", " ('francisco', 0.45953232937844013),\n", " ('tour', 0.45953232937844013),\n", " ('spielberg', 0.45953232937844013),\n", " ('struggle', 0.45911782160048453),\n", " ('language', 0.45902121257712653),\n", " ('visual', 0.45823514408822852),\n", " ('warner', 0.45724137763188427),\n", " ('social', 0.45720078250735313),\n", " ('reality', 0.45719346885019546),\n", " ('hidden', 0.45675840249571492),\n", " ('breaking', 0.45601738727099561),\n", " ('sometimes', 0.45563021171182794),\n", " ('modern', 0.45500247579345005),\n", " ('surfing', 0.45425527227759638),\n", " ('popular', 0.45410691533051023),\n", " ('surprised', 0.4534409399850382),\n", " ('follows', 0.45245361754408348),\n", " ('keeps', 0.45234869400701483),\n", " ('john', 0.4520909494482197),\n", " ('defeat', 0.45198512374305722),\n", " ('mixed', 0.45198512374305722),\n", " ('justice', 0.45142724367280018),\n", " ('treasure', 0.45083371313801535),\n", " ('presents', 0.44973793178615257),\n", " ('years', 0.44919197032104968),\n", " ('chief', 0.44895022004790319),\n", " ('shadows', 0.44802472252696035),\n", " ('closely', 0.44701411102103689),\n", " ('segments', 0.44701411102103689),\n", " ('lose', 0.44658335503763702),\n", " ('caine', 0.44628710262841953),\n", " ('caught', 0.44610275383999071),\n", " ('hamlet', 0.44558510189758965),\n", " ('chinese', 0.44507424620321018),\n", " ('welcome', 0.44438052435783792),\n", " ('birth', 0.44368632092836219),\n", " ('represents', 0.44320543609101143),\n", " ('puts', 0.44279106572085081),\n", " ('fame', 0.44183275227903923),\n", " ('closer', 0.44183275227903923),\n", " ('visuals', 0.44183275227903923),\n", " ('web', 0.44183275227903923),\n", " ('criminal', 0.4412745608048752),\n", " ('minor', 0.4409224199448939),\n", " ('jon', 0.44086703515908027),\n", " ('liked', 0.44074991514020723),\n", " ('restaurant', 0.44031183943833246),\n", " ('flaws', 0.43983275161237217),\n", " ('de', 0.43983275161237217),\n", " ('searching', 0.4393666597838457),\n", " ('rap', 0.43891304217570443),\n", " ('light', 0.43884433018199892),\n", " ('elizabeth', 0.43872232986464677),\n", " ('marry', 0.43861731542506488),\n", " ('oz', 0.43825493093115531),\n", " ('controversial', 0.43825493093115531),\n", " ('learned', 0.43825493093115531),\n", " ('slowly', 0.43785660389939979),\n", " ('bridge', 0.43721380642274466),\n", " ('thrilling', 0.43721380642274466),\n", " ('wayne', 0.43721380642274466),\n", " ('comedic', 0.43721380642274466),\n", " ('married', 0.43658501682196887),\n", " ('nazi', 0.4361020775700542),\n", " ('murder', 0.4353180712578455),\n", " ('physical', 0.4353180712578455),\n", " ('johnny', 0.43483971678806865),\n", " ('michelle', 0.43445264498141672),\n", " ('wallace', 0.43403848055222038),\n", " ('silent', 0.43395706390247063),\n", " ('comedies', 0.43395706390247063),\n", " ('played', 0.43387244114515305),\n", " ('international', 0.43363598507486073),\n", " ('vision', 0.43286408229627887),\n", " ('intelligent', 0.43196704885367099),\n", " ('shop', 0.43078291609245434),\n", " ('also', 0.43036720209769169),\n", " ('levels', 0.4302451371066513),\n", " ('miss', 0.43006426712153217),\n", " ('ocean', 0.4295626596872249),\n", " ...]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# words most frequently seen in a review with a \"POSITIVE\" label\n", "pos_neg_ratios.most_common()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('boll', -4.0778152602708904),\n", " ('uwe', -3.9218753018711578),\n", " ('seagal', -3.3202501058581921),\n", " ('unwatchable', -3.0269848170580955),\n", " ('stinker', -2.9876839403711624),\n", " ('mst', -2.7753833211707968),\n", " ('incoherent', -2.7641396677532537),\n", " ('unfunny', -2.5545257844967644),\n", " ('waste', -2.4907515123361046),\n", " ('blah', -2.4475792789485005),\n", " ('horrid', -2.3715779644809971),\n", " ('pointless', -2.3451073877136341),\n", " ('atrocious', -2.3187369339642556),\n", " ('redeeming', -2.2667790015910296),\n", " ('prom', -2.2601040980178784),\n", " ('drivel', -2.2476029585766928),\n", " ('lousy', -2.2118080125207054),\n", " ('worst', -2.1930856334332267),\n", " ('laughable', -2.172468615469592),\n", " ('awful', -2.1385076866397488),\n", " ('poorly', -2.1326133844207011),\n", " ('wasting', -2.1178155545614512),\n", " ('remotely', -2.111046881095167),\n", " ('existent', -2.0024805005437076),\n", " ('boredom', -1.9241486572738005),\n", " ('miserably', -1.9216610938019989),\n", " ('sucks', -1.9166645809588516),\n", " ('uninspired', -1.9131499212248517),\n", " ('lame', -1.9117232884159072),\n", " ('insult', -1.9085323769376259)]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# words most frequently seen in a review with a \"NEGATIVE\" label\n", "list(reversed(pos_neg_ratios.most_common()))[0:30]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Transforming Text into Numbers" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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WU0gI3cKUzJIlS+xKGkiJpFkIXHzxxeakk04apjTnRo0aZX75y18OO1/kDzZmpI4bbrih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\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "\n", "review = \"This was a horrible, terrible movie.\"\n", "\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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4nb799ttmyZIlLeslHQN9WpN91nx5boSslh0hMlL33XffbX19Jk2aJKcq/U1f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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review = \"The movie was excellent\"\n", "\n", "Image(filename='sentiment_network_pos.png')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Project 2: Creating the Input/Output Data" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "74074\n" ] } ], "source": [ "vocab = set(total_counts.keys())\n", "vocab_size = len(vocab)\n", "print(vocab_size)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "['',\n", " 'inhabitants',\n", " 'goku',\n", " 'stunts',\n", " 'catepillar',\n", " 'kristensen',\n", " 'senegal',\n", " 'goddess',\n", " 'distroy',\n", " 'unexplainably',\n", " 'concoctions',\n", " 'petite',\n", " 'scribe',\n", " 'stevson',\n", " 'sctv',\n", " 'soundscape',\n", " 'rana',\n", " 'metamorphose',\n", " 'immortalizer',\n", " 'henstridge',\n", " 'planning',\n", " 'akiva',\n", " 'plod',\n", " 'eko',\n", " 'orderly',\n", " 'zeleznice',\n", " 'verbose',\n", " 'amplify',\n", " 'resonation',\n", " 'critize',\n", " 'jefferies',\n", " 'mountainbillies',\n", " 'steinbichler',\n", " 'vowel',\n", " 'rafe',\n", " 'bonbons',\n", " 'tulipe',\n", " 'clot',\n", " 'distended',\n", " 'his',\n", " 'impatiently',\n", " 'unfortuntly',\n", " 'lung',\n", " 'scapegoats',\n", " 'muzzle',\n", " 'pscychosexual',\n", " 'outbid',\n", " 'obit',\n", " 'sideshows',\n", " 'jugde',\n", " 'particolare',\n", " 'kevloun',\n", " 'masterful',\n", " 'quartier',\n", " 'unravelling',\n", " 'necessarily',\n", " 'antiques',\n", " 'strutts',\n", " 'tilts',\n", " 'disconcert',\n", " 'dossiers',\n", " 'sorriest',\n", " 'blart',\n", " 'iberia',\n", " 'situations',\n", " 'frmann',\n", " 'daniell',\n", " 'rays',\n", " 'pried',\n", " 'khoobsurat',\n", " 'leavitt',\n", " 'caiano',\n", " 'sagan',\n", " 'attractiveness',\n", " 'kitaparaporn',\n", " 'hamilton',\n", " 'massages',\n", " 'reasonably',\n", " 'horgan',\n", " 'chemist',\n", " 'audrey',\n", " 'jana',\n", " 'dutch',\n", " 'override',\n", " 'spasms',\n", " 'resumed',\n", " 'stinson',\n", " 'widows',\n", " 'stonewall',\n", " 'palatial',\n", " 'neuman',\n", " 'abandon',\n", " 'anglophile',\n", " 'marathon',\n", " 'chevette',\n", " 'unscary',\n", " 'eponymously',\n", " 'spoilerific',\n", " 'fleashens',\n", " 'brigand',\n", " 'politeness',\n", " 'clued',\n", " 'dermatonecrotic',\n", " 'grady',\n", " 'mulligan',\n", " 'ol',\n", " 'bertolucci',\n", " 'incubation',\n", " 'oldboy',\n", " 'snden',\n", " 'plaintiffs',\n", " 'fk',\n", " 'deply',\n", " 'franchot',\n", " 'cyhper',\n", " 'glorifying',\n", " 'mazovia',\n", " 'elizabeth',\n", " 'palestine',\n", " 'robby',\n", " 'wongo',\n", " 'moshing',\n", " 'eeeee',\n", " 'doltish',\n", " 'bree',\n", " 'postponed',\n", " 'gunslinger',\n", " 'debacles',\n", " 'kamm',\n", " 'herman',\n", " 'rapture',\n", " 'rolando',\n", " 'tetsuothe',\n", " 'premises',\n", " 'bruck',\n", " 'loosely',\n", " 'boylen',\n", " 'proportions',\n", " 'grecianized',\n", " 'wodehousian',\n", " 'encapsuling',\n", " 'partly',\n", " 'posative',\n", " 'calms',\n", " 'stadling',\n", " 'austrailia',\n", " 'shortland',\n", " 'wheeling',\n", " 'darkie',\n", " 'mckellar',\n", " 'cushy',\n", " 'ooookkkk',\n", " 'milky',\n", " 'unfolded',\n", " 'degrades',\n", " 'authenticating',\n", " 'rotheroe',\n", " 'beart',\n", " 'neath',\n", " 'grispin',\n", " 'intoxicants',\n", " 'nnette',\n", " 'slinging',\n", " 'tsukamoto',\n", " 'stows',\n", " 'suddenness',\n", " 'waqt',\n", " 'degrading',\n", " 'camazotz',\n", " 'blarney',\n", " 'shakher',\n", " 'delinquency',\n", " 'tomreynolds',\n", " 'insecticide',\n", " 'charlton',\n", " 'hare',\n", " 'wayland',\n", " 'nakada',\n", " 'urbane',\n", " 'sadomasochistic',\n", " 'larnia',\n", " 'hyping',\n", " 'yr',\n", " 'hebert',\n", " 'accentuating',\n", " 'deathrow',\n", " 'galligan',\n", " 'unmediated',\n", " 'treble',\n", " 'alphabet',\n", " 'soad',\n", " 'donen',\n", " 'lord',\n", " 'recess',\n", " 'handsome',\n", " 'center',\n", " 'vignettes',\n", " 'rescuers',\n", " 'pairings',\n", " 'uselful',\n", " 'sanders',\n", " 'nots',\n", " 'hatsumomo',\n", " 'appleby',\n", " 'tampax',\n", " 'sprinkling',\n", " 'defacing',\n", " 'lofty',\n", " 'opaque',\n", " 'tlc',\n", " 'romagna',\n", " 'tablespoons',\n", " 'bernhard',\n", " 'verger',\n", " 'acumen',\n", " 'percentages',\n", " 'wendingo',\n", " 'resonating',\n", " 'vntoarea',\n", " 'redundancies',\n", " 'red',\n", " 'pitied',\n", " 'belying',\n", " 'gleefulness',\n", " 'bibbidi',\n", " 'heiligt',\n", " 'gitane',\n", " 'journalist',\n", " 'focusing',\n", " 'plethora',\n", " 'citizen',\n", " 'coster',\n", " 'clunkers',\n", " 'deplorable',\n", " 'forgive',\n", " 'proplems',\n", " 'magwood',\n", " 'bankers',\n", " 'aqua',\n", " 'donated',\n", " 'disbelieving',\n", " 'acomplication',\n", " 'immediately',\n", " 'contrasted',\n", " 'reidelsheimer',\n", " 'fox',\n", " 'springs',\n", " 'toolbox',\n", " 'contacting',\n", " 'ace',\n", " 'washrooms',\n", " 'raving',\n", " 'dynamism',\n", " 'mae',\n", " 'sky',\n", " 'disharmony',\n", " 'untutored',\n", " 'icarus',\n", " 'taint',\n", " 'kargil',\n", " 'captain',\n", " 'paucity',\n", " 'fits',\n", " 'tumbles',\n", " 'amer',\n", " 'bueller',\n", " 'redubbed',\n", " 'cleansed',\n", " 'kollos',\n", " 'shara',\n", " 'humma',\n", " 'felichy',\n", " 'outa',\n", " 'piglets',\n", " 'gombell',\n", " 'supermen',\n", " 'superlow',\n", " 'enhance',\n", " 'goode',\n", " 'shalt',\n", " 'kubanskie',\n", " 'zenith',\n", " 'ananda',\n", " 'ocd',\n", " 'matlin',\n", " 'nosed',\n", " 'presumptuous',\n", " 'rerun',\n", " 'toyko',\n", " 'mazar',\n", " 'sundry',\n", " 'bilb',\n", " 'fugly',\n", " 'orchestrating',\n", " 'prosaically',\n", " 'maricarmen',\n", " 'moveis',\n", " 'conelly',\n", " 'estrange',\n", " 'lusciously',\n", " 'seasonings',\n", " 'sums',\n", " 'delirious',\n", " 'quincey',\n", " 'flesh',\n", " 'tootsie',\n", " 'ai',\n", " 'tenma',\n", " 'appropriations',\n", " 'chainsaw',\n", " 'ides',\n", " 'surrogacy',\n", " 'pungent',\n", " 'gallon',\n", " 'damaso',\n", " 'caribou',\n", " 'perico',\n", " 'supplying',\n", " 'ro',\n", " 'yuy',\n", " 'valium',\n", " 'debuted',\n", " 'robbin',\n", " 'mounts',\n", " 'interpolated',\n", " 'aetv',\n", " 'plummer',\n", " 'competence',\n", " 'toadies',\n", " 'dubiel',\n", " 'clavichord',\n", " 'asunder',\n", " 'sublety',\n", " 'airfix',\n", " 'stoltzfus',\n", " 'ruth',\n", " 'fluorescent',\n", " 'improves',\n", " 'rebenga',\n", " 'russells',\n", " 'deliberation',\n", " 'zsa',\n", " 'dardino',\n", " 'macs',\n", " 'servile',\n", " 'jlb',\n", " 'apallonia',\n", " 'crossbows',\n", " 'locus',\n", " 'mislead',\n", " 'corey',\n", " 'blundered',\n", " 'jeopardizes',\n", " 'disorganized',\n", " 'discuss',\n", " 'longish',\n", " 'tieing',\n", " 'ledger',\n", " 'speechifying',\n", " 'amitabhz',\n", " 'bbc',\n", " 'chimayo',\n", " 'pranked',\n", " 'superman',\n", " 'aggravated',\n", " 'rifleman',\n", " 'yvone',\n", " 'radiant',\n", " 'galico',\n", " 'debris',\n", " 'waking',\n", " 'btw',\n", " 'havnt',\n", " 'francen',\n", " 'chattered',\n", " 'scathed',\n", " 'pic',\n", " 'ceremonies',\n", " 'watergate',\n", " 'betsy',\n", " 'majorca',\n", " 'meercat',\n", " 'noirs',\n", " 'grunts',\n", " 'drecky',\n", " 'tribulations',\n", " 'avery',\n", " 'talladega',\n", " 'eights',\n", " 'dumbing',\n", " 'alloimono',\n", " 'scrutinising',\n", " 'geta',\n", " 'beltrami',\n", " 'pvc',\n", " 'horse',\n", " 'tiburon',\n", " 'huitime',\n", " 'ripple',\n", " 'loitering',\n", " 'forensics',\n", " 'nearly',\n", " 'elizabethan',\n", " 'ellington',\n", " 'uzi',\n", " 'sicily',\n", " 'camion',\n", " 'motivated',\n", " 'rung',\n", " 'gao',\n", " 'licitates',\n", " 'protocol',\n", " 'smirker',\n", " 'torin',\n", " 'newlywed',\n", " 'rich',\n", " 'dismay',\n", " 'skyler',\n", " 'moonwalks',\n", " 'haranguing',\n", " 'sunburst',\n", " 'grifter',\n", " 'undersold',\n", " 'chearator',\n", " 'marino',\n", " 'scala',\n", " 'conditioner',\n", " 'ulysses',\n", " 'lamarre',\n", " 'figueroa',\n", " 'flane',\n", " 'allllllll',\n", " 'slide',\n", " 'lateness',\n", " 'selbst',\n", " 'gandhis',\n", " 'dramatizing',\n", " 'catchphrase',\n", " 'doable',\n", " 'stadiums',\n", " 'alexanderplatz',\n", " 'pandemonium',\n", " 'misrepresents',\n", " 'earth',\n", " 'mounties',\n", " 'seeker',\n", " 'cheat',\n", " 'outbreaks',\n", " 'snowstorm',\n", " 'baur',\n", " 'schedules',\n", " 'bathetic',\n", " 'incorrect',\n", " 'johnathon',\n", " 'rosanne',\n", " 'mundanely',\n", " 'cauldrons',\n", " 'forrest',\n", " 'poky',\n", " 'legislation',\n", " 'womanness',\n", " 'spender',\n", " 'crazy',\n", " 'rational',\n", " 'terrell',\n", " 'zero',\n", " 'coincides',\n", " 'thoughout',\n", " 'mathew',\n", " 'narnia',\n", " 'naseeruddin',\n", " 'bucks',\n", " 'affronts',\n", " 'topple',\n", " 'degree',\n", " 'preyed',\n", " 'passionately',\n", " 'defeats',\n", " 'torchwood',\n", " 'sources',\n", " 'botticelli',\n", " 'compactor',\n", " 'kosturica',\n", " 'waiving',\n", " 'gunnar',\n", " 'stiffler',\n", " 'fwd',\n", " 'kawajiri',\n", " 'eleanor',\n", " 'sistahs',\n", " 'soulhunter',\n", " 'belies',\n", " 'wrathful',\n", " 'americans',\n", " 'ferdinandvongalitzien',\n", " 'kendra',\n", " 'weirdy',\n", " 'unforgivably',\n", " 'chepart',\n", " 'tatta',\n", " 'departmentthe',\n", " 'dig',\n", " 'blatty',\n", " 'marionettes',\n", " 'atop',\n", " 'chim',\n", " 'saurian',\n", " 'woes',\n", " 'cloudscape',\n", " 'resignedly',\n", " 'unrooted',\n", " 'keuck',\n", " 'hitlerian',\n", " 'stylings',\n", " 'crewed',\n", " 'bedeviled',\n", " 'unfurnished',\n", " 'reedus',\n", " 'circumstances',\n", " 'grasped',\n", " 'smurfettes',\n", " 'fn',\n", " 'dishwashers',\n", " 'roadie',\n", " 'ruthlessness',\n", " 'refrains',\n", " 'lampooning',\n", " 'semblance',\n", " 'richart',\n", " 'legions',\n", " 'gwenneth',\n", " 'enmity',\n", " 'assess',\n", " 'manufacturer',\n", " 'bullosa',\n", " 'outrun',\n", " 'hogan',\n", " 'chekov',\n", " 'blithe',\n", " 'code',\n", " 'drillings',\n", " 'revolvers',\n", " 'aredavid',\n", " 'robespierre',\n", " 'achcha',\n", " 'boyfriendhe',\n", " 'wallow',\n", " 'toga',\n", " 'graphed',\n", " 'tonking',\n", " 'going',\n", " 'bosnians',\n", " 'willy',\n", " 'rohauer',\n", " 'fim',\n", " 'forbidding',\n", " 'yew',\n", " 'rationalised',\n", " 'shimomo',\n", " 'opposition',\n", " 'landis',\n", " 'minded',\n", " 'despicableness',\n", " 'easting',\n", " 'arghhhhh',\n", " 'ebb',\n", " 'trialat',\n", " 'protected',\n", " 'negras',\n", " 'rick',\n", " 'muti',\n", " 'tracker',\n", " 'shawl',\n", " 'differentiates',\n", " 'sweetheart',\n", " 'deepened',\n", " 'manmohan',\n", " 'trevethyn',\n", " 'brain',\n", " 'incomprehensibly',\n", " 'piercing',\n", " 'pasadena',\n", " 'shtick',\n", " 'ute',\n", " 'viggo',\n", " 'supersedes',\n", " 'ack',\n", " 'cites',\n", " 'taurus',\n", " 'relevent',\n", " 'minidress',\n", " 'philosopher',\n", " 'bel',\n", " 'mahattan',\n", " 'moden',\n", " 'compiling',\n", " 'advertising',\n", " 'rogues',\n", " 'unimaginative',\n", " 'subpaar',\n", " 'ademir',\n", " 'darkly',\n", " 'saturate',\n", " 'fledgling',\n", " 'breaths',\n", " 'padre',\n", " 'aszombi',\n", " 'pachabel',\n", " 'incalculable',\n", " 'ozone',\n", " 'sped',\n", " 'mpho',\n", " 'rawail',\n", " 'forbid',\n", " 'synth',\n", " 'guttersnipe',\n", " 'reputedly',\n", " 'holiness',\n", " 'unessential',\n", " 'hampden',\n", " 'asylum',\n", " 'bolye',\n", " 'strangers',\n", " 'rantzen',\n", " 'farrellys',\n", " 'vigourous',\n", " 'cantinflas',\n", " 'enshrined',\n", " 'boris',\n", " 'expetations',\n", " 'replaying',\n", " 'prestige',\n", " 'bukater',\n", " 'overpaid',\n", " 'exhude',\n", " 'backsides',\n", " 'topless',\n", " 'sufferings',\n", " 'nitwits',\n", " 'cordova',\n", " 'incensed',\n", " 'danira',\n", " 'unrelenting',\n", " 'disabling',\n", " 'ferdy',\n", " 'gerard',\n", " 'drewitt',\n", " 'mero',\n", " 'monsters',\n", " 'precautions',\n", " 'lamping',\n", " 'relinquish',\n", " 'demy',\n", " 'drink',\n", " 'chamberlin',\n", " 'unjustifiably',\n", " 'cove',\n", " 'floodwaters',\n", " 'searing',\n", " 'isral',\n", " 'ling',\n", " 'grossness',\n", " 'pickier',\n", " 'pax',\n", " 'wierd',\n", " 'tereasa',\n", " 'smog',\n", " 'girotti',\n", " 'spat',\n", " 'sera',\n", " 'noxious',\n", " 'misbehaving',\n", " 'scouts',\n", " 'refreshments',\n", " 'autobiographic',\n", " 'shi',\n", " 'toyomichi',\n", " 'bits',\n", " 'psychotics',\n", " 'barzell',\n", " 'colt',\n", " 'shivering',\n", " 'pugilist',\n", " 'gladiator',\n", " 'dryer',\n", " 'reissues',\n", " 'scrivener',\n", " 'predicable',\n", " 'objection',\n", " 'marmalade',\n", " 'seems',\n", " 'spellbind',\n", " 'trifecta',\n", " 'innovator',\n", " 'shriekfest',\n", " 'inthused',\n", " 'contestants',\n", " 'goody',\n", " 'samotri',\n", " 'serviced',\n", " 'nozires',\n", " 'ins',\n", " 'mutilating',\n", " 'dupes',\n", " 'launius',\n", " 'widescreen',\n", " 'joo',\n", " 'discretionary',\n", " 'enlivens',\n", " 'bushes',\n", " 'chills',\n", " 'header',\n", " 'activist',\n", " 'gethsemane',\n", " 'phoenixs',\n", " 'wreathed',\n", " 'sacrine',\n", " 'electrifyingly',\n", " 'basely',\n", " 'ghidora',\n", " 'binder',\n", " 'dogfights',\n", " 'sugar',\n", " 'doddsville',\n", " 'porkys',\n", " 'scattershot',\n", " 'refunded',\n", " 'rudely',\n", " 'insteadit',\n", " 'zatichi',\n", " 'eurotrash',\n", " 'radioraptus',\n", " 'hurls',\n", " 'boogeman',\n", " 'weighs',\n", " 'danniele',\n", " 'converging',\n", " 'hypothermia',\n", " 'glorfindel',\n", " 'birthdays',\n", " 'attentive',\n", " 'mallepa',\n", " 'spacewalk',\n", " 'manoy',\n", " 'bombshells',\n", " 'farts',\n", " 'lyoko',\n", " 'southron',\n", " 'destruction',\n", " 'flemming',\n", " 'manhole',\n", " 'elainor',\n", " 'bowersock',\n", " 'lowly',\n", " 'wfst',\n", " 'limousines',\n", " 'skolimowski',\n", " 'saban',\n", " 'koen',\n", " 'malaysia',\n", " 'uwi',\n", " 'cyd',\n", " 'apeing',\n", " 'bonecrushing',\n", " 'dini',\n", " 'merest',\n", " 'janina',\n", " 'chemotrodes',\n", " 'trials',\n", " 'authorize',\n", " 'whilhelm',\n", " 'asthmatic',\n", " 'broads',\n", " 'missteps',\n", " 'embittered',\n", " 'chandeliers',\n", " 'seeming',\n", " 'miscalculate',\n", " 'recommeded',\n", " 'schoolwork',\n", " 'coy',\n", " 'mcconaughey',\n", " 'philosophically',\n", " 'waver',\n", " 'fanny',\n", " 'mestressat',\n", " 'unwatchably',\n", " 'saggy',\n", " 'topness',\n", " 'dwellings',\n", " 'breakup',\n", " 'hasselhoff',\n", " 'superstars',\n", " 'replay',\n", " 'aggravates',\n", " 'balances',\n", " 'urging',\n", " 'snidely',\n", " 'aleksandar',\n", " 'hildy',\n", " 'kazuhiro',\n", " 'slayer',\n", " 'tangy',\n", " 'brussels',\n", " 'horne',\n", " 'masayuki',\n", " 'molden',\n", " 'unravel',\n", " 'goodtime',\n", " 'interrogates',\n", " 'bismillahhirrahmannirrahim',\n", " 'rowboat',\n", " 'dumann',\n", " 'datedness',\n", " 'astrotheology',\n", " 'dekhiye',\n", " 'valga',\n", " 'kata',\n", " 'wipes',\n", " 'hostilities',\n", " 'sentimentalising',\n", " 'documentary',\n", " 'salesman',\n", " 'virtue',\n", " 'unreasonably',\n", " 'haver',\n", " 'cei',\n", " 'unglamorised',\n", " 'balky',\n", " 'complementary',\n", " 'paychecks',\n", " 'mnica',\n", " 'wada',\n", " 'ily',\n", " 'prc',\n", " 'ennobling',\n", " 'functionality',\n", " 'dissociated',\n", " 'elk',\n", " 'throbbing',\n", " 'tempe',\n", " 'linoleum',\n", " 'photogrsphed',\n", " 'bottacin',\n", " 'hipper',\n", " 'titillating',\n", " 'barging',\n", " 'untie',\n", " 'sacchetti',\n", " 'gnat',\n", " 'roedel',\n", " 'cohabitation',\n", " 'performs',\n", " 'sales',\n", " 'migrs',\n", " 'teachs',\n", " 'nanavati',\n", " 'fresco',\n", " 'davison',\n", " 'obstinate',\n", " 'burglar',\n", " 'masue',\n", " 'dickory',\n", " 'grills',\n", " 'appelagate',\n", " 'linkage',\n", " 'enables',\n", " 'loesser',\n", " 'patties',\n", " 'prudent',\n", " 'mallorquins',\n", " 'nativetex',\n", " 'suprise',\n", " 'drippy',\n", " 'quill',\n", " 'speeded',\n", " 'farscape',\n", " 'saddening',\n", " 'centuries',\n", " 'mos',\n", " 'improvisationally',\n", " 'neccessarily',\n", " 'transmitter',\n", " 'tankers',\n", " 'latte',\n", " 'mechanisation',\n", " 'faracy',\n", " 'synthetically',\n", " 'thoughtless',\n", " 'rake',\n", " 'ropes',\n", " 'desirable',\n", " 'whitewashed',\n", " 'donal',\n", " 'crabby',\n", " 'lifeless',\n", " 'perfidy',\n", " 'teresa',\n", " 'bulldog',\n", " 'cockamamie',\n", " 'rasberries',\n", " 'notethe',\n", " 'captivity',\n", " 'chiseling',\n", " 'smaller',\n", " 'clampets',\n", " 'alerts',\n", " 'tough',\n", " 'wellingtonian',\n", " 'aaaahhhhhhh',\n", " 'dither',\n", " 'incertitude',\n", " 'florentine',\n", " 'imperioli',\n", " 'licking',\n", " 'disparagement',\n", " 'artfully',\n", " 'feds',\n", " 'fumiya',\n", " 'tearfully',\n", " 'lanchester',\n", " 'undertaken',\n", " 'longlost',\n", " 'netted',\n", " 'carrell',\n", " 'uncompelling',\n", " 'reliefs',\n", " 'leona',\n", " 'autorenfilm',\n", " 'unfriendly',\n", " 'typewriter',\n", " 'shifted',\n", " 'bertrand',\n", " 'blesses',\n", " 'tricking',\n", " 'fireflies',\n", " 'zanes',\n", " 'unknowingly',\n", " 'unnerve',\n", " 'caning',\n", " 'flat',\n", " 'recluse',\n", " 'dcreasy',\n", " 'chipmunk',\n", " 'dipper',\n", " 'musee',\n", " 'cousin',\n", " 'shys',\n", " 'berserkers',\n", " 'eve',\n", " 'conflagration',\n", " 'irks',\n", " 'restricts',\n", " 'parsing',\n", " 'positronic',\n", " 'copout',\n", " 'khala',\n", " 'swiftness',\n", " 'higginson',\n", " 'imprint',\n", " 'walter',\n", " 'sundance',\n", " 'whispering',\n", " 'thematically',\n", " 'underimpressed',\n", " 'uno',\n", " 'expressly',\n", " 'russkies',\n", " 'discos',\n", " 'shaping',\n", " 'verson',\n", " 'prototype',\n", " 'chapman',\n", " 'trafficker',\n", " 'semetary',\n", " 'unrealistically',\n", " 'lifewell',\n", " 'rivas',\n", " 'consequent',\n", " 'katsu',\n", " 'titantic',\n", " 'jalees',\n", " 'ranee',\n", " 'shipbuilding',\n", " 'gambles',\n", " 'dispenses',\n", " 'disfigurement',\n", " 'bright',\n", " 'cristian',\n", " 'puertorricans',\n", " 'constituent',\n", " 'capta',\n", " 'jewel',\n", " 'erect',\n", " 'farah',\n", " 'despondently',\n", " 'avoide',\n", " 'inconnu',\n", " 'headquarters',\n", " 'sanguisga',\n", " ...]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(vocab)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "layer_0 = np.zeros((1,vocab_size))\n", "layer_0" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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WU0gI3cKUzJIlS+xKGkiJpFkIXHzxxeakk04apjTnRo0aZX75y18OO1/kDzZmpI4bbrih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\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'': 0,\n", " 'inhabitants': 1,\n", " 'goku': 2,\n", " 'stunts': 3,\n", " 'catepillar': 4,\n", " 'kristensen': 5,\n", " 'goddess': 7,\n", " 'offing': 49797,\n", " 'distroy': 8,\n", " 'unexplainably': 9,\n", " 'concoctions': 10,\n", " 'petite': 11,\n", " 'paramilitary': 24759,\n", " 'scribe': 12,\n", " 'stevson': 13,\n", " 'senegal': 6,\n", " 'sctv': 14,\n", " 'soundscape': 15,\n", " 'rana': 16,\n", " 'immortalizer': 18,\n", " 'rene': 67354,\n", " 'eko': 23,\n", " 'planning': 20,\n", " 'akiva': 21,\n", " 'plod': 22,\n", " 'orderly': 24,\n", " 'zeleznice': 25,\n", " 'critize': 29,\n", " 'baguettes': 25649,\n", " 'jefferies': 30,\n", " 'uncertainties': 61695,\n", " 'mountainbillies': 31,\n", " 'steinbichler': 32,\n", " 'vowel': 33,\n", " 'rafe': 34,\n", " 'donig': 68719,\n", " 'tulipe': 36,\n", " 'clot': 37,\n", " 'hack': 12526,\n", " 'distended': 38,\n", " 'cornered': 37116,\n", " 'impatiently': 40,\n", " 'batrice': 12525,\n", " 'unfortuntly': 41,\n", " 'lung': 42,\n", " 'scapegoats': 43,\n", " 'pscychosexual': 45,\n", " 'outbid': 46,\n", " 'obit': 47,\n", " 'sideshows': 48,\n", " 'jugde': 49,\n", " 'kevloun': 51,\n", " 'quartier': 53,\n", " 'harp': 61948,\n", " 'unravelling': 54,\n", " 'antiques': 56,\n", " 'strutts': 57,\n", " 'tilts': 58,\n", " 'disconcert': 59,\n", " 'dossiers': 60,\n", " 'sorriest': 61,\n", " 'craftsman': 49412,\n", " 'blart': 62,\n", " 'dependence': 37120,\n", " 'sated': 61698,\n", " 'iberia': 63,\n", " 'sagan': 72,\n", " 'frmann': 65,\n", " 'daniell': 66,\n", " 'rays': 67,\n", " 'pried': 68,\n", " 'khoobsurat': 69,\n", " 'leavitt': 70,\n", " 'caiano': 71,\n", " 'attractiveness': 73,\n", " 'kitaparaporn': 74,\n", " 'hamilton': 75,\n", " 'massages': 76,\n", " 'horgan': 78,\n", " 'chemist': 79,\n", " 'audrey': 80,\n", " 'yeow': 55655,\n", " 'jana': 81,\n", " 'dutch': 82,\n", " 'pinchot': 24773,\n", " 'override': 83,\n", " 'dwervick': 63223,\n", " 'spasms': 84,\n", " 'resumed': 85,\n", " 'tamale': 66259,\n", " 'calibanian': 49636,\n", " 'stinson': 86,\n", " 'widows': 87,\n", " 'stonewall': 88,\n", " 'palatial': 89,\n", " 'neuman': 90,\n", " 'abandon': 91,\n", " 'lemmings': 65314,\n", " 'anglophile': 92,\n", " 'ertha': 61706,\n", " 'chevette': 94,\n", " 'unscary': 95,\n", " 'spoilerific': 97,\n", " 'neworleans': 67639,\n", " 'metamorphose': 17,\n", " 'brigand': 99,\n", " 'cheating': 41603,\n", " 'clued': 101,\n", " 'dermatonecrotic': 102,\n", " 'grady': 103,\n", " 'mulligan': 104,\n", " 'ol': 105,\n", " 'incubation': 107,\n", " 'plaintiffs': 110,\n", " 'snden': 109,\n", " 'fk': 111,\n", " 'deply': 112,\n", " 'franchot': 113,\n", " 'henstridge': 19,\n", " 'cyhper': 114,\n", " 'verbose': 26,\n", " 'mazovia': 116,\n", " 'elizabeth': 117,\n", " 'palestine': 118,\n", " 'robby': 119,\n", " 'wongo': 120,\n", " 'moshing': 121,\n", " 'mstified': 12543,\n", " 'eeeee': 122,\n", " 'doltish': 123,\n", " 'bree': 124,\n", " 'postponed': 125,\n", " 'debacles': 127,\n", " 'amplify': 27,\n", " 'kamm': 128,\n", " 'phantom': 18893,\n", " 'boylen': 136,\n", " 'rolando': 131,\n", " 'premises': 133,\n", " 'bruck': 134,\n", " 'loosely': 135,\n", " 'wodehousian': 139,\n", " 'onishi': 70389,\n", " 'encapsuling': 140,\n", " 'partly': 141,\n", " 'stadling': 144,\n", " 'calms': 143,\n", " 'darkie': 148,\n", " 'wheeling': 147,\n", " 'ursla': 15875,\n", " 'subsidized': 49420,\n", " 'mckellar': 149,\n", " 'ooookkkk': 151,\n", " 'milky': 152,\n", " 'unfolded': 153,\n", " 'degrades': 154,\n", " 'authenticating': 155,\n", " 'writeup': 12548,\n", " 'rotheroe': 156,\n", " 'beart': 157,\n", " 'intoxicants': 160,\n", " 'grispin': 159,\n", " 'cannes': 61718,\n", " 'antithetical': 70398,\n", " 'nnette': 161,\n", " 'tsukamoto': 163,\n", " 'antwones': 44205,\n", " 'stows': 164,\n", " 'suddenness': 165,\n", " 'vol': 61720,\n", " 'waqt': 166,\n", " 'camazotz': 168,\n", " 'paps': 55042,\n", " 'shakher': 170,\n", " 'terminate': 63868,\n", " 'kotex': 56419,\n", " 'delinquency': 171,\n", " 'bromwell': 25214,\n", " 'insecticide': 173,\n", " 'charlton': 174,\n", " 'nakada': 177,\n", " 'titted': 24791,\n", " 'urbane': 178,\n", " 'depicted': 54491,\n", " 'sadomasochistic': 179,\n", " 'hyping': 181,\n", " 'yr': 182,\n", " 'hebert': 183,\n", " 'waxwork': 12990,\n", " 'deathrow': 185,\n", " 'nourishes': 24792,\n", " 'unmediated': 187,\n", " 'tamper': 37143,\n", " 'soad': 190,\n", " 'alphabet': 189,\n", " 'donen': 191,\n", " 'lord': 192,\n", " 'recess': 193,\n", " 'watchably': 61023,\n", " 'handsome': 194,\n", " 'vignettes': 196,\n", " 'pairings': 198,\n", " 'uselful': 199,\n", " 'sanders': 200,\n", " 'outbursts': 72891,\n", " 'nots': 201,\n", " 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'acomplication': 241,\n", " 'contrasted': 243,\n", " 'muzzle': 44,\n", " 'amphibians': 72141,\n", " 'springs': 246,\n", " 'reformatted': 49443,\n", " 'toolbox': 247,\n", " 'contacting': 248,\n", " 'washrooms': 250,\n", " 'raving': 251,\n", " 'dynamism': 252,\n", " 'mae': 253,\n", " 'disharmony': 255,\n", " 'molls': 72979,\n", " 'dewaere': 12569,\n", " 'untutored': 256,\n", " 'icarus': 257,\n", " 'taint': 258,\n", " 'kargil': 259,\n", " 'captain': 260,\n", " 'paucity': 261,\n", " 'fits': 262,\n", " 'tumbles': 263,\n", " 'amer': 264,\n", " 'bueller': 265,\n", " 'cleansed': 267,\n", " 'shara': 269,\n", " 'humma': 270,\n", " 'outa': 272,\n", " 'piglets': 273,\n", " 'gombell': 274,\n", " 'supermen': 275,\n", " 'superlow': 276,\n", " 'kubanskie': 280,\n", " 'goode': 278,\n", " 'disorganised': 45570,\n", " 'zenith': 281,\n", " 'ananda': 282,\n", " 'matlin': 284,\n", " 'particolare': 50,\n", " 'presumptuous': 286,\n", " 'rerun': 287,\n", " 'toyko': 288,\n", " 'bilb': 291,\n", " 'sundry': 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936,\n", " 'dashboard': 12667,\n", " 'unknowingly': 937,\n", " 'flatmates': 51897,\n", " 'unnerve': 938,\n", " 'caning': 939,\n", " 'shortland': 146,\n", " 'recluse': 941,\n", " 'dcreasy': 942,\n", " 'scratchiness': 24911,\n", " 'pms': 30930,\n", " 'chipmunk': 943,\n", " 'tkachenko': 49537,\n", " 'dipper': 944,\n", " 'europeans': 61601,\n", " 'berserkers': 948,\n", " 'shys': 947,\n", " 'monte': 68505,\n", " 'eve': 949,\n", " 'luxury': 61828,\n", " 'conflagration': 950,\n", " 'water': 46389,\n", " 'irks': 951,\n", " 'positronic': 954,\n", " 'cushy': 150,\n", " 'swiftness': 957,\n", " 'underimpressed': 964,\n", " 'imprint': 959,\n", " 'sundance': 961,\n", " 'aida': 31951,\n", " 'thematically': 963,\n", " 'uno': 965,\n", " 'expressly': 966,\n", " 'russkies': 967,\n", " 'discos': 968,\n", " 'shaping': 969,\n", " 'verson': 970,\n", " 'blushed': 61831,\n", " 'prototype': 971,\n", " 'lifewell': 976,\n", " 'trafficker': 973,\n", " 'crucifixions': 62188,\n", " 'unrealistically': 975,\n", " 'rivas': 977,\n", " 'consequent': 978,\n", " 'katsu': 979,\n", " 'titantic': 980,\n", " 'jalees': 981,\n", " 'ranee': 982,\n", " 'gambles': 984,\n", " 'dispenses': 985,\n", " 'disfigurement': 986,\n", " 'bright': 987,\n", " 'cristian': 988,\n", " 'subculture': 37268,\n", " 'capta': 991,\n", " 'jewel': 992,\n", " 'erect': 993,\n", " 'avoide': 996,\n", " 'inconnu': 997,\n", " 'headquarters': 998,\n", " 'babbling': 1000,\n", " 'pac': 1001,\n", " 'performace': 1003,\n", " 'dorrit': 1004,\n", " 'runners': 1005,\n", " 'sentimentality': 1006,\n", " 'marred': 1007,\n", " 'commemorative': 1008,\n", " 'helpers': 1012,\n", " 'chiles': 1011,\n", " 'snowy': 1013,\n", " 'cheddar': 1014,\n", " 'neath': 158,\n", " 'outshine': 1016,\n", " 'nadu': 1019,\n", " 'wellbeing': 1020,\n", " 'envisioned': 43779,\n", " 'fanaticism': 1021,\n", " 'morrisette': 12687,\n", " 'sesame': 1024,\n", " 'gran': 1023,\n", " 'marlina': 1025,\n", " 'artificiality': 1030,\n", " 'coinsidence': 1027,\n", " 'founders': 1028,\n", " 'dismissably': 1029,\n", " 'dracht': 66299,\n", " 'scavengers': 1031,\n", " 'neese': 12685,\n", " 'pangborn': 1034,\n", " 'elmore': 1039,\n", " 'bristol': 71162,\n", " 'lillies': 1035,\n", " 'parkers': 1036,\n", " 'skipped': 1038,\n", " 'clipboard': 1042,\n", " 'jucier': 1041,\n", " 'haifa': 1043,\n", " ...}" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "word2index = {}\n", "\n", "for i,word in enumerate(vocab):\n", " word2index[word] = i\n", "word2index" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \n", " global layer_0\n", " \n", " # clear out previous state, reset the layer to be all 0s\n", " layer_0 = 0\n", " for word in review.split(\" \"):\n", " layer_0[0][word2index[word]] += 1\n", "\n", "update_input_layer(reviews[0])" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 18., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_target_for_label(label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'POSITIVE'" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[0]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_target_for_label(labels[0])" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'NEGATIVE'" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[1]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_target_for_label(labels[1])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 3: Building a Neural Network" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "- Start with your neural network from the last chapter\n", "- 3 layer neural network\n", "- no non-linearity in hidden layer\n", "- use our functions to create the training data\n", "- create a \"pre_process_data\" function to create vocabulary for our training data generating functions\n", "- modify \"train\" to train over the entire corpus" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Where to Get Help if You Need it\n", "- Re-watch previous week's Udacity Lectures\n", "- Chapters 3-5 - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) - (40% Off: traskud17)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " # set our random number generator \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews, labels)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self, reviews, labels):\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " \n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 = 0\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " self.layer_0[0][self.word2index[word]] += 1\n", " \n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output * (1 - output)\n", " \n", " def train(self, training_reviews, training_labels):\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", " self.update_input_layer(review)\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # TODO: Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # TODO: Update the weights\n", " self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(np.abs(layer_2_error) < 0.5):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " if(i % 2500 == 0):\n", " print(\"\")\n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", " self.update_input_layer(review.lower())\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] > 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):587.5% #Correct:500 #Tested:1000 Testing Accuracy:50.0%" ] } ], "source": [ "# evaluate our model before training (just to show how horrible it is)\n", "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):89.58 #Correct:1250 #Trained:2501 Training Accuracy:49.9%\n", "Progress:20.8% Speed(reviews/sec):95.03 #Correct:2500 #Trained:5001 Training Accuracy:49.9%\n", "Progress:27.4% Speed(reviews/sec):95.46 #Correct:3295 #Trained:6592 Training Accuracy:49.9%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-62-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlayer_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_0_1\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_1_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# train the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):96.39 #Correct:1247 #Trained:2501 Training Accuracy:49.8%\n", "Progress:20.8% Speed(reviews/sec):99.31 #Correct:2497 #Trained:5001 Training Accuracy:49.9%\n", "Progress:22.8% Speed(reviews/sec):99.02 #Correct:2735 #Trained:5476 Training Accuracy:49.9%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-64-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlayer_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_0_1\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_1_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# train the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):98.77 #Correct:1267 #Trained:2501 Training Accuracy:50.6%\n", "Progress:20.8% Speed(reviews/sec):98.79 #Correct:2640 #Trained:5001 Training Accuracy:52.7%\n", "Progress:31.2% Speed(reviews/sec):98.58 #Correct:4109 #Trained:7501 Training Accuracy:54.7%\n", "Progress:41.6% Speed(reviews/sec):93.78 #Correct:5638 #Trained:10001 Training Accuracy:56.3%\n", "Progress:52.0% Speed(reviews/sec):91.76 #Correct:7246 #Trained:12501 Training Accuracy:57.9%\n", "Progress:62.5% Speed(reviews/sec):92.42 #Correct:8841 #Trained:15001 Training Accuracy:58.9%\n", "Progress:69.4% Speed(reviews/sec):92.58 #Correct:9934 #Trained:16668 Training Accuracy:59.5%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-66-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m 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gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# train the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Understanding Neural Noise" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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id8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ\nVhiVSUggUwTGox0Em8OBOG2b0rSftCIjaRErLj39zQ7QJ5xwQnGFqqSBQ6AWRITtokeNGtX58Gb7\n1ltvxXbGpk2b7Nuvn2fMmDF222m3MiKc2U9Lfj4nnXSSrZP6kSRpcMry04Xr8X+vW7euUwd5qI9z\nWSSqzThook9WYVrmiCOOyJo9dz6HI+0oQpxpuGlvxxAQxOmfFAvyhadk4vKed955lhQQK8YREkzq\nRQmYMwWEU/AzzzxjV+0wFZN0eimvHo6MlEmy8urYxvzr1683gZ9ZpEWuje1Vm0pCoN/OLb6zJ86q\nfIKmjfjss88+Qzt27Bih3vXXXz8irZ+ffK+//vqIfH6acBnOOTRJGl9/0vvi57/66qtj9XT1+Xm7\nOauS3i87fExdaQXHsiASZ9pshaZ37TjxxBMLKzeYaqqFg2bSBtEPOF1mkbCDatIycIK95557bP8H\nFhPrRBoMKKn1oP5rr712WDlZ25JU9yTpsuKSpGylGY5AW5zdh7dKv/qNQKXLd++///5gLIqWgITY\neAgrV67sJMBycdVVV3V+Rx2Q7+STTzavvfaaDVYVlaZXGeRJkiaqbHfuuuuuc4cjvi+44AJz8MEH\nG6YSegkWFNJ3E+oaO3asmTVrVrdkw65t3brV7L///sPOVfXjiSeeKKzqCRMmGO6BJghv71k3dOs1\nJdOt/VgPsJDwwZLBPbZ48WLruEyYeO4L7iesjGHB2vHzn//cbjgYrIQxfDDNH3/88eGklf12vjFl\nTwlV1sCaVIxFDqsqy+YlQiAPApVPzQRvwyawYFifi127dhl+O/GJClMubJLlhMiMwRLZjq9GYBVw\nl+xAdMcdd3R+Rx2QPmB99hM3gCdJE1W2OxdYMjp1BJYUd9p+sz9KEvHb5WPFYBtYkzpFgE3ctFQn\nkXdAfsz0dRGmnoqQ8ePH26mBIsoqswxHJLJMXaSZkunVBqaDcCTFj4T/B+7TuXPnRpIQyrrooovM\nggULbFrilMybN69WJMS115GRqlYLOT3a/M09EyzJ7tv0W5uxHPi29dsEE57aCAbEYSoQfyPolM4n\nICf2ejhfVJwOf5qHKRpf/DJJFyVJ0oT18Mvx8wcEwr9kj8NTLK5tXIyammGKyS8zjBX5aadLg25J\n5aabbhriU6U4vflmesbHI6temOUxF9dVmBbJO3WQN39dsSlDL6a+wFxSPAJM7TKlJxECeRGo1CKC\nVWPvvfcOxqE/Svi3e8tnu3AnweAbOa3xmc98xiWxVhGmH6KEuAa9JEmabmWceuqpIy5/9KMfHXau\nm0MuCZlecoI1JIwN+6Scc845Lon5yU9+0jnudYCJHetBXsHR1Dmdpv3262Z6BjM/02+9cPHzNekY\nSwafPFMGzpLSpHZXqSsWHzCXZaTYXnAO4XWakiu2hSqtnwhUSkQOOOCAxG198803O2nDA7q7sPvu\nu7tD++1IzLCTwY9wuvB1fidJE5XPnQuTBs5DHHyJ08+lCSwE7tAwUEcN9L4vyttvv91Jn+QgrE+S\nPGWmefnll60/TJNjgcThw2CIpF0Z45dHGUlXyfj5Bv1YZKT4O4AXBlbLSIRAEQhUSkSKaIDKEAJ1\nR8C9PUYFHUuje1zgsjRlDGpaR0YcIRxUHIpqN4sIiKkkEQJFINAYIsKOnU6effZZdzjs27cgcKHK\nN34ccMMStoBEWU38PL5VBsfUYB6u6+fLX/6yn73rMasi4qauumbUxVQIMJUCAclLQjQlkwr2yMTO\nGiUyEglP4pPEnwFLh2fijEooBGIQqHT5boxOkadZVuiEFR+ssggvfw1iI7gkBj+ScePGdX73+wAf\nDAKY+eITKPTrRUQOOuigTnbyQmSKIlcszQwTt05lKQ5YNfH5z38+RY7eSXvh0ruEeqQoijz0Y0qG\nOl544QXrW8W9i7hluhxDpCZOnMih4X+R+4f/v6YNRrSDtvLJSw4tGAP4B2sIS78lQqAoBBpDRIgN\nwuANCUH+8R//0Xzta1/rkBGisfrLfX0nzqLASlNOOLYHEVD9eCBJ9INI4dCL7wTt/tSnPmW3UHcE\nizKJZukwCVbNpDKXFkFEnC5psCkzLVYerD1VCo6RRYY2Z0omj4NrHBZMGXEPEQti586dlmiwpPvs\ns8/ukGRXLwM3eiAsm9+4caO9F8k3efJku1UAG+01QRwZof1NI1JV48u9vWzZMhMEsqtaFdXfJgTy\nLrtJm99f/hq1jDYYVDvLUQOch0VXDS9/5XrUJyAsI5aC+unilrkmSePrT3pf/Py9jmmnL1HLd7ke\nri+uXPKnkbovc03TFj8tkT6rXJZMZFGWjBYlZSzVXb16dScaKnjlqYP2EqU1WPE0FAzwFnvONUEC\nC2OhfdWENufVkb4merFECBSJQGN8RIIB2EYO9QN8cS4sWE0ef/zxwqYwwuUn/R2EkY9NSqCzpNMP\nOISRvptgNVm7dm23JCOusfqCN+G2Ccu83RRCv9uG1QAp6i2b8opcJfPwww+bQw891FxzzTVm/vz5\n1sLB1JqzemTBC+sCZnqCm7HL7vbt263ObHxX9yWzbn8a50ycpf2Dlmf58uWt2ihz0Pqvru1tFBEB\nRBwyIRphQgIBYcAm9kYdpguIrxFYM+zUiut8YoGge1wkV5cu/E16nF/D5AYCQptfeumlxMTGlc0A\nwlx/mx7CDHyQK8Km91scjuBalDAVUkR56Mauu46AbN682ZQxjQKhYaM79MbPhH4ocmO9onD1yxEZ\n8dHofgwx5l4q497pXrOuth2BUZhX2t5ItS8aAfxLcDokxHcbhEEPosrbeT8Fp9Sse8bE6VmUoytv\nsJB2wraff/75fQ3HTX+ce+651odk0aJFfa07Dte480X79cTV0+TzbCOBXxlkUyIEikSgcRaRIhs/\n6GXhZIg5vQ3CoLdixYq+e/M7wpBlz5g43IuakoFoQkLoY8hmkTrG6e6fJ+omTrsE2psyZUqtrW9g\ng0WH/pREI4C18cwzz4y+qLNCIAcCIiI5wGt6VgYKTK1uWqHJ7fnwhz9sp71Gjx5tBxMGlDIHFd6g\nHQkpGre8UzLoxhYFrLYqcvVOlnYywLM5GtOI6FT3e01kJLqX3f9SHn+i6JJ1VggYo6mZAb8L2mJu\nZQrikUcesYOe36XuAerOFTGFgsWCwb4op1SnG995yQ16YX1AcGDutxXEVhzzB2fZSy65xE6dlYFd\nTLWZTufth0yV1jgT1jUCLOLcLBECRSMgIlI0og0rz00D5H0Lr7rZrAZZuHBhzy3peSP3I9yyKiWN\nQyh4IWnyJMWmiLJxSiUQWd1IiMOgaWSkCOLq2t7Ub8gtOEDOyrjvm4qL9C4OARGR4rBsbEm87SBN\ndULDGoIzJKtB0gqDPyTMCZFr497WITFYGMp6GOd9C8e69fTTT9eWhDiMm6In+tLn9HedLEsOx359\nQx5vueWWvjuB96t9qqd6BEREqu+DyjVwVhH8CeIG4cqVjFHAva3hkFnE/DXlgYMvzm+gzLfjvCTE\nrVBpylsrlps99tjDLF261Ie6lseDTkZmzpzZqMi5tbyJpFRXBEREusIzOBcJQMVg3u+lr3kR5iGJ\nlDmgYXHxY9MUQXj8duedknFk7N577+05NeXXW+Vx03Qu2xpWZV90q9u9pDCdOchWoW4Y6Vp+BERE\n8mPYmhJY1TB16tTGxBUp2wrgrCNh4oHVwZe8lpK81pB+kDG/vUUdu/7DAtWEQS4vYSwKt36WAwln\nX6EyiX4/26O66omAiEg9+6USrXjrg4w04c26bF0ZdCAiSaaq0CWrA2xeEkJ+yGNTBvPwjc0UDRF+\nm7IaY9DICM8DNhRlqb9ECJSFgIhIWcg2tNwmrGqAILBENdhorZQBLO9gQ/4kDrB56+EWYyBnx9ym\nRscFA1YuNWnVVhH91oTHgyP7/r3cBL2lY/MQEBFpXp+VrjHmWCJy4i+SxCJQukJeBZCQE044wXzs\nYx8rZZUPD9+iV8a4KR6vGZ0onuFpHz9Nr+OmW0Nc+5oYowIyktRi5trZtO+2xBhqGu6DqK+IyCD2\neoI2MzisXLmyVmTEWUIOOuggc8455xSySsaHgoE9r7+HX16347ADbJZ66aM27BXU1Ddv7kcISd3I\nerf7Ls01LFV1fBlJ0walbQYCIiLN6KdKtHTTNHXwGWGwYp+LSZMmdSwhRRIHyspjnUjTQVGmfdqX\nxs+EQZCYJ02a0uiGEb4Is2fPbtzOrm0lIzgSX3755Zli83TrZ10TAlEI/On/CyTqgs4JgQMPPNDs\nt99+ZtasWea3v/2t+fjHP14JKFgP2MX1i1/8ornqqqs6OvDGtm3bNvN///d/mVddMJC88sorfSMh\nKI9jKRYQX/baay/rK0Gb+KAX6SAtfH79618b0jj55je/af7nf/7Hhkx355r+vX79evP3f//3jWrG\nO9/5TsOHe4h+a4vcdttt1g+LiMUSIVA2ArKIlI1wC8rnbf2iiy6yLVmwYEHfBm0GYJww33jjDbNk\nyZLYeknHwJ3WRJ41X54uzWp5ccTE1X3TTTdZX5nzzjvPnWr0N32BRarJjpFZ+7ZuHce91iZrW93w\nlT4jEdDuuyMx0ZkQAgzwzBWzTJQPvgkMHGUJD0Ic5XjDZGkncQy6TZsQgpsPA0FScfqnJS9Jy49K\nR51Z35pxoAUD93nttdfMe97zntrvZhuFQ9Q5+s/tnBx1vQnn6Js092Dd2sT/HYJlatq0aaVtZVC3\ndkuf6hEQEam+DxqjAdYJpgsQBlQCaTGXXJRgeYHk8Da2a9cu+3ZMfIkkwa7cQM1A4B6ocXpRD8Lg\n108pyp8DQrNlyxa7dLefRKpsrPD/2bp1a9nVlFp+k8nInDlz7P/zihUrzNlnn10qTipcCPgIiIj4\naOi4JwIM+GyOh2PlhAkTrEMb88gQCEhJLxIQrgDigPWDMnBYZKvxZ555xsyfPz8TUWAgYKB2Fo+o\n+pwFJXytzN+0E92KEAgNb6xtE1YAvfrqq41vliMjaf8Xqm7422+/bZ3BV61aZadD2fYh7v+oal1V\nf7sQeEe7mqPW9AsBCAkWEj5YGB588EGzePFi+yBjOmX//fe30yoQi7BANNiqnp1iCUrGZ+HChcOi\nN+YZuLES8ABFL99ikKfMcBvS/EaXrFMyUfVgNRg7dmzUpUafmzhxoiWhjW7EH5SHjHD/QXqTWPTq\n1macwlk102+rYd1wkD79QUBEpD84t7oWBns/RDcPYCwmzz//fGS7cXxl+qWbhcC9VXZLE1n4H07y\nAOWNFPLBChWmlLKW1a2eJNewYBRZN9NWWA8k9UaA/4umkhFeDrB8SoRAPxAQEekHygNWh7NC5B18\nsSJgTcj6VsabKGWsW7fOnHTSSZX0QlVWmEoam7NSCCPTAm0SR0a4F7Pex/3GAxKydu3afler+gYY\nAfmIDHDn173pzqqRda7dzW+zHwvHWcvJilPRUzJZ9WhKviZOYSTB1hFzdz8myVNVGv7nmGJta19U\nhavq7Y6AiEh3fHS1YgR4iLuVOmlUwSSOuLdQymEg6OdgUNQqmTTtVtp6IuDuw37ef1mQWLNmzTC/\nqixlKI8QSIuAiEhaxJS+7whgsnfEIknlTIfw4HcPf5fHvZmmKcvlTfutKZm0iJlc03Dpa+t/Dnc/\n1pWMfPnLXy7Ul6n/CKvGpiIgItLUnhsgvTET80nyAHcEIM607AgK6coS9CxylUxZetatXCxIrJxp\nszgy0g8ynBZHR9TT5lN6IZAXATmr5kVQ+TsIMAC/8MILZseOHZ1lmG6ZLoncsl53zMqPI488MpEp\nmAe4s3R0KvQO8P9IujIGkoIjLeVhbYkjLV7xqQ6LXiUTrnyfffYxjz76aPh043/7m/41vjFdGsC9\nzP0KGSli8Of/7vvf/755/fXX7Z43xAPx/++ozxE8/gf5vxs3bpysH136SJf6i4D2mukv3q2rjYcp\nMURY7bBz5077wDv66KPN+PHj7RJdGuxWz5CWwYaPe2i++OKLNt/06dPN5MmTh8USiQLLWTz8azyI\nebBneaijE0TEvan65WY5jtIvSznd8lDH3Llzbdj9bumado0AWgixaQZBuGe5d7Pet+7/jii7BLjj\n/w6Suvfee1v4wv93nGRJ/fbt220Yd/5f+Z875ZRTbPyfogn5IPSh2lgMAiIixeA4UKXwAGU/imuu\nuca2m4fgGWeckemBSgGQgU2bNplFixZZUsIge/7550daKsIPbx7kSB4ikYfI2Mr/8KcIXfzy4o7B\ngDgsELqsA1lc2VWeZ8sABlJ2e87Tn1W2IW3d9GVSSx5lP/zww+aWW26x/zNJyXucTtw7Tz75pGF3\na/4HL7zwQjNjxoyBwT4OF52vAIEhiRBIgcDq1auHgkFiKCAfQxwXLd/5znds2dQR7DA7FAy2I6oI\nHtz2PN/BNMiI61lOUA9155G8+ZPUTR18Dj/88KFvfOMbSbI0Jg197vrUtbMfmNYBoF7tDIj/UDCt\nYj9l/N+BexBJdSgYgux31P9dHXCSDu1EwLSzWWpV0QjwoAoCHdkHYa+HZhF1Uwdkh4cvD+Gw3HPP\nPZEkJZwu7W/qzfIQLgsTyvU/rj3XXnvtEJ+2CPcXfR0lfvuLIp5R9VR9Luoeor38H0DSyiAg4TZT\nX2AVsfXxPyYRAv1AQESkHyg3vA4eSLwpYaHotzgLDIOuIwg8sDlm8CpDKDfNgEfaNOm76Uzd/sAb\nl9a9Icddb9p5+pc38l4Czknw6VVOXa/7ZCTq3u+X3ugBMYSUuP+7ftWtegYPAfmIVDAd1pQqmb/G\nDwR/kAceeCCzD0je9jKX/elPf9r87ne/M8GAZT7+8Y/bIsv0yaBs2p/EkTCPgyr1BINrB6I0q3hY\nIkwAKueU2CmkgQfsvrxkyZLUbQF7J+ARWA7cz8Z+06b77rvPOoBX2b/c/3PmzDE4lFf5/9/YjpTi\niREQEUkM1WAl5CE0ZcoU22j2najao54B+4tf/KLdN+app57qEIQ8JKBXj4JBYKHoOjimrd+V6erO\nM3h+6UtfMmyA1/TNyXDAhPBu3rzZwZLp2yd1OPMmIZGZKio50xVXXGG+/e1vm3vvvdcccMABJdfW\nu3hWMy1YsMCu0moqpr1bqRRVIiAiUiX6Na3bkZDDDjusNoMcOkGGeEivXLly2EMxLRlICzvlR1kq\nGPiQXm/h5HdS5ABJ/RAZLCq9dHD11/GbvYDOPvtsc9pppxWmXpjwRfVfYZUVWBD398svv2w3naMN\ndelXyOIll1wy7P+uwGarqAFHQERkwG+AcPPrSELCOjoygrUCcoLODMplvq1FxRuJI0A+8UD3MqdO\nwAJpqlUErKZOnWotT2Va3ei/wNfBYlUkGbQFFvTHJyFlYpFVXZGRrMgpXy8ERER6ITRg1+v+MHTd\n4fRkmgZhoOHtscwHOGQH0gPh8UmIP8ihS5nEg/J9QSfq86er/Ot1P8Y3ZP78+YVaQ3q1mT6ExDqp\ng7WE6Y+77rrLbNy4sdR72LU56zcxR4j3U3c9s7ZP+apBQESkGtxrWSsPmauvvrr0t9OiGn/ccceZ\nYEmxmTdvni3SJwdF1REuh0EMB8K//Mu/NKNHj7aXqx7IGMTQyZGysM51/V0XvX0iWYW1xFmFmkIm\nCTxHGPmHHnqorreW9GoYAiIiDeuwstR1b9ZVeumnbVuUzmWQkfAbNKGxISFVExAfL0gZUxxNCY/O\n4A9+WCbKnFLzMUpyHO7rsvuY+qjjuuuuM+edd14SFStPg85HHXWUXVHTFJ0rB00KdEVARKQrPINz\nEYfBIG5Ax7rQlJaHTcWQEySvkx+Exon/luwTnX5MBzkden2jC2QEsznh9ussTRrIfGtJnhVOcf3B\nyif2immadcFZcfjO+78Wh43ODw4CIiKD09exLXUPFef8GZuwphcgUWz45awBPllIqrI/4JAnys8j\niuSQD7+UOjyMb731VvOv//qv5j//8z9rZWXw+wASwrLwOq3I8vXrdkz/+zFfou6RbvnD1yivyaue\nmu4oHe4P/a4QgcGL4aYWhxEggmI/wkeH6y3qN1EgAyIwLAKkH6Eyqp6AdA2L0JkkemRcmUT7pLwq\nBd1oA1FwwaJqfeKwIHoqWwUkwTuujLqcB3P34R5IK2BRRbTitHrGpafNwdCVO6pwsCOwLYey7rzz\nzrjqBup8EECugwm49FvoB+rlE+yUXnr1/W9h6U0qpoLgja3TEeF/jm7Xiqm9f6W0JVQ4+3H4D3UG\nOn8w5qHpBg03aKdBmTzdhPp6pemWP+u1qHoZ4OpGRtCTcOFtISHh/grfX+Hr4d9uEAeXJgv3Gp88\n4p6nfA+SuIGeb4iHL1UTEXRx/XLiiSf6qpVy/CcBCJIGIzBq1CjjPg8++GDqlhAcjDDOTZe5c+fa\n5Y9+O1599VU7TcFUDYIp3X3SLPN1JnS/7PAx5VE2dTH90A9BLz7hKQJiiuD8iM/Ihg0b+qFK1zrc\ndMybb75pA3Wlwb5rwTW6yNScu7fcfcC9wIc+CstXvvIVEwzgtV6qG9Y56vdll11mFi5cmPmeJ6w/\nAdwQ/ocl9UHA9ccTTzxhsowtqVpSCr1pQaGODQZgjjAXdrvW76ajn/uEWXUvXdryVuba+bd/+7dD\nX/va16xlwllDirBSpC2DusG2TElSh9s0zbcUlalTVNlgh3Um71tzVNlNOedbS9x9WTeLVR4ssUZm\nmdoNticY2meffezzi+9BE/fc5jvts7sfWIX7h99liSwiwV0wqPLkk0+awFze+Lcy+o83T1aL8NbN\nG6lbEureTrP2MeVSRhpxdePIWoagE2/gfLoJIdOJTcGSbKwjZekTpQNWEFaEsKQYJ9qmRn6Nalva\nc/QT9xAfjm+77Tbjr8RKW17d0hOef8WKFanVCgZfs2PHDptv9uzZw/LzBu4svV/4whfstRtuuKFz\njmsXX3yx2bp167B8/g+sLYcffviwPJx76623/GTDjimPcl3dY8aMsdYA8rhzfIfL4HdYP9JxLqzj\n9OnTbVl+xWeeeaY95ywPfvspBwmm8YbpgJ5hCadZt27dsCSbNm0y4Om3BX1cvX5i7tFzzjnHnqKf\nHn/8cf9yscdFMhx8KXxrQaCptSYEjRhWTRA0q/MWDxMOX/fL4DgsMDPfmYZ64ury8ybVjzy+Dml9\nRHC+8tuIbmeddVYs6/XrAgvyMy/n2gVGYR0oz10Pfydl18zZZ3mT8TGt0zFvmzjehoU30iwWiqz5\nXP3M/6e1pri8Ud95ysMqEgyC1jKRBYsofaLOoaOri/urzLqi6m/CuWAH6SE+bRH6nGcQ32nEf+7x\nzPOFZ5h7rvEs9Z+H7rz7DuflGdotPc/TKAdMynFlhr+vv/76Ydf8MYuywunDv/3nd5Jnt99+ynJy\n0UUXdeqKsiJRj6ub674Vw7/m0vjf4ByWgHx0yivTV+SPLQxrkOJ32o4nPSA5EHwAwh0QvsnodD+v\nK8P/DudJqx9N9/9J/Juo17UsnR2uy2+Lf8wN7CTJzezSxn1TdtEDBVjTh/QpOkb1Fee4RhrSkqco\nYbCNahMkJe2DsigSQTlp6w7jQZucWT98LelvymCKhH7nO295fr2U7QgIpvqisPPraMsxDrtF41P1\n/x1twvE9qYQH73C+8DjgPwfDx+EBshsJcXl5BvmDNMdRzyqXPvztP7P853c4nf/b1Zfk2R1uv8Mn\nfD5MqHyiwrETn1D4OoWPw2MdOvtpwvW58vN+F0JEsnR8eMB2Het3qg8kDU16s1CGL1n08/UId07c\ntayd7Zfnd3rUMXUgSW5mH4PwcZz1IJwu6W/6z/8niNK92znyunsgaZ1R6brNV6d5+KdJG6VH+Bx4\nRxGkcLqo33nyRpWHHryRQ9qwIHGcpb3oxXJhLB/0Ld9ZyonSsc3nwKooqcv/HfdQGl8k//kfJhJg\nEx5wSeMGQcaB8PPPDfLhfP6zu9s1Xx/6x88XtoZw3T2rwlYUP1/4mnt2u76nHPdBN1/CurprYWLg\n10can0z59fljjI8l7fCxDBM0yvTzhuvjehGS+z8iDJivaLdrKO830L0du44BENfZrqGU7a7z7dcV\nvllcJ3TTods1Xze/nrDe/jU/T5rO9vOF20U7/DbTTl/8a7QnqfD2wqBdhKCj/w/g65TmmDJcv2XV\ni4dh3AMRqwSDZy9hoM5KGrqVTZlJ6vfLIH1ea4pfXviY+4BBBEJCX/FmC6FwOIa/Sct9A4nhQ1qm\n98rUMaxzk39D1MC4CKnT/x33APdCUvFfWsIvnJQRfjaHx4KwRcVd98v1Le1OL38M4bnrhOe1e1ZF\n5eOcu863q88vj+dXWPxne/j57JcXvhZuv1+u30YfOx8TdHHkzD/v6+7KJJ3//A7r4hOVKGxcOXm+\nczurfutb3wra9nsJlDSzZs1yP63zYNBRnd+3335755iDYIVD5zfLDRcsWND5zUZme++9d+c3B34Y\n5HBdV155pY3W6DK88sor9jCPfq6sJN84JLllaKRftmyZGTdunM1KO+644w4TdLb9HdzEsY4/4Xad\ndNJJJrjZbD7+sNlUERLcnDYaad6ycNKiz2lTXqEMygo7gqUpF4yfeeaZyCxu2Wiv5bUBYejpCBpZ\nQY+TwcBty8XZNIk4p1Snd5I8adMcf/zxNqz/5s2brTMc/4PsIxInu+++u11miW7gtHTpUrtzbpk6\nxunSxPMBYTN77rlnbtXr9n/HMy7Ns+mFF17oYLDvvvt2jqMOgsF8xFjgnq3h9H65RFsOy+TJkzun\neF7TH3xYouokKl/UOdLzvAoGYPvZvn27LQKHUJxicSb1xwRXft7vY445plPEf/zHf3SOn3322c7x\nJz7xCesQzYnXXnutcz4gXCOw9J1SSfiTn/ykk56D/fbbr/P7xRdf7BwXefCOvIVl6Xgajhx55JF2\nt1dICOI6jRvPJzT2YvDHv1mCNzh3uvP90ksvdY7dQR79XBlJvpN2tmtruLNdHVHt+sAHPuAu1+6b\nOCRhEkL/BSza7LHHHubggw+O1JkYHzy4gjfyYf1KWZQJscwiYfIaLoMVLQyicSthul0Ll5XlNwM2\ndVNP3IZqEKXAEhKrY5Z6k+RxusVhk6QMpemOQFEvAHX7vyNU/apVq7o33rvKxpFOeE50kwMOOKDb\n5WHX3BjCyZNPPnnYtagfkJCwjB8/Pnyq8xI54kJwgjICK4K54IILoi4Xfs5vF89LXoIhZt/73vc6\ndbGNgpPA4uEO7bPWrcLpnAwddCOUP/vZz0Kpi/mZm4hk6XhHRGgCb/v33XffsMHMt5S4ZobfkllW\nlUTy6pekDtIU1dlJ25VUr7h0WA1YdpdXIBK+8A+ZZNM1SCgC4eANYuLEiZ1iKDMrEekU0uXAEYHw\ngJskcFmXYlNdou6ofWrQASIS1i1V4UrcegTq9n+HtS+NhF9e0uStU1pISDDV1nmJdrph2R47dqxh\nFsAfg9z1PN+Mn7zo3X///bYYLCG8gC1evNj+xirsP0/z1NWvvH/Sr4ri6qEjwzclb8uS8hHoZT1I\nooFvpeKfLwkJCZfrLGPuvF+mO1f0N29w4YiXZU3JxOkejjfi4ny483H5dF4I+P8jTfq/cz2H1bQM\n8csNnEU70yZu+iT8HfUMxGoVlvAY5a7z4uWIBnWTjjq+/OUvR1r1Xb6838QFcoIlhLY68adlOMd0\nqhMITBiD8G9077fkJiJ5O56gR2HhXNgC4ltRSO/m48J5w7/z6hcuL+53Ezo7TveizvMGkFXy5M1S\nJ29wWB6cv0jZUzJxOjq/keXLl1v/kbRvlnHl6vzgIJDnfydP3jwI77XXXp3s3aYCOokSHhxxxBGd\nlElfaCEjzn+PzFE+ZlHnSEvAQCcXXnjhMP8LXrIdSXFpivomAJoTLCG+fv60DGkOOuggl9RgPUGv\nrJJmmixNHbmJSJaOdwoSzc2Zl7gRcKRBYJXOzOTSQkT8myXKx4JpDRcxDmchJI9+ru4k30V2dpL6\n8qZhXjYc8S9vmf4/Zdqy8uRNW5dLj+UBX4x+Tsm4ut238wc577zzrC6OGLnr+hYCvRDI87+TJ28v\nvbpdf9/73te5/OMf/7hznPfAd+TEZ8ONA5TLy60fNZWoq05cBFF+48fn5+PY+fa59FHfLKZwgzzT\nzZ/61KeikkWe86f2IxOETrrpGXfa6Rc1LYP/iHshZ2xFL//Zzzjsj53hKKtEq3biynG/C/sOzDK5\nhKU+gTKdj7+cNWj0sNgSQSM6dYWXDJEvvO6a374EJshOPdTpL/UML99lyRKSVT90de3y20SZcdf8\n8/7yXadHcJN0ykQvJ36+cJtJQ/1OFzDwxZ3nO6ynny587JZlhs+n/e0v7UIH+oG+TSqk9dtHGZSZ\nVVgemWZZcvDgGPrGN76Rtbpc+aKW87Jcl/P9FnAAO+4Lt0QXHMMfAqGRJvBRqETPfuNSdH0O37zl\n1u3/jvs2sOYlbpb/P8+zMiz+czvueeA/+xhrnPjPUz9N+Nh/BpM/fL3bb1dfeNzplsevD12j0ro0\nfvtJFyU+hq4sfzmvnydcnksf/ga7sPjjlj/mhtPl+R3dwpQlZul4n1TQUDd4+f9gYVCS3izhzsii\nn58nPMDHXcva2X55eYiIu6nczdytG4t6IEb9M6AHDxf6kutRH66Rxunsf4fx7taO8DUCbKXZYI3B\nl4G/34N/N8LRL32oB7yIawH+fDuiAS5RH9Jz70BQyJMnIFq47wbhN5imIcpxmNTt/y5tu8LPcvf8\nd+31n6VpiQhlxz1b3HMm6hnj1+nSue8w4XBEhG9/oHbp+WaMYyxy5yjDlygd3bM7rIufzx2DmSvb\nfXcjCnH3jMvLOOTaFVdHuJ9curzfhRCRtB2PtcI1nm//puh2jVopbAYAACaLSURBVMaGO8gvh2M6\nNwxWWv2oxycHvn69rmXpbL+utESk282MrnGS9sERVw5YR+kQ7pekv6P6L67uqPNpIjz6Az549Euo\nCwtEN0E3yEoZgjXDEQmIB7976ROnB21xAdEgJRCVrGXF1dGm8/QpOOWVuv3fpX0BoP3+cyM8gPrP\n+bRExGHLs9ivg2cQ5CDqGevycI363POKZzO6cN6d49sfYxizfMLh8lBmHIHhWjgf5VIX4ref83Hi\nt89/oY9LT51hndA3PMa5/PSLa3dcP7i0eb7jW5ih1KQd74MHCGEJW0vCLA0w/TQA1Q1MV35S/UhP\nea4Dwp3U7Rp503a2X17UPwn1O11oty/dbmY/XfiYgS6NKTWc3/+NDn6fOl3TflMGZeURBlgG1iQS\nJh/h30nKSJPGTX8kzZM2fa9yaR+DYFmEwREc7iusJpJoBPi/KIKs1en/DkILGUkj/mAbfq6lKacf\naf1nMAP+oIhPsBxJKqPthRKRMhRUmeUhwIBU5Fs3/6w+qUpKRMgTJntZW530IR9FOnwLSdb64/Ll\nsXCga56Bi7oJvw1BSDtYxLWn23n0hRByf0Xh3C3vIFxLQ5aT4FGH/7ssfY1VwU1rJHmbT4JF1jT+\nixTPI//ll5dDpyfPF9IOgtA/7hkOJmXKKAoPKpMMIAJXXHGFjXzKio0iBe/05557zgZ5Y437L37x\ni2HF/8Vf/IUhWixLnj/ykY8MW/I2LGHKHxs2bLDr93utBHDxQ4KBeUQNxPLgfJEhy6MCl42ouMeJ\nrGWAybnnnmumT59u5s+fX2i7eqhsWJIcvOkaljWyZYPk9wjcfPPNNvzAjTfeWCgkVf3f8f9EAL6A\n8KZuDytSXETSgFCVGnujm3K+Ht3ScS2wDGSKl9Sr3Lpd9zEpvc1lshyVXW8E2KiqqA24qm4p0wKz\nZ8+2/gq9dOn1lt7req/y/etYnPJYM/yy0lpV8N3ACpJ0qsqvq6hjdOYe41MUDkXpVkU5YMAqrdGj\nR7cGjyz+IT72zhpR9lu3X2fUse8bEpCCjjXAP677FFJUu7Kc861V/bAAaWomSy+1JA8PRQYqBoum\nC235q7/6K/uQh0jEkYm48377KauIKSvqoqwihfKStIE5ewb/ItpRhP7oU/RUYBF69aMM+oA+4+P6\nAyyqJIhFtjtvW/B1cYN9UVO0WduHH4TvF+H0wsEzyn8vaz11z0c/0HampPxpqrL01tRMgPYgC9Mz\nSNFm4n5j+vDDD5tbbrllWKRDoqU6IQCQm26JmpJx6dx3t+kblybu2wUpK3O/mG6b5tGnRHRcu3Zt\np81xuvbzPHqxWRtTZ20OY8+9409TRG1uyLTVI488MmxH8X72RVF1cR9OnTp1WHuLKlvlDA4CIiKD\n09eRLXXzu8GbWq0GrUhlu5w89NBDrQ/EaaedFpkKchBMRdldKknAXjO9CEmWsO/gSV39GGij/Ebq\nSkJcpzgy0vT7zbXHffukN8m9xT0CQSFfr/vQ1VHH79NPP90cffTR5tJLL62jetKpIQiIiDSko8pU\nk8GBEL9NfZhgDbnmmmvM5s2bY2EKk4okb60UFs4XW0FwIYoYdEtfxDWf+OAEedddd5mNGzfWmlTW\nnSwl6Rf6Opgm6yTNYv2iv9gjhNDgTRT+N7CGtI1UNrEvmq6ziEjTe7AA/RnMeIvDnNy0tzPeLI86\n6iizcOFCc/zxx0eiQfuQbm2LG1gon/y9LBw8lKNM8JEKFXwSHbH2/PM//7N5+umne+pacPWZimP3\n0MCHpTGracCYAddJEX1NmZSzZs0au+rEld2Ub1lDmtJT9ddTRKT+fdQXDdnxeMuWLY17O0uidxqr\nhgObPE7+93//13z0ox+NtDK4ASrLG7ErP++3G9BuvfVWEzc1lbeOovND7sDs3nvvjSWQRdeZtjz/\nHsDHqBcZTVs+6fEVWbRoUe2tWOG2Ob27WSHDefRbCMQhICISh8yAnWcww7IwZ84cU3RckbKgZKDA\nNMx3nLWDa3lJAthE+ZcwmHKtjAEqDWZMdbCV+tKlS9Nkqzytm1Kry1QS/ek7mea9b5ICjGUhWHnS\nGOsQ1kMsWk215CTtF6XrHwIiIv3DuvY18YDBVIwJuurBtRdYSawADCxIHEnpVYd/nfooD1z4JmDb\nu9/9bhPEg6hsSgb93KDQjYz57ajbcdXmfXBzksTJ1KUt8pv7CdLTBIsW/wdTpkyxLwBN9Skrsu9U\nVjEIiIgUg2NrSuEt9ZJLLqn1Ekv3MAwCIHVddlyENcTvWEdseGv2fQTi/Ev8vGUdVz2Q520XfdRP\nh8cq+6obVi4CLkt6ua/rKjNnzrTWt6Y62NYV10HXS0Rk0O+AiPbXeVWDIyF77rlnV3+WokkIMFE3\nUzS9pq78t+yyfAvQx1lDmr5qoUwyRZ8V7WQK9mXIv//7v9upUVbS1NEiWefnQhn9oTL7h4CISP+w\nblRN7qGzePHi2jwUHQkByG7BupzloogpGddplEn9DBBpSE54ICzS/E8fsV9P0/dxcVYR3z/D4Z7l\nu19EMItucXnc/bV169ZaWiTd86Db/11c23ReCPRCQESkF0IDfJ2HT10iYfKg/vSnP232228/u8rA\nRUmN6p40RCEqf/gclgfqc8QGcoE+Wd5ayecPuP4UT7jebr/Rgby01enVLX3drxGQrtsS7G76hzHt\nl5NpN53SXEN/xPWjmx6tg88I9xk+IYhIiIVBf0pA4E//XyAllKsiW4BAsNmRHfhZTbPvvvsaBosq\n5MEHH7R+BGeccYYdrN75znfGqlEGCWGA2GuvvTp1Uj9Levnupksng3cAocEq4j7btm0zP/7xjy2x\n+fWvfz2sHi/biMNvf/vbxr09j7jYwBPvete7zLPPPmu453oJg+Mrr7xiMWMQZ/oLUuYw7ZW/TtfD\nJATdDjzwQPu/NmvWLPPb3/7WfPzjH69EZf6XWA7O0vXbb789cvl6JYqp0tYhIItI67q0+AZhETjz\nzDPN/vvvb4gG6d7ciq9peIkMOCwnfuyxx6wVhCWO3awQUQ/14SWm+8WDuJvFomjSQ3t9f4Zu0zhY\nq5ocDTfcE/QdlgzfWuSn8Z1My/S78ess+7jX/cp1rIDIggULci9DT9oe7sOvfvWrlnxcd911PX2i\nkpardEIgFoGydtNTue1CgF1fb7rpJrsjIzupBgNGaQ10dQWEZ2jGjBmdHWw5HwzUsfUm2ZU2NrN3\ngXqSlpU0nVd84kMwpnz3QS8n7HjaDQuXrknffptcH0S1vUltitOVvk36P8T/Hf8LZf/foWvgjG3r\nmjZtWmL94tqo80IgKQImaUKlEwIgwMOTB2LAbO13kYMhZbuHLg/CqEHeDVDh3ohKG06T5Dc6pGkT\n6fn0Q9CLdr700ksW/37U2c86zj333KGrrrrKtjFNH/RTxyLqynLPcN/7/3dF3e+0h7L5v4MI8lm/\nfn0RzVQZQiAxAn8SayrRBSEQgQDTMjfeeGPHhE6ERT5M2TBVkVYwuRMumjKYiti+fbuN2Eicgiin\nQ3wsOE9dmJARTNjkzSvognSb/gnXAR7o4XQJXy/yN3rR9t/97ncmIGpFFl2Lsg4//HDzZ3/2Z7aN\nafqgFsonVKLXdExcMdz37v+OKTn8R/DZYouDLP936IFTLHFBmOrC52bJkiV248i4PZvidNN5IZAX\nAfmI5EVQ+Q3BmPDjCN6k7H41DJJjx461PgzAwxJTHnY7duywaO3atcume/755+3vyZMnm1NOOcVM\nmjQplUMcxIEHdPCGGUlabOEJ//Aw7+YP0qsY8kcRp175slyHuL366qtdg7llKbfqPGCIL0Rbg2Vl\nJSFx/cL/HRF+2eiQD5sIsqpswoQJnSzjx483r7/+uv0d9393xBFH9M3vq6OYDoSAh4CIiAeGDvMj\ngGUgMKvbFR08+BCsHOyF4j8gJ06caK0YWBTyyDe/+U3zvve9L5UVw6/P6ZuXRFAOA00/3uSxPiFt\nC7HdZiJSNAnx72GO3X3MSqpu/3cQk7333rsv92lYR/0WAnEIiIjEIaPztUfAPdyxikB+0pIJ8vMA\nL4o8YKGBWKFPmdJWIkJfYDkLJpbLhK/vZbv7NC/p7rviqlAI9AkB+Yj0CWhVUzwCTMm4gR8Swhs1\ng1kSyeIP0qtcCA2ESJINgbIJXDat8uVy95lISD4clbvdCIiItLt/W9u6KJ8MyAhvn+4NNK7x5GVg\nKGNwcIQorm6dHxwEnIWsjPtscFBUSwcBARGRQejllrURohG3SsZNs7g3Ub/pWEscgSnz7RvdepEh\nXy8d/x4BMGvLoO1ISJn3me4bIdAWBERE2tKTA9QONyUT12Rn7YB0OGGQ45PWj8TlT/NN/ZCepNNE\nacpuc1r6FSfmpotISNN7UPr3G4F39LtC1ddeBBh48ZFgWa5bKhjVWre0Fw9+9tVI8xbsLBpR5frn\neBN10yR/+qd/av76r/+6MKdUv564YywzSXWNKyPuPMuhN27cGHe5seeDwFqN1d0pLhLikNC3EEiO\ngIhIcqyUMgIBrAxPPvmkDUrmYhkcdthhNobI3LlzI3IYu7SXJb2LFy82q1atMkE0Rxugi03t3NRK\nVEbqipuSiUrPOVZh/OY3v4m7XOp54pIwMHVrUxYFxo0bZ/HOkrfOeYh3wb3QVBEJaWrPSe+qEdDy\n3ap7oKH1E5VxxYoV1voxffp0Q1CyD3/4w5mWrrrATOzwOXr0aDN//vzI4GZpLQykd0HKIDFYbIom\nBb26j3qRNFafXmXSjjYucyXKJ4Ht2PG1aSIS0rQek751QkBEpE690QBdIA3BnhdW0zjCkKcZPsG5\n9dZbO4NSGhLipojC/iBx5/PomyRvGt2TlEcawnsTkjvcxqT565gOa9dTTz3Vd7KYFwuRkLwIKv+g\nIyAiMuh3QML282ZPJE/8P3yCkDB76mQM3uynseeeexq2vGfgTWJVSGL5KIMY9Gpg0XWCCb4i8+bN\n61V1I64zmLPfEA6rTRKRkCb1lnStKwJaNVPXnqmRXlgpePNm/h5n1H6Yzqlv8+bNZurUqdZcn2T/\nEQYFpNf0C2VDDLCQ9EuKXNIL2WJPkfvuu8+2o19tKLOeBx980DDF1yThHoIca4luk3pNutYRAVlE\n6tgrNdKJN++VK1faHXGrmgaAYFx00UXWOnL33XdHPvgZFJw/SFL4KJdBJImlJWmZ3dJlfXsmn7+i\nBFKDzk2dyojCCIvXwoULTVN2fi3awhWFic4JgUFBQERkUHo6ZTuxFpx//vnm5z//ufn617/et8E6\nTk30mTNnjnnzzTfN2rVrO2Qkr99HkqmcOJ2ynE8ygIWJRxzBYgt4lkmzPXyTxfkdYQFrgiTpwya0\nQzoKgbogICJSl56okR4M7lOmTLEa+YN+HVTEQvPyyy9bMoKefHpNxfTSOy+Z6VW+f526ID++zgxs\nvsQRDz8Nx5SDVaRXgLdwvrr9Pv30083RRx/diN2ERULqdvdInzYgICLShl4suA0sowxbHgquIldx\nkJFvf/vb5t577zUHHHBArrL8zAwySUmAny/t8Te/+U2bhaXKSJ4pL7BAmmoVAXP8gPA9qruvhUiI\nvdX0RwgUjoCISOGQNrtAzP0EJqubJcRHFVM+JIRAZUmcWP28vY6L9htx1ha/Xucsm4eAuPKabhVh\npQxEhBVZdRaRkDr3jnRrOgIiIk3vwQL1Z4A/99xz7UqMfjlwZlWfAZ7pozIGsTx+I2HiQeAxfxrG\nb29Rg9vNN99snnnmmcJJma9rGcfLly83ixYtsqujyii/qDKL6qei9FE5QqBtCIiItK1HM7aHAZRp\nCSwNTVm5gPWCN+o1a9bkmt6IgswRil5WC5fOldGNeLg07pu8YX8Rdy3NN+UcddRR1pn3vPPOS5O1\nsrRl9l2RjRIJKRJNlSUEohEQEYnGZeDO4heCLF26tFFtL/utmoHI9xuBOPhBt9IQjyhgGZCLiEVB\nOeiJr0WcBSaq/irOQZzKsmYV2R6RkCLRVFlCIB4BEZF4bAbmCg/cpjgMRnUKVhEsAWVYAyAezz33\nnHn3u99t98FxMTyi9Mh6rqgBj8Bzl1xySe3DpEN633777VpPJRXVJ1nvCeUTAoOEgIjIIPV2TFub\ntHwyqglFEiksC1HBw/L4jUTp7J8raoqGMv3lzXVchVJ3/egLrEq9puT8/tOxEBAC+RAQEcmHX+Nz\nFzmIVwkGZOrUU09NbRUJEw9/GibcnjIHKYgOUoSTsBvs6xCIzsfQ6VXXFVlFEkK/3ToWAkKgOwLa\na6Y7PrFXDz/8cDNq1Cj7YRdUX9x5vjdt2uRf6nrMfht+3q6JC7p4xx13mLlz59Y+hkOv5hICnhUY\nvQTi5X8Y+Hn7dZ9uVgSukY78DFpFCnr4vid5yiamyGGHHWZ1hWhVLWAFUXSB6LphXJWuIiFVIa96\nhYAxtSUiDO5uUGbQlxSPAA/fZcuW2UGi+NL7W6Jb6QNJ8MUnHRw7wuG+swyK5MWC4awYfn15jh3J\nyVOGywsZYZdkLDw49FYlECFW9Oyxxx61jU0jElLV3aF6hcDvEXiHgBhcBNavX29mzJhRyHRAHVD8\nxCc+YYmVrwuDexnCyhRHRoqYTnE6QhwYvItY+cIuyd/5znfMrFmzzCOPPGKIN1Kkrk7nqG8GdzYo\n/PznP2/uueee1FNmUWWWcU4kpAxUVaYQSIdAbS0i6ZrR/9QvvfSSGRoash8e9E2URx991L6tNlH3\nsM4EY/vYxz5mp8KctaMsEuLqdoN6kdMfzkLDAFmEgMHGjRvNIYccYvelgYwUVXacfqzewQpCkDUc\nP8tYzRRXd5rzIiFp0FJaIVAiAsFgWit5/vnnh4LmRn6Cee8Rut55551DnPfzcG7Hjh2RaV26s846\ny16//vrrO3ldBpeGb/Thc+KJJ9p0lI34dbpzcfkff/zxTn7KpCzOheWBBx7o6EK6KEnT3qj8/rlg\nIB0K/BL8U40/rqJNwSqbocDyUCh2RZeHcgEpGJo2bdoQGF177bWF9j0YrF69eiggPPbDcZ0FfcFD\nIgSEQPUIRI92FeqVlIhANBw58ImDO95nn32GXn/99WEtYRB31yEi4fwusUvDt09U+O1IR1IicvXV\nV3fq9Mv1y3L1diMiWdrryo365iHMgNQ2YaANppwqaRbkgQGuKCmDjKAbfX/55Zfb+/LYY48dCqZO\nMg3KjnxQFvcS2NedgNB+kRBQkAiB+iDQWB8RfBueeOKJYDyPlmDgNieffLJ57bXXDNEvw3L//feH\nT0X+vuqqqyLPJz153XXXxSa94IILzMEHH2yOPPLI2DTuQt72unLc91tvvWUmTpzoflbyPX36dOP6\nIfiXKEQHtpMPCGglYeqZBmGahumVYGDO3R6Cp+GHUkRZvjL4n+DMOn/+fIOfEFN0AWG2SbgnwBAZ\nP378sP+drVu3ml27dplXXnnFvPjii2bLli0mIB922fRll11WuJ5WiYL/aDqmYEBVnBAoAIHa+Ygw\nKDMoBZaHTvMC64M9h18GwjJXn4SQljx8AqtCJx9kxP/dufCHA8oNLDCdvOHr7jcPaVd+Fn+QOP0o\nn71deklR7fXrYbB2A45/vqrj4C21kKoDS5gdKAspLEMhzsm0CL8RCEhRS3qjmgJhwqGVsP7U89RT\nTxmWQSMQjsWLF5sFCxZ0Pq+++qq9dsoppxhWtfE/we7H+IAUTZZsRQX/cc7Fro8KLl7FCQEhkBWB\n4GFSS2EKJGiT/TAN4kvwsOxcY+ojLHF5/fOUzTRQlLh6+Xa+JOF0aaZmwnnDegQPfZskbmoma3vD\n9fq/b7rppiE+VQrYOqzj+iKtfkxnMEVQtWD+L2pqpahyqsakyvrxhWqbP1SVeKpuIVAkArWziAQD\nU0954YUXOmmi3uonT57cuU4Qpbi37SRTIuxjkkeI9hmWj370o8NOMU3STYpqr18H5nWsB3UR97Zd\nF33y6oG1gamaIoKfuSW9eXUa1Pwu3ksTrDaD2kdq92Aj0EgiArlwgh+IC3zmvsMDbBQRYVomiey+\n++5JksWm2XvvvUdcC/usROnnZyqivX55HLPpWJRu4XT9+v2lL33J4IPQNoGMuCmBrG0reklvVj2a\nmE8kpIm9Jp0HDYFGEpFB66Q6t9ePgOuIYNJv56hK+5xz8Q033NA6QuJ8EvL4jVBGsNqlzrdC7XQT\nCaldl0ghIRCJQCOJiG/N8J1NgzmrjlOpf1zlmz9OoWEJW0B66VdGewm53WtKKKx32b8hI6xSwjrS\nNmFagE84BH2adrqpnjR5BjWtSMig9rza3UQEGrl894gjjrAbaAE4vgVJfD2q6hyiS5500knDqn/2\n2Wc7v5lG6kVEymjvhAkTrBWio4gOSkfA9xvptstvN0XKWtJLnVhsmB6DEG7fvt1s27ZtmCqQV+4b\npivHjRtnfWCGJajJD5GQmnSE1BACCRFohEVk586dw5pzzDHHdH4Ti8Pf/Za3/IsvvrjjN1L1hnnE\nEfH1YykuOjs555xz3GHsd1ntZYlm24SBlAGzzpLHbwSrCrEwigjTThmEY585c6YN/37mmWeaFStW\nWOjGjBljd2VmZ2b3YdkuAvnnHFNwOHMTNt4N/jZBhX+cHnJMrbATVLUQSIlAIywivKHx0GOKglgi\nZ5xxho1t4Jw4Gdj9wd3HgAdm1dJNPxe3oZuOZbSXYFXEicgrrFBieqxICTvzpikbcsVbe90Fnw8G\nTawQzockqc6kdzsJJ83jpyMv8XUWLlzYCUgWhHzvGQsEAuULRCZYWmwee+wxax1Br9mzZ9vYJH66\nfh2LhPQLadUjBApGoMi1wEWWxV4sQVOHfQIi0qkiICcjQrSH0xOvwxc/fodflp+GY78cYntECfld\nunA97jzf4RDx/rVwvrg4ItSfpb1RertzxFQI3hrdz9Z8VxniPQuIgb9Q5vDqafdKcTFW6HdiyBQZ\nV4N2VLnXjOKEZLn7lEcI1AOB2k7N4FcRDNSxsS7wq1i3bp1NE+wZE4zvfxQiofKWniUK6h9LKeaI\nMOa8fWLNcYK+AdFKpV/R7XWm6zwrOVx76vS9atUqc+CBB9ZJpa664DdCX6R1YiUfH2cF6FYJlgsc\ngKdOnWqj6bL65tJLL+1pAelWZvgauhCldfPmzTZ0/DXXXGOnbfpxf7k63D0d1k2/hYAQqDcCo+BD\n9VZR2pWFAL4BbNde123a07Z7w4YNJtiAzQ6GafPWIT1kJK0Ta68pGq5DyPfff3/ry9HPwRrfkc9/\n/vMmsL5Y4lMGxpAQ2gQRkggBIdBMBGprEWkmnM3SGufD5cuXN0vpLto+99xz1uehS5JaX8rixNpt\nSS99ixVkzpw5dk+YfpIQgMbqgvVlzZo11iG2CAdbvwNFQnw0dCwEmouALCLN7bvcmjMw4BgazK8X\naqbPrVjGAljaysZtaZ0/M1ZXWjamW+ibpO1w0zM+0bjiiivMypUra4EHbYEMvfnmm2bt2rWFWC9E\nQkq7/VSwEOg7ArKI9B3y+lSIOZupjGXLltVHqYyasAx19OjRiQfvjNX0JRuEgk9SvxHSMtjzQSAh\nrCjDGpGUzJTZMO4zdvjFT2rKlCkdPbPWKRKSFTnlEwL1REAWkXr2S9+0YrDDfM+g1eR59g984APm\nkksuMTNmzOgbdv2oiP5J6jdCWhyjISFFWR6KbqMjSVn1EwkpukdUnhCoHgFZRKrvg0o1wMdg4sSJ\n5u67765UjzyVYw3B55qYJgzG/idPuXXIm8ZvhGBuTMd8/etfry2pvPHGG81+++1nzj///NTwioSk\nhkwZhEAjEJBFpBHdVK6SDNxYRfj2/QzKrbW40g899FC7ZJTlo2GhTb7gE1OH6QpfpyTHvfxGGKSx\nnNRlOqZbm5hCYorm2GOPNfPmzeuWtHNNJKQDhQ6EQOsQEBFpXZdmaxAm87ffftvO5WcroZpcLBFl\nVQZOqkmEQZDB2pekUx9+niqOne5YSXzhPMuwcQhtylJsiAXh4em7cHv8tnEsEhJGRL+FQLsQEBFp\nV39mbg2DGQPyrbfeWlmI7rTKF2UFoJzwjsi9Bse0uhaZHiuPT54IVrZlyxa7RLfIesoui+XFixYt\n6hr3JdzWsnVS+UJACPQfARGR/mNe2xp56DNF04QlsGVbAcJTOiwNrtO0FeTJORejW1OXYGMV4Z4j\n5khY6IM6E8KwvvotBIRANgRERLLh1tpcTHXcddddZuPGjZ2Bro6NZQBjOSjOj/0QfDQY7H3xrRL+\n+X4do9MXv/hFs++++yb2teiXbknrceQ3vGpLJCQpgkonBJqPgIhI8/uw8BbkXWJZuEKhAuuiX3hK\np9+OsBARrCHoccABB4RQas7P008/3e6B46wiIiHN6TtpKgSKQEBEpAgUW1hGXQZ7H1qmY9hMra5x\nMtAv7Ahb5pQOfYT0yyrk90WRxxAP9sNhwzyRkCKRVVlCoBkIiIg0o58q0ZKBbv369TZIVtVLXhnk\nWfKJZA2GVQWIUVM6Rfk9QHKa4M+TBHeWYF9wwQXm4osvTpJcaYSAEGgRAu9oUVvUlIIR4E0bnxH8\nMapcTcNbMg6N06dPt/FCnJNmwc0tpTgcXMNOrrTHlyxTOuw0DDmsmiD67chzPG3aNLsXTZ4ylFcI\nCIFmIiCLSDP7ra9aOyJA5NJrr712xMBaljJYQb761a+a22+/3Vx33XWNiZGRFo+oKZ1ejrBYq3bf\nfffGOqmGMcLPBcIbdggOp9NvISAE2oeAiEj7+rSUFjFY4p9BCPG5c+faEN1lWiYI2059+++/v7XK\nhK0KpTSyRoWGHWFRzZ/SYSpjyZIlw87VSP1MqrRpqikTAMokBAYUARGRAe34rM3GOrJgwQLz/PPP\nW0LCioeiSAJkZ/Xq1TbIFfotXLjQHH/88VlVbV0+N6Xz29/+1hxzzDGNjR0S1zEzZ860EWKbEh02\nrh06LwSEQDoEtOldOrwGPjVv5Q899JANzb19+3a7fJQBhCiZOGamFcgH1g/KwFfikUcesQSEFRQi\nIcPRBHs+7373uw0+FQiWk7bIhAkTDPeURAgIgcFCQBaRwervwlsLkWBlzaOPPmoee+wxM3r0aDud\ncvTRR9u62NnXF3aI3bVrl3nllVfMiy++aEOTM6ieeuqp5oQTTijMuuLX2bZjSB8B55YuXdqqpuGA\nu3jx4saFqm9VJ6gxQqACBLRqpgLQ21QlfiLseut2vuUNHbKxY8cOSziYxvFl7NixZsyYMeaUU04x\nn/vc51rl4+C3s8xjiBzWg7YJFjGJEBACg4eAiMjg9XmpLW7TktJSgVLhIxDAWRXfI4kQEAKDhYB8\nRAarv9VaIVBbBHB6zuJnVNsGSTEhIAQSISAikggmJRICQkAICAEhIATKQEBEpAxUVaYQEAKpEZA1\nJDVkyiAEWoGAiEgrulGNEALNR4Coqm5ZcvNboxYIASGQFAERkaRIKZ0QqAkChHZXvI2adIbUEAJC\nIDcCIiK5IVQBQqC/CIwbN85s27atv5X2oTZWzBxyyCF9qElVCAEhUCcERETq1BvSRQgkQIAN8Vat\nWpUgZbOSEOSOGDMSISAEBgsBEZHB6m+1tgUIEEQOy0GbwrvTLUTaPfLII1vQQ2qCEBACaRAQEUmD\nltIKgZogMGnSJLNu3bqaaJNfDUjVzp07DQHxJEJACAwWAiIig9Xfam1LEJg8ebLdeLAlzTGbNm0y\n06dPb0tz1A4hIARSICAikgIsJRUCdUGAnYmxIrQl9saiRYsM5EoiBITA4CEgIjJ4fa4WtwQBLAhf\n+cpXGt+a7373u3ZaBnIlEQJCYPAQGDUUyOA1Wy0WAs1HAGvIhz70IfODH/zA4MDaVDn99NPN0Ucf\nbS699NKmNkF6CwEhkAMBWURygKesQqBKBNgkbuLEiebuu++uUo1cdWMNIX7I+eefn6scZRYCQqC5\nCMgi0ty+k+ZCwPqJEFeE8OgQk6aJrCFN6zHpKwSKR0AWkeIxVYlCoG8IsNz12muvbeS0xvLly80b\nb7wha0jf7hZVJATqiYAsIvXsF2klBBIj8Ktf/cpgFbnuuuvMeeedlzhflQmdf8uaNWusn0uVuqhu\nISAEqkVARKRa/FW7ECgEAXwtpk6dap566qlGBAU77rjjzLHHHmvmzZtXSPtViBAQAs1FQFMzze07\naS4EOgiwembu3LnmzDPPNFhI6ixXXHGFVU8kpM69JN2EQP8QkEWkf1irJiFQOgIM8i+//LJZu3Zt\nLZf0ot/69evNxo0ba6lf6R2kCoSAEBiBgCwiIyDRCSHQXARuvPFGc9hhh5kpU6bUzjICCVm5cqV5\n4IEHREKae4tJcyFQOAIiIoVDqgKFQLUI+GSkDjv0MlXkLDVN8WGptgdVuxAYLARERAarv9XaAUEA\nMoIzKE6hGzZsqKzVrI7BOuOmi7S7bmVdoYqFQG0REBGpbddIMSGQDwGcQe+9915z7rnnmpkzZ/Z9\nqoY4ITjRQoiwhDQ5DH2+nlBuISAEuiEgItINHV0TAg1HgI3k2IsGIdbIzTffXHqLWEqMJYYddYkT\notUxpUOuCoRAoxHQqplGd5+UFwLJEYAgLFiwwEYz/exnP2sjmhZppXj44YfNihUr7N4xTQqulhxB\npRQCQqAMBEREykBVZQqBGiMAIbnjjjvMsmXLzIwZM8wpp5xiJk2alGnqhLIef/xxc/vtt5vRo0eb\nOXPmmE9+8pOZyqoxZFJNCAiBEhEQESkRXBUtBOqMAI6kTz75pHnkkUfMqlWrrC8HS3/HjBljxo8f\nb3bbbbcR6rNT7q5du8yWLVtsnkMOOcRMmzbNnHHGGY2I6DqiQTohBIRA5QiIiFTeBVJACNQDAawb\nW7dutUTjmWeeiVRq7NixHaJy4IEHNnLH38iG6aQQEAKVISAiUhn0qlgICAEhIASEgBDQqhndA0JA\nCAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUhICJSGfSqWAgIASEgBISA\nEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJCQERE94AQEAJCQAgIASFQGQIiIpVBr4qF\ngBAQAkJACAgBERHdA0JACAgBISAEhEBlCIiIVAa9KhYCQkAICAEhIARERHQPCAEhIASEgBAQApUh\nICJSGfSqWAgIASEgBISAEBAR0T0gBISAEBACQkAIVIaAiEhl0KtiISAEhIAQEAJC4P8Di13nEo+f\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \n", " global layer_0\n", " \n", " # clear out previous state, reset the layer to be all 0s\n", " layer_0 = 0\n", " for word in review.split(\" \"):\n", " layer_0[0][word2index[word]] += 1\n", "\n", "update_input_layer(reviews[0])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 18., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "review_counter = Counter()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "for word in reviews[0].split(\" \"):\n", " review_counter[word] += 1" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('.', 27),\n", " ('', 18),\n", " ('the', 9),\n", " ('to', 6),\n", " ('i', 5),\n", " ('high', 5),\n", " ('is', 4),\n", " ('of', 4),\n", " ('a', 4),\n", " ('bromwell', 4),\n", " ('teachers', 4),\n", " ('that', 4),\n", " ('their', 2),\n", " ('my', 2),\n", " ('at', 2),\n", " ('as', 2),\n", " ('me', 2),\n", " ('in', 2),\n", " ('students', 2),\n", " ('it', 2),\n", " ('student', 2),\n", " ('school', 2),\n", " ('through', 1),\n", " ('insightful', 1),\n", " ('ran', 1),\n", " ('years', 1),\n", " ('here', 1),\n", " ('episode', 1),\n", " ('reality', 1),\n", " ('what', 1),\n", " ('far', 1),\n", " ('t', 1),\n", " ('saw', 1),\n", " ('s', 1),\n", " ('repeatedly', 1),\n", " ('isn', 1),\n", " ('closer', 1),\n", " ('and', 1),\n", " ('fetched', 1),\n", " ('remind', 1),\n", " ('can', 1),\n", " ('welcome', 1),\n", " ('line', 1),\n", " ('your', 1),\n", " ('survive', 1),\n", " ('teaching', 1),\n", " ('satire', 1),\n", " ('classic', 1),\n", " ('who', 1),\n", " ('age', 1),\n", " ('knew', 1),\n", " ('schools', 1),\n", " ('inspector', 1),\n", " ('comedy', 1),\n", " ('down', 1),\n", " ('about', 1),\n", " ('pity', 1),\n", " ('m', 1),\n", " ('all', 1),\n", " ('adults', 1),\n", " ('see', 1),\n", " ('think', 1),\n", " ('situation', 1),\n", " ('time', 1),\n", " ('pomp', 1),\n", " ('lead', 1),\n", " ('other', 1),\n", " ('much', 1),\n", " ('many', 1),\n", " ('which', 1),\n", " ('one', 1),\n", " ('profession', 1),\n", " ('programs', 1),\n", " ('same', 1),\n", " ('some', 1),\n", " ('such', 1),\n", " ('pettiness', 1),\n", " ('immediately', 1),\n", " ('expect', 1),\n", " ('financially', 1),\n", " ('recalled', 1),\n", " ('tried', 1),\n", " ('whole', 1),\n", " ('right', 1),\n", " ('life', 1),\n", " ('cartoon', 1),\n", " ('scramble', 1),\n", " ('sack', 1),\n", " ('believe', 1),\n", " ('when', 1),\n", " ('than', 1),\n", " ('burn', 1),\n", " ('pathetic', 1)]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review_counter.most_common()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 4: Reducing Noise in our Input Data" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " # set our random number generator \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews, labels)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self, reviews, labels):\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " \n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 = 0\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " self.layer_0[0][self.word2index[word]] = 1\n", " \n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output * (1 - output)\n", " \n", " def train(self, training_reviews, training_labels):\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", " self.update_input_layer(review)\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # TODO: Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # TODO: Update the weights\n", " self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(np.abs(layer_2_error) < 0.5):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " if(i % 2500 == 0):\n", " print(\"\")\n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", " self.update_input_layer(review.lower())\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] > 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):91.50 #Correct:1795 #Trained:2501 Training Accuracy:71.7%\n", "Progress:20.8% Speed(reviews/sec):95.25 #Correct:3811 #Trained:5001 Training Accuracy:76.2%\n", "Progress:31.2% Speed(reviews/sec):93.74 #Correct:5898 #Trained:7501 Training Accuracy:78.6%\n", "Progress:41.6% Speed(reviews/sec):93.69 #Correct:8042 #Trained:10001 Training Accuracy:80.4%\n", "Progress:52.0% Speed(reviews/sec):95.27 #Correct:10186 #Trained:12501 Training Accuracy:81.4%\n", "Progress:62.5% Speed(reviews/sec):98.19 #Correct:12317 #Trained:15001 Training Accuracy:82.1%\n", "Progress:72.9% Speed(reviews/sec):98.56 #Correct:14440 #Trained:17501 Training Accuracy:82.5%\n", "Progress:83.3% Speed(reviews/sec):99.74 #Correct:16613 #Trained:20001 Training Accuracy:83.0%\n", "Progress:93.7% Speed(reviews/sec):100.7 #Correct:18794 #Trained:22501 Training Accuracy:83.5%\n", "Progress:99.9% Speed(reviews/sec):101.9 #Correct:20115 #Trained:24000 Training Accuracy:83.8%" ] } ], "source": [ "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):832.7% #Correct:851 #Tested:1000 Testing Accuracy:85.1%" ] } ], "source": [ "# evaluate our model before training (just to show how horrible it is)\n", "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Analyzing Inefficiencies in our Network" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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kXFTbwvXm0k/K0NchI8Tp5BOdPl86uRb3F2dRbZa6Cu3Jq/u4UPq0XIfglrLm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\nEDAEykSgaYlXsXhBrCAwEKcwyeIc15588sm8xZGmEGmCdEHSCpWVqyI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SVSxyls4QMATi\nRMCIV5xoWlklI5AUcqMVx72IWzGppBD90E1LOS5KifuSme7DcV8QMCxf//uL813XQ7/lJl42QVeZ\numOsXpeMG+tjvYx0pa77TGFDoGEQMOLVMF2Z3oZAvhgI+WqwEktOHAhAaiTeB52SaImLameUi1La\nEZVezgn5kiknIF96vi/I13/3+W83c9ZtiZ8+QtqUb9+tW3f3jW/s0xCrKORrp10zBAyB5CKwUXJV\nM82aBQHip4j5gijU82tHiBaz67OhR1pIF/dJlMWL9miJclEyEz/yhS98we/10kPMUn/XXXe5vTvt\n0xCkiwZ2PfTb7h+ffuLban8MAUPAEKgHAmbxqgfqVmckAkJ8hIBBJmohWLkk2J99Nb5erEU7CtUR\n5aKUwP1w3JcE3Q8/8+dup52/ltqg+jAmMq/XipUrwpfstyFgCBgCNUHALF41gdkqKQYBCBcuM4gP\nhIA9WzUtT2Jtg+QRN1UrslcMHnGnAUcw1hIO3IeAHXbYYdlFt1e9+IL7/g9+oLOk+lgW86bd9XZr\npxpIU94QMATKRsAsXmVDZxmriQDWL6xPDJCQL+LA4iJFWH4gXLgTEfa4F00+R2DRokUOArZ27Vp3\n4okn+uNGwoY1HDt8vZ2/rxqpXdYWQ8AQSAcCG6ZDTdOy2RDAMgP5IuAeK9huu+2Wjb3id6kiZAuC\n1apVK18uhIuyjHS1RLNbt26ObZtttgmsXSe3vNgAv3bdbXe3bt0HDdASa4IhYAikEQFzNaax15pI\nZwgYGyQJEsaGJQy3GRYwriGy9z+CP+JCY8/2yiuveBeaBM7HZT2T+hptT5A9uG2xxRaN1jS3xx5t\n3dw5cxquXdYgQ8AQSAcCRrzS0U9NryVEC3cjGyKECouVBMf7C//+A7Fig2jhqgwTM53WjqMR+MJG\nX3RYhxpNGpFMNlofWXsMgUZGwIhXI/duA7eNwGgLjq5uBzOxaiPK13be2f3hiccbsWnWJkPAEEgB\nAhumQEdT0RAwBAyB2BDo2KG9W/nnlbGVZwUZAoaAIVAKAka8SkHL0hoChoAhYAgYAoaAIVABAka8\nKgDPshoChkD6EHj2OZs8NX29ZhobAo2DgBGvxulLa4khECsCsoxQrIUmoLBXX3vN/eCkUxKgialg\nCBgCzYiAEa9m7HVrsyFQBAL/+udn7q9vv11ESktiCBgChoAhUCwCRryKRcrSGQJNhgBfjb711psN\n1+qnnvqja9+uXcO1yxpkCBgC6UDAiFc6+sm0NARqjgDE6ze33FTzeqtd4V9efsltueXm1a7GyjcE\nDAFDIBIBI16RsNhJQ8AQ+HxR7W5uydLHGgqMF4KpJGxC3YbqUmuMIZAqBIx4paq7TFlDoLYIHHBg\nF/fgQ4trW+n/397d9ER1hmEcv+YDsNRUUDMJLEhdYayg3dD6/sZAurOFIU2qSNGkTaoZSXShKGqi\nCRo7VYkVW0QDTtqmMbY2gcS2GKDMoo3ESGUjfgLWyjNqAqiYyDkz5z7nPyvmDPOc+/nds7hyXp7j\n495ciHwyOcniuz4aMzQCCMwvQPCa34dPEYi0wNo1lRr8+6/QGAyPjGhHojY082EiCCBgT4DgZa9n\nVIxA3gTcsy4fjN1XWNa+yvT16sO1VXnzY0cIIIDAXAGC11wR3iOAwCyBmto6XbrUOWubxTe3bv+e\nO83owiQvBBBAoFACBK9CybNfBIwINO/ZrVu//qLJJ7aXlrja1aXmlhYj6pSJAAJhFSB4hbWzzAsB\njwTi8bhWrvpA31+56tGI+R/GHe36Z3hIDfWsWJ9/ffaIAAIzBWJPp18zN/A3AgggMFcgm82qoqJC\n//53XyveL5/7ceDf7/y0XlVVldq3lyNegW8WBSIQcgGOeIW8wUwPAS8E3GKqR462qa2tzYvh8jpG\nx7nz09d2PeZoV17V2RkCCLxJgOD1Jhm2I4DALIGWL5tzp+tckLHycndjnj/bocOHD8ktCMsLAQQQ\nKLQAwavQHWD/CBgRcMEl/V06F2SsrGafSqX0WUMDK9Ub+Y1RJgJREOAaryh0mTki4KFAR8dZZTIZ\n/djdreIl73k4srdDffX1Nxoff6iff8p4OzCjIYAAAgsQIHgtAI+vIhBVgf0HUhobG1M6/W0gw5cL\nXdnRkemAeJNTjFH9kTJvBAIqwKnGgDaGshAIssDJE8dVXl6upqY9gVvfy4Uut+7YmTOnCV1B/hFR\nGwIRFSB4RbTxTBuBhQrMDF9BuObLLfD68vRiz/UeHoS90AbzfQQQ8EWA4OULK4MiEA0BF74qV6/W\n541J3ei9WbBJu7sX3dE3d01X15XLhK6CdYIdI4DA2wQIXm8T4nMEEJhXoLU1pfYT7TrUelC7duf/\n1KNb3uKTutpcAHQX0rNsxLzt4kMEECiwAMGrwA1g9wiEQcA9eHrw3qBisZg+rq7WsfZTvk/LPQao\nJlGnTF9vbpkLFwB5IYAAAkEX4K7GoHeI+hAwJtDf368LFztzq8Vv2LRFjcl6T+987LzcpT/uPH/2\nYupgSslk0pgQ5SKAQJQFCF5R7j5zR8BHARfAuq9d18ULaX2xq0mVVWu0ZfPGdwph7ujW3bt/qu9G\nj5YUF6tx+pqyRCLBaUUf+8fQCCDgjwDByx9XRkUAgRcCExMTGhgY0O3f7uha9w/aUVOr0tIyLVq8\nWGVlpSoqKnrFanQ0q6mpKT36fzz3nerqj7Ru/Xpt37aVC+df0WIDAghYEiB4WeoWtSIQAgF3JCyb\nzeqpYhoaGn7tjEpKSrRsaYmWL1+WC1rxePy1/8dGBBBAwJoAwctax6gXAQQQQAABBMwKcFej2dZR\nOAIIIIAAAghYEyB4WesY9SKAAAIIIICAWQGCl9nWUTgCCCCAAAIIWBMgeFnrGPUigAACCCCAgFkB\ngpfZ1lE4AggggAACCFgTIHhZ6xj1IoAAAggggIBZAYKX2dZROAIIIIAAAghYEyB4WesY9SKAAAII\nIICAWQGCl9nWUTgCCCCAAAIIWBMgeFnrGPUigAACCCCAgFkBgpfZ1lE4AggggAACCFgTIHhZ6xj1\nIoAAAggggIBZAYKX2dZROAIIIIAAAghYEyB4WesY9SKAAAIIIICAWQGCl9nWUTgCCCCAAAIIWBMg\neFnrGPUigAACCCCAgFkBgpfZ1lE4AggggAACCFgTIHhZ6xj1IoAAAggggIBZAYKX2dZROAIIIIAA\nAghYEyB4WesY9SKAAAIIIICAWYFnu/zIiInRwogAAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='sentiment_network_sparse.png')" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "layer_0 = np.zeros(10)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "layer_0[4] = 1\n", "layer_0[9] = 1" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.])" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "weights_0_1 = np.random.randn(10,5)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([-0.10503756, 0.44222989, 0.24392938, -0.55961832, 0.21389503])" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0.dot(weights_0_1)" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "indices = [4,9]" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "layer_1 = np.zeros(5)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "for index in indices:\n", " layer_1 += (weights_0_1[index])" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([-0.10503756, 0.44222989, 0.24392938, -0.55961832, 0.21389503])" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_1" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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apXPHaKqJjr744WSiQNCj0ezqd865210y9hI/u3m/vfdKvdUE4BGUGvC09FTb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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='sentiment_network_sparse_2.png')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 5: Making our Network More Efficient" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self,reviews):\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes*-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " self.layer_1 = np.zeros((1,hidden_nodes))\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output (1 - output)\n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 = 0\n", " for word in review.split(\" \"):\n", " self.layer_0[0][self.word2index[word]] = 1\n", "\n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def train(self, training_reviews_raw, training_labels):\n", " \n", " training_reviews = list()\n", " for review in training_reviews_raw:\n", " indices = set()\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " indices.add(self.word2index[word])\n", " training_reviews.append(list(indices))\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", "\n", " # Hidden layer\n", "# layer_1 = self.layer_0.dot(self.weights_0_1)\n", " self.layer_1 = 0\n", " for index in review:\n", " self.layer_1 += self.weights_0_1[index]\n", " \n", " # Output layer\n", " layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # Update the weights\n", " self.weights_1_2 -= self.layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " \n", " for index in review:\n", " self.weights_0_1[index] -= layer_1_delta[0] * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(np.abs(layer_2_error) < 0.5):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " \n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", "\n", "\n", " # Hidden layer\n", " self.layer_1 *= 0\n", " unique_indices = set()\n", " for word in review.lower().split(\" \"):\n", " if word in self.word2index.keys():\n", " unique_indices.add(self.word2index[word])\n", " for index in unique_indices:\n", " self.layer_1 += self.weights_0_1[index]\n", " \n", " # Output layer\n", " layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] > 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):1581.% #Correct:857 #Tested:1000 Testing Accuracy:85.7%" ] } ], "source": [ "# evaluate our model before training (just to show how horrible it is)\n", "mlp.test(reviews[-1000:],labels[-1000:])" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
JudoWill/ResearchNotebooks	AgingAnalysis.ipynb	1	2622580	null	mit
GentleZhu/gentlezhu.github.io	markdown_generator/PubsFromBib.ipynb	103	8764	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Publications markdown generator for academicpages\n", "\n", "Takes a set of bibtex of publications and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html)). \n", "\n", "The core python code is also in `pubsFromBibs.py`. \n", "Run either from the `markdown_generator` folder after replacing updating the publist dictionary with:\n", "* bib file names\n", "* specific venue keys based on your bib file preferences\n", "* any specific pre-text for specific files\n", "* Collection Name (future feature)\n", "\n", "TODO: Make this work with other databases of citations, \n", "TODO: Merge this with the existing TSV parsing solution" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pybtex.database.input import bibtex\n", "import pybtex.database.input.bibtex \n", "from time import strptime\n", "import string\n", "import html\n", "import os\n", "import re" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#todo: incorporate different collection types rather than a catch all publications, requires other changes to template\n", "publist = {\n", " \"proceeding\": {\n", " \"file\" : \"proceedings.bib\",\n", " \"venuekey\": \"booktitle\",\n", " \"venue-pretext\": \"In the proceedings of \",\n", " \"collection\" : {\"name\":\"publications\",\n", " \"permalink\":\"/publication/\"}\n", " \n", " },\n", " \"journal\":{\n", " \"file\": \"pubs.bib\",\n", " \"venuekey\" : \"journal\",\n", " \"venue-pretext\" : \"\",\n", " \"collection\" : {\"name\":\"publications\",\n", " \"permalink\":\"/publication/\"}\n", " } \n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "html_escape_table = {\n", " \"&\": \"&\",\n", " '\"': \""\",\n", " \"'\": \"'\"\n", " }\n", "\n", "def html_escape(text):\n", " \"\"\"Produce entities within text.\"\"\"\n", " return \"\".join(html_escape_table.get(c,c) for c in text)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "for pubsource in publist:\n", " parser = bibtex.Parser()\n", " bibdata = parser.parse_file(publist[pubsource][\"file\"])\n", "\n", " #loop through the individual references in a given bibtex file\n", " for bib_id in bibdata.entries:\n", " #reset default date\n", " pub_year = \"1900\"\n", " pub_month = \"01\"\n", " pub_day = \"01\"\n", " \n", " b = bibdata.entries[bib_id].fields\n", " \n", " try:\n", " pub_year = f'{b[\"year\"]}'\n", "\n", " #todo: this hack for month and day needs some cleanup\n", " if \"month\" in b.keys(): \n", " if(len(b[\"month\"])<3):\n", " pub_month = \"0\"+b[\"month\"]\n", " pub_month = pub_month[-2:]\n", " elif(b[\"month\"] not in range(12)):\n", " tmnth = strptime(b[\"month\"][:3],'%b').tm_mon \n", " pub_month = \"{:02d}\".format(tmnth) \n", " else:\n", " pub_month = str(b[\"month\"])\n", " if \"day\" in b.keys(): \n", " pub_day = str(b[\"day\"])\n", "\n", " \n", " pub_date = pub_year+\"-\"+pub_month+\"-\"+pub_day\n", " \n", " #strip out {} as needed (some bibtex entries that maintain formatting)\n", " clean_title = b[\"title\"].replace(\"{\", \"\").replace(\"}\",\"\").replace(\"\\\\\",\"\").replace(\" \",\"-\") \n", "\n", " url_slug = re.sub(\"\\\\[.\\\\]\|[^a-zA-Z0-9_-]\", \"\", clean_title)\n", " url_slug = url_slug.replace(\"--\",\"-\")\n", "\n", " md_filename = (str(pub_date) + \"-\" + url_slug + \".md\").replace(\"--\",\"-\")\n", " html_filename = (str(pub_date) + \"-\" + url_slug).replace(\"--\",\"-\")\n", "\n", " #Build Citation from text\n", " citation = \"\"\n", "\n", " #citation authors - todo - add highlighting for primary author?\n", " for author in bibdata.entries[bib_id].persons[\"author\"]:\n", " citation = citation+\" \"+author.first_names[0]+\" \"+author.last_names[0]+\", \"\n", "\n", " #citation title\n", " citation = citation + \"\\\"\" + html_escape(b[\"title\"].replace(\"{\", \"\").replace(\"}\",\"\").replace(\"\\\\\",\"\")) + \".\\\"\"\n", "\n", " #add venue logic depending on citation type\n", " venue = publist[pubsource][\"venue-pretext\"]+b[publist[pubsource][\"venuekey\"]].replace(\"{\", \"\").replace(\"}\",\"\").replace(\"\\\\\",\"\")\n", "\n", " citation = citation + \" \" + html_escape(venue)\n", " citation = citation + \", \" + pub_year + \".\"\n", "\n", " \n", " ## YAML variables\n", " md = \"---\\ntitle: \\\"\" + html_escape(b[\"title\"].replace(\"{\", \"\").replace(\"}\",\"\").replace(\"\\\\\",\"\")) + '\"\\n'\n", " \n", " md += \"\"\"collection: \"\"\" + publist[pubsource][\"collection\"][\"name\"]\n", "\n", " md += \"\"\"\\npermalink: \"\"\" + publist[pubsource][\"collection\"][\"permalink\"] + html_filename\n", " \n", " note = False\n", " if \"note\" in b.keys():\n", " if len(str(b[\"note\"])) > 5:\n", " md += \"\\nexcerpt: '\" + html_escape(b[\"note\"]) + \"'\"\n", " note = True\n", "\n", " md += \"\\ndate: \" + str(pub_date) \n", "\n", " md += \"\\nvenue: '\" + html_escape(venue) + \"'\"\n", " \n", " url = False\n", " if \"url\" in b.keys():\n", " if len(str(b[\"url\"])) > 5:\n", " md += \"\\npaperurl: '\" + b[\"url\"] + \"'\"\n", " url = True\n", "\n", " md += \"\\ncitation: '\" + html_escape(citation) + \"'\"\n", "\n", " md += \"\\n---\"\n", "\n", " \n", " ## Markdown description for individual page\n", " if note:\n", " md += \"\\n\" + html_escape(b[\"note\"]) + \"\\n\"\n", "\n", " if url:\n", " md += \"\\n[Access paper here](\" + b[\"url\"] + \"){:target=\\\"_blank\\\"}\\n\" \n", " else:\n", " md += \"\\nUse [Google Scholar](https://scholar.google.com/scholar?q=\"+html.escape(clean_title.replace(\"-\",\"+\"))+\"){:target=\\\"_blank\\\"} for full citation\"\n", "\n", " md_filename = os.path.basename(md_filename)\n", "\n", " with open(\"../_publications/\" + md_filename, 'w') as f:\n", " f.write(md)\n", " print(f'SUCESSFULLY PARSED {bib_id}: \\\"', b[\"title\"][:60],\"...\"(len(b['title'])>60),\"\\\"\")\n", " # field may not exist for a reference\n", " except KeyError as e:\n", " print(f'WARNING Missing Expected Field {e} from entry {bib_id}: \\\"', b[\"title\"][:30],\"...\"*(len(b['title'])>30),\"\\\"\")\n", " continue\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
mrcinv/matpy	oma/kolokviji/OMA, 2. kolokvij 2012_2013.ipynb	1	109285	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Installed tikzmagic.py. To use it, type:\n", " %load_ext tikzmagic\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/gregor/.virtualenvs/oma/lib/python3.4/site-packages/IPython/core/magics/extension.py:47: UserWarning: %install_ext` is deprecated, please distribute your extension(s)as a python packages.\n", " \"as a python packages.\", UserWarning)\n" ] } ], "source": [ "import math\n", "import sympy\n", "from sympy import latex, solve, Eq\n", "from IPython.display import HTML, display\n", "from sympy.abc import x, a, b\n", "\n", "%matplotlib notebook\n", "%install_ext https://raw.githubusercontent.com/meduz/ipython_magics/master/tikzmagic.py\n", "%load_ext tikzmagic\n", " \n", "sympy.init_printing()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# OMA 2. kolokvij 2012/2013" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. naloga" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "V polkrog z radijem 1 vcrtamo pravokotnik ABCD tako, da oglisci A in B lezita na premeru, oglisci C in D pa na loku polkroga. Koliksni naj bosta dolzini stranic pravokotnika, da bo ploscina pravokotnika maksimalna?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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zQkuQBQAgyB6NvO6JvddVD6a/IifeIc/zymKdHWTP7C1HbeRMAEgzxHiMUPl4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\nKReu5aDqHtuIOn+zg2CoRTB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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%tikz s 400,400 -sc 1.2 -f png\n", "\\draw [domain=0:180] plot ({cos(\\x)}, {sin(\\x)});\n", "\\draw (-1,0) -- (1, 0);\n", "\\draw [color=red] (-0.5, 0) -- node[below, color=black] {2a} ++ (1, 0);\n", "\\draw [color=red] (-0.5, 0.8660254037844386) -- (0.5, 0.8660254037844386);\n", "\\draw [color=red] (-0.5, 0) -- node[left, color=black] {b} ++ (0, 0.8660254037844386);\n", "\\draw [color=red] (0.5, 0.8660254037844386) -- (0.5, 0);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maksimiziramo funkcijo $P(x)=2ab$. Velja tudi $a^2 + b^2 = 1$. Namesto ploscine bomo maksimizirali njen kvadrat (ki ima maksimum v isti tocki kot prvotna funkcija." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAALsAAAAcBAMAAADLp9MPAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nbcIO9TgFxgAAAABJRU5ErkJggg==\n", "text/latex": [ "$$P{\\left (b \\right )} = 4 b^{2} \\left(- b^{2} + 1\\right)$$" ], "text/plain": [ " 2 ⎛ 2 ⎞\n", "P(b) = 4⋅b ⋅⎝- b + 1⎠" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "P = sympy.symbols('P', cls=sympy.Function)\n", "eq1 = Eq(P(b), (2ab)2)\n", "eq2 = Eq(a2+b2, 1)\n", "equation = Eq(P(b), solve([eq1, eq2], P(b), a2)[P(b)])\n", "equation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAvBAMAAAACzbekAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAInarRM2ZVBDdiWbv\nuzJCz3LGAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVQoFbWRsQ7BUBSG/1aJVtJYTewMBgMT\nj2A0SHRiZLJqmGwdbUwGk9kbSLyCxEMYkEidc93T3u6c4T/na869vckH6KrGXEJoJRMPVj+DhVkG\n1xnCBNb4epNvxQgreHdB74g2cBGko2egK9t1YBQqdHq0Eqm1bQg06AF2jdF50XOWdJ/LBLdJUXwD\nJ4VjldMyNjzYgcJqUNrxMIdVppZ7uCE1P4DN6L/mlKgc9h3uGA44R3H85I5FoJpEno/8vZSLJH7+\nO9MPXW76ITT9EJp+CLUfmqTYT1rsxyjlJ+WvH2HtR1D70Sh+NCZ+vpz4+QDG90XydfieogAAAABJ\nRU5ErkJggg==\n", "text/latex": [ "$$\\frac{\\sqrt{2}}{2}$$" ], "text/plain": [ " ___\n", "╲╱ 2 \n", "─────\n", " 2 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "P = sympy.lambdify(b, equation.rhs)\n", "x = sympy.symbols('x', positive=True)\n", "solve(Eq(P(x).diff(x), 0))[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. naloga" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Naj bo \n", "$$f(x,y)=3x^2-3y^2+8xy-6x-8y+3.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Izracunaj gradient funkcije $f(x,y)$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAAUBAMAAAC5TlbCAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", "text/latex": [ "$$\\left ( 6 x + 8 y - 6, \\quad 8 x - 6 y - 8\\right )$$" ], "text/plain": [ "(6⋅x + 8⋅y - 6, 8⋅x - 6⋅y - 8)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x, y = sympy.symbols('x y')\n", "f = lambda x, y: 3x2 - 3y*2 + 8xy-6x-8y+3\n", "f(x,y).diff(x), f(x,y).diff(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Izracunaj stacionarne tocke funkcije $f(x,y)$." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAH4AAAAVBAMAAAByPkciAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAZpkQ3Ynvq81UMrtE\ndiLw+n06AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABrklEQVQ4EZ2UMUjDUBCG/+Sl2NI2uugqHcQu\nQiY7lSoiTpWAi4sguIgg1EFxDIK4Zupog6tDuzmI2KmbtFS0k6i7g1pEJ/HeS1KTl4iQGy69/97H\nXe5eCkApIJmd3XKOLSejieo45JTXBDybmSRWtQhNbZKTLLMlCXKYttkqoQukx/Cl2aEMSPEUsO6h\nMTzy//EXQMNwSyfiP4GuHeDLg5e7HvWo1USjMfXLc9DFVFboBHsnvufyYxbFplLV6Il57hDXv1lH\nnm+KbZPTP4Cm6a6u7tAgjNQw80QJz6L1dbuIbMvP61SfeBwB5zskMqRHOX4myjPsYrzn817/GKt6\nlyiQi+eRG6Lp+Dxofg0bOCGBvz9143DvW7Q+VAv3fhq4BKYN9/35JcoYj8gbv+kYPlsDLd03uj/F\nwP3p2td45rnA/CuWf1g81Vbujf8Q84dmM5qbuDrclR7KhQmedfevHnztQVviwsjYfv9bBHz/YBsD\nJ8CLhOxOJYEmINmovqS7oRlSD1FphwQKBP/X9896oeOLuArFPHA7Oo7oQlDC8k3fCQsUddpcyiT+\n/1sDfgDNr2XH9LJoSQAAAABJRU5ErkJggg==\n", "text/latex": [ "$$\\left \\{ x : 1, \\quad y : 0\\right \\}$$" ], "text/plain": [ "{x: 1, y: 0}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sympy.solve([f(x,y).diff(x), f(x,y).diff(y)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. naloga" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Izracunaj odvod funkcije\n", "$$\\frac{\\cos(x)}{\\sin(x)}.$$\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFUAAAAxBAMAAABZvvglAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAAAElFTkSuQmCC\n", "text/latex": [ "$$- \\frac{1}{\\sin^{2}{\\left (x \\right )}}$$" ], "text/plain": [ " -1 \n", "───────\n", " 2 \n", "sin (x)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = sympy.symbols('x')\n", "f = lambda x: sympy.cos(x)/sympy.sin(x)\n", "sympy.simplify(f(x).diff())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "S pomocjo substitucije izracunaj nedoloceni integral\n", "$$\\int \\frac{\\cos(x)}{\\sin(x)}.$$\n", "" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJUAAAAqBAMAAABIEATcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n5QAAAABJRU5ErkJggg==\n", "text/latex": [ "$$\\frac{1}{2} \\log{\\left (- \\sin^{2}{\\left (x \\right )} \\right )}$$" ], "text/plain": [ " ⎛ 2 ⎞\n", "log⎝-sin (x)⎠\n", "─────────────\n", " 2 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sympy.simplify(f(x).integrate())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "V zgorjem racunu poleg konstante znotraj funkcije $\\log$ manjka se absolutna vrednost (sympy racuna v kompleksnih stevilih), tako da je pravi rezultat\n", "$$ \\frac{1}{2}\\log(\\sin^2(x)) + C = \\log(\\sin^2(x)) + C.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "S pomocjo pravila za integriranje po delih izracunaj\n", "$$\\int\\frac{x}{\\sin^2(x)}.$$" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAA2BAMAAABaYdL2AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ngDffizMz+OivXNpE/j/6i56B/wKSb5X7Mx9FYwAAAABJRU5ErkJggg==\n", "text/latex": [ "$$\\frac{x}{2} \\tan{\\left (\\frac{x}{2} \\right )} - \\frac{x}{2 \\tan{\\left (\\frac{x}{2} \\right )}} - \\log{\\left (\\frac{1}{\\cos{\\left (x \\right )} + 1} \\right )} + \\log{\\left (\\tan{\\left (\\frac{x}{2} \\right )} \\right )} - \\log{\\left (2 \\right )}$$" ], "text/plain": [ " ⎛x⎞ \n", "x⋅tan⎜─⎟ \n", " ⎝2⎠ x ⎛ 1 ⎞ ⎛ ⎛x⎞⎞ \n", "──────── - ──────── - log⎜──────────⎟ + log⎜tan⎜─⎟⎟ - log(2)\n", " 2 ⎛x⎞ ⎝cos(x) + 1⎠ ⎝ ⎝2⎠⎠ \n", " 2⋅tan⎜─⎟ \n", " ⎝2⎠ " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = sympy.symbols('x')\n", "f = lambda x: x/sympy.sin(x)2\n", "sympy.simplify(f(x).integrate())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tudi to resitev se da poenostaviti v \n", "$$ \\int\\frac{x}{\\sin^2(x)} = \\log(\|\\sin(x)\|) - x\\cot(x) + C.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. naloga" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Narisite lik, ki ga omejujeta krivulji $y=e^{2x}$ in $y=-e^{2x}+4$. Izracunajte ploscino lika.\n" ] }, { "cell_type": "code", "execution_count": 277, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/ Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f3b688886a0>" ] }, "execution_count": 277, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from matplotlib import pyplot as plt\n", "import numpy as np\n", "x = sympy.symbols('x')\n", "f = lambda x: np.exp(2x)\n", "g = lambda x: -np.exp(2x)+4\n", "fig, ax = plt.subplots()\n", "xs = np.linspace(0,0.6)\n", "ax.fill_between(xs, f(xs),g(xs),where = f(xs)>=g(xs), facecolor='green',interpolate=True)\n", "ax.fill_between(xs, f(xs), g(xs), where = f(xs)<= g(xs),facecolor='red',interpolate=True)\n", "plt.title(\"Liki med dvema krivuljama.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Izracunati moramo ploscino rdecega lika." ] }, { "cell_type": "code", "execution_count": 287, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHgAAAAUBAMAAAC0fOTAAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM3dMlTvq5l2Zoki\nu0Rn3bgMAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAB3ElEQVQ4EZWUTygEURzHv/sfO7O2PXDgtCkH\nxRZxoEwpOSjKhYtWSo6DckCZg6xIORKHzclxk2JzMOWClDlwUNSE+7b+5KCM33tv1q4dpnxr3+/f\n+/Te++17A/xbF4Ko0t1IaeVeAfzTmZ+TJAM4W78GFkrygbGSgLm1CH+SqVF/5r06PCpOTMSK+dpU\nvhCMCGcKmCBPLoP3gFAUwSTCGwUAqCyHx4FFxQl3AcEEQq/wJF3gluhvsJQAvHkGo9kFplJ3lK/s\nyW6biMyvbhm0QY0jPtrnkSsc+aAynblHxy1yUV+aTimnOZLLALuusE8VcCcwoMwglKDQN8iRDhqH\nEGtlavhu2H483h6PN/IZK2yUVf8bUJ1ZFnDQZMkKjYYB5gk5um0fT1YD9HdXG1dKDVtUwJuMcYWH\nIenfK2vn2QNG8G0HNFwCjywWcqzs11Cp84ZNAjlzVEzjDesDdtwbdnp8NEOAbKBXQRMenvoZHtYA\nqfM4Rc1bYzFXxezznO3a17PFsl7oYSy+69LaoQmvZdEOECGKXIsMddkpGy4r3KB+aINydD2F2F1z\nqt6ZAgIGUEU/7BWqpQ+jkPvDetro+INUlHV7RsmT/IMppuuy20ssYh8Drjvb/ssUP0Nfai57V6fk\ncMUAAAAASUVORK5CYII=\n", "text/latex": [ "$$-1 + 2 \\log{\\left (2 \\right )}$$" ], "text/plain": [ "-1 + 2⋅log(2)" ] }, "execution_count": 287, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = sympy.symbols('x', real=True)\n", "f = lambda x: sympy.E*(2x)\n", "g = lambda x: -sympy.E*(2x)+4\n", "intersection = sympy.solve(sympy.Eq(f(x), g(x)))[0]\n", "result = sympy.integrate(g(x)-f(x), (x, 0, intersection))\n", "result" ] }, { "cell_type": "code", "execution_count": 289, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKkAAAAPBAMAAABtvvLvAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", "text/latex": [ "$$0.386294361119891$$" ], "text/plain": [ "0.386294361119891" ] }, "execution_count": 289, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.evalf()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3+" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-2.0
zzsza/TIL	Google_Cloud_Platform/02. BigQuery.ipynb	1	5074	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# BigQuery" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## 사전작업\n", "1. [Google 로그인](https://accounts.google.com/Login)\n", "2. [Project 생성 및 선택](https://console.cloud.google.com/project?_ga=1.105140352.267165872.1487136809)\n", "\n", " 단, 처음에 체험판 등록하면 300$ 제공\n", "\n", "3. [BigQuery API 등록](https://console.cloud.google.com/flows/enableapi?apiid=bigquery&_ga=1.139222832.267165872.1487136809)\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "![BigQuery_UI](https://cloud.google.com/bigquery/images/bigquery-web-ui.png)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Left Side( the navigation bar )\n", "- COMPOSE QUERY : 쿼리 생성\n", "- Query History\n", "- Job History\n", "* 의문점 : Job은 뭐라고 정의해야할까-\n", "- project명\n", "- Public Dataset : 샘플 데이터로 실험 가능\n", "\n", "## Right Side( Query Editor )\n", "- SQL Query 문 작성\n", "- format Query를 누르면 조금 더 형식을 맞춤\n", "\n", "\n", "## Keyboard shortcuts\n", "\| Windows/Linux \| Mac \| Action \| \n", "\| :---: \| :---: \| :---: \| \n", "\| Ctrl + Space \| Ctrl + Space \| If no query is open: compose new query. If query editor is open: autocomplete current word. \| \n", "\| Ctrl + Enter \| Cmd + Enter \| Run current query. \| \n", "\| Tab \| Tab \| Autocomplete current word. \|\n", "\| Ctrl \| Cmd \| Highlight table names. \|\n", "\| Ctrl + click on table name \| Cmd + click on table name \| Open table schema. \|\n", "\| Ctrl + E \| Cmd + E \| \tRun query from selection. \|\n", "\| Ctrl + / \| Cmd + / \| Comment current or selected line(s). \|\n", "\| Ctrl + Shift + F \| Cmd + Shift + F \| Format query. \|" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# BigQuery 설명\n", "\n", "- 관계형, noSQL도 아님..! 가까운 것을 찾으라고 하면 NoSQL과 유사하다고 보면 됨\n", "- 맵리듀스도 아님\n", "- 오픈소스도 아니라는 점-!\n", "- 데이터셋 - 테이블 - 스키마 구조\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# 본격 BigQuery\n", "\n", "- [BigQuery 설명 페이지](https://developers.google.com/bigquery/docs/query-reference?hl=ko#having-)\n", "- [BigQuery Web UI](https://bigquery.cloud.google.com/welcome)\n", "\n", "- COMPOSE QUERY 클릭\n", "\n", "~~~\n", "#standardSQL\n", "SELECT\n", " weight_pounds, state, year, gestation_weeks\n", "FROM\n", " `bigquery-public-data.samples.natality`\n", "ORDER BY weight_pounds DESC LIMIT 10;\n", "~~~\n", "\n", "\n", "- Public Datasets을 클릭하면 각종 데이터를 볼 수 있음" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "## Data load(in local)\n", "[baby names data](http://www.ssa.gov/OACT/babynames/names.zip)\n", "\n", "- in Web UI, create new dataset 클릭\n", "- 이름을 설정한 후, create new table 클릭\n", "- 나머지 설정!\n", "~~~\n", "SELECT\n", " name, count\n", "FROM\n", " babynames.names_2014\n", "WHERE\n", " gender = 'M'\n", "ORDER BY count DESC LIMIT 5;\n", "~~~" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Reference\n", "[Quickstart Using the Web UI](https://cloud.google.com/bigquery/quickstart-web-ui)\n", "\n", "[빅쿼리 공식문서](https://developers.google.com/bigquery/docs/query-reference?hl=ko#having-)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Word\n", "- incurrying : 초래하는" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
statsmodels/statsmodels.github.io	v0.12.1/examples/notebooks/generated/markov_autoregression.ipynb	4	483030	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Markov switching autoregression models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook provides an example of the use of Markov switching models in statsmodels to replicate a number of results presented in Kim and Nelson (1999). It applies the Hamilton (1989) filter the Kim (1994) smoother.\n", "\n", "This is tested against the Markov-switching models from E-views 8, which can be found at http://www.eviews.com/EViews8/ev8ecswitch_n.html#MarkovAR or the Markov-switching models of Stata 14 which can be found at http://www.stata.com/manuals14/tsmswitch.pdf." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/travis/miniconda/envs/statsmodels-test/lib/python3.7/site-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", " from pandas.util.testing import assert_frame_equal\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "import requests\n", "from io import BytesIO\n", "\n", "# NBER recessions\n", "from pandas_datareader.data import DataReader\n", "from datetime import datetime\n", "usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), end=datetime(2013, 4, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hamilton (1989) switching model of GNP\n", "\n", "This replicates Hamilton's (1989) seminal paper introducing Markov-switching models. The model is an autoregressive model of order 4 in which the mean of the process switches between two regimes. It can be written:\n", "\n", "$$\n", "y_t = \\mu_{S_t} + \\phi_1 (y_{t-1} - \\mu_{S_{t-1}}) + \\phi_2 (y_{t-2} - \\mu_{S_{t-2}}) + \\phi_3 (y_{t-3} - \\mu_{S_{t-3}}) + \\phi_4 (y_{t-4} - \\mu_{S_{t-4}}) + \\varepsilon_t\n", "$$\n", "\n", "Each period, the regime transitions according to the following matrix of transition probabilities:\n", "\n", "$$ P(S_t = s_t \| S_{t-1} = s_{t-1}) =\n", "\\begin{bmatrix}\n", "p_{00} & p_{10} \\\\\n", "p_{01} & p_{11}\n", "\\end{bmatrix}\n", "$$\n", "\n", "where $p_{ij}$ is the probability of transitioning from regime $i$, to regime $j$.\n", "\n", "The model class is `MarkovAutoregression` in the time-series part of `statsmodels`. In order to create the model, we must specify the number of regimes with `k_regimes=2`, and the order of the autoregression with `order=4`. The default model also includes switching autoregressive coefficients, so here we also need to specify `switching_ar=False` to avoid that.\n", "\n", "After creation, the model is `fit` via maximum likelihood estimation. Under the hood, good starting parameters are found using a number of steps of the expectation maximization (EM) algorithm, and a quasi-Newton (BFGS) algorithm is applied to quickly find the maximum." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the RGNP data to replicate Hamilton\n", "dta = pd.read_stata('https://www.stata-press.com/data/r14/rgnp.dta').iloc[1:]\n", "dta.index = pd.DatetimeIndex(dta.date, freq='QS')\n", "dta_hamilton = dta.rgnp\n", "\n", "# Plot the data\n", "dta_hamilton.plot(title='Growth rate of Real GNP', figsize=(12,3))\n", "\n", "# Fit the model\n", "mod_hamilton = sm.tsa.MarkovAutoregression(dta_hamilton, k_regimes=2, order=4, switching_ar=False)\n", "res_hamilton = mod_hamilton.fit()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>rgnp</td> <th> No. Observations: </th> <td>131</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovAutoregression</td> <th> Log Likelihood </th> <td>-181.263</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 13 Mar 2020</td> <th> AIC </th> <td>380.527</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>14:00:33</td> <th> BIC </th> <td>406.404</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>04-01-1951</td> <th> HQIC </th> <td>391.042</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 10-01-1984</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> -0.3588</td> <td> 0.265</td> <td> -1.356</td> <td> 0.175</td> <td> -0.877</td> <td> 0.160</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 1.1635</td> <td> 0.075</td> <td> 15.614</td> <td> 0.000</td> <td> 1.017</td> <td> 1.310</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Non-switching parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.5914</td> <td> 0.103</td> <td> 5.761</td> <td> 0.000</td> <td> 0.390</td> <td> 0.793</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L1</th> <td> 0.0135</td> <td> 0.120</td> <td> 0.112</td> <td> 0.911</td> <td> -0.222</td> <td> 0.249</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L2</th> <td> -0.0575</td> <td> 0.138</td> <td> -0.418</td> <td> 0.676</td> <td> -0.327</td> <td> 0.212</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L3</th> <td> -0.2470</td> <td> 0.107</td> <td> -2.310</td> <td> 0.021</td> <td> -0.457</td> <td> -0.037</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L4</th> <td> -0.2129</td> <td> 0.111</td> <td> -1.926</td> <td> 0.054</td> <td> -0.430</td> <td> 0.004</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0]</th> <td> 0.7547</td> <td> 0.097</td> <td> 7.819</td> <td> 0.000</td> <td> 0.565</td> <td> 0.944</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0]</th> <td> 0.0959</td> <td> 0.038</td> <td> 2.542</td> <td> 0.011</td> <td> 0.022</td> <td> 0.170</td>\n", "</tr>\n", "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation." ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "================================================================================\n", "Dep. Variable: rgnp No. Observations: 131\n", "Model: MarkovAutoregression Log Likelihood -181.263\n", "Date: Fri, 13 Mar 2020 AIC 380.527\n", "Time: 14:00:33 BIC 406.404\n", "Sample: 04-01-1951 HQIC 391.042\n", " - 10-01-1984 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.3588 0.265 -1.356 0.175 -0.877 0.160\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 1.1635 0.075 15.614 0.000 1.017 1.310\n", " Non-switching parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.5914 0.103 5.761 0.000 0.390 0.793\n", "ar.L1 0.0135 0.120 0.112 0.911 -0.222 0.249\n", "ar.L2 -0.0575 0.138 -0.418 0.676 -0.327 0.212\n", "ar.L3 -0.2470 0.107 -2.310 0.021 -0.457 -0.037\n", "ar.L4 -0.2129 0.111 -1.926 0.054 -0.430 0.004\n", " Regime transition parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "p[0->0] 0.7547 0.097 7.819 0.000 0.565 0.944\n", "p[1->0] 0.0959 0.038 2.542 0.011 0.022 0.170\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n", "\"\"\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_hamilton.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We plot the filtered and smoothed probabilities of a recession. Filtered refers to an estimate of the probability at time $t$ based on data up to and including time $t$ (but excluding time $t+1, ..., T$). Smoothed refers to an estimate of the probability at time $t$ using all the data in the sample.\n", "\n", "For reference, the shaded periods represent the NBER recessions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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brn/+AWC1roVuDoRy8qZvOo7lqoZf/eQ5uAX08cxc8L6wY4ABfvB+5tFFPPvYIv7y4XIHb9NhqKi9wVsyb4mi4u6vXMKHvpxvk40iYtsLNqNl8+IFg6LADKwdmxmZ1hhjMGwX9Upv8I6SzdcaGlYbGn7iW47hwcst/EUByd/MBW/hND+5r+Y/dsNaHc0QeaRMsBzmm10AoF5RoRds984Y80vzgtKXxPzhA184i/d/aRO2O1/XgWByUcG7KgxrcnMbCTMH2dxyGRwG1APMO1o2d7Da4KVkL3/qfhxfreKeM8Vzns9u8F7rBu+qRqW/WUyH9crmBTSsbXVs/8YzZfCea5zbbMOwGZ7YLH69bJbY8oLNPimbp0Zw7djKKHgLpSMO8+ayOe+6RkRYq2uF/L5mMnivNTQs17o3T01TevIoZQSXzYPMW4HtMtgFCpLBhjhmyTdLEunhugwXtrnJ55Er89VFbBTzFsG77OtRnsiDefvBO5jzbmho6g6cgDpkOwx7ptOz+appCnSreN/XTAbvIOsGgKpKMGy31M1DLJeh2pfzBoBOgWq9rzS7jf6lbD6/uNLU/e//kSutKR/NZLHdsaEQl2TDIEvFRiO4dmTVqEWc737ZnAHYC5S1bntMfDXw/dUqSuFSlMCcBO+apoABKOD5jw3LYX6+DOjKP0VynPcybxm85xXnNjnrrqmEh+eQea/UNSgUNq8pIJtLZSoSYu1oVJTMmLceIZsDvY1awpSTuqYUcrM1U8G7bTq41rJCgzdQbinXdPqYt/eZ2gUK3lf2gsy7OMclMVmc2+QB+4UnF/HY9c7IhW+rbZVaFQtiu+NE5rsB2WEtDsQ6fXipmlmpmGDOwVKxNT94B5h3iGehrknmnTvO7wya1QD4Qa/MN4zlMFS1YjPvjT0LQhyQOb35xbmtNoiAbzm1BMcFvnY1usnFRtPEHb/3IL54oVgTm9JiWHc1ANBUgkpSNh8GwbwPLVdyzXmveI7ync4g814NBu+C9tSYreAd4jQHgsy7vAGlv857QeS8C3RRbTQtHFvh577MKofEeDi/1cHh5TqecbgBYHjee6tjw2HAtdZsTP/b7ti+HBuFWkFl2KJArNOHl6vY6tiZNEgRaYpGCtm8plEhv6+ZCt5nveB9op95z0DwDqvzBorGvE2cWOXnXhrW5hfnNts4ub+B9UUN64vaUMe5uCdnpR58u2Nj38Lw4F3VlFKvRXlDqHaHlypw3F5DWVqInHd/hzVgMHgTgJX6YM67aKmdmQre57cNHFysoFHpdXrWPMZaZjZoOu5AhzUA6BRkR+h6DVqOr/LewXJxml+c3+rg5L4FAMBthxeHBm/hjZiFzZ7jMuzow2VzoLhMrigIMm8gm1pv320eYN6LVQWq0jsWdLtjY7muQlO6a21NU+Cw4hmeZyp4n90adJoDwcYI5V0g+pu0iNyNbhWjVGy7Y8NyGY6vSdl8nmE5Li7tdHBiH5fMbzu8gLPbBppG+AI8S8x7V7fBEF3jLSBl8+EwHQYCcHCJN0rJolwsLOdNRFip9TZqCdt81QvaWGemgvf5baOnLarArMjmwVKxRsFy3mIgyZHlKlRlNpiURHJc3O7AZcCJ/Zx5P+0w//9XI9i3uE6K1GwoLUY1aBGoqTJ4D4PpDWESju8sTGt6SJ034HVZ63QJ0FbH9l3oArWKKO8r1jU6M8F7V7exrdsRzFvI5sU6+XHheH15g6Vivtu8IIvAhteg5dBSFVVVkcx7TnF+izvLhWz+1EP8/1HSuZDNZ4F5h5UZhYEz7/J/3rwgiMq+BY95ZyKbi5x3b/39akPFbl/OO4p5F2WtFZiZ4N11mtcHftetrSzWyY8L0zvunlIxUeed4bD6cSCY9+HlCioqSeY9pxA13if3c9l8pa7h5FoNj2wMZ97WDATvrbjMW+a8h8J0GGoa+Qw4K+Zd1xRQX/Oc/v7mO0OCd9E2XDMTvM9GlIkB5S8VM7wWqEHmrSqEqkqF2Q1u7JnQFMJaQ+PMu2AXusRkcG6rDU0hHFnpbqJvO7yAhy9L2VygKnPeQ2F6zFtTCcs1FVvt8csIDZsNSOZAb/BmjGFb5rwnj/PbBhQCjq1UB35XVcstm4uLJpjzBoo103tjz8KhpQoU4psKKZvPJ85tdnB0rQ4tcK3edngBV1tWT/tcAXMGZfP+nGk/eM67/J83L5gBf8++hpaNbO64qFUGW9auNFTsdBwwxtA0HDju4OZL5rxzxqVdEweXKj3SskDZmXdXNu+9+BoVtTB13leapu8OlbL5/OLcVtvPdwvcepD/fPraIPsWcvmsBO/lmgpNDe9rLlCbgRHFeSJYWbNvIZvgrVvRzNtyGTqW6zPwgeCtFbND58wE75bpYLkWNclHjOEr5w0jZPNKgZn31T3Lr8uUhrX5RbDGW2DZm9AUdq1aM1Qqtq2P7q4GiBHF8v6Igum4PvNea2jZlIo5bmTwBnh/8yjlRMrmOaNjuQPNWQTELq5oskdcdGXz3h19vaJAL8BIUJcxbOxZPvPmsnk5z7VEeuiWg6tNwzerCQjDaNg14RvWZuB6GdXXXEC6zYfDCjLvhpaRYY31NGgREKNbd3Xb3ySsLUS5zYv1nc1M8G5bTk/f2iAUIlSU8gYUEbxr/cxbKwbz3u7YsF2Gw0uceVdUZSYWY4lkOL/FZfETfcxbdAYMuyZmLecdL3hLt/kwmIFW0GsNbigbt7/5MMMawJuziOli/eqJCPpF+85mJ3ibrj+sIwxVjUrrgDa8AF0JY94FCN5XmtyIFMx5S9l8/iDmePczb3HdhknFs+Y2H1XjDXDmbbkMzgxsWPJAb867ApcBTWO89cSw3VDmveox7x3d8ceP9n+HRe3QOTPBe5hsDnh5ppIGFHHc/Wa8RkUpRG/zDW+OdzfnXd6NkkR6nPOYd3/OuxpDNi8782aMYbvjxJbNgfIaaPNGv9scGL/W27BZz1ASATH6c6djY6fjoK4pA0Fe/J1esPgxQ8HbwUJ1CPNWlfLmvK3BOm+AM+9OAZq0iAYth0TOW5Oy+Tzi3GYbNU3BweW+qX6+bB7GvGdjMMme6cB2WazgXfbS1bwRZN7ifO7o43l7RJOWfqzUurL5dsfGamOQAGoKT7tK5p0TOPOO/ji1MsvmEYa1hqYUoknLxp6JitegBZCy+bzi/BYfSNLfxaoyJFjNymCSuA1agKAMK++RMATnOIjxqttjB28WGh80lbBUVX23eVTag1cIFOsajRW8iejlRPQoEZ0moreG/P4GIvoUEX2RiL5MRN+Z/aFGw3YZTIfFkM2LdfLjIqw9KuAx7wLkvDea3GmueIt2VdZ5zyXObbUHzGoAn97EDaPROe+y53/j9jUHpGw+Cqbt+kTFl83HDN6G7YbK5gB3nIvZGFGbr3qlON0sBUYGbyJSAbwTwCsAPA3Aa4noaX1P+zkAH2aMPRfAawC8K+sDHQYRwIYa1lSltI0RIpl3RYXtsqmbfTb2TBxa6na2k3Xe84lzm50Bs5pAVOMeXzZ3y329RJUZhUEG7+Hgvc35OVqpayAA2530wdtyXDgMoYY1gOe9dzqebB5Rp1/Tipd2jcO8XwjgNGPsccaYCeCDAF7Z9xwGYMX79yqAi9kd4miI4D1KNi8rGzT93uaDhjUA6Ey51ntjz8Kh5Yr/s+ywNn/Y1S3sdKwBs5pANUL56rrNcz283JFMNi9mx64iwPYCrSAqmkJYqatjMe+O5xkKy3kD3HEuZPNI5l3AfvRxgvdxAOcCP5/3HgvibQB+mIjOA7gbwE+EvRARvZGI7iOi+65evZricMMhiucXqtGyebXEjRHERRNWKgZgquVijDFc27NwcLEbvDnzLue5lkiHi9u8TOz4vnDmzVMpg9epuE7Kzrzj9jUHgk1ryv2Z80BYZc2+RmUsw1rHHB68V+oarrVMdCx3RPAu1poWJ3iHNert/xSvBfA+xtgJAN8J4I+IaOC1GWPvZozdzhi7/eDBg8mPNgKxmHeJA4oRkfNueD9PM+/dsVxYLvNLLgC+ybBdNnZjBYnyoGXwBXK5Xgn9fSWi696s5Ly3OnZomVEYilo3XASInhbBFOFaQxuPeXvBOyrnvVrXcK01XDmpVYphDg4iTvA+D+Bk4OcTGJTFXw/gwwDAGPscgDqA9SwOMA5ErfPIJi0lDd6m7UIhLiEFIRaKaQbvXW/RXgn0lRdlHlI6nx/oI6TJqDGxs1IqtjPE7NQPIZuXdT3KE2FEZd+CNh7z9q7NKHInGrUA0cG7rMz7XgC3ENFNRFQFN6Td1fecswC+AwCI6Dbw4J2dLj4CPvMeIpvXCpiziAvDdgYkcyCQ855i8N7x5MIg865KWXDuIIJ31D0YJZvPylSxuK1RgW5gKquBNk+IIUzBVtBrY/Y3H5nzDnxvQ4N3wTZbI4M3Y8wG8CYAnwDwCLir/CEi+kUiusN72lsAvIGI/gHABwC8jrHJaabxDGvlbRwSLJ0IolGAnPeuN0YvyLyH9bKWmE102U148K5EpK1mpcNa3NaoQHDKYbk/cx7w5zgExh/va2hoGi7slJsdXcjmkcx7dPAuIvmLdbUxxu4GN6IFH/v5wL8fBvBN2R5afHQsz7A2NOdNhds5xYUREbzFTpKnDabTb2fXk7NWQpl3Oc+3RHL4pqCoyX4RjXv8wSQlv1a2OjZuiiiT60dNLfeUwzzRzXkHDWsaGIDtjoX1pVrEX0Yjjts8+F5hqFfKKZsXHt2c9+gmLRMUBDKDYbuoKGHMm39eIVlOA2KAfXD3Wh0yiEJiNiHMPFHBO7rOe3aYd/yct9zcRkHI5tUA8675CmO6da7tu83DvNfcbQ4AKgFLtfDrt4iT4GYieAvZeJjTUwSUon0BcWDa7oDTHChGzjvMsCZl8/mDkCYjc94RaatuqVh5r5WO5cCw4/U1B4KyefnWorzRbUjVXe/GXbt95h2xsRQ575W65neJ7IfIeReJ/M1E8G5bLmoaQQ1hpwJ+eUYB2okmhWE7oczbr/Oe4iKw07HRqCg9mwspm88fRkmTUbK57TdpKe+1ctUbzLO+GC94qwpBU8qbxssTYczbX09SrnPdSojw+CBk82Gbr7qmwGXFMuHORPDmTeejJXMg4PAsYSunkTnvKW5ImobTw7qBIPMuzoUukS90i1dEaGr4khJV5+3nvEvMvC/u8pG4R1fi52PLPCgpTwhyFXSbV7Uxmbfvxwi/NuuagqpKQ4O3L90XYIqjwEwE747lDjWrAV334jSd2WkRFbxVhVBVaaqfaadj+zkjgarM6c0dOpYTKUsC3G3ev5ljjM1EzvuSH7yrI57ZBe+VXb61KG9067wHmbeRMuctVKGoJi1EhNW6NpJ5B1+rCIin8xQcHcsdOssb6O7kysq8w+q8AZ73nnbOe6Xeu2h35xXLxWleoI8I3rUQ5u24vFWjqvB/l7XL2qVdA5pCWF8M7y4XhqimNfMOI2SOw7jrScd0UFUpMp8NAD/1rSdweCn6+xOBf5rm4H7MRvC23diyeRmZt2m7WKhEGCmmHLx3dBs3H+gtkZGGtfmDbg2/Bznz7r0eRD/zhYqKpuGUOHibOLJcHeq56UdNkznvMIRNUKyO6VfqWE5P3XgYXnLz2tDfF5F5z4hsHj5oPYjuJJ/inPy4MGwnVDYHgIamTpd562HMW6gc5dsoSaRDx3RGBG8aMByJYC5SXmUlohd3zUSSOSBKV+X90Q+/zjsgcVcyYN5RRsq4kME7J8TJeYuAUkbmbVjRsnm9okCf0oaEMYamHpLzlsx77sBz3sNLNR3WK40LGV1MAyyr4/zSjoljq8mDd9GafhQBYe1Rx015xmHeo9A1rMngnSn0GLJ5EXMWcWE64YY1gLOWaTHvtsVn7/Yz74p3s8ngPT8YlfMWTCp4TQgDm1DNymhaa5sOtnU7BfMu76CkPGF4Q5iCRQt+nXfKdU63JPMuLOIY1sYtN5gmDCt8MAngMe8pBe9dnb/vagTzlrLg/EC3nKGDgcKkTxG8yhy8hdP8WIIyMaDcI4rzhDDnUsBclkQ2/w9/9hV88Atnex7rWI7fkjYtRI24DN4Zo2PHyHmrZWfe4Z+vUVH89rCTRtOTkPpl8+7NJheneUHHclDXhnSzG70AACAASURBVDDvkMY9th+8Pdm8hMH7oh+808jmcnPbD8Ma9PckMaz97SNX8OlHewdatk0nskwsLvzxy1I2zw6W48JyRjdp8TusleyGYYxF1nkD02be/EJe7TeshUikErMN3XJjMW8rhHkL1axktyYA4OKuASBZjTfQnbUg0Yuwta7bHnV04LQcF9dbRs9j3LA2Zs67gGnX0gfvttdbO36TluKc/DiwHAbGENoeFQAa2vRy3k2Dv28/89YUgkJSNp8njGrSUg1RY2Yh531p10SjosTuay7AB12U7/PmDR68e9dyTSEQ4rVHNW0X1/fMnsd0a3zmXStgqXHpg3fL5FOtRpeKiT7g5QreIgAOY97TCt7+UJL64KIdNUVKYjahm6Pc5oNqzCzkvC/u8DIxGtIAJAxVybxDEVYWS8Q7ScZRTU3bxfVWb/DmKZ1smLfMeWeIthe8F4ZIdkBXtivbYBLREjC6w5oK22VTWfia/kSxQdbBO0iV61xLpIduj67zBnrZk2DeCyXOeV/aNRLnuwHuwSlbCm8S0CPKYisxgrfrrYM7HavnOutkkPPWFEJFIRm8s0TLCyCjmLdChIpKpWPeYR2HgqhPUc7ZNVwsVhVoIcdWjRhEITF7iOM7CZfNy53zZozh0q6ZaCCJQE3jde+2TC31IKohVRzmHUzTbbW77DuLOm+Af2fSsJYhWobHvEcEb4D3Vy4b8xY7yGG9zQE+WW3SaBrOQL5bIKwdpsRswh+5GKvOO8i8RYe1cjZp2dEdtC03HfMuqYE2bxhWuDmXM+/hgTN4Lq/tcdOa2FhmFbyL5Jkqf/D2dkLDnK4C1RgXQNEwink3pjjTe9dwBpzmAlHzmyVmD0L1qceq8x7MeddLmvNO6zQHiuleLgKiKmvC2uv2I/j7TS/v3Z3lPX6oq2mKlM2zRDumYQ3wgveMMe/6FIN303CxHJLvBqLnN0vMHsQCOVw2H6zz7nebO6xc18ulneSjQAW6BtpyrUd5IyvZXDjOO37wzoB5q1I2zxStmKViAJfuypfzHmVYEzv4yS98u4aD1UYE89YG5zdLzCb8BXLIPRhW5215THvRY+xlS7NcTNldDShv6Wre4B3WBq+jijI6eFshsrlu8sdqEU2ukkAy74zhu81HNGkB+O6tSHV6cdCVzaM7rAHTYt5OqNMckIa1eUJ65t1bKla2vd7lpomVuorF2ui1px9SNg9HVM67qikwRxCvIPMWsrkItlnlvIuk3JY+eO95hrVhu36BMua8zZhu80m3SHVchj3DxWpEcwpZ5z0/EFJiHLd5b513r2xulS3nvZOuTAzono+ykYm8MZZsbg/K5oLcZZHzrmuyVCxTtE3edF6N6EAWRK2UzNuTzaM6rHkL5qRl86ZugQFYiWAdFVnnPTfw2U3iOu/e3uZOyYJ32jIxIOA2L1AwKAIiDWvKaMNaMLiLFqnZ5rylbJ4pWoYdi3UDXHopG/P2ZfOIi29ahrWttgVgsDWqQE3K5nMDsSFOI5ur1JU0y8S8Xa/GOy3zLmvHxyxxbc8YSBtEBe8kzLuiEq7t9brNx23Swl9DGtYyRdt00Ii5qypzzntYb3Ng8nXe214ThLDWqICs854n+DnvhINJLIehoirQvGu7TMx7s+3AclkqpzkQZN7lWo+yxKve9Vn85idP+z/bjgvHZaiErOdx6rxFGubIar2b8xaGtUxy3kqhPAqlD94tw47lNAeEbF6ckx8HIzusTYl5b3vMu3+WtwDPec/vwjRPiOM2VxWCqgyWilVU8oN3mbIsl5v8+k/jNAcCbvM5Zt5XdnWc2Wz7Pw8z51YT1HkfXWng+l6vbJ4J81ZlzjtTtMz4snmc/rhFwyjDmqrwpv2TZt5bI5h3TVNgSOY9F4jjNge8fvd9I0GrPcG7PNfL5T0evI+upmTeavGmVE0SrstHHW8GxncOIypxZHNBFo6u1dEyHXRMBx3fsJYN8+5YDlhB+hHEinpE9HIiepSIThPRWyOe8wNE9DARPUREf5LtYUajZTixGrQAxZM94mBUnTfA3bqTXgQk85YQ6MRojwoMViBYDoOmkt8bv1TB22PeR5bHzHmXbD3KCiIQb7aswGP8XES2Rx2xxvnMe7UBgJvWMmXeGoGx4ow6HjmElohUAO8E8E8BnAdwLxHdxRh7OPCcWwD8BwDfxBjbIqJDeR1wP9qmjUP1+DnvsjFvccEOC971ijIF2dwEAViKdJvLUrF5gW46IBq9QHLmHQjeLp/drBBBpXL1Nm96Q3nSBoUizoeeJMSmpYd5W9HMuxKj3bII3sfW6t5rm92c95D1My4Ee9dNFzUteW1/1ohz5b0QwGnG2OOMMRPABwG8su85bwDwTsbYFgAwxjayPcxotIzhc4SDEHkTt0Q7fNPpLnBRaGjq5A1rHQtLVSWyRE8s1EWRmCTyg267qGvqyJnW1T41hhvW+N9oKmEKs3VSw3Tcsdhcdc47rIlc/1bL8teI4bK5AsdlQ6ewGcKwtsKD9/U9Ex3LQVWLXqeSoGgzveNcfccBnAv8fN57LIinAHgKEX2GiO4hopeHvRARvZGI7iOi+65evZruiPvQNhMY1jQxHKE8u13Dcv2JTFGoVxR0JryD32pbWI7IdwPhTTkkZhMd04k1GIgPl+gdTCKuE02hUjFvw2GRXQ/jQCE+H3peDWtCcTAd12+0NSxF2B1sE73O9cvmohRtlBcjLkT8KFPwDtuy9N9lGoBbAHwbgNcCeA8RrQ38EWPvZozdzhi7/eDBg0mPNRQtw/HLpUah29WoGCc/Dkxn9CD5xpRk8+VqnF7W5VmQJdKhYzmxOlgNyOaOC03hf6cpVKqcdxZjJqsFa7c5SQTX4C0v7z3KsAYML63zg3ePbJ5h8PY2a0Wp9Y4T9c4DOBn4+QSAiyHP+XPGmMUY+zqAR8GDea4wbRem4yaQzcuXZ4rLvCdf521FOs2BYFOO8pxriXTQLWfoOFCBMNlcLMoVtVzB27DHY95AOUtXs0Lwc4tuaCIwhw0REX0uhnmWRPDet1BFTVNwvWWibcVTheKgjMz7XgC3ENFNRFQF8BoAd/U9504ALwEAIloHl9Efz/JAw9DtqRzfsAagVF3WDHt0bq2hTYF5d0wsDxnIEDa/WWI2EVea7G/cE8x5qyVj3qbDxnYwl9FAmxWCBEqUnfpu8xBFQ6zdw2q9Tcfx+gkQ1pdquLZnZMu8C1YhMPLqY4zZAN4E4BMAHgHwYcbYQ0T0i0R0h/e0TwC4TkQPA/gUgH/HGLue10ELtMQs75g3UXcMX3luGNMe7WxsVKfAvFtWZF9zoMu8pWw+++hYzsgyMcAr9wnpsAYAFUUpVZMW03EjWxbHRRlLV7NCD/PeE8E7urImDvGyAj6EA0tVXN8z+cYyI+Yt3OZFkc1HlooBAGPsbgB39z3284F/MwBv9v6bGMTEmLh13pVSMm9npGze0JSJ5s4sx0XTsLFciz6uagyDicRsQLfcWOymqhJ29N6pYj2GtZIx7+XG+MxbBu8Q5j3EsDZKNhdr5f5FHryrmpJhzrtYXfFK3WFtzxCyedz2qCXMeceQzSed897peENJYsjmknnPPjpmPOZd1QZlc63EOe9xa4drWvlmLWSFYAC87vUhH1bnXY0RvI1A8D6wWMN1TzaPc23GgV8qVhDmXerg3TaEbB7vJqqVsLYyuJuMQk1TYLlsYoMdRHe14cxbGtbmBboVr9dCf6ONoGGtbDlvy2Ej78tRqKiyVEwhYKvVK5tH9Tbnz4k+X6bt+n+7vlTFtRav887asFaU+FHq4N0yRw9ECCKO9FI0xGHe4oKdVDrAnyg2LOctaurL1HlDIhXiGtaqfXXewZx32WRzw3FDXdFJUFMn39a4KBAB8MhKdwLY2LK5E2DeS1WYtotrTSO2MjsK9RI2aSkskua8/ZxFQU5+HMQxrE3aiNdl3sNkc2lYmxfEZTf9bvNgzpvL5rkdYuYwM6jzrmkEo0RrUZYQa9WxtUY3eA+VzT0lb2jO2/Gft3+RT3trGjYWqrGsXSMhjku0XJ02Sh28W17OO26HtS5DLcbJj4M4hjV/NvCEmPdWHOYtDWtzg7hu82qIbK4FDGtlmudt2uPL5tKw1jt727Bdv9SrH0kNaweWugNjssp58wmOimTeWaBlJBv3VsZ+wnFk80kPORCGtaUhOW9Z5z0fYIxBt9yYwbvLvBljsNxuaY9WIsMaYyyTOu+appSKSGQJ3eKdI9eXaj2yedQ59deTIecr6ENYX+zOWc/KbQ545uCCxI9yB29/Vmuy9qhlumFM20VthLIw6eYBbXO0y79b512ecy2RHOJeitekhWC5DC5jfqCuBJh3WXqbm44LhnB5NwnmnXnXKyr2LVSxq9uwHHcoUUlqWAsy78aQNs5J0aiq0m2eBdomd7nGnRhT9Jz3n95/Hj/0nnt6HjNsF1V1+MI46Z7tusU7GWlDzntVMu+5QCeBaTQ4rEZcF37OWynPVDGxYRmbeas08c6IRQFXaxTs94LsVtuEYUX7e3zZfIi6aDi9dd4CWTLvRkUtTIVAqYN3y7CxmMCM0L0AinHy+/HFc1v4zOnrPbvLJMx7UoqCbrkj1Q5Z5z0fEAtZLLe51jUxiuuiEpTNS3KtiE3y2Mxb42Mu51Gd0m3OvPcv8CC72TK5bB6x1sXx0ARz3vWKiqUajw2NjAxr4nUl884AbdPBwhDTVD+IqNB5pj2dpwGuee0CXZf587yHYdKyubjxhkHWec8H/PkCMUeCAvyaEAFLDJwoU6mYP0AjA+YNFFcJzBO65aCuqT5D5sE7Wjb3B5MMnSrm9KyVQjrPlHlXVWlYywJ7CZk3wHdORb1ZxFzbq00+ZUcEvtHMe7KlYnoMd7Ff510SNiWRDmIhG1XOCAQ3dF3ZvCfnXZbg7Q/QGN9tDpSr42NW0C0X9WpY8A6/jkSabmjO2+ltaCVeO8ucd10rTvwodfBumzYWa8mCd5GZd1PvDd7d3NqoOu/JlooZ1ugxrCIfLmXz2YZYyJIwb8txA7J5+YK37o+uHH+eN3+9YgSDSULMgBcBdkvI5kM2RDVNGVHn3atSHvAc51mVigGSeWeGluFgIWHruyIzb+Ge32jqAOLv8LtzyidnWBt1QxDRQF2vxOxBBLK4HdYAzryFbC6u3TI1acmKedcm3BmxSDC8NWRtoQKA9zc3rOH+nuoI4tXfSno9D9lc5ryzQdtMI5sXtyXhXj/zjplbE7L5xAxrMXLegNfLuiwWYolUSOY2DzOslY95Z5XzrpZwRHFWEG7ziqpgtVHxmPfwbpI1TY1d5w0Ect4Z9TYHBPkrxvdV6uDdMpIZ1gB+ARR1pxuZ847dpGVSzHu0bA6IphzFuNAl8oGQEOPWeQOcIQ3kvEvUpEU47MeeKlbCEcVZIUgA9i9WOfMeJZtXlNF13j05by6bL1Syc5s3qrJJSyZombZfDhAXRWbeAznv2Mx7sh3WhFN0FLhsXo4FWSIdxEIWtz0qwGXzbpOW7mASl/EKi6IjO+ZdvhHFWSG4huxfrPI67xHdJKtqtGzOu9715rxfcutBfN9zj+PoWj2z425UZM47E7QNJ3HT+Ukzb8tx8SPv/QLuP7M58nniwry618+8hy+MmkJQaXLMO24v6/5BFBKzh0TB26/zdv1ruxqQzQHAcosfyHQ/550N8y4Kk5skdMv15ez9i1Vc3xvepAXgzDtKNvevp0Dwf9LBJfz6Dz7H3yBmARG8GZv+ulba4M2lNxeLiQ1rk2XeV3Z1/P3XruKex4cHb9GnHQgy7/jGGN5qcTKfa5SxRKB/frPE7KGTwm1uhuS8y9TUx2feYwaF+c55dxuy7F+oYrNlQh/SpAUYzrxFUB/VE2Nc1CoqGCtGi+3SBm9hlFlIXCo2WeYtxmfuesM8oiAk8/2LVWw0DTDGErVh5E7M4rjNAW6kk7L5bEMEnjjzBULrvD3GLVoc2yXY7OkJNtXDMOm2xkWB6/K1zZfNl7hsLoaVRGHY2u0H7zG/k1EQ3o4ifGelDd6irCop865NmHmL8Zk7I4K3MKvdtL4I03axq9v+BRmnAUZtgsw7bs67Ig1rM4+O5aCiErQYjKe3ztvrsCZKxUrUF6C7qR5XNvdy3nNmWBPnzzesLVRhOXw6XZayeR4QClMR8t7lDd5esEvapKVemSzzFuPuRgVv8XmetL4IgEvnRoLdZFWjiS0Cuh3PbS5LxWYfHTOeCgP0Mm+rbzBJt6lP8Td7YpM8rkQrgn9R6oYnha5PYnCISFrDmmUz/zl5QjDvInxn5Q3e3slbTFwqpgztj5s1fNlcHyGbe8H7VCB4m45oPRkv5z2JgSuW48JxWTzZXFWkbD7jMGwndhOM4FSxqJx3GYaTGLYDTUHsaYZRECN1ixAIJglBMoKlYgLDct61SnSdt1gr82be4pgl8x4DbS/YJXWb1yc80i22bK73Me89w99kxLkga+pk0gH9u+ZhqKhUCiYlkR5JmHdQNjf7ZHMRCMtgcNSt0cOC4kBTCFVNwZ5pj37yDMH3SYQy72FNWqKZdxKVchwI2VzmvMeAz7wTl4rx8iVnQvWkgnnHznkfHJTN4xnWhjftzwrdGy+eu7gMOUyJ9OhYSZi3aAca3mENAOwSlIoZtjP2OFCBxaqKtjH9QDBJ6H2NfWLL5kNMuZNym4t23O0CqCWlDd5tb7eatMOaCDqTynv7zLsdL+d9bK2Bqqpgo6l3DWuxJOrJGNZ85h1zipRRAiYlkR5iOlQcqAqgUPhgEsHArRJ4JDjzzih417SeMtF5gD+JLqlsPoR5T9ptLoP3GBBMNWmHtUl3I9vygnbTsId2jxKlYktVDQeXax7z9vI4MXaTVW0ybfvEMcVZsKuaZN6zDjEdKg6IyFdjLIdBUwgKCebNn1OGJi2G7YzdoEVgsar5lTPzgn4CsFBV/aA7TDYfNphk4m5zGbzTQ0hNaaaKAZNj3tse82asa0oLA59NrkJRKBC8XRB12ckwVNXJuM2T1PXKDmuzD91yEg1+qHomRt7KsntdC+ZdBsNatsxbRWvOZHOjL+dNRDjgse9Rdd6m7YZ2N5OyeYkgdqvJDWuTZt4mPHIxtFHLnm5jqc4/iwjeYj4t0eiFYuKyecxe1mUwIEmkh54g5w10u+5ZDoMWCIBqiUrFDNsZu7uawGJN81XEeUHYGrJvQQTv4YY1INzUOCnZXAw5aRdALYn1SYno5UT0KBGdJqK3Dnneq4mIEdHt2R1iONqmg3pFSVyuIS6OiTHvloVjqw0Aw01re0Z3yMrB5Rqu7RkjG/UHUdUmUyqW1LBm2qwQfYAl8kHcPvcCVU+NsRzWy7zLFLwtN1PZvAiBYJLoLxUDuuM7R+W8gfDWpJOWzUvhNiciFcA7AbwCwNMAvJaInhbyvGUAPwng81kfZBhaRvJZ3sBkmbfluGgaNk6tLwAYwbwNG0t1Ppj+4FIN11sm2qYdy6wGeKViE+i3m6RUrKoqYABKsB5LpETHdBMGb495u27PwAitTL3NM3SbL8yhbN5fKgYEmffo4B1W6z0p2byqKdAUKo1s/kIApxljjzPGTAAfBPDKkOf9ZwBvB6BneHyRaBl24u5qQIB5T2DnJMrEbtjPy79GM29+bAeXa2AMuLitx74YqyrBtN3cRyp2EsrmQDlqdyXSwUghmwvmHfRyaKXqbe6OPctbYKk2v4a14HWzf3G0bF4tAPMGOPsuS/A+DuBc4Ofz3mM+iOi5AE4yxv5y2AsR0RuJ6D4iuu/q1auJDzaIlukkNqsBAeY9AZYqysROHeDMe2jw1ntlcwA4v9WONb0L6AbKvKfdJCkVK9OkKIl04LJ5/AWzoiowbT6YpKIMBu8ybPQM28lszORCdX5LxeqhwXu4YQ0IJ16TYt4AN62VxW0etsX0V2MiUgD8OoC3jHohxti7GWO3M8ZuP3jwYPyjDEHbHI95TyJnseX1Nb/xQFzm7cnmXvBOwrxFn+S8c/m6PSh5RaHby7r4C7JEcliOC9tliZg3nzTHB5P0yOY+8y7+Ro8P0MiKeauwHBbZ9nMWIWTzYKA+ttYAEbBcj17Ti2BYA7hi0C5DzhucaZ8M/HwCwMXAz8sAngHg00T0BIAXA7grb9NayxiTeU8ieHuy+Yl9DagKDe1vvmfY/oV7yAvepuPGznmLQJl3Lt/oa7Aw9Ji07vxmiXJAtxz83J1f8Teeo54LxJvlLRCUzXtLxeazw5qolpkn9m14oz+DVTR3PPsYPvZvvgkHlmqRf+fL5iFr3ESDd1VDpwCpjjif9F4AtxDRTURUBfAaAHeJXzLGdhhj64yxU4yxUwDuAXAHY+y+XI7YQ9tMZ1gT7HYSJhFR471vsYrVRiWSeTPGeJ23l/NeD1zAcd3mggnkvSlJ1tvc65olg3dp8JULO3j/PWdxz+PXRz63v1NWHFS8Ou+onHcZNnpZ1nmLVNk85b31kAqFqqbgOSfXhv5dt1IoPOdN1L2O8sRCVS3HYBLGmA3gTQA+AeARAB9mjD1ERL9IRHfkfYBRaBlO4taoALDS4DfLqF7jWUAw730LFazUNex0wm9Q3eKTusTGol5RseKx8NilYt5iknejFt3iN0msrm/SsFY6ND11KE7tsW7y7zWJbC7c5qbDeq6h7lSxYl8rjDGPeWeU8/bWsHlynOtWvJHC/RD+n1C3uRO/J8a4WCiIYS0WdWWM3Q3g7r7Hfj7iud82/mGNRsu0E7dGBfhCU1GHS9hZYbttoqopaFTUocy7afDHlwL5noPLNezqdvLgnbNsrlsO6poa6yYRC7Kc6V0eiDa9sYK3PegaHgVR501wS9mkxXIYXIbMct6L88i8E4yRDaI72CbcsDYJyRzg1/vVpjGR9xqG0nZYaxtO4u5qAG/Ft1KPDqRZYqttYt9Chb9noxJZ5y123cu13uANDC+dCMJvYJC3bG7HdxdXfdm82AuyRBd+8NZHBxPhuE3mNu92WKuGyOZFT7GIwBGnZXEc+LL5HOW8w2TzOBjKvO1sxrTGQVGYdymDt2nzecCLKQxrALA6JJBmia225TcfGPaeYqFc7AnedQDxDRhd2Txv5u0mGAFZnjymBEcS5t0JqdcdhaowrLnhOe+ib/R8p3RmhrXiyOaT6BMBAB0rvhE3iC7zjgjek2LeMninh9jxL6SQzQFgeYiEnSW22ybWFngeeyWObB74PId85l08w1rcXbM0rJUPe0aCnLcwLyZym/M67/5SMVUhKFT8UrHupL/ZY94v/W//G7//2Sdyfx89wSS6IGpDhkqZziRl8/K4zQuHPe/ElY5561Zon2/BvJfrg7J5fOY9mRI4PcGuWRrWyodEOe8EDXsEqlrQsNYbADWFSsO8swoUfqnYlIOB7bg4u9nG6Y293N/LSCubj2iPOlHZ3HKmPrOhlMG7bQzKzEmwUtewGyOnNy448+4Gb8thoSUG4sYNMu+DS8mYt99hLe867yQ5b1nnXTokynmnqPMODibpL+vhwTveteK4DO/81Gm/HHNSyI95T1eGFe+/08n/fKZ1mw9tjzph2Zyx/LtZjkIpg3fLk80XU5SKARjq/M4KjDFstS3sX6z47wmEl6iF57yTGtYm1GHNc5vHgZTNywcRvIfNnhcQLDRpb3MGUSvdu/xoSvwmLQ9d3ME7PvEo/ubhK7HfOwtknfOuVxQoNH3ZXKTuxDyGPMFNr+mZd1Sd96SCt/ApTLtFaimDt2DeadzmAHznd56yx65uw3GZL5uv1KODt1gox5PNJ1UqFn/XLGXz8sGv887JbS6uCYZBx3YS2fzCVocf54SDXtbMm4iwWJ3+cBJxHicSvBMOsxEYaViboGwOYOotUksZvH3mnTJ4rzYqsF2Wq2NQyHlB2RwAdkMatezpNjSFeiRyYViLuzCKv807551kfrOs8y4fBPOOE0ySTJgTCC6w/QFQTSCbX9jmwbs5gfRXEEbGOW9AjAWdcvD2zuMkjLwdMx3zJiJUNWXqhjVx7NM2raWLflOGuNDTdFgDAoFUt1LnzUch2F0t+J6hsrlhY6mu9TQ+ObBUw699/7PxT25Zj/V+KgEKTaLDWvwbT9Z5lw+CgcVh3oblgCi+LwPoZdtaX/CuJGDe52eEeQM8XdaasgQr1L9JBG/ddmNPS+xHTVMie5tXFybFvHnMmHa5WEmZ96DBKwmGSdhZYauPeQ9ry7pnhPdpf/XzT+DQSj3W+xER6hW1ULJ5RdZ5lw5CNo+T8+4k6LYnEAx6/TKnqsQvFesy7/yDTRBZ57wBriAWhXnvGXaum23X5RPUklQoBFHTlMipYpPOecvgnQJtzxmZZqoYMFzCzgr+UJI4zFu3h47Ci4t6Rc3dsMYnAsU77woRNIVkzrskYIyhqdsg4ovhqGupYzmJnOYAemq7+3PelQTXish5T1w2z7jDGsCNt+0pu82DCkaepMbwRwqnDd5qOPOeZJ23MKzJnHdyCOad3rCW/3CSrZaQzTnzXq6LDUOEbJ6BfF/XlPyZd0KnqOioJVF8GDafzy38FqPKl5J02xPoZd6DOe+4g0mmlfMOm0U9Lhar2sTl/34E0yR5rotJphKGYSjznrBhTbrNU6Bt8lpjNeX4ty7zzu8i3W6bIOLOdoAvTMt1bWjOe1xw2Ty/C8pxeX1uMnexImXzkkAM6zmy2gAwunypYzmJc5e9k8R6/7aiEuwY7Tn3DNu/j2Yl592esvkpmCbJ03EuPDlpmXdVU0LnN0xUNq/InHdqROWI42IyOW8Lq41KzwZjpR7e2Y3P8h4/eFdzZt56CndxrUK5qwES2UCw2KOez2IUq9XN5CU/Qbm5X3pWFQrtntUPIZkrNL2cd9bBe2/asnkP886vUUua3gBB1DSlEE1agOm7zUsZvNtjBjuRX85zLCifKFbteSyqOcyebvdMFEuLvHPe3XaY8S+bldpkutlJjA+xgB9d48F7FKs9ZRpxRwAAIABJREFUs9n2Jfa4qGrRwVsjxGLeF7bbAICb1hdjueKzhGE70BRKrfqFYbGqTp15i572QL7MO01vgCCqmhI9z3vCwVsy7xRomU5qsxoAaKqCpVq4hJ0VtgJDSQREf/N+ZJbzroSXUWQFMbEsiUlpraFhWwbvUkAw7WOebB5c0Pux2TJxemMPt5/an+g9euu8+zqsqfFKxQTzfuqRlankvNNKvlHgsrkzkYleUdgzbKx7LZknIZunmSoGeIa1PoLCGOPBe0I5b6EayOCdAm1zfJmZDyfJ78bfalnYH4N5O16zmMxy3pNg3gluvNWGip3O9MfnSYyGCNZHVkfL5vc9sQkAeOFNyYL3MNk8bm/z89sdVFWFM2/Tzi3ondts417vcwoYtpOpWQ3otnmeZpe1pm7j2FodRBMyrI1RKtYvm9suA2ODm8G8oHoNtfJuiDUKpQzeLWM85g0g0jyWFYJDSQRWGoPvOW7NehB1LV/DmnjtuKViALBW17AjmXcpINIbR73gPcxtfu8Tm6hqCp51YjXRe/QY1kIHk8Rj3kfX6lhtVMBYfkHvf/zdP+LH//iBnsfyYt7AdJncnmFjtVHBSj3fuQ9CGcxSNhc/T0o2B7zJYpJ5J0drTMMaEC1hZwU+DnRQNu+/MUTOLovgXavkbVhLfuOtNjQ0DQfOFCVBiXjwDWtro2XzLzyxheecWEu0kQNGMW/EKhW7sN3B8bWGr1blJZ1f3O7gesvsYfaG7WbPvL21bJrlYns6T92tNiq5TmpLo94FEca8pxO8NRm806BtOqlbowqs5DjTW7ccdCwH+xYHZXPd6m1+IW7YTGTzkHxQljDSyObCHDhlN63EaIiN5KHlGoiiW6S2TRsPXdjB7af2JX6Pak/w7p8qFk82v7DFg7cwnuYV9C7v6nBc1rPh1i0ndb42Cj7znuI9Inw3awsVbOcpm3vrU3q3uTrIvJ3JB+9GVUXHkm7zxGiZ4xu8VnMM3sLwEWZYA3o7uzUzZN71vJl3ihrN1YZw9svgXXQ0dQsLVRUVVcFSVYtskfrFs9uwXYYXJMx3A8MHk8SRzQ3bwUbTwPF9Df+eyatc7MqODgDYDDDRfJg3v5+mzrzrWu7jkrvq3Rh13n0ExWfeE8p5A3zzIZl3CrQNJ3V3NYEsczuMMfzqX30VD1/cBdDta95fKrYS0iJVNMLIJnjnm/PumMll87U6v0l3DFnrXXQ0A216F2vR/bbvfWITRMDzb0zOvCtDmLeqjG7ScmmbB1TOvCv+cWeNpm75w0I2W93gzQfzZG1YEznv6QRv12XYM3m56tpCFTsFLhULlc2nxLxl8E4I03ZhOq6/W02L1UYFLdOJ3Y5xGK42DfzWpx/DG/7wPmy3zcBQkl7mHRa8s5TNaxUVhu3mNqc8jVNUMu/yIFiyuFSPbtl57xObuO3Iit/sKAmG5bwrCsEa0aRFtEU9vq8rm+cRvK/sGv6/g8GbM++sZfPpMu+25YAxeMxbm4hsPg7zjjSsTZB5L1TzJUpxULrgLXanC2PL5iKojH/DXPdu7gvbHfz0R7480Ne8+57dUaQCmRrWtOhh9VkglWwuc96lwa5u+Wx2qaaFBkXLcfHAme3EJWICROTL5YMd1gDLHRG8vRrvE2sLuea8r+zq/r97gneuzHs690h3DapgrVHFdtvMrfxu3N7wNU2F7bIe0jWvbvPSzfMWUta4zDvIgvf3GcuSQtzcr3jGEXz8wcu4tMMXmMjgHdjZirzici05i+mHCKpGDuUsQEq3ubfA7kjmXXgEZfPlCOb90MVddCwHL0jYnCWIikowHTaQ867EMKyd3+6AiNeiC7k0j5z35Z2I4J0D8xYpwGmNBRVVBUt1blhzGbBn2qmUlVHgUwmVRGNkgxC99E3HhaZ2/w1MWDavaHIwSVK0vQs8iyYtQDbDSQTzfvM/fQpe9vTDeMjLfQ/I5iE91cWud3FM9zzQDap5NWpJU+ZRryioa4qUzUuAPaMbvJdqWqjb/N6v86YlL0jhNBcQuW5NGext7rhsaNrnwlYHh5frqGoKFirqUFf8OLjS5MG7qir557w9IjJqilteEArLslcqBiC3vDc/f+nXOiGNB6Xzacnm025pO7Xg/djVvVR/5zPvDErFgGy6CW3u8fzY/sUq3v7qZ+OE54Ttv0jDboyWaaNeUfxd5DgQuei8cjGG36Ql2bGuNlQpm5cATd3yFaAow9q9T2zixgMLOOQNL0mDqkqoKDTAvoSMPox9X9hu4/g+XoeuKISlnHrnX9nRsVzXcGilljvz1lQF9YoytQ5rQd/NaobrIgC8+cNfwl8/dNn/Oc0Y2SAE8zbCgreUzSeDONODwiCY97hu87D8c1pstvj4z7WFKlSF8P7XvwiPXxvcnFQ1BY2K2vOeTd3GUgaSOdBlxHmVi+lemUxSyWu1rsngXQI09e5o2qXaYKkYYwz3ndnCtz/10FjvU1WVgXw3AH/YhxUYMnFus40T+xr+NXdhu4Pnnuyy/uVaPrOwL+/qOLJSR6Oq5s68Ad6oZWqyecB3I0SPLPqbb7ZM/NkDF+C6DP/s6UcA8DGy45w/sXEKxg9jCsG77pmDHZdlOqQmCWJ9WiJ6ORE9SkSnieitIb9/MxE9TERfJqK/I6IbR72m7bJUAVzcqON2WMtyLOj1Fp8gJr7EU+uL+PanHg59bn8dZVCqHBeCEefFvHXLSTSURGC1rmFHl6ViRYbosd+f8w5K2I9fa2GzZeKFY+S7Ac6wwyROsfbaHvM+c72Fb3n7p/C+zz7hH+PlHd1n3vw4K/nkvHcNHFmtY/9iNXfmDQwvzcsbzUC5qkj1bWcwFvRrV5oAgK9fa/mPjS2b+6bc7honct5Z198Pg2jPPU3H+chPS0QqgHcCeAWApwF4LRE9re9pXwRwO2PsWQA+CuDtcd58o6mPflIfhFQxboe1sIYpabHZMmOb3vr7m+/pVib5biBgWMvLbW45qQYKrDW03HLepzf28G8/9KXUSo4Eh2BfQbc5Y70O6LPX+SjOmw8vjfVeVZVCmbfIgYvF+Owmf7//+tdfw8aujqtNA5bDcHytG7yX6uGu+HGxsavj0HJv8LYczrTyYN4LVXVqM727372GtQxlcxG8H7/W8jeBuu2O1aGuS1C697soL+zvG5AnFgowFjTOp30hgNOMsccZYyaADwJ4ZfAJjLFPMcba3o/3ADgR582Djs64yGqQR73CpbusmHfc4N3PvFuGk0mZGBAwrKXcDX75/PZQCZIPZUh+g6w28pPNP/XVDXzsixd6dvcSySFSOcuBOm+gtwzrslc+dXiMfDfAF9lhwdv2ysWuNg3/GH757kf8Od69zDt72dxxGTaaBo6s1rB/oRu8xaY4D+a9VNOmZoDaC5iAhRcoC9n80cs8eDd12zf1cgKQPsgK5m0GS8Wm0qSF3x/TdJzH+bTHAZwL/HzeeywKrwfw8bBfENEbieg+IroPAC6mCN6i/++4U8WIKLPhJJstEwcSBO+e9qhGHjnv5BeUbjl49W99Du/7zNeHPieN5LVaV7FnurEmRiWFqMe9vJv8WpLoQizgQbd58HGge64PenOf06KqDWfels1Z2jXPCPqj33QKd37pIv70gQsAgBNB5h1Rjz4Oru8ZcFyGIyt17F+qomM56JhOoNoiB+Y9Rdl8z7A9MqOgXlFRryiZMW/xnT5+lW+ujTFlc7+XhTV9tzkAtKfY3zzOpw3LxofaQYnohwHcDuAdYb9njL2bMXY7Y+x2ALjs1UMngWDe4xrWgOxapCaSzev9OW8r+5x3Cgn5atOA6bj4+rV25HPSSl5r3ufLYjffjw2PnV1JsRGU6KIZIpsDvWVYV3YNHFisjs1wqiNy3laAeTcqKn7mZU/F8bUG/uTzZwH0M+9K5sFbdFc7vFLHfq9Xw2bbzJl5q34lzaTRb5oVjVrGAWMMj15u4htvXgcAfN0z8KZV7wTEue/JeU/BsNYoiWx+HsDJwM8nAFzsfxIRvRTAfwRwB2PM6P/9wBsT4VIa2dzbJWbh8MtispjjMmy1EzDvhQquNg2881OncW6zjT3dzjznnYZ5C/+BkCbDkFbyEt3stnIYNSjYYJprSaKLYKMOIJx5b+zqY0vmAPDqZx/ED98+aOj0ZXNHMG8T68tVNKoq3nbH0wEA+xYqPRv35bqWuWEtmB4Qm/LNPbM7zz6XnPd0mXeQQKwtVMbeaF/ZNbCr23jJrQdRVRU87qW1dNsZr1RMyOb2lGVz7zNMUzaPQ/nuBXALEd0E4AKA1wD4F8EnENFzAfwOgJczxjbivHFFpZQ5b2dsp7nASqOCnTEDynbbBGOIzbz/+fNO4MELO3jHJx7FOz7xKABkJpuH1UDGhcgvit7RYTAsB2sLybvRrYng3co+eIvjlrL5eGjqfbJ5SN/wK00dh1fGk8wB4BtPrYY+rgVKxQD+3QqJ/qW3HcJ3PvPIANNZrmkwbBem7Wa2eItr6chq3T+WzbYJVeHHklfOe3qlYlaP72Ylg8lij3pmtduOruDGAwv4uiebd8yMZPOwUrEpyOaFDt6MMZuI3gTgEwBUAO9ljD1ERL8I4D7G2F3gMvkSgI949ZhnGWN3DHvdiqqkYkttwx67u5rAaqOCc5vRTDMOhJllf8w84DOOr+IjP/aNOLfZxl3/cBF/98gVfMOTD4x1DALd9qhpmLcXBHf0yNrFjuXgSBrDmjdZLA/m7cvmcxi8L2x38HMf+wp+/Qefk2pTFcRuX/AWzVp6c94GnnEsPPBmgf7gfW3PwA37FwBwj8pvvvZ56G8xEOxvvl8b7xwIXNnRoRCwvlTzA+pmy/DfKy+3ect0wBhL3To0LYIDaQBgrVHxnf5p8ehl3mXyKYeXcdP6om8ozapUrL/DWlVN33I1Dbo57wIHbwBgjN0N4O6+x34+8O+XJn3jiqqkZt7jmtUEVvvKttJAuCjjyuYCJ/cv4MdfcjN+/CU3j/X+QYzTYW3Dy/NZDsPVJq9x7Yeesme66G++2cpW3mwZth9c0lxLZcdfPXgZn3r0Kh44uxXZVyAu/HIhv8OaaNnJH7cdF9f2jLE6q41CN3hz2fxq08DzAmNHlZAN5ZKXo9/T7bFnFAhc2dVxcLkGVaGubN6ycHhFdBhUgYzX7MWaBsdlMOx85hIMw57h4MS+7rlbW6jgy+fHZN6X93BwuYb9i1XcdHARn370KhyXeb6ZLHLegVIxxw01QOaJrtu82Ia1XFBRCRtNPfFIzraZHfNeqfOc9zgjNH3mndHCMQ4qKoEoXYc1IT8D0XnvtHXeeeW8BeterKpzKZs/cHYLQLf+ehw0dQuaQj6r7C8Vu7pngDFkIptHodukxYXtuNhsmyOd7YINZ1E1IiC6qwF8jVAVwmbL8ANGPh3WejdLk8Se0Subry1Ux27S8rUrTdx6eBkA8KT1RZiOi3ObbZi2m2oNEaiFNWnJMGUSFwuVchjWckFFVeCy7gIcFy0jS+Zdge11lkqLtMw7DxAR6prac2HHxUZT900Y57fC895pW0PWNAV1jTLPeQup/BnHV7HZMlN97jLji2e84L05+H2Ztov333MmdnmemCgmpMeapqKqKn7O23dgL+fIvNVuk5bNFveSrC+PCN4hxrpxcSVgzFMUwr6FKjZbVqC3fz4d1oDpBIM9vVc2X21UoFtu6n4RjsvwjxtN3HqEB++b1nlTn696UnousvmEg3dZ3Oa5QHTDSZr3bhl2poY1YLxd++YeD0j7ChC8Ac4K0jDvjaaBZx7n+cyL2+HfiT6GpLdaV7GZE/N+1gl+3EL6nwdc2un4fRLC8pOffnQDP3fng/jUV2P5R3nes69kcamu+S70KwETV14Ius2vejXeo5k3v4ezLBe7stubNtq/WMmdeYe5+ycBxtjAdz/uxMVzm23olttl3gcXAQAPX+ImtsZYsnnIYBJn8sGbz3gofpOWXCByFElzlW3TydSwBozXCnCzZWClrk20Nd8w1Ctqqh3z1aaBm9YXsdqohMrmrteLPm1rw+Wamjnz3vACyrNOrAGYr3KxB85sA+Aydpjp8jHP3fugN552FIITxQS4A5pfS+JcH8pVNu92WBNpnIPLwzfFXXk/G9lctxzsdKyekrj9i1VstaxAqVj2zHvBZ96TDd6G7cJyWJ9sLvqbpzunwmn+FI95H1isYrmu4eGL4zNvTeVlwgOy+YTXXyLCQkVFp8i9zfNCl3kna9TSMrOrixbDScbpb369ZeLAmB2nskS9oiZu0uK4zDMj1XB8rYELIbL5uKyDM+9sDWsbTQNVTfHluXnKez9wdgs1TcE/e9oRnN1sD/g2HvdG7j50YSfW6+3qgwNyFgPdy67sGlAVwoHF/IO36TBc8xStg0vDmf5ySEnbOBBkoj94Xw8y7xxY3pK3pk26v3l/Zz2AN2kB0jdV+prXFvWWQ1wuJyI8aX0Rj1waP3gDvCSsf6pYNYdUxig0qtp8yuaqQmhU1OTM23Ay6a4GZMW843dXmwRqmpKYeW+2TLgMOLhcw/F9jdBab/GaaRssrNTUsbs29YM3Dan5C+08dVl74OwWnnViFU8+uIiO5fjeCwHRFOMrMYP3Xkjw5uM2u7L5waVaruMP1YBhTTDv9VHMu5Zx8Bbpgb7gvdky82Xe3po2acNacByogM+8U96vj15p4uT+Ro9CetP6or+ujJt2qFWUqcvmAC8Xm0u3OQAcXa3jUgK2ZNouTMf1nZnjYsVzQY/TZa1wwdubM5sEorvaoeUu8+5ncrot+jqPkfPO3LBm4NByHSt1DQtVdW5kc91y8OCFHTzvhn244QCvg+7Pez9+dQ9VVcFG0/Al72FoGpafPxZYCgz9uLybTYOWYagE6ryv7RlYrKojN+r1Sq+xblx0c/vdz7p/oYrtjuWzrHyY95SCtzEYvMclNUGnuYAwrQHjb36qqtLX29xBdcKlYgAP3nPJvAFufrk0pKNXP0Q+aKFAOe/rCYaSTAL1FMx7w88v8uDdMp2BVIIwwaXdNa/U+AKb5XCSjaaOQ8s1EBGOrNTnplHLQxd3YDkMz71hn9/EJJj33myZ2GpbeMlTD3rPH533boYw76Wa5jOzjd18a7wB+Kxe9BoY5TQXWMqwReoVP7ffy7wZ66YOtBzyqwtTKhUTm54ew9pC+nXRtF08frWFp/QHb8+0BmCsUjGAG42DZaeWw6bCvBvVOc15Azx4J5HNReP+rJi3YBppgzdjDFsFY971ipq4w5qQKA8t1/2hD+f7TGv+RKWUN95KDl3WNpqGL5kfXqnPTc5bmNWed+MaTuzzmHeg1lvku7/7WccAjJbOGWMD5UKAYN78e7/S1Huk5DzQz7zjTi/Lcizo5R0DC1XVL0EDupUkl3Y6ubBuoFsqNunhJH7OO2BWXK5pUBVKlfP++rUWbJf5PhSBJ60HgveYsvnNB5dw2rvGgekY1oA5Z95HV+u40uTj9+KgHZg7mwVUhbBc01KXiu12bNguK1jwTl4qdrWPeQMYMK11xyGmlM09Q05Wk8U6poOmbuOgx86SbgTLjAfObuHk/gYOLddRr6g4tFzrkc3F+MVnnVjFk9YX8eCI4K1bLmyXDcrmXs5btxxst63cZfNuzttj3jGDd5ZjQa94DVqCrTaFSe/Stp5LvhvgXhVVoSnI5r0DaYDuuOQ0jVq+GmiLGsSpQPBujEm+bjm8hDPX277jfBp13gD3/8xt8D6y2vCdznHgM++M3ObAeE34r7f4cR9YKk7wrmmqn5+Oi41dHct1DfWKimNe8L643R+8vXGIaWVzj3lnlfcWeXrBvI+sctncjbkRLCsYY7j/zBaed0O3begN+xd6gvdj13i++8S+BTz9+OpI2VxIzmGyuW65/rWQt2xeUbpNWq7tGf7GbBSW61rP6NJxcGVXHyiHE5vzizkybyLC4hSYXJhhDeApxTQb7X84t4OapuDJB5d6Hl+qaTjkfZ/jyuY3H1qC4zI84Y0v5oa16bjN0zayyQJTDd7HvEYIcY1Ggnln5TYHxFjQdDd+tzVqkUrFes0ccXB1z/BvrPWlKmqaMuA4z8KwBmQ3WUx0/BLHfWSlDttlA67rWcOF7Q42msZA8D7Xx7xvPLAAVSE88/gKLmx3hm6amiHlQkB3QRdMPotxoMMgct665WCrbcVm3sv1SmbtUYOtUQVE8G7qdm7MG+CK4qSbtER996spSc39Zzbx7JNroUxYNGsZt1TslkOc1X/NqyefmmxeUSdelx/E1HPeAHA5Zq23uLCz6rAGACt1LbXbvEitUQV4nXdS5s1d2wBnAMfXBsvFdHPMnLenlmTVZc13yHssyS8Xm/G89wNneb77+YGBHSf3L+DSru7Xvj5+dc9fKMUUsGHSef84UAEhpT7m5Rfzl815b36R/ojNvDMKeowxbOwaONzXRW7fYjedUMtRnl2saRMPBnu6DU2hgc+1tpA8eLdNGw9d3MXtgWszCOE4Hzfn/aSDi1AI+McNfl0a05LN5zvnzSXa2MzbO1ELGcrmq430u/YiDSURSNNh7WqfRHl832Cjli7zTu82B7LLeff32j6aUMUpKx44s4VGRcVTA4agG/YvgDHOym3HxdnNNp7kyZZP91rePngxOnj7E8X6ct7CtOUH7xz7mgsERwWvx0xHLdezyXlfb5kwHXeAedc01VchcmXeVXUqTVqWAj3tBdZSyOZfOrcN22W4/VR48H7BqX1YX6qO7VmqV1TcsH8BpzcE83Zy3VRFgdd5F3wkaF7Yt1BBVYs/17tlhudnxjuGKu4/s5Vqjm4hg7fGDWtxP49gG4cCwfvYagP/P3vnHSbXVR783ztte9/VqmvV3OVuGTeMMcYtYHpMSTDF9JKEjwQSvnxJIA0CISQEAsSADZhiIBjs4A7uRZJtyZJt1ZV2tZJW2+v08/1x752d3Z1yZ+bOzr3a83uefXbmtjnnzp3znreeF806xBYzqWLFDV6hgI+6kHO53v3jYUJ+X6qgRMqKc4Jr3o/vG+DsVc2z0pXm5nrHEioV3dtUE2R1a20ezdsMWprzu6pLCe9JQoGZe11Ogj6hz7TE2dW8rXz0UtfCfumIVRmsYd6+1roQE5F42XzeYGreFSjSkmk8NXzehf1Wt3YbC+Wct7o14/43nrOCN5y9IuPSroWysbOBPceMSWUsoRZ8SVAwhHfcLBtdCc2/opq3iBiFWmz7vE3N26FUMYDTVzQyOBnNWFUsH4MTUepC/gVffzcXlmZgt1DLRCTOdCwxT/MemIjM0uBLjTYHI+XGKZ/38THDWmAN1u1m9S+7VdZ2Hxvng7dtqejM2aJvZJqP/WhbXjPl7mPj7D42wTVnLJ21fVXLjPDeZ5oS1y+ZCRg6Y0UjLxzOHrRmx2ze2VhVkmC0SzDg48iIpXnb93knkqrknFsrpe6MFY3z9lnpYm70ed+/6xhfufflopY2Ho9kEd61IcYjcduZQABbDg5zUmd9Kk98LiLiiOAGo/TqgYFJYolkxSqsWWNhpcaQiq+msbSx2rbP29K8nQxYswJ/tprLKxbC0GSEVhdFmkPmVXdykcrxTvNnrsgQcV5qkRYwrBxO+byPjc+OCvb7hI76Ktua98+29HDPzmM8eWCw4M92OsL0ru1H+M32Izzw4rG8x4nAtZtmC+8lDVWEAj56hqbYP2AK7/Z04d3EoaEpRrOYQccz5Poa743f2chUbEFM5gABny8lhG1r3taKXCWaznccHmF1ay3NtfN/01ZcS1k17yJ8qN9/vJubb9vC1x7cm1oQpBAylcUFw2Wh1ExsST6SScW2Q8Octyaz1u00GzvriScV+49PkkgqQv6FV6AsOeR0oRa7imTFhXchmvdkJE510OdofeVTljZQG/KzrQjhPTgZdVWkOczMBu0WaulPK9BiYRVqOTxLeJcWsAam5u2Qz7t/LDJPoBSS6/3oXkNoP3NgqKDP/f3u45z5t/emCqE4wZaDRhse2TOQ9RilFHftOMKFa1tnfVdgrDm9qqWGQ4NT7D8+SVtdaJb2YwWt7czi906ZzbNo3lD+SHMLq8xlQ1XAtpXHEj5jJQvvUTaZy8vOpaV2YTTv4cmoraA1pRT//NuX+H937uSyjR2IwN07jhb8mRNZNG9rpT67Ss3u/nHGw/GswWpOY7k2rGe6UrXNwdmV4GKJJB+6bautYysvvJtrbOfnTkYTjkaag7HE3Nmrmtl6qAjhPeGu0qgwI7ztFmpJL41qkVHzjicI+X0lmb1aa4MOporNz8ddarPK2sBEJLXC0TPdhQnvHzx5kGg8yYM218nOh1KKLaav8JE9A1l/By8fG2dv/wTXm1XT5mLleu8/PpmKNLc4I0/Q2ng4Tl3IP29SnD6ol3Mp0HQsX77d0qiQvrJY8RPD4ckoPUPTqTXt52LVciin5n39mcuYiMb5q1++kNcE/pmf7+Abv9vHOy5czS3vPp/NXa38744jBX+mEbA238x9+vJGaoJ+25Nb6xnOFqzmNOs76hGZKf1bqWhzwNGI82/+bp/txYQqL7ybqokl7OXnToTjjkaaW5y3poUXj4wXXN3IbYuSwIzZ3G662Exp1JnBcmlTNT6ZXWUtEkuWnOLhlM87HEswFo7PajOYhVpsaN6P7zO07s1rW3m+Z9S2GXxgIsJDptDOpSUXwoGBSQYno1zQ1WJMKo5m9k3ftf0IPoFrTl+acb+V671/YIJ17bMLZLTWhVjRXMOOLH7viXB8ntYNs1MyF0rzDpiat93SqDATJV9Kupg1YJ6ZRXi3pnze5RsyL17fzp9ceRK/fPYwP3r6UNbjdh8b5ydbenj/pWv5+zecQcDv47pNy9jTP8GeAk3n41kC1oJ+H+euaebpbntKzZbuIToaqlK19stNTcjPypYaV2jeTpnNd/WN8bUH9/C6szJP0OdSceFtpWXkM3fGE0ke3zfIqUvnB5OUyrlrWkgkFc/3jtgny7tgAAAgAElEQVQ+RynFkMsWJYF0s7ldzduI2rYWaQHjh9vZWE3vHLN5qYF5LWYQTKTAPPS5zPjpZwuUzsZqxiPxvIP4Y3sGaKwO8N5LuogmkmzvtTfT/dVzfcSTiks2tPHUgcGS+wEzGsufvuYkIPOkQCnFXduPcNH6tqx+4FWttYxH4gxMROdp3mA844/vHci4MEymFcXAMMdb6wiUO8fbIpTSvO3/rvItCzoWjnEkT1yNJbxPzya8ay3Nu7y+1Y+/egOvPKmDv71zFzuyPJdW7MMHLl+XCiK0ghj/94XCTOcTkVhGnzfABV2tvHR0LGMg5dznaMvBYc5f07IgQY0WG5c0pDTvqgrVNgdnNO9oPMmnfvY8TTUh/u71p9s6p+LC2yrHuac/94zx4T3HGZiI8ObzVjrehnNXGaaeQvzeE5E40UTSdZq3pR0XonmnR21bWEuDWjghvE9dZky87tmZOzArH6mVn+YIsmVN+SeCSike3TvAxevbuXBtG2DfdH7H1l7OWtnEey5eSziWLCrIcS5bDg7RUhvkovVtnLK0gUf2HJ93zItHxtk/MMn1m7LPyNM1nnVzSlMC3HDWcgYnozy8e/71B8ajWQdwSyN3t+adO2Dtz37yPK//j8dSRWwysaN3lK622lmT2HQWQvMGY8L01T88m7b6EB/+4daMQYZ37TjC5q7ZsQ+djdWcv6aFuwswnccSScKxZNbU281drSg1f1z8/uPdnPv5+1K/m2NjYXqHp2cVDloINi6pT03YgoGFTxWrCZoBaw74vP/jwT28eGSMf3zTplRmQz4qLrxPXdbIiuYa7tjam/O4O7b20loX4oqTlzjehqbaIBuX1Bc0GLsxxxvSfd6FCe+5rGipSeXbGtcr3Wx+5SlL2Liknq8/uLekGuSWn36uQLFTZa17cIrDI9NcurGdlroQG5fU87QNv97OvlFePDLGW85bySvWtxHwCY86YDrf0j3MeabGctnGdp45MDwvAOauHX34fcLVp3dmvY6V6w1k1LwvP7mD1roQv9h2eNb27oFJnjk4xGUb2jNe1xrYF0p4By3NuxDhbUbJZyq2dHBwkgdeOsbx8Qj37co+aTSC1Zqz7m+pWxjNG4wx5evvPJejo2G+ct/Ls/btTsU+LJt33rWblvHS0XHbwZSTGdbyTuec1S0EfMLTaZNbpRTfe7yb8XCc93z3GbYdGk7zdy9MpLnFhrR0yEpEm9c4ZDZ/8cgYX//dPt507gquOi37b3wuFRfefp/wjgtX8/i+wVQlp7mMTEW5f1c/N5y9vGy+jfPWtLDt0IhtoZIqjeqyVDFrcMkWsDY3EKZ/LIvwbq7hyEg4FQQUjpeueft8wsdevYGXj41zb46BNB/ZNO+lNjTvR/caAvdSU1hdsLaVbQeH8+az3rG1l5Dfx+vOWk59VYBzVjenrlUsAxMR9g9Mpga9yzZ2EE0keerA7MHyN9uPcPH6NtpyCDQr1zvgk4x+x6Dfx+vPWs59Lx6bZQb93uPdBHzCuy5ak/G6VjDTgglvn/H7tpsmBjPWgUzuklufOIhfhI6GKn78TGY/8uBEhMMj02zKkN9t0bZAmrfFuatbePO5K7n9mR760yajlsl8bq4/FG46z7SWdzo1IT+bVjbNClrbdmiYAwOTfPrqk2mrD/Hu/36aHz9ziOqgj9OXO+/SzEX6ymWVjTYvTXh/5b7d1Ib8/PUfnFbQeRUX3gBvPX8lAZ9w+1OZf1y/fr6PaCLJW8pgMrc4d00Lo9OxVJ5sPoYm3LcoCcwMLpk075GpKK/914f55u/3pbalL0qSztWnLyWeVHz7kQOp6zmhdVy/aRldbbX8+4N7iioqAYbmHfBJKn3HIhU/kUPzfmzPACuaa1hjaqqbu1oZj8RT0eeZiMaT/Oq5Pq46rTOVA3zphg52HB4tKQDPsvRcYEbobl7bSlXAxyO7ZyYFj+wZ4ODgFH+QQdNKp64qQFtdiNWttSntdS5vPGcF0XgyZVodC8f42ZYeXnfm8nnpZxb1VX7qQn5HqxrmwjJ/FqJ5+03f/Fyf92Qkzk+39HDtpmW868I1PLJnYNYCLhaWv3vTiuya99Kmala11rCxc371tXLxkSvWk0gq/uvh/altd2cwmVusaK7h7FXN/O8L9kznM2t5Z/9uN3e18nzvSGo8uWNrL7UhPzdd3MWPbn4FTbVBHtljVP3L9tyVi/RCRJWMNi+lSMsLh0e5b9cx3n/puoz1BXLhCuG9pKGaq89Yys+29mYUOnds7eWUpQ2cvjxzMIkTWP4au6bzIRcuSgIzmnemIi1/95td7Omf4Cv37ebQ4BTReJKhyWjGgeCsVc1cv2kZ33lkP/3jYcKxpCNaR8Dv4yNXbGBn3xi/e3m+/9UOx8bCLGmompe2VhPy01QTZNeRsYwWlERS8fi+AS7d0J7y8V+w1tB6c/m9H3q5n6HJ6KzJ46Ub21FqJnK9GLZ0DxEK+FKpXNVBP5vXtqb83jv7RvnoD7exvqOO6zblFt4A56xu5oIcpsszVzaxvqOOX2wzXFQ/faaHyWiC91yyNus5bXVVrGxZmAhiMIq0QGGaN5glUucI7188e5jxcJybLl7D2y5YiU/IqH2/kApWy645Vgf9PPLnry6L2y4ba9rqeMPZK/jhUwcZmIiw+9g4e7KYzC2u27SUFw6PcWhw/iRlLpbwzqZ5gxG0FksonusZYTqa4DfPH+HaM5ZRVxVgRXMNt9/8Ck5b1sgNZ68ovIMlUm+2AajYqmJQmub91ft301gd4D2XdhV8riuEN8A7L1zN6HSMu7bPnjXuOTbO872jZdW6Ada119FcG5wlvJ/cP8iPnjqU0aQ66Fqfd2bN+8GXjvGLbYd5++bVBHzCP9z9Ymod9WwD5f+5+mSi8ST//sBeRwLWLN54zgpWNNfwtSK17+PjETqymHGvPHUJd20/whu/8Tjb52QPvHB4lLFwnEs2zvh3VzTXsKK5Zpbwfuilfq788u947b/+njf952P83a930dFQxWVp5521somG6gCP7i1uAgLwTPcwZ61soirNovHKjR3s6Z/giX2DvPuWZ2ioDnDb+y7MGA0+l2/90fn845s2Zd0vIrzp3JU80z1M98Ak33u8m81drVkLkwD85XWn8o13nVtYx0rAqlFdSJ43GOli45EZd4BSilsf72bTiibOXd3CsqYarjh5CT/b0jsvUnp77yjr2utotHGPF5qPXrGeaDzJtx/Zn9NkbnHtGYZg/+s7X8hoZUgn21re6Vh5288cGOLeXUcZj8RnjcWrWmu5+5OX8fbNq233yUksv3clNO+A30fI7ytaeD/fM8L9L/bzgVeuK+rZc43wvmhdG+va6/jhUwdnbb9jWy8Bn/CGc8o7sxMRzlvdkhLeP3jyIO/8zlP85S938NZvPs6BgcnUsePhGNt7R6gK+Byts+4EVgWodL/m6HSMz/5iB6csbeBvX386H71iA7/deZRfPdcHzPcdW6xtr+Ptm1dz+9OHODwy7ZjwDvp9fPhV63n20AgPvZy92IlSir3943z3sQP85S938LUH9vDzrb10D07SmaXNX37rWXzlbWdxeHiaG77+GJ/+2fM8vneAaDyZ8lFfvL5t1jmb17by9AFjcZqtB4f58A+NCkfr2uupCflprw/xp685adZiIAG/j4vWtfHInoGiJiDT0QQ7+0bnBflcdpIxQfjjW54ikUxy6/suTGVk5MPny1872vod/clPnqN3eJr3XNKV8/ilTdUZo9fLxUzAWmGT4vqq2SuLPb5vkD39E7z74q6UleXGzavpH4/MK7DzQo7KapVmXUc9rztrObc9cZBfPns4q8ncYlVrLZ+99hSe3D/IlV/+PX9/1y5GpqJMRxOMTEXpH58piJVtLe90mmtDnNzZwNPdQ9yxtZeVLTVcuHZhA9NysdEU3pVYVQwMa1+x0eZfvX83zbVB3n1xV1Hn23Jkicg1wL8BfuA7Sql/mrO/CrgVOA8YBP5QKdVdSENEjMC1L9z1Irv6xjh5aQNbuof4xbbDvOrkJQX5wIrl3DUtPPBSP3/5yx386KlDXHFyB9duWsYXfrOLa//tYT5x5UZ6hqb51XOHmYomeM2pnQua12iHupCfFc01/Mu9L/Py0XE+ceUGvvXwfgYmonznjy8gFPDxvkvXcvvTh/jX+3YDuU2Un7hyIz/f1st42NkVld56/kq+/ch+PnDrVj7+6o185Ir1qYF73/EJvvvYAR54sT9VOrepZvb6wq89LbP2YWmXV53WyVfv38NtTx7kZ1t7qa8KEAr4OG1Z47xn6YKuVn757GEefKmfT/3seTobq/nJBy/K+8xdtrGde3cd4+DgFMuaq7n9qUPcteMISxqqWdNWS1d7HZu7Wulqnx/9/XzvCLGEmldO8uTOBpY0VDERifO992yeFVHrBCuaa7hoXRtP7B9kRXNNQdGtC0HA76OxOjDLGmGHucuCfu/xblrrQrNiBa44uYPOxip+/PQhrjaL3Rwfj9A3Gs5aWc0NfOyKDdz5fB+HhqZ4/2XZXRwWH7x8PTecvYIv3/sy33n0QCpuxeLkzgY++ZqNjE1bq8nl1vouWNvCHVt7icSTfOLVGx1bXMQJTjKXxq2pkBJVE/Sz9dAwf/aT59h+eJRjo2GuOr2TGy9YzQVd2fPenz00zEMvH+fTV59sy6qWibzCW0T8wNeBq4Be4BkRuVMptSvtsPcBw0qpDSJyI/DPwB8W2pi3nLeSL97zMp/88bMMTkYZmoxSFfDZemCdwPJ7/+ipQ9x0cRefu/5UAn4fr9zYwWd+sZ0v/vZlqoNG1O47LlzDWS6crQf8Pn798Uv59iP7+f7j3fx6ex9KGeY3S7uoDvr53PWn8qEfbANyl77saKji5svW8W8P7HF09bSqgJ//+cgl/M2vd/Kv9+/mvheP8qHL1/M/z/Zx/4vHCAV8vObUJXxiYweXbmhnVWst4ViCI6NhjoxOp/zE2WioDvJ//+A0PvXak3hs7yAPvnSMR/cOZKwTsHmt8b1/8LatNNcGufW9m21NFi/d2AHAF+95ied7Rjk8Ms0pSxsYmBjjnp1HiZsazsXr23j75tW89vTOlFDaYprp5+bGigjfeff5VAX8nLy0PMFRbzx3BU/sH+Smi7tmWRPcwHmrm4ta3rGhOsCOw6N84NYt7Owb4/DINB+9Yv2sZzbg9/G281fxHw/t5fe7j3PG8saUv9vNwntjZwPXbVrG/+44ktNkns7Spmq+9NazeM8la7n/xWME/EJ1wE9SKW5/+hAf+eG2lIstl88bjMntD540YgXefG553ZeF8vqzllMT9KeWwF1oOpuqeb5nhOPjETataOKslc3cs/Mov9h2mHXtRqzK+V0tnLumhYaqALuPTfDbF47ys609tJSgdYM9zXszsFcptR9ARH4M3ACkC+8bgL8xX98B/IeIiCrQnthcG+LGC1bxy22HueKUJVx9+lIuP7ljwSJdz17VzCtP6uA1py7hjy/qSm1f2lTNd2+6gO29o3S112Ut5OAWWutC/MU1p3DzZev49iP7OTQ4xSeu3DjrmKtPX8pF69p4unsor6C6+ZXr+Pm2XtY6/ANpqQvxbzeewzWnL+Vz//MCH/vRs7TUBvnklRv5o4vWzGtXddDP2va6gtpRGwpw1WmdOTXM9R31tNaFCMcS3HLTBaxps3f9rrZaVrbUcPeOo5y1sol/evOmVDBcLJGkZ2iKu3cc4fane/j47c9Say4fm1SKyUicjUvqM0aYnpkj39gJ3nC2EXVe7jiSYrgpR/BcLta11/O/Lxxl7/EJzlvTwnsu6eJdr5if/vaHF6zilkcP8O5bngaM1DqR7JXV3MLfv+EM3n1RV06TeSZOW97IaXNSuN5zyVp+s72Prz2wh7FwPBV4lY3Nppl889rWWfUE3EB10G+7nGg5uPW9mwnHErNSKT8fPZ27th/hp1t6+Mbv95F4SCFiZFAcH48gAuevaeHjr95YkmyTfPJVRN4CXKOUer/5/o+AC5VSH0s75gXzmF7z/T7zmIE51/oA8AGA1atXn3fw4Gz/toVSqiBz9MGDB4lEInmPq6qqYs2azPmsbidfH4vpW/94mF19Y7zKRgRtIqnmLVxh977bad/gRITneka4eH17RUxgD+8+TmNNkLNXFSY4tx0aZnQ6xqtO6sj6zCaTikf2DvDAi8dS99EnwpWnLuEyU3s/ESnk+YDSfp9KKSLxpC3rUP9YmJ19Y+wfmKR7YJLOxio+9uqNec9Lp9C+ZaLS41EiqYgl7N2zL93zEpeftCQlyMtJpntb6r2q1Pc1GYnzXM8IW7qH2Xd8ggvXtXLVaZ15J2EislUpdX7OY2wI77cCV88R3puVUh9PO2aneUy68N6slMqaR3P++eerLVu25PxsjUaj0WgWG3aEtx2HVy+wKu39SqAv2zEiEgCagMLWWtRoNBqNRmMLO8L7GWCjiKwVkRBwI3DnnGPuBN5tvn4L8GCh/m6NRqPRaDT2yOstV0rFReRjwD0YqWK3KKV2isjfAVuUUncC/w3cJiJ7MTTuG8vZaI1Go9FoFjO2Qt2UUncDd8/Z9tdpr8PAW51tmkaj0Wg0mky4K8lTo9FoNBpNXrTw1mg0Go3GY2jhrdFoNBqNx8ib5122DxY5DmSu0uJd2oGBvEd5F90/b6P75210/7xNIf1bo5TKWcGpYsL7REREtuRLrPcyun/eRvfP2+j+eRun+6fN5hqNRqPReAwtvDUajUaj8RhaeDvLtyrdgDKj++dtdP+8je6ft3G0f9rnrdFoNBqNx9Cat0aj0Wg0HkMLb41Go9FoPIYW3jkQkVtEpF9EXkjbdpaIPCEiO0Tk1yLSaG7vEpFpEXnO/Ptm2jkhEfmWiOwWkZdE5M2V6M9cCumfue9Mc99Oc3+1uf088/1eEfmaiEgl+jMXp/qXtv/O9GtVGge/v7eb77eLyG9FpL0S/clEgb/Bd6b9/p4TkaSInC0itSJyl/nb2yki/1S5Hs3Gif6Z+zw/xohIUES+b25/UUQ+m3bONSLysjnGfKYSfcmEU/0z9/tF5FkR+Y2tD1dK6b8sf8ArgXOBF9K2PQNcbr5+L/B583VX+nFzrvO3wBfM1z6gvdJ9K6J/AWA7cJb5vg3wm6+fBi4CBPhf4NpK983J/pnv3wT8KNt37NX+mdv7rWcS+CLwN5XuWzF9nHPeJmC/+boWuMJ8HQIe8eIzmq1/5vsTYYx5B/DjtO+sG2Nc9QP7gHXm9/c8cFql++ZU/9LO+zNzjPmNnc/WmncOlFIPYyxxms7JwMPm6/sAOzPc9wL/aF4zqZRyRRWhAvv3WmC7Uup589xBpVRCRJYBjUqpJ5TxBN4KvKH8rc+PE/0DEJF6jB/WF8re6AJwqH9i/tWZFpNGoK/cbbdLCb/BtwO3m9eYUko9ZL6OAtuAlWVpcIE40T+TE2GMURjPYQCoAaLAGLAZ2KuU2m9+fz8Gbih32+3gUP8QkZXA9cB37H62Ft6F8wLwevP1W4FVafvWmmaP34vIZQAi0mzu+7yIbBORn4lI5wK2t1Cy9e8kQInIPWY//tzcvgLoTTu/19zmVgrtH8DngS8DUwvXzKIpqH9KqRjwYWAHhtA+DfjvhW1yweT6DVr8IbOFG5D6Pb4OeKBsrSudgvp3Ao0xdwCTwBHgEPAvSqkhjPGkJ+18r44x2foH8FXgz4Gk3Q/Rwrtw3gt8VES2Ag0YsycwvpDVSqlzMM0fpq8jgDHLf0wpdS7wBPAvC99s22TrXwC4FHin+f+NInIlhtY2FzfnHxbUP9OnuEEp9cuKtLZwCu1fEEN4nwMsxzCtf3beVd1Ftj4CICIXAlNKqRfmbA9gCLyvKaX2L1Rji6DQ/p0oY8xmIIHxHK4FPiUi6zhxxpiM/RORPwD6lVJbC/mQgIMNXhQopV7CMEEiIidhmDpQSkWAiPl6q4jsw9B2tmJobNbg/zPgfQvcbNtk6x/GbPf3ljlORO7G8PX8gNkmyJW4yOw6lyL6NwGcJyLdGL+XJSLyO6XUqxa46bYoon9j5nn7zO0/BVwTEJSJHH20uJEMWjdGkYw9SqmvlreFpVFE/wY5McaYdwC/Na1B/SLyGHA+htadbn3w6hiTrX/nAK8XkeuAaqBRRH6glHpXrs/RmneBiMgS878P+BzwTfN9h4j4zdfrgI0YASUK+DXwKvMSVwK7FrjZtsnWP+Ae4EwxIncDwOXALqXUEWBcRF5h+kz/GPhVBZpuiyL69w2l1HKlVBeGxrrbrYIbCu8fcBg4TUSsFYyuAl5c2FYXRo4+WtveiuEXTT/nC0AT8CcL19LiKLR/J9AYcwh4tRjUAa8AXsIIANsoImtFJIQxeblz4Vtuj0L7p5T6rFJqpTnG3Ag8mE9wAzraPNcfxuz2CBDD0FzeB3wS2G3+/RMzVereDOzEiITcBrwu7TprMAIYtmP42lZXum+F9s88/l1mH18Avpi2/Xxz2z7gP9LPORH6l7a/C3dFmzv1/X0IQ2BvxxACbZXuWwl9fBXw5JxrrMQws74IPGf+vb/SfXOqf+Z2z48xQD2G1WAnxuTj02nXuc48fh/wV5Xul9P9m/P92oo21+VRNRqNRqPxGNpsrtFoNBqNx9DCW6PRaDQaj6GFt0aj0Wg0HkMLb41Go9FoPIYW3hqNRqPReAwtvDUajUaj8RhaeGs0Go1G4zG08NZoNBqNxmNo4a3RaDQajcfQwluj0Wg0Go+hhbdGo9FoNB5DC2+NRqPRaDyGFt4aDSAiN4nIow5dq0tElLn0ZtkxP2tDked2i8hrsuy7TEReznSsiPyliHynuBYX3MY3ikiPiEyIyDkL8ZlOYbZ5XaXboTnx0MJbs6CIyKUi8riIjIrIkIg8JiIXLHAbFlS4ehWl1CNKqZOz7PsHpdT7YUHu578AH1NK1Sulni3TZ5QFs837K90OzYmHHrw0C4aINAK/AT4M/BQIAZcBkUq2y82ISEApFa90OyrMGow1kPOi75dmsaA1b81CchKAUup2pVRCKTWtlLpXKbUdUqbrx0TkX0VkRET2i8jF5vYeEekXkXdbFxORJhG5VUSOi8hBEfmciPjMfT7z/UHzvFtFpMk89WHz/4hp1rwo7Zr/IiLDInJARK6d81n/LSJHROSwiHxBRPzmPr953oCI7Aeuz3UTTPPzZ0Vkl/lZ3xWRanPfq0SkV0T+QkSOAt81t98sIntNa8WdIrJ8zmWvM+/XgIh8Ke0+rBeRB0Vk0Nz3QxFpnnPuBbnakqUPfyMiP8hyPy8327kp7fglIjItIh0ZrpXxuxKRKhGZAPzA8yKyL0tblIh8VET2AHvMbaeIyH1mO14WkbelHV8jIl82P29URB4VkRpz3ytMy9CIiDwvIq9KO+8m8x6Pm8/HO83tG0Tk9+a1BkTkJ3PatsF8net5vclsR8bnT6OZh1JK/+m/BfkDGoFB4PvAtUDLnP03AXHgPRgD9heAQ8DXgSrgtcA4UG8efyvwK6AB6AJ2A+8z970X2AusA+qBXwC3mfu6AAUE5nx2DLjZ/OwPA32AmPv/B/gvoA5YAjwNfNDc9yHgJWAV0Ao8NPf6c/rZDbyQdvxjwBfMfa8y78E/m32uAV4NDADnmtv+HXg47XrK/MxWYLV5H95v7tsAXGWe14EhaL9aQFt65xz7GvP13wA/yHE//xP457T3nwR+neV+ZP2u0vq3IcdzpYD7zPbXmN9RD8ZzFDDv2wBwunn814HfASvM7/pi8/6swHg+r8NQbK4y33eY1xwDTjavsSztercDf2WeUw1cmqnt5H5ebyLH86f/9N/cv4o3QP8trj/gVOB7QC+GkLoT6DT33QTsSTt2kzn4daZtGwTONge4CHBa2r4PAr8zXz8AfCRt38nm4BjIImxuAvamva81j1kKdJqfVZO2/+3AQ+brB4EPpe177dzrz7kH3XOOvw7YZ75+FRAFqtP2/zfwxbT39WZfusz3Crgmbf9HgAeyfPYbgGcLaEuxwvtCDAHqM99vAd6WpU1Zv6u0/uUT3q9Oe/+HwCNzjvkv4P9hCNhp4KwM1/kL0iYN5rZ7gHdjCO8R4M3pz4F5zK3At4CVWdq2gfzPa9bnr9K/Wf3nzj9tNtcsKEqpF5VSNymlVgJnAMuBr6Ydcizt9bR5ztxt9UA7hs/8YNq+gxjaE+Z15+4LYAjibBxNa+eU+bIew+caBI6Y5tQRDGGwJO2zeuZ8Vj7mHp9uBj+ulAqnvZ/VF6XUBMYkZkXaMRmvZ5qrf2ya+seAH2DcO7ttKQql1FPAJHC5iJyCIcDuzHJ4Md/VXNL7sAa40PquzO/rnRgTsXYM7TiTCX4N8NY5510KLFNKTWJMCj6E8RzcZfYL4M8BAZ4WkZ0i8t4M1873vEL250+jmYcW3pqKoZR6CUMLP6OI0wcwtLM1adtWA4fN130Z9sUxJgeqwM/qwdCa2pVSzeZfo1LqdHP/EQyzc/pn5WPu8X1p7+e2b1ZfRKQOaGOmr7mu94/m9c5USjUC78IQNHbbYods9/P75uf9EXDHnAlJOrm+q2La0AP8Pu27alZG1PeHMZ6bMLA+wzV6MDTv9PPqlFL/BKCUukcpdRWGyfwl4Nvm9qNKqZuVUssxtOn/lPmpe/meV42mILTw1iwYZhDRp0Rkpfl+FYb5+clCr6WUSmBErP+9iDSIyBrgzzA0SzD8kH8qImtFpB74B+AnyohEPg4kMXysdj7rCHAv8GURaTQDrNaLyOXmIT8FPiEiK0WkBfiMjct+1Dy+FfhL4Cc5jv0R8B4ROVtEqsy+PKWU6k475tMi0mLe00+mXa8BmMAIJlsBfLrEtmQi2/28DXgjhgC/Ncf5ub6rYvgNcJKI/JGIBM2/C0TkVKVUErgF+IqILBcj2PAi877+AHidiFxtbq82g/ZWikiniLzenDhFMO5pAkBE3mo908Awxvn4OAoAACAASURBVEQikd4gG8+rRlMQWnhrFpJxDF/oUyIyiSG0XwA+VeT1Po5hmt0PPIoh5G4x992CITweBg5gaFsfh5RJ8u+Bx0zz6CtsfNYfY5g9d2EM0HdgaGBgaGD3AM8D2zACrvLxI4wJwX7z7wvZDlRKPQD8X+DnGFr+euDGOYf9CtgKPAfcheEnB/hbjICtUXN7prbZbkuW9mW8n0qpXoz7oYBHclwi63dVDEqpcYy4gxsxtPqjzAQAAvwfYAfwDDBk7vMppXqAGzAmMMcxNPFPY4yTPozntM8853KM2AKACzCe6QkM18AnlVIHMjQt1/Oq0RSEFUmr0WgWCBHpxogGv7/SbSk3InIL0KeU+lyl26LRnEjoIi0ajaYsiEgX8CbAUyVNNRovoM3mGo3GcUTk8xgukS9lMSFrNJoS0GZzjUaj0Wg8hta8NRqNRqPxGBXzebe3t6uurq5KfbxGo9FoNK5k69atA0qpeesApFMx4d3V1cWWLVsq9fEajUaj0bgSEclbpVGbzTUajUaj8RhaeGs0Go1G4zG08NZoNBqNxmNo4a3RaDQajcfIK7xF5BYR6ReRF7LsFxH5mojsFZHtInKu883UaDQajUZjYUfz/h5wTY791wIbzb8PAN8ovVkajUaj0WiykTdVTCn1sFmjOBs3ALcqo1TbkyLSLCLLzGUUNRqNZtGglGLboWF2H5ugoTpAQ3WQhuoAVQEfIb+PgN9HR0MV9VV6WYmFpn8szN7+CaZjCaaiCUIBH689rRORucvbewMnnqAVGEvnWfSa2+YJbxH5AIZ2zurVqx34aIODBw8SiURS749NxNg7GOHc5bXUBGeMC1VVVaxZs8axz11I9h/oZmffGEfGYxwdjzE4Hef6k5voajFWOaxE3+be91x4+d5riqOQ5wO89YzM7duRsRgP7Bvj/r3j9I3Hcp7bVuvn9hvXeaq/C8nBgwf5n+39rGkJcdqSGqD0Z+PgwYO844cvc3hs9nfzpWtXcNayWlvXcNv35YTwzjRtyVgwXSn1LeBbAOeff75jRdUjkQiDsQC3bTnGs4cnODIWBeDTV6zijZvaU8dNTEw49ZELzm1bjvLdrYOztjXVVnPGqjagMn2LRCLU19fbOtbL915THIU8H+CtZyQcDtMz5ePR/aM8emCUvQNhBDh3ZT3vuXAZ566sZzqWZCKSYCKaIJZQxBKKB/cM87t9o9TV1TE5OVnpbriSSCTCd7YM8sr1TWxeZxQZK/XZ6B6Y4PBYjHeet4QrNjQT8Akf+OluthyJcsnGJbau4bbn0wnh3QusSnu/EmPB+gXlSw/28GL/FBeubuTGc5bwH48cpm/M/qzf7ewZCLOsMcQ//8E6ljWGeP1/v0AsoReV0Wgqwc93jvCtpwfwCZy5rJ6PXbqcV29sYWlDKOd5vaMRfrdvlERygRrqQRJJxXgkQTjm3E167sgUANef2kZXazUA561q4JH9o3zishWeNJ07IbzvBD4mIj8GLgRGF9rfPRZO8HzfBO86v5MPXrQcgJ88209/HvOVlzg0GmNDew0b2g0zUsAnxJJaeGs0leDYRIzaoI+f33Q6TTX2h9GgzxASsaSW3tkYjyQAiMQdFN5907TVBlhjuhkBLlvXxBPdY+wfCrO+rSa1/fhEjN/sGuTdF3Tic7FQt5MqdjvwBHCyiPSKyPtE5EMi8iHzkLuB/cBe4NvAR8rW2iw81TNJQsEr1zWntnU2hOifiC50U8pCLJGkbyw668EL+IS4nr5rXErP0BR7B8OVbkbZiMYVtSFfQYIbIOA3hbe2mmVlLGKMa2GHhLdSiuePTHHeqoZZGvYlXU0APLJ/dNbx33qyj28/eYQDQ+5+fu1Em789z34FfNSxFhXBE4cm6KgLcsqSmdnTkvogz/edGD6lQ0NTxJOkzD0AQb/WvDXuJJFUvO/7zzA8EeZX72vPf4IHiSQUIX/hNa4szTueVOQ2sC9exsKG5h2OOTO+7Ts+wdB0gvNWNsza3lEf5LTOWh7dP8pNFywFoH88yj0vDQMwNBmbpZG7Dc9XWAvHEmw5PMWl65pmzao6G0Icn4yS8JCAu+2Jbq7/2iMY86EZ9vYbgRJdLTPC29C8vdM3zeLhF9t62X1sguOTcYamThzXVTrRhKIqUITwNgW+1ryzM+aw2fzxfUag73kr5wdPXrquiV3HphiYNJ7Tnzx3nLgpMwYm4458frnwvPB+bO8A4bjileuaZm1fUh8kkYShKXd/Aek8vm+QnX1j9A5Pz9puCe/VLbM177iHJiaaxUE4luAr9+2mqSYIwO7j03nO8CbReJKqQOH+0IBPm83z4bjw3jtIZ32A5U1V8/ZdZsqNRw+MMhaO86sXBrh0rbFt0OUTT88L7/t2HaM26OPcObOqTjPq00t+7wMDhpl/e+9sH8y+4xO01fqpr/KntgX9ogcAjeu49YlujoyG+eJbzgRgd/9UZRtUJiJFa94zZnNNZpz0eSeTiif2D3J2llzuda3VLG8M8ej+UX65Y4CpWJKbX7GUmqCPwUktvMtGIqm4/8VjbF5ZmzJHWXTUGzP/Y+PeEN5KKQ4OGgPd9sMjs/bt659gddNsD1nA59M+b42rGJ2O8fWH9nH5SR1cffpSljUEefmE1byL9HmnAtZ0sGk2xi2fdxHC+yv3vsxPt8zUDNt1ZIzR6RhnL8/suxYRLlvXxJaecX7y3HFesaaBjR21tNUGteZdTp7rGWZgIspFa+b7MjrrLc3b3V+ARf94hOmY8dDuSNO8lVLsOz7J6ubZwjvo1z5vjbv45u/3MRaO8RfXnALA+rYqdh8/MTXvYn3eKbO5nnhnZTRiBawVLrx/sqWHz/x8Ow+93A/AE6a/O5vmDYbpPJpQjEzHedd5nQC01QUY1D7v8nHvzmME/cLmlfO/mMZqP1UB8YzmbZnM17TVsqN3lKT54z42FmEiEmfVHM076HOH2VwpxZce6uHx7tH8B2tOWIYmo9zy6AHecPYKTlveCMDGtioOj0YZj7h7ECyGSKI4n3fKbO6C365bsaLNowlFUhV2n2IJRVLBJ370LHv7J3h83wDrO+poq82eWHXm8noaq/2c1lnLOSsMRbC9LqjN5uVCKcW9u47xinVt1IX88/aLCJ31Ic9o3t2m8L7hrOWMR+J0DxrvrWC1VXM074BfXFHooXsozC93DPCdJ49WuimaCnJgYJJIPMnrz16e2rahzQgQ2pPDdB6JJ/mH+w9y3CO/U4tovEiftw5Yy4sVsAaFB63FEkmuPr2TUMDHzbdu4ekDQ1y8Pne6YsAnfPUNG/j8tWtTGUuttcFUBLpb8azw7h2e5sDAJK85tTPrMUsagvR7RfMenCTk93H1GUa+oRW0trd/HGC+2dwlqWIPmwUOXuqfYt/gienf1ORnPGwMdI3VwdQ2S3jnijjfc3ya3+wa4rk+d9WNzkexAWsBK1VMm82zMh6ZEdiFms5jiSRdbXV884/Oo3d4islogovXt+U975QltSxrnBlj2+oCTMWSKVemG/Gs8B4xgwlWNGdPol/iMc17VWsNJ3c2UB30zQjv4xM0VAVorZltXXBLkZZH9o+ypqWKgE+4e9dQpZujqRDjYcM03lA9Y55sqQnQXhfk5RwR58PTxnlumIgWQiyhCPm12bwcjIYTqdiAcLxws3nQ7+OCrlb+8U1nsrq1Nq/mnYn2OmMS6ma/t2eFtzUjqslgMrfobAgxOBXzRFpG98AUa9vrCPh9nL68iR1mxPm+/knWL6mfVzjf7wLNe3Aqzq5jU1xzSiuXrG3kty8NVbxNmsqQSXgDnNxRk1Pztoq4eKmYEhjm3JLM5h7r70KhlLEoyRIzW6gQs3kiqUgkVSrz6C3nreThP7+CptpgnjPn02ae4+aIc88K76moMVjkEt5L6oMkFa73XSSTiu7BSbra6gA4c2UTLxweI55Isvf4BBuWzI+md4Pm/cQhw9T5ynVNXHdqG8PTcZ48OFbRNmkqg2U2b6iePVCetKSWg8PhrObPlObtIWEWTyRJKIo0m+tUsVyMR+IklGE1hcLSxax7GiwikHAubSnN272yw7PCO2xp3sEcmreVLuZyv/fRsTCReJI17TPCezqW4NmeEY6PR1jfkUF4+3wVD3p5/OAkq5qr6Gqt5qI1jbTUBLjrxcH8J2pOOCYicXzCvODRkzpqSCrYO5BZ+7YqIFZ6IloIljZYVLS5TxdpycWIKSyXNBSueVvCu5j8+7lY0emDLq7Q6VnhPW1DeFsPwDGX+72tyPK1pua9aYWxOtovth0GyKh5BypcHnU8HOO5I1NcZtaUD/iFq09p4dEDowy72NSkKQ/j4Tj1VYF57p2TOow0zmz53taz4iV3i6U4lFakxTv9XUiGpwxFy1oXvZCANeueWv7yUmiqCeD3ac27LExFjR9QbS6ft0c07+4BY2DrajcGunXtddRXBbhrex+QWXhXukjL714+TjzJrJry15/aRiIJ9+4erli7NJVhLBybZzIHWNoQpLHan7XS2vCU98zm4ZTmXXyRFi9NVhaSIVN4Wz7vQszm8ZTZvHSx5hOhzeXpYp4V3tOm8K7OIbzrqvzUhXye0LxDAR/Lm4zIeZ9POGNFI2PhOCG/j1Ut8yPqg77K5nnft+sYTdV+Tl9al9q2vr2GU5bU6KjzRch4OD4vWA2Megsnd9RmrXE+5EXhbWrexRVp0aliFj1DU6lYCYsRU3hba1MUYjaPWsLbAbM5GLneWvMuA5bwzmU2BzNdzOWa94GBSda01uJLM/ecudIwnXe116ZyQ9MJVFDzjsaTPPRSPxetrsM/x0R1xYYW9gxMMxZ2r69I4zzj4VhG4Q2G33v/YDhjkNbQtGk295Awi8SK961qs/kMb/rG4/zb/XtmbRu2fN6W5l2E2dwJnzdAe13A1atSeld4xxIE/ZJ3ltXZEHT9ymLdA5N0tdfN2nbmSsMcnclkDobmnVCVSbF5cv8g45E4F6+um7evwVz5LKoHp0XFRCSe0WwORsR5LKk4MBSetT2eUKlSmF4yI4fjxWveAR2wBhiL2Bwfj7C7f3ZxnpGpKMJMnnVR0eYOCe+2Om02LwvTsQTVebRuMDTvY+Pu/QKSScXBISPHO50zzaC1DRkizWEm5aQSg8CuI0Y62Kal8835Aa1ZLEqymc0B1rcZ69B3zxHeVpoY4IpSv3aZMZsXPnz6fYJPdKpY34gRA3HIDNa1GJ6K0VDlS8UyFSK8o3FLeJcesAZGrvfIdNy1Ey3vCu9oImewmkVnQ4jh6Xjqi3UbfaPTRONJ1rTNXlxlVWsN//imTbzjwjUZzwv6jK+uEhpLzLyX1RkGr5lUGHfeb015yCW8Lf/l3En08PTMe5f+PDMSKSFgDQzte7H7vA8PG8K7d3h6lvVwaCpKY5WfkF8QIFJAhbVyaN6KmaBKt+Fd4R1L5PV3w4zvxK1lUq1IcytNzEJEePvm1Sxtqs54Xsp3VoFBIJZIIgKZMjK0T2/xoZRiPByjviqz2bwu5Kehyj9vhb90f6KXKqxFUqlixWl4lc4UcQN9o4bwjidVSgsHw2zeWO1HRKgO+gryeVsasmPCO5Xr7U7Z4V3hHbVnNrdm/W71ex8wzUZzfd75qKR5OmrWD56b0wtp6xUv8sFpMRGJJ4klVFbNG4y83bnCO70egJeEmSVQitW83VBgqdJYmjcYUecWw5OxVNxMld9XmM/bYbN5u8urrHlXeMfsmc0tzdutfu/ugUmqAj6WNmbWsLMRqKB5Op5IpszjcwlW0BevqQxWXfPGHMK7syHIsTkTaMvn3VTtXzQ+b7CW813cv4/ekemU5fRgmvAemYrSWGXc16qgFJcq5kCeN8yUSHVr0Jp3hXc0kbOuuYVVI9etmvdBs6a5r8CqQJVcFziWSGb9gWjNe/GRra55Op0N8wNHh6bihPxCY3XAU5O9Un3e2mxuBKydtaqJoF84lK55T8VoqjbG9epAYWZzp1PFWk2zuVvTxTwrvKei9nze1UEfTdV+1/q8DwxMpiqrFUIlfd6W2TwTVk66lwZjTWlkW1Esnc6GEOORBJORmfWRh6ZitNYGCfiEuHuXTZ5HuFSfd4ULLLmBw8PTrG6tZWVLLYcGDeEdjiWYjiVSZvPqgK+o2uZO+byDfkN2aM3bYcKxBDWh7INFOm4t1KKUomdoOrWaWCFUssxiLJfZXJd/XHTMCO/cmjcwy3Q+PBWnpTZgCG8PCbNSfd6VLLDkBiLxBP3jEZY317CqtTaleVt1zRst4R0s0OedcNbnDYbpXPu8HcaINrfX/CX1QVdq3uFYkmgiSXNtqOBzK+lbjucwm1fSIqCpDJbZvL4qV8CaFXsyI7yHpuK01gQqvshOoYTjCQI+5lUXtEvQJ4varXR01Mj3X9Fcw5rWWg6aQbtWdbXGatPnHShMeM/keTsn1tpcXCLVs8LbrtkcjECa8Yj77HKT5prkdVX2+pFOqkZyRTTvXGZzvV7xYsOW2bx+fq738HSa2dxDwjsSS5bkV13sAWtWpPmK5hpWt9YyFo4zOhVL1TVvLNJs7nSqGEBbXcC1y4J6VnhPF2A2D/rFlcLEbn32TFSyzGI0kcz6A9HrFS8+xiNWtHl2s3lbXRC/wFFT804qxfC0YTb3miYajieK9neDkSq2mM3mh8287hUtNaw2i1MdGppKpQ42VqeZzWPFFGlx3myulPu+L1vCW0SuEZGXRWSviHwmw/7VIvKQiDwrIttF5DrnmzpDIqmIxpO2hV7IpTNda1nTuhzmxmwEK6jhxhPJrD8QHW2++EiZzXNo3n6f0FE/k+s9Hk6QSEJLbQC/xzTvcCxRVF1zi6BLx6OF4vDINCKwtKma1a2G8D44NJlaDtTSvIs2mzuUKgbQXhskllSutNzm7aWI+IGvA9cCpwFvF5HT5hz2OeCnSqlzgBuB/3S6oelMx/Kv5Z1O0O/OogiW2dxOyttcKikkc5nNdZ734mM8HKcu5M/rA+5sCKbM5laOd4vp8/ZUhbV4siTNO+D3lqXBaQ4PT9NRX0VVwM+q1hnNe2SyNLO506li4O5cbzu93AzsVUrtV0pFgR8DN8w5RgGN5usmoM+5Js7Hzlre6QT9kkrgdxNWP2qLMJtXUkhGbWjei9ksuNgYD8dyat0WnQ2hVLT5kGkiba0NEvSY5h2JJUoSEEGPRdc7Td/oNCtajEWN6qsCtNeHODRomM3rQv7U2FJ4nnc5Atbcm+ttp5crgJ60973mtnT+BniXiPQCdwMfz3QhEfmAiGwRkS3Hjx8vorkGhfqKg34hkTT8bG5iMmIFrJViNq9QqlhWzdsMpPPQYKwpDWNRkuz+boulDSH6J6Ikkio1GLaaqWJe0kTDsWTpZnMP9ddpDg9Ps7x5ZkVCK11sZCo6K/OmOugjllS2J3axRBKfFJ8FkAmva96Z7sTcu/l24HtKqZXAdcBtIjLv2kqpbymlzldKnd/R0VF4a00KN5u7Uxu0+lGc2bxyQjJuw2y+mAenxYaxlrc9zTuRNLTuuWZzL2ne4VhpAWtem6w4STKp6BsNszJNeBvpYlMMT0VpqZuZBFoTJLum82gimSoS5RRtLq5vbqenvcCqtPcrmW8Wfx/wUwCl1BNANdDuRAMzkRJ6djXvCgq6XFgBa3YnIelUckISs2M2d9m91pSPMZuad6eZ6310PMbQVAy/QFNNwHupYiX6vIMem6w4ycBkhGg8mTKbA6xureXI6DT94xFa0jRvqwiOXeEdTyhH/d0AtUEf1QGfZzXvZ4CNIrJWREIYAWl3zjnmEHAlgIiciiG8i7eL52HKDPSys6oYzJQxdNts1zKb19pMeUsnFbDmslQxv89Yh9eNqXma8jAejtnWvMEo1DI0Fae5JoBPxCyP6q7fZi7CsQShEiKa3RpAuxBYOd7Lm9KEd1sdSQW7j43PEt7VBQrvXEpFsYgIbXUBb/q8lVJx4GPAPcCLGFHlO0Xk70Tk9eZhnwJuFpHngduBm1QZE+PCRZrN3aZ5T5eiefsqlyqWy+cNi1uzWIyMh+M5VxSzWFo/I7yt0qiA5zTvcDxBVYlmcy/110n6RszqanM0bzCUq5baGQuOJbztBq3lG5eKpabAMq0LhS2VTyl1N0YgWvq2v057vQu4xNmmZccyN9v1FbvVDztlRq0W88AFKmg2N3ze2QevxezTW4yMh2M5S6Na1FX5qQ/5TeEdo6XGGKi95vM2KqzpgLViODxi1DFPD1hb0zazMNPcgDWAcFxBVf5rR+PZY3FKIeh3Z1EdT1ZYKzza3FrpqmxNKoqpSLyoYDWorNlca94ai1giSTiWtOXzBsPvfXTcCFizllz0XIW1WIJQCdHmgUW8qtjh4WkaqgI01cw8L0bOtzGepGve1ja7Wm8skSzJnZGNkEtTjT0pvMMFRmm7VvOOJooymYPhW/ZLhfK84/mFt9vutaY8TNioa57O0oZQyudtmc39Pm8VaQnHk1SVkuft0tTVheDwSHiWyRzA55OU6bylLoPPuwCzecDBNDELt1oSPSm8p4rI8wZjHWo3UYrwhsotLRhP5jOb+7TmvUiwsxxoOp0NIQ6NhAnHk7SaWlbAJyQUrqwfPZekWZq5tNrmizcj4/DI7Bxvi5Twzmg2r6zPO+T3uS5eCjwqvAtNFQulzObu+gKmovGiIs0tgr7KPFR2zOZunKlqnGfMrGtuV/PubAgRiRvPRkuNaTZPVQssQwMdxjKflloeFcCFltiy0zcyzYoMwntVJuFdsNlcOVrX3MKtC1t5VnhXBXz4bJpITkSzOVSmRrJSKmdtc3CvmUnjPHaWA03HyvUGUj5vL9UGsFx2pVVYc2fdiXIzEYkzOh3LqHmv66gDoL1hfp53QT5vh1PFwCqv7b7vqni1r4JMRxMFBXoFKxjclYupaIL2+lD+A7MQrEB+bKr4f8AHZP5RLeZUmMWGtaJYQ5U9s/nStME53WwOXhHeluZdWm1zcF/Fx3KTWse7Zb7wfst5K1nWVMOyphrGjxnbqoNmhbUKp4oFK+SezIc3Ne9ooqDFPNyreZdoNvcvfNSqtaBCrsAQI9rcfWYmjfNMRArVvGeEt2U295bwLl3zTqV5eqC/TtJnreOdQfOuDQW46rTOWdsKNZtH81gEiyXo97lS8/ak8J6KJWyvKAbuNVN50WweM/2V2uetgcLN5m11QSzLZqpIi4eEmVXty4mANbeNR+WmN4fwzkTAzKixbTbPkwVTLEYqo/uUEU+azcPRhO1gNXCz5l2a8K7EUopWwE6uwBBtNl88pMzmNqPNAz6hvT7IVHRmoJ3RvMvTRiexNO9Si7TA4jObHxqcJBTwsaTBRsUVjNKkVQUsC1qO8qjgXmXEk5r3dKwwoefW2uaG7774+VMlAsMsc3gwj9ncbfdaUx7Gw3GqAr6CimMsbQiltG6YEd5eyPWeMZsXP3R6abLiJAcGpljbVmc70BiM+2zXZF3WVDEXjmee1LynognbZjpIr7Dmni8glkgSTSSpOwHN5jrafPFgrChW2DDytrOXMBlJpN4HF5vZ3KVrLZSbAwMTbFzSUNA51cFCNO9y+byFWFK5rg6BJ4V3OJags9Ge6QXSF/Fwz80vtD57JoIVKIZiz2yui7QsFoy1vO2ZzC2u2NA8673fgwFrpQlvU5lw0XhUbuKJJIeGprjqtKUFnVcdsL8oiFEetTxmc3CfpcSzZvOifN4uGhysZU3rbCzokI1KmKdjNopU6Nrmiwe7y4HmwktmZEuQOGE2d9N4VG4Oj0wTSyjWtdcVdF5VgcK7XJo3uO/78qTwnio0z9uFPu+pEpYDtajEakyWthDw6WhzjeHzLlV4W79PL/m8HQlY80B/nWL/wCQAazsKE97VAV8Bed7lSxWzru8mPCm8jWhz+wOGiJgr+bjn5he6MlomjGjzhVVX7Eebe0CN0pTMeDhmu0BLNrykiTrh8/ZSXrtTHDhuCu8CNe/qoBSQ511ezdtt35fnhLdSiqlYgppQYU0PuUwbnIx422yeKyVDa96LByc0by8Js4gj5VHdZwksN92DkzRUB2irK6yiZKE+73KkilkTNbcVavGc8I4lFImkKlhjdZsfdqrAZU0zUYmo7hmft4421xhLgtY7JLxdWAdjHjNmcydSxRbPb+TAwCTr2usQKUy42s3zTiQVSuXOgimWgAsDnsGDwju1oliB+dFu0wanzFSZuhLLo1bM551LeLtsoqQpD8mkYiJaeLT5XAIe0kQj8SQ+gVIWr3Jr9HI52X98smCTORipYhEbN2rGIliePG9w32TLe8K7SF9x0GWJ9la0eUkV1irQp6gds7musLYomIjGUQoaHdO83f/MhGMJqgL+gjXIdKxgTzeNR+UkHEvQNzpNVzHC26bZ3M64VCxudXN4T3inNO/Cmm4l2ruFaYfM5gstJO2YzYN+H0nljcFYUzyF1jXPRsqM7LIiGJkIx5JUB0sbNt0aAFUuDg5OoVThwWpgP1UsZgUSlmk9b9DCu2RmNO8CzeYu88NOOmQ2X/DyqAl7FdZg8QxOi5VC65pnYyZVrOQmlZ1wLEF1CRkisPgWJjkwMAHAuvb6gs+tDvpIJPOPJTEb41KxuHVhK+8J75gx2y9UYw36fa66+dPROCKUNIs30t8qkyoWyBNtDu6bqWqcxWnN2wvPSySeLKlAC6StouaB/jrBgYEpALraaws+t8oUnPn83uX0ebuxQid4UXhHjS+pUF9xyGULqk+ZK6OV5jsTEklILqC50ZbZPPWwe0CV0hTNhCm860tId4SZ8qhecLM4oXkvNsvUgYEJOhqqirLQWMpNJJ77Xi2Iz9tl35fnhLcV6FVowFrAZT7vyWiC2hJM5lCZpQUt31KuGa5/kfn0FitjDpvNPeHzjiepKlF4+811qt00HpWTAwPFRZoDVJv59PmEtx13XrGEXGop8ZzwtgK9Cp39uq1Iy3Q06db4HQAAIABJREFUXlKkOVRmRmgJ5Jxm80WmWSxWLLO5U9HmbhscMxGJJah2ICjKSKd0oEEewMrxLgbLRRHOY8Urq9lc+7ydwSqSUKjgc5vP29C8HTK/LeCgF7XxI9E+78XBjM/bmfKoXhBmTmjeYK4IuAh+H2PhGAMT0aLSxMBlZnOXfV+eE95TReZ5u0/zLl14V2KdcrvreYP7ZqoaZxkPxwj4pOTUKS/5gJ3UvBfD76N7oLia5hbWvc4bsBbPH4tTLNrn7RDF5ke7rWTnVDReus+7AkIylkgaPjtfrmjzxbde8WLEWMs7UFLQJRgLB/l93hDeTgSsweIpZHTAFN7Fms1nhLfNVDGd5z0bEblGRF4Wkb0i8pksx7xNRHaJyE4R+ZGzzZwhHE0gUvh6uiGXmc2nnDCbVyJgLZlMaUrZcGtqhcZZxh2oa25RiYJDxeBEqhiYpY0Xwe9j//FJRGB1W+FpYgBVplUnX6EWy+edb2wqhpBLlwTN+8sTET/wdeAqoBd4RkTuVErtSjtmI/BZ4BKl1LCILClXg4tNsQq6LEDECeE9KyXLmTE0L7G4ymuaCrjUzKRxFieWA7Ww0h7djlOa92Ixmx8YmGRlSw1VgeLumV3N204sTrG41a1jp6ebgb1Kqf1KqSjwY+CGOcfcDHxdKTUMoJTqd7aZM0zHEkWtge26hUmicWpLzI+thJCMJZJ5TVNeqlWtKZ5jYxHaG6ocuVbQ53Pd4JgJJ8qjwuIym68torKaRUp455nZWVaMcpRHTaX2uUh+gD3hvQLoSXvfa25L5yTgJBF5TESeFJFrMl1IRD4gIltEZMvx48eLavB0NFFUPXD3Ce8EtaWWWayA2TyezL9m7oyPyAOqlKZoeoanWNVS48i1An73aTZzUUoRjjvk814kK+91D07SVaTJHGaEdzivz7t8mrd1XbdZSuz0NNNIPbcXAWAj8Crg7cB3RKR53klKfUspdb5S6vyOjo5C2woUr3lbPm/lgkIQyaRiOuag2XwBH6poXKVWRcqGl8pdaopjPBxjZCrGqtbiB+Z0/B7QRGMJY81oJ3zeAZ8PM/b2hCWZVIyH4zTXhoq+hnWvoxVMFbOu60Xh3QusSnu/EujLcMyvlFIxpdQB4GUMYe44xQo9N60ZHI4nUIrSzeYVEJKxRDKvaWqxrZq0GOkZmgZgVYszwtswmztyqbIRjhdXICoTi0HznjJnJ/VVJayc6DcyEezWNi9Hqhi4z3IL9oT3M8BGEVkrIiHgRuDOOcf8D3AFgIi0Y5jR9zvZUIupaHFmK6vEXdQFplwrV730aPOFT8mKJQoxm7vrYdc4R8+wsdjEqlaHzOYe0LytAlFOFGnxQn9LZSpiFPEpNSW2OuAjnGcssVO2uRQ8KbyVUnHgY8A9wIvAT5VSO0Xk70Tk9eZh9wCDIrILeAj4tFJqsBwNDseK9Xmb4f4umN5PRYorNDOXShQPiCUKMJuf4IPTYqZnyBTeDmneXvB5R2LG2OFEkRY3CgOnmYg4s3BNdcBnQ/POX7a5FEJ+9wVU2rqrSqm7gbvnbPvrtNcK+DPzr6xMRxPUthQhvH0u0rzNZU3rSnyoZ6/eVbo2YAd70eYLX/lNs7D0Dk9TXxWgudbJVDF3Py+RuHOa96IwmztkYawK+CqaKgbuK/IFHqywVqzZ3NJSo27QvKPFVYmbS6ACvuVYIplyQWSjElHwmoWlZ2iKlS01JVdXswj43BfNO5ewg5p3wOe+ACincUzzDubXvMu5qhiY5bVd9n15TniHS8jzBpdo3qbZvM6p8qgL7vPOE7Cmo81PeHqGpxyLNAdvFGkJF7miYSYWQ4U1a/nmUgNzqwM+W6li+co2l0LQ777sAM8J72Irk1kCxx2atxXI4VCe90L7vG1XWKv8vdY4j1KKnqFpx/zd4BGftzl2OFIe1QPR9aUyESk92hxMn3e+gDUbgbSl4NVUMdeglCohz9s9ZvNiF1eZSyWWBLVjNvfS+syawhmcjDIdSzgWaQ6GMHO7z9tJzXsxlEe1os1Lje2psmE2j9qwCJaCGwMMPSW8rS+wusgKa+COql+TTpnNK7BIvB2zeaqc4Ak+OC1WnI40B+OZcfvzkvJ564A1W0w4mCqWf1WxZNlyvMFQ/tz2fXlKeKeiF4vxefvco3lbZvNSNe9K5FPHE8rWDNfvO/F9eouVnmGzQMsi83lHUkVaHKptfoL/PmaUlNLN5nlXFYursqWJgVURz13fl6eEdynmZkvg5CtwvxA4VqSlAqvdRBNJWz+SxaBZLFYszXulQ3XNwRs+b0vzLnaFrHQWhdk8Gqcq4MsbI5OP6qCPqC2fdxk174A2m5fEdCrFqnAzTEpLdYXmnSDk95X8sPl9gk8W1hVg1zwVdOFMVeMMvcNTtNWFSvZlpuOFVcVmfN7OaN5JdWKvvDcRiZecJgZQFZD8ZvNk/qWKS0EHrJVISnh7PFVsOhov2WRusdD5ojGbZvPFoFksVnqGplnpoMkcvFEu1Mna5gEXxeCUi6logtoSI81hxmyea1GpWLzMAWu6SEtppMzmRa4qBu7weU9GEyX7gSwCC+w7ixViNnfZw65xBieXArUI+MX1qVNWeVQnNLygWYXwRBbeE5F4yUG5YKTmJVXu2B6j8mM5U8XcV0TIU8K7lEAvN1VYK3ZN8kwsdAqDfbO56DzvE5BEUtE3Mu1osBp4R/MOBXz4HCgEshgW75mKxh1xrVgV7aZzVEkpd6pYyIXKiKeEd7gEzdtVqWLReMnpExYL7YuxazbX0eYnJkfHwsQSytE0MfCG8I7Eko6URoXFYTafiCScEd5mjIHlNs1ELJFMWTPKgRvdgJ4S3qVFmxs/lnzJ/gtBsVXiMhHw+RZMSCaTikTSnvDW0eYnJqkcbwcLtIA3FiYJx4pbVyETbkpdLRdTkXjJ1dUAakzhbVleMxFLqLKazUN+w3Qfd9Fky1PCu5QUq5TP2wU3f9pB4W1o3gvTJ+tzbPm8XRjgoSmdchRoAWuyR86gpEoTiSepciDSHNxlCSwXkxFnLIyWpXUqh+YdX4AKa+AuN4enhLdlNilm9ptaYzpPysFCMBmNl1ys38IIWHPkUnmxHlw7Pm83mpk0pdMzPI0ILGuudvS6/grULCiUcCxBtQM53pA2HrlIGDiNU6liluY9GcmueUdtuvOKxY2WEk8J71J83lZOdDRR+aVhpqOJoqrEZcLQWBZI845ba+bqaPPFSu/Q/2/vvMMkKevE/3k7TQ67Mzuzs3kXdmF3gQU2kE5ABREUMICCguHwp6io6J0enp6H4U5Pz3CGE1FQT1SiYUFwyXnZBLvL5jBp4+TYPZ3f3x9V1dM70zPT1V2pm/o8zzzTXV1dXW931ft9vznEzOpSQwqVpFMI9fANNZt7izvaXEppmHuwPGU2n9znbXaeN0DEAfJDo6CEdyiawOcRBHIMGvF7hSNWToo5yTjfmVWrd81s7s/i+/cXQH9mF/0oaWLGmswhTRN1cIZCJJ40pEALpGlyRSq8I/Ek8aQ0JGAtpXlP6vM2t6tYILXYcs6cVlDCO9eOYhpO8cOOxBLGmc0tNE9r3102UZ1utHlxohRoMTZYDZxVAXEiwrGEYRYHXwGMNx80LdmIehZagHIoMonmbXKRFp+DUo01Ckt455kf7fcK26PNo/EksYQ01mxuleatmc2ziOp0o82Lj0g8QcdQ2FTN28nXTDhmoObtwAAoIwka1A4URs3mk2ne0YTMu4b6ZDippbRGYQnvPH1Ofo/9ZnMt6M4ozdtK87Tmn8s2VaxYJ6Y3Kkf6RpDS2G5iGqMBXM6ZHMcSjicoMThVzMnjzQdN0BpZpGVqn7e5Fda0z3EKBSW88w2A8HmF7V9+KKb1uDXO/GaZ5q1+ji8Ls7nPrbBWdPQEowA0VpcYfmzNLOlkV0sklqTE4CItxerzNlLz9nsFXjF5nrfpqWIe59QJ0Sgw4Z1foJcTNG+j2oFq+KwMWFMnmkC2ZnMHT8Qu+hk2cEIeSyFo3pG48UVanDzefDCqlzeAEIIyvyd1zEwoRVrMjza3W36kU2DCO79ye36vsH2lqwVdGFse1aJUMT1mc4u7nbmYTyg1IZspvJ17zYRjScPyvJ1ohjUSIzVvUEqkTqR5SynNr23uc95iq7CEdyS/aPOAE8zmUYPN5hZGdaeizd3a5m9IggZfu+lomo1VNQtyQdG8Da5t7oCiUWagWWmMKNICUOYTBCfweWtBjmb6vDVXoat550goll+XGp/H/mhzzWxuaFcxywPWsjObO1mLctFP0OAJOR2vw83IiaQklpCGpYoVe5630e7BMr+H0AQV1vRYBHMl4MAYhcIS3pH8U8XsXjmN5j8aFW1uvc87u2hzD/GkdHStahd9pCZkA5pNjMXpZnOtuqPxqWLOEQZGYnR8RJnPM6HmrVkvzEwVc+LvVVjCO5rIKwBCEXR2C28Tos0dmCrm8wgk4NC52CUHgpG4UuHQhEnS6eVRt7T1ATC9ImDI8QohQC8fQtE4Xo8wLDp/Mp+3pg2bmyrmRpvnTDIplQpreWisPgdp3saVR7WuJagen7e/AFJ/XPQRjChuKyGMnySdqNloROIJbl+7k/l15Vy5YpYhxyz+Ii2KomXUtVLqExNWWNPiJMztKlagPm8hxNuFEHuFEAeEELdNst81QggphFhl3CkqaL2889W87fZZjApvI6PNrTFP6/J5F0Ctahd9BPO0fE2GkzXRXz7fTHN3kK9ftdywVDFtvE4SBkaiLfSMoszvmbBIi2Y2t8Ln7aTrc8rRCiG8wM+Ay4FlwPVCiGUZ9qsCPgtsMPokwRiNNeAIzTuOEMb5zrSoVSsW8LrM5kWuWbwRCRnYynYsTi2Peqg3xE+ePsAVp8/k4lMaDDuuEAKfx1nCwEiCUYOFt88zYXlUTSEzM8/biYutbEa7BjggpWyWUkaBe4GrM+z3TeC7QNjA80sx6ivOJ9rc/pulvTdEQ1WJYeYkv4WBPlEdZnOn+zBd9DMcya/OwmT4HKjZSCn597U78XoE//bOcfpK3vgcEINjFprZ3ChK/YJQNJHRwhizwOcdKFCz+WzgUNrzw+q2FEKIs4C5UspHJjuQEOLjQojNQojNXV1duk7UCM3b7/XYGnAgpWRjSy+rF0w37JipspIWaCxxnali4DxNyiV3QpG4BWZz51wvT+7u5Ok9nXz+kiU01ZjQSc0hXQ7NwHCzuc9DIikzzt9WpIp5PSBw1uIym9FmmqlTV5wQwgP8EPinqQ4kpbxTSrlKSrlqxowZ2Z8laZp3vhXWbBTeh/tGODYQ5pyFxglvv4XmRr3R5oBbZa2ICEYThsVqjGXUUuOcyfGx149RXxngIxcsMOX4PgdUfDSLYJ7VMMei9fTO5PfW5iUzU8WEEPi8goiDfq9sRnsYmJv2fA5wNO15FXAa8KwQohU4F1hrdNCaEZq33WbzjS29AKw2UHhbqbHoizZX9nHN5sVDMBKn0oQcb3DmYq83FGVmTalpGp3PI4q2n3fQYCuNJryDGQq1RFMBa+aZzcEZvTHSyeaq3AQsFkIsFEIEgOuAtdqLUsoBKWW9lHKBlHIB8ApwlZRys5EnGowYYTYXJKV9q/uNLb3UlPlZ0lBl2DGtNE/nFm3unMnYJT/MDFhLpU45aHLsC8WYVm5MXncmnFB3wiyMNpuXqrXFM2neWqqYGfUH0vE7oLx2OlOOVkoZB24B1gG7gfullDuFEN8QQlxl9glqjMTyD1gL2FyScGOr4u/2eIxbIWqmIivmvFgiic8jsgq2c6PNiw+jg5DS8abcP86ZHPtDUWpNFN6+Ii4hbHS0udbTO1PEuRU+b3Ce5p3VtyulfBR4dMy2r02w78X5n9Z4jGgxd0IzAPPuyYx0DoVp6Q7ygTXzDD2uldHmsYTM+gZxog/TJXcSapEk06LNHRiw1h+KMa3cb9rxfR5n1co2ingiSTiWNLT7nGY2H8mgeUctyPNWju8s4V0wFdZGDGjokWqonpi4L6xZbGpRyiuuMdDfDdaazaPxZNZ+JTfavLjQAkbNaAcKzivSkkhKBsMxUzXvYjWbh7SCWgbGR5T5lesjk887lSrmM9nn7TBLScEI76ABed52NlTf2NJDecDL8lnVhh7XyuIW8WSSQJaFEFyfd3FhZlMSUMzmHuGcAMeBkRhSYrLmXZzC2+he3qCkisEU0eYec8WZz+PWNs+JkWiCEp8n5RvLBTtNcxtaelk5f5rh6QypQB8rAtbiMusbxPV5FxdG92fOhNcjHFNOty8UBTA1YM3nFUXZz9uI4OKxaBUpJ/V5m1hhDZxRXjudghHeRgRABGzSvAdCMfZ2DLHGwOIsGlZHm/uzNE2l8s9d4V0UhCLG1uTPhN8jHHO99KvCu9ZEzdtpwsAozOj7nsrzztCcZLTyowVmc1fz1k8omqAsz6YAdpnNN7f1IqXx/m4YNRVZMelFE8msg0K0/VyzeXEQTPm8zTGbg1LFyilm5L5gDDBZ8y5Ws3nUeLN5iTp3Z9K84wmLUsUcttgqHOEdSeQdAOGzKVVsY0svAa+HFXNrDT/2qOZt+KHHEU9I/Fmazb2u5l1UmOHHHIvPQeVCLTObO0gYGMVoZpBx14rXI9Se3hP7vN1oc4cSyrOXN9ineW9o6WXF3BrD2gmmY2VlKl1mcwt98S7mE4waH0E8Fq9HOCa1sD+kaN61FWaazYszJmR0oWfstVIR8E0QbW5dqpiTFluFI7wNKLfnt0HzDscS7DgyYGgzknRSmrfTzOYObfHokhuhSP7ZHlPhpNSpvlAUn0dQZbKlwUmanFGYYTYHJdMhc5539pUf88Fpv1fhCO9oIu/oRTs078N9I8STkiWNxpVETcfSVDE9RVoc2OLRJXdGNW8zo82dY6npC8WoLfcb1ro3E0Xr8zbJxVIR8E0YbZ5t5cd88HuFmyqWC6FoPO9Vv9+GQhDHB5T25jNrSk05vqWpYgkdRVpcn3dREUxp3uaZzX2OMpubWxoVnGeGNYpUqpjBbsLygHdCn7fZJnNwlmUICkp4F6bmfXRgBIAmk4S3lZq3npvE5/q8i4pgNE7A5zF1knRawJqZBVrAWeM1kmAkTnnAa2gPB1A0+Yl83mabzEENWHOFt35C0URepVEhLdrcQuGtad6N1eZq3tb4vLM3m3uEUFN/im9yeiMSiiRMLdACzjIj94fMLY0Kzks9Mgqz+r6X+SfWvLOt/JgPTmtMUhDCW0pJKBrPO/UgpXlbeMMcGwhTVxEwJdIcSKVuxSxJFUvqyqX0eYQbsFYkaNqUmfg8znGzWKF5e9XFipTOGLNRmNX3vaJkYp+3JWZzh7k5CkJ4R+JJkjK/piRgj9n8+MCIaf5uUIJ8wDqzuU+Hecrv8bjCu0gIGrB4ngplsWf/5CilNL2XN4DPC1IqTVCKCSPikzJRHvBmrLCmp9thPmhujqRDfq+CEN6aqaQQU8WODYRpqikz7fhCCMvKSuq9SYq5X/EbjVA0/yJJU+HziFSpSzsZiSWIxpOWmM2h+FxLw5G4KS6WiTTvqI5A2nyww3I7GQUhvIMG5ZimIrMt1LwV4W2e5g3KuCxpCarTPOWkWtUu+TEcyb+3wFQ4pUhLX0grjWp+wBo4RxgYRdCAapiZKA94CceS4ywVsbh1ZnNwTvprQQjvkZgx7Qi9AoSw7mYJReMMjMRMNZuDouFak+etb4Xr9zqnS5RLfoQi+Wd7TIVTfN59Qa0piVWad3HdI8FonHIzNG9VedPkgYaVqWJgT0vpTBSE8DYqx1QIgd/rsezL1yLNTde8PcKiPG/XbP5GxYiuflPhlGjzfqs0b4dpckYRjMSpNMPnrSpvoTHpYvGkdali4BxLSUEIb60knhFBECVej2Vf/rGU8DbP5w3Wad56zeZutHnxEIxYE7DmBEtNX8gazTvVl6DIenqHIom8raSZ0JS34Jh0sahVZnOH/V4FIbyDUeOauwd81mnexyzTvD3ExgdhGo6SKqYn2tz1eRcLwag5E3I6PodcL/2pjmLmat52BNCajZSSYNScgDVNeRtbqMWqPG9fSvO2YLLNgoIQ3qGocU0RrBTex9Xqaqb7vC3QcBNJSVKCT6/Z3NW8C55YIkk0njTFFJqOzyH9vLWANdM1b3Ut5IQxG0U4pqT1mpEqpll+xhZqsSpVTFtsOaW+eYEIb+M0b7/XY9nNcmwgzLRyv2kFWjSsiDbPpWeu3+NxhCblkh9abq0ZQUjpOKVcaF8oSkXAa7o25yvCgLVhVSs2o0iLZvkZmy6mNSYxm9Foc/uvUSgw4W2Ezy3gs9bnbba/G6wJDIumhLfeaHNnXOguuZNq8WhytLlTUsWsKI0KxRltbqSVdCwpzXtMoZZoIonfovKo4Eab62JEvSDyrbAGELAw2tyKHG9QfcsmC0lNg9ajjbjR5sVByKT+zGOxKmtiKvpCUaZVmOvvhjQfqkMCoIxg2KR2oDBqeQ1l0Lz1lG3OFTsqdE5GQQjvYDSB3ysMMWP5fR7LqjiZXRpVw4poc0078Hn0Rps740J3yZ3hiNbL23zN2wlaaL8FpVGhODVvrR2oWRXWYLzPO25RVzGnuTkKQniPRBOUGeQ3LvF6iMbNjxYMxxL0hWKWad6mm83jOZjNHeLDdMmew30hTr99HTuODKS2hQyqcDgVPo8zan1b0csbnCcMjEBzsZibKjZe87YiYE3LtHED1nQQNLA0o1XR5lbleIMWsGbuZ2gTjF6zuZvnXVjsODLAUDjOtsP9qW1BA2NOJsPrEGGmNCWxwGzukPEaSTAVsGb8tVLi8+ARGXzeFpdHdUpqX0EI71As/17eGlY1VD+mpolZoXn7LOjepR1fj9nc73VG3q5L9rT1hAA40jeS2hZM+TFNzppwgDBLJCWDYYsC1lLCoHjukVRmggnBjUIIKgLjm5PEEtKaPG/1I6zsjTEZWY1YCPF2IcReIcQBIcRtGV7/ghBilxBiuxDiKSHEfCNPMmRgdaeAz2NJhRytNKoVPm8rUsVyNpu7mndB0darCO+j/WnC26KANU3ztnPBNzASQ0rzC7SA84SBEfSPKAVuqsvM+f7KS8a3BbU6VaxgNG8hhBf4GXA5sAy4XgixbMxurwGrpJRnAA8C3zXyJENR4zTvgM9rkeZttdncojxvXWZzj+vzLjAOqcL7SJrwNlObSiclzGwMcuxLVVdzfd650DEYoTzgpcqkhd5YzTuZlGptczdVLBNrgANSymYpZRS4F7g6fQcp5TNSypD69BVgjpEnGYomDMsx9XuFRT7vEWrL/YYtOibDiuIW2uJAT0qGFSlsLsaSyWw+bFXAmgOKYPSn6pqbr3k7rcWkEXQMhmmsLkUIczTh8hLvCdHm2kLPCrO5036vbEY8GziU9vywum0ibgIey/SCEOLjQojNQojNXV1dWZ9kKBo3bOIosahIy/GBMDOrzTeZg0Wad1xLFcv+pnRKxSyX7IglkhzpH8HnERwfDKcmqVA0TpnfmzJrm4VPaGZzGzXvoNZRzHzNu0QVOGMbbRQyivAuMe345QHfCXnemovFkq5iBVgeNdO3knFGFkLcAKwCvpfpdSnlnVLKVVLKVTNmzMj6JENR43oJW1WkxaoCLaDlU5v7GdEczObaokJKV4AXAsf6wySSkhVza0nK0biNYDRherAapJuR7bterDSbl/kEFQFv6nsuBjoGIzSaqLSUB8Zo3jmUbc4VXwEWaTkMzE17Pgc4OnYnIcQlwFeAq6SUEWNOT8FI4W1VP+/jA2Gaas33d8NoBL2ZQlKbUPWYzbWL3SFWJpcpaOsNAnD+SXXAqN87ZGCq5mRol5adhX20Xt61FlRYE0LQVFuWykwpdKSUKbO5WVQEfCd0FYtaKLw9Qjim5zxkJ7w3AYuFEAuFEAHgOmBt+g5CiLOAX6AI7k6jTzIUjRvWFCHgM78xSTiWoCcYpckis/m0Mj/xJAyOxKfeOUfiOdwkqdQft8paQdCuBqudt0gV3qrfeziSMN3fDWk+RRvLhfaFovg8wrSAq7E01ZSmglsLnYGRGJF4koYqM83mYzVv/UpFPljZlXIqphyxlDIO3AKsA3YD90spdwohviGEuErd7XtAJfCAEGKrEGLtBIfTTTSeJJaQlBtUYS3gU3Kikyb6iDsGrUsTA5hRqWgJxwfNmwS0Fa5Ph2/JCQFILtnT3hMi4PNw1rxpQJrmHY2b3pQEwCvsX+z1hWLUlvtNC7gay6yaMo72F4fw7hhUDK5mznsVJSdq3qlYHAt83mBtY6upyGp5KaV8FHh0zLavpT2+xODzSjESNbYdoaY5RhNJSj3mTEhWpokB1FeMCu9TZlaZ8hm5rHA1zTuelJjvQXTJl7aeEHOnlVEW8FJfWZLSvIPRBLUm5e2m43NAnrdVpVE1mmpL6R6OEI0nLYmYNhNNabHC5y2lRAhhqc9b+5xCMpvbSiimpakYVNvcNyq8zUILQGmqtVbz7jBR887FbO6EACSX7GnvDTFvejkAs6eVpTRvpTyxBQFrDkjF6QtFLSnQojFLXeCbee9aRUp4V5mreceTMjV/W+nzBkV5KaRoc1sJGlwgQlvdmum30DRvq1LFNM27w0Tf2egKV18/b8DN9S4ApJS094aYX1cBwJzashMC1qzweaeKtNgovK3q5a2hmZjTK9oVKprwbjA1VUxtC6rKhdFWxdaYzUsKyedtNymzuUGTR8psbsIPEEsk+fVLLfz82QPMri2zJEIXlAuqqsRDx5CZPm+1trmuaHNlX1fzdj69wSjDkfg4zTuZlASjCVMaTYzFCWZzyzVv1TpXDEFrHYMRasv9lBoUn5QJrUx2KKbIhTey2dwa6ZIHqbrKBuZ5g/Gr+/UHe/jaX3ewv3OYNy2u59+vXG7o8aeivtzH8QFDM/ROINVVLEeft4uz0SLNU8K7toxoPEl3MEIwEje9NCqkCW+bAtbsZ1g2AAAgAElEQVSklGpHMQt93qrZvDiEd9hUkzmQqliptam13GzuIM3b8cJb07yNq21uvOY9EIrx4bs3MrOmlDtvXMmlyxoti1bVqCv3WeTz1m82dzVv56MJ7/l1ivCepdYoaO0OEU9KS6xImvC2q8vWSCxBNJ601GxeUeKjutRXFLneHUMRU03mMNrZTqtKF7Owwho4K9rc8WZzozsaaSs0I4MO9nUOEU0k+fpVy3nb8pmWC24wX3hrE6qeEpl2a1Iu2aPVNJ+bpnkD7OsYAoyzfE1GqkiLTZNjz7BSXa2uwtrciFm1xZEu1jFgboEWGHWfapp3LG612VzYWocgHccLby0hv8wgP4oWbW6k2Xx/xzAAJzdUGnZMvdRX+Ogejpg28cUSSQJej66FiRttXji094ZorC5J+StnT1OE935VeBuVqjkZdvu8xy5grGJmTantmreUkst++Dy/W9+a0/sTSUnXcMT0IF3N5z2qeVttNvcScTXv7NBWWEZp3maYzQ90DlPm96a0FTuoK/eRlNA1bI7fOxZP6jZNuWbzwqG9J8T86RWp5zVlfqpKfOxNad5Wms3tmRy18rCa68AqmmrKbPd5dw9H2dsxxHP7unN6f08wQiIpTW1KAkpXMSDVnOSZvZ0EvB5Tq7qlY1VvjGxwvvCOGZsqll6kxSj2dw5xUkMFHgsawk9EXbny/WhVjowmnpS6mpLAaN6uG7DmfNp6g+M0ztnTylJWJSsbk9hlNtcqzFmV4qkxq6aU3mCUcMy+7mIt3crCZc/xwZze36nOOw1Wad6RBDuPDvDAlsN85IIF1FVaJLx9hVXb3FZCkQQeMWruzhczNO+DncOcPMM+kzko0eaAaR2KookkPo++38CNNi8MwrEEHYORcRrn7NoyeoKKH9iagDXlv13XS2tPkLnTyixfhGsNjOzsLtbSrSzSDveNMBiO6X6/du6m+7zTNO//+Ntuasv8fPrNJ5v6mem4mrcOlI5iPsOCwIxOFRuOxDk6EGZxozllSbOlThXenSblesfiSQK6zeZunnchcGhMmpiG5vcG4yxfk2G72bxntEiNlczSCrXY6Pdu7gqmHu89PqT7/VqNCbOtFlqPi0e2H+Plgz3ceskSaiwo3avhpFSxAhDexuaYapV4jIo2P9iprFhPslnzri3z4vMI01bvOZnN3YC1gkAL1JqXQfPWKPYiLVqFubELGCvQqqwdszHivLk7SK1anGbPMf2m847BCEJAfaW5kfo+r4eAz8PWQ/0smlHBB86ZZ+rnjcVJRVoKQHgb18sblOAQj4CDaSvNfDigCu/FjfYKb48QNFSVmNZZLJpI6o7odH3e9hGOJfj1Sy1E4lP7UVM53pNq3uYLb81abYfPu3s4SiiaYIHFwWowWqjFzK6AU9HSHWTNgunUlPnZdUy/5t05GKa+skRXBcZc0dIW//XypZZFmWu4mrcOQtE4ZQZOHBUlPpY0VrHtUL8hx9vfOYzfK8ZNfHbQUF2aChwxmlg8mdKMsiXVz9shK9U3Eut2HufrD+/i0dePTblve2+IioCX6WPym9M1bysC1oQQBLweYjYs9tpTkebWm83LAl6mlfttq2+eSEraeoIsmlHJ0qaqnILWjg+GTY8012ioKuWCk+t469IGSz4vnYDP46aKZUsomjC8QMSKObVsO9yPlPlPEgc6h1lQV2HJinMqZlaXmrZ6jyX0tyx0G5PYx6bWXgCe2t055b6tPUHm1VWMiyvRNG8hjKuzMBU+r0gV3rCS1u7MrgOrsDNd7EjfCLGEZFF9BafOrGbv8SGSOu/ZjkHzc7w1fnfTGu68cZUtxbACqtncCNmRL/ZLnCkIRhOGlUbVWDG3lv5QLOXry4cDnUO2m8w1ZtaU5txZTErJfz66e0KLRDwp9ZvN3Whz29jU0gfAc/u6JrV8SCnZdqif5bOqx71WX1FCwOehwsCA0anweYQt10tbbwghYM40e2o1zKottU3zblYjzRfOqGBpUxWhaCLlSsmWzsGw6WliGg3VpZY1fRpLwOtBSmfMaY4X3iPRuOEFIlbMrQFg2+H8TOfhmHKR250mptFQXcJQJE5QLWyjh5buIHc+38xPnzmQ8fVoLmZzt0iLLQyEYuztGGLFnBqGwnE2tfROuO/BriB9oRirF0wb95rHI5hdW2ZJpLmGXQFB7T1BZtWUUeKzbqzpKFXW7NG8tRzvhfUVLG1SFnF6TOfReJKeYNT0piROwIqW0g9sPpTVfo4X3kYHrAGc0lhFqV+JWMyHlu4gSQkn25wmpqGZrXKpcb5RneCf29uVMc8zF7O5FdHm0XiS19r7TDt+IbK5Tfktb71kCQGfh6f2TGw636ya11ctmJ7xdStb24J9wrutN2R5ZbV0mmrKGBiJpSqHWUlLd5CqUh91FQGWNFbhEegKWtPSU63yeduJ36SulBrhWIJvP7Ynq30LQ3gbHCzj83o4fXZN3kFrWqS5UzTvUeGtP2htY0svPo8gmkjy+M6Oca/HEvrN5kII082gv325lXf/78upBhrFzjN7O7nnlbZJJ/lNrX34vYLzTqrj/JPqeGp3x4Q+uk2tfUyvCLCoPnOg1sfetNDSIhg+r7AlVay9x17hbWdf7+auIIvqlZiHUr+XhfUVutLFtPmmscbVvPPlgS2H6VULI01FAQjvuClpKivm1LLj6GBeK6gDncN4BCyaYX2EaiYa8tC8N7T0cumyRmbXlvHI9qPjXo8l9Nc2B0X7NlPzfmyHEk39+M7jpn2GU+gPRfnsH17jq3/ZwfnfeZofPLGPngy17De19nL67BpK/V7eemoDrT2hCVMjN7f1smr+tAl92hef0sA1K+cYOo7J8GcZbS6lZNfRQUMCh4bCMXqCUeZNt+8+TvX1tiHXu6VbiTTXOLWpmj06CrV0qvPNG8lsbmRXSo1EUvLL55s5c25tVvs7WngnkpJwLGmKz23F3Fqi8SR7cshp1DjQOczc6eWpTkx2oxV70BtxfrgvxJH+Ec5ZOJ13rmjixf3d9I1Z/cUSyZwi6v1e8zTvzsEwr7Yr1pMndo23FhQbv3i+meFonO9fu4LVC6bz46f2c+F3n+FA5+g1HI4l2H64n9ULFTP4W5Y2AvD0nvHfT+dgmLaeEKsnMJnbgT+LaPNkUvL1h3dxxY9f4I8bs/MPToYWuGqr5q0Kb6urrIVjCY70j7AwzfKydGYV7b0hhrIsk6opC28Es7nRFTrTeWzHMdp7Q9x80UlZ7e9o4T1icFOSdLTVzdY8gtYOdA6z2MY2oGOpLPFREfDq1rw1f/eahXVcecYs4knJ38dosrGETF24ejDTbP64KrDfdeYsth0eMLWfuZm0dge56Teb6Bqa2N3RNRThNy+1cuUZs3jvyjn88kOrePzzFyKBnz1zMLXftkP9xBKS1fMVgTy7toylTdU8mSFlbHObEiuwKkOwml34PJ5J+79H40k+d99WfvNyK6V+D39+7XDen9k+QXlYK2msUQSf1fXNW3tGg9U0tKC1bF1Rxwcj+L2CaeXW9kG3g5TZ3GDhLaXkF881s6i+gkuXNWb1HkcLb23lZ2SRFo0508qoqwjk7PeOJ5I0dw9zkoOENyh+p1yEd3Wpj1NnVrF8VjUL6yt4eNuJpvN4jmbzGZV+dhwLmpIXuW7ncRbUlad8soWqff/yhWae2tPJr15onnCfnz97kGgiya2XLE5tW9JYxfVr5rF229FUfXJNIK+cPyqQL1nawJa2PvpDJ1pTNrX2Uur3sHxWjZHDyQu/d9TNIqXkxf3dvLC/i+2H+znYNcxNv93Ew9uO8uXLT+WWN5/MptY+juSZYuUEzbvE56W+MmB5X++WrvHC+1RVeO/O0irZORimoarU1q6KVqHVUdfqAhjF+oM9vH5kgI9fuAhvlt+jbcI7Gz/on187AsBZWfoA9CCEYMXc2pyFd3tviFhCOiZYTWNmdanu1fvGll7WLJyOxyMQQnDlGU280txzQpOTaA4BawDXrJjB/u4Rthwx9mIfGImx/mAPly2fyckNlSyoKy9I4T0cifOX147g9QjueaVtnIAFODYwwj0b2njv2bNP8E2CElDmEXDn84rg39jSy5LGSqalVUt7y6kNJJKSZ/d2nfDeza19nDm3VncWgZn4vaOa9993HOeGuzZw410bueqnL/HW7z/Hywd7+N41Z/CJi07iqhWzAcYtNPXS3hukriJAVal1DS4y0VRTxlGLfd7N3eOF96yaUqpLfezOMmitY8i66mp2s2bhdGZWl/L7DW2GHveO55uZUVXCu86anfV7bLtrW7qHJ/WpjEQT3PVCCxcumcFps83RDFbMqeVA1+TnMRH7UzXNnZEmptFYXaor2rxzKExzd5A1C0f9nu9cMYukhMdeV0zng+EYkVgiJ+F92SnTmFHh577txqZzPbOnk3hS8rblMxFCcOmyRtYf7GE4yxz3YCTOM3s6ba+U9NetRwhGE/znu08jGE3w65dax+3zk6cPIKXkM29ZPO61ppoy3nPWHO7ffIiOwTCvtvWNS/taMaeW+sqSVHAfKIuGnUcHHOXvBq3CmiSWSPK9dXtZ3FDJAzefx68+tIr/vnYFf/rk+Vy7ai6gVEM7c24tf92an/Bu6wnZVlktnaaaUus17+4gjdUlJ6QDCiF0Ba11DEZMbwXqFPxeDzecO48X9nenso3yZduhfp7f18VHL1igK37KNuEdiSe59d6tJCbwh963qZ2eYJRPX5yd8z4XzpxXi5Tw+uEB3e89kOom5oxIc43G6lI6h8JZlzfUKnGtWViX2raksYpTGqu4+6UW3nfHes76xhMMReLMqNK/uvZ7PVx3VgPbjo8Ymo+9budxGqpKUlaZS5fNJJpI8twY7TITUko+d+9WPvqbTfxhY7th56QXKSX3vNLOsqZq3rdqLpcua+Q3L7eesADZcWSA+zcd4rrV85g7gU/2ExctIppIcttD2xmKxFkzRiB7PILrVs9l3c4OHlNrnW9t7ycpJ87vtgsl2jzJ/ZsP0dwd5F/efiqrF0znkmWNXLNyDivGWOGuPnMWu48Nsj+PVMG2npAjehOcMrOKfR3D/MuD2xkY0a9Q5EJLd/AErVtj6cwq9hwbnHB+BiXY7UdP7qOlO2hbZTo7uG7NPAJeD79b35r3scKxBP/8wDZmVpdyw7nzdb3XNuE9q7aMp/Z08r11e8e9Fo0n+cXzzaxeMI1zFtVleLcxrJijaPR6g9ae39fFHc8e5JTGKttNbWOZWV1CLCHpy2B+zcTGlh7KA95x5TGvXTWHtp4QoVicmy9axP2fOI9PZhkFOZarTqujKuDhjucOTr1zFoRjCZ7d28WlyxpTfraV86cxvSLAE7umThm768UWntzdQUNVCf/xt920G1AmNxe2Hupn97FBPnjuPIQQ3PLmkxkYiXHPK4pJbkNzD9ff+Qr1lSV85i0T51ovmlHJFac18Yy6cMkUgPbZty5mxdxavvTQdg71htjU2otHwNnzjHdJ5YPPIxgcifE/T+5n9YJpUzafeMcZTXgErJ3AdC6l5MdP7eczf3wtY258JJ7g6MAI82xoSDKWT7/5ZG6+6CQefPUwl/7gOUvSH5u7hllYP971d95J9QSjCf7nyX3jXpNS8vcdx3jr95/jR0/u5/LTZmYdIV0M1FeW8M4zmnhwy+GcrLbpfP/xvezvHOa715xBtU5ZkpXwFkK8XQixVwhxQAhxW4bXS4QQ96mvbxBCLJjqmHUVAT54zjzueO7guIjRv7x2hGMDYT5lcnGI2vIAC+rKM/q9k0nJHze2861HdrGlrTdlXv39hjY++ptNzJ5Wxq8/utrU88sFzXyVbbrYhpZeVs6fNs4kftM/LGTH1y/jkc+8iS9edmrKJ54LFQEvVy6t5fFdHYaYml7c381ILMFly2emtnk9grec2sDTezonTeN4tb2P7zy2h8uWN/LnT1+AVwi++OA23Y0YjOD3G9qpCHi5+kzFz7Vibi1vWlzPr15oYe22o9x490Yaqkt46FPnT1k3+pOqhWpWTSlzpo3XIgM+Dz+9/iwAPvPH11jf3MMpM6sdt/j0ez0c7ArSORThtstPnbKmekNVKeefVM/abUfHuUCklHznsT384Il9PLztKB/99aZxpYMP940g5fh2qHZQ6vdy2+Wn8pdPXUBdZQkf/90WPn/f1nEVD7e09fKun73Eu//3JW57aDt3v9jCptZe3a1U+4JR+kKxjAV6LluuWDp+/PSBExYR4ViCz967lZvveZWqUh/3fvxcfvqBs6mrfGP4vDU+fP4CgtEED23JPdvhleYefvViCzecO48Ll8zQ/f4phbcQwgv8DLgcWAZcL4RYNma3m4A+KeXJwA+B/8rmw2+/ajnnLprOvzz0Oj94fC+HekMkkpKfP3eQ02ZXc3EOA9LLqgXTeWp3J198YBvNXYpg2Xt8iGvueJkv/+l1fv1yK+/9+Xou+t6zfOJ3m/nKn3dw4eJ6Hvzk+cyqdZ6pSKtylCni/HBfiL9uPcKWtl6SSUl/KMrejqFxZlZQ/F6VBpbFfNeyGkp8ivadq6CUUjIYjvHw9qNUlfo4d4xV5tJljQyG46nUt7H0h6Lc8vtXmVlTynevWcHs2jL+7cplbGjp5Tcvt075+Z1DYZ7Z28kfNrSnortzZSAU4+FtR7n6rNknfM+fuvhkuocjfPaPr7GsqZoHbz7/hNacE3Ha7BquXzOP962eO+E+c6eX81/vPYOth/rZ2NKbsZ653WiLyEuXNbJyfnYm/atWzKKtJ8T2NPeXlJJvP7aHXzzfzI3nzud/rjuTTa29fPTXm05wS7Q7INJ8LKfPqWHtLRdw6yWLWbvtKJf/6AU2tfaqpTN3c+0d6+kailDi87Bu53G+8cgurr1jPav/40n+6f5t/H3HcXYcGWDHkQF2Hh2gtTtztkeLmiaWqciUEIJvves0Tp9dwxfu38bBrmGOD4R53y/W88j2o/zz25bwyGf+Ydw9+EZhxdxaVsyt5f/Wt+U0nw2FY/zzA9uYN72cf71iaU7nkM3svAY4IKVsBhBC3AtcDexK2+dq4Hb18YPAT4UQQk4RDeT3evjfD67kiw9s4yfPHOAnzxxg6cxqWrqD/PyDZ1vSyehfr1hKZYmPP25s56FXD3P+SfW80txDVamPH7xvBZcua+TxnR38ZesRnt7TyUfOX8BX37HUES1AM6GVSP39K+1sUdOGuoYirG/u4VDvaDBMY3UJS5uqkRJTXRMatWU+3r9qLr9d38Zftx5hZk0ps2rKmD2tjNm1ZcxSG2Ds7xhm17FB9h4fYjgSx+/14PcKpITeYDSVX/nus2aPi5K+cPEMSv0evnD/VpY0VtFUU8q0igChSILBcIzdxwbpGo7w4M3np1I+rl05h3U7jvNff99DXWWA/lCMw30hjg9GGIkmiMQThGMJ2npCdI7Jw16zYDrvPns2Sxor6QvG6A1G6R+JEo0nlb+EpDzgZUZVCQ1VJUyvCJCUkmhc8vSeDiLxJB9YM++EY567aDpvVy0KP3j/Cl3VBb/9ntOn3OeK05v44Dnz+P2Gdsf5u0GxEHgEfOmyU7J+z2WnzeSrf9nBb19u5T1nzyEYjfP8vi5+v6GdD503n69ftRwhBB4huPW+rXzk7o187E2LKA94eelAN2BPH+/J8Hs93HrJEi5cMoPP37eV9/9iPU01ZRzpH+H6NfP4yjuUeUtKSddwhM2tfTyxq4Mndh3noVfHa4PTyv2snD+dVQumMaOyBJ9XpHo7ZPJ5g2IJuOPGlVz5kxf5f7/dzLDa9OiXN67ikixzkYuZj5w/n8/ft427X2qhqtTH0f4wPcEIfq+HUr+XEp+H6lI/dZUB6ipKKC/x0tYTZH/HMC8d7OFo/wj3f+K8nCuIiqmibYUQ1wBvl1J+TH1+I3COlPKWtH12qPscVp8fVPfpnui4q1atkps3b049P9I/woObD3P/5kPUlvt5+JZ/yNpMu2/fPiorp07ZGh4eZsmSJRlf6xqK8KsXm/nTq0d408n1fPWdy5hecWLRgWRS2pbLONUYtbHFE0ku/eHzqcITAqgs9bF6wXTOW1THmoXTOdg1zGOvH+fZfZ0EvB42fuWSnKrEZfu9a+c3b+FJ/OnVIxzqC3G0f4Sj/SMc6Rvh+GAYbfHq9QhOmqF0N6ot8xNLSuKJJFLC9MoA9RUl1FcFuGhJw7jfB+D+TYd4ek8nxwZGODYQpj8Uo7LUR1Wpj+pSPx9708KUmVqjczDM2370PP0hxTxZ4vPQVFNKecBHid9DwOthdm0Zy2fXsHxWNXUVAdbtPM6fXjtC8wRlR0GpxjRZMYeV86fx0CfPz+r7M5JwLMHarUd5V4YFkJHouT5AuUZCZQ209QTH/UZT8cl7tvDYjhN9xB8+bz63q4Jb42/bj/G5e187oXBQTZmfrV+7VJeyoHdsmZhsPjphv0icbz68iw0tPdx+1XIuPmXiOIBYIsmrbX0MjMTQRtgXjLKlrY8tbX2p1DCNqhIfr37t0kkzSV4+0M0Nd21gVm0Zd314NafMNDfDJtN3m+13peeYehl7DpF4ggu+8wzdaoliIaC2zE88IQnHExOmQ/s8ggX1FXzovPl86LwFGfcRQmyRUq6a7HyyEd7XApeNEd5rpJSfSdtnp7pPuvBeI6XsGXOsjwMfB5g3b97KtrbxuXJSSqREl5Bsa2sjEpk6PaqkpIT58/VF9DmFqcaYy9hC0TihaIL6HP1V2X7vMPn5xRNJOoYiDIVjLKirsKXcbHtPiK7hCHOnlzGjsiSriVxKyc6jijZfVxFgekWAmjI/pX4vPjVnPhpP0j0coXMoQl8witcj8Hs9BHyCk2dUUVPuLJ+zkei5PiC/+7MvGGXr4X4qAj7KA15qyvwTRud3DUXoHAozEk0QiiZoqinVnfKpd2yZsGM+6gtGGQzHiCcliaSkttxPQxY1yfccH6SppixlsTKTTN9tvt+VWb9Xa3eQjsEws2rLaKwuPWExnEhKBkeUuvk9wxGC0Tjzppczv65iyrRbo4T3ecDtUsrL1OdfBpBSfjttn3XqPuuFED7gODBjMrP5WM3bxcXFxcXFJTvhnY3NbBOwWAixUAgRAK4D1o7ZZy3wYfXxNcDTU/m7XVxcXFxcXHJjSk+5lDIuhLgFWAd4gbullDuFEN8ANksp1wJ3Ab8TQhwAelEEvIuLi4uLi4sJZBXmJqV8FHh0zLavpT0OA9cae2ouLi4uLi4umXBmvpOLi4uLi4vLhLjC28XFxcXFpcBwhbeLi4uLi0uB4QpvFxcXFxeXAmPKPG/TPliILsDYjub2Uw9MWFWuCHDHV9i44yts3PEVNnrGN19KOWlzD9uEdzEihNg8VWJ9IeOOr7Bxx1fYuOMrbIwen2s2d3FxcXFxKTBc4e3i4uLi4lJguMLbWO60+wRMxh1fYeOOr7Bxx1fYGDo+1+ft4uLi4uJSYLiat4uLi4uLS4HhCm8XFxcXF5cCwxXekyCEuFsI0SmE2JG2bYUQYr0Q4nUhxMNCiGp1+wIhxIgQYqv6d0faewJCiDuFEPuEEHuEEO+1Yzxj0TM+9bUz1Nd2qq+XqttXqs8PCCF+LIQQdoxnLEaNL+31tenHshsDf7/r1efbhRB/F0LU2zGeTOi8Bz+Ydv9tFUIkhRBnCiHKhRB/U++9nUKI79g3ohMxYnzqawU/xwgh/EKI36rbdwshvpz2nrcLIfaqc8xtdowlE0aNT33dK4R4TQjxSFYfLqV0/yb4Ay4EzgZ2pG3bBFykPv5H4Jvq4wXp+405zteBb6mPPUC93WPLYXw+YDuwQn1eB3jVxxuB8wABPAZcbvfYjByf+vw9wB8m+o0LdXzq9k7tmgS+C9xu99hyGeOY950ONKuPy4E3q48DwAuFeI1OND71eTHMMR8A7k37zVpR5lUvcBBYpP5+24Bldo/NqPGlve8L6hzzSDaf7WrekyClfB6lP3k6pwDPq4+fALJZ4f4j8G31mEkppSOqCOkc39uA7VLKbep7e6SUCSFEE1AtpVwvlSvw/4B3mX/2U2PE+ACEEJUoN9a3TD9pHRg0PqH+VagWk2rgqNnnni153IPXA39UjxGSUj6jPo4CrwJzTDlhnRgxPpVimGMkynXoA8qAKDAIrAEOSCmb1d/vXuBqs889GwwaH0KIOcA7gF9l+9mu8NbPDuAq9fG1wNy01xaqZo/nhBBvAhBC1KqvfVMI8aoQ4gEhRKOF56uXica3BJBCiHXqOL6kbp8NHE57/2F1m1PROz6AbwLfB0LWnWbO6BqflDIGfBJ4HUVoLwPusvaUdTPZPajxfk4UbkDqfrwSeMq0s8sfXeMrojnmQSAIHAPagf+WUvaizCeH0t5fqHPMROMD+BHwJSCZ7Ye4wls//wh8WgixBahCWT2B8oPMk1KehWr+UH0dPpRV/ktSyrOB9cB/W3/aWTPR+HzAPwAfVP+/WwjxVhStbSxOzj/UNT7Vp3iylPLPtpytfvSOz48ivM8CZqGY1r887qjOYqIxAiCEOAcISSl3jNnuQxF4P5ZSNlt1sjmgd3zFMsesARIo1+FC4J+EEIsonjkm4/iEEO8EOqWUW/R8iM/AE35DIKXcg2KCRAixBMXUgZQyAkTUx1uEEAdRtJ0tKBqbNvk/ANxk8WlnzUTjQ1ntPqeZ44QQj6L4eu7hRBPkHBxkdh1LDuMbBlYKIVpR7pcGIcSzUsqLLT71rMhhfIPq+w6q2+8HHBMQlIlJxqhxHRm0bpQiGfullD8y9wzzI4fx9VAcc8wHgL+r1qBOIcRLwCoUrTvd+lCoc8xE4zsLuEoIcQVQClQLIe6RUt4w2ee4mrdOhBAN6n8P8FXgDvX5DCGEV328CFiMElAigYeBi9VDvBXYZfFpZ81E4wPWAWcIJXLXB1wE7JJSHgOGhBDnqj7TDwF/teHUsyKH8f1cSjlLSrkARWPd560eH6UAAAFPSURBVFTBDfrHBxwBlgkhtA5GlwK7rT1rfUwyRm3btSh+0fT3fAuoAW617kxzQ+/4imiOaQfeIhQqgHOBPSgBYIuFEAuFEAGUxcta6888O/SOT0r5ZSnlHHWOuQ54eirBDbjR5pP9oaxujwExFM3lJuBzwD717zuMVql7L7ATJRLyVeDKtOPMRwlg2I7ia5tn99j0jk/d/wZ1jDuA76ZtX6VuOwj8NP09xTC+tNcX4Kxoc6N+v5tRBPZ2FCFQZ/fY8hjjxcArY44xB8XMuhvYqv59zO6xGTU+dXvBzzFAJYrVYCfK4uOLace5Qt3/IPAVu8dl9PjG/L5ZRZu75VFdXFxcXFwKDNds7uLi4uLiUmC4wtvFxcXFxaXAcIW3i4uLi4tLgeEKbxcXFxcXlwLDFd4uLi4uLi4Fhiu8XVxcXFxcCgxXeLu4uLi4uBQY/x8jqQ9naU7eGwAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 504x504 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(2, figsize=(7,7))\n", "ax = axes[0]\n", "ax.plot(res_hamilton.filtered_marginal_probabilities[0])\n", "ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='k', alpha=0.1)\n", "ax.set_xlim(dta_hamilton.index[4], dta_hamilton.index[-1])\n", "ax.set(title='Filtered probability of recession')\n", "\n", "ax = axes[1]\n", "ax.plot(res_hamilton.smoothed_marginal_probabilities[0])\n", "ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='k', alpha=0.1)\n", "ax.set_xlim(dta_hamilton.index[4], dta_hamilton.index[-1])\n", "ax.set(title='Smoothed probability of recession')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the estimated transition matrix we can calculate the expected duration of a recession versus an expansion." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 4.07604747 10.42589381]\n" ] } ], "source": [ "print(res_hamilton.expected_durations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, it is expected that a recession will last about one year (4 quarters) and an expansion about two and a half years." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kim, Nelson, and Startz (1998) Three-state Variance Switching\n", "\n", "This model demonstrates estimation with regime heteroskedasticity (switching of variances) and no mean effect. The dataset can be reached at http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn.\n", "\n", "The model in question is:\n", "\n", "$$\n", "\\begin{align}\n", "y_t & = \\varepsilon_t \\\\\n", "\\varepsilon_t & \\sim N(0, \\sigma_{S_t}^2)\n", "\\end{align}\n", "$$\n", "\n", "Since there is no autoregressive component, this model can be fit using the `MarkovRegression` class. Since there is no mean effect, we specify `trend='nc'`. There are hypothesized to be three regimes for the switching variances, so we specify `k_regimes=3` and `switching_variance=True` (by default, the variance is assumed to be the same across regimes)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the dataset\n", "ew_excs = requests.get('http://econ.korea.ac.kr/~cjkim/MARKOV/data/ew_excs.prn').content\n", "raw = pd.read_table(BytesIO(ew_excs), header=None, skipfooter=1, engine='python')\n", "raw.index = pd.date_range('1926-01-01', '1995-12-01', freq='MS')\n", "\n", "dta_kns = raw.loc[:'1986'] - raw.loc[:'1986'].mean()\n", "\n", "# Plot the dataset\n", "dta_kns[0].plot(title='Excess returns', figsize=(12, 3))\n", "\n", "# Fit the model\n", "mod_kns = sm.tsa.MarkovRegression(dta_kns, k_regimes=3, trend='nc', switching_variance=True)\n", "res_kns = mod_kns.fit()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/travis/build/statsmodels/statsmodels/statsmodels/base/model.py:1354: RuntimeWarning: invalid value encountered in sqrt\n", " bse_ = np.sqrt(np.diag(self.cov_params()))\n", "/home/travis/miniconda/envs/statsmodels-test/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n", " return (a < x) & (x < b)\n", "/home/travis/miniconda/envs/statsmodels-test/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n", " return (a < x) & (x < b)\n", "/home/travis/miniconda/envs/statsmodels-test/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n", " cond2 = cond0 & (x <= _a)\n" ] }, { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>0</td> <th> No. Observations: </th> <td>732</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>1001.895</td> \n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 13 Mar 2020</td> <th> AIC </th> <td>-1985.790</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>14:00:38</td> <th> BIC </th> <td>-1944.428</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>01-01-1926</td> <th> HQIC </th> <td>-1969.834</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 12-01-1986</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.0012</td> <td> 0.000</td> <td> 7.136</td> <td> 0.000</td> <td> 0.001</td> <td> 0.002</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.0040</td> <td> 0.000</td> <td> 8.489</td> <td> 0.000</td> <td> 0.003</td> <td> 0.005</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 2 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.0311</td> <td> 0.006</td> <td> 5.461</td> <td> 0.000</td> <td> 0.020</td> <td> 0.042</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0]</th> <td> 0.9747</td> <td> 0.000</td> <td> 7857.416</td> <td> 0.000</td> <td> 0.974</td> <td> 0.975</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0]</th> <td> 0.0195</td> <td> 0.010</td> <td> 1.949</td> <td> 0.051</td> <td> -0.000</td> <td> 0.039</td>\n", "</tr>\n", "<tr>\n", " <th>p[2->0]</th> <td> 2.354e-08</td> <td> nan</td> <td> nan</td> <td> nan</td> <td> nan</td> <td> nan</td>\n", "</tr>\n", "<tr>\n", " <th>p[0->1]</th> <td> 0.0253</td> <td> 3.97e-05</td> <td> 637.835</td> <td> 0.000</td> <td> 0.025</td> <td> 0.025</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->1]</th> <td> 0.9688</td> <td> 0.013</td> <td> 75.528</td> <td> 0.000</td> <td> 0.944</td> <td> 0.994</td>\n", "</tr>\n", "<tr>\n", " <th>p[2->1]</th> <td> 0.0493</td> <td> 0.032</td> <td> 1.551</td> <td> 0.121</td> <td> -0.013</td> <td> 0.112</td>\n", "</tr>\n", "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation." ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "==============================================================================\n", "Dep. Variable: 0 No. Observations: 732\n", "Model: MarkovRegression Log Likelihood 1001.895\n", "Date: Fri, 13 Mar 2020 AIC -1985.790\n", "Time: 14:00:38 BIC -1944.428\n", "Sample: 01-01-1926 HQIC -1969.834\n", " - 12-01-1986 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.0012 0.000 7.136 0.000 0.001 0.002\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.0040 0.000 8.489 0.000 0.003 0.005\n", " Regime 2 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.0311 0.006 5.461 0.000 0.020 0.042\n", " Regime transition parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "p[0->0] 0.9747 0.000 7857.416 0.000 0.974 0.975\n", "p[1->0] 0.0195 0.010 1.949 0.051 -0.000 0.039\n", "p[2->0] 2.354e-08 nan nan nan nan nan\n", "p[0->1] 0.0253 3.97e-05 637.835 0.000 0.025 0.025\n", "p[1->1] 0.9688 0.013 75.528 0.000 0.944 0.994\n", "p[2->1] 0.0493 0.032 1.551 0.121 -0.013 0.112\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n", "\"\"\"" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_kns.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we plot the probabilities of being in each of the regimes; only in a few periods is a high-variance regime probable." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x504 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(3, figsize=(10,7))\n", "\n", "ax = axes[0]\n", "ax.plot(res_kns.smoothed_marginal_probabilities[0])\n", "ax.set(title='Smoothed probability of a low-variance regime for stock returns')\n", "\n", "ax = axes[1]\n", "ax.plot(res_kns.smoothed_marginal_probabilities[1])\n", "ax.set(title='Smoothed probability of a medium-variance regime for stock returns')\n", "\n", "ax = axes[2]\n", "ax.plot(res_kns.smoothed_marginal_probabilities[2])\n", "ax.set(title='Smoothed probability of a high-variance regime for stock returns')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filardo (1994) Time-Varying Transition Probabilities\n", "\n", "This model demonstrates estimation with time-varying transition probabilities. The dataset can be reached at http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn.\n", "\n", "In the above models we have assumed that the transition probabilities are constant across time. Here we allow the probabilities to change with the state of the economy. Otherwise, the model is the same Markov autoregression of Hamilton (1989).\n", "\n", "Each period, the regime now transitions according to the following matrix of time-varying transition probabilities:\n", "\n", "$$ P(S_t = s_t \| S_{t-1} = s_{t-1}) =\n", "\\begin{bmatrix}\n", "p_{00,t} & p_{10,t} \\\\\n", "p_{01,t} & p_{11,t}\n", "\\end{bmatrix}\n", "$$\n", "\n", "where $p_{ij,t}$ is the probability of transitioning from regime $i$, to regime $j$ in period $t$, and is defined to be:\n", "\n", "$$\n", "p_{ij,t} = \\frac{\\exp\\{ x_{t-1}' \\beta_{ij} \\}}{1 + \\exp\\{ x_{t-1}' \\beta_{ij} \\}}\n", "$$\n", "\n", "Instead of estimating the transition probabilities as part of maximum likelihood, the regression coefficients $\\beta_{ij}$ are estimated. These coefficients relate the transition probabilities to a vector of pre-determined or exogenous regressors $x_{t-1}$." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the dataset\n", "filardo = requests.get('http://econ.korea.ac.kr/~cjkim/MARKOV/data/filardo.prn').content\n", "dta_filardo = pd.read_table(BytesIO(filardo), sep=' +', header=None, skipfooter=1, engine='python')\n", "dta_filardo.columns = ['month', 'ip', 'leading']\n", "dta_filardo.index = pd.date_range('1948-01-01', '1991-04-01', freq='MS')\n", "\n", "dta_filardo['dlip'] = np.log(dta_filardo['ip']).diff()100\n", "# Deflated pre-1960 observations by ratio of std. devs.\n", "# See hmt_tvp.opt or Filardo (1994) p. 302\n", "std_ratio = dta_filardo['dlip']['1960-01-01':].std() / dta_filardo['dlip'][:'1959-12-01'].std()\n", "dta_filardo['dlip'][:'1959-12-01'] = dta_filardo['dlip'][:'1959-12-01'] std_ratio\n", "\n", "dta_filardo['dlleading'] = np.log(dta_filardo['leading']).diff()*100\n", "dta_filardo['dmdlleading'] = dta_filardo['dlleading'] - dta_filardo['dlleading'].mean()\n", "\n", "# Plot the data\n", "dta_filardo['dlip'].plot(title='Standardized growth rate of industrial production', figsize=(13,3))\n", "plt.figure()\n", "dta_filardo['dmdlleading'].plot(title='Leading indicator', figsize=(13,3));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The time-varying transition probabilities are specified by the `exog_tvtp` parameter.\n", "\n", "Here we demonstrate another feature of model fitting - the use of a random search for MLE starting parameters. Because Markov switching models are often characterized by many local maxima of the likelihood function, performing an initial optimization step can be helpful to find the best parameters.\n", "\n", "Below, we specify that 20 random perturbations from the starting parameter vector are examined and the best one used as the actual starting parameters. Because of the random nature of the search, we seed the random number generator beforehand to allow replication of the result." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "mod_filardo = sm.tsa.MarkovAutoregression(\n", " dta_filardo.iloc[2:]['dlip'], k_regimes=2, order=4, switching_ar=False,\n", " exog_tvtp=sm.add_constant(dta_filardo.iloc[1:-1]['dmdlleading']))\n", "\n", "np.random.seed(12345)\n", "res_filardo = mod_filardo.fit(search_reps=20)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Markov Switching Model Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>dlip</td> <th> No. Observations: </th> <td>514</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>MarkovAutoregression</td> <th> Log Likelihood </th> <td>-586.572</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 13 Mar 2020</td> <th> AIC </th> <td>1195.144</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>14:00:51</td> <th> BIC </th> <td>1241.808</td>\n", "</tr>\n", "<tr>\n", " <th>Sample:</th> <td>03-01-1948</td> <th> HQIC </th> <td>1213.433</td>\n", "</tr>\n", "<tr>\n", " <th></th> <td>- 04-01-1991</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 0 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> -0.8659</td> <td> 0.153</td> <td> -5.658</td> <td> 0.000</td> <td> -1.166</td> <td> -0.566</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime 1 parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>const</th> <td> 0.5173</td> <td> 0.077</td> <td> 6.706</td> <td> 0.000</td> <td> 0.366</td> <td> 0.668</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Non-switching parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>sigma2</th> <td> 0.4844</td> <td> 0.037</td> <td> 13.172</td> <td> 0.000</td> <td> 0.412</td> <td> 0.556</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L1</th> <td> 0.1895</td> <td> 0.050</td> <td> 3.761</td> <td> 0.000</td> <td> 0.091</td> <td> 0.288</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L2</th> <td> 0.0793</td> <td> 0.051</td> <td> 1.552</td> <td> 0.121</td> <td> -0.021</td> <td> 0.180</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L3</th> <td> 0.1109</td> <td> 0.052</td> <td> 2.136</td> <td> 0.033</td> <td> 0.009</td> <td> 0.213</td>\n", "</tr>\n", "<tr>\n", " <th>ar.L4</th> <td> 0.1223</td> <td> 0.051</td> <td> 2.418</td> <td> 0.016</td> <td> 0.023</td> <td> 0.221</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<caption>Regime transition parameters</caption>\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>\|z\|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>p[0->0].tvtp0</th> <td> 1.6494</td> <td> 0.446</td> <td> 3.702</td> <td> 0.000</td> <td> 0.776</td> <td> 2.523</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0].tvtp0</th> <td> -4.3595</td> <td> 0.747</td> <td> -5.833</td> <td> 0.000</td> <td> -5.824</td> <td> -2.895</td>\n", "</tr>\n", "<tr>\n", " <th>p[0->0].tvtp1</th> <td> -0.9945</td> <td> 0.566</td> <td> -1.758</td> <td> 0.079</td> <td> -2.103</td> <td> 0.114</td>\n", "</tr>\n", "<tr>\n", " <th>p[1->0].tvtp1</th> <td> -1.7702</td> <td> 0.508</td> <td> -3.484</td> <td> 0.000</td> <td> -2.766</td> <td> -0.775</td>\n", "</tr>\n", "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation." ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Markov Switching Model Results \n", "================================================================================\n", "Dep. Variable: dlip No. Observations: 514\n", "Model: MarkovAutoregression Log Likelihood -586.572\n", "Date: Fri, 13 Mar 2020 AIC 1195.144\n", "Time: 14:00:51 BIC 1241.808\n", "Sample: 03-01-1948 HQIC 1213.433\n", " - 04-01-1991 \n", "Covariance Type: approx \n", " Regime 0 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.8659 0.153 -5.658 0.000 -1.166 -0.566\n", " Regime 1 parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.5173 0.077 6.706 0.000 0.366 0.668\n", " Non-switching parameters \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "sigma2 0.4844 0.037 13.172 0.000 0.412 0.556\n", "ar.L1 0.1895 0.050 3.761 0.000 0.091 0.288\n", "ar.L2 0.0793 0.051 1.552 0.121 -0.021 0.180\n", "ar.L3 0.1109 0.052 2.136 0.033 0.009 0.213\n", "ar.L4 0.1223 0.051 2.418 0.016 0.023 0.221\n", " Regime transition parameters \n", "=================================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "---------------------------------------------------------------------------------\n", "p[0->0].tvtp0 1.6494 0.446 3.702 0.000 0.776 2.523\n", "p[1->0].tvtp0 -4.3595 0.747 -5.833 0.000 -5.824 -2.895\n", "p[0->0].tvtp1 -0.9945 0.566 -1.758 0.079 -2.103 0.114\n", "p[1->0].tvtp1 -1.7702 0.508 -3.484 0.000 -2.766 -0.775\n", "=================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n", "\"\"\"" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_filardo.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we plot the smoothed probability of the economy operating in a low-production state, and again include the NBER recessions for comparison." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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WMTw8nGojlpaWUi3fKlXThs0YygW9I+8Xuh0p9lGn9k0nqVarGBoawl9/5whu3T2NTeNFjJcNfHLXKZjQ8SvXbpL+d59dqmK8XIhUkd71+YegaYT/+LVr8chTB7FgafjcAyfw3v+ewvVXrsOtu6exc/0IfnBkAVefN4b7Ds1j2dYxPDzcl5+Ton2kOQeYFoOhU88fY61cG+x5/vODr9yGF2yfgM0YfvZze/FfT8zhVRdPtGEr+5tqtQq9UAQA/PObL8ZffXsKZ5fN1DcoYesWn+9dh6ZwzlgBJgP+8nsncHyJ4cYXbE61vl4/3hXxJDknxB0HMlIjpgCc6/l9C4CjEtabS95z6268+MN34tEjs93elIHn/z05g1t3T+P6K9bh1rftxCeuvwiv27kGn951As+cWZH6XmeXqnj+n3wTX9p9JHSZlZqF3VOzeNvzt+LK81bh2vNH8eOXr8Unrr8YW1eX8dkHTuL8VWU8dnwR68eKeM+LtgAAqlboPaFCkZnv7p/Fyz66G/cfmuv2pnSFmmUDAAo6v4HViPDGZ63F3pNLeGJa7nliUOBOSIBAKBua9HPYfMXEnhOLeNUlq/Evb74Ea0cKmPWMsikUMpFRCN8G4Oec9IjnAZjtZ3/wvpMLODa7gjfddA9ml2pd3ZbdRxfwtpv34n23H5Be+PUCe04sYaig4V3XbQYRgYjwusvWAACOzVWkvtfjx+axXLPw+LH50GWOz/LPYMuqoYbHx8sG/vaNF+ADrzgf/3T9xfj8z+7Ex/7HRZgo8wGZqnOhVihks+f4Iv7Pfx9A1WJ4cnq525vTFUSRVtTrIzkvv3gVAODBo4td2aZex3bqXo0AQyfUJBfCD04twGbA1eeNg4iga4CtTpOKNhFrjSCizwN4EYC1RDQF4P8CKAAAY+yjAG4H8GoA+wAsAfj5dm1sHpier2DLqiFMzSzjsaOzuPaCtV3blu/un8X+M8s4Pl/FnhNL+KfrL8Lq4ULXtqfTTJ2tYMtEqWGi2aphfkjPLJtwDlMpPHmCF8BTM+FDLEdneaGxeXKo6bmJIQMvv3g1AGDjOB9WrJj8zF41lSKsaA+ffeAEhos6DNPG8fnsQ9e9SF0Rrus+I0VubbNs9d1rBSYKYY1Q1DXphfB9h+YxXNBw+YYRAIBOBIupz0rRHmIVYcbYmxljGxljBcbYFsbYJxhjH3WKYDDOOxhjOxhjz2KM9W3fZNOycXqxgusu5K2q9x4PVwc7wf7Ty9i+egh//eMX4MxSDf94V986UgKZmq1g80TjTOVVQ04hvCR3GO0JpxA+MhOuqh07yxXhjQGFcBBiqFYpwop2cGaphu8emMWrLlmNzRMlHJsbzEJYKMIFjyIsbP6qDm4NUZRqAAyN3JsNWTxybAHP3jQCw2NnsVUhrGgTqrNcCk4vVsEYsHPTOFaPFF2VsFvsP72CHWvLuHT9MLavGcLpxe5aNTqJZTMcna3i3MnGQniooKFkEM5ILoSfPC4U4fBC+OhZ/tzGiXKidWpEzkVEneAV8vnq3jOwbOB1l63BhvHiACvCzdYIIgKhrmwq0iH2GxGhqBNqku8oVmoM4+X6gLWmrBGKNqIK4RRMz3Pf6TljJVy0ftRVCbvBfMXCyYUatq/h6mNBJ5gDJG+cXKzBtBm2+AphIsLq4QJmluXdFDDG8OQJnvl7erGK5aoVuNzR2RWsGSmmShQp6uRaJBQKmXzzqbPYuX4YW1eXsWGsiONzVXeS0yBRDbBGAFwVVsPtrSHU2XZ5hG3G4A3n0ZUirEjIQ1PzeMcXn8LJFDf+qhBOwcl5PvS9bqyESzaM48nj87C7VHw+M8OL8u1ruPpoaAQzuD7rS47O8kJ3y0Sx6blVQ4ZUa8SJuQrmVkw85zw+webI2WCf8NGzy9g4mUwNFhR1+TOuFf3PO25+EDf/4HTo8zXLxlPTy7hy8ygA7ktfqtmYrwzeTVfQZDmA3zSr2qo1vJPl2uERthiDRo1WFnWaVMTxxMkl/OaX9+OhIwv4+lMziV+X20L4iZNL+NNvHMrVcP/JOa8iPIbFqoUjZ7szE/uZGX63s8NRhA2NUBugsaMjjt/RrwgDTiG8LK8QFsr/Sy45B0C4PeLY7DI2TSTzBwuKhnx/naK/mV2q4fZHjuHLj58NVXj3n15BzWa45Byer7lhjN8wnljIz/m0U9QCPMIAL64GaBBNKnVFmNzRSJmKrW1zFVigEXVNdFL0DtwOxrB5ooh7DiaPi8xtIXzT3cdw22OnccPn9+JUTophYY1YN1bCxRvGAABPJJgw9+m7n8EbPvJ9/NX/e1Ja0XPgTAUjRQ3njPJkBEOnXM+Anlms4h/vfFra0OyRuRpKBmHtSHMyxKphuYrw4TNcAf4RJyEktBA+u4JNCSfKCZQirEjLfc+cAWPA9KLZEIk2dbbixijuPcmP2UvW80JYJJUMZiHMz7n+rmRqAlbr1D3CQMHxMJgSz2M2Y9A8H5euqZsWRTzLNRtjJR0v2jGJHxxZxGKIjdFPLgvh4/M13HNwDi/YNo7TSybueSYfQfAn5yuYHC6gZOi4cD0fctw3vRD7uk/ffRB7js7ib77xFPYclfO3HF+oNUSH5X3S1btv+QH+7Kt7sXtKTiOSo3PVpug0waqhAmaWTWlFt+lcSM9dNYSCToGjAHMrNcxXzMQT5QQFnVBVHmFFCu47cBpFXQMB+K89p/GvD53EZx84gZ+7eS9u+PxefP/ALPaeXMJYSccmpwAWivDx+cErhINSIwCuCKs6uDWEt1oncr3XMifMWQw+a4SKT1PEUzFtlAwNz986DtNm2HU42TwuGS2WpXP73rMgAt79onOx++hePHp8Ea91GiV0k5PzK1g3yofix8sFjJUNHIuxRhybXca+kwt45WUb8NXHjmNmSc7MbctmbrQMwO/K8zxZ7qCjqg4X5bSmPrtihWYmrxo2YNoMi1U5BabYrwVDw6bJoUBF+ITTTGNDykK4ZJBShBWpuPfAGVx53iTmF5fwbw+fch9/9sYRVC2G935lP8bKBi45Z9i9URwv6xguaDi5MHjducKtEaRUxhZpUISd/SpTiLFsBu/HpdR7RRIqJkPJ0PDsjaMYK+m45QfTeMG2+DbquSuEGWP4xtNzuOb8cWwYK2Ln+mHsOZGP7j/T8xWcM173pG6eHMKRs9Ed3b6/j09oec2zN+Krjx3HrKQ0A5s1eqiMnKdGzCzyGwAtQMFtBcvmKngQIkv47Iqc2YPCcmJohA3jZbfo9bJS40X3cDHdV4pbI5QirEjG/EoNjx6ZxY0vvgAjdglEs/iNF23BRNnAhrEiVkwb7/jiU3hyehkXn1O36RAR1o0WcKrL3TC7QdWyUdSpafSICKq4apH6ZDlyC2GZ1x9ujah/XqqznCIJFctGySAYOuGd123GH3/9ED73wAn81GXjka/LnTXi6elFnFgw3Sr+sg0j2H96JbHXo52cnK/gnLG64rdxooxjs9GK8Peemsba0SKu2c67iskrhBtn1fLUiPye1GecC7Asu4I/XsfL6obuctkRJ3hdI4yVDSxWm9crLqh6ym8Ut0bk93NT5ItdB2dgM+Ca7Wvw0gvG8fHrL8bO9SPYPFGCrhFGijo+/PoduHbrOF58waqG1xZ1DYN4z1WzWJMaDHAhQX3zWsMbn1ZvDCSzEEbD+V1ZIxRJqNS4NQIAXnPpalxz3hi+6Bk1CyN3hfC3n5wGADzvfD4Z7bINw7AZsPdEeGvbTmDZzCmE64rwpskht4lCGPfsP4Nrd6zFqmHu0TsrSZFhvhOFkXNrhEDWycxmaFAMvKxyLBMzy7IVYQ3DRQNLATdl4u8K8ixHUTTUZDlFcu7dfwYFndwovyDWjhTw4dfvwKXORDnBoCqgvBBuvtQRqRbLrdKoCDseYdnWCM3rEVaT5RTxCI8wwK/F560qY7kWf/efy0L4vIkiNjoWhJ3rea/x9/7Xfvze7Qe6tl3fePwEqqaNKz0XoE2TQ5hZqoU2WKhZNo7PrWD7uhEUdA0jRV2aImwxNMyq5fFp+T9TyBreshlrsIZ4ca0RkhRhcbHUCBgp6VioNK+XeSaPpKGkk7JGKBJz74HTePaWSQy14LUfVE+ssEb40QhKEW4R5t741/OZ5VojVHyaIj3cGlEvjMqGhpUEk9FzVQifnFvBPftP46pzR9zHJoYMvPFZa1E2NOw53j2v8GfuOYhNE2W87NJz3Mc2Oc0TjobYI04t1OPWAGBiqCBNEfZbIwp6vq0RAlmKlGWH2xAmhoQ1Qp4ibGjcYzhcNLAUUAiLWjatB7qga8oaoUjEUtXEI1OzuGbb6pZeP6iqWpg1ghdXXdigPsDbUEPM1ZCpCDd1ltNUQw1FPHyyXP3AKRl8pDxu5Cc3hfDDU2fx0x+7G4ZGeMWFjcbm33rxufjRHZOodOmbcODUIr771Cm85ZrzYHiqr41O84Qwe0S9AQcvmCeGi5hdlpMa0TRZLsfWCNOjeMoqhP03Al4MjRLfCSbB9AzTjRR1LNWsJnXC9cyl/EaphhqKpDx48CxMm+HqFgvhQbVGVE3WlCEMODcGShNuCZsxEPjws1CEZY1IMsYcj3CjIjyI7cEV6fBaIwCgXNCcx6OPna6nRjDG8Dv//gi+cP9hTA4X8JlfvAYTtebWocUu5q0+eoRn375s5/qGxzc7zROOhSRHnJyvd6IDgMmhgjRrBGMM3jowz6kRMx4VXJYnz/J5pP3IDGC3bNsthIdLBhgDVkyrISHCdu0TKT3Cuta1GzxFb/GIcx56zvnh/uAo9AEtJmqWHawIa6rFcqt4J7MZkj3C4rzd6BEeTFuPIh0V00bJc9Mr/l+JEZu6rgjftvsovnD/Ydxw7VZ897dfjB8OOcl3swOXUFH8qsL68TKIENpmWXSiE5FrMq0R/sBxQ+MnijxO/ji9WHH/L684bZxM4UcjeZ32/IowACxWGm0XQSfvJHTzBk/RW4jjZCRlRJ+ABtQaUQ2xRhAGc3/IgLH6xGDZHmFvIoWAWyPUh6WIRuQIC0oJFeGuFsIV08L7v/QYnnPeJN73mksxVg5ukADwIeQkXo92IN7TX+QUDQ3rRkuhEWon51dABKx1mnBMDstThG2bNXhkhU8rj6rw6YW6HUSeNSJ6YpqukTRPmfAIA8BIiRchS74INSvg5J2Eoq7luiOgIj+0eowJ9AFtSlCzwqwRg7k/ZOD18Mr2CHsTKQTKz62IgzGGFdN27RAA3KK4EiM2dbUQ3n14FrPLNfzPF+5o8N4GUWxDREtSrIhh77BOYwC3RqweLrrxMhPDBZxdrkkZnvR7qNoRai4LMWkQgLSZv/5e9H50iX5IrgjzNxN2iGZFuEVrhMHTPtQFWRGHbfPiI21En2BwFeEwa4Rqsdwq3utPUXKOcD23vf6YNqD+dkVyxPFX8nzXy6IQjjk2u1oI37ufe4Gv3ho/+aNoiC9b528L6xOhmk+m29eO4MCp4DSLk3MVNzEC4NaIqmm7Xciy4M8RFmp1Lq0RDYqwnHVadgJFWNKhYlleRZhbI/yKcKse4Xa0J1X0J2aMHSiOQZ1wxBXhgEIY8kaNBg2vIiyEHvnWCG9nOdVQQxGNUH0brBFO3ZhrRfi+Z87gkg1jWDVSjF1WKMLd8AmLgiqo8Nq+bgTHZlewGBCpNT2/gnPG653oJof43ynDHmH549PaEGEji0aPsExFOLoQlhbVxuoFiFCE/VnCrXqES+5xrcb9FNHYLGshPJgRVFwRDm6oMYg3BjLwKsKyb+ZtN4qy/tigRv8pkiN8wP4cYe9zYXStEGYAHjg4kzgT0x1+6cLEItebF7C3dqwbBYBAVdjfiW5iiHugz0qIUPO3oBxMj3D485rErlGWzWDofkXYaloGANKOWrvtSVWWsCIGyw5vIpMETRvMlsKhLZYlJssMGt7UooLk+DRxvW1KjVAfliKCuiLszRGWmBpBRK8koieIaB8RvTfg+fOI6FtE9BARPUxEr45b53LVwlLVwjXb1yTZBBSN7inC4gsYrAjzQvjp6YWm10z7CuFJp/WvjOQIvzpk5HiIfd6jnsoqTuPUMZmT5RpTI4RHuFERZgEn7yTUj2ulCCuisezoUZA4NMjz6PcS1RBrBEFFclI7/u8AACAASURBVLWKV4iRrgiL0TWvNYKUjUURjSh2G60RkhRhItIBfATAqwDsBPBmItrpW+x9AG5hjF0J4E0A/iFuvfMrNWgEPC9pIawn83q0g7DUCAA4f80wNAKenm5UhGeWqjBtFqgIy7BG2KxRfcyzImx5zmCyRiItOyZHWOKMcMu23ZPycDFEEW51spzkiSaK/iWrNYKIMIi3WzXLDkyN0DVljWgVb0OjomyPsKelvUDT1GQ5RTTBHmF51oirAexjjO1njFUBfAHAG3zLMACiHdwEgKNxK51druF529dgdQJ/MFD3UnZD8YyaLFcu6Niyahj7fYqw20zD4xF2C2EpinBzjjCQz8ly3hOkvIYa4Z3lACd3UtJV37Q8irATn7bonywXEPmTBNf7rrKEFTFktUbo2mAqwmHWCFJNGlqmoaGG5PkpYjVakzVCyuoVfUql1lwIlxOGLCQphDcDOOz5fcp5zMsHALyViKYA3A7gnXErrZg2XnX5hgRvz+lmaoQVYY0AgB3rRvD09KI7XM4Yw8e/s995btRdzrVGSPEIswaPrJgMIsunJRNvZzYZd/WiBWekNUKqIlz3CJcMDbpGWPLHpwWoGEmoH9f5+9wU+SJugmgchMH0CFfDcoShWiy3irehhq4RdJJpjRDX2/pjOqmGGopogibLif+vSGixHHTm9a/1zQA+xRj7CyJ6PoDPENHljLGGqpWI3g7g7QBQ3HABXnFZ8kK44Cpn3VCE+c+wwmv7ulHc+eQ0Lvu/d+DGF1+Aimnh3x86gvf82EW4eMOYu9xQgQ+ry4hPa1aE+U8zhwWVaXNFxpKUl1v3kIUvo0mMT/PmCBMRhot6gCLcoke4IQ2l9SJH0f+YlgRFOH+nh7ZiM+aef/xompqA1So2moUYWSJMUHyaJjEFSNGf1D3CzZPl4kZckxTCUwDO9fy+Bc3Wh18E8EoAYIzdTURlAGsBnPQuxBi7CcBNALD6/EuZ1zYQh+sR7maOcMhF6KWXnoN7D5zGquEi/v5b+wAAb3v++bjxJRc0LCdeL6cYZA3qY74VYa7IrNRsKRdit8FJjCIsS0GwWT1HGOAT5vyKcFTTlSga01C63vFckWOszB7hwfNZCpUyMEeYmhUdRTJsu7GxS0EnedYIEZ/WYI0YvJs4RTqCPMK6RihoJEURvh/AhUS0DcAR8Mlwb/EtcwjASwF8ioguBVAGMB210m1rRxK8dZ1SF1MjoibLAcC1O9biK++8DrbN8NdffxKrR4p427VbmzpAiS+2DBXCZo0nijw31DBt5qYjyNi+oF70fmSqX16PMAAMl3QsVP2pEfxn2qHrbqahKHoLW0pDDYkb1AOI4iwwRxgEmynjaSs0CzEkvaFGozWCcnltU+SHIGsEAJQLWqylNrYQZoyZRHQjgDsA6AA+yRh7jIg+CGAXY+w2AO8B8HEi+g3wm+wbWMx03LQjfEU3oqV7HuG4a5CmEd798osjl5HVIcd/InJTI3JYUAlFGJCjSIkTrhGjCJuSjhWvRxgQinBjIWwlKM6D6OZxregtrBhffByD2FBDXAADFWENqDX3QVIkgKHxGl7QZCrCAdYINbFREYOrCPu+6yVDjiIMxtjt4JPgvI+93/P/PQB+JNHWtoirnHXFI8yLTr/C2wr8zlbCNvlaDMsONZeJaTMUDHmFcBIbgq4RApr9tYRp2ygV6l8V7hH2TZZzVYzWPMJx8S4KhW2z1DdaXgaxxXJdEQ62RuTwdNkT+BNMCrqGqjRFmP9U8WmKNARZI8TvcR7hnjEldtMjbGUckvQi6wvdUznCdj3HU0YETpKEBtmd5byf/0jJwJJ/spzbWa7VHGGlCCuiyXoeGsTCL8oaMYg3BrJgvutPQSdpo5FBneV0RxFWn5cijJWQQrhsaPltsZyWYhdTI+Iya9Mgy+tk+7apnuWYv4LKtJh7IZJhCwk6UfrRJc4yNu3GyXLDRb05Pi0mWSQMMdKRx46AinyR9Tw0iLm5kdaIAbwxkIU/tUjmZDkhlugN1oj6+yoUQVRMGwWNmq7BJUNDJebY7JlCuNBF5SzrJBUvPNZLjiLc0GI5x5PlLM9kORl39HG5zgA/ScuqLZsU4aLRFJ+W1Efup5vHtaK3yHoe0gc4NUI11JBL0GQ56fFpnurEnWg+YMevIjkVkzWpwYBQhPvEGqFrBEOj7ijCdvpYrDBkKJWMMTA0Fl1iMlceG5R5J8vJqPeSFJ2GJu+mgCvC9a/KSKk5Pi2q+2AUJeURViTEzFgI0wCmRtQV4YAWywN4YyCLJkVY0+R3lmtBEb77mVm889+fkpLVr+gtKqbdkCEs6CtFGODDW11RhFm2SSpeNAkdz4ImixXybI3wKMJSJ8tFfCgyZxn7lbiREm+o4VW347Kmw9A1fpLP4+emyBd2xhxhfQCtAEI4CVOEVR3cGoyxZo+wLEXYHfGrPyZG/+LEjbuemcMDUwu4ZffJyOUU/QcvhJtL2lI/KcIAv6vvVo6wNGuEhNSIoFm1+Z4sV+/sJLMQjrJG6JIsKECzR3ioqMNmaPhy1bvdpTtOiAgFnVSOsCIW/0z9tJDEtuO9gttQI0ApUopw69hoo0c4YHSt3owq+rWHz1YAAJ/ddRJzKyobb5AIs0aUDOqfyXIAP5l1QxHO2tHJi65lb6gRpD7WrRH5O7Gbtl1XhCVsX32yXPgyuiYvM9WyWcNJud4W2W5YBkifjy3WFxfvolD4j8O0DOLksChrhPIIt44/yq8o0SMcaI1wPr64G5epsxVsW13GQtXCN546K2V7FL1BxQq2RpQL/agIdyNH2JacGiHJGuE9t+e9oUY9NSL7+urxaXHWCFmKsN2gCAfta5YgySKMkqEUYUU8NsumCMv8TvQKcTnCA7Y7pNEU39kGRTitNaJm2Tg+X8ULd0xg/VgB9x6ck7I9it6gUouwRvSVR7hbirDk1IisqqgopAkBxVkOJQ6vR1hGaoRpxxed3BqR+a0ANH/+BTfyzKsI85+t3DAV9PgWkApF5hxhbfAKP3GDGaQIaxJEiUGFofGmrKhr8hRhcS5tsEbwn1FvcXSuCpsB506WcM1549h1eD6X10NFe6iYduD3vK9SI4AueoRl5ghLaLHM3BNF43o1ymdnOcvypkZInCwX8ZHI9P/5PcIFrbkQrttV0q+/qHcnDUXRW1gsfSqJFw2DZwUQ39HgyXK8VbAiPbbdvoYaQefSJPFpU44/eMtkGdecP46lmo3HTy5L2SZF/qlYNsqFYEXYtAEzQmzqsUKYuuKllJkjLKOhhuuR9RXnhiZv5q5MGlMjsq8vSTtjqYqwxaB77jpcP7bnxG87s6hbacM9VNCxVLPiF1QMNJZtI6CeSwwN4OQw1WK5PTTHp0n0CAeM+LnWiIi3cAvhiSKuOncUOgH3Ty1K2SZF/uGT5YLi0/hjKxG1o9G2rWoDxQSz/9qBxVrzfgahScgRDlNEDU3eXblMvB5hqakRkZPl5A17Woy5xS8AGM4bm3ajItyqf3O8rGNuRRXCimgsO9t5iGeYS9ygHiDOGjFoNwayYGC+1Ah5OcJBCTyuNSLiAJ6arWC0qGNyyAAR4dmbRvG9ZxacqDdJ+aeK3FINsUYI3/ByNfwa22OKcHdm18vNEa63kMyyPXxdPkVYYpajTKSnRiSaLCfXGtHgEXYzm+vrz9J0RRXCiiRkHZkicCuADJ9+rxBljVCT5VrHshsL1bbEp3mtERHxaTZj+PyDJ3HPwTlsmSy5Re/LLlqFQ2erePzYvJTtUuSbqsUCW6mXAlKe/PReIdwFxVNqi2UJEzTC/Kh5tEbYNoPN4EmNkKkId3CynE/9ABo9woyxBs92GibKBuYqKvNSEU3WGEfx2kEq/sRwfXAhPHgKuSyCGmpYTJbQwX96hQUh9AVdPx4/sYS/+94RnF028aM7JtzHX3zBJHQCbtt9NPM2KfKPaTN3tNaLGM2NElF7qxDuYmqE1BbLWT3CIZ3VCpq8u3JZiBNXUQ+/o291ndGT5eQMezLGmmbriy9WoyLc+jEyXjYwv2JJawCi6E+yxjiKlw5SUoLl5N0G7TeZo0aDBm+oUf9djJLJuD67c0A81YmrCAecIx89zn3AN791J2547gb38ckhAz+8eQRfffRY5m1S5J+axdzj0EvRvV73SyHcpdn1WVubepHhXRXWCr/IoedQEa6rt5pjC+ktRVi8V0NqhPAIN6RGpO8qJxgv6WAAFqsqQk0RTlZFuB5Bla9zRDvxD+F7US2WW8d/U1ZIMPyceN1BDTUi4tMePbaI9aMFrBstND23eaKAM4vVzNukyD812w4c+XGPzb5RhLuUtyq/xbKkHGHfCV5mhI0sTE8hqUuYKAgk8wjrTjRS1sLbzSz2TpYLyGy2fUOFaRgv6wCA+YryCSvCydpiua6qydqi/GNFWJZ0Gix1XCY8NaL+u1BvZXYObbRGUMNzXh47voTLN44ErktGSpMi/9iMwbKDLVDisags4d4qhLvUgctirU+E8iOjGOwlj7Bl1dVbkhRgH9R5yI8WceJMQ5AibASoH1lGDcbLPLxFFcKKKGwJLZaB3laEF6oWfvO2pzG9kEzli7p5UIpw6zA0JjGIfSzj+mO7I371x8Imy51arOH4fBWXbQguhDV1szMQCAEwyhrRX4pwl3KEZaVGyLhDdU8UPZAjLCLGDJ2gS7rwmJ7iOowkLTkTvZfH2iEoutYIeR5hAJirDJBUp0iNKUkR7uXCYP/pCu56Zg6Pn1hKtHxU9KXKEW4dbgWr/y72sRxFmP8MtEb41i/aKF8eUghzi5z6kPsdMSnWiFCE+yY1omRosFjn2wjLtEaQhPg0d+jIt00y+73LwuvnleURTtLFzZ1lLOmmo1ERdtQPn0e4VbVOWSMUSbAZa7DopEXUFayH77eEjz7peS5KEVY5wq3DO8s1F6oyFeEG60VAQ42v7j2DP/vmYWxfU8bF5wwFrkuGFVGRf+qNc5pL2iQe4d5qqOGZ/WdoesfeV3aL5ayqdlRDjbx96b0eYRnRcUBwL3o/sobqxOu97yXuML2dlLKMGoyX+LE8pwphRQRZPcJ6HyjCi04oftIuZtyyFPycyhFuHRvM5xGWMwIHeDqnet5ADMiJG5dP338cH737GJ6zZRR//OptgQUQXwcXKVRTjf6mFmGNKMhKjSCiVxLRE0S0j4jeG7LMTxPRHiJ6jIhuTrLetLgSd4eTI6S2WJaQGiFe7i/OC7q8NpeyaEiN0ORYI8JaTHuRdWIO9AhrQakRrRcpY8ojrEhA1pEpcXj2sgq6WEuvCIeJGCpHuHUY81sX5BXCwZ3l6taLimnjY3cfw3XbJ/BXb9jhWsuCkLldivwiLJhBk+WSeIRjFWEi0gF8BMCPAZgCcD8R3cYY2+NZ5kIAvwPgRxhjM0R0Tpo/IimiO1mnkyOyxhZ50UhijnCAIryYsynhjYqwJMXA+RujWiyLfZPdIyzey6MIG80NNawMioOhEUaLuiqEFZHYGSftisKipwthxxqR9BpgR3iESeUIt4xlN6bkRDW8SEuwNUKsn8/+ZwB+eMtoqBLs3y7ebCHzpilySt0a0b74tKsB7GOM7WeMVQF8AcAbfMv8MoCPMMZmAIAxdjLBelNTcmMwOq8Iy7RGZE4yCFFEDU3LXXya1yMsLz6N/4yMT2ujIhzUYplFXHCToNosK+LginDrr3cV4XzdK6cirUfYjNhnmhOxqEgPQ4iHV+ZkuQZrRP0mLsoP6kfrg5s/RTy1gOu0QCjClYzWiM0ADnt+n3Ie83IRgIuI6PtEdA8RvTJoRUT0diLaRUS7pqenE7x1I0WJod1pkK0IZ918ccfsrwPzmBrhLSRJ0uSUumKQIDUi4/uZnkJeYAQ01BAdrFplvKxjXqVGKCLgmbiDrgg7HmFpk+W4f1SRDts3b0amRzgoHtObgV2NUP/8iEXydl1UyMWMuDkSxXFWRTjoaPMfVQaACwG8CMCbAfwTEU02vYixmxhjVzHGrlq3bl2Ct25kqMg3t9MduCxbZo5w9uSEsM5qhp6/L7zXWqATSVGj6naL8GXcobqMCnm9kK+/mZsa4dnXWYuU8bKhJsspIrEzTpZzWyzn7ByRhrpHOLs1Qjys6uD02HajIizLigbwz4wAX06xs37G6n7QBOdb3eMtVvQv1cgcYTnWiCkA53p+3wLgaMAyX2KM1RhjBwA8AV4YS2X9aBEAcGK+sy0T7YxDkl6ktFh2J8s1Pl7QtNzGpxk6SQs3D+o85Ec8Z2asvIMyiwua8Ah7rRHZ7DNcEVaFsCIcM+NkuX4o/Ooe4TST5YKf8w63K9LR1FBDZmqE3dwNsP5ZpVOENY9HWNG/iNHZqBzhrKkR9wO4kIi2EVERwJsA3OZb5j8BvBgAiGgtuFVif4J1p2LDGC+Ej3e4EJZpjSAJk+XskEIwj9YIb0MKTZJHOGgyhR9d0kXOHaYLik/zWSOyqHXjJaUIK8KxQ0aB0tAPDTWWUnqEozo+iotfL++PbtHUYllmZ7mABB5vV8SoYXA/ShEeDIRHOOjmSPQwyKQIM8ZMADcCuAPA4wBuYYw9RkQfJKLXO4vdAeA0Ee0B8C0Av8UYO53yb4llpKRjrKTj+FznFWFp1ggJPtnQ1Ag9f4Wwa+MgkpKYAQT7dv2I52TlCHtN+O66fQ01shwi42UdCxVb+RUVgSSJDIyjL+LTUuYIW3ZUagR/vId3R9fwJ5iImlTWZGj/9dY7GS8qIcCPzEYfivwSd0wUdYqcW5aooQZj7HYAt/see7/n/wzAu51/bWXDWLHjinCUqpAWmakRgYpwzqwRXmsBT43Ivs6wFtNeZHnWrID4NCJqymzOmjVdNDQw8C900ZBzrCn6B/fmV8ZkuR4uCtJ6hM0Ia4Ss7pODCJ8sV//dtaJJuP74183XL56rf/aJPMISLRuK/CJudMKOCSOmkVlPtVgGgPVdKIStDM0S/GgSJowJ1dDvo8qjNcLrESbZHuGIo9eQdAIUJ3Z/LAuPqmtsqJFl1KAeyaaSIxTNBFl00kKeYqJXSRufZjMWGKkEAATlEW4VxhDoEZaiCAdMcGyIT3MO4CSCgddSoehf4iL1Cjqh0k+F8IbxIo7PVTs6hGzb2ZQYL7omQ6V01hXYWS5fhZQ/NULG52alUISz3hiEJXQUdGq4GFss2zEiLtZ5U/QV+SDJMR+HzGKlG9g2S+0Rtuzwfab1wY1Bt+A+3vrvusRJaUHpKHVrRP2zN6KUEHe75HmXFfmlFiJYCQp6nynCG8YKWKrZHZ1hnzUj1osUa0REjrBl5ysX05sjzDOU5RXCUYWne9GX5RHW/YWw1qDesoDhvDSI9Xc6I1vRG4j72yw3W+KVvTpMvFSz3NzOpCMnPNYw+DlN0jliEPHPiZBpu7EDPjOvsiuKnmIKj7D6jPsb1y4TckwUNMqcGpErupEckbcWy/XAcd9wfUC+bbfxTmzTJHmE6+pY+DKyZjHXFeHGr4qhN/qxs6ZGuIpwzhR9RT5wR1YynIbEOSxH98mpmF+puf9PGp8Wlb0svtG9qpB3E78VTJOovFqsef5LY3xaeFSWH5lpFor8EpUaIR7vL0V43CmEO5gckbcWy/X4tMbHS874VKdbUEfhbUihkTzFgNDoUfMjS+0JG5I2NK3BhpLVI6ysEYoo3NSIDIHmbkONHi385ldM9/+JrRERjW7E4726P7oJa1KE+U9ZneX89UxjQ43oiVFeNDUhciAIm8sjiEuN6L1CWCnCodaAcoF/nCsRdz6dxqsI65JyhM0EDU4MSZ61sKi2gk8R5j7y1t+noKwRigjskHkBadB6fHKYVxGW4REWD/fo7ugq/gnk7k2FFI9w87VN81gvqi3kCKtCuL9xJ1CGHBN9lxoxOcQT3+ZWOu0RllgIZ/xOhinCZWcW7UotP8WUiB8zNAIRQYbgmUSh1ySdAL2pF178HmGlCCvaiasIZzhjixu1XvVLzjmK8EhRS+URDmvFrqwRrdOsCEer6wdPL+KOx44nWrcVE59mpsoRVqr/IOBOlhsUa4RGhGJMFIZssmbEepGZGuEvvEqGsEbkpxBuyBEmORP5kkxelJUfaQbkCAOAoWu+1Ihsx4gohFV8miKIejfFDIqwUNV6tCZYcArhySEjZYvlGGtEr+6QLhLWUMMKmePw2XsO4p03P5ToJiy4s1y9oK3GTIzyorKiBwPT5nbJsEOioBMq/WSNALgFoJPD/1KtEVI9wvm3RngVVVmpEWaCGxPxhZA1Wc7vPSro1DCxzX9hSIu4k1WFsCKIsBi/NMhqMtMthEd41VAhRY5w+D4TDyuxMB1BI5LeeLMgKqaNqmVjZine0hjUWa4+muFRhJM01FDWiIGgatko6BQ6b6igaaj1kyIMAGWjs4WwHfDFbBVdRmpESGrCkKMI58ka0ZwaISleJ+bj8AawZyHMI+zv4mdnjNgTJ3U1u1kRRN0akSE+zW0p3JvHmPAIrxo2EuelWxHzCeoKeW/uj24hdldQakSYIizOa9MLldj1B8en1T+ragprhFKEBwPTCm+cAzjWiL5ThA2to8UeV4TlrEtmi2X/3U8pz4qwmxohZ51xk4Zkx6f5w9sLutbwxcrahtu1RuTos1PkBxnWCO/M+15kfsWERsB4WU+XGhHTUEMVSekQuyvQIxxy+hJdOE/OxRfCQRMcvR5k02bunJM4ZM0VUeSbmsUib4wKGvrLIwxwL2wnfbBZM2K9aERgLJsq4yrCvk+vnEePcBtSI6JUHkGcZy0pYvv9CkVBb2yxbNks0Yk5DNcaoU7YigDCRibSQD3uEV6uWSgZhKKupfAIh++zXt8f3YIFWSNizrfi+D05n0wR9p/fvZPlqpadqJkGIDfWTZFfajaLTBEp6Fr/FcKd9Ai7Soy0yXLZ71DtgKEpoF4I50sRrqdGaJRdDefrjE9oiFMokmKHKMKGTg1qM2PZoq3E+k3lEVYEIMMjrLvFRG8WBRXTQlHXnPbmKawRIbus1/dHt7ACrj9xXlzLLYRXYtcflMDjzYU3LZaomYb3daoQ7m/MOEW4f60RnTmwXW+eNEWY/8zyvXRzhP3xaYWce4QlRMcBwfE6fvQYz1pSwj3CzakRWXKERcSTmiynCCKsm2QaXAW0R4uCSo0rgbwQTjpZLtyyRMoj3BJ1j3D9MXHuC7OiifkUJ+cquPHmB3Hb7qOh6w+aLKc3KMIstSKs5l70NzXbjpw8GRefZrRjo9pNuaDh7LIZv6AEwppXtIqMSVxhqRGlPCrCno4v0jrLpcoRzvZeXkXbC2+oIS9HWAzrJL3AKwYLmakRvVoTiJnhRSe6kLF4O1KUNcLdH/k5XfYENpqvP3rMTYVI2Hno0Ax2T83i9keOQSPgtc/e1Lz+AGsEEYHg9QgnUx3UhMjBIG6UoKD1qyLcKWuEhNnaXmTEuYQpwvUc4fx86bvVWU62R7i5s5yvoUbGpisqR1gRhXvzO8DxaUIRNjQCQ7Kb3KjRI1UktYY4pTZMlotJvRGK8MNHZgEAIyUDH/3208HrD4mi1DT+3qk8wpI6jCryTdVimRTh3i2EOzT8L74/sqwR7pB9JkW4cV0CQyMUNMqVNcJyMn+JnM5yknrRJ/UIZ06NCOlhbviGZ6PySpPgXkiUIqwIQBR9mVos93h8GvcIk1sEJYlQE+efIOoKeW/uj27BohThsELYeVzs6ivOnUQl5DoVFo+pExdSUnmEe9wOpEhGLYEiHEVPFsKlDk6WE4WbpDpYyhczrMUy0PlmI3F4m1/oTmJGVjrbWS5EEda0hoYaPDWi9fcppLi4KwaPukWr9XW4inCPFn5Vi/sA3e9KgptGK6BLmaDXFfJu4U7W9jwW6xH2nNc2Tw5h1XAxdNkgjzAAd7J1Go+w+IyVItzfmDZDIeLkGJc53ZOFcNnoXItlO6QQahUZBVq9OG/eplKHm43EYdm2q6ZqmpyLcJTKI5AVpO5VtL0YemNDDRZxwU2CyhFWROF6hGVMluvRmqBSs1E0NNdPnyRCzVbxadJxC2HPftUcD2+oIuz5rC5aPwojYqg6LLdfI/7eNSu5R1h89koR7m9ic4T7sxDmOZKduJOX0dHJiybBGlG/KDY/V+5wxnIcXkVYI4k5wjEfh7c3fRbMkAxpv0c4Krg/CaqznCIKcRwnHRIOQhQXvVoUVEw+Wa6uCEef52zGB/HDdlm9xXJv7o9uIfaX/3SnUZQiXH/84g3jTSNqXsLmW+ga78paayE1oldHQRTJMG17AAvhQucaR8jo6ORFxkxlxhgIwYpwuZA/j7DhLYQlXIStkMkUXlzlPaPnNix+yR/hZLNsE5lEgRM1s1UxuMg4D3HNrnc9sVXTRlHzeIRjvttxiT+q61hrBFkjAL6fwwpO064rdhdvGG0aUfOvP9gawc/9NdtOfEMoa66IIt/EKcJFGR5hInolET1BRPuI6L0Ry/0UETEiuirJelulkzFhshXhuJiZpNsUtjl5s0ZwRZh/Xjw1Ivs6LduOVYRl5UeG9TA39EZFw07gW45CTZZTRCGloYZQhHv0EKuYFgoGJY4ajJvo3Otxct0iyBoB8HNumPBgWjaeu3U1fvm6bXjJJeubRtS8WIy5uepeeFfWdIqw1uOjIIpk1Ozg67Qg7sYpthAmIh3ARwC8CsBOAG8mop0By40BeBeAe+PWmZVOthKW4c3zIqPTTdhkAiB/1gjLU0gSyVGjLJvFqq9ETm5xxvezbBt6wJeooJGbZQpEB/cnQSeAoDrLKYKxmARFuMdn0FdMp6GGJkZPov+O+kTX4Od7PUWjW4RN1o7qHGrZDKMlA7/3mp2YGCrwHPaQ49AOub7pmqMIp/EIK0V4IIj1CEtQhK8GsI8xtp8xVgXwBQBvCFjuDwB8CEB8D8WMuK2EO2ABEKKftBbLEhThsHgZoLPRcknwp0bIaagRnJjhJ8qzlhQz5E7TsEX99QAAIABJREFUcK6u4oYmSdvnKIh4PmqSCUCKwUPGpF1xsu9pa4TTYhlI5hEG4hVh5R9Nh9hdwmoj0LVwgadmNdoZDF2LsEYEd+kUc0xqFkPRSJoa0ds3f4pk1Kz2p0ZsBnDY8/uU85gLEV0J4FzG2FeiVkREbyeiXUS0a3p6OsFbB+O2Eu5A44i6NULO+mSkRth2eDvfvMWnWR4/l8wWy0kUehmFd1hChX94liXwLcdh6EoRVgQjw6IlY6JuNxGT5ZJ7hPlPlRohF7G7/NdEPSIn3vJ1gytohJptB6rxYROP+RwTxyOc8HugGmoMBmYHUiOC1uAeVUSkAfgrAO+JWxFj7CbG2FWMsavWrVuX4K2D6ahHWPZkOQktlqMSCvLpEZYbn2ZG3Ah40bXw4bc07xU0DOfP/Y3ybSfFkLC9iv5Exnmo1z2xoqGGkdAjHOerFtdGpRamIyy+M1oRbhxZM3QNjAUvb7NgFZ9PlkvpEVbdAweCWkxqRNzxkqQQngJwruf3LQCOen4fA3A5gDuJ6BkAzwNwWzsnzLke4U5YI9rWYrn1dURNzCobGiq1/Hzp/akRMvx4SdsZayQnRzio6BZ/kxjey+oRBvixoVIjFEHImCzXy8PE3tisYsKElajGQ4BXEe69/dFNxN5qSo2IU4QbrBHh3t2wEU8+2Zo5XcSS5gjzn2oScn8T11kubgQhydF0P4ALiWgbERUBvAnAbeJJxtgsY2wtY2wrY2wrgHsAvJ4xtivBuluibo3ovclyMho9RCnCebNGeFMjok6UaUiSIwzwE6eMQjhIERYnYmFlsO3gOLs0FHRS1ghFIOI4TjokHEQvtxQWRW9jjnDcZDn+M77FspxtHBTCUyPCz7embbvXAQCunzPI5x0Wj6kRP8+2ogj3qh1IkQzeWa6N1gjGmAngRgB3AHgcwC2MsceI6INE9Pp0myuHcjesEZIUYRkqhBUxWazsWCPyMhPaqwjLi09L5hGWMVku3CMsrBFeRTjTW0HXKFHbWMXg4aqbEhThXszNFaN/RW8hHPN3RDUeAlSL5VYJTY2IsL55c4QBjyIccL4LEzrcyXIpPMIAv3m0VOv6vsWyGWxWn7cTRFxqhJHkjRhjtwO43ffY+0OWfVGSdWahkx7huJnHaZGRGsFiPMIAjxYqJZxZ2068HmEiOcOyVsisYj8yJsuZISddd7Kc6fUIZ1SENYqdCa8YTNyJX5mSSfjPnNwjp6JiWQDgpEYkzRGOtpOo+LTWqKdGNKJT+BwH02oUFFyfd0CBaoecS3WNX08sO97z6UXTKJMVUZFvaglGy/q0sxz/ozrhEZbhzfMiIzXCivIIC9tITiLULE8hqUtqsZzUIyxOnFkIi0VzrRHOzGcZqRG6prxsimDcHOEMZ2wZN+HdQpzrCyk8wnG2NhWf1hquNcK3X7UIoYO3wK0fvMUIRTisS6dGhIqTFJWm1bhShPsbcQy1e7Jc7uikNULGkKQXeakRwc91ct8kwasERLXgTLXOhB5h7knOth8qpo1iQJsjMdRSs1johSEthlKEFSHYMUVdEqiHCz/RJMhrjYi7aQzzsgpUfFpr2CE3ZVEpPU2KsKa5j/uJskbUj4PkpQv3LideXNFjCEW43fFpucPQCDp1yiPMf0qqgyWmRoRZI/jjeSmEvbOFpeUIJ+gsB9Q7EWWhGlII1yfLMc8QbLb3MjSK9T0qBhM5qRH8Zw/Wwah6C2Gtbv+Kwu0sF+MRVtaIdIjdpfnMEWGKMGOsaTKT4fq8g6wRwce595of5/lseJ2uFOF+RnzPo46JkaKOT94QHmTWk4UwEfG83E7Gp0nLEeY/MzXUYNGT5YD8WCMaUyPkeISjOut5kaUIl4IUYc/wbFiuZloMjVzPsULhZeAny5ncI9yYGhFjjUjoEe7F/dFNxLHoP92FKcL1mzhPaoTr8w7zCDe/r6bVFeE4ha9huyJaPyt6n1oCa4ShEV5yyfrQ53uyEAa4F7bSCWuE5NQIGT69uPg0AB3ZN0loTo2QFZ+WQBGm7Be5amghXI9PE39SVh95QSeYSrlQBGBKsEb0cnya1xqhOyOCcYqw+CrFeYRVHZwOsbv85zuNgo8tcew25Aj7cti9hHeW8yjCaQphCTGaivxSP75aL2d7txDuUAc1Ga1NvUhrsdwjHmFv/Bg51oisQ5FRkwW9yDgBVq0Qa4Q4kdusPpEp4yGia/Ez4RWDiQxrBBGB0JsNNfzeUEOP78JoxViWZMzXGESEiBuYGhHi+QUaZ/VHKsJ2mDWiPlkuKiqr6XWqEO5rXEU4y2iZrI3pNNwa0f6DW3aLZTk5wuGFYCln1ogGRdiNK8q4zoRd3LSIOJ+kVE07cGKG4TmR13M1M1ojSE2WUwRjSyiEASfCsAdrgqpPCSzqWqwiHHfuFt/qXrwx6CYMITnCFDwRUxTHXsUurLMcY3ztwdYItGSNkHEdUOQXcR4oZoiL7dlCuGPWiDYpwlkK4bB4GQAYcqwRyzkphHlHITFZjj+W1a+VXBHObo2omBZKht70uHfmui3pZsnQgxUVhcKSNFehV/2SwiMsYpAKevxNo/jqh527VWpEa4R2lgtRXoXdK4kiHJXA402NSFMIG3r2PHlFfnELYWWNaB8yguy9SEmNYCz0g5sc4j1SZpbN1t9AIl5FWNZQZPLOchKsEWHxaQ2KMH8s680ST43Ixw2MIl/ImqtAIT7OvOPtLAeIQjhhakTIPnPb3ffg/ugm9WK18XEtZE5GkEc4LAIvqhug7kkdSpUaQZQ5PUiRX2puITyIirChYblmtf196i2W5axPRmpEVHzYWElHQSOcWaq1vH6ZNKZGOIVwxlovsSIsabJccCEs4n+YZwg201upHGFFKEntQHFo1KPxaVajElgy4kcE64k/wc+Lh1V8WjrE/iIkVYSbPcJujrDvYmCFqM1A4/k1zcQoXTXU6Gvq1ogBVIS3TJZw4MxK2+0R+bRGhBeCRITVwwbOLOVPERbnrqyKlM2S5gi3c7JcvcWyuDBkVesMTVkjFMFYtpxRKRmjJN2gUmu0RpQTxGe6o3kx8Wlq2DwddWtE4+NhwoNpCWtEs0e4ajYub0dMPPaeX9Oof2qyXH+TJD4tjp4thJ977hgqJsMjxxbb+j5xbTrTUrdGZC2Ew7dn1XAhX4qwp6EGkH0o0kxsjcjWYtm2GWoWC4xPKxXq1ghL1mQ5LT4bVTGYWLYtZVSqZ60RvtSIJNa4uKQNd3Su93ZHVwnz8eohN1nB1ohgRTjKZuatc4w01ghVCPc1YrSoNIge4Su3jELXgF2H59v6PrlssRxjDcirIixOnCxDrccYA2PJbAhZc4vFFyxIERYX5Irp8QhLabGsTtiKZiy7UVFrFVlNbTqNPzWilKAQjlIXgXpnNGWNSEfdGtGIpsWkRngV4ZAc4agGVt7CWynCCsFAK8IjRR2XbxjBPQfn8NDUfNssErlUhO3oQnBNnhRhS25qRF3liV9Wp+DA9qRE9bUvFXRnGcstLLIeItwaoRRhRTNJuynGIavNeaepmDY0qquC5YLWNKzuJ26ynIz5GoNI2GS5cEWYn9O8n0NoaoQdvG6g0YqRNkdYxaf1L9VBniwHcHvEk9PLeMe/78M3n55ry3vICLL34vrSMnwvozrLAcCqYQMzS2YuhkD9neWA7F31gGQ2hLATc1KEChVojXAeq5q2NB+5kWAmvGIw8TamyUJY1mveETGGIvIsiTUibqRGxrl4EAmLTwtNjQhQ7OrWCF9qRORkOec6QsC60ULi7eVpE+pD7ldqgzxZDgDecPlaXH/FOgDA3Ep7EiTkWyOc9Wb2CIc/v2akAIsBszmIUPOmRpCEySlR8Tp+Cga5+aOtIF4blCNsaAQirlTJaroi4tPUUK3Cj9zUiN47vqqm7frygaST5aJHj8TeVEVSOtxrou/xuNQI7/HrNtRoyhEOH4EVj523qhwoToShq0nIfc3AK8JrRgp453WbAQCVNh3o0nOERYvlLKkRdrQivNrJEj7T5UKYMQYzUBFufZ1miqKzpMdfLKMQinDQnSYRuRFOYQpJWgyNR1upoVqFn7jvfFJ6NjXC1+GxZFDyyXJhirCW/cZ8EBF7i8ivCIcUwm70Xf3zK4jUHd91u35+b35fISJtX1NOtb1Z54oo8k3NZiCkm0Dpp6cLYYB/+Yp6vQe5bKyYCRdpkeERtmIU4dUjfNjozGJ3fcLzFROWzTA5zLdHhkd41vE+j5WbVVo//GLZuiIcNVmOr19vsEbIyBEGsiVdKPoTWdaIXm2xXPEpwqUEnUXjblDFw724P7qJOH37lXY9ZLJckL1QKMJ+j/CjJ5YBADvWDjWtR4gaFwQ8F4XyCPc3VdNGQaemG7M09HwhDHC/ZrVNk4xkdXQSyEmNiFZEVw87inCXkyPOLFQBAKtHigDk5HZOL1QAAKuG4gvhok5YydB0pRoxWQ4Qof5W5HBeGkQhrCLUFH7i5gUkRetRv2TVtBssSmVDQ9VikYJCnI1KWSNaQxS7/oYaYYpwzW72CLvWCN/ydx9cwNqRAi5e11zsHpxZAdBaIaxU//6lZgdHnKahTwrh9inCsoocgQxFmDEW6ZFd4yiwp7ucHHF6kRfCq/yFcIYLz/Q8L4SF/SOKomONaNUTKRQnrxLVsH5DQ6VW9whnuSMFvIWwOmkrGpE5Wa4Xi4KKaTXckJbFZNWIm0YzZhIrEfH9oQrhVIjd1ZwaEXxds9zUiCBrRP3zq5gWdh1ZxI9sGw88lx44zQvh7WvSFcKGUoT7mqrFMkWnAQkLYSJ6JRE9QUT7iOi9Ac+/m4j2ENHDRPQNIjo/01alpKjHD5O1itUmRThri+WommukqKGoE0512RpxximE1ziFsAyP8KkUinDJ4O/X6mhBMkXY9gwVyimEVYSawo83fSULhN60AvitEaIQjpoDYCeY38H3Rw/ukC4SmhoRMlmu5uYIU8Oymi/e8t79Z7BcY3jBtonA933e+eMAgA1jyRMjgN71xSuSUbNY6DU6KbGyGhHpAD4C4McATAG4n4huY4zt8Sz2EICrGGNLRPSrAD4E4PpMW5aCkqG1bbJcHlss82HS8A+eiHDeqhIOzlRafg8ZnFl01FunEBbXoywnJaEITyZShPkbrtTswOSHOKImywHcI9yYGpH6LRpwfXPqpK3wkbSteBw6UY/Gp9kNw5+iKF6JGAm0Epy7efEmaSMHhNDUiFBF2CmEfapdQddQ83SWe+TILADgis2jge/7h6/ehsWqlXrkzdBVIdzPVC2WKTECSKYIXw1gH2NsP2OsCuALAN7gXYAx9i3G2JLz6z0AtmTaqpSUjPhw9VaRnRohY4KGbbPYdqvb1wxh/+nl1t9EAmKy3pqREoD6BSlLfNOphQpWDRcSqWPiwllp0SdciSmEiz6PcObUCOfltTaNbih6FythW/E4qEfj0yqmjaLnZtb9bkd8V+wEN6gaenN/dBOxu5KmRgj7g78zYkHXGhThpaoJjYDhECtaydCwejidGuxul/qM+5aaxVA02l8IbwZw2PP7lPNYGL8I4L+zbFRaSgah0q7Jcm6RI2d9mgSPsM3iC/Ptq8s4MV/DYqU9+cpJOLNYwVBBx1CRX8Dcvz2jR3jdWCnRsiWPItwKbg/zEDW5Hp8mJ0dY3CiYtiqEFY1Ythx7VljTg7xTqQV7hKMi1ESNFaUIk/IIpyYsJScuNcIvXhh6YyfNxYqFoYKWea6FH0O1WO5ruEe4/ZPlgo7KwKOKiN4K4CoAfx7y/NuJaBcR7Zqenk6+lTHwgqRdirDkyXISsivjPMJAfULB/jPdU4VPL1ZdWwTgTY1ofZ2nFqpYO5qsEBYG+lYj1ISSHDYjtVTQG3KEsx4jYnvbNbqh6F1sxhK1FY+jV1ssL1UtjJS8qRHxN7lJLEtcxZSzjYNCXItlv8IuVF+/NcLQNLcZAsAV4aGMs/+DCPMuK/qDWoesEVMAzvX8vgXAUf9CRPQyAL8H4PWMsUBzKmPsJsbYVYyxq9atW9fK9gZSMhq/UDKR3WJZl6CKxnWWA4Ada3no+NPOTNtucKapEOY/s6ZGJFaE3Qk1rRXCcTnCRV1DVaZHWMWndZwjZ5fxa597AFd88Gs4fGYJh04v4enphW5vVhOyrBG9mhqxWDExUqrPCygX4q0RlpOuE6UwqtSI9NRTIxr3q7hR8x9epqsI+60RPkW4yhVh2ehqslxfU7XszJPlkrz6fgAXEtE2IioCeBOA27wLENGVAD4GXgSfzLRFLcDj09pnjaCYk2kaZHQzshJ0mVo/VsRwQcP+HBXCMiYKnlqoJFaEhTWi1WMjNjWiINcjLIZ7lzNkHyui+c6T03jLx+/BUtWEadn41c8+gDufmMbscg237DqMX/iX+/GOzz3Y7c1swrLlTJajHs0RXqiYGPMUwqUk1ogEkXO9mqvcTWyIuMjGx8U1yW/tEr83KcJ6Y6zZUsXEUEGuLUK8jyqE+xcZk+Vip94zxkwiuhHAHQB0AJ9kjD1GRB8EsIsxdhu4FWIUwK1OwXiIMfb6TFuWgnYrwrJsEQJdy2be5x7h6GU0ImxbXcb+U120RixUccG6+gzgeo5wa+tbrJhYqlqOIhy/kqI7fNqiIhyTI1xycoTFeT+rR1ioIYuV7jZC6Wdu2XUYdz19Gv9459PQNcLDU7P4+7dciX+9/zBu+s5+VEwbRLzwGi3FJ5N0ClnxaRrVvbO9gmnZqJh2oyKcZLIcix/JU4pwety4SL8iLEb8fB+JGRCfBvAs4ZpPES63wxqhbnb6Gj5Zrs3xaQDAGLsdwO2+x97v+f/LMm1FRkp6Gz3CkmKLvOgZfWlcpY7fpvNXl3HfobnW3ygjZxarbjMNIHuGssgQ5opwvNJdSpA1GkUlNkdYR9WyPRF7Lb2Ni1BDFqv9qQhXTRvvuXU3fv2lF+KCc4IjktqNyLT+u2/uAwC85lkb8ZpnbYRpMXz3qVNOqgLw6JFZPG/7mq5sYxDyOsv1XkqCmPDLC2H+/yQ5wnzkLHrdvdpyupuIG6loRbju567Hp8WnRoy0wRqhGmr0Nx1rqJF3yoX2tliWrQhnnamc5AQPAGtHCphZNrtyN7xctbBcswI9wq1eiEWGcPrUiNYVYY2aT+Du+g0NlZpVbzkqSRFe6lNF+NCZJXx591Hc9fSprm3DfMVEQSf86EXr8EdvvBx/86YrQER4xWUbsH68hF994Q4AwMNTZ7u2jUHYkjrL9aI1Yr7CYxhHS83xaVKsEapISgULScmpTwRvXF5kBQemRngWXqq0xyOsJsv1NzWLodTuhhq9QMnQsGLy2aqyo1csW95EOUHW3udJi/NVQwYsG5ivdH7y1Zmlxq5yQPbouLoiXASWYhaGJzUiw2S5qCGXemc5OckiYsZ0vyrCZ51jYn6le4X+woqJHetG8elfuLrh8aGijnt+56UAgC/94Ch2T812Y/NCsZicQjis6UGeaVSE+TFULiQohFm8XUlZI9ITlhohfvd7hK0Qa4Shaw3t5BerJoaMstyNhYpP63eUIuyQdVJUFEkSGtKStbuTlXCbVg3z+5yzy50vPE7McetCYHxai3/60bN8nRvGk50sk6hGUVTN6I50ohC2lEc4EWeXuLK30MW/L8r7S0QgIlxx7iR2H86fIizDGtGLVgBxvHg9wsUEUYPcVx29biKo+LSU1AvhYEXYf20TnTL9N3IFjRo8wkoRVrRCzbY70lAj99Q7iMk/o8mare1Fy6oIs/gcYQBuF56Z5c4qjF98YApvuukeAMCWVcPu41nj0/ZNL2ByuNBQXEfh3iC13FnOilSEi+6EHb7+rE1XdI1QMjQsVvuzEJ5xFOGFLirC8ysmRsvRA2HP3jKBqZllTM0kGHboELIU4V60AogbQ+8NDBGhbGgxinD8yJlG1HOe6W7DXCtY4+Pi8PQXnZZtw9CoabSWN9Rg7jqXahbK7UiNUIVwX1M1WUfi03JPXfmTX/AlOZmmJXNqhJ1MfXQV4Q4XHh//7n5sWzOCL9/4AuzcNO4+njU+bd/JBVywbjSx/UXcJbY6UlAxo/MJhVq85FgZZCh2IyUDS13sBthOhCLcTcV7oWJirBzdpvW1P7QJBZ3wD3c+3aGtimd6voLJofTtZf30ohUgqBAG+IS5qMlydoJufL24P7pNaEONkMnQphV8E1fQNdc/XHHy2NuWI6w+477Eshksho401Mg9pYwxWVHY7VCEM6ZGiKD4OFYN8QtHJxXhqZkl7D0+j5/84c141paJhucoo0f46ZML2LEuedpAQSMQZZssF9ZVDqjHqon1y1DsRkp6/1ojlh2PcBf/vvmV+Fi0zZNDePPV5+GW+w/j0Onuq8JnFqs4MVfBJRvHMq9LI+q5+LSFkEK4VCAJinDvxcl1G5vxdrN+QSJMETbt4Ba4hlZXhIWY0I7OcrqmgbHebCSjiEZEnHaixXLuyRqTFUV7coQlNNRIUHRNlA1oBJztYCH8rb28n8pLL13f9JwoFFu5OZ9ZrOL0YjVV7BYfPtUzFcJxk+UAnpABZO8sBwAjRaOPrRGOR7ibk+UqNYzFWCMA4Jev2w7TZrjzyY73B2pi73EegXjpxvGYJeOhnoxPa/YIA3WPfhg8NSJ63Rp6L0Wj27CQOSp6iNBhWna4IuwoQuIzbosi7KxSqcL9h9v0SnmEs7fSjeLJkwtYPyF3JmuWoRrXn5XkfTTCRNnoqDXiG3tPYtvakUDlNkwxCOLTdz+Dn/zHu7DrmTMA4La9TZs/WypED59GUYlRhEWRLDrBybBGDBd1Vx3pN0RqRLcmy9UsGys1u6FDWRgbne/8mcVquzcrlsePzQMALtmQvRDuydSIqkiNaJy4GmeNsBJYyHrxxqDb2AiOioxWhIMLYZHv6yrCbSmEtcDtUvQ+Yn6O8gijfYXw8dkV7D58Fi/f2axuZoEyTFix3Bm4yZZfPWx01Bqx5+gcrjp/VeBz9dSI+L/9K7uP4YGDM/gfH7sb3993CvtOtlYIt1cR5hfmZcke4W6mKrSTbqdGCCU6brIcwKOdxsqGu83dZO+xOawdLSbOz46C5whL2KgOMr/Cs5/9CS5lQ0MlwmNmsfhufFoP3hh0Gzskxz4sNSLMI8wnyzmKsDMK1o7Ocq4irD7nvqPe9EopwnWPsOT4tK/tOQ4AeMVlG6SuV9daH44TJ5mkRdeqYaNj8Wk1y8b0QgWbJocCn09aCDPG8PixOfzElZuxbc0IfuvW3bj3wBmUDC103WGUC9Ezy6NIkiMM1BVhKR7hYv9OlhPWiG7lCId5TcNYNVx0ky66yd7j81LUYICf8HtNAV2smE22CMDJj6+F/y1hBZsXrQdvDLoNQ/D1R9QiptWsCBsBkTqGVs8RFue8oTakRghFWHWX6z/cQjjjDVRfFMJCFpetCN/x2HHsWDcivR0sT41o7bW2m1mbbPnVw4WOKcIn5ytgDNg0GWwlqadGRK9namYZ8xUTV21djb+8/gqcmK/gPx46govWj6UuNssFveX4tLgc4aLPIyzDSj5c0vvWIzzrWiO6o7L+//bOOzyO6lrgvzuzRVr1asuW5V7BNtim95bQEgLBBFJo7yVfCJC85KV+SV7ywksIEJKXvEcKPDBJCCWQggFjU0JxsI0t927Lsi3JkqzetW3mvj9mdr2Wd6Xd1Uqypfv7Pn3anZ2ZvXP2zJ1zzz33nA6v9b0DZY0Ikedxho33kSJomOw92sncFCyUAyvF36nmGev2BclwnWgIW4Pc2Pd2PCnnNKEWUSWKKaM/f2JlBQqaJo6ooRHH8giH+ryhyRph/Ve/8+jDnyKP8KipLAepNYSllGw41Mpnz5mcsnOGGEznW9feC1gL4eIhL334YoTr2qy2jc+J5RG2/g/0IN5Zay0OmjchmzMm5fL6Vy6ioqGLOeMTNwbcTj1pj7AvaMSVPi3VHuHRmjUiZFR6AyYBwxz0St9ECYVGxLNYDiD3JPAIVzR24Q+aKVkoByAQmPLUqiBhpbw78TfL9zjYUdcd87h4qoKeiiWnRxoZo2Lfscpy0TzCMUIjwjHCtiE8FKERuvIIj1aOxQgrQ3hIDOH23gD+oMnEvMSm4uNBH0SC7+1HrNKvswrjixfM8zjoDUh6/QbprtjezVRQ125VfiuJsbhQi+Ex6Mvuug40AbPHWYbvrHFZzBqXnEfMWlAzXFkjUhMjPBpLLHsDBr0Bg8JMN01dPrp9QXI98RVGSRWJh0Y4qWzqGsomDcjmKqvC3Zll0ePuE0XTTr3Kct3+6KER47JctPYGYy5qjacCp3YKVtobaYwYBZ1CWSP6OnmChtlPaEQoa8QQLpZLYG2K4tRCpU+LIC2cRzh1no7GTh9AShao9EUbRNaIHUfacTk0JufF1658Owl/6HqGknrbEB4fyxCOs0PaVdfB1MKMlBju7kGGRvRrCDtTnzUiw6XjD5rHlR4dDbT3Wt7gSfnWwHIk4oRDhnAiHuG27pENjdhS1Uaux8mUAs/AO8eBxqlnEHT5jOiGcKY1kGroiu61t9KnqYIaqUbGCI0I2bp9Pa+GKWOGRhzLIzx0oREhb/RweYQ/rGzmtW11tJ4EGWdGO6EYYbdKnzY0HuEG23AsHgJDWB9EieVtNe3MLckecDV0iJlFluHxzt6hz4da1+4lw6XHTE91zGPQ/3l21XakbCp4oBRL/eE3Bkifpvc1hJP6muPw2LIbbQvmQiEGoZLbI5E5oiOBrBFgLZbr9AVHdFCyubqVMyblxl1NcSC0U7DcbLcvSKb7xEHx+CxrkH+0M/pgxZQMmANehUYkjilljMVy0T3CASN6aISVPu14j/BgDZpohGcih0Hv23r83PX0Bu59dhOX/uxdqltGviDPaEZ5hCMYioIaQ+kRTrbEsmlKdtZ2sGBizsA728wu9jCr0M2zH1YN+WrxuvZeSnLTYz60Q5v7u/Y99R0caetfKnThAAAeYUlEQVTlnKn5KWlTmlNPuvS2LzCQR/j49GmpiRG2zjnaFsy12p7VUjvUaCTioMMxwu44F8tlWPuNVAq1Tm+A/Q1dnDEpN2XntLJGpOx0w0KXN/piuXFZlke4vjO6563LZwwYO6jSpyVOrMVysWKELY9wlNAI3coaIaWkxx/E49JTMqvWFz2Gp3ooeHrNIXr8Bo8uXYgpJV95fnM4RZwi9aj0aRHomsChkbTBE42GTmuafyg8wsnm8jzU3E2XL8j8BAxhgOvm5LL3aCcbD7cm/qUJUNfujRkfDJGV5WJf/MtbatE1wbXzS1LSpjRn8jHCvjjTp4XOnwqvXWgKuGeUGcLtdnnlSbZHeCTKLHf5AuiaIC3O6ddQDHPbCC2Y21bTjpSpiw8GS0dPNcMvVvq0okwnAjgaxRBu7QlwsMXLaSUZ/Z77VIgRNkzJHU+t54FXd50Uqe+kjN7XxcojHIhVWS60vynp9ht4ogx2UsFwFdTo8gV5es0hrpw7jk8uLuWBG05nU1Ubb+0e+eqUoxVVUKMPLj15gycajZ0+0pxa3AtrEkFPMmvEu3sbAZhfmpghfPn0bPI8Tr750jaau4YuVri+3cv47NiGcGi0H2uALKXkla21XDijkILM1AxA0px6v2VYYyGltNKn9XOD9a0slxKPsD0F3DXKQiOOdlh6V5Zvh0aMQIxwp9fKPhDvgCXPY3mERyqF2vv7G9E1wRmlKfQIn2IxsVJKuv3BqP2wU9coyHAeZwjvaejhJ28dZt1hK/PM2ZP6X2SrCXFSGJf98ddNNby3r5En/3mQW363lkUPvMm+o50j1h4TSTQHXLh/N070CEerLBfyEgcMSY8veELlwFQxXIvlnni/kraeAPdfPgOAa+eX4HHprDnQNKTfO5ZJVfq0UWMIux0ipaERDZ0+irLcKYvNiySZrBFH2nr5+Zv7OG9aQcJpxNKdGo/fvoQjbb3c9sQ6KhpS34kGDZOGzv49wqHFFNE6JG/A4N9e2EJNay83LZqYsnalOZOrLBeKYU3vx0sR8gj3+FMYI+wKxQiPLo/w6v2NTMxNZ3qx5aEbiRjhLm90gyoWebZHeDhSqD23vorv/HVbuF8wTMnLm2u5ZFYROZ74QjniYag8oFLK8ILIVNIbMDBl7LjucVnO42KEl+9o4tVdLfzP6lqy3Dqzi/tfZHiyh0Z4AwaPvrGPhaU53Ly4lJ21HbR0+/mgYuSMKzOmR9j6f4JH2JRhr2wkIeM4YJpD7BG2F8slm7w/Dho7ffzf6kqunT+ehXYok8uhcdaUfNYeaB6y7x3r+MIxwsoQBsCta0lnB4hGY6eP4qzYRt1gSCZrxE9f34MpJQ/fvCAp4/ysKfk8dedZNHf5ueaXq7nt8XUcaExdaqjaNi+mjJ1DGKJnjQh5Y36yYjcvb6nlGx+dzccXTkhZu9z2YrlEvT6hNHX9FTLoW8glFfFtIUNtNKVQ8wYM/lnRxJVzi8PFLEbEI+xLzBDO9YRihIfeEH5idSXPra/m4VV7AFhX2Ux9h5cbz0zdoBBCldRSbxCs2lnPwv98g+t+tZqLH36H7/99R0rOGxowRQuNABif5TrOI7zpiNWntXmDLC4duACPOMlDI5764CD1HV6+c+1cHrl5Adt+8BEKMlzssnOtpxop5YB9ZcysEeJYqEMkhmmGwyAicUQYqD3+YHh9RKqJVegjlfxkxW68QZOvf2T2cdvPm17A/oaucKhlsvT4gzy4Yje/effAoM4z2vCrynLH43KIFMcI+yhK0fR8XxLNGtHQ6eX17XV85pwyJuUnn0bpghmFrPjKRfzLhdPYfqSd/35rf9LnisQXNPjGS1tx6RpLpsSOZ+y7qnjj4RbOffBtvv7iVp5Zd5g7zpvMvZfNSKkXPs1e0JZoeEQ4f+uk2NcjhMDlOFYmNBWGsCe0WG4UeYTXHGjCGzC5Yu44PE4dIUYoRtgbJDvOqnIQ6REe2tCImtYeKhu7mZibzu/eq2TVznqeWXeYLLeDq+aNS+l3iThDI1bvb+SCn/6D9/c1xnXeVTuPkpXmwOOyft8XN1anJFStusUq0pMd0yPsor7Tj5SS5u4AVa0+rpiZiy7gwmkDZ57ROHlDI1q6/fzmnQNcObeYc6cVIITAoWvMm5DN7vrEDGHTlHzuyQ/7NaS8AYNbH1/H1/68NSwTb8A44VllxswjbP3vawgHjehp7Art9Te/fGsf+452xZ3WMFGGOn3ayh11/G3zEe67bAbTio6vQnv+9AIA1lW2JH1+KSW3Pb6O371fyaNv7A2nKVWkrqBGXIawEOJqIcReIUSFEOLbUT53CyFesD//UAgxZVCtSgK3nlxoREVDJ3ctW89VP3+P17bVhbc3dvoozh46Q9jo4xV9aWMN9z+3mde3153QkbxYXkPQlNx2dtmgv3tcdhrfvmYONy8uZdXO+kF7u554v5LzHvwHHx5s4ZGlC/otfBH2GEjYXtPO7U+up9dv8NLGGnLSnXztqtkxj02WUPiCL0Hd2HS4lelFGQNOS0emV0tNjHDIIzw4Q7HTG+Dupzdw+1PrB5U2aOWOev62uWZQ7fjTuioyXDrnTMtH0wSZLsewe4QbOrxUNHaRnR6/Iexx6bh0bchDI97fZ01zP377Yk6fmM19z27i9R313H3h1PBALlXocSyWe3dvA3ct28CRtl6e/OfBAc8ppWT1/iYum13Mi188nwduOB1vwBz09L1hSh54dRf5GS4unlkUdZ9xWS78hqSxK8Bm2xt826Ji/nrX6Vw9Z+DMM5ogPJA9WWjo8HKgsYt7ntlItz/It66ec9znc0uy2Xe0K6G0fm/uPsrq/U089k5FuNR4JFJKfvDyTj482MLfNh/hlW11eAMG1/xyNdf9zz+pau6J2Dd6Wjpdi+4RDpoyanqra08v4ZYlpfx+7WEMU3Lf5TPjvp5E0GK0KxX4ggY/XL7Lum/t2OBITpuQQ3aag1U76pP+jvUHW9ha0849l07HkJJn1h0eTJNPGtp6/Gyqir2A/8PK5n6v1TAlr22vZ3yWM+50srEYcAgmhNCBx4CrgBpggxBiuZRyV8Ru/wK0SilnCCFuBR4CPjWoliWI26FR09qDN2DE/fCQUvLNl7ZR0dBFcXYaX31hCw2dXqYVZdLeGxgyj7AmjnmETVPyjZe28ZdNNXhcOq9srWXO+CxuXlwKQE1rL3/fcoTzpxecMNocDEuXlPL0mkO8vKWWO86fktQ51lU28+MVu7lwRiGfv3gal8yK/rAKEQoTM0yT7/59O5lpDl6570LKD7dSlOVOaSxkiJAueIMGOcR3fiklm6vbuGJO8YD7uh06nVhGXWpihK32xptHWErJluo2TpuQE54eauz0ceey9eFS1c9vqObT5yQ+iDrY1M2Xn9+MP2iy6XAb379+XkJTUGsPNHP/c5to6vJz/+UzwiWpM9McdPkS87K2dPvRBHFVo/MHTf5od6Buh8bBpm5W7qin2xfknkunxf2dQghyPc5BF9WoaLCMlli5sd/f10hJThrzSrL55a1ncuNjH7B0yQT+7crUGwZC9J8+zRsw+P7LO5hamMEFMwr5w9pD1iLYfmL/99R30tTl46KZhQCcMy2fDJfOW7sbuGJuch7tioZOHlq5ly3VbfziUwvJy4j+uy+ckIFDE3zr1Uo8Lh2PU2NWkSfuB+OUfDfPb23hxfJqli6ZlFRbU0Vbj5+HVu7l+Q1VSGmFXv3iU2cws49zYW5JFv6gSWVjN7PjWC8ipeTX71SQn+GipdvPM+sOc+OZE9lwqJXJ+R6Kstz86u39vFBezRcvmc7aymb+4+UdfLC/iYNN3WS6Hdzw2D/5/d1ns6A0F0MS1SOsxfAIxypsommCn960gHOnFXDutAIm5KZTUZH6eFrHEBrCL5bXUN/h5ZGlC6Ia+7om+Nx5k3nsnQN8qbad0yZYC92PdnipbOzG49KZNS7ruOJRTV0+CjJc4ZnRP5fXkOl28OXLZ1LR0MWfPjzMWVPzMUyTibmeuHQgEUxTUt3aQ5cvyIziTH7y2m4qGrv4zWcXR51Rq27pwRc0mVEcn30ipWT51lp+9Moumrv93HXBFO6+YCoNnT4aOrzMLcmmurWHz/+hHG/AZEJuGuOz0ynJSTuuH3hpYzW76zr47mUlg55Fjmcu4mygQkpZCSCEeB64AYg0hG8AfhhqH/C/Qgghh3HO6aOzcnh0dT03/XoNZ5blUpyVRna6A7dDx6mLqII61NTNpqo2HvrkfK4+rYRbfreW/3zl2GUNpUe4qcvPi+XVlB9q5S+barj/8hl8+YqZvL6jnodX7uG/XtsNQLpTZ+GkHL573dyUtuG0CTksLM3hxyt2U93SQ2leOul2HkcprekvU1oLH2paethV1xGeUq1v99LQ4UMIKwvAE7cviasKXMgj/NLGGvYd7eLntyykODstZanSohEyhH/73gGmFWWSbr+PVM2+StrRG6Cl2x9X2qqQR1iI1KRPCy0YWVfZHP49gHBZ4jSnTlGWG00IAobJqp31rDnQzGkTsrnt7DJau/28uLGGxk4fy+46i9++e4D/em0Xq/c3kp/hCj8UegMGWWlO8jNcZKc70YT1+wis/wHT5KWNNbh1jVvPmsQf1h5ma00b184vwaVrdHgDHGntZVy21Tn5gyZtvX6KMt109AbYXN3GmgPNTCnw8NSdZ7EgIvNBptvB3vpO/lxejbT1LKRvUkpM89g2n/3Af2WrlVbv9vMmU5ydhku3wlKktOJI01265e2Ukle31rG28tgD1e3QWFiay69uO5PFkxNLRZbncbHhUAuPrNpDcVZaeCDQ7QtS3dJDdWsvOelOphZm4NQ1nLrAGzBYf6iVHUfamVzgCYfZLCrL5aOnjSfP4yJoSkwpaejw8s7eBj5xxkSEEEwvymTD964MDxpSjSYsL9b/ra6krt2LP2jicetkuBykOTU2V7VR3dLLn/71HCbmpvP0mkM88OouzizLxalr6Jo4zqiREtYftGR9ke21dTt0Lp5VxJu76plXkhX+ncDqT9p7Azg1jYBpUnG0C6eu4XHrODSBP2jS0Olj1c560p06X//ILD5xRuw46VlFHn56/VS+u+IgQUNyw+mFCXmH7lhUSE23xjf/so1NVW0snpxnxckCSJBIpLT6iNA16Jo12Nxd30lpbjqFtsOkN2CQ5tRId+p0+w3aegJkpTnISXfi1IWVHcEfZFdtB9npTsvAlZKgKWns9PHChmraegPcef4UZo/LYuGk3KiDp3klljH19JpDLCrLJWhKun1BVu9v4miHl1yPkwWluRRkuPC4dNZWNrO1pp2f3DifV7fV8vDKvTy8cu8J573vshl87apZVLX0cNsT63ihvJor547je9fN5bNPfsitj6/jkllF7DraGzWMIeQl/sfeBiQgbJk0dfmiVpYDyxi+aVFp3L9XMoQM1Luf3sDiyXlcNLOQNKdu66XEMC29NE2JYd+XhinD2zLcDgKGSVVLD5PyPBjSknev32T51lrOmJTLhTMKY37/Fy6ezjPrqvjK81tYVJaLL2jy+vZ6/LZHX9cEM4szmVqYQV27ly3VbSyclMvVp41H12DF9jo+ceZE0l06X7liJncu28AdT60HrOfO9QsmUJTpxqkLnLqGw/4feq9rVr8OVoVPIQSFma7jnlehV37D5I9rD7On3lpQb6UftdLfffqJdZw/vZB0p05Tl4+mLh/js9N4dn0VAUMyucDD1MIMSvPSyUl3ogkR/vMFDXr8Bl2+IIeauik/3MrC0hw+cto4ln1wiGUfHDpBblMKPDh1jS8+swl/0MSpC+aVZJOZ5iBoSNYfamFRWS6XThv8QEAMZKsKIW4GrpZS/qv9/nPAOVLK+yL22WHvU2O/P2Dv09TnXF8AvgBQVla2+PDh6G7vqqoq/P7EpiNdLhdbWh38+p0KGjp9tMRZ3nDhpFz+es/56JplWNS3e9lb30n54VY+f9HUlKXxiuTbf9nG8xuqw+/vPH8KP/jYvLBimqakwxtAIMhOj57uKREZuVwuyspO9Ag2dHr50Su7eDUiJCQaTl0we3xW+OGc53FRnO1mb30n375mDmdNia/4RdAwOffBt2nq8nPBjAL+ePc54WmrRInn+l0uFz53Hl98ZiMHm7oTWhSjCXjra5cM6IW/5XdrWX+wheIsN+u/e2XCbezb3rKyMi555B0ON59Ykcjl0MKLA0JkuR187rzJ/Lm8mia71Oz0ogx+tnQhZ5blUdPawyOrLM9aty9I0LQe7GlOjU5vMJzxIhoOTfDgTfNZumQSK3fU872/bw9/hxBQmOmmucsXlmvoYa8JmFGcyaWzi7n/8hnhBXIh7lq2nnf2xhd7ClZ86HULSmju8vPGrqMD7u/SNR68aT6XzC4iYJgUZ6UlHbbyw+U7ebG8Gm/QPMGjlOV2UJrvobnLF65EGWJibjrnTM2norGLC2YUUpDh4qWNNeEHTCSXzynmxzeeTkk/C02jkUw/+d6hHh569wj+oHmc0Ra56ORTSybxwCdOB+L/rRaW5vDyfReG37+16yhf+tOm8MM+FiU5aZhS0uMzCJoSl0Mjzalx/YIJfOnS6Sf0v7GuuTdg4tBEwqvHXS4XBeMm8Ogb+/j92kMJeQ1L89Jp6PDFvMbQ/dCXcdluOnqD4bSLYPU3Sybn88OPn8a8Cf3HNgcMkysefY+qPlXLphR4mDkui8ZOH7tqO8Ltcjs07r1sBvdeNoPatl5e31GHS9c4syyPyqYuun0GC0pzjhusVrf08Ku39/PlK2YyKd9DfbuXh1buYVNVK27N5JpZOXxsbp+0frqDb62sZVNV63GzDkVZbn7wsXlcv2DgRdDJ6HR/uFwuiksm8sT7lRzt9PLWrgbqO5KLsc1yO8JrG3RN4HZolOV7ePCm+QM6TZZvreUXb+7DGzAIGJIr5hTz8TMm0OkNsqu2na017dS09uDUNS6bU8zyLbUcabPi49OcGn+55/ywN9kbMHhnTwO5HherdtbzytZafEGTgGEStI35wVCW7+HzF00lM83B6v1NXDSzEJeu86NXd9LeG8AbMMlyO8jLcFHV0sM1p4/n3GkFrD3QTHVrD9UtPfT4DQwpIwaPggyXTqbbQXa6k1uWTOKO86ega4IdR9rZfqSd7DQnE/PS2VffiRBw2ZxijrT28tUXtnDToom09QTY19BFty+IL2hw+exi7rpgKp3N9XHZAmVlZQghNkopl/T9PB5DeCnw0T6G8NlSyvsj9tlp7xNpCJ8tpYw5z7FkyRJZXl7e73cPBn/QpMcfxBc0TzAeIhmfkzbo8nyJEjRM6uyAd10TTMhN7AGYarwBg16/QY+9MELTxDEPoYDsNGfKYhW9AQNfwCQrzZG0EZwM/qBJW48/5qK5vmONDJcj5nRsJL6gQXOXn+x0Z8pyTnsDBp3eYNgzZUpJnsdFmlMnYJjhQZ5T18hw67gdVoq49t6AvWAp/nZ4AwYd3oDl9bK9sBLLu1OQ6Tru3rByuhoYhiTNpeF26PiDJr1+A4cu8Lh02nsDpLv0fj2avqBBQ4fvBD075kGwvOuasK4xUve8ASN8T4diJDNcDnoCQSt2URNkuh0xswwki2FKWrr94YeM26GR63GGB6kBwyRoSAKmVZY71vU3dlqGk25fn8uhxRXukUpCA+3sNGf4HgwaJr0BgwzX8fellJJe++EdtB+0fR8ZoZCVvmEzhilp7vKFFykJAQJBTrqTgGkiTYYkHCpZWrv94SwVof5A2LMkobaD5T30OHXyMlyYpqQnYCClJN2p47WfO+lOnaw0Z/i+DJoSpyZwO3RyPM7wDEpI57PTHFGrr8VCSksfe+x7z6Vr5EdMpxumtPp1O1RwKPLhx6LTGwhXktQ0a1A4FGlIkyGk+/6giS9oomnCuhc1q8/TNRHepmtWv9TtMxBAXoaLth4/Loc2ZKneQkgp8QbMsK4l8qw0TasfinbPhjz5Ld1+e5ZD2t937PiBbCLTlOEZ0FjFbiKvI1SJ8GTQgcEYwucBP5RSftR+/x0AKeWDEfussvdZK4RwAPVAUX+hEUNtCCsUCoVCoVAoFBDbEI5nCLoBmCmEmCqEcAG3Asv77LMcuMN+fTPwj+GMD1YoFAqFQqFQKBJlQP++lDIohLgPWAXowFNSyp1CiB8B5VLK5cCTwB+FEBVAC5axrFAoFAqFQqFQnLTEFegipVwBrOiz7T8iXnuBpaltmkKhUCgUCoVCMXSMmspyCoVCoVAoFApFIihDWKFQKBQKhUIxJhkwa8SQfbEQjcBQ1gosBAZX43NsouSWHEpuyaNklxxKbsmh5JY8SnbJoeSWHKmW22Qp5QklcEfMEB5qhBDl0dJkKPpHyS05lNySR8kuOZTckkPJLXmU7JJDyS05hktuKjRCoVAoFAqFQjEmUYawQqFQKBQKhWJMMpoN4cdHugGnKEpuyaHkljxKdsmh5JYcSm7Jo2SXHEpuyTEschu1McIKhUKhUCgUCkV/jGaPsEKhUCgUCoVCEZNTxhAWQjwlhGgQQuyI2LZQCLFWCLFdCPGKECLb3j5FCNErhNhi//024pjF9v4VQohfCSHESFzPcJFCub0rhNgb8VnxSFzPcJKI7OzPFtif7bQ/T7O3K51LTm5jSucSvFc/EyGXLUIIUwhxhv3ZmNI3SKnslM7FlptTCPF7e/tuIcR3Io652pZbhRDi2yNxLcNJCuV2yN6+RQhRPhLXMtwkKDuXEGKZvX2rEOLSiGNS189JKU+JP+BiYBGwI2LbBuAS+/XdwAP26ymR+/U5z3rgPEAArwPXjPS1nSJyexdYMtLXcxLLzgFsAxba7wsA3X6tdC45uY0pnUtEbn2Omw9URrwfU/qWYtkpnYt9r34aeN5+7QEO2c8MHTgATANcwFZg3khf28kuN/v9IaBwpK/nJJbdvcAy+3UxsBHQ7Pcp6+dOGY+wlPJ9oKXP5tnA+/brN4FP9ncOIUQJkC2lXCstSf4B+ESq23oykQq5jVUSlN1HgG1Syq32sc1SSkPpXJiE5DYsDT3JGMS9ehvwHIzNPg5SI7uxSIJyk0CGEMIBpAN+oAM4G6iQUlZKKf3A88ANQ932kSRFchuTJCi7ecDb9nENQBuwJNX93CljCMdgB/Bx+/VSYFLEZ1OFEJuFEO8JIS6yt00EaiL2qbG3jTUSlVuIZfYUzvfHwnRrDGLJbhYghRCrhBCbhBDftLcrnbNIVG4hxrrO9XevhvgUx4w5pW/HSFR2IZTORZfbS0A3UAdUAT+TUrZg6Vd1xPFjVecSlRtYRvIbQoiNQogvDGdjTzJiyW4rcIMQwiGEmAostj9LaT93qhvCdwP3CiE2AllYIy2wFK5MSnkm8DXgWTvmJFqnNhbTZiQqN4DPSCnnAxfZf58b5jafLMSSnQO4EPiM/f9GIcQVKJ0LkajcQOkcxJYbAEKIc4AeKWUo3k7p2zESlR0onYPYcjsbMIAJwFTg34UQ01A6FyJRuQFcIKVcBFxjH3vxMLf5ZCGW7J7CMnLLgf8G1gBBUqxzjmQPPBmQUu7BmlpFCDELuM7e7gN89uuNQogDWJ6nGqA04hSlQO1wtvlkIAm5lUspj9jbO4UQz2Ld3H8YgeaPKLFkh6Vb70kpm+zPVmDFQT2D0rlk5Pa20rl+5RbiVo73aKo+ziYJ2aF0rl+5fRpYKaUMAA1CiA+AJVje4Ehv+5jUuSTkVimlrLWPbRBC/A1L394/4eSjnH5skiDw1dB+Qog1wH6glRT2c6e0R1jYK3qFEBrwPeC39vsiIYRuv54GzMRSujqgUwhxrj3ldTvw8og0fgRJVG72tEShvd0JXI81lTHmiCU7YBWwQAjhsWPBLgF2KZ2zSFRuSucs+pFbaNtSrJhMAJS+HSNR2Smds+hHblXA5cIiAzgX2IO10GmmEGKqEMKFNcBYPvwtH1kSlZsQIkMIkWUfk4FlCI45fYN+bRKPLRuEEFcBQSllyp+rp4xHWAjxHHApUCiEqAF+AGQKIe61d/krsMx+fTHwIyFEEGtK4osRMTn3AE9jBa2/bv+NWlIhN1sRV9kPBx14C3hiGC9jREhEdlLKViHEz7EeChJYIaV8zd5P6VyCchuLOpfgvQrW/Vojpazsc6oxpW+QMtm5UTrXn9wes1/vwJqaXial3Gaf5z6sQa0OPCWl3DlsFzECpEJutrPpb5YdhwN4Vkq5cviuYmRIUHbFWPekCRzh+FCllPVzqrKcQqFQKBQKhWJMckqHRigUCoVCoVAoFMmiDGGFQqFQKBQKxZhEGcIKhUKhUCgUijGJMoQVCoVCoVAoFGMSZQgrFAqFQqFQKMYkyhBWKBQKhUKhUIxJlCGsUCgUCoVCoRiTKENYoVAoFAqFQjEm+X8xqQZr+qMe+AAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(12,3))\n", "\n", "ax.plot(res_filardo.smoothed_marginal_probabilities[0])\n", "ax.fill_between(usrec.index, 0, 1, where=usrec['USREC'].values, color='gray', alpha=0.2)\n", "ax.set_xlim(dta_filardo.index[6], dta_filardo.index[-1])\n", "ax.set(title='Smoothed probability of a low-production state');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the time-varying transition probabilities, we can see how the expected duration of a low-production state changes over time:\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x216 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "res_filardo.expected_durations[0].plot(\n", " title='Expected duration of a low-production state', figsize=(12,3));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "During recessions, the expected duration of a low-production state is much higher than in an expansion." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
bioinformatica-corso/lezioni	laboratorio/lezione17-09dic21/esercizio4-biopython.ipynb	3	85837	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Biopython - Esercizio4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[MAFFT](https://www.ebi.ac.uk/Tools/msa/mafft/) è un tool di allineamento multiplo sviluppato da EMBL-EBI (European Bioinformatics Institute - European Molecular Biology Laboratory) per sequenze di DNA.\n", "\n", "Usare MAFFT (scegliendo ClustalW come formato di output) per allineare i 14 genomi completi di SARS-CoV-2 presenti nel file `covid-sequences.fasta` sequenziati nel novembre 2021 e scaricati dal sito di [NCBI](https://www.ncbi.nlm.nih.gov/sars-cov-2/). Il primo, con identificatore `NC_045512.2`, è il genoma di riferimento.\n", "\n", "Trovare tutte le variazioni rispetto al genoma di riferimento.\n", "\n", "---\n", "\n", "Variazione: posizione della colonna di allineamento in cui esiste almeno un genoma che ha mismatch con quello di riferimento.\n", "\n", "Esempio di allineamento con variazioni in posizione 8 e 13:\n", "\n", " REF AAGCTGATTGCACGC-T\n", " G1 --GCAGAGTGCAGGCCT\n", " G2 --GCCGAGTGCACGCCT\n", "\n", "Variazione 5: `T` nel reference e `A` in G1 e `C` in G2.\n", "\n", "Variazione 8: `T` nel reference e `G` sia in G1 e G2.\n", "\n", "Variazione 13: `C` nel reference e `G` in G1.\n", "\n", "Variazione 16: `-` nel reference e `C` sia in G1 che in G2.\n", "\n", "---\n", "\n", "Si richiede di:\n", "- costruire il data frame delle variazioni in cui le colonne sono tutte le posizioni 1-based delle variazioni e le righe sono indicizzate con l'identificatore del genoma.\n", "- estrarre il genoma con più variazioni e quello con meno variazioni\n", "- ottenere il data frame delle variazioni \"complete\", cioè in cui tutti i genomi variano rispetto al riferimento.\n", "- produrre il data frame delle variazioni \"stabili\" in cui tutti i genomi variano allo stesso modo rispetto al riferimento. \n", "- ottenere la lista delle posizioni in cui c'è un gap nel genoma di riferimento.\n", "- ottenere la lista delle posizioni in cui c'è un gap in almeno uno dei genomi (diversi dal riferimento)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Installare il package `Bio` di Biopython." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "!conda install -y -c conda-forge biopython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importare il package `Bio`." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "import Bio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importare il package `AlignIO` che è il package per manipolare file contenenti allineamenti multipli in diversi formati (tra cui `clustal` che è quello del file di input)." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "from Bio import AlignIO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Leggere l'allineamento in input" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il package `AlignIO` mette a disposizione la funzione `read` per leggete un allineamento:\n", "\n", " AligIO.read(input_file_name, format)\n", " \n", "e restituisce un oggetto di tipo `MultipleSeqAlignment` che è un oggetto iterabile contenente oggetti `SeqRecord`, uno per ognuna delle righe dell'allineamento letto." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "alignment = AlignIO.read(\"mafft-alignments.clustalw\", \"clustal\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La lunghezza dell'allineamento in input (numero di colonne della matrice di allineamento) è:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "29903" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alignment.get_alignment_length()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trasformare l'oggetto in una lista di oggetti `SeqRecord`." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[SeqRecord(seq=Seq('ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGATCTCTTGT...AAA', SingleLetterAlphabet()), id='NC_045512.2', name='<unknown name>', description='NC_045512.2', dbxrefs=[]),\n", " SeqRecord(seq=Seq('---------------------AGGTAACAAACCNACCAACTTTCGATCTCTTGT...AAA', SingleLetterAlphabet()), id='OL700521.1', name='<unknown name>', description='OL700521.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('---------------CTTCCCAGGTAACAAACCAACCAACTTTCGATCTCTTGT...AAA', SingleLetterAlphabet()), id='OL700526.1', name='<unknown name>', description='OL700526.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('----------------------------------------------TCTCTTGT...AAA', SingleLetterAlphabet()), id='OL700531.1', name='<unknown name>', description='OL700531.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('----------------------------------------------TCTCTTGT...---', SingleLetterAlphabet()), id='OL700532.1', name='<unknown name>', description='OL700532.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('----------------------------------------------TCTCTTGT...---', SingleLetterAlphabet()), id='OL700537.1', name='<unknown name>', description='OL700537.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('----------------------------------------------TCTCTTGT...AAA', SingleLetterAlphabet()), id='OL700524.1', name='<unknown name>', description='OL700524.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('----------------------------------------------TCTCTTGT...AAA', SingleLetterAlphabet()), id='OL700530.1', name='<unknown name>', description='OL700530.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('----------------------------------------------TCTCTTGT...AAA', SingleLetterAlphabet()), id='OL700538.1', name='<unknown name>', description='OL700538.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('----------------------------------------------TCTCTTGT...---', SingleLetterAlphabet()), id='OL700543.1', name='<unknown name>', description='OL700543.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('-----------------TCCCAGGTAACAAACCAACCAACTTTCGATCTCTTGT...---', SingleLetterAlphabet()), id='OL700544.1', name='<unknown name>', description='OL700544.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('----------------------------------------------TCTCTTGT...AAA', SingleLetterAlphabet()), id='OL700541.1', name='<unknown name>', description='OL700541.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('----------------------------------------------TCTCTTGT...AAA', SingleLetterAlphabet()), id='OL700533.1', name='<unknown name>', description='OL700533.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('-----------------TCCCAGGTAACAAACCAACCAACTTTCGATCTCTTGT...---', SingleLetterAlphabet()), id='OL700545.1', name='<unknown name>', description='OL700545.1', dbxrefs=[])]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alignment = list(alignment)\n", "alignment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Eliminare i gap iniziali.\n", "\n", "Trovare il più lungo prefisso di soli simboli `-` delle righe dell'allineamento. Supponendo che tale prefisso sia lungo `g`, eliminare da ogni riga dell'allinemento il prefisso di lunghezza `g`.\n", "\n", "Ad esempio il seguente allineamento composto da tre righe:\n", "\n", " GTATGTGTCATGTTTTTGCTA\n", " --ATGTGTCATG-TTT-----\n", " ----GTGTCATGTTTTTG---\n", " \n", "presenta un più lungo prefisso di soli simboli `-` di lunghezza `g=4` (terza riga). Eliminando da tutte le righe un prefisso di lunghezza 4 si ottiene:\n", "\n", " GTGTCATGTTTTTGCTA\n", " GTGTCATG-TTT-----\n", " GTGTCATGTTTTTG---" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "import re\n", "\n", "gap_list = [re.findall('^-+', str(row.seq)) for row in alignment]\n", "gap_size_list = [len(gap[0]) for gap in gap_list if gap]\n", "gap_size_list[:0] = [0]\n", "leading_gaps = max(gap_size_list)\n", "alignment = [row[leading_gaps:] for row in alignment]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AAA', SingleLetterAlphabet()), id='NC_045512.2', name='<unknown name>', description='NC_045512.2', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AAA', SingleLetterAlphabet()), id='OL700521.1', name='<unknown name>', description='OL700521.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AAA', SingleLetterAlphabet()), id='OL700526.1', name='<unknown name>', description='OL700526.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AAA', SingleLetterAlphabet()), id='OL700531.1', name='<unknown name>', description='OL700531.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...---', SingleLetterAlphabet()), id='OL700532.1', name='<unknown name>', description='OL700532.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...---', SingleLetterAlphabet()), id='OL700537.1', name='<unknown name>', description='OL700537.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AAA', SingleLetterAlphabet()), id='OL700524.1', name='<unknown name>', description='OL700524.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AAA', SingleLetterAlphabet()), id='OL700530.1', name='<unknown name>', description='OL700530.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AAA', SingleLetterAlphabet()), id='OL700538.1', name='<unknown name>', description='OL700538.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...---', SingleLetterAlphabet()), id='OL700543.1', name='<unknown name>', description='OL700543.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...---', SingleLetterAlphabet()), id='OL700544.1', name='<unknown name>', description='OL700544.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AAA', SingleLetterAlphabet()), id='OL700541.1', name='<unknown name>', description='OL700541.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AAA', SingleLetterAlphabet()), id='OL700533.1', name='<unknown name>', description='OL700533.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...---', SingleLetterAlphabet()), id='OL700545.1', name='<unknown name>', description='OL700545.1', dbxrefs=[])]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alignment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Eliminare i gap finali.\n", "\n", "Trovare il più lungo suffisso di soli simboli `-` delle righe dell'allineamento. Supponendo che tale suffisso sia lungo `g`, eliminare da ogni riga il suffisso di lunghezza `g`.\n", "\n", "Ad esempio il seguente allineamento composto da tre righe:\n", "\n", " GTGTCATGTTTTTGCTA\n", " GTGTCATG-TTT-----\n", " GTGTCATGTTTTTG---\n", " \n", "presenta un più lungo suffisso di soli simboli `-` di lunghezza `g=5` (seconda riga). Eliminando da tutte le righe un suffisso di lunghezza 5 si ottiene:\n", "\n", " GTGTCATGTTTT\n", " GTGTCATG-TTT\n", " GTGTCATGTTTT" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "gap_list = [re.findall('-+$', str(row.seq)) for row in alignment]\n", "gap_size_list = [len(gap[0]) for gap in gap_list if gap]\n", "gap_size_list[:0] = [0]\n", "trailing_gaps = max(gap_size_list)\n", "alignment = [row[:len(row)-trailing_gaps] for row in alignment]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='NC_045512.2', name='<unknown name>', description='NC_045512.2', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700521.1', name='<unknown name>', description='OL700521.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700526.1', name='<unknown name>', description='OL700526.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700531.1', name='<unknown name>', description='OL700531.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700532.1', name='<unknown name>', description='OL700532.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700537.1', name='<unknown name>', description='OL700537.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700524.1', name='<unknown name>', description='OL700524.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700530.1', name='<unknown name>', description='OL700530.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700538.1', name='<unknown name>', description='OL700538.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700543.1', name='<unknown name>', description='OL700543.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700544.1', name='<unknown name>', description='OL700544.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700541.1', name='<unknown name>', description='OL700541.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700533.1', name='<unknown name>', description='OL700533.1', dbxrefs=[]),\n", " SeqRecord(seq=Seq('TCTCTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...AGA', SingleLetterAlphabet()), id='OL700545.1', name='<unknown name>', description='OL700545.1', dbxrefs=[])]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alignment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creare la lista degli identificatori dei genomi" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "index_list = [row.id for row in alignment]" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['NC_045512.2',\n", " 'OL700521.1',\n", " 'OL700526.1',\n", " 'OL700531.1',\n", " 'OL700532.1',\n", " 'OL700537.1',\n", " 'OL700524.1',\n", " 'OL700530.1',\n", " 'OL700538.1',\n", " 'OL700543.1',\n", " 'OL700544.1',\n", " 'OL700541.1',\n", " 'OL700533.1',\n", " 'OL700545.1']" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creare il dizionario contenente i dati per costruire il data frame\n", "\n", "- `key`: posizione 1-based della variazione (posizione della colonna nell'allineamento in input)\n", "\n", "- `value`: lista dei simboli allineati coinvolti nella variazione (il primo simbolo deve essere quello del reference, mentre se un genoma non presenta una differenza con il reference si deve inserire la stringa vuota)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "df_data = {}\n", "\n", "reference = alignment.pop(0)\n", "\n", "for (i,c) in enumerate(reference):\n", " variant_list = []\n", " is_variant = False\n", " for row in alignment:\n", " variant = ''\n", " if row[i] != c and row[i] in {'A', 'C', 'G', 'T'}:\n", " is_variant = True\n", " variant = row[i]\n", " \n", " variant_list.append(variant)\n", " \n", " if is_variant:\n", " variant_list[:0] = [c]\n", " df_data[str(i+leading_gaps+1)] = variant_list" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'186': ['C', '', '', '', '', 'T', '', '', '', '', '', '', '', ''],\n", " '210': ['G', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T'],\n", " '241': ['C', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T'],\n", " '1048': ['G', '', '', 'T', '', '', '', '', '', '', '', '', '', ''],\n", " '1244': ['G', '', '', '', '', '', '', '', '', '', 'A', '', '', ''],\n", " '1371': ['A', '', '', '', '', '', '', '', '', '', '', 'G', '', ''],\n", " '1616': ['C', '', '', '', '', '', '', '', '', '', '', '', 'A', ''],\n", " '1684': ['C', '', '', '', '', '', '', '', '', '', '', '', 'T', 'T'],\n", " '1843': ['G', '', '', '', '', '', '', '', '', '', '', 'T', '', ''],\n", " '1889': ['C', '', '', '', '', '', '', '', '', 'T', '', '', '', ''],\n", " '2462': ['C', '', '', '', 'T', '', '', '', '', '', '', '', '', ''],\n", " '2929': ['A', '', '', '', '', '', '', '', '', '', '', 'G', '', ''],\n", " '3037': ['C',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T'],\n", " '3096': ['C', '', '', '', '', '', '', '', 'T', '', '', '', '', ''],\n", " '3259': ['G', '', '', '', '', 'T', '', '', '', '', '', '', '', ''],\n", " '3792': ['C', '', '', '', '', '', '', '', '', '', '', 'T', '', ''],\n", " '3923': ['C', '', '', '', '', '', '', '', '', 'T', '', '', '', ''],\n", " '3948': ['A', '', '', '', '', '', '', 'G', '', '', '', '', '', ''],\n", " '4181': ['G', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', '', ''],\n", " '4201': ['G', '', '', '', '', '', '', '', '', '', '', '', '', 'T'],\n", " '4414': ['A', '', '', '', 'G', '', '', '', '', '', '', '', '', ''],\n", " '5164': ['G', '', '', '', '', '', '', '', '', '', '', '', 'T', 'T'],\n", " '5184': ['C', '', '', '', '', '', '', '', '', '', '', '', 'T', 'T'],\n", " '5192': ['C', '', '', '', '', 'T', '', '', '', '', '', '', '', ''],\n", " '5213': ['T', '', '', '', 'C', '', '', '', '', '', '', '', '', ''],\n", " '5284': ['C', '', '', '', '', '', '', '', '', 'T', '', '', '', ''],\n", " '5584': ['A', '', '', '', '', '', '', '', '', '', '', '', 'G', 'G'],\n", " '6013': ['A', '', '', '', '', '', '', 'G', '', '', '', '', '', ''],\n", " '6040': ['C', '', '', '', '', '', '', '', '', '', '', 'T', '', ''],\n", " '6402': ['C', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', '', ''],\n", " '6408': ['C', 'T', '', '', '', '', '', '', '', '', '', '', '', ''],\n", " '6616': ['A', '', '', '', '', '', '', '', '', '', '', '', 'G', ''],\n", " '6865': ['G', '', '', '', '', '', '', '', 'T', '', '', '', '', ''],\n", " '7124': ['C', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', '', ''],\n", " '7393': ['G', '', '', '', '', '', '', '', '', '', '', '', 'T', ''],\n", " '7926': ['C', '', '', '', '', '', '', '', '', '', '', 'T', '', ''],\n", " '8131': ['G', '', '', '', '', '', '', '', '', '', '', '', 'T', ''],\n", " '8174': ['G', '', '', '', 'A', '', '', '', '', '', '', '', '', ''],\n", " '8349': ['G', '', '', '', '', 'A', '', '', '', '', '', '', '', ''],\n", " '8642': ['G', '', '', '', '', '', '', '', '', '', '', '', '', 'A'],\n", " '8829': ['C', '', '', '', '', '', '', '', '', 'T', 'T', '', '', ''],\n", " '8956': ['C', '', '', '', '', 'T', '', '', '', '', '', '', '', ''],\n", " '8964': ['C', '', '', '', '', '', '', '', '', '', 'T', '', '', ''],\n", " '8986': ['C', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', '', ''],\n", " '9053': ['G', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', '', ''],\n", " '9072': ['C', '', '', '', '', 'T', '', '', '', '', '', '', '', ''],\n", " '9194': ['A', '', '', '', '', '', '', '', '', '', '', '', '', 'G'],\n", " '9868': ['T', '', 'C', '', '', '', '', '', '', '', '', '', '', ''],\n", " '9891': ['C', '', '', '', '', '', '', '', '', '', '', '', 'T', 'T'],\n", " '10029': ['C', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', '', ''],\n", " '10171': ['A', '', '', '', '', '', 'G', '', '', '', '', '', '', ''],\n", " '11083': ['G', '', '', '', '', '', '', '', '', '', '', '', 'T', ''],\n", " '11201': ['A', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', '', ''],\n", " '11332': ['A', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', 'G', '', ''],\n", " '11418': ['T', '', '', '', '', '', '', '', '', '', '', '', 'C', 'C'],\n", " '11456': ['A', '', '', '', 'G', '', 'G', '', '', '', '', '', '', ''],\n", " '11514': ['C', '', '', '', '', '', '', '', '', '', '', '', 'T', 'T'],\n", " '11562': ['G', '', '', '', '', '', '', 'T', '', '', '', '', '', ''],\n", " '11669': ['C', '', '', '', '', '', '', '', 'T', '', '', '', '', ''],\n", " '11956': ['C', '', '', '', '', '', '', '', '', '', '', '', '', 'T'],\n", " '12049': ['C', '', '', '', '', '', '', '', 'T', '', '', '', 'T', 'T'],\n", " '12115': ['C', '', '', '', '', '', '', 'T', '', '', '', '', '', ''],\n", " '12793': ['G', '', '', '', '', '', '', '', '', 'T', 'T', '', '', ''],\n", " '13019': ['C', '', '', '', '', '', '', '', '', '', '', '', 'T', 'T'],\n", " '14014': ['T', '', '', '', '', '', '', '', '', '', '', 'G', '', ''],\n", " '14408': ['C',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T'],\n", " '14599': ['C', '', '', '', '', '', '', '', '', 'T', '', '', '', ''],\n", " '14925': ['C', '', '', '', '', '', '', '', 'T', '', '', '', '', ''],\n", " '15120': ['C', '', '', '', '', '', '', '', '', '', '', '', '', 'T'],\n", " '15240': ['C', '', '', '', '', '', '', 'T', 'T', '', '', '', '', ''],\n", " '15451': ['G',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A'],\n", " '15982': ['G', '', '', 'A', '', '', '', '', '', '', '', '', '', 'T'],\n", " '16466': ['C',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T'],\n", " '17236': ['A', 'G', 'G', '', '', 'G', '', '', '', '', '', '', '', ''],\n", " '17763': ['T', '', '', '', '', '', '', '', '', '', 'C', '', '', ''],\n", " '18360': ['A', '', 'G', '', '', '', '', '', '', '', '', '', '', ''],\n", " '18468': ['A', '', '', 'G', '', '', '', '', '', '', '', '', '', ''],\n", " '18657': ['C', '', '', '', '', 'T', '', '', '', '', '', '', '', ''],\n", " '19006': ['G', '', '', '', '', '', '', '', '', 'T', 'T', '', '', ''],\n", " '19017': ['C', '', '', '', 'T', '', '', '', '', '', '', '', '', ''],\n", " '19170': ['C', '', '', '', '', '', '', 'T', '', '', '', '', '', ''],\n", " '19220': ['C', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', '', ''],\n", " '19524': ['C', '', '', '', '', '', 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'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A'],\n", " '22000': ['C', '', '', 'T', '', '', '', '', '', '', '', '', '', ''],\n", " '22227': ['C', '', '', '', '', '', '', '', '', '', '', '', 'T', 'T'],\n", " '22335': ['G', '', '', '', '', '', 'T', '', '', '', '', '', '', ''],\n", " '22427': ['G', '', '', '', '', '', '', '', '', '', '', '', 'A', 'A'],\n", " '22917': ['T',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G'],\n", " '22995': ['C',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A'],\n", " '23284': ['T', '', '', '', 'C', '', 'C', '', '', '', '', '', '', ''],\n", " '23403': ['A',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G'],\n", " '23587': ['G', '', '', '', '', '', '', '', '', 'C', 'C', '', '', ''],\n", " '23604': ['C',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G'],\n", " '24110': ['A', '', '', '', '', '', '', '', 'C', '', '', '', '', ''],\n", " '24208': ['C', 'T', 'T', '', '', 'T', '', '', '', '', '', '', '', ''],\n", " '24280': ['T', '', '', '', '', 'C', '', '', '', '', '', '', '', ''],\n", " '24410': ['G',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A',\n", " 'A'],\n", " '24989': ['C', '', 'T', '', '', '', '', '', '', '', '', '', '', ''],\n", " '25088': ['G', '', '', '', '', '', '', '', '', 'T', '', '', '', ''],\n", " '25339': ['C', '', '', '', 'T', '', 'T', '', '', '', '', '', '', ''],\n", " '25439': ['A', '', '', '', '', '', '', '', 'C', '', '', '', '', ''],\n", " '25469': ['C',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T'],\n", " '25553': ['C', '', '', '', '', '', 'T', '', '', '', '', '', '', ''],\n", " '25658': ['C', '', '', '', '', '', '', 'T', '', '', '', '', '', ''],\n", " '25702': ['C', '', 'T', '', '', '', '', '', '', '', '', '', '', ''],\n", " '25913': ['G', '', '', '', '', '', '', '', 'A', '', '', '', '', ''],\n", " '26107': ['G', '', '', '', '', '', '', 'C', '', '', '', '', '', ''],\n", " '26144': ['G', '', '', '', '', '', 'A', '', '', '', '', '', '', ''],\n", " '26146': ['T', '', '', '', '', '', '', '', 'C', '', '', '', '', ''],\n", " '26514': ['A', '', '', 'G', '', '', '', '', '', '', '', '', '', ''],\n", " '26753': ['C', '', '', '', '', '', '', '', '', '', '', '', 'T', ''],\n", " '26767': ['T',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C'],\n", " '26951': ['G', '', '', '', '', '', '', '', '', '', '', 'C', '', ''],\n", " '27005': ['C', '', '', '', '', 'T', '', '', '', '', '', '', '', ''],\n", " '27507': ['A', '', '', '', '', '', '', 'C', '', '', '', '', '', ''],\n", " '27527': ['C', '', '', 'T', '', '', '', '', '', '', '', '', '', ''],\n", " '27539': ['T', '', '', '', '', '', '', '', 'C', '', '', '', '', ''],\n", " '27603': ['C', '', '', '', '', '', '', '', '', 'T', '', '', '', ''],\n", " '27625': ['C', '', 'T', '', '', '', '', '', '', '', '', '', '', ''],\n", " '27638': ['T',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C',\n", " 'C'],\n", " '27752': ['C',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T'],\n", " '27874': ['C', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', '', ''],\n", " '27916': ['G', '', '', '', '', '', 'T', '', '', '', '', '', '', ''],\n", " '27999': ['C', '', '', '', '', 'T', '', '', '', '', '', '', '', ''],\n", " '28086': ['G', '', '', 'T', '', '', '', '', '', '', '', '', '', ''],\n", " '28153': ['C', '', '', '', '', '', '', '', '', '', '', '', 'T', ''],\n", " '28236': ['C', '', '', '', '', '', '', '', '', '', '', 'T', '', ''],\n", " '28326': ['G', '', '', '', '', '', '', '', '', '', '', '', 'T', 'T'],\n", " '28461': ['A',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G',\n", " 'G'],\n", " '28657': ['C', '', '', '', '', '', 'T', '', '', '', '', '', '', ''],\n", " '28748': ['C', '', '', '', '', '', '', '', 'T', '', '', '', '', ''],\n", " '28881': ['G',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T'],\n", " '28895': ['G', '', 'T', '', '', '', '', '', '', '', '', '', '', ''],\n", " '28916': ['G', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', 'T', '', ''],\n", " '29050': ['G', '', '', '', 'A', '', 'A', '', '', '', '', '', '', ''],\n", " '29095': ['C', '', '', '', '', '', '', '', 'T', '', '', '', '', ''],\n", " '29119': ['C', '', '', '', '', '', '', '', '', 'T', '', '', '', ''],\n", " '29402': ['G',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T'],\n", " '29409': ['C', '', '', '', '', '', '', '', '', '', '', '', '', 'T'],\n", " '29509': ['C', '', '', '', 'T', '', 'T', '', '', '', '', '', '', ''],\n", " '29543': ['G', '', '', '', 'T', '', '', '', '', '', '', '', '', ''],\n", " '29648': ['G', '', '', '', '', '', 'T', '', '', '', '', '', '', ''],\n", " '29700': ['A', '', '', '', '', '', '', '', '', '', '', 'G', '', ''],\n", " '29742': ['G',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T',\n", " 'T'],\n", " '29781': ['G', '', '', '', '', '', '', '', '', 'T', '', '', '', '']}" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Creare il data frame\n", "\n", " df = pd.DataFrame(df_data, index = index_list)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "df = pd.DataFrame(df_data, index = index_list)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>186</th>\n", " <th>210</th>\n", " <th>241</th>\n", " <th>1048</th>\n", " <th>1244</th>\n", " <th>1371</th>\n", " <th>1616</th>\n", " <th>1684</th>\n", " <th>1843</th>\n", " <th>1889</th>\n", " <th>...</th>\n", " <th>29095</th>\n", " <th>29119</th>\n", " <th>29402</th>\n", " <th>29409</th>\n", " <th>29509</th>\n", " <th>29543</th>\n", " <th>29648</th>\n", " <th>29700</th>\n", " <th>29742</th>\n", " <th>29781</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>NC_045512.2</th>\n", " <td>C</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>G</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>...</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>G</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>G</td>\n", " </tr>\n", " <tr>\n", " <th>OL700521.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>...</td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>OL700526.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>...</td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>OL700531.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>...</td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>OL700532.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " 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<td></td>\n", " <td>T</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>OL700530.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>...</td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>OL700538.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>...</td>\n", " <td>T</td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>OL700543.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td>...</td>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700544.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td>A</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>...</td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>OL700541.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td>G</td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " <td>...</td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>OL700533.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td>...</td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>OL700545.1</th>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td>...</td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td></td>\n", " <td>T</td>\n", " <td></td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>14 rows × 156 columns</p>\n", "</div>" ], "text/plain": [ " 186 210 241 1048 1244 1371 1616 1684 1843 1889 ... 29095 29119 \\\n", "NC_045512.2 C G C G G A C C G C ... C C \n", "OL700521.1 T T ... \n", "OL700526.1 T T ... \n", "OL700531.1 T T T ... \n", "OL700532.1 T T ... \n", "OL700537.1 T T T ... \n", "OL700524.1 T T ... \n", "OL700530.1 T T ... \n", "OL700538.1 T T ... T \n", "OL700543.1 T T T ... T \n", "OL700544.1 T T A ... \n", "OL700541.1 T T G T ... \n", "OL700533.1 T T A T ... \n", "OL700545.1 T T T ... \n", "\n", " 29402 29409 29509 29543 29648 29700 29742 29781 \n", "NC_045512.2 G C C G G A G G \n", "OL700521.1 T T \n", "OL700526.1 T T \n", "OL700531.1 T T \n", "OL700532.1 T T T T \n", "OL700537.1 T T \n", "OL700524.1 T T T T \n", "OL700530.1 T T \n", "OL700538.1 T T \n", "OL700543.1 T T T \n", "OL700544.1 T T \n", "OL700541.1 T G T \n", "OL700533.1 T T \n", "OL700545.1 T T T \n", "\n", "[14 rows x 156 columns]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Estrarre il genoma con più variazioni e quello con meno variazioni" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Determinare la lista del numero di variazioni per genoma (per tutti i genomi tranne quello di riferimento)." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "variants_per_genome = [len(list(filter(lambda x: x!='', list(row)))) for row in df.values]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[36, 40, 41, 45, 45, 45, 42, 47, 45, 41, 42, 40, 41]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variants_per_genome.pop(0)\n", "variants_per_genome" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In alternativa:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "variants_per_genome = [df.shape[1]-list(df.loc[index]).count('') for index in index_list[1:]]" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[36, 40, 41, 45, 45, 45, 42, 47, 45, 41, 42, 40, 41]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variants_per_genome" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estrarre il genoma con più variazioni." ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'OL700538.1'" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index_list[variants_per_genome.index(max(variants_per_genome))+1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estrarre il genoma con meno variazioni." ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'OL700521.1'" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index_list[variants_per_genome.index(min(variants_per_genome))+1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In alternativa, per estrarre il genoma con meno variazioni:" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>difference</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>OL700521.1</th>\n", " <td>120</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " difference\n", "OL700521.1 120" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "null_df = pd.DataFrame((df == '').sum(axis=1), columns=['difference'])\n", "null_df[1:][null_df[1:]['difference'] == null_df[1:]['difference'].max()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In alternativa, per estrarre il genoma con più variazioni:" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>difference</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>OL700538.1</th>\n", " <td>109</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " difference\n", "OL700538.1 109" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "null_df[1:][null_df[1:]['difference'] == null_df[1:]['difference'].min()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Determinare il data frame delle variazioni \"complete\"\n", "\n", "Selezionare dal data frame precedente le sole colonne relative a variazioni \"complete\"." ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "df_complete = df[[col for col in df.columns if all(df[col] != '')]]" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>210</th>\n", " <th>241</th>\n", " <th>3037</th>\n", " <th>14408</th>\n", " <th>15451</th>\n", " <th>16466</th>\n", " <th>21618</th>\n", " <th>21987</th>\n", " <th>22917</th>\n", " <th>22995</th>\n", " <th>...</th>\n", " <th>23604</th>\n", " <th>24410</th>\n", " <th>25469</th>\n", " <th>26767</th>\n", " <th>27638</th>\n", " <th>27752</th>\n", " <th>28461</th>\n", " <th>28881</th>\n", " <th>29402</th>\n", " <th>29742</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>NC_045512.2</th>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>...</td>\n", " <td>C</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>G</td>\n", " <td>G</td>\n", " </tr>\n", " <tr>\n", " <th>OL700521.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700526.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700531.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700532.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700537.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700524.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700530.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700538.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700543.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700544.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700541.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700533.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700545.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>14 rows × 21 columns</p>\n", "</div>" ], "text/plain": [ " 210 241 3037 14408 15451 16466 21618 21987 22917 22995 ... \\\n", "NC_045512.2 G C C C G C C G T C ... \n", "OL700521.1 T T T T A T G A G A ... \n", "OL700526.1 T T T T A T G A G A ... \n", "OL700531.1 T T T T A T G A G A ... \n", "OL700532.1 T T T T A T G A G A ... \n", "OL700537.1 T T T T A T G A G A ... \n", "OL700524.1 T T T T A T G A G A ... \n", "OL700530.1 T T T T A T G A G A ... \n", "OL700538.1 T T T T A T G A G A ... \n", "OL700543.1 T T T T A T G A G A ... \n", "OL700544.1 T T T T A T G A G A ... \n", "OL700541.1 T T T T A T G A G A ... \n", "OL700533.1 T T T T A T G A G A ... \n", "OL700545.1 T T T T A T G A G A ... \n", "\n", " 23604 24410 25469 26767 27638 27752 28461 28881 29402 29742 \n", "NC_045512.2 C G C T T C A G G G \n", "OL700521.1 G A T C C T G T T T \n", "OL700526.1 G A T C C T G T T T \n", "OL700531.1 G A T C C T G T T T \n", "OL700532.1 G A T C C T G T T T \n", "OL700537.1 G A T C C T G T T T \n", "OL700524.1 G A T C C T G T T T \n", "OL700530.1 G A T C C T G T T T \n", "OL700538.1 G A T C C T G T T T \n", "OL700543.1 G A T C C T G T T T \n", "OL700544.1 G A T C C T G T T T \n", "OL700541.1 G A T C C T G T T T \n", "OL700533.1 G A T C C T G T T T \n", "OL700545.1 G A T C C T G T T T \n", "\n", "[14 rows x 21 columns]" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_complete" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Determinare il data frame delle variazioni \"stabili\"\n", "\n", "Selezionare dal data frame precedente le sole colonne relative a variazioni \"stabili\"." ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "df_stable = df_complete[[col for col in df_complete.columns if len(df_complete[col][1:].unique()) == 1]]" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>210</th>\n", " <th>241</th>\n", " <th>3037</th>\n", " <th>14408</th>\n", " <th>15451</th>\n", " <th>16466</th>\n", " <th>21618</th>\n", " <th>21987</th>\n", " <th>22917</th>\n", " <th>22995</th>\n", " <th>...</th>\n", " <th>23604</th>\n", " <th>24410</th>\n", " <th>25469</th>\n", " <th>26767</th>\n", " <th>27638</th>\n", " <th>27752</th>\n", " <th>28461</th>\n", " <th>28881</th>\n", " <th>29402</th>\n", " <th>29742</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>NC_045512.2</th>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>G</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>G</td>\n", " <td>T</td>\n", " 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<td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700524.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700530.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700538.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700543.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700544.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700541.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700533.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " <tr>\n", " <th>OL700545.1</th>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>...</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>T</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>G</td>\n", " <td>T</td>\n", " <td>T</td>\n", " <td>T</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>14 rows × 21 columns</p>\n", "</div>" ], "text/plain": [ " 210 241 3037 14408 15451 16466 21618 21987 22917 22995 ... \\\n", "NC_045512.2 G C C C G C C G T C ... \n", "OL700521.1 T T T T A T G A G A ... \n", "OL700526.1 T T T T A T G A G A ... \n", "OL700531.1 T T T T A T G A G A ... \n", "OL700532.1 T T T T A T G A G A ... \n", "OL700537.1 T T T T A T G A G A ... \n", "OL700524.1 T T T T A T G A G A ... \n", "OL700530.1 T T T T A T G A G A ... \n", "OL700538.1 T T T T A T G A G A ... \n", "OL700543.1 T T T T A T G A G A ... \n", "OL700544.1 T T T T A T G A G A ... \n", "OL700541.1 T T T T A T G A G A ... \n", "OL700533.1 T T T T A T G A G A ... \n", "OL700545.1 T T T T A T G A G A ... \n", "\n", " 23604 24410 25469 26767 27638 27752 28461 28881 29402 29742 \n", "NC_045512.2 C G C T T C A G G G \n", "OL700521.1 G A T C C T G T T T \n", "OL700526.1 G A T C C T G T T T \n", "OL700531.1 G A T C C T G T T T \n", "OL700532.1 G A T C C T G T T T \n", "OL700537.1 G A T C C T G T T T \n", "OL700524.1 G A T C C T G T T T \n", "OL700530.1 G A T C C T G T T T \n", "OL700538.1 G A T C C T G T T T \n", "OL700543.1 G A T C C T G T T T \n", "OL700544.1 G A T C C T G T T T \n", "OL700541.1 G A T C C T G T T T \n", "OL700533.1 G A T C C T G T T T \n", "OL700545.1 G A T C C T G T T T \n", "\n", "[14 rows x 21 columns]" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_stable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Ottenere la lista delle posizioni in cui c'è un gap nel genoma di riferimento." ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "ref_gaps = [col for col in df.columns if df[col][0] == '-']" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ref_gaps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Ottenere la lista delle posizioni in cui c'è un gap in almeno uno dei genomi (diversi dal riferimento)." ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "other_gaps = [col for col in df.columns if any(df[col][1:] == '-')]" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "other_gaps" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }	cc0-1.0
erikdrysdale/erikdrysdale.github.io	_rmd/extra_unequalvar/unequalvar.ipynb	1	260065	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Vectorizing t-test and F-tests for unequal variances\n", "\n", "Almost all modern data science tasks begin with exploratory data analysis ([EDA](https://en.wikipedia.org/wiki/Exploratory_data_analysis)) phase. Visualizing summary statistics and testing for associations forms the basis of hypothesis generation and subsequent exploration and modeling. Applied statisticians need to be careful not to over-interpret the results of EDA since the p-values generated during this phase do not correspond to their formal definition when used in a highly proscribed scenario. Speed is often an asset during EDA. I recently encountered the problem of needing to assess thousands of AUROC statistics (discussed in the past [here](http://www.erikdrysdale.com/auc_CI/) and [here](http://www.erikdrysdale.com/auc_max/)), and found the bootstrapping procedure to be too slow for high-throughput assessment. By relying on the asymptotic normality of the AUROC statistic (which is an instance of the [Mann-Whitney U-test](https://en.wikipedia.org/wiki/Mann–Whitney_U_test)), rapid inference could be performed because an analytic solution was available. However, I needed to develop code that could properly address the bottlenecks of my analysis:\n", "\n", "1. Use only the moments of the data (mean, variance, and sample size)\n", "2. (Possibly) accounting for unequal variances\n", "3. Vectorizing all functions\n", "\n", "In the rest of the post, I'll provide simple functions in `python` that will vectorize the [Student's t-test](https://en.wikipedia.org/wiki/Student%27s_t-test) and the [F-test](https://en.wikipedia.org/wiki/F-test) for the multiple comparisons problem. Each of these functions will rely on only the first two moments of the data distribution (plus the sample size). Using only the sufficient statistics of the data helps to reduce the memory overhead that other functions normally have. Means and variances can be computed quickly using methods that are already part of `pandas` and `numpy` classes. The functions in this post will also be able to account for unequal variances, which to best of my knowledge, is not available in existing `python` packages for the F-test.\n", "\n", "## (1) Student's t-test for equal means\n", "\n", "Suppose there are two normally distributed samples: $x = (x_1, \\dots, x_n) \\sim N(\\mu_x, \\sigma^2_x)$ and $y=(y_1,\\dots,y_m)\\sim N(\\mu_y,\\sigma^2_y)$, and we would like to test the null hypothesis that $H_0: \\mu_x = \\mu_y$. If the variances of the distributions were known in practice than the average difference of the two means would have a normal distribution,\n", "\n", "$$\n", "\\begin{align}\n", "\\frac{\\bar{x} - \\bar{y}}{\\sqrt{\\sigma^2_x/n + \\sigma^2_y/m}} \\sim N(0,1) \\hspace{2mm} \| \\hspace{2mm} H_0 \\text{ is true},\n", "\\end{align}\n", "$$\n", "\n", "So that a test statistic with a known distribution could be easily constructed. However, since the variance of the two distributions needs to be estimated in practice, the statistic seen above would actually be the ratio of a normal to a chi-squared distribution, in other words a [Student's t-distribution](https://en.wikipedia.org/wiki/Student's_t-distribution):\n", "\n", "$$\n", "\\begin{align}\n", "d &= \\frac{\\bar{x} - \\bar{y}}{\\sqrt{\\hat\\sigma^2_x/n + \\hat\\sigma^2_y/m}} \\label{eq:dstat} \\\\\n", "d &\\sim t(\\nu).\n", "\\end{align}\n", "$$\n", "\n", "When the variances are not equivalent, some modifications need to be made to the degrees of freedom parameter ($\\nu$), using now classic derivations from [Welch](https://www.jstor.org/stable/2332510?seq=1) in 1947:\n", "\n", "$$\n", "\\begin{align}\n", "\\nu &= \\begin{cases} \n", "n + m - 2 & \\text{ if } \\sigma^2_x = \\sigma^2_y \\\\\n", "\\frac{(\\hat\\sigma^2_x/n + \\hat\\sigma^2_y/m)^2}{(\\hat\\sigma^2_x/n)^2/(x-1)+(\\hat\\sigma^2_y/m)^2/(m-1)} & \\text{ if } \\sigma^2_x \\neq \\sigma^2_y\n", "\\end{cases}.\n", "\\end{align}\n", "$$\n", "\n", "Because the test statistic $d$ is only a function of the first two moments,\n", "\n", "$$\n", "\\begin{align}\n", "d = f(\\bar x, \\bar y, \\hat\\sigma^2_x, \\hat\\sigma^2_y, n, m),\n", "\\end{align}\n", "$$\n", "\n", "A function can written that takes uses only these sufficient statistics from the data. The code block below will provide the first function `tdist_2dist` to carry out the testing and return the test statistic and associated p-values from a two-sided hypothesis test.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import modules needed to reproduce results\n", "import os\n", "import plotnine\n", "from plotnine import \n", "import pandas as pd\n", "from scipy import stats\n", "import numpy as np\n", "from statsmodels.stats.proportion import proportion_confint as prop_CI\n", "\n", "def tdist_2dist(mu1, mu2, se1, se2, n1, n2, var_eq=False):\n", " var1, var2 = se12, se22\n", " num = mu1 - mu2\n", " if var_eq:\n", " nu = n1 + n2 - 2\n", " sp2 = ((n1-1)var1 + (n2-1)var2) / nu\n", " den = np.sqrt(sp2(1/n1 + 1/n2))\n", " else:\n", " nu = (var1/n1 + var2/n2)2 / ( (var1/n1)2/(n1-1) + (var2/n2)*2/(n2-1) )\n", " den = np.sqrt(var1/n1 + var2/n2)\n", " dist_null = stats.t(df=nu)\n", " tstat = num / den\n", " pvals = 2np.minimum(dist_null.sf(tstat), dist_null.cdf(tstat))\n", " return tstat, pvals\n", "\n", "# Useful short wrappers for making row or columns vectors\n", "def rvec(x):\n", " return np.atleast_2d(x)\n", "\n", "def cvec(x):\n", " return rvec(x).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a rule, I always conduct statistical simulations to make sure the functions I have written actually perform the way I expect them to when the null is known. If you can't get your method to work on a data generating procedure of your choosing, it should not leave the statistical laboratory! In the simulations below, $\\mu_y = 0$, and $\\mu_x$ will vary from zero to 0.2. At the same time, both variance homoskedasticity ($\\sigma_y = \\sigma_x$) and heteroskedasticity ($\\sigma_y \\neq \\sigma_x$) will be assessed. To further ensure the approach works, the respective sample sizes, $n$ and $m$, for each of the `nsim`=100K experiments will be a random integer between 25 and 75. In order to avoid an inner loop and rely of pure `numpy` vectorization, a data matrix of dimension 75 x 100000 will be generated. To account for the different sample sizes, if $n$ or $m$ is less than 75, the corresponding difference in rows will be set as a missing value `np.NaN`. The `np.nanmean` and `np.nanstd` functions will be used to handle missing values.\n", "\n", "Note that in all of the subsequent simulations, the type-I error rate target will be fixed to 5% ($\\alpha=0.05$), and 100K simulations will be run." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 800x350 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8786008044325)>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Parameters of simulations\n", "nsim = 100000\n", "alpha = 0.05\n", "nlow, nhigh = 25, 75\n", "n1, n2 = np.random.randint(nlow, nhigh+1, nsim), np.random.randint(nlow, nhigh+1, nsim)\n", "se1, se2 = np.exp(np.random.randn(nsim)), np.exp(np.random.randn(nsim))\n", "mu_seq = np.arange(0,0.21,0.01)\n", "tt_seq, method_seq = np.repeat(['eq','neq'],2), np.tile(['neq','eq'],2)\n", "holder = []\n", "np.random.seed(1234)\n", "for mu in mu_seq:\n", " # Generate random data\n", " x1 = mu + se1np.random.randn(nhigh, nsim)\n", " x2a = se1 np.random.randn(nhigh, nsim)\n", " x2b = se2 * np.random.randn(nhigh, nsim)\n", " idx = np.tile(np.arange(nhigh),[nsim,1]).T\n", " # Find which rows to set to missing\n", " idx1, idx2 = idx < rvec(n1), idx < rvec(n2)\n", " x1, x2a, x2b = np.where(idx1, x1, np.nan), np.where(idx2, x2a, np.nan), np.where(idx2, x2b, np.nan)\n", " mu_hat1, mu_hat2a, mu_hat2b = np.nanmean(x1, 0), np.nanmean(x2a, 0), np.nanmean(x2b, 0)\n", " se_hat1, se_hat2a, se_hat2b = np.nanstd(x1, 0, ddof=1), np.nanstd(x2a, 0, ddof=1), np.nanstd(x2b, 0, ddof=1)\n", " # Calculate statistics and p-values\n", " tstat_neq_a, pval_neq_a = tdist_2dist(mu_hat1, mu_hat2a, se_hat1, se_hat2a, n1, n2, False)\n", " tstat_eq_a, pval_eq_a = tdist_2dist(mu_hat1, mu_hat2a, se_hat1, se_hat2a, n1, n2, True)\n", " tstat_neq_b, pval_neq_b = tdist_2dist(mu_hat1, mu_hat2b, se_hat1, se_hat2b, n1, n2, False)\n", " tstat_eq_b, pval_eq_b = tdist_2dist(mu_hat1, mu_hat2b, se_hat1, se_hat2b, n1, n2, True)\n", " # Find hypothesis rejection probability\n", " power_neq_a, power_eq_a = np.mean(pval_neq_a < alpha), np.mean(pval_eq_a < alpha)\n", " power_neq_b, power_eq_b = np.mean(pval_neq_b < alpha), np.mean(pval_eq_b < alpha)\n", " power_seq = np.array([power_neq_a, power_eq_a, power_neq_b, power_eq_b])\n", " holder.append(pd.DataFrame({'mu':mu,'tt':tt_seq,'method':method_seq, 'power':power_seq}))\n", "# Power comparison\n", "di_method = {'eq':'Equal','neq':'Not Equal'}\n", "res_power = pd.concat(holder).assign(nsim=nsim)\n", "res_power[['tt','method']] = res_power[['tt','method']].apply(lambda x: x.map(di_method))\n", "res_power = res_power.rename(columns={'tt':'Variance'}).assign(nreject=lambda x: (x.powerx.nsim).astype(int))\n", "res_power = pd.concat([res_power.drop(columns=['nsim','nreject']),\n", " pd.concat(prop_CI(count=res_power.nreject,nobs=nsim,method='beta'),1)],1)\n", "res_power.rename(columns={0:'lb',1:'ub'}, inplace=True)\n", "\n", "plotnine.options.figure_size = (8, 3.5)\n", "gg_power_ttest = (ggplot(res_power,aes(x='mu',y='power',color='method')) +\n", " theme_bw() + geom_line() +\n", " geom_hline(yintercept=0.05,linetype='--') +\n", " scale_color_discrete(name='Variance assumption') +\n", " geom_linerange(aes(ymin='lb',ymax='ub')) +\n", " ggtitle('Vertical lines show 95% CI') +\n", " labs(y='Prob. of rejecting null',x='Mean difference') +\n", " facet_wrap('~Variance',labeller=label_both) +\n", " theme(legend_position=(0.5,-0.1),legend_direction='horizontal'))\n", "gg_power_ttest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 1 above shows that the `tdist_2dist` function is working as expected. When the variances of $x$ and $y$ are equivalent, there is no difference in performance between approaches. When the mean difference is zero, the probability of rejecting the null is exactly equivalent to the level of the test (5%). However, when the variances differ, using the degrees of freedom calculation assuming they are equal leads to an inflated type-I error rate. Whereas using the adjustment from [Welch's t-test](https://en.wikipedia.org/wiki/Welch%27s_t-test) gets to the right nominal level." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## (2) Checking power calculations\n", "\n", "After checking that function's test-statistic has the right nominal coverage on simulated data, I find is useful to check whether the power of the test can be predicted for different values of the alternative hypothesis. For some test statistics, this is not possible to do analytically, since the distribution of the test statistic under the alternative may not be known. However, for the student-t distribution, a difference in true means amounts to a [noncentral t-distribution](https://en.wikipedia.org/wiki/Noncentral_t-distribution).\n", "\n", "$$\n", "\\begin{align}\n", "T &= \\frac{Z + c}{\\sqrt{V/\\nu}} \\\\ \n", "T &\\sim \\text{nct}(\\nu,c) \\\\\n", "Z&\\sim N(0,1), \\hspace{3mm} V\\sim \\chi^2(\\nu), \\hspace{3mm} \\mu \\neq 0\n", "\\end{align}\n", "$$\n", "\n", "The statistic $d$ from \\eqref{eq:dstat} can be modified to match the noncentral t-distribution:\n", "\n", "$$\n", "\\begin{align}\n", "d + \\underbrace{\\frac{\\mu_x - \\mu_y}{\\sqrt{\\sigma^2_x/n + \\sigma^2_y/m}}}_{c}.\n", "\\end{align}\n", "$$\n", "\n", "The power simulations below will fix $n=25$, $m=75$, and unit variances when $\\sigma_x=\\sigma_y$ and $\\sigma_x=1$ and $\\sigma_y=2$ in the heteroskedastic case." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 800x350 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8786007901169)>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n1, n2 = 25, 75\n", "se1 = 1\n", "se2a, se2b = se1, se1 + 1\n", "var1, var2a, var2b = se12, se2a2, se2b2\n", "# ddof under different assumptions\n", "nu_a = n1 + n2 - 2\n", "nu_b = (var1/n1 + var2b/n2)2 / ( (var1/n1)2/(n1-1) + (var2b/n2)2/(n2-1) )\n", "mu_seq = np.round(np.arange(0, 1.1, 0.1),2)\n", "\n", "# Pre-calculate power\n", "crit_ub_a, crit_lb_a = stats.t(df=nu_a).ppf(1-alpha/2), stats.t(df=nu_a).ppf(alpha/2)\n", "crit_ub_b, crit_lb_b = stats.t(df=nu_b).ppf(1-alpha/2), stats.t(df=nu_b).ppf(alpha/2)\n", "lam_a = np.array([mu/np.sqrt(var1(1/n1 + 1/n2)) for mu in mu_seq])\n", "lam_b = np.array([mu/np.sqrt((var1/n1 + var2b/n2)) for mu in mu_seq])\n", "dist_alt_a, dist_alt_b = stats.nct(df=nu_a, nc=lam_a), stats.nct(df=nu_b, nc=lam_b)\n", "power_a = (1-dist_alt_a.cdf(crit_ub_a)) + dist_alt_a.cdf(crit_lb_a)\n", "power_b = (1-dist_alt_b.cdf(crit_ub_b)) + dist_alt_b.cdf(crit_lb_b)\n", "dat_theory = pd.concat([pd.DataFrame({'mu':mu_seq,'theory':power_a,'method':'eq'}),\n", " pd.DataFrame({'mu':mu_seq,'theory':power_b,'method':'neq'})])\n", "\n", "# Run simulations to confirm\n", "np.random.seed(1234)\n", "holder = []\n", "for mu in mu_seq:\n", " x1 = mu + se1 * np.random.randn(n1, nsim)\n", " x2a = se2a * np.random.randn(n2, nsim)\n", " x2b = se2b * np.random.randn(n2, nsim)\n", " mu_hat1, mu_hat2a, mu_hat2b = x1.mean(0), x2a.mean(0), x2b.mean(0)\n", " se_hat1, se_hat2a, se_hat2b = x1.std(0,ddof=1), x2a.std(0, ddof=1), x2b.std(0, ddof=1)\n", " stat_a, pval_a = tdist_2dist(mu_hat1, mu_hat2a, se_hat1, se_hat2a, n1, n2, var_eq=True)\n", " stat_b, pval_b = tdist_2dist(mu_hat1, mu_hat2b, se_hat1, se_hat2b, n1, n2, var_eq=False)\n", " reject_a, reject_b = np.mean(pval_a < 0.05), np.mean(pval_b < 0.05)\n", " holder.append(pd.DataFrame({'mu': mu,'method':['eq','neq'], 'power': [reject_a, reject_b]}))\n", "res_theory = pd.concat(holder).merge(dat_theory).sort_values(['method','mu']).reset_index(None, True)\n", "res_theory = res_theory.assign(nreject=lambda x: (x.powernsim).astype(int))\n", "res_theory = pd.concat([res_theory.drop(columns='nreject'),\n", " pd.concat(prop_CI(count=res_theory.nreject,nobs=nsim,method='beta'),1)],1)\n", "res_theory.rename(columns={0:'lb',1:'ub','method':'Variance'}, inplace=True)\n", "res_theory = res_theory.assign(Variance=lambda x: x.Variance.map(di_method))\n", "\n", "plotnine.options.figure_size = (8, 3.5)\n", "gg_power_theory = (ggplot(res_theory,aes(x='theory',y='power')) +\n", " theme_bw() + geom_point() +\n", " geom_linerange(aes(ymin='lb',ymax='ub')) +\n", " facet_wrap('~Variance', labeller=label_both) +\n", " theme(legend_position=(0.5, -0.1), legend_direction='horizontal') +\n", " labs(x='Expected power',y='Actual power') +\n", " scale_y_continuous(limits=[0,1]) + scale_x_continuous(limits=[0,1]) +\n", " geom_abline(slope=1,intercept=0,color='blue',linetype='--'))\n", "gg_power_theory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 2 shows that the power calculations line up exactly with the analytical expectations for both equal and unequal variances. Having thoroughly validated the type-I and type-II errors of this function we can now move onto testing whether the means from multiple normal distributions are equal. \n", "\n", "## (3) F-test for equality of means\n", "\n", "Suppose there are $K$ normal data vectors: $x_1=(x_{1,1},\\dots,x_{1,n_1})$ to $x_k=(x_{k,1},\\dots,x_{1,n_k})$, and we want to test the null hypothesis of $\\mu_1 = \\mu_2 = \\dots = \\mu_K$ against an alternative hypothesis that there is at least 1 inequality in the means, where $x_{k,i} \\sim N(\\mu_k,\\sigma^2_k)$. As before, the variances of each vector may or may not be equal. When the variances are equal, the sum of squared differences between the total mean and any one group mean will be chi-square. Similarly, the sum of the sample variances will also have a chi-square distribution. Hence, the F-test for equality of means is the ratio of the variation \"between\" versus \"within\" the groups,\n", "\n", "$$\n", "\\begin{align}\n", "R &= \\frac{\\frac{1}{K-1}\\sum_{k=1}^K n_k (\\bar x_k - \\bar x)^2 }{\\frac{1}{N-K}\\sum_{k=1}^K (n_k - 1)\\hat\\sigma^2_k}, \\\\\n", "R &\\sim F(K-1, N-K) \\hspace{3mm} \\text{ if } \\sigma^2_k = \\sigma^2 \\hspace{3mm} \\forall k \\in \\{1,\\dots,K\\}\n", "\\end{align}\n", "$$\n", "\n", "Where $N = \\sum_k n_k$. To account for heteroskedasticity in the data (i.e. non-equal variances), both the test and degrees of freedom need to be modified using an [approach](https://doi.org/10.2307/2332579) Welch proposed in 1951.\n", "\n", "$$\n", "\\begin{align}\n", "R_W &= \\frac{\\frac{1}{K-1}\\sum_{k=1}^K w_k (\\bar x_k - \\bar x_w)^2 }{1 + \\frac{2}{3}((K-2)\\nu)}, \\\\\n", "w_k &= n_k / \\hat\\sigma^2_k \\\\\n", "\\bar x_w &= \\frac{\\sum_{k=1}^K w_k \\bar x_k}{\\sum_{k=1}^K w_k}\\\\\n", "\\nu &= \\frac{3\\cdot \\sum_{k=1}^K \\Bigg[ \\frac{1}{n_k - 1} \\Big( 1 - \\frac{w_k}{\\sum_{k=1}^K w_k} \\Big)^2 \\Bigg]^2}{K^2-1} \\\\\n", "R_W &\\sim F(K-1, 1/\\nu) \\hspace{3mm} \\text{ if } \\sigma^2_k \\neq \\sigma^2_{-k} \\hspace{3mm} \\text{for at least one }k\n", "\\end{align}\n", "$$\n", "\n", "The `fdist_anova` function below carries out an F-test for the equality of means using only the empirical means, standard deviations, and sample sizes for either variance assumption. In `R` this would be equivalent to using `aov` for equal variances or `oneway.test` for unequal variances. In `python`, it will replicate the `scipy.stats.f_oneway` function (for equal variances). I am unaware of a `python` function that does a Welch-adjustment (if you know please message me and I will provide an update with this information). As before, because the function only relies on the moments of the data, it can be fully vectorized to handle matrices of means, variances, and sample sizes. \n", "\n", "The simulation below assesses how well the two F-test approaches (homoskedasticity vs heteroskedasticity) do when the ground truth variances are either all equal or vary. To vary the signal in the data, I generate the $K$ different means from $(-\\mu,\\dots,0,\\dots,\\mu)$, where $\\mu$ is referred to as \"mean dispersion\" in the subsequent figures." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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iJRb5RXw+5s2dy7xhV6BRVRStluN3bePP336Nvc+pUWkj9PPVg0Tt5+T3qHWkUn7tjOkMtGew0uNFByhAZ2M61+Zn87G7ksd27qeLKZ2LnXbOtFnRRblYks+oxgmrKp97vCxwedgbDDIgM4On2+fQ4YSOzH3pn6w6tR9BQxo9aqr43dln1N++L4RIHSlXYL799tu89dZb9T+vXr2aIUOGcOedd8YxKiGEOLJtP/zAvAHDUDXa+ufKfio4jtk7/8eEKBWYQiS7qnCEr7w1XOq0c1x6GsGqSvoXtCdNp+Nkq5nSYIjF5R6m7C3lzeJyLsy2M9hhw6yT8QxjwRdRWF5RySJXJX5FYViWjfuy25NlqDvVrFg8n1N3buXyW/5AaUUF+fmnxqSAFkLEXsoVmNdeey3XXnttvMMQQohjUhWFwLb/sel//0N7Yh8i+l9OtiI6Pdszs+IYnRCJ5Y1iFw6DnqvzstGqKsX+avQHXXHLTTNwfbscRuZmscxdxUJXBe+VuhmaZeOCrEycaXKlrDW4Q2E+LPew1F2FUavhQqedoY7MBoV9xOejYsH7ZI8YhTY9PY7RCiFiIeUKTCGESGRKMIj/h++pXr+OLXv28G1hZ37semL9c0kHaBSFTLPM7ycEwMZqH596vDzapQC9RoOiqkd8r0Wn47IcBxdl2/miqpqFrgoWuzyclWnlYqedLiZjDCNPXXsCQRa4Kvi00kv7tDRuaOfk7MwM9NpDb7P1fLgArdmM7dwhHPnICSFShRSYQgjRyiI11fi++Rrv+rX8r6SE77v2ZOMpZ+I+YwgnpBs4z25jZXEpxZEwEZ0ejaKg0cCInj3iHboQcRdSVF7ZV8awLBvdzI0vDvVaDefYMxiQaWVjjZ+FLg9/2baHE81GLnE66JthRhvD52FTgaqqbPIFmO+qYIPXx0kWE3/u2I7eVvMRn98MV1Xi+Wghudf/Do1ejxqj6VeEEPEjBaYQQrSCULmLmg3rqF6/jv9VVrHxxD780G8YFWlGTjClc6nDxpk2a/3zSUOzbLy5r4wfq2swazWM6dSB4y2mOGchRPx94KrAryiMzstu1voajYaTrWZOtprZEwiyqNzD87uLyTHouchp51x7BmnyLOBRKarKl1U1zHdVsN1fy1mZVh4/vqBRV4Mr5s/FkJOH9ayzYxCpECIRSIEphBBRoKoqwb17qFm/lur169hWG2RjnzPYOOxKPGlpdDebuCzTyhkHFZUHM+t03FiYj6IoFBcXky+38QnBvtogc8vc/KEgD0sjprI4lgJjGjd1yOXqvCyWlFfydkk5b5eUMzwrk+HZmWTq5bToYLWKwicVXhaWe/CEwgx22LijMJ/cRj7PGnKVUbliKe0m3oVGingh2gzpSYUQoplURSGwdQs169fiXb+OHfo0Np12Nt9fPhaPIY0eZiNX/FxUOg5TVAohjkxVVV7dV8ZJFjP9bNaobjtTr+c3edlcnuNglcfLIpeHD1wezrFncHG2nQ7GtKi2l2yqwhGWuCv5qNyDBg0XZGdyXlYmGfqmFfnuee9hPK4zZhkNW4g2Rc54hBCiCeoG6fmOmvXr8G74ip8ys9h85jl8P+YWPDoDPS0mrrRZOcNmwS5FpRDN9qnHyxZfgGe6dWy1uSfTtFqGZWUyxGFjg9fHwnIPf9y6iz5WMxc77ZxsMcV03tJ4K64Nsajcw8qKKrJ+HrG3ubcQB/fuwbt6FR3ufbBN7UMhhBSYQghxTJGaGvwbv617pvK7b9iVX8jms87lu9MGUanV0dNi4qpMK6fbLNjlFjshWswbjvB6sYuRuVnkxWB6Ea1GQ5HNQpHNwnZ/gIUuD0/8tI+OxjQuyrZH/Qpqotnmr2Wxx8WXVTV0NRm5tTCP0zIsLRoEqXzOO5hP7oWpR88oRiqESAZyJiSEEIcRKndRs34dNRvWUfPjJnZ3OYHNZ57LtwMvoUqj5SSLiZE/F5Xy3JYQ0fVmSTmZej0XO+0xb7uLychthflckxfiQ3cl0/e7eKuknPPsGZzntGNNkb93RVX5utrH/LIKNvkCFGWYebhzB7pHYXCxwPZt1KxfS+HDj0chUiFEskmNXlIIIVpIVVWCe3ZTs34tNevX4d/1E3tO6sOmMwfy7UVX40XDSRYTozKtnG6zYmvis0hCiMbZVONnZUUVD3fugD6Ot1Y60wyMzXdyVU4WK9yVLC73MM9dyWCHjYuy7Y0e6CbRhBSV1ZVeFrg8FAeDnJOZwQ25WXSM4u3A5bPfwnr6maR36hyV7QkhkosUmEKINktVFAL/+5Ga9euoXr+WoKuMfUVn8cPwy/nG7sSrwskWEyOtJs6w28iUZyqFaFVhReWVfaUMdtiiciUtGsw6LRc57QzNtLDeV8vCcg93lO/kDJuFS5yOJs3NGU++SISl7ioWl3sIKirnZWdyQVZ7MvU6wuFw9Nr54Tv8mzbS8fFnorZNIURykbMlIUSbogSD+Dd+R/X6tdR8/RUhn5/9/c7lh5HX802GnWpF5WSrmattdbe/WnVawuEwerliKUSrm++qoDqscG0z57xsTTqNhn6ZVvplWtnsq3tOc9L2PXQzG7kk285ptpY9s9haykNhFrk8LK+oxKLTcanTwWCHDZOubuAeVVWj1paqqpS/9xa2AYNIy28fte0KIZKLFJiA1WpFr9c3qZNVVbV+nWh2zkdr7+B/Y9FequaXyrkdaEfyayhSXY3v2w3UrF+H77tviGg07D9nCBtvuJWvTVZqFJVTrGZG2yyclmHBelAxmQz5AeiP8VxYfn5+QvdzybKfW9Lewf/Gor1ky68kGGJOWQU3t8/BotMedVvxzE+j0dDDbKRHx3z21wZZXF7Ji3tKcBh0XJhlZ6A9A6Ou+XM+Riu3XYFaFrg8fF5ZTYExjQntcjgz01p/2/Gvj1k09mP1V2sJ7tpF/sS7jri95uani8I8qEKI2JACE+jbty8Oh6PJt4g4HA4URUFRlFaK7FCRSCRmbaVyfqmcG0h+AOFyF76v1+P/+isCWzajWqzsPWcIG88ZztdpZnyKwskWE1dnmCmymg6axF09bF+QaPkdbp2jmTBhAkDC93OJvp9bSvI7PFVVeXVvKSeY0jnDYmzU72ki5Jej0zIu18GV2TaWe6qZW+bm3VI3g+1WhjsycDRzQKDm5qaqKj/4AiyqqOLbmgAnm438sSCXk83GuucrIxGOtGdb+rupKgru2W9jHTwMbJlHPYbNyS8jI6NF8QkhYkcKTGDDhg2ccsop5OTkNHodRVEoLy8nOzsbbTPmh2oqVVWJRCLodLqYzCeVyvmlcm6Q2vkFtm2l5uuv8AUC5JwzCGPHTg3iCO7ZVTfy6/p1BHf9hCY3n73nDOb7S0ezXqvHryj0spgZk2mhKMOCtRHfiCfL8SsrKzvq66+++ipXXXVVwvZzybKfm0vyO7rVlV42+2v5+/GFGAzHHjwn0fJz6PWMyEvnspwsVld6WVReyeKKKs62WbnYaaeTMb3RbTUnt4iq8mVVDfNdFewMBOmXaeWJLgUcZzp2u9H63az67BPCFW6yL70C3VEK6+YeO6/X2+zYhBCxJQUmUF1dTTgcblLHqtFo6teJ5QTCsWovlfNL5dwOtJOK+Xn/+zklU/4JOh2oKntXfEy7O+9Bm5ZW9zzl+nWEy0rRH9eFPQOG8G2nrqxXIKAo9LaaGWezUmSzHHSlsmkS/fgd64pPcXFxUvRzib6fo9Gu5NdQdSTC68XlXJnjoJ0xrdHtJGJ+aToNg7MyGeSw8W21jwUuD/dt28MpFhMXO+30tpqPGW9TcgsoCisrqljo8lAViTDUYeOPHdvhbMYIty3Zl2ooRMW893BceAl6W+Yx22nOsYvl1X8hRMtIgSmESHhqJML+6VNY3m8w3/U4Ba2icvq3a+n3jyfRaDQYTjyZPZeO5NsOx7E+GK4rKo0mrs+0UpRhxizP7giRsN4qLseq03Kp8+i3eScTjUZD7wwLvTMs7AzUssjl4eld+2mXlsbFTjsDMjMwaJtfGFeGw3xYXsnH7kr0Gg0XZGcyLCuzUXdltIbKlctQg0Hswy+KS/tCiMQiBaYQIuFFvFV8cM75rD+lCOXnE6gPB15AIC2d8PCL+ao2RFBR6a03cEO2g1MzLJhbMMiGECI2tvj8LKuoYlLnDi0quBJZJ2M6vy/IY3ReNh+5K3mj2MVbJeWcn1VXFDZlTt39tUEWujx84vGSk6bn2jwn59hbVqy2lBIIUDF/Lo5LrkBrSoypZYQQ8SUFphAi4dXqDazrc0aDZYpOx6dnnktvnY7ftrPTV4pKIZJKWFWZtreMgfYMTkyQOS9bk8OgZ3ReNlfkOPikoopF5ZXMK6vgXEcGF2XbaZ+ehisY4q2ScvZUB+iilDE634lNr2OLz898l4d1VTWcYDZyR2E+p2aYE2JaFM+SRWj0emyDh8U7FCFEgpACUwiR0ALbt7Hj9ddgxPhDXgvrDdzdsV1CnGQJIZpmocuDJxxmTH6HeIcSU0atlvOz7ZyXlck6bw0LXR7++L9dnGwxsdUfIKioRIDdHi/rvDXkphnY5q/ldJuFR7sU0M1sjHcK9SLVXjyL5+O8ZhzatMY9PyuESH1SYAohEpIaDuP+YA4VC+YRGHYhOupGSuTnYlIHdDEbpbgUIgmVBkPMLnVzY/scMppwi2gq0Wo0nGGzcobNyv98Af61pwS/8su8kBGgKqLQTqPhH9060i498Qq4ioUfoLM7yOh/brxDEUIkECkwhRAJp3bvbkqnvkzQU8H6O+7nfb2JUywmtvuDVP08kqDToOeOwvw4RyqEaCpVVXltXxndzEbOscvchgDdzEZOtJjYFwyhHrRcr4FTrOaELC7DFW4ql35I3k0T0chAakKIg0iBKYRIGKqi4PloIe7Z7+A7sz/vjLmFfZEIE9vncHZmBgFFYYcvQEV5Oad2aIexmZOYCyHiZ01VDd/X+Ph7144xnWIk0XU2paOtqLtyeYCiQucmzKEZS+7355DWoRDLaWfGOxQhRIKRszMhREIIlZZQ8spkanfvYvPNd/COxUGPNANPd+hAlqGuqzJqtXQ3Gymu0pEWg8nVhRDR5YtE+Pf+Mi53OmifgFfl4mmww8Z31X7WVFWjA8LA8KxMTs0wxzu0QwRLiqn6dAXt77pXviQQQhxCCkwhRFypqkrVJ8twzXqd4Em9mHf3JDYHI4zJc3Jelk1OXoRIIW+VuDFptVyekzpzXkaLVqPhjsI8fqjOYJurnB45Tk6wJl5xCeCe+w6mE3pgOumUeIcihEhACVFgVldX89JLL7F+/XpMJhOjRo3ioosOnax35cqVvPzyy/U/q6pKbW0t9913H2effTbfffcdDzzwAOnpv9xOMnLkSEaNGhWTPIQQTROucFM6fSr+zZvYef1NvJnVjnZaHU92bZ+QzxwJIZpvqy/Ax+5KHjiuvdyBcAQajYYTLSYcXj35CTRa7MFqd/5E9ZovKPjro/IFoBDisBKiwJwyZQqRSITp06ezf/9+Jk2aREFBAb169WrwvkGDBjFo0KD6n7/66iuefvppioqK6pdlZmYyY8aMWIUuhGgm738/p+z1V4l0PI7Ff36ENbVhRmbbuSzHgU5OWoRIKRFVZdq+Us6xZ3BSgl6VE41TPvttLH2KMHbtFu9QhBAJKu4FZiAQYPXq1Tz//POYzWaOP/54hgwZwtKlSw8pMH/t448/ZsCAAQ2uWAohEluk2kvZjFepXr+O0mtu4I0Ox2NGy9+OL6SzSf6WhUhFi8s9lIfC/PW4tjXnZarxb9mM77uvKXzs7/EORQiRwOJeYO7duxeAjh071i/r0qUL8+bNO+p6Xq+XL7/8kscff/yQ5ePGjcNgMHDqqacybtw4MjJkGHQhEkHN1+spnT6ViCOLT+55lKVBhYsyM7g6L0tumRMiRZUFQ7xb4uaG9jnY2uicl6lAVVXK35tFRr8BpBcUxjscIUQCi3uBGQgEMJlMDZZZLBb8fv9R11u5ciX5+fn06NGjfllBQQEvvPACBQUFuN1uXn75ZZ5//nkefPDBBuu6XC5cLhcAZWVlBINBABRFaXTcB97blHVaQlVVFEVBUZSYPPOQyvmlcm6QmPkpfh/lb72Bd/UqKlnCAcoAACAASURBVK8czRsn9CKkwl875dHTYmpSvImYXzS1Vn4Gg6HJ243lvk6V/XwkbTU/VVWZvq+MzqZ0zrVZohZPouTXGhI1N9+3Gwhs20rOhN+3KLZY5yeEiL24F5hGo/GQYrKmpuaQovPXli5dyrBhwxosczgcOBx1I9M5nU5uuukmbrnlFmpraxvcRjt79mymTZtW//PAgQMBKC4ubnL8paWlTV4nmaRyfqmcGyROfpHtWwnOfouIzsCnt97LsjQLRRqVKy1pGL2VFHsrm7XdRMmvtUQ7v/HjxwPSz/1aKucG8c/v22CYb6qD/CnTSElJSdS3H+/8WlMi5aYqCoG3Z6I//SzKIwo0ox/5tUTKTwgRXXEvMDt0qHseY/fu3RQW1t1ysWPHDjp16nTEdbZv386uXbsYPHjwUbet1WpRVRVVVRssHzFiRH1RWVZWxuLFiwHIz89vdNyKolBaWkpubi7aGNzap6oq4XAYvV4fs2+/UzW/VM4NEic/JRikYs7b+D7+EP+Fl/Nm336UhyPc0S6H022WZreXKPm1lubmd6zCcfr06YwfPz5h+7lk2c/N1Rbz80cUPti2m0uddnrnZkW1vUTIr7UkYm7Vaz4nUF5O+3seQJ9pb1F7zc3vwJ1nQojEF/cC02g00r9/f2bOnMntt99OSUkJy5Yt45577jniOkuXLqWoqKj+auUB3377LXl5eeTm5uLxeJg6dSp9+vTBaGw41LfT6cTpdAJgs9lIS6ubDqE5HblWq43ZB8CBtmI5LHgq55fKuUF88wvs2EbJtJeJBAJs/NODvEc6vdLSuK9zDpn66HQ7cvyaJhQK1W833rEcTqrs5yNpi/m9V1KOQavlqtysVoshlY9fouSmhsNUzHsP+/ALSXNE74uCWOUnhIi9uBeYADfffDMvvvgiN9xwA2azmTFjxtC7d28ARo0axUMPPcRJJ50E1J0kffLJJ9x2222HbGf79u08//zzeL1eLBYLp556Ktdff31McxGiLVPDYdzz51KxYB7hc4fw7oDz2F4bYkK7HAbaM2TONCHaiO3+AB+WV3K/zHmZ9Ko+W0mk2ov9wkvjHYoQIkkkRIFptVq57777DvvaO++80+Bng8HAzJkzD/veK664giuuuCLq8Qkhji24dw+lr0wmVO5ix633MCs9g04aLU917UhumiHe4QkhYiSiqkzbW8bZmVZ6yZyXSU0JBnHPm43josvQWZr/aIMQom1JiAJTCJG8VEWhaskiPHPfQzntDOZfdwvrA0FG5zq4KNuOVq5aCtGmfFReSWkwxL3HtYt3KKKFKpd+BEDmsAviHIkQIplIgSmEaLZQWSklr7xM7a5d7P/drbyRmYtdhSeOL6TQmH7sDQghUkp5KMw7peWMzXdij9Lz1iI+Ij4fFQvfJ3vEKLTp0p8LIRpPen8hRJOpqkrVJ8txvfU6mm49WPLHSXwaCHOZw8bInCz0WrlqKURb9J/9Ljoa0xnisMU7FNFCnsXz0ZrN2M4dEu9QhBBJRgpMIUSThD0VlL42Ff/mjVSMmcCM/E4QgYc6d6C75ejz1wohUtf6ah/rvTU82bVQbo1PcuFKD54li8i9/ndo5Eq0EKKJpNcQQjSad83nlM14DW2HDnxx72Ms9IcYajVztTMT68/T/Qgh2p5ARGFGiZuLnXa5PT4FVCyYhyE3D+tZZ8c7FCFEEpICUwhxTJFqL2UzXqP6qy8JjLqOGV164g1FuKdTO/pYzYTD4XiHKISIo/fK3Gg1Gq7KcRz7zSKhhVxlVK5YSrtb70IjU8wIIZpBCkwhxFHVfLOB0temoMnM5Lv7HmN2UOG09DQmtM8hQ69DVdV4hyiEiKMd/loWl1dyd4dc0qUgSXruee9hPK4L5t6nxjsUIUSSkgJTCHFYit+P663Xqfp0JZHLRzDr5DPYXRvilg559M+0opFnrIRo8xRV5ZV9pZxps9LbKs9gJ7vg3j14V6+iw32TpI8XQjSbFJhCiEP4f9xEySuTQatl2z0PM0vR0U2r5e/dOpJtkG5DCFHnY3cl+2pD/LEwP96hiCgon/M25pN7Yep+YrxDEUIkMTlTFELUU4JB3LPfxrNkEZrhFzH7rMF876tlTH4252VlysiQQoh67lCYt0rKuSbPicOgl2exk1xg+1Zq1q+j8OHH4x2KECLJSYEphAAg8NN2Sqe+jBLwsf+PDzBDZyJXUXmyayHt02WEWCFEQzP2u+iQnsawLJnzMhW4Z7+N9fQzSe/UOd6hCCGSnBSYgNVqRa/XN2mwElVV69eJxSAnB9qI1YAqqZxfKud2oJ2m5KeGw1QsmEfFgnnozj6XRUMv5vPqAFdlZXJ5jgOdRnPU7SR6ftFo7+B/Y9Fec/LTH2Ouuvz8/ITu55JlP7ekvYP/jUV7rZnfBm8NX1ZV8/jxBWhIvfwO197B/7Z2W7HOzb9pI/7NP1D4f8/E5G+9OfnpdLpWjEoIEU1SYAJ9+/bF4XA0+fYeh8OBoigoitJKkR0qEonErK1Uzi+Vc4PG5xfavxfXq1MIu124b7mL/2Rkkx4MM6lTPl2M6aiRCI39q0jE/KIp0fNzOI4+PcSECRMAEr6fS/T93FKpkF9AUXhtXxkXOGx00Osa/E6lQn5Hk4qfUaqq4pn7Dtb+56J15sTkVufm5JeRkdGKEQkhokkKTGDDhg2ccsop5OTkNHodRVEoLy8nOzsbbQyGZVdVlUgkgk6ni8nIbqmcXyrnBo3LT1UUKj/+EPd7b6Hv05dV4yfyUXWAC2xWRudlkdaE/ZKI+UVTsuRXVlZ21NdfffVVrrrqqoTt55JlPzdXKuX3QXE5aDT8Jj8b/c/bTqX8DieVP6Oq131JcPdu8ifedcw7IaKhufl5vd5WjEoIEU1SYALV1dWEw+EmfWhoNJr6dWI5lHes2kvl/FI5twPtHC2/UFkpJa9MJrjrJ3wT/sC/ne0J1Ib563HtOdlqblG7iZBfa7abyPkd66pDcXFxUvRzib6fo9FuMue3K1DLonIPd3dsh+kwtywme36NaTeVPqNURcE99x1sQ4ZhyHYm9LGL5dVxIUTLSIEpRBuhqireVSsomzUDw/Hd+Orex5hXHaC/ycj17ZxY5PkWIcRRKKrKtL2lFNksFNks8Q5HRIF39SrCbje2Cy6NdyhCiBQiBaYQbUDYU0Hp9Kn4N20kfM31TO94AiX+ILcX5nNmpjXe4QkhksDyiir21Aa5q2O7eIciokANhXDPew/7BRejk+cbhRBRJAWmEClEqQ1Q/c0GwsXFBPsWYSzsiPfLLyj7z6vo89vx4/2P8ZYvzMl6HX/q2A67QboAIcSxeUJh3iwu5+q8bLKk30gJlSuXogaD2IdfROyGSRJCtAXyKSFEighXVbL3/x4i5CoDjYY9c98hvXMXanf+hHbEaN448VS2+APc0M7JYIctps8tCSGS24xiF/npBoZnZcY7FBEFSiBAxQdzcVxyBVqTCSUGI8cKIdoOKTCFSBGumf+mxuPhf126U5uWTsH+PeRu38b+O+7j30YbBarKU107kpdmiHeoQogk8o3XxxeVdXNeauWLqZTgWbIIjcGAbfCweIcihEhBUmAKkSJc+/YxecwtVGQ60KoKEa2OKz+ay8a9+7n8zE5c4rTLyaEQokmCisKr+0q5IDuTziZjvMMRURCp9uJZPB/nNePQpqWhqmq8Q4qa3/3ud43K55VXXolBNEK0XVJgCpEiFpw1BE+mHUWnQ6FuRNi551/JfSYNvXMccY5OCJGM5pRVEFZhVG52vEMRUVKx8AN0dgcZ/c+NdyhR94c//AGAQCDAv/71L84//3xOPPHEOEclRNsjBaYQKcD7xWfsynQQ0f3qT1qjgfz28QlKCJHUdgdqme+q4M7CfEw6bbzDEVEQrnBTufRD8m66FU0KTk3Vt29famtreeSRR+jatSsff/wx/fv3p1OnTvEOTYg2RT4xhEhiSm0tpa9NoeSVyVjMZvjVrUGqRkOmXr5HEkI0jaKqvLKvjL5WC6fbZCqjVOF+fzZpBYVYTjsj3qG0Cr/fz0MPPUR+fj4PPvggEydO5LHHHqOkpCTeoQnRpiTEmWd1dTUvvfQS69evx2QyMWrUKC666KLDvveyyy4jPT29fgTMnj178vDDD9e/vmDBAt577z38fj9FRUXceuutmM3mWKQhREzV7t1NycsvEAkG2XLPI+wL1S3XACqgA4oyLHQypsUxSiFEMlpZ4eWnQC3Pdu0Y71BElASL91O1agXt774vZUcRf+SRR+jQoQO33XYbAKeddhrXXXcdDz/8MJMnT45zdEK0HQlRYE6ZMoVIJML06dPZv38/kyZNoqCggF69eh32/c899xwFBQWHLN+wYQNvvfUWjz76KPn5+Tz33HNMmTKFu+66q7VTECJmVFXF++lKyt6YjnrqGcwbfjnf+YNMaJ9DYbqBheUe3D4/p2bZuTTHkbInEkKI1lEZDjOzxMWo3GycMup0ynDPfRdT9xMxnXRKvENpNYWFhUycOLHBsoEDB+L1euMUkRBtU9xvkQ0EAqxevZqxY8diNps5/vjjGTJkCEuXLm3ytpYvX87QoUPp0qULZrOZMWPG8Nlnn1FbW9sKkQsRe4rfT8nUlyib8Rrl193Is4MuxqXAE10LGZJlo5vFxO0FedxiM3KZ045OikshRBO9UVxOjsHABdky52WqqN35E9VffkH2iNEp/aXjr4vLAy655JIYRyJE2xb3AnPv3r0AdOz4y204Xbp0YefOnUdc54EHHuC6667j0UcfZdeuXfXLd+7cSefOnet/7tSpE4qisG/fvlaIXIjYqt35E7sfvh/fTzv44p6Hec7RnrMzM3isSwHt0+U2WCFEy31X7eMzj5fftc+RL6hSSPnst7H0KcLYtVu8Q2lV77777iHLZs+eHYdIhGjb4n6LbCAQwGQyNVhmsVjw+/2Hff/jjz9O9+7dCYVCzJkzh0mTJvHyyy9jNpsJBAJYLJb692o0Gsxm8yHbcrlcuFwuAMrKyggGgwAoitLouA+8tynrtISqqiiKgqIoMfn2MZXzS7bcVFWlavnHuN+eiX/AIGb1H4YrrHBPxxx6W82AiqL8MrhPsuXXVJJf8xgMhiZvN5b7OlX285EkQ351c16WMTzLRmdjWpPWTYb8WiKZP6MCWzbj++5rCh558rDbTKVjt2nTpkYtE0K0rrgXmEaj8ZACsKam5pCi84CTTz4ZqDtZGjt2LCtWrGDTpk0UFRVhNBrx+XwN3u/z+Q7Z1uzZs5k2bVr9zwMHDgSguLi4yfGXlpY2eZ1kksr5JUNuqt9PcM5bRLZs5vtrf8vc3EI6KxHuzkgno7qK4uqqI66bDPm1hOTXNOPHjwekn/u1VM4Nmpbfh74gvlCEgWqoWb8n8ZDKxy8auamqSu2s19H1KcKtN0ACHdfWOHaTJk06ZNkDDzwQ9XaEEEcX9wKzQ4cOAOzevZvCwkIAduzY0eg5iw7+tq1Tp07s2LGjvmDcuXMnWq2W9u0bzgM4YsSI+veUlZWxePFiAPLz8xsdt6IolJaWkpubi1bb+ncaq6pKOBxGr9fH7BvGVM0vWXILbNtK6ZR/ErRk8OGfHmFtWOGa3GzOz7KhPcp2kiW/5pL8Du9YBcH06dMZP358wvZzybKfmyvR89tbG2SZew+3FeRyXDOmJUn0/FoqWT+jfN9soHjPLgon3onBmXPY9yTLsTtw55kQIvHFvcA0Go3079+fmTNncvvtt1NSUsKyZcu45557Dnnvrl27CIVCHHfccYTDYWbPnk0wGKR79+4ADBkyhH/84x8MHDiQvLw8Zs6cyYABA0hPT2+wHafTidPpBMBms5GWVvf8WnM6cq1WG7MPtwNtxfIB/VTOL1FzUxUFz0eLKH9vFuXnX8obvc7EoNXyty4dOM6Ufsz1D0jU/KJF8muaUChUv914x3I4qbKfjySR81NVldf2u+iVYebMzIxm38qfqPlFQzJ+RqmKgnvOO2QOGkZ6bt6R35fix04IEXtxLzABbr75Zl588UVuuOGG+tFfe/fuDcCoUaN46KGHOOmkk/B4PEyePBmXy0VaWhpdu3blkUcewWqt+7a1b9++XH311TzyyCP4fD6Kioq4+eab45maEE0S8VZR8spkarZs5qs//In56RkMyrBwXTsnRvkgFkK0gk88Xrb7a3m6W8eUHmG0ran+8gtCJcW0/9P98Q5FCNHGJESBabVaue+++w772jvvvFP//7169TrmRLmXXHKJDEctkpL/x00U/+ufVOflM+fOB9mlwO3tczkzs+m3qwkhRGNUhSPMLHYxMjeLXJnzMmWo4TDuOe9gH34h+kx7vMMRQrQxCVFgCtGWqYpCxYL3cc97l5+uHM1bx59MoSGNpwryZJJzIUSrmlnswmHQc6FTipBUUvXpSiI11dgvvDTeoQgh2iApMIWIo7DHQ8m0l/Du3sWKO/7CpzojI512rshxHHUgHyGEaKmN1T5Webw82qUAvfQ3KUMJBnG/PxvHRZehO2jqtrbg/vvv5+KLL2bAgAEAbN++nfnz5zd4zx133BGP0IRoU+ShLiHixLfxO3ZPupc9BiPTfn8vPxitPNylA1flZklxKYRoVSFF5dV9ZQzLstHNbIx3OCKKKpd+BEDmsAviHEns7dy5k6Kiovqfc3Jy+PLLL3E4HDgcDj755JM4RidE2yFXMIWIMTUSwT3vPdwL5vHNmAm83+44TreaubF9DmadLt7hCSHagA9cFfgUhdF52fEORURRpKaGioXzyB4xGm1640cdTxWKomA0/vKFyYFBIMeNGwfAwoUL4xKXEG2NFJhCxFDYXU7xv/4f7ooKFtz9EFt0Bn7XLodz7M2bGkAIIZpqf22QeWUV/L4gF4t8qZVSPB8uQGexYjt3cLxDiYucnBw2bdpEz549Adi4cWP9tHRCiNiRAlOIGKn5Zj2lr/yLHX1O450rxpGdnsaTBXnkp6fFOzQhRBuhqnW3xva0mOhnkxGqU0m40oNnySJyb/gdGn3bPL275JJLeOKJJ7j44otRFIVFixZxww03xDssIdqcttkDCRFDajhMxbtv4l72MZ/e8HuWO/K4NCuTUbnZ6LVy1VIIETufVVbzoy/AMzLnZcqpmD8PQ24e1jPPjncocXP++edjsVj44osv0Gg0TJw4kX79+tW//tJLL8UxOiHaDikwhWhFobJSiie/wP5whNl3TaJKb+AvBXmcYjXHOzQhRBtTHY7w+v66OS/zZAqklBIqK6Vyxce0u+1uNNq2PX7jgAED6keR/TW5XVaI2JACU4hWUr12DSWvTeHbc4Yxr/cZnGQ189cOedj08syTECL23iwpx6bXcbHMeZly3PPew9j5eMy9T413KEIIIQWmENGmBIOUv/0GxZ9/xoc3TORbq51rchxc4LSjbePfLAsh4mNzjZ8VFVU83LmDzHmZYoJ79+D9/FM63DdJbnsWQiQEKTCFiKJg8T6KX/5/bDdn8O7E+zGlp/O3glza63XywS+EiIuwovLKvjIGO2x0t5jiHY6IsvI5b2M+uRem7ifGOxQhhACkwATq5knS6/WoqtrodVRVrV+nKes114E2YtHWgXZSNb/Wys37xWqKX3+V1RdcycfH92Sow8bY/GwMGg2RSESOXRTbk/wOpT/GqJH5+fkJ3c8ly35uSXsH/xuL9g7kN7/cQ1U4wjW5Wa3Wvhy/6LbV2NwC27dSs34dBQ/9X7NjS5Zjp5MpdYRIGlJgAn379sXhcBAOh5u0nsPhQFEUFEVppcgOFYlEYtZWKucXzdyU2gDuWa+zZ+P3zLvxLopNVm7Pz6YowwyKwoGM5NhFj+R3+HWOZsKECQAJ388l+n5uqVjntz9Qy5yyCibkZ2FEbfLxbyo5ftHR2NzK352F+bQz0HUobPGxTfRjl5GR0YoRCSGiSQpMYMOGDZxyyink5OQ0eh1FUSgvLyc7Ozsmz9WpqkokEkGni82tlqmcXzRzC+7dTfHLL/BN+07MvfFuulhM/L1DLlmGX/605NhFl+R3eGVlZUd9/dVXX+Wqq65K2H4uWfZzc8Uyv+pIhB+r/VRVVbE6rNLdbORcR2artivHL3oam5tv43cEtmym8P+eOeYdDEeTLMfO6/W2YlRCiGiSAhOorq4mHA43qWPVaDT168Ty2bpYtZfK+UUjN1VV8a5awd63Z/LhlWNY164jo/KyudRpR3uEbcqxi147kt+hjnX1ori4OCn6uUTfz9FotzXb2xmo5W879lITUVABFbi3Y37MBhiT4xedNo6Vm6qquGe/je2cQaS3ax+1dhP52MXyCqsQomWkwBSiiRS/n9L/vMKWnbuYfdOfwGzikYJ8upqN8Q5NCNHG/WPXfmoiCgduPNQA/ykup6/NGs+wRJTVfLWW4O5dtLvt7niHIoQQh5ACU4gmqN25g30vv8BnPfvy4Zib6W/PYHy7HEw6mX5ECBFfgYhCSbDhlWwVKA6GCCgKRpkmKSWoikL5nLfJHDYcfVZ2vMMRQohDSIEpRCOoqkrlsiXs+GAu74+6gZ2OHH7fIZcBdhl0QAiRGAwa0AK/HjZFp4E0mSYpZXhXryLsduO4+PJ4hyKEEIclBaYQxxCpqaH0tSlsqKph7o13kWcx82RhPnlphniHJoQQALhDYV7ZVwrU3RZ7YPIHLXBVTtYRnw0XyUUNhXDPew/HhZegy7DFOxwhhDgsKTCFOIrAtv+xe+pLfHTGQFYPPoUrchyMyM1CLydrQogEoKoqqzxeZux30cGYxtNdC/nBF+DTiiqCwRBDc7MYlpUZ7zBFlFSuXIoaDGIfflG8QxFCiCOSAlOIw1AVBc9HC9m0fCmzfzMevy2TBwvz6WkxxTs0IYQAoDwUZtreUjbW+Bmdl82F2ZloNRo6GNMZas+guLiYfIctpqOsitaj+P1UfDAXx6VXoDXJZ5EQInFJgSnEr0S8VRRPm8wqg4nF426ld6aVm9vnYtXr4h2aEEKgqiorKry8XuyiozGNv3ctpF16WrzDEq3Ms2QRmrQ0MgefF+9QhBDiqKTAFOIg/h83sf21qcwdfDFbCo5jXPschsoVACFEgnAFQ0zdV8qPNQFG52dzflamPF/ZBkSqvXg+XIDz2uvRGOT5fyFEYpMCUwjqbomtWDCPdevWMefqG7FlWHm8sB0FRrkqIISIP1VVWV5RxRvFLo4zpvNU147kp0uh0VZULHgfnd1BxtnnxDsUIYQ4poQoMKurq3nppZdYv349JpOJUaNGcdFFhz7AvnnzZmbNmsXWrVsB6N69OzfeeCPt27cH4LvvvuOBBx4gPT29fp2RI0cyatSo2CQikoKqqqgBP6paN85i2ONh37SXWJRbwCe/Gc8FTjvX5GWTJnPGCSESQGkwxLS9pWzxB7g2L5vz5KplmxKucFO57CPybroVjU4e1RBCJL6EKDCnTJlCJBJh+vTp7N+/n0mTJlFQUECvXr0avK+mpoZhw4Zxzz33kJaWxsyZM/nb3/7Gyy+/XP+ezMxMZsyYEesURJKo/upLtr/xbzyGdNyhIO3PHcRP69byzvlX4HbmcU9hPn0zLPEOUwghUFSVpe4q3ixxcbzJyN+7dpTpkaibqiNYWoJaUwPkxzucVud+fzZpBYVYTjsj3qEIIUSjxL3ADAQCrF69mueffx6z2czxxx/PkCFDWLp06SEFZlFRUYOfr7jiCubMmUNVVRU2m8wHJY4usH0bsz//Lx+OvxNVq0WjKFywcjGbz70AW0FH7i/Mx26I+5+EEEJQGgwxZW8p2/wBxuQ7GeqwyVVL6p6T3//C0yg+HwBl5w4m94bfoUnRO06CxfupWrWC9nffJ2MBCCGSRtzPpvfu3QtAx44d65d16dKFefPmHXPd77//HofD0aC49Hq9jBs3DoPBwKmnnsq4cePIyMiIfuAi6azb/COLB10IP39Iq1otiwdfxFXbvmdk5w5y8iaEiDtFVfnYXcmbJeWcYDLydNeO5MhVSwDClR72/eNJ1Nra+mXe1asw5OaRdckVcYys9bjnvoup+4mYTjol3qEIIUSjxb3ADAQCmH41n5PFYsHv9x91veLiYqZMmcLNN99cv6ygoIAXXniBgoIC3G43L7/8Ms8//zwPPvhgg3VdLhculwuAsrIygsEgAIqiNDruA+9tyjotoaoqiqKgKEpMvsVMtfzClZV8HwyjUyJEdL/82usiEbwOJ6gqys/PZEabHLvokvyax/DzyJOJ2s+lyn4+ksbkVxIMMXVfGT8FarkuL5vB9gw0Gk2zYkzE/FrKv3ULaijUcGEkQuWSxajhMFqzBa3ZjNZsRme2HPSzBU16eoviiuXv54Fj5v9pO9VffkH7vzxSN3aAfEYJIZJE3AtMo9F4SDFZU1NzSNF5MJfLxaRJkxg5ciQDBgyoX+5wOHA4HAA4nU5uuukmbrnlFmpraxsM/DN79mymTZtW//PAgQOBuqK1qUpLS5u8TjJJ5vxURUHZvpXQl1+wtcrLT/2HwWE+nzW2zGYd+0SXzMeuMSS/phk/fjwg/dyvJUJuiqryWW2Yhb4QXfRa/pSRjqPWR0mJr8XbToT8okH1+wn+9ws4TFESCYeo/GYDaiAAAT+q3weBQMM3abVgNKExmdAYTXDgX6MRjcmMxmSqe91o/OX/Teaf32sEvSEmxZfqqyH06UrUCjfFe3ej7X4iHosV5DNKCJFE4l5gdujQAYDdu3dTWFgIwI4dO+jUqdNh319eXs5f//pXhg8fzuWXX37UbWu12sN+6zdixIj6orKsrIzFixcDkJ/f+MECFEWhtLSU3NxctDF49kNVVcLhMHq9PmbfMCZrfpGqSryffcKetWtY264jG869CLfZTE+zCU2NH42i1D+DqdXpuLBb91YdOEOOXXRJfod3rMJx+vTpjB8/PmH7uWTZz811pPz214aYtq+MXbVhxrdzMvDnq5Ytgdbn/gAAIABJREFUlSj5tVTtrp1ULV9C9X8/R/Pz1Ugl4P+l0NRoyL3+Rqynn9UwHkVBCfhRfD4UX03dvzU1KL4aIn5fw+XeSpSS/XWv+epeUwO/uotKr0dr+vnKqOWXK6Vas6VuucXS4Aqq1lT3/weWa/THPt2K1FSz95n/I1xZCZEwADq9ntzsLLSG1psyK1n+9g7ceSaESHxxLzCNRiP9+/dn5syZ3H777ZSUlLBs2TLuueeeQ95bXl7OX/7yFwYNGsTIkSMPef3bb78lLy+P3NxcPB4PU6dOpU+fPhiNxgbvczqdOJ1OAGw2G2lpdR13cz6EtVptzD68D7QVywf9kyU/VVHwb95IxcrlbKjwsL7vWWwe+VtyDXqGZ9s5156B3aBnmz/AtD2llNQGyTMbualDLu2M6cduoAXk2LUOya9pQj/fWpio/Vyq7Ocj+XV+iqqyuLySt0rKOdli4plunchuhUHGkvHvRA2FqF63hsplSwhs3YKp58nk3TwRS58iwp4KSqa9TO2ObajpRnJGXI3tzLMP3YhWi86aAdbmjcGgRiIofl990RmpqSHk9aIJ+H9e/stroQo3tTV1xWrk56JVDdY22J4mLa2+ANWZzWjN1oNu5a0rVP1bNhP2VDS4Sht2uaj5YjWZg4Y2K49G5Zrif3tCiNiLe4EJcPPNN/Piiy9yww03YDabGTNmDL179wZg1KhR/H/27js+qip9/PhnWqZm0nuhBBCsgAiCKFKsP1EWFQs2RFG3uGJb3LWsX3VdV9eya1mxsOraFVR0VaSoiLJIUURROunJpLfJtHt+f4SMjKEkYTJJJs/79eIFc+t57lzOzDPnnnPuuusujjjiCJYsWUJJSQmLFi1i0aJFwf2feOIJUlJS2LFjB48++ij19fXY7XZGjhzJ5Zdf3l1hiQjw72mt3LluLf/L7M+GMZNxm80cHxfLHYlxDLVZQj4w86wW/pKXTWlpKenp6fLhJoSIuGKPl6cKyynyeLkqM4WTwtRq2dv5KiuoW7GUus9XoHxeYsdPIPXKa4jJzApuY0pKJnvenWiaRmlpKbEdaJHvCJ3BgMERi8ERi4mOt/Ipv39PwtkQkoy2tpoGWltTGxvwucpaEtWSojaPAOt0OvzVVV0SoxBCdJUekWA6HA7mzZu3z3VvvPFG8N8XXXQRF1100X6PM23aNKZNi86R5MTPlFK4f/yByk+Xs6a+kXUjxrDjV5fR32TgvOREToh3YJfJqIUQPYymFIsrqnmzvJqjHFYeys0lsY9PjaQ0DfcPm6hd/gmNG9YSk5VD4q/OJ3bsePS/ePqoN9EZjRicTgwdmEKt5uP/UvHGK8HHYwGUFghJsIUQojfo259solcJ1NVRt+ozfvrmG1ZnD2Dj8ZNRJhPjE+K4OtHJAGvv/TIihIhuRR4vTxWWUerzMycrlfFxjj7dahlobKR+1WfULv8En6scx6gxZN12F5bBh/XZ6xI3+VQa1n9N87YtoNODFsA+8rg2/UuFEKKnkwRT9GitrZXlKz/jS7eH9UcfR+HUixgaY2RWahKjnXbM8pirEKKHCijF+xU1vFVexVF2CzflZpDYh+e19OTvpnbZx9R/tQqDw47z5Ck4T5qEMT6+u4vW7XRGI1m33k79mq+o3r2LxLzBOEaN7rMJtxCi95IEU/RIgfo6ar/4nO9++J7VWQP4fswkLHo9JycncGNiHJnmrhtRTwghwqGg2cO/isop8/q4JiuF0TYLpj74SOyBBu3RSXeGEDqDAceYcTT0G4g9PV2SSyFEr9T3PulEj9XaWln05Res8gRYd/QoKk49jOHWGK5PTWJ4rB2jfNgKIXo4v1IsdlXztquKkbF2bumXQZzBgN/vP/jOUaQ9g/YIIYSIPpJgim4XaKin5ovPWPvTVtbkDGTzcRNJ1MGk1CROTozr84NgCBFpStNoWLMab2kxfqMJdeoZECNPDbTH7mYP/yosp8Ln4zfZaRzvbOlr+cv5mKOV0jTcm7+ndtkSGr9ZFzWD9gghhGg/+eYuuoVSiuYtP7Ljqy/5wg/rjxxB48QhHGc388e0FA63W9FLa6UQEac0jZLHHqRp08Y9A41oFG9YS/a8O9GZ+m7fwYPxK8W7rmoWuqo4LtbBvP4ZxBn7zkdsoKmRus9X0PDZchm0Rwgh+ri+8+kneoRAQwNVq1by1c6drMkewPZjTyILjXPSUzg+1kaC2SxfRoToRg2rv2xJLgMBIACAZ9dOapd/QvxpZ3Zv4XqoXW4P/yoqo8oX4HfZ6Rwf5+juIkWMZ/cuapcvof6rVejtduJOnoxzwmQZtEcIIfowSTBFl1NK0bz1J35avZqVGNhw+HACGQMZ57AxKz2FgVYzQJ/rnyRET+QtLW55pHPvhQE/DevWYB06jJjsXBmYZQ+/pljkquIdVzVj4hz8sX8KTmP0X5t9DdqTOuc3mI88GpP8SCiEEH2eJJiiywQaG3B9uZJVuwtZnZtHwcjxDFJ+Ls3KYGxcLBbDz9OL9JX+SUL0dMakZNr8d9Tp8BYXUnDXbejMZiwDB2HJG4xl0GAseYMxxLZ/MvlosdPdzFNF5dT6A/w+N53RzuhvtTzQoD1KKfmRUAghBCAJpggzpRTurVv4bt1aVupMbDzsSGJSB3CS084NGWlkWWSgECF6Mue4E6lb8QmewoKWx2T1eoxJyeTefT/K76d5+1aat22ledsWapZ8iPJ6MKWlYxk0ZM+fwcRk5aCL0vlpfZpioauKd13VjItzcHn/FGKjuNVSaRruHzbJoD1CCCHaTRJMERaBxgZKvvqSz4tL+F/OIMqPHM0Rys+vc7IYFReLUS+PTAnRG+hMJrL+eDe1Sz/CW1pCc4yZrGnnobfaALAPPxb78GMBUIEA3sJ8mrdtxb1tC9X/fQ+/qxydxYol7xetnPbe38K33d3MvwrLqQ8EuDE3g1FOe3cXqcsEGhup/+Izald8IoP2CCGE6BBJMAGHw4HRaOzQY5pKqeA+kXi8s/UckXqUtD3xKaVwb9vKum82sNJg4YdBQ3EmZXNyQiyTMtJIjjGFbHuw87Vnu3CQ9y7859v770icT+Jry3iQEUvT09PbXc/pTCbiz5iKpmlUVlais1r3vZ9eT0xuf2Jy++OcdAoA/toaPNu30bx9K+4ff6Dmw8Uonw9TZlZLwrkn6TRlZIW0cvbk6+zVNBa6qllcUcMJcQ4uS0/GYTR0+DNj77+7WmfvI0/+LmqXf0LDXoP2xE6YhDHu50F79nW83hLfoZxv77+7+lzRGlvreToTn0H6fgvRa0iCCYwYMYKEhIQO9x9JSEhA0zQ0TeuikrUVCAQidq79xac1NVKwZg2fuSr5ut8g6oeOZLjm48acdI502ILTi3SmP06k4uur711XkvjCpzPxJSQkHHD97NmzgY7/v+xwWewOzEcPx3z0cOIA5ffjLczHs30bnu1bqVq8iEBlBTqrDfPAPMwDB2HOG4x5QB56m63HXedtbg/PlFbi1jTmZqUw3GEDOt/fsKfFBy2D9jSt/5r6FUvxbN+KZdgRJM++FusxI4IDOrU33p4YXzjJZ1R4dCa+2NjYLiyRECKcJMEENmzYwFFHHUVKSkq792n9ZT8pKQl9BPoaKaUIBAIYDIYufTxJBQKsXbGCn+rqiNE0Jo4YTkreIJRSNG7fxlcbN/JFjJ2t/fJIS8zgjOQETs5MP+SREyMVH0Tve9dK4guv3hKfy+U64PrnnnuO6dOnR76eMxoxDRqCfdCQ4CJ/TXWwH6dn8/fUffg+KuD/uZVzT19OU3pml13zg8Xm1TTeKq/m/coaToqP5dL0JOyH0ILSE+8jX2UFdZ8uo/7z5Sifj9gTJpA6+xpiMrI6fL6eGF84yWdU+HQ2vvr6+i4slRAinCTBBBoaGvD7/R2qWHU6XXCfSPZH6crzKaV49YMPWDzwcIzJfpROx9KKeq7+9i3Wazq+zh2Eb9CRjCbAzAFZDHHYwl6WSFzPaHzvfnkeia9rztuT4ztYC1NpaWmPqedMCYmYjhtD7HFjgJYWtObdu2ja+iPeHduofudN/FVV6O12LHktyaZl0GAsAwaht1rDUoYDxbalyc2/isppDihu7ZfBiNjw9bXs7vto34P2zAjboD3dHV8kziufUYd+ns7EF8kWViHEoZEEUwQV7trJ4oGHg06H39TSf7LRaudtWzy65BQuSE1mfHYmNkN0jg4phOgeOpMJS94gjP36YzQaW76AVlXuGTzoJxq/WU/Vu2+DphGTnbsn4RyCJW8wprT0sH0p9moar5dV8d/KGiYmOLkkPQlbL+v3pXw+Kl59ifr/rULTNCrGnkjKRZeieb0yaI8QQoiIkASzD9Gam/FVVVJbXUVFbR2VjU1UeTxU+zWqdTrcPh+6IUei9vqyoRkM1Mcl8ORxw7ux5EKIvsaYmIRjdBKO0ccDoHm9eHbvDD5aW/nmqwRqqtHHxv7cypk3GMvAPPTmA7fEeTWNDbX1FDV5GNnsob/Nyo+NLa2WPqWY1y+TY2JtkQgz7MpfeJb6r75omWIGqPt0Ge4fvsNfVYXBYcd58hScJ03CGB9/kCMJIYQQnSMJZhRQSqG53TRXVVBRXUNlXR2V7maqPD6qNY0avZ4ao5k6m516Ryw+kxWcVkw2P3E+D/FagAQ9JHi9qF/0h9BpGskBmTxbCNG99DExWAcfhnXwYUBLveevrAgmnI3r1lC16E1QCnNOvz0JZ0viaUxJDbbSNfj93LnxR0oNJvRK483thRyt+dhoiGFygpOL05N71FMaStNQXi/K60XzeVE+L8rr2/drTzP1X3wWegAtgK+0hLTf3IBj5HHBQXuEEEKIriIJZg+nlCLQUE9NZSVlVdVUNzVR1dxMlS9AjYIag5HaGDN1dgeNNgfobOicVhzmZuL8XhKURoLBQH+ziSSbjSSnk+TYWBLNJux6fZtHowJrN/BljA0NHXql0CvFFcMGd1P0QgixbzqdDlNyCqbkFGKPHweA5vHg2bWD5m1baN62lYqvXyJQV4vBGRccOOglRxJlcUloBgMaLcnWRr2Ra5NimZCRut/zKaUgEED5vGh7Ej7l8/38ujXR2/u1b0/it2f7gNcDfj8Ek0PfnuN40fbs23rc1mNwoH5nej26mBj0phh0MTFwgKlq7MeMlORSCCFEREiC2Qnf/vQTK4vL8Pr9jK2sYuxRR3bqOErTcNfVUlFVRWVtHZWNjVR6vFT7A9Sgp8ZootZipd7uwG80QYyTGJ2VOKObOM1PApBrNJBkMZPksJPsjCPZGUu8yYixk31qfnPscIbs2M3m6hoMfh9nDzuM3Dhnp44lhOidGvwB3i6vosDjJdbv5RKfnyRzTHcXC00pmjVFs6bt9UfhDmh49rx2J2XQnJBG87En0BzQaHI30VTfgNvtpsnnpRE9AUPoR59OUzQuXkRRVfleyeKeRK/1314vHGjOPqOxJdEzmdDFxPyc+LW+NrUkgHqzGb3ZjNERu2ebn9cHX+/5d8tyUzCBbD1W8PU+EsaCe+7As2vHz4mpwYBlQB76mO5//4QQQvQNkmB20KcbNvC0wQ5J6QCsQYdr9RrOPn50yHZaIEBNdTWu6moq6+qpdLup8vqoDmjU6A3UmGKos9posraMTqgz2HGY9cTrWh9Z1dHPZCTJZiXZYSMpPo54i5XYGFOXDluu0+k4Na8/UzSN0tJS0mMdXXYuIUTP4w5o/HF7AZU+PwFAD3y/o4gHB+cQd4AWsl9SSuEJSQYV7j3/9uz17+bAnsRQ03D7A3gguJ97z/rW1559JHg6wKLXY9XrsOj1LX8Meiytr6024uz2PdvoWb3hG6o0LaQ7gKbXE2+1Yj38yJbkzmRCH0zw2vdad5B6WamWuTNbBzHqKhm/nUvxww/gLdgNQExmNum/ndtl5xNCCCF+SRJMoLm5maKiopBh/rOzs9Hr9eTn54ds+0xlAyo9Fl9DPd49czI9H/Dz3SuvQ0oy9TEWimpqqY+xENjzZcxmMJJqMWP1NhFTV0ecQUdWTAwJ3kaGJMWSkZqKu6YGtJ8HprBaraSkpNDY2EhlZSWqtpaKykpqjEYyMjKIiYmhuLg4pMwWi4XU1FSampqoqKgIKXdqaioWi4XS0lK8Xm9wudlsJi0tjebmZsrLy4PLNU0LDgleVlaGx+MJrjOZTGRkZODxeCgrKws5T1JSEna7HZfLhdvtDi43Go1kZmbi9XopLS0N2ScxMRG73U5FRQVerzf45Uuv15OdnY3f76e4uDhkn/j4eJxOJ1VVVTQ0NASX63Q6cnJyCAQCFBUVhewTFxdHXFwc1dXVFBUV4fV6g8l6Tk4OAAUFBSH7OJ1O4uPjqampoa6uLmTd/u4Rh8NBYmIidXV11NTUhHyxzM7OxmAwUFBQ0PLI3R52u52kpCTq6+uprq4OOV5GRgYmk4mioqKQYdp/eY+02nvi6nDeI263u81ciykpKVgsFsrKyggEAsH37kD3SHJyMjabrcP3iMPhoKKigoaGBlwuF16vN3hN93WPJCQkEBsbS2VlJY2NjcHl7b1HWudca33/Bg4ciFKqS+6RvWVkZAAt9+LeicjB7pGDaW8991l1LSWNHgzO+GA91wjcX+VikM2KLTUVn15PcWEhzf4AXk3RrDQ0sxVdXBx19XXUVdfgVYrWO9yanILeZKKprJQYpTDrdZj1ehxWG4kpyRiam/HXVmPSgdVgxKnXMzg9HYfFTGNZGUZNa9nHqMdptZKdno5qbqahqhLTXlMdtLmHFRAAs9FMWloKzc3NmKrL+F5TaDo9Sq/H5nQypLKc3BPG02A0Ba/Dfu9hBUmxzp/ruaqf34sD3cMJCQlYLBYqKipC7vuuqOdy7r6fsq1b2J2fT2reIIrr6qGuXuo5ek89t/dn1IHukXDUc3u/d/369evyek7TNAx7Wt87co/4fD6EEL2E6uOKiorUhAkTFC1fRYJ/amtrlVJKmUymkOWDLrxEXfDdVnXMTX9os8+9b76tXlm5SjmczpDll19+uVJKqaeeeqrNPj/99JNSSqmcnJyQ5eeee65SSqmXXnqpzT7r1q1TSik1bNiwkOWnnXaaUkqpRYsWtdnns88+U0opddxxx4UsP+GEE5RSSn3yySdt9nnxxRdVIBBQEydODFl+zDHHKKWUWr16dZt9Xn/9daWUUmeffXbI8ry8PKWUUps2bWqzz7PPPqs0TVMXXnhhyPLU1FSllFK7du1qs8+jjz6qlFJqzpw5IcttNptSSqmKioq278+99yqllJo7d26bdZqmqebm5jbL582bp5RS6s4772yzrq6uTimllNFoDFn+29/+Viml1N/+9rc2+xQXFyullIqPjw9ZfsUVVyillHryySfb7LNlyxallFLZ2dkhy8877zyllFIvvvhim30+/vhjFQgE1NChQ0OWn3766UoppRYuXNhmn88//1wppdSoUaNClo8fP14ppdSSJUva7PPf//5XaZrW5v/Q8OHDlVJKffnll232eeONN5RSSk2dOjX0/9agQUoppb777rs2+zz//PNKKaUuuuiikOVpaWlKKaV27tzZZp/HHntMKaXU1VdfHbLcbrcrpZRyuVxt9rnvvvsOeI+43e42y2+77TallFJ33HFHm3X19fVKKaUMBkPI8t/97ndKKaUeeOCBNvsUFRWpoqIiFRcXF7J81qxZSimlnnjiiTb7bN26VRUVFam77rpLFRUVHXI9N+Si/ddz81avU3/fXaxscaH38JQLL1LLq2rVLQ8/0mafrzd9r9z+QI+s566/6SbVsHtnl9dzzzzzjPJ6veriiy8OWS71nNRzrX9a67lffhZGsp5TSkWkntuwYYMKBAIdqucWL16s7rrrLrV06VK1du3aQ/6zdOnS/daZQohDo1PqQJ1Kol9xcTH/+Mc/OPfcc0lLSwsu398vcndu3k5zRha+psaWFkyliPH5eOjwPHJycjAajRQWFob8uhqOX23VXr8wRrIFc8CAAbhcroi0YLaWratbMCsrK9mxYwcpKSlR3YKZm5tLaWlpRH7Zby1bJFswU1JSIt6CCV1zj+wtIyMDl8uFz+frUAumy+Vi/vz5zJkzh8zMzJD1Hannvqlv5IWaRkxxP7dgGoAZaYmMj3eSmZnZJfVcRUVFyCOk6enpXVrPBXw+XCXFHDFiJHa7vcuf1GhtwaypqenyFkyp53p3PVdeXs7u3buD7120tmBmZWVRVFTU7nvE5XKxePFiTjzxROLDMM1OTU0NK1eu3GedKYQ4NFGXYDY0NPDEE0+wfv16rFYrM2bM4Mwzz9zv9sXFxfv9UrYv327+kQebFUqnQ0fLY1a/VW7GDe/aeSJVhPrvtNJa+2Cmp3dpn89WkYwvmmMDiS/cekt8B6rLOlLPKaWYX1zOp9X1GHU6/Eox0mHjpn4Z6Lsw/t5ynTtL4guvaI4vmmODzse3ceNGFi5cKAmmEL1A1PXBfPrppwkEAixYsICSkhLuvPNOsrOzOfroo8Ny/GOGDeX+oiJW79hJc7OHcYMGkjfgsLAcWwghuptOp2NOZionxjspafagb6jnpOy0Lk0uhRBCCBE9oirBbG5uZtWqVTz66KPYbDby8vKYNGkSS5cuDVuCCZCTlUVWRkbwFzghhIgmOp2Ow+1WhlrNlHqaItKqIYQQQojo0PXPXkRQaz+D3Nzc4LKBAweye/fu7iqSEEIIIYQQQvQZUdeCabVaQ5bZ7faQDvYAFRUVwY7/rdMdQOiw5wfTum1H9jkUSik0TUPTtIj1kdj7764WyfiiOTaQ+MItWuIzmUwdPm4kr3W0XOf9kfjCK5rji+bYIPLxCSEiL6oSTIvF0iaZbGxsbJN0vv322zzzzDPB1xMmTABoM6Jbe+w98mo0iub4ojk2kPh6u3DHN2vWLEDquV+K5thA4uvNojk2iP74hOjLoirBzMrKAlqGYG8djn3nzp3069cvZLtzzz03mFS6XC4+/PBDgA71p9Q0jfLyclJTU6N2lLdojS+aYwOJL9x6S3wHSxwXLFjArFmzemw911uuc2dJfOEVzfFFc2zQ+fh+OeWMEKLniqoE02KxcMIJJ/Dyyy9z/fXXU1ZWxrJly7j11ltDtktOTiY5ORlomdspJiYGoFMVeescVV1NKRU8VyQH3Ijm+KI5NpD4wiVa4vP5fMHjdndZ9iVarvP+SHxdI5rji+bYIHLxCSEiL6oSTIBrrrmGxx9/nCuuuAKbzcbMmTM55phjurtYQgghhBBCCBH1oi7BdDgczJs3r7uLIYQQQgghhBB9TrsSzIcffrjdB9TpdMydO7fTBeounX22vzMDZnSG0WgkISEBl8uF3++PyDkhuuOL5thA4guX3hJfe+qwnlzP9Zbr3FkSX9eI5viiOTboeHzV1dUANDQ0hOX84TqOEKKtdiWYN998c7sP2NsSTJvNhslkYuHChd1dFCGEOCQmkwmbzdZmudRzQohosWHDhrAdy2g07rPOFEIcGp1SSnV3IbpbTU0NTU1N3V0MIYQ4JDabjfj4+H2uk3pOCNHbtY52Gy4HqjOFEJ0nCaYQQgghhBBCiLBo189AVVVVHTpoYmJipwojhBBCCCGEEKL3alcLZkfnRgoEAodUKCGEEEIIIYQQvU+7WjCff/75iE6+K4QQQgghhBCi95E+mMjgF0KI6CCD/AghopkM8iNE7xC+/6W9VE1NDU888QQ+n69D+5lMJmbNmsWCBQs6vG9npKenM3v2bJ577rmIzI0VzfFFc2wg8YVbb4rPZDLxm9/8ps0Xpt5Qz/Wm69wZEl94RXN80RwbRD6+AzEajfz2t7+VJFOIMOtwC+akSZMOus3y5cs7XaBIKy4uZv78+UyfPp3k5OTuLs5+tU6EXF1dHdGJkCMlmuOL5thA4uspKioqWLhwIXPmzCEzMzNkXW+o53rLde4sia93i+b4ektsW7duZcWKFYwYMQKHw3HIx2toaGDDhg37rDOFEIemwy2YNputTX/MqqoqNm7cSHx8PCNGjAhb4Trj/fffZ/ny5ezatYuxY8dyyy23tGu/5OTkDlUwmqZRWlpKeno6er2+s8VtN6UUfr+flJSUiPSHjeb4ojk2kPjCLZri68n1XDRd532R+MIrmuOL5tig8/FVVFQA4HA4pMVRiB6uwwnm+++/v8/l5eXlnH322Vx66aWHXKhDkZiYyIwZM/jmm2+or6/v1rIIIYQQQgghRF8Stj6Yqamp/OEPf2DevHmcf/754Tpsh40bNw6AHTt2SIIphBCiT/FripfLKlhVU4+maZxIBTPTUzDqZSR40b2UUry3dj0fKAM+vYHDtu3k16NG4LTZurtoQogwC+sgP5qmUVJSEs5DCiFEn+BvqGfpkk/Y5fNjDQQ449gRpB42tLuLJXqZ50tcfFZdR+ts1Euq6vAquDortVvLJcTitet4zexE7Xks9juTiXvXbeT+E0ZjiMCjwEKIyOlwgrl+/fo2y7xeL5s3b+buu+9m9OjRYSlYV6qoqAg+y+9yuQgEWj6KNU1r9zFat+3IPodCKYWmaWiaFrE+Env/3dUiGV80xwYSX7hFIr6A18Njyz5l/aAj0Gkt466tqmrk7i1bSBs0KCznMJlMQM+t5+Q+OnR+pVhRXcfeI/cFgBXVdVyRnoShC6+rvH/hE62xfewDZf05kQwYjOQnJLMjP5+83NwuO68QIvI6nGCOGjWqTQXUOhDtmDFjeOaZZ8JTsi709ttvh5TzwgsvBOjU8Nzl5eVhK1dPFM3xRXNsIPH1Jlu3bWPdgMNaftnf8/2rwWbn1W07uTAMoyUCzJo1C5B67peiKTavUuxrWHgNKCotJSYCiV+kRdP790vRFptvP62U5S4X9piYCJdGCNGVOpxgrlixos0yi8VCdnY2WVlZYSlUVzv33HOZMGEC0NKCuWTJEqBlLqj20jSN8vJyUlNTIzrKm9FojNivw9G682/6AAAgAElEQVQaXzTHBhJfuHVFfJq7CW9BPpWFheyoqeVbXwC9M4XAXvFoBiMVdke766WDJY4LFixg1qxZPbaek/soPPLcRexs9rB329dASwy5GRlddk6Q9y+cojW2I7//kf9ZbQQMLV89dZqG1ePmmKGHYbMf/Ie01ifPhBA9X4cTzNbErKcKBAIEAoHg4x5erxe9Xo/R+HOoycnJwbngnE4nBoMBoFMVuV6vj9gHQOu5IvHh3Sqa44vm2EDiC5dDiU8phb+yAk/+bqqLCtleW8eugKIgNo6itCxq0gdiSgmQW1OBtqceaqUP+Ek3GsIWY+uE5j21npP7KDxuyE1n7tbd7HnSGgOQHGNCp9N16Xnl/Qu/aItt8KBBFGzZSkFWPwBszW5udsbgiHV22TmFEN2j04P8FBcXU1hYSHNzc5t1J5100iEV6lC8/vrrvPbaa8HXq1atYtKkSdxwww3dViYhRPRTPh+eokK8BbupLixgR0Mju5WewqQUitOzqRpwJEZNI1vzMdBiZlxSIoMcdrIsMRgYwiPrN7LWaAFApxS2gI+ZI4d3c1Sit9nu9qBTcFf/TDzVVTgSE7l7VwlLqmo5LUnmDhTdY1tTM/9paOZX2zbz2/6ZlFfXcvjhh2N3SnIpRDTqcIK5Y8cOLr30UlavXg383P9Sp9OhlEKn0wUHzekOF198MRdffHG3nV8IEf0CdXV4Cnbjyd9FbVERO5rc5BtjKErNoDgzl8qjxmJQGtlKI89u5fj4ePJsFnLMMfudLmLuyKP5tLCY7VU1GDU/5xwxlASLOcKRid5MKcUiVxWnJMVxmM1CaZ2edKuFqzNTeLq4nP5WM4fZrN1dTNHH1PsDPLKriOGbNnD66OOwDT4MU2kp1jD1LxdC9DwdTjCvvvpq8vPzmT9/Pocffjgx0jFbCBGllKbhLS3GvWsn/sIC6kqK2en2UGiPpSgjh5KsHFw5w9ArRbYO8mJtjIl1MNBqJsdsxtSBuQd1Oh0Tc7KYkJVBaWkpcTGmLoxMRKN19U0Ue3zM65cZsvykBCfb3B4ezS/l/rwc4k1hnaFMiP3SlOKJwjIsNVWcX7wD+4Uzgg0TQojo1eFPmTVr1vDCCy8wffr0riiPEEJ0C83txlOYjzd/N56C3dQVFbLbF6AoMYXinP4U5w7FNew4dEC2QU9erJ1RNit5VjM5lhhiZB430Y2UUix0VTExwUmCydhmiovL0pPZ6fbwWEEpfxqQhTEKR5QVPc87rmp+qm/kurdeIPOWPwafdhNCRLcOJ5hZWVnBQXGEEKK3UUrhr6psSSTzd+Ep2E1DcRH5GChOz6Kk/yCKh42i7NiT0QFZJiP9bRb+n83KQJuFfpJMih7o24Ymdjd7uDFn36MEG/U65uamc9u2Al4treTSjOQIl1D0Nd81NPFWeRWXLFvMwLEnEJORefCdhBBRocMJ5r333stf//pXTjzxRBITE7uiTEIIsU/eokIavvsGX0MDvhNPxpx24Ck3lN+Pt6gwmEh683dTX1JEsc1JcXY/SgcMpmjMZEotNtDpyIoxMtBm5XSrmYFWM/0sZmJ0uohOvyBER7W0XlYzId5J8gEerU40Gfl9bjr37iwiz2ZmXFxsBEsp+pJKn59/FJQyuaqUw/O3kXjdr7u7SEKICOpwgvnCCy9QWFhI//79GT58OPHxoaPS6XQ63n333bAVUAghABq/Wc+O+U9QmJmDTtNoXPEJA+b+AeugIQAE6lsH3snHuyehbCwrozQxhdK8IZT0y6PwsJGUxlhQOh2ZMSYG2iycajUz0GKmn9WMZR8tk/I4l+jpvm90s62pmd9kpx1028PtVmamJ/F0UTk55hhyZCApEWZ+pXisoJQsPZz40nySr/41erOlu4slhIigDieYDQ0NDBo0KPi6vr4+rAUSQohfUprGurffZMGs39O854tKbEM9Vz/zL7LS0loSy7paStOzKB9yOMWDjqTwuAmUmMxo6MiIMTHQamaK1cIAq5kBFjMWgzzmKqLDIlc14+NjSWvnwFBnJsWztcnDw/ml3JeXjU26vYgweqW0gnKvjxuWvYd98BDsx43p7iIJISKswwnmihUruqIcQgixX/7aGv5z5nk0my2oPa2MDXYHr5xyDgNMBgomT6XYEEMASN+TTE60mhlgtdDfYsYmyaSIUj81uvmh0c3fB+e2ex+dTse1WancvqOQpwrLmZubjl4e/xZhsLq2gY8qa7nZ34Bx/dek3PM36VogRB8kY5ULIXq8OvTUxcaFLNMMBkpTM+gXH8tJNgsDLWYGWM3SGiP6lIWuasbGOcg0d2zKMItBz4256fxpewHvVVQzLUXGVBCHptjj5emiMi5Ijifx7//AcdqZxGRmdXexhBDdQH7WF0L0aA3r11Jx7x2wj76QZh3ckJvB1OQEjnDYJLkUfcq2pmY2NjTxq5SETu2faY7huuw03iirYmNDU5hLJ/oSj6bxSH4ph9ttnPDVCpTfT+LZ53Z3sYQQ3UQSTCFEj+Svqabk8YfZ/OLzvDpjFnqdDt1eSaYexbkZKd1YQiG61yJXFaOc9kMaqGe008HU5Hj+WVCKy+sLY+lEX6GU4rliFx5N42qzjtoP3iP5wkvQW2RgHyH6KkkwhRA9itI0aj9dxq7bbuLzuBSeuOpG9KlpPDQol4vTkxlgiSHXoGN2RgpnJcUf/IBCRKFdbg/r6puY3snWy71dkJZEf4uZR/JL8WpaGEon+pLl1XV8VdvA3NwMml57CcvgIThGj+3uYgkhupH0wQTS09MxGo0dmo5AKRXcJxLTGLSeI1JTJkRzfNEcW+t5emt83pJiXC88Q3F1DYuvuoECq52L05KYnOBEr9NxljmeMxOdVFZWkhQfG7bzHkhvef+MxgNX5z29nust1/lQzrf334dqkauKEQ4b/S3mfR6zI/HpgN9mp/GnHYUsKHYxJyu1w+WR9y+85+otse10e/h3SQVXZCST8tMmSr/7hpy7/3rA43U2PoN0gRCi15AEE5g9ezYAfr+/Q/slJCSgaRpaBH/xDQQCETtXNMcXzbFB74tP+f3UffwBVR+8x9rTzuajocM5zGbh/vQkkk1GtECAvSPpbfF1VGfiS0g4cEtWb6nnevp1PlThiK/I42VNXSN35qYf8P3sSHw24HcZydxTUMpAs4mT9/yA01Hy/oVHb4itMRDgkYJSxsTaGG82UPLyCzgnn4o+7cD3JXQuvtjYzt2TQojI63CCeeWVV+53nV6vJy4ujhEjRjB9+nRsNtshFS5SnnvuOaZPn05KSvv7c2ma1tKKkpSEfh+Ts4ebUopAIIDBYIjIkN/RHF80xwa9L77m7dtw/Xs+xToD7/12HqUmC7PSk5gQH7vP4/W2+Dqqs/G5XK4Dru/p9Vxvuc6dFc74FpdWcqTdytBY+3636Ux8Q2KNzMpIYUGJiwF2K3nW9vehk/cvfHpDbJpSzC92YdXruSorlYb330H5vCRNOx/9QZ6m6Gx8Mu+6EL1HhxPMr7/+mrKyMioqKoiLiyMlJQWXy0VtbS3JyclYrVYeffRR/vSnP7F8+XLy8vK6otxhVVpait/v79CHhk6nC+4TyTmeInW+aI4vmmNrPU9viE9rbqby7depWraEteddwke5gzk61sbNmakkmvZfNfWW+A7lPJ2J72AtBr2lnuvp1zkc5z2U85V4vHxZ28CdA7IOeJzOxjc5MY7tbg+PFJRxf14OTmPHHkuU9y885+jpsb1fUcPmRjd/ycvBUFVJzfvvkDr7WgztaFjobHyRbB0XQhyaDv809tBDD+F0OlmxYgXV1dVs2bKF6upqli1bhtPp5Omnn2bz5s2YzWZuvfXWriizEKKXa/x2A/l/upntu3bx/I13sWLAYVyTncbNuRkHTC6F6OvecVUz1GZhmN3aZee4IiOZOKOBfxSUokWoT6XoPb5vaOL1skquy0ojwxxDxSsvYh44CMeYcd1dNCFED9HhBPOWW27h7rvvZsKECSHLJ06cyF133cXNN9/MkCFDuO2221ixYkXYCiqE6P38dbWU/usfFPzzYT7/f+fxz3NmkuF08tDgXMbv55FYIUSLcq+PlTX1TE9N7NLzxOj1zM1JZ3ezh9fLKrv0XKJ3qfb5+UdhGWckxTM6zkHjxg00fruelEuvlPpbCBHU4aaCrVu3Eh+/76kBEhIS2L59OwB5eXm43e5DK50QIioopahf9TkVr75E2aDDWHjjXdTpDFyfmcKYOEd3F0+IXuFdVzV5VjNHdmHrZauUGBPX56Rz/65i8mwWRjvl/2lfF1CKfxSUkh5j4qL0JJTPR8XLLxB3yumYs3O6u3hCiB6kwwnm0KFDeeihh5g4cWLIID6NjY08+OCDHH744QAUFxeTnp4evpIKIXolX3kp5S88R/2ObXx16TV84kxmXGwsl2ckE9vB/l1C9FWVPj+f1tRxc25GxFqKjnLYmJGWyFOFZWTnxZBpjonIeUXP9FpZJUUeH38dlINRp6Pqw/fRmptJmnZedxdNCNHDdDjB/Oc//8kZZ5xBdnY2EydODA7ys3z5cvx+Px999BEAGzdu5Nxzzw17gYUQvYMKBKhZ8l+qFr1J2XHjeOv6i3DrdNyUmcqxzv2PfimEaGuxq5p+ZjPDHZEdnf2c5AS2N3l4OL+UewdmYzF0/aimouf5uq6BDypquK1/JokmI74KF9XvLyL1iqvRW3vHjAFCiMjpcII5fvx4tm7dysMPP8zatWv54YcfyMjIYM6cOcydOzfYavmXv/wl7IUVQvQOzbt24lown8aaalZdcyNLzQ5Odjq4JD0Ju0yWLUSH1Pj8LKuu4/qctIj3c9PpdFyXncqfthfyr6Jyft8NZRDdq9Tj46nCcs5PTeSoPT9wVLz6Eub+A3GMHd/NpRNC9ESdGq4xPT2dv/3tb+EuixCil9M8HqreeYuajz+gbMqZvDHyBAI6HfMyUzkmVn7lFqIzFlfUkBFj4tgDzHvZlWwGAzfmpnP7jkL+W1nD/0tO6JZyiMjzahqPFpRwmM3COSkt73vjd9/SuGEtOXf/VX5sEELsk8wHIIQIi6bvv8P1wrN49AY+m3s7K3QxnOJ0cFFaElZ5rE6ITqnzB1haVcs12anou/HLfI7FzLVZqfyzoIz+FjNHRPhRXdE9FpRU0BDQuL1/GnqdrmVgn/8sIG7yaZhzcru7eEKIHqrDCabb7eaee+7hrbfeorCwEI/HE7K+dQJdIUTfEGiop+KVF2lcvYqSX13A60OOwaDXc0dWKodHYLRLIaLZBxU1JJmMHN8DRnEdGxfLtiYP/ygo4y+DckiSOWuj2qfVdaysqePugdk49gzIVv3xB2huN4m/Or+bSyeE6Mk6/Onwm9/8hldeeYXzzz+fWbNmERNz6KPKNTQ08MQTT7B+/XqsViszZszgzDPPbLPdp59+ypNPPhl8rZTC4/Ewb948xo0bx3fffcftt9+O2WwObnPeeecxY8aMQy6jECKUUoqG/32J6+UX8CalsPzW/2NlQMcZcQ4uSEvCrJdWSyEORUMgwJKqGmZlpHRr6+XeLkpPYoe7mUfzS7hrQDZGfc8olwiv3W4PzxW7uCw9hTyrBQBfZQXV7y0i5fLZGGzSgi2E2L8OJ5iLFy/mwQcf5He/+13YCvH0008TCARYsGABJSUl3HnnnWRnZ3P00UeHbHfyySdz8sknB1+vW7eOBx98kGOPPTa4LC4ujhdffDFsZRNCtOWrrMD1wnO4f9hE4YWX81rWQKwGA3f3S2WITVothQiHjyprcRqNnBAf291FCTLqdPw+N53bthXwYmkFV2amdHeRRJg1BQI8UlDKGKedUxKdweUVr76EuV9/Ysed2I2lE0L0Bh1uYjAYDBx22GFhK0BzczOrVq3ikksuwWazkZeXx6RJk1i6dOlB9/3kk08YP358SIulEKLrKE2jZsl/yf/jTTRoGv/9w//xdHp/TohzcH9etiSXQoRJU0Djw4oazklOwNBDWi9bxRuNzM1NZ1l1LZ9X13V3cUQYKaX4V1E5Rp2Oq7JSg4P4NG3aSOO6NaRceqUM7COEOKgOt2Bee+21vPTSS5x66qlhKUBRUREAubk/dxYfOHAg77zzzgH3q6+vZ82aNW2mQ6mvr+eyyy7DZDIxcuRILrvsMmJje86vv0L0Vp6CfMoXPI2vpIT8K67ltfh04jDwfwPT6WcyYpRHYoUImyVVNVgNek7qQa2Xextis3J5egrPFLvItZjpZzn07jKi+31QWcO3DU38JS8Hy546Xfn9uP7z75aBfXL7dXMJhRC9QYcTTLvdzsqVKxk7diynnHIK8fHxIet1Oh1z585t9/Gam5uxWkNbPex2O263+4D7ffrpp6SnpzN06NDgsuzsbB577DGys7OpqqriySef5NFHH+WOO+4I2beiooKKigoAXC4XgUAAAE3T2l3u1m07ss+hUEqhaRqapkXk18Noji+aY4Pwx6f5vNQsfoeaDxejjR7L+xfPYV2zj3MSYpmWkoAB8Pv9vTa+g+nt718rk8nU4eNG8lpHy3Xen47E16xpfFBRw3kpiehRaJrq8PkiEd/keAdbmtz8Pb+EewdkYtkTY19//w5Vd8W2uaGJV0sr+XVWKhkmY/D8NR+9j9bUQPy0c8NSpkjHJ4SIvA4nmH/4wx8AyM/P53//+1+b9R1NMC0WS5tksrGxsU3S+UtLly5lypQpIcsSEhJISGiZpyk5OZk5c+Zw7bXX4vF4Qh6jffvtt3nmmWeCry+88EIASktL213uVuXl5R3epzeJ5viiOTYIT3yBHdvwvvMmyuvlh1m/5t2ENBJ8fuY6LWRqXirKysJQ0s6R969jZs2aBUg990s9MbZP3T50mmKop5HS0qZDOlZXx3eWXrFDC/DIzkKucpgjPhhRT3z/wiWSsdVrikdrmxlrNjLA3UCpuwEArbaG5vcWEjN1Oq66eqirD9s5o/m9E6Kv63CCGe5fnLKysgAoKCggJycHgJ07d9Kv3/4fw9ixYwf5+flMnDjxgMfW6/UopVAq9Nffc889lwkTJgAtLZhLliwBID09vd3l1jSN8vJyUlNT0Ufg0UClFH6/H6PRGLFfh6M1vmiODcITX6Cpkao3XqFp5aeoKWfyztiT+dbt4byURP5fUlxIn7DeGF9H9Jb4DpY4LliwgFmzZvXYeq63XOfOam98Xk3js60FnJOaSE5SXKfPF8n4bkny8acdRXyhM3F+WlKffv/CIdKxBTSNZ3aXkGKOYc6ATIx7xVe26A3MOf3IPOMsdGEqS2fja33yTAjR83X7JFYWi4UTTjiBl19+meuvv56ysjKWLVvGrbfeut99li5dyrHHHhtsrWy1ceNG0tLSSE1Npaamhvnz5zN8+HAsFkvIdsnJySQnJwPgdDoxGFrmd+pMRa7X6yP24dZ6rkh2sI/m+KI5NuhcfEopGteuwfWfBejsdnb+4W5e04xkKnggL5esffSz6k3xdUa0xOfz+YLH7e6y7Eu0XOf9aW98n+4ZNGdKUlxYyhWJ+DIsZn6bncpD+aUMdtg4NoJzdva09y+cIhXbG+VVFHh93D8wm5g934cAmn74jsa1/yPnz3/BYAz/18VIxddTTJ06lR9//JGtW7fuc/1TTz3Fr3/9a3766SeGDBlySOdaunQpp5xyChs2bGD48OGHdCzRdZYvX86aNWuYN29eyPJnn32Wq6++murq6jZdEXuLdtUY69evZ9iwYVitVtavX3/Q7UeOHNmhQlxzzTU8/vjjXHHFFdhsNmbOnMkxxxwDwIwZM7jrrrs44ogjgJYvSZ999tk+p0nZsWMHjz76KPX19djtdkaOHMnll1/eobII0Vf5q6twvfg8jRs3wK9m8OYRx/JDk4cL0xI4Iym+x8zDJ0S08mmK91w1/L/k+F43j+yIWDvnJMXxZGE59+WZSTeburtIoh3W1zfybkUNN2enkhzz83sWHNhn4imY+w3oxhJGj5kzZ3LRRRfx9ddfc9xxx7VZ/8orrzBq1KhDTi4BRo8ezVdffRWWY4mus3z5ch5//PE2CeY555zDkUce2asHKW1Xgjlq1ChWr17N6NGjGTVq1H5/vVNKodPpgoPmtJfD4WhzcVu98cYbIa9NJhMvv/zyPredNm0a06ZN69C5hejrlKZR9+lSKt98FVNWDtv+dB+vNwfor+Bvg3LIMMvokEJEwmc1dXiVximJnX80tjtNS4pjp8fHwwUl3DMwu9clyX1NudfHE4VlTE9J4Gh76LgXNUs+JFBfR+K5M7qpdNHn7LPPxuFw8Morr7RJMPPz81m1ahUPP/zwIZ0jEAigaRpOp5Pjjz/+kI4luk9KSgopKb17juF21f4rVqzg8MMPB1qy7f39WbFiBcuXL+/SAgshwsdbXETR/XdT8caraBdcyr8vnsOrzQEuSkvmjgFZklwKESF+pXjXVc2ZSfFYDb0zMdPrdPwmO5XmgMYzRa424x+InsOnKR7NLyXPamZ6Smh3I391FVXvvkXy+RdjsEfucedoZ7PZmDZtGq+//nqb8UxeffVVdDodF1xwQXDZLbfcwlFHHYXD4SA7O5uZM2e26Ws/fvx4pk2bxvPPP8+QIUMwm81s2rSJpUuXotPp+Oabb4LbPvjgg4waNQqn00laWhpTp05l27ZtIce75JJLGD58OMuWLeOYY47BbrczZswYNmzYELKdpmk8+OCDDB06FLPZTHp6OhdccAH19T8PAvX9998zdepUnE4nDoeDqVOnsmPHjoNep/bE/fnnn3PiiSfidDqJjY3l6KOP5qWXXmr3+uzsbG644YaQY7722mvodDoKCwsB2LZtGzqdjpdffpkrr7ySuLg4UlNTeeSRR4CWFuchQ4bgdDo577zzqK2tDR6r9fp/9NFHTJs2DbvdTmZmJg888EBwm9tvv5377ruP2tpadDodOp0uOHjps88+i06no6amJrh9ZWUlV155JcnJyVitVo4//niWLl0aEkPr/fDaa68xZMgQHA4HU6ZMYefOnQe97uHWrhbM1gFxAE4++eSuKosQIkKU30/1B+9StXgR1qOO4cc/3ssb9c0M0el4cFAuqTHyeJsQkfRFTT0NAY3TDmFgn57AYTBwU24Gd+woZFCVmdOTemf/oWj3QqmLGn+Aef0z0et07J3uVLz2H2KycogdP2G/+4vOmTlzJv/5z3/49NNPmTRpUnD5K6+8wqRJk8jIyAguc7lc/PGPfyQzM5Py8nIeeughJk6cyKZNm4JjhwD873//Y9euXdx33304nU4yMzOprKxsc+7CwkKuv/56cnNzqa2t5amnnmLcuHFs2bIlpJ9fUVERN954I/PmzSM2NpZbb72V6dOns3XrVox7+uJed911PPfcc9x0001MnjyZuro63n//fZqamoiNjWXr1q2MGzeOESNG8MILL6DT6bj33nuZMmUKP/74IzEx+//x+mBx19TUMHXqVCZMmMDrr7+OyWTi+++/DyZ4B1vfUbfddhsXXHABb731FgsXLuTGG2+krKyMlStX8tBDD1FdXc3vfvc7brvtNp588smQfa+66ipmzpzJddddx8cff8y8efNISkriqquu4tprr6WoqIi33nqLTz75BIC4uH3X/36/n9NOO43du3fz17/+lYyMDJ588knOOOMMli9fzoknnhjcdu3atZSWlvLAAw/g9/u54YYbuOyyy1i5cmWn4u+sDvfaHjhwIIsWLQr2kdzbpk2bOPvss9v1C4UQonu4t23BtWA+gYYGuPZ65idmkt/g4fKMFCYmxEZ0gBUhBGhK8Y6rmtOT4rDv9cWxt+pnNXN1Vir/KixjgMXMYfYDTzsmImtlTT0rquv484BsnEZDSEtz0+bvaVjzFdl33Re2UWPFz6ZMmUJqaiqvvvpqMMHcvHkzGzduZMGCBSHb/vvf/w7+OxAIMHr0aPr3789nn30WkpxWV1ezfv36kOT0u+++a3Puxx57LOR4p5xyCikpKSxcuJArr7wyuK6qqoqVK1cG55k3Go2cccYZrF27luOPP57Nmzczf/58/va3v3HLLbcE9zvvvPOC//7zn/9MamoqH3/8cXCawOOPP56BAwfy73//mzlz5uz3Gh0s7h9//JG6ujoeeOABhg0bFryurQ62vqNOOukkHnzwQQAmTpzIW2+9xZNPPsnu3buDg41u2LCBl19+uU2CeeqppwZbLU877TRKSkq45557mD17NtnZ2WRlZWEwGA76OPN7773HunXr+Pjjjzn11FMBOP300xk2bBh33313SEtmbW0t3377LUlJSUBLy+d1111HaWlph0aRP1Qdrj127dqFx+PZ57qmpiYKCgoOuVBCiPDT3E24XnqeovvuwpR3GBtvvZu7rUnY9HoeGpzLpESnJJdCdIMvaxuo8fs5M4pa+06Mj2VyYhyPFpRS4/N3d3HEHgXNHp4tKueS9GQG20JH2Fd+PxUvLcB58mQs/Qd2Uwmjm9FoZMaMGbz99tt4vV4AXn75ZSwWC9OnTw/Z9v3332fs2LHExcVhNBrp378/AFu2bAnZbsSIESHJ5f58+eWXTJkyhaSkJIxGI3a7naampjbHy8nJCSaXQLCLXOujo8uWLQNg9uzZ+z3XkiVLmDZtGgaDAb/fj9/vJzk5maOOOoqvv/76gOU8WNyDBw/G4XBwzTXX8Oabb7aZvuZg6ztq7+TUaDQyYMAARo4cGTKTxZAhQ6ioqMDtdofs+6tf/Srk9fTp08nPz6ekpKRDZVi5ciUJCQnB5BLAYDAwY8YMvvjii5AfiY499thgcglt379IaVeC2dzcTFVVVbDJva6ujqqqqpA/xcXFvPPOO2RmZnZpgYUQHde4YR35f7qZpk0bUbfcweMTTue9ukbmZKVya78MkkzdPmOREH1Sa+vlKYlxxBp7f+vl3i5LTyYlxsSjBaX4pT9mt3MHNB7JL2VErJ3T9zGQVKl/pygAACAASURBVO2yj/HX1ZJ07oXdULq+Y+bMmVRXV/PRRx8BLf0vzzrrLJxOZ3Cb1atXM23aNHJycnjppZf46quvWLVqFdDynXxvqampBz3nzp07Oe200wCYP38+q1at4uuvvyYxMbHN8X45LUbr46yt21VWVmI2m0lMTNzv+SorK3nooYcwmUwhf9asWXPAhqj2xJ2UlMSSJUuw2WxccsklpKWlMWnSJL7//vt2re+ofV2P/V2jXzbA/fK9aX3d0QSzurqatLS0NsvT09PxeDw0NTUdsLzQ9r7pau36VvnAAw/wf//3fwDodLrgTbovf/7zn8NSMCFEx9Wt+pyqd9/G39hI2bAjSPzV+VS/+xYN677GecZUVp4wmYVVtRxrMnJLbgbxklgK0a2+rmuk3OvjrOToab1sZdTruCEnndu2F/ByaQWXZ/TuURF7M6UUTxeVA3BNVmqbp1X8NdVUvfMWyRdeisEhA/t0pdZHRV999VVSU1PZsWMHf//730O2WbhwIUlJSbz22mvBuUK3b9++z+O158mjDz/8kKamJhYtWhSc+sLr9YYMItNeSUlJeDweqqqq9ptkJiQkMH36dK655po26/ZOpH+pvXGPHTuWjz76CLfbzbJly7j55puZPn06P/30U7vWWyyWYAtyq6qqqvZdgA4oLy/f5+v2tDjvLTExkbKysjbLS0tLMZvN2Gy2zheyi7Tr2+W0adPo378/SimuvPJKbr/9dvLy8kK2iYmJYdiwYTKhqxDdpH71KsqffQr2tBQ0rv+axnVriOk3AO32+3g4oKeqpoHfZaczxmmXx2GF6GZKKRa5qpic6CSuCyay7wkSTUZ+n5POfTuLGGy1MC6+987r1pt9VFXLhvpG7s3L3ucoxdVvvkpMZhbOkyZ2Q+n6nosvvpiHH34Ym81GfHw8Z555Zsh6t9uNyWQKJlnAfqfoaw+3241OpwsO0gMto6b+cjTb9pg8eTIACxYs4KabbtrnNlOmTGHTpk2MHDkyJIb2lLMjcVutVs466yy2bt3KzTffjM/nw2QyHXR9dnY2mzdvDjlW60A74bRo0SKmTp0afL1w4UJycnKCCWZMTMx+ux3ubfz48TzyyCMsXbo0+MhuIBDgrbfeYvz48T3y+1y7PtGOOeaY4KA+Op2Os846K+T5XiFE96v5cDGlSal8NeJ4mi1WBhTuZPSGNWw881e82uhnTJyDP/ZPwRllj+EJ0Vutr2+i0OPl1n7R3bXkcLuVi9OTebqonBxLDDkWc3cXqU/Z0uTmPyUVXJedts9r7/7xB5q+Xk3WHffIwD4RMnPmTO69914WLFjA7Nmz24yqesopp/D444/z+9//nrPPPptVq1YdUoI5efLkYCPRVVddxaZNm3j44YcP2Jq4P8OGDePqq69m3rx5VFRUMGnSJBoaGnj//ff5y1/+QlpaGvfccw+jR4/mjDPOYPbs2aSlpVFSUhIcPXfGjH3Pr9qeuN99911eeuklzjnnHHJzcykpKeGJJ57gpJNOwmQyHXQ9tAxIdP3113PvvfcyZswY3nvvPdavX9/xC3sQS5YsYd68eUyaNImPP/6Y1157jaeffjqYEA4bNozm5mYef/xxxowZQ1xcHEOGDGlznLPPPptjjz2Wiy++mPvvv5+MjAyeeuoptm/fzjPPPBP2codDh2uSSZMmsXv37n2uW79+fcQ7kQohWux2xPHUpdex/qhj+f6wI/nvyWfy9pnT2VhVw9zc/8/enYc3VaUPHP9mbZKmbbqnG4UCAi4g4oaoZRMFER1FQEAE/Y3gxuCOiiwjOqOOAg7uoo4zuDAKrjACCoqyuKEsIiA73fcmTdMs9/z+KI0tLdC0adLlfJ6nD+Su78m9ucl7zz3nWJmeZpXJpSS1EkIIlhcUMyg6kpgO8Kj6iNgozokM55nDuTi83lCH02GUe7wsPJzHoJhILm6g9lh4PBT+503MlwzE0KVrA1uQWkLPnj0555xzEEIwfvz4evNHjRrFE088wfvvv8+oUaPYuHEjH3/8cZP3d/bZZ7NkyRK2bNnCyJEjWbZsGcuXL/c9Luuvl156ifnz5/P+++9z5ZVXcvvtt+NwOAgPDweqO73ZsmULUVFR3H777Vx++eU8/PDDOJ1OzjrrrBNutzHlPu200xBC8PDDDzNs2DDuvfde35AkjZkPMHXqVGbMmMHixYsZM2YMiqIwb968Jr0XJ/Paa6+xc+dOrrnmGt5++22eeOKJOj3oXnPNNUybNs2X6N5+++0Nbker1fL5559z5ZVX+oaNyc/PZ+XKlXWGKGlNVMLPkZBHjhxJ9+7dfQON1nbfffexd+9ePvroo4AF2NKys7N55ZVXuPXWW/3qoEhRFF+Xv/5U/zeVEAKPx4NWqw1KVXh7Ll97LdsjX29ivyUWcVyZZidaOD0+rsX2K8/NwGpq+U52LWsL17m28j431fHl+8VWwVOHclh4WjrxLTDubKjL1xCnV2HW/qNY9Tru6WRF3Yzj3BrLFyiBKpsiBH8/lI3dqzCvSyo6df24Sz9fSfHHy0me/xRhluhW/dnbtm0by5cv55JLLqnXkUlTlJaWsmHDBr+vi5J0KmvXruWyyy5j69atHbbpoN9Xri1bttQZf6e2QYMGsWnTpmYHJUmSf0pXr6REpa6XXKoQuA2tr/G3JHVk1bWXJVwaHdkiyWVrZdCouaeTlZ0VlXxUUBLqcNq9D/KL2VdZxYw0a4PJZXXHPv8l5rqxaMyybawkSYHj93M5dru9TgPa2tRqNTabrdlBBZvVakWr1eJPZa4QwreOn5XATVKzj2Dsq2Y/7bV87alsQlEoeuctytd/SeKdM6nfB5qKZL1/57bfMchzM+D7a0r5tKfoJKa1X+fayvvcnP3V/LvL4WSvw8ltKQkttu/W+jlJ0uu4PSWBBUdyyTCG0dvctBtgrbV8gdpXc8v2i93BioIS7u1kJV7X8Oe+8L2l6BKtRFw6CCVI7yM0vXwajWziIUlthd8JZq9evVixYgVXXHFFvXkffvghPXr0CEhgwVQzWKzH499g0NHR0SiK0qReuJrKG8S2K+25fO2hbEqVk8JXX6Bq317y7nmY/YoOvUqFVwjUKhUeIZiUEINFrfL73G4KeW4GTlPKV3vQ54a0letca3+fm8vr9fJBfjH9I8OJbeHPZmv9nJxtCuPKmEj+eSSPv3ZOIr6JbVBba/kCoTllK3R7WHw0j5ExkfQ2hjV4jjn37Ma++VusD81BOZbktfbPXlPbC0pSsA0dOjRoN2xaK7+v6jNmzGDy5MloNBpuvvlmkpOTyc7O5o033uC1117j9ddfb4k4W9SSJUu49tpriY9v/BhdiqJQVFREbGxs0Np/eL1eNBpN0NpItNfytYeyeUpLyF/0NF5HJb/eO5u3K1xcGx/NFTFR/GCzU1hu5+y4GLqGGwOyv5OR52ZgNbV8BQUFJ53f2q9zbeV9bqqa8u2rcrPL4eTpbmmnrHVujtb+ORlnjeNglZt/Zhcyt0syej9jbO3la47mlM2jCJ7PySXdEMZYa1yD7VyF10vJO/8i4pKBhHfv0WY+e23xCbkaHo+H3377LeDb1Wg09OrVK+DblaTm8vvbbdKkSeTl5TFv3jxefvll33Sj0cjf//53brrppoAGGAy5ubl4PB6/Lqwqlcq3TjDHnwnW/tpz+dp62aqyjpDz7JOoYuNYO/lOvqpwcmdqom98uUstkeQ6HVhNhjZZvsbspy0fv8bspynlO1VNWFu5zrX297m5VhSWcEGkmdQWHqqjtX9ONCoVd6VZeXjfEd7IKWRqSkKrPzdr9tuav6OW5hVS5Pbw925paE6QvJWtW4OnpJiU62+os/3W/tkLZg1roAkhGjXeob/kY8NSa9Wk26f3338/U6dOZePGjRQXFxMbG0v//v2bNJ6OJEmN59i5ndzFz6Lqex5Lh47icKWL2V1S6G4yhDo0SZJOYX9lFb/YK3mqW1qoQ2kVIrUa7u5kZe7+LLqbDAyJiQp1SG3axlIba4rLeLRLClEnqB33lJVSvHwZsdffgCZC/mYLhYbGOWyqffv2BWxbkhRITX4+JzIyssF2mJIktYzyDevJf/NVvKOu45XT+6H2KszvmtqheqGUpLbso+Iyzo0Ip1ML1162JV2NBqYkxfN6Tj7phjC6yZtlTZLldPFydj7jrbH0PEnTiKJlb6NLsBI5cGgQo5MkqaNpUsOFwsJCZs6cyZAhQ+jRowc7d+4EYNGiRWzevDmgAUpSRyeEoOiD98h/81XKbr6Nf5x2NvE6HfMyZHIpSW3FIWcVP9kr+VP8yTtj6ogGx0RyqSWSBUdyKfe03ccgQ8XpVVhwJJc+ZhMjYk88PmTl3t3YNm4g7sYpqILQblWSpI7L7yvMTz/9RPfu3Xn77bexWq38/vvvvufKs7KyWLBgQcCDlKSOSrjd5L28mNI1/+PQ3Q+xwGJlgCWC+9OTMGnkDwRJaitWFJTQJ9xIhlHWXjZkclIcUVoNi47k4u3gvS/6QwjBa9n5eIRg2knasQqvl4K3Xifi4kyM3QL3iKYkSVJD/P6Fevfdd9O/f3/27dvHv/71rzrd8F5wwQWyBlOSAsRrt5P19ONU7tnFd/c+yhIMTLLGMzkpHk0QO7WQJKl5spwuviuv4JpY2cbwRPRqNfekWTnsrOK9vKJQh9NmrCkuZ0t5BXenWTGdpMOXsnVr8BQVEnv9DUGMTupIDh48iEqlwul0hjoUqRXwuw3m999/z/Lly9HpdPV69IqPjyc/Pz9gwUlSR+XOzyX72afwGo18fMdMfqny8GB6En0iwkMdmiRJflpRUMwZ4Ua6ydrLk4rT65ieZuVvB7PpZjRwfpQ51CG1avscTt7KLeD/khNIP8m55Skvo/iDZcRcNxZtZPu+yfHnP/+5UeMPvvbaa0GIJnQGDhzI5s2b6wyF1KNHD3788ccQRiV1JH4nmOHh4ZSXlzc47/Dhw8TGxjY7KEnqyCp/30POoqdx9TyD/wy7ljKPwl8zUkiTHYNIUpuTU+Xi2zI7szonhzqUNuEss4mxibG8kJVHikFPSpg+1CG1SjaPlwVHcrnUEsnA6JP3Blv033fQxicQNfiyIEUXOrfffjsATqeTl156icsvv7xNjRMp3G4qN32Dt7AATVw8xv4Xo9I1ra+FhQsXMm3atABHKEmN4/cjspdffjnz58+nqOiPR1hUKhWVlZUsWrSIESNGBDRASepI7N9vJvvJx7BdMoTFQ65GrVHzeNc0mVxKUhv1UUEJPUwGTj9Jz55SXaPiLJxlNvHs4RwqvUqow2l1FCF4/mgeZo2ayUlxJ1228vc92L75ivgO0rFP3759Of300/nkk0/o1q0ba9asISYmhr59+9b5a42E203xwqewLX8Px9dfYlv+HsWLnka43QHdz7vvvktGRgbR0dHcd999XHjhhbz55psAzJ07l3HjxvmWdTqdqFQqDh48CMCqVas455xziIyMJC0tjUcffbRRNcZSx+N3DeaTTz7JgAED6N69O4MGDUKlUjFr1ix+/fVXVCoV8+fPb4k4JaldE0JQuupTit5/h5xJt7IkIY1zw43cmpKAvgP8KJCk9ijf5WZDqY0HZe2lX1QqFbelJDJr3xFeyspjRpr1hJ3XdEQfFZSwx+Hkia5pJ/1+EIpC4b9fJ+KiSzB27xHECEOnsrKSefPmkZyczPTp0/nhhx947LHHePzxx0lMTAxpbFH/fBqAPD/W8Rw6QP49t59wfrjRCC++0ejt7d69m5tvvpmPP/6YzMxMnnzySX744YdGrx8eHs6bb77JmWeeyY4dO7jsssvo06cPo0ePbvQ2pI7B71+uKSkp/Pzzz9x1113k5OTQtWtXioqKmDBhAj/88AMJCQl+B2G323nyyScZO3YskydPZuXKlSdcdtSoUVx//fWMGTOGMWPGMHfu3DrzP/30UyZPnszYsWN56qmncDgcfscjScFU3bvfEopWLGPHXQ/yYlwqV8VFc0dqokwuJakN+7iwhC7GMM6StZd+M2nU3NMpiZ/tDj4tKg11OK3GdruD/+YXc3tqAtawkz86Wb5uLe78fGLHjA9SdKE3b948UlJSmD59OgDnnnsuN954Y73fih3BPffcg8Vi8f3dcsstvPfeewwfPpyhQ4ei0+l46KGHiI5u/NBJl156Kb1790atVtO7d29uuOEGvvrqqxYshdRW+V2DCWCxWJg3bx7z5s0LSBAvv/wyXq+XN954g5ycHGbPnk1qaiq9e/ducPkFCxaQmppab/rWrVt59913+etf/4rVamXBggW8/PLL3H333QGJU5ICTamsJPfF53Ac3M+X98xmg6LmzpQELrJEhDo0SZKaodjtYX1JOfd0SkKlUsnHyJog1aDntpREnjuSS4YhjDPMplCHFFLFbg//PJLHlXEWzo08eQdI3vJyij54j5hrx6CNOvHYmO1NWload9xxR51pmZmZ2Gy2EEUUOs8++2y9NpjTpk2jU6dOvtcajYaUlJRGb3PLli3MnDmTHTt24HK5qKqq4k9/+lPAYpbajyYlmIHkdDr59ttvWbhwISaTia5duzJ48GDWrl17wgTzRL788kuGDBlCRkYGABMmTODee+/l9ttvJyxMtmGTWhdPSTHZC57CIeCDO2dy2AuzuyTR3WQIdWiSJDXTx4UlpIaF0beDJ0XNdWGUmb0OC4uO5PG3bmnE6kL+syUkPEKw6EguKWE6xiWeujPFovffQRsb2yE69qnt+OSyxsiRI4McSX1ld90PwGmnNTwOaU0bTE/WEd80bWonYv5y/wk7+tm3b59fMSQnJ/PLL7/4Xnu9XrKysnyvzWZznSf/cnNz66w/fvx4brvtNlauXInRaOTuu+8mJyfHrxikjqFRz9/17t2bHTt2AHDWWWfRu3fvE/716dOHQYMG8eCDD1JcXHzKbdec2LXvqGRkZHDo0KETrjNr1ixuvPFG/vrXv3L48GHf9EOHDtGlSxff6/T0dBRFITs7uzHFlKSgqTp8iCN/nUVJbDyvTpxGqUrD/K6pMrmUpHag1O3hi+Jyro2Plm0HA2C8NZZUg54Fh3NwKx2zJvid3CJyXW6mp1lPOQ6yc99eyjesJ/7Gm1GdZGzM9ui///1vvWkffPBBCCLxn0qnI2bGA0RcOxZT5hAirh170uSyKa6//npWrVrFl19+idvt5sknn6SkpMQ3v2/fvmzYsIH9+/dTUVFR70lFm81GdHQ0RqORH374gbfffjtgsUntS6NuBfbr14/w8HDf/0/1hWmz2ViyZAm7d+/mww8/POmyTqcTo7Fu+5Tw8HAqKysbXP6JJ56gR48euN1uli9fzuzZs3nhhRcwmUw4nU5fnFDdUYDJZKq3rcLCQgoLCwEoKCjwjeepKI3vra5mWX/WaQ4hBIqioChKUH6wtOfyhbpsju2/kPfCIvIyh/JG34vICNNxV0oiJo06IDGFunwtTZavaXTHfqS01utce3mfAT4pLMGq13KO2ejbfnsqX0Nasnwq4K6UeB7Zn8W/cgq4OSmuXZXveMeX7btyO6uKSnk4PYmoU3xPCEUh/63XMV84gLBupzXq/WlP5+auXbsaNa21Uul0mC4dFJBtzZgxg/vuu8/32mKxcPToUV599VVuueUWSktLufnmmzn33HN9ywwZMoRJkybRr18/LBYL8+fP9/UwC/DCCy9w7733MmPGDDIzM7n++ut9v6clqbZGJZhvvPFHD1W1T7ST+eijj7jxxhtPuZzBYKiXAFZUVNRLOmuceeaZQPWPpYkTJ7Ju3Tp27dpFv379MBgM9Tr1cTgc9bb1wQcf8Oqrr/pe13TJfPyjAI2Rn5/v9zptSXsuXyjK5vluE66PP2D7n8bzfpeeXKRVM0oH5QX5NDy6bNO152MHsnz+mjJlCiCvc8cLdNnsimBNaSVjw/Xk5/nTX2TLaE/HbqJJy+KScvaV27AJCFfBFe4ceujaZy1dfn4+BV6FF8ucDDfqiLGVkWsrO+k67i0bcefmoBl/U5M+68HUEufm7Nmz602bNWtWwPfT2q1fv/6E8yZMmMCECRN8ry+88MI68xctWsSiRYvqLF9j9OjRJ+wxtnPnzrKtueTTrMYMQghycnJISEhAq627qczMTP7973+fchs1jYuPHDlCWloaAAcOHCA9Pb1RMdS+25aens6BAwfIzMwEqh+ZVavVJCfX7SL+uuuu8y1TUFDA6tWrAbBarY3aJ1TfecvPzychIQF1EHr6FELg8XjQarVBu8PYXssXirK5XS5sHy/HvnoVW267j1XGSG6yxnJZTFTA99eejx3I8p3IqX5MvvHGG0yZMqXVXufayvt8Ksvyi4nVe7i8UwrqWuVoL+U7kWCUL04ILI7DHPBUP3VUCLxiq+KhTkmcaW7ZnnqDdfyEEOx1VLK/sJgusTH8J6+YM80mbkhLrHM+NcRrK+fI2lXE/ul6oro33M7vRPtsC+emrCmTpLajSQnm559/zpw5c9i6dSsej4fvv/+ec845h1tvvZXMzEwmTJiAxWLh6quvPuW2DAYDAwYMYOnSpUyfPp28vDy++OILHnjggXrLHj58GLfbTefOnfF4PHzwwQe4XC569Kge32nw4ME8++yzZGZmkpiYyNKlS7n44ovrdfATFxdHXFz14MSRkZFojrVRaMqXsFqtDtqXd82+gtmmpz2XL1hl87qqKF7yErbfdvHp3Y+yQ63jwbRE+kSEn3rlZmjPxw5k+fzlPjZYd2u9zrWH97nC6+Xz4nKmJMWhPa7tW3so38kEo3y77Q5KjiWXvv0C/8krYpwqFp1KhfbYn+7Yn1at8k3/Yz5+xxiM8ilC8FJ2PhtK7WgAb0UuRpWKORkp9c6nhhQuX4Y2OgbLZVeg8uOYt/dzU5Kk4PM7wXznnXeYOHEio0ePZsqUKdx2222+eV27duWNN96oU53eGFOnTmXx4sVMnjwZk8nEhAkT6NOnDwBjxoxhzpw5nHHGGZSWlvLiiy9SWFiIXq+nW7duzJs3D7O5urvuvn37MnbsWObNm4fD4aBfv35MnTrV3yJKUsB4beXkLPoHxU4n793xIOVqDX9NTyLNIHs1lqT25n9FZURo1AyQwwy1iEpFQQ14j5t+tMrFP4/k4RECdyMf0dOqqJ94HpeM1k5QNajQIAjTaOolrrW3oauX0HKC6X9so+b1V6U2vi21Q60yOoVgr8NJ31PckHTu30f51+tImTm7w3XsIzXP5s2bQx2C1A75nWA+9thjzJgxg2eeeQav11snwTzjjDNYsGCB30GYzWZmzpzZ4Lxly5b5/t+7d29efPHFk25r5MiRraI7akly5eaQs+BJcq0pvHXtTcTqw3g8PYlIrfzyl6T2xuFVWFlUyoTE2FP28ik1TZcGbsxpgBFxFiZYq59KEkLgBTxKdbLpFsKXeHqUP/5//PQ6r4XAffyyisClKFQpChWCesufan+N6c5GRXWNbG1aFeyrrDppgikUhYJ/V3fsY+zRq7FvpyRJUovxO8Hcv38/I0aMaHBeeHg4ZWUnb4AuSR1B5Z7fyFn0Dw70z+TffS/inHATU1MTCJN3liWpXVpTXIZBreZSS2SoQ2m34vU67kqzsvhoLl5RnYz1CjdyfUKMbxmVSoUW0GpUBHLQp+a2U1SOS1Z9iWmt6auLythUZq+TjArArDn5Y6TlX6/DlZNN0l/uO+lyUuuwZ8+egG1LI39TSK2U3wmm1Wrlt99+Y8iQIfXmbdu2rdGd80hSe2XbvJHc117g5+snsSIlg+viLFwVHYFOtjWRpHbJqSh8WljC6IQYtGpZe9mSLowy08PUmcOVTpylpfRLsaJtA9dWtUpFmEpFGFRXuzYgVqtlq82BU1FQji0WqdFw8UkeufbabRS9/y4x14xGa4lugcjbl7Fjx/Lee+8Ffb9ardbXX4gkdQR+J5jjx49n7ty59OzZk4EDBwLVdwx37NjBU089VeeRWUnqSIQQlHz2EQUr3ueLW+9mk9nCnakJ9I804/F4Qh2eJEkt5IvicrQqFYOiZe1lMETrtERpTOTay0/Zs2pbEqfX8bduabydW0hWhYPO5nAmWuMwn6SWquj999BGRWEZenkQI229XnnlFYYMGULXrl1DHUodKpXK11+IJHUEfieYc+fOZefOnVx22WXExsYCMHz4cAoKChg5cuQJ21JKUnsmPB4K3lpC/k8/suIvj3BUZ2B2ehLdTQY5LpQktWOuY7WXI+Oi0beBmjSpdUvU6/hLaiK5ublYrScfxsN5YB/lX31B8gOzUGmbNepcu1FUVMT9999PWloaQ4YMYeDAgURGto4bP17v8d1TBYZ8TFZqjfy+Iun1ej766CPWrVvHmjVrKCwsJCYmhqFDhzJ06NCWiFGSWjWvw0Hu8wvILS1j6W0PoNXrmZ+eRLxeF+rQJElqYetKyvEKGBrTOn7ESh1Ddcc+b2A+vz+mXmeEOpxW46GHHqKsrIz169ezZs0a/vWvf3HeeecxZMgQ+vXrF7K43G4327ZtC/h2NRoNZ599dsC3K0nN5VeCWVVVxeLFixk2bBiDBg1i0KBBLRWXJLUJ7qJCchY8ycHYRP49firdwo3clWbFdIpOGSRJavs8iuDjwlKujLMQJmsvpSCybViPK/soSXfdE+pQWp2oqCiuvvpqrr76avbu3cuaNWt49tlnMRgMuFyuUIcnSR2CXwlmWFgYjz76KOedd15LxSNJbYbz4AFyFj7F9nMvYtnZ/bksJpKJ1jg5RIEkdRBflZbjVBSGxUSFOhSpA/Ha7RS+/w4xV1+HNjrm1Ct0YN27d6d79+78+c9/ZuPGjXzzzTehDonTTjstYNvat29fwLYlSYHk9yOyZ599Nr/++iuXXnppS8QjSW1Cxc8/kf3iIjZcO4G1KRlMTopnWKz8kSlJHYVHCD4qKGFErAWjfGJBCqLi5cvQRERiuWx4MecI6gAAIABJREFUqENpM3Q6HZmZmWRmZoY6FEnqEPxOMBctWsTEiRNJSEhg+PDhGI3GlohLklqtsi9Wk/3ef/jk5unsiozhwbRE+pxkEGxJktqfb0tt2LwKV8gbS1IQOQ8eoGzdGpLvf0R27CNJUqvl99Vp8ODBuFwurr/+egBMJlOdQYdVKhVlZWWBi1CSWgmhKBS9t5Qjm75l2R0zqTCYeCw9mVSDPtShSZIURIoQfFhQwhWxUYTLHhylIKnu2Od1zOddgOn0M0MdjiSFzMCBAxk3bhzTpk0LdSjSCfidYN577711EkpJ6giUqiryXnme/fn5vH3rvcSajMzvlESkVv64lKSOZnOZnWKPh+GxllCHInUgtm+/xnX0MEl3zAh1KFIrN3DgQL799lt27dpFt27dAPjtt9/o1atXo4ZOW79+PePGjSM3N/eEy7z55pvccsst9Z5kXLduneyrRWraOJjtjdVqRavV+jVeoRDCt04wxjms2UewxlRsz+Xzt2yeslJyn/sHOyNjeXfs/3FulJk/J8ejV6sbtb48doHfnyxffdpTPC7X2q9zbeV9VoRgeUExl0VHEaFp3DWgZn+1/21p8nMS+P3V/rel93V82bwVdgqXvU30qGvRRMcENI62cuzkeI/+iYyMZPbs2bz99tstto/zzjuPzZs3t9j2pbZLPsAP3HLLLQB4PB6/1ouOjkZRFBRFaYmwGtRSA/U2pD2Xr7Flc+dkk/fcP9jU90I+7XMBf4qN4urYKFSKgsfP90Ueu8CR5Wt4nZNpK9e51v4+f29zkOfycIXF7Pd7Ca2/fM0lyxcYx5et+INlqMPNmAcPa9J51xit/dhFRES0YEStg1sIvrFXUuD2Eq/TcLHZiK6JTw3eddddPPPMM/z8888NjpVZXl7OjBkz+Oyzz9DpdIwbN44nnngCt9vN8OHDqaqqwmw2A/D999/Tq1cvv/Z/+PBhpkyZwnfffUfv3r259NJL2bRpE+vXr+fgwYN06dKFyspKDAYDAOPGjaNnz57MnTuX8vJyJkyYwJYtW3C73fTv35+XXnqJTp06Nem9kIJPJpjAkiVLuPbaa4mPj2/0OoqiUFRURGxsLOogjH8mhMDr9aLRaILyiHJ7Ll9jy1b5269kPb+Q/101hu9SM7gzJYH+UWa/9yePXWDJ8jWsoKDgpPNb+3WuLbzPQgg+Li5jSHQksYYwv/bXFsrXHLJ8gXN82aoOH8S2fi1J9z6E7tiP8UBqK8fOZrO1YFSh5xaCp3KLOeL64wbCJnsl91tjmpRkWq1Wpk+fziOPPMJnn31Wb/706dPJzc1l9+7dOBwORo4cyd/+9jfmzJnDqlWrTvmI7KmMHz+e3r1789lnn7Fz506GDx/O6aef3qh1FUXhpptu4r333sPr9XLzzTdz++238+mnnzY5Him4ZIIJ5Obm4vF4/LqwqlQq3zrBbJMarP215/I1pmy2jRs4+J83+WDS7WRHxTA7PYnupuZ9sctjF7j9yPLVd6pajbZynWvN7/NPNgdHq1w8kJ7c5Bhbc/kCtV9Zvubvw/dZFYLC/7xJeL/zCT+zd4vvtzUfu2DWsLaEp8OO9Th9KK/R6xxwebj9cP4J5xs14bxxkvUfeOABMjIy2LBhQ52bi4qi8M477/Ddd99hsViwWCzMnj2b++67jzlz5jQ6vh9++AGLpW5b9Ly8PPLy8ti4cSOfffYZBoOBfv36MWHCBLZu3dqo7VosFkaPHu17/cgjj3DJJZc0Oi4p9OTgXZJUixCC4o8+YNd/3+X1W+/FERvP411Tm51cSpLUtgkhWFFQzMDoSGJ08t6sFBy2jRuoOnSQuBtuDHUoUhsUFRXFgw8+yEMPPVRnekFBAS6Xi86dO/umde7cmaysLL+2f+6551JaWlrnLywsjOzsbKKiooiK+mMYp/T09EZv1+FwMHXqVNLT04mMjOTSSy/FbrdTVVXlV3xS6DQqwbznnns4cuQIAF9//TV2u71Fg5KkUBAeD/mvvcjPW3/ilZv/gtViYW5GKvF6XahDkyQpxLZXVHKgsoqr407e1lWSAsXrqKBo2dtEj/oTuti4UIcjtVF33XUXBw4cqPN4aVxcHHq9nkOHDvmmHTx4kJSUFIBm12QnJydTVlZGeXm5b9rhw4d9/69p2+lwOHzTaj+O+8wzz/Drr7+yefNmysvL2bBhAxC8jqik5mvUbdhFixYxbtw40tLSGDRoEJs2beL8889v6dgkKWi8FRXkLn6WLeFRLB89hctio5hojUMjh+SRJAlYnl/MJZYIecNJalGekmKKPvoA59EjZFdVoQoLI/ryK0MdlhQA91dVjxF/2mmnNTi/oTaYnfTak7bB3Ldv3yn3azQamT17No888ohvmkajYdy4cTzyyCP85z//weFwMH/+fCZOnAhAYmIiJSUllJSUnLIDuYZ06tSJCy+8kIcfftiXLC5dutTXUVBcXBypqan861//Yvr06Xz66ads2rSJgQMHAtXtbY1GIxaLhZKSEh577DG/Y5BCq1E1mImJiWzZsgWovnsgx8GU2hN3QT5HHp/Dys49+GDgCCYlxXNTUrxMLiVJAuDXikp2O5xcEy9rL6WW4ykr5cich7BtWI/y+x7cRw4h3G5EG297KDWOTqXiAWsMY2MiGBJpYmxMRJM7+DneLbfcUi9RfO6554iNjeW0006jX79+XHrppb5HaXv27MnEiRPp1q0bFouFXbt2Nbjd77//HrPZXOevpqb07bffZufOncTFxXHnnXdy880311l3yZIlLF68mJiYGD777DOuuuoq37wZM2bgcrmIj4/nggsu4LLLLmv2eyAFV6NqMCdOnMhf/vIXZsyYgUql4sILLzzhsjWNtyWpNfJW2Cn+9COqDh2kKL0z4Wf24ehrL7J8xGh2p3TigbQk+kSYQh2mJEmtyPL8YgZEmbGG6UMditSOlX2xGq+jAmollF67DdvGr4kaPCyEkUnBolOpGBSA3yDr16+v81qr1bJ3794606KionjzzTdPuI0lS5awZMmSE86fPHkykydPPuH8zp07s27dOt/rl156iU2bNvleDxs27IQ1sMnJyfXKMHXqVN//j58ntT6NSjCfeuophg4dyq+//so999zDXXfdJceikdocr8PBwXmz+N6aRklMItFZOfRZs4qlk+6gPDGJx9KTSTXIH5CSJP1hj6OSnRWV/KOb/M6TWpanqBCOu0GvQoW3Vjs2SZKktqDRXeENGzaMYcOG8dFHH3HbbbfRs2fPloxLkgKu+KsveHnoKI4mJqMSgAp+Or0vZx/Zz8iLzidSqwl1iJIktTLL80u4INJMirz5JLUQV3YWpatXYdv8bb15QvGiT2t875uSJEmtgd99rdeu7hZCYLfbMZvNsl2m1OptUOs5mpiMovnjtM9KSuEClVcml5Ik1bO/0snPdgdPdksLdShSOyOEwLFjG2WrV+LY/guGHr1InDYd2zdf4dj2M2jU4PUScclAws85N9ThSlKzTZs2jWnTpoU6DClImjSY11dffcW8efPYuHEjbrcbnU7HgAEDmDt3rhwIVWp1hBA4fv6R7JJSsNa9EyyA0tj4hleUJKlDW5FfwrkR4aQbwkIditROKC4Xto1fU7b6f7jycoi44CJS5/4NQ+cuAJj7nUfFjl8oPrCf2O49MPU6Q97AlySpzfE7wVyzZg0jRozgtNNO46GHHsJqtZKTk8P777/PkCFDWLlyJUOHDvVrm3a7neeff56ffvoJo9HImDFjGDFiRL3lfvvtN9555x1+//13AHr06MH//d//kZycDMD27duZNWsWYWF//BgYPXo0Y8aM8beYUjtQnVj+RPGH77NXEey7ahxQ94tapVKRXGugYUmSJIBDziq+t1XwRNfUUIcitQOekmLKvlhN2fovQAVRA4eSPGQYWkvdnj1VajWmM/tQHpeI0WqVyaUkSW2S3wnmrFmzGDFiBB9++GGdC9+cOXO45pprmDVrlt8J5ssvv4zX6+WNN94gJyeH2bNnk5qaSu/evessV1FRwdChQ3nggQfQ6/UsXbqU+fPn88ILL/iWiYqK4q233vK3WFI7UpNYFn34Pr+pNHxzxXXsjbBwXkQ4VFWRX+VGQaBGRarRyJCYqFCHLElSK7Miv4SzzSYyjIZQhyK1Yc4D+yhdvQr7d5vQW5OIu/4GzP0vRq2XbXo7mprfzMf35tocGo1s3iO1Tn4nmNu3b2fevHn17qqpVCpuu+02rr32Wr+253Q6+fbbb1m4cCEmk4muXbsyePBg1q5dWy/B7NevX53X11xzDcuXL6e8vJzIyEh/iyK1M7UTyx26MDYMH80hcxQDLBFMjYsmxaCnSlFYXVTKwdJyOlsiuTzWgl7dqOFgJUnqILKcLraU25mbkRLqUKQ2SCgKFT9+T+malTj37MbUuy/J98zEePqZskayg9LpdJxzzjmhDkOSgsbvBNNsNpOVldXgvKNHj2I2m/3aXs22ag97kpGRwYcffnjKdXfs2EF0dHSd5NJmszFp0iTfh3nSpElERET4FZPUttQkloUfvs8vhnA2XDmGbFMEA6MjmREfTaJe51s2TK3mylgLuW4n1lgLaplcSpJ0nBUFJZwebqSHyRjqUKQ2xOtwUP71OsrW/g9veTmRF2eSMGUq+qTkUIcmSZIUVH4nmKNGjWLmzJmkpqZy+eWX+6avXr2aRx55hKuvvtqv7TmdTozGul/i4eHhVFZWnnS93NxcXn755ToDr6amprJo0SJSU1MpLi7mhRdeYOHChTz66KN11i0sLKSwsBCAgoICvMcGNVYUpdFx1yzrzzrNIYRAURQURQnKHdC2UD4hBI5ftlL48XJ+Co/im5HjKDSGMzgmkgdiLcTqqk/v48vQFsrWHLJ8gdVeyqfT6fzebjDf69byPue53HxbZuOR9KSAlru1lK+ldOTyufPzKFv7P2wbvkJtMhE19HIiLhmE5tgNd39jbE1lawnBLp8kScHnd4L59NNPs337doYPH05kZCSJiYnk5eVhs9k477zzePrpp/3ansFgqJdMVlRU1Es6ayssLGT27NmMHj2aiy++2Dc9Ojqa6OjqBvNxcXHceuutTJs2jaqqqjod/3zwwQe8+uqrvtfjxo0DqpNWf+Xn5/u9TlvSGssnhEDZ/SuV69awNTqBr68ci91o4iKDjoEGHRF4cBcVcqqj2RrLFkiyfG1boMs3ZcoUQF7njnd82d6rqKKzVk10eSm5trIQRRU47fnYQejKJ4RAObAPz7df4/1tJ+rUNHR/uh7NGb1xajQ47Xaw25u1D3nsJElqq/xOMKOjo9m0aROffvop33zzDSUlJcTExHDxxRdz5ZVX+v3IYUpKdRuXI0eOkJZWPdbYgQMHSE9veGDhoqIiHnnkEYYNG3bK2lK1Wo0QAiFEnenXXXcdmZmZQHUN5urVqwGwWq2NjltRFPLz80lISAjKY5ZCCDweD1qtNmh3GFtb+WpqLPM/+ZDN0fF8c80kqgwGroi1cEVsFOZGNnZvjWULJFm+wGor5TtV4vjGG28wZcqUVnudaw3vc4HLzQ/FR7i/k5Uksymg+2sN5WtJHaV8GiGo+H4zZWtW4Tp6hPB+5xP18FwMXbsHbF/y2DWs5skzSZJavyaNg6lWqxk1ahSjRo1qdgAGg4EBAwawdOlSpk+fTl5eHl988QUPPPBAvWWLiop4+OGHGThwIKNHj643f9u2bSQmJpKQkEBpaSmvvPIKZ599NgZD3V4A4+LiiIuLAyAyMtLXC1dTLuRqtTpoXwA1+wpmJwGtoXzVieVP5H7yEd/EJPLtqBsQYQZGxsdwWUwUJk3T4msNZWtJsnyB0V7K53a7fdsNdSwNaQ3v86fFZXQ2htEnIjzgMbSG8rWk9l4+d1kp5V+sxr7+C4THQ2TmYJL+cj+62LgW26c8dpIktVVNSjADberUqSxevJjJkydjMpmYMGECffr0AWDMmDHMmTOHM844g9WrV5OTk8OKFStYsWKFb/3nn3+e+Ph49u/fz8KFC7HZbISHh3POOedw0003hapYUjPV1FhmffYRX8cls+mqG9Dr9VyTEMvgmEgM8otJkqQAKXZ7WFdSzt1pSbKnT8mn6shhSlevxL7pWzQxsURffS2RAzJRG+TwNZIkSSfSKhJMs9nMzJkzG5y3bNky3/9vuOEGbrjhhhNu55prruGaa64JeHxScNUklkdWfcL6hFS2XHkD4WF6xiXGkmmJkMOKSJIUcJ8UlpAapueciMA+Giu1PUJRcGzbSunqVVT+ugPj6WeSeOcM9L3ORKfXyxsQkiRJp9AqEkxJgurEsuKXnzj0v5Wss3biuxHjiNHrmGyNY4AlAq38UpckqQWUejysLS7nztREmTx0YIrTSfk3X1G2ZhWeoiIiLrqYuMeeIiytk6+doiRJknRqMsGUQq46sdzKvs9X8WVKZ34cMZYkvZZpSfFcGGlGLX/wSZLUgj4rLCVRr+O8yPBQhxIQ7oJ8Krb9jLu8HPeASwhLSAx1SK2au6iQsrWfU/7Vl6i0WqKGDCNy0FC0kVGhDk2SJKlNkgmmFDJCCBzbfmbPms9Zm9KFrcOvJ12vY0ZyAudEmGRiKUlSixCKgvP3PXiPHqVUo2F1sZ1bkxPaxTWncvcusv/xt2O9pwuOrvyY5PsewtijV6hDa3Uqf99D2eqV2H/4Dn1qJ+Im3ETE+f1RHRszVpIkSWoamWBKQVeTWO76Yg1r0rqy7fLr6K7Xcl9iLGdHmmWvcpIktRilqorsZ/+Oc/cuUKupBM4fNY7+vTJCHVqzCSHIfX4hwuOGY8NzCZWK3BcW0WXRSyGOrnUQHg/2H76jdPVKqg7sI/zsfqQ8MAtDj17y8WhJkqQACWiCqdPpZDsF6YRqEsvt675kTXo3fr3sT5wRpmN2SiI9TQY8Ho/8gpckqUUVffAeRwuL2Tz4ShxGE52yDzP0k2V4L+6POi4eqL5WoSgIrxe8HoTHg/B6a732IrzV0/D9v3o6x5arXsdT67Ubr8uNSghQvL5tcmw7wlNr2WP7/ON1zfyaZWv246m1HS+K241wVtYtsBB4y0rJfXkxuvgEtDExaGNi0UbHoo2JRW0ydYjrrtdup/yrLyj74nO8DgeRlwzEOu1OdAmNHxdWkiRJapyAJpiTJk1CUZRAblJqB4QQOLb/zE9ff8WaTqexZ+jV9A3T8tcUK91NBt8ykiRJLclTXsb+A/t5fuJUvGoNilrNjtPO5GBKOuMevg+VimNJo583SVUq0GhQabWoNBpUGu1xrzWg0YBag1qn871WabS+ZTi2nEqrRR1mqPVaAxrtsWVrtlXz/2Pr1mxDrSb3+YX14z82z7lvL57vivAUFyFcVdWhh4VVJ5y1/6Jj6rxWG41tNgl15WRTumYVtm++RhMZieXyK4m4ZBAak+wtWJIkqaUENMFcsmRJIDcntXFCCCq2/8z333zDms49OJB5Jecb9TyZYiXdGBbq8CRJaseUqiqqDh3Aue93qg78jnP/PjyFBay6ZgIejQah1lQvp9Gwo8dZlEWG0zPJ6ksIVVrtscTuVImeFlUjHuuvebpHq9W2aLIWP3EyBf9a4ntEFpWK+Em3EJU5uE4siqMCT3ExnuIiPCVFeIqK8JQU49y72zfdl4QaDA0mntroGLSx1bWhaqOxxcrkLyEElTu3U7p6FY5tWzGc1oPEW+8gvG+/6uMmSZIktSjZBlMKuJrEctOmTazp0pOjl1zBRSY9d6QmkRKmD3V4kiS1M0JRcGUdxbn/d6r2VyeTrqwjAOhTO2HI6Irlqj+Rl55BfpnTl1zW0AgFz3n9CY+ODEX4ARU1cChaSzS2LRuprHQSnzkYc99+dZZRqVRows1ows2EpXVqcDtCCJSKijrJp6e4uvbTuec332vhclVv02BEGx2NNibuj8dwj/3pYmLRxsSgNgam1tBbYacq6yhKpROR+EcPuYrLhW3TN5StXokrN4eI8/uTOudxDF26BmS/kiRJUuM0KcEsLCxk4cKFbN68mZycHJKSkrjwwgv5y1/+Qnx8fKBjlNoIIQT27T+z4bvvWdulJ3n9h3Kp2cC9qUkk6mWvfJIkNZ8QAk9RIVX79+Hc/zvOA/uoOrgfUVWFNj4BQ5euRAy4FENGV5wpndju8rDN7mC7vZIyu4dwnQ61V0GpVYvoVWtINRpCWKrACj+7H8befcnNzcVkbVobQ5VKhcZsRmM2E5aW3uAyviS0uAh3cSGuggKUslK8JcXVSeixhFS43QCojUZf288/2oL+UQta8zjuyVT8/BO5Lyz0JbZ555xL/PibKP/qS8rWrwVFEDVoKMlDhqGNjmlS2SVJkqTm8TvB3LJlC1dccQVer5fBgwczYMAA8vPzee655/jnP//J6tWrueCCC1oiVqmVEkJg2/4L63/4kbVde1FyfiaDzUYeSUsmVicrySVJajpvhZ2qA/tw7q/+q9r/O97yMtThZgwZXTH2PJ3o4VdhyOiGYo5gt6OSX+wOttkdHDqQTaRGQ2+zkQnWWHqbTahUMGvfUUo8HhDgBcYkxJBikE9X+Kt2EqpP60RYA48AVyeh9mO1oEXHks7q2s/K3bsaTkJrdUJUuzYUjZbcxc9Wd3h0jGPrjxz66Qd0ySnEXjeWiP6XoA6TTTAkSZJCye9f/3fccQdnnHEGn332GVFRfwxCXFZWxvDhw7nzzjv5/vvvAxqk1DoJISjf/gtrt/7Mlxm9sPcbwGWR4YxMS8KilYmlJEn+EW43zkMHqTpQnUg6D+zDnZuDSqsjrHNnwrp0I+KC/hi6dkMbX/1oZI7LzXd2B9uK7fx6pAAPgh4mIxdFRTAtxUS6QV9vfMsnu3Vic5mN3JJSzk6M53Sz7PClpVQnoRFozBGEpXducBkhBIrd9keb0FrtQit/+9WXlAqPu6GV0SVa6fT4P9psR0SSJEntjd9ZwM6dO/nvf/9bJ7kEiIqKYubMmYwdOzZgwQWL1WpFq9X61ZOpEMK3TjB6QK3ZR7B6Wz1Z+YQQlO/4hf9t28m6jJ64+lzA5VHhXJmajFmraVKcwSxfRz52LbW/2v8GY3+yfPVpT3FTp7Vd54Si4M7LOfao6z6qDvxO1eFDoCjokpIxZHQj6rLhGDK6oU9Nq+50B6jwevmxopLt2QX8YndQ6PZg1evobTYyPSaR001GDJq6ne4cH7tRrSIzykyRp4pYY5g8jwK0v9r/+kttjkBvjkDf6cSP41Z8v4W8l577owOjYzQxsc3ad2PI76jA7q8p5dPIDpokqc3wO8Hs1q0bpaWlDc4rKysjI6PtDVZ9yy23APg9fmd0dDSKogR1aBav1xu0fR1fPiEEpTu38/muPXzVtSeccQ5XRIUzLNmKUaMGmj8GarDK19GOXTDI8gVOU8oXHR190vmhvs55SktxHdxH1YH9uGraTVZWorFEo+/SFWPf87BcOxZ9epc67fAUIdhdWcX2ilK2VzjZ56xCr1ZxhsnAyJhIzjIZSKjdxlsoeDyNi1WeR4HXkuULO6s3WmsSnvw8qNmPSkXkZVcEbfxt+R0VGE0pX0RERAtGJElSIPmdYD799NPccccdpKWlkZmZ6Zu+fv165s6dy+LFiwMaYDAsWbKEa6+91q8OihRFoaioiNjYWNSN6KK+uYQQeL1eNBpNiz4G5HVVsf6LL9lTWYVe8TKsVw9STz+Doh3b+GzXHr7O6IGuZ2+ujo3kstRkwgJU9mCVD9rvsashyxdYbaV8BQUFJ50fzOucUllZPUTI/t99nfF4S4pRGYwYumRgyOiGZejlhHXp6uuIpfb7XOzxss3u4Be7gx0VlTi8ChnGMHqbTYy3xtLNZEDbzGMhz6PACkr5tFpSZ86h4N9LcO7dgzAYiBs9joi+57bM/mqR31GB09Ty2Wy2FoxKkqRAalSCedZZZ9W56JSVlTF48GCioqKIj4+noKCAsrIyoqOjefDBBxk+fHiLBdwScnNz8Xg8fl1YVSqVb51gtvtoyf0JRWHJ/1azvktPVEKgEoJvnG4y33qLr87sh6lbL8bGRzM4NQWdumViCMb72R6P3fH7keVrmf225vKdqgbHn+ucUBTsmzdSlZuNU6uDK6484fiBwuPBlXXkWCc81cOEuLKzQK0mLC2dsIyuxF43FkNGN3TWpAbHjKxSFH61V/Kzzc4Oh5OsKjfRWg19zCZuSY7nrHATEdrAPh4nz6OW229L7k8bFUXSnfegKAq5ubmYrdZ2Vb6afbTHY1d7P00pXzBrWCVJap5GJZj9+vWrcxHo16/fSZaW2qoD+/ezruvpANS0ihAqFbuS0piUEM2laSnNrjWQJKl1E4pCzoIncfy6A5VKjRAK2Vt/IOWhOah0OjwF+ceSyb1U7d9H1aEDCLcbXaKVsIxuRA4cgqFLN/Sd0lHrG+6ZVQjBkSpXdW+vNge/OZwA9DSGMcgSSZ8IE6lhetlpiyRJkiS1QY1KMN98880WDkMKBsXpxFVYQHFJCQVl5RQ6KihyuSn2CkrUGlxuN6puvRC1ahgUjYaKiCgGd0oNYeSSJAWLffO3OH7dAV4vguoag6qDBzgyZyZemw3FbkMTEUlYRjdMZ/Uh+urrMHTpisZsPul2yz1edhx77HWb3UGJx0tqmJ7eZiMj4yz0NBlQK0q9YS4kSZIkSWpbmjWWRFZWFsXFxcTExJCSkhKomKQmEB4PruIiSoqLKSwtI99RQXGViyKPQolaQ6lOT2l4BDZzBF6NCSwmTKYqot0uooVCjFZNuNfDruMeX1N7vSR6G+gaXpKkdsmVm4NKpaJO345CQaXVEj/pZgxduqKNiz9lEugRgr0Op68t5YHKKkwaNWeFmxiTWD0mZe1xcoUQeILYoYkkSZIkSS2jSQnm0qVLefTRRzl06JBvWnp6OvPnz2f8+PEBC06qJhQFd1kphfkFFNtsFFZUUOSsosjrpQTEoH/AAAAgAElEQVR1dfJoDKfcHIlXqwNzHCZ9BBZXFdHCS4xaTecwPXHhJhKiooiLjCBWr2uwgx7V1m2s0lTXJADoFS+39D4j2EWWJClEdLFx9YcO0GoxX3AREef3P+m6eS43v9iqayh3VjioUgTdTQb6RYRzc1I8GcawemNSSpIkSZLUvvidYL7zzjvceOONDBs2jDlz5mC1WsnNzeXdd9/lxhtvRK1WM27cuJaItdWwlZfzy5692Ox2wvR6YuPimrU9r6OC4sIiCkpLKLTZq5NHt4cSVBRrdZQaTNjMEXi0OjBYMKqNWPROohUvMWoVnfQ64sJNxEdGEG+xEGsIw9DEnucm9e1Nj6PZ7CooQuVxceUZvYgzyUHIJamjiLjoEsrWf0FBUTHFkZFY7Dbiw/RYhlxeb9lKr8LOikq2HXvsNdflJl6npY/ZxG2piZwRbiRcjl0nSZIkSR2K3wnm3/72N6ZMmcKSJUvqTL/pppu45ZZbeOKJJ9p1gnng8CEezy3BYTBDtJn3DuXyQHEJp5/WvcHlFZeL0uIi8otLKLDZKHI4KfJ4KBFQrNFSFmakLDwCj04H6nAMRi0WdSXRXg8xKjhLryPWaCQ+wkx0RATWyAiMAe5N8XgXpCZzXnL1jYMYg6FF9yVJUuui0un4ZerdLC0oQRyrbRwVFc54oxFFCA46q6ofe7U52FPpRIuK081GroiNorfZRJJeJ9tQSpIkSVIH5neCuWfPHp555pkG540dO5alS5c2O6hgczqdZGVl1enmPzU1FbVazeHDh+ss++T23ThS06lyOHDZbKgUhceOOhi9ezcqs5lSRbA/r4BSlZpyvR670YSIjCLMEo3apcJQYiNKcROFIEGr5aJIM0lRBryOSqJMRoyREUAERqOR+Ph4KioqKCoqQggFT0kR+bYykpKS0Ov1ZGdn14nZYDCQkJCAw+GgsLCwTtwJCQkYDAZyc3NxuVy+6WFhYSQmJuJ0OsnPz/dNVxTF1yV4Xl4eVVVVvnk6nY6kpCSqqqrIy8urs5/Y2FjCw8MpKCigsrLSN12r1ZKcnIzL5SI3N7fOOjExMYSHh1NYWIjL5fL9OFWr1aSmpuLxeMjOzq6zjsViITIykuLiYux2u2+6SqUiLS0Nr9dLVlZWnXWioqKIioqipKSErKwsXC6XbwyutLQ0AI4cOVJnncjISCwWC6WlpZSXl9eZd6JzxGw2ExMTQ3l5OaWlpdVtyzwetFotqampaDQajhw5UucxxPDwcGJjY7HZbJSUlNTZXlJSEjqdjqysrDrdtB9/jtSoPXB1IM+RysrKemMtxsfHYzAYyMvLw+v1+o7dyc6RuLg4TCaT3+eI2WymsLAQu91OQUEBLpfL9542dI5ER0cTERFBUVERFRUVvumNPUdqxlyrOX4ZGRnVvZ+2wDlSW1JSElB9LtZO1E51jpxKY69zexxOXi8qRx9lwW234bLZeDcHvjt0mPwqN57YODLMJlLKS5liDKOL0YBO5SEcL7Fh+iafw4WFhb7PiUqlwmq1tuh1TlEUCgoKiIqKIjw8vMWvc9HR0RgMBgoLC+usI69z1eR1ru51rvaxO9k5EojrXO1jl56e3uLXOUVR0Bx7ssGfc8Ttlv1BSFKbIfyUkpIiFixY0OC8Z599VqSkpPi7yZDKysoSmZmZguqROXx/ZWVlQgghdDpdnendxk0QY7fvFX3ufbDeOjd/+JmY/dW3whQRUWf6dWPHCrvLLV588cV66+zevVsIIURaWlrdda67TgghxL///e966/z4449CCCF69epVZ/rll18uhBBixYoV9db56quvhBBCnHfeeXWmDxgwQAghxJo1a+qt89Zbbwmv1ysGDRpUZ3qfPn2EEEJs3ry53jrvvfeeEEKIUaNG1ZnetWtXIYQQO3bsqLfOa6+9JhRFEePGjaszPSEhQQghxMGDB+uts3DhQiGEELfeemud6SaTSQghRGFhYb115s+fL4QQ4u677643T1EU4XQ6602fOXOmEEKI2bNn15tXXl4uhBBCq9XWmX7nnXcKIYR46qmn6q2TnZ0thBDCYrHUmT558mQhhBAvvPBCvXX27NkjhBAiNTW1zvTRo0cLIYR466236q3z+eefC6/XK3r27Fln+hVXXCGEEGL58uX11vn666+FEEKce+65daZffPHFQgghVq9eXW+dlStXCkVR6n2Gzj77bCGEEBs3bqy3zrJly4QQQlx11VV1P1vdugkhhNi+fXu9dV5//XUhhBA33HBDnemJiYlCCCEOHDhQb51FixYJIYT485//XGd6eHi4EEKIgoKCeus8/vjjJz1HKisr601/6KGHhBBCPProo/Xm2Ww2IYQQGo2mzvS77rpLCCHEk08+WW+drKwskZWVJaKioupMnzJlihBCiOeff77eOnv37hVZWVlizpw5Iisrq9nXudNumHjC69yvhw4JIYSIjo6uM/2mm24SQog2d5375JNPhBCixa9zr776qnC5XGL8+PF1psvrnLzO1fzVXOeO/y4M5nVOCBGU69zWrVuF1+v16zr3ySefiDlz5oi1a9eKH374odl/a9euPeE1U5Kk5lEJcXxvDid377338tJLL/Hss88yZswYoqOjKS0tZdmyZdxzzz3cdtttPP300/5sMqSys7N57rnnuO6660hMTPRNb+iOnBCCB/ZnoYpP8N3ZB1ApCs9kpNKlS2e0Wi1Hjx6tc3c1EHdtRa07jMGswezSpQsFBQVBqcGsia2lazCLiorYv38/8fHx7frOfqdOncjNzQ3Knf2a2IJZgxkfHx/0GkxomXOktqSkJAoKCnC73X7VYBYUFPDKK69w6623kpycXGe+P9e5L4rLWOmoQhP5Rw2mBhgcHck1CTEkJye3yHUuVDWYp59+elBrMEtLS4NSgymvc233Opefn8+hQ4d8x6691mCmpKSQlZXV6HOkoKCATz75hEsuuQSLxUJzlZaWsmHDhgavmZIkNY/fCWZVVRXjx49nxYoVqFQqtFotHo8HIQTXXXcdS5cuRX+CwbWDwW638/zzz/PTTz9hNBoZM2YMI0aMOOHy2dnZJ/xR1pDlGzfzX3OMb6xIteJlaEkBN2deHLAyNKT2F0Aw2jcpikLu/7N35+FRlWfjx7+zT2aSyTZJJhthU9xBsG6gKLgXKwLihgtal1br1srPKiCubW19a/u64VL62lIVBcG1bO5YdyzuC8QQQpbJzCQzySSznfP7IySCCZBlZs5kcn+ui4tkcpb7Pid5ztzznPM8dXW4XK6uNyeJlMz80jk3kPzibbDkt6e2rC/tnDcS5TffbqVdUVAAHWDU6fjD6HJKLIlr2wfLce4vyS++0jm/dM4N+p/fpk2bWLFihRSYQgwCfX4G02KxsHz5cj799FPeeustfD4feXl5TJo0iYMPPjgRMfbJ4sWLicViLFmyhNraWhYuXEhZWRmHHHJIXLY/4+gjsX/4MW8E24kpKkdajJxxzNFx2bYQQmgtz2TkzlFlPLa9gdpQBAcql5UXJ7S4FEIIIUT66FOB2d7ezo033sgFF1zA4YcfnhIF5c7a29vZsGED9913HzabjVGjRjFlyhTWrVsXtwIT4OTDxnNikj9hFEKIZCmxmFk4ouyHnoYMi9YhCSGEEGKQ6FNlZLVaWbJkyS7PE6SSzucMhg0b1vXayJEjqaqq0iokIYQQQgghhBgy+nyL7NFHH817773H5MmTExHPgLS3t5ORkbHLa3a7vVtB3NjY2PXgv9vt7hpQYOcBK/amc9m+rDMQqqqiKAqKoiTtGYmd/0+0ZOaXzrmB5Bdv6ZKfyWTq83aTeazT5TjvjuQXX+mcXzrnBsnPTwiRfH0uMG+//XbmzJmD0Wjk1FNPpbCwsFuDlJeXF7cA+8JqtXYrJltbW7sVncuXL+fRRx/t+v6cc84B6DaiW2/sPPJqOkrn/NI5N5D8Brt45zd37lxA2rkfS+fcQPIbzNI5N0j//IQYyvrVgwnwm9/8hhtvvLHHZXYeYjyZSktLgY4h2DuHY6+srKSiomKX5WbOnNnVA+t2u1mzZg0ALper1/tSFIWGhgYKCwvTdpS3dM0vnXMDyS/eBkt+eysclyxZwty5c1O2nRssx7m/JL/4Suf80jk36H9+P55yRgiRuvpcYP7tb39LSgPUH1arlYkTJ7J06VKuueYa6uvrWb9+PfPmzdtlOafTidPpBDrmdjIYDAD9asg756hKNFVVu/aVzOOfzvmlc24g+cVLuuQXiUS6tqt1LD1Jl+O8O5JfYqRzfumcGyQvPyFE8vW5wLz44osTEEb8XHHFFdx///1cfPHF2Gw2zj//fMaOHat1WEIIIYQQQgiR9npdYH7++ecsXryYyspKSktLmTVrFieccEIiY+uXzMxMbrrpJq3DEEIIIYQQQoghp1cF5ttvv83UqVOJRqM4nU68Xi+PPvooDzzwAFdeeWWiYxRCCCGEEEIIMQj0qsBctGgRBxxwAM8//zzl5eX4/X7mzp3L/Pnz06bA7O/D4/0ZkbE/jEYjubm5uN1uotFoUvYJ6Z1fOucGkl+8DJb8etOGpXI7N1iOc39JfomRzvmlc27Q9/x8Ph8ALS0tcdl/vLYjhOiuVwXmpk2bePjhh7tGZnU4HNx7772MHDlylxFbByObzYbJZGLFihVahyKEEANiMpmw2WzdXpd2TgiRLjZu3Bi3bRmNxh7bTCHEwOhUVVX3tpBer+fdd9/l8MMP73otFothMpn46KOPOPTQQxMaZKI1NTURDAa1DkMIIQbEZrORk5PT48+knRNCDHad06nEy57aTCFE//X6rzRVpyaJh5ycHGlghBBpTdo5IYQQQiRDr3swbTZbt/mKWlpaur2u0+lobm6Of6RCCCGEEEIIIVJar3owb7311kTHIYQQQgghhBBikOtVD6YQQgghhBBCCLE38XtSehCTwS+EEOlABvkRQqQzGeRHiMFhyBeYTU1NPPDAA0QikT6tZzKZmDt3LkuWLOnzuv3hcrm49NJLefzxx5MyN1Y655fOuYHkF2+DKT+TycRVV13V7Q3TYGjnBtNx7g/JL77SOb90zg2Sn9+eGI1Grr76aikyhYizIX+L7Pbt23nkkUeYMWMGTqdT63B2q3MiZJ/Pl9SJkJMlnfNL59xA8ksVjY2NrFixgssvv5ySkpJdfjYY2rnBcpz7S/Ib3NI5v8GS27fffstrr73GoYceSmZm5oC319LSwsaNG3tsM4UQA5N2PZgvvvgir776Kt9//z1HHXUUN954Y6/WczqdfWpgFEWhrq4Ol8vVbXTdRFBVlWg0SkFBQVKmjEnn/NI5N5D84i2d8kvldi6djnNPJL/4Suf80jk36H9+jY2NAGRmZkqPoxApLu0KzLy8PGbPns0nn3xCIBDQOhwhhOi1mq1bqayrRxeJUOB0ojebtQ5JDFJhRSE8tG9QGpTaYgqrPU1Ut4YZ6WnmpPwcTPr0mYd8a00N677dTEs4wri6Oo4dP17rkIQQCZB2BebRRx8NwJYtW6TAFEIMGi+9vYF/OgrQWR0oGXrGvPUfbpl0JGaLRevQxCDSEovx1+o6NrW0AXBIuJZrh7mwGwwaRyb2pi2mcPPmatzhCArwXr2Hd/0t3DqiDGMaFJlfbd7CXf4QsdxCQMd/dDoq33iLiyYfo3VoQog4S7sCszcaGxu7brVwu93EYjGg47aN3upcti/rDISqqiiKgqIoSbuFZef/Ey2Z+aVzbiD5xVsy8vu+aiv/dBSg6vV09jl96yziyQ3/4YLjjo3LPkwmE5C67Zz8HsXHX7fW8UWwrev7z1vb+N/qOuYNK07YPkHOXzy80ujDHYnQ+RSkAmxpC/GC28vhDntC9gmgqhCLRTEYYiTy1P1tWx3RXCfqTrfFvpJfzMn19RQWFCRux0KIpBuSBeby5ct59NFHu74/55xzAPo1elpDQ0Pc4kpF6ZxfOucGkl8qURUFgkFirS0EgkECoRD+cAR/TCGgqnjbQ+hHH0BspzdeisHIVxjiNqrj3LlzAWnnfiydcouoKpta23Z5LQZ80tLGttpajEko/JItnc7f1tYQsR/d1RwDnnb7eNrt0ySmeLJbbbsUl52+/b4KZccH/UKI9DAkC8yZM2cyefJkoKMHc82aNUDHUN29pSgKDQ0NFBYWJvUhfKPRmLRPh9M1v3TODSS/eOspP1VRUFoCxPx+YgE/wUAAb7CNpvZ2miJRmhQFPzqa9Qb8JjMBSwYBexattkzUbAcApmgER6gdRzRCjrcR9Ue3wOkUhSyd2ut2aW+F45IlS5g7d27KtnND8fco3iKKCr7KHn/mcrkSWmDK+Ru4creP935USBqAC135HOEY+Kipu5Osc/eHd96nypqBsvPt2qrKmJEjcObn73X9zjvPhBCpb0gWmE6ns2uofofDgWFHY9efi5Rer0/axa1zX8m4eHdK5/zSOTdIv/xUVcX/+nr8771DOBIheMLJOI6aFJ9tx2LEAgFiAT+xQDMxf4BIwE9TMEhTKIwvHKZZBb9OT7PBSMBsJWDLpCUzk4A9i3BmIWSCTlWxR8JkxyJkqwrZOh2jjEZyLGbyMqzkZmWSZ7eTazZj1eu6jlc4HGHeBxtxZzqIGYzodtz2N72iPG7nsHO+uVRt5+TvZOAsepiQZWNjIMjON446DHoigDmBecr5G5jGcIR3/S0YdB23rHY60J7Bifk56BP94YBel/AC8/KR5Sxy+4mqKqASMxiZ2eym8OB9ErZPIYQ20q7AjMVixGKxrmclwuEwer0eozHtUhViSKlZ+SxLVDNfnTobnaoy9ov/Mje4FufUE7stq0ajO4rFADF/846vO74PtrbgC0dojkRpUqFZpydgMhOwZ9Fi7ygYW7IctBT98KyQSYmRHYuSjUqOXk+Rych+Vgu5tgxybTZyTUZyjEYcRgOGfrxBM5tN3D7uIP724UYqTWbs4TDnlLk4YOSIAR83MbRcWVrEVV9XElZBBxxkz8AbibJgyzbmDSvGZZGRiVPN5mA7f9xaS6nFzM3DS/jQ30qVr4nRebkcm+tIaHGZTGUZVq75v7v5+uSf0Wq2ML7YxYRJR2sdlhAiAdKu6nr66ad56qmnur7fsGEDU6ZM4brrrtMwKiHEQCjhMIsNdrZUjCRm6Gi2PjlwHEu++C8X/e0RIgE//nCYpphCswrNJjMttsyOgjErmxZHNi1ZRQQKRxAydgx2o1NVMlWFHCDboCfHbKI0w0qu1dJVMGYbDeQYDBiVGCaTKaGf7jvsNq6bPHGXOeKE6KtPWoIYdHoe2qeMFreb0uJiQir877Y6btmyjRvKXRyYadM6TLHDf5oDPLitgWNzsphbUoBRp2NqroO6UBBXTlbaFJcA3lXLKSosYNwpJ1NfXy9tnBBpLO0KzPPOO4/zzjtP6zCEEHHU0tLCtyP33eW1mMHIJweM5dtIhIDFirrjjZhZVcnWQ67RSI7ZRInFTK7RSLbRSK7JQI7RQPaO4rE3vY2qqhJVkzNSpRADoagqq9w+TsrLJttopG3H73eGQc9vhhXzZL2Hu7/fztySAk7Iy9Y42qFNVVWec/t4tsHL+S4np+VnJ/XW4mQLb68hsOFNSubNT+s8hRAd0q7AFEKkl2iTj8aXX4BxE7v9TNXpuGh0BXkmU0dvo9FIxk7PNgoxlHwUaKU+HOE0Z/fiUa/Tcb7LSanFzGPbG9jWHuaCYme/bukWAxNWFB6paeDDQCu/HlbMhAROQZIqvM89Q8YBB2Hb/8CkTS0jhNCOFJhCiJQUawnge/l5Pv3sM1Yf/1PM4RBRg7FrBEJDLMZYq4mJOQ6NIxVCe6qqstLtY2qug2yjcbdv4o/LdeAym7h3ay3bw2GuLXdh33lUT5FQzdEof6qqwxeNctvIMiqsFq1DSrhQVSUtH75H2YI7tA5FCJEkiR96TQgh+kBpa8O7ajkf3bmIh+1OHjnrEkpHj2bBfiMZbv1hgJL97VauGl2hXaBCpJDPWtv4vi3ENGfOXpfdz57BXaPK8UViLNi8jbpQOAkRiur2ELds3gao3DlqaBSXAJ7ly7CPm4B15GitQxFCJIn0YAohUoISDuN/bS1V69fw2k+O5f3zr2R/ewZ3FTsZmWEF4K59h9McieJuaGBUSXFSpicQYjBY6fYxKScLp9nUq+ULzSZuH1nG/TsG/7m+3MVBMvhPwmwMtPLX6joOzbJzZWlhQqeMSSVt335N8NNPKL/9D1qHIoRIIikwhRCaUqNR/G+/Qf3Lq3hz//G8NecqCq0W5hU7GZtp2+V5Sp1Oh8NoIKiX58aE6PRNsI0vWtv40z7D+rRehkHPr3cM/vM7GfwnIVRV5RVPM/+sa2RmYR4zCnKHzDPiqqriefYpMo84Gkt53343hRCDmxSYQghNqIpCy/vv4l75DB+UjmD9eb/AaLVySVE+x6TZ8PxCJNIqt4/DHXZK+zHHZefgP2UWM4/K4D9xFVVV/l7r5g1fgKvLizg6O0vrkJKq7fNPaf/2a4b97n+0DkUIkWRSYAohkkpVVYL//ZjG5U/zqS2bdWddQrPVxvTCPE7Nzx4yt44JEQ9V7SE+CgT53aiyAW1nctfgP3Uy+E8ctMRi3Le1jupQmIUjStnHZtU6pKRSVRXP8qdwHHMc5iKZ71KIoUYKTCFE0gS//Bzv8qf4ti3EutPP5vvMbE7Kz+bMgjwcRnkzK0RfrXL7GJtpY0TGwAuYMfYM7hxVxp+qalmweRs3VhRT3I9e0aGuLhTmnqpajHodd40s6/Vzsemk9eMPCVdX47r6Bq1DEUJoQApMIUTCtW/ZjGf5U2zbvp3Xpp/Hf/OLOCo7k2uK8ikagm++hIiHulCY/zS3sHBEady2WWg2cdvIMu7fVs98Gfynz75obeN/ttYyxmbl6jIXGYahd0eGqih4VzyNY8oJmPKdWocjhNCAFJhCiIQJ1VTjXbGM+i+/ZMMZZ/POqSPYz57Bna58RsWhx0WIoez5xib2tVnZ354R1+12DP7j6hr85+KSAk6UwX/26jWfn8e2N3BKXg7nu/KH7HPkLe++Q6TRTcm06VqHIoTQiBSYgMvlwmg0oqpqr9dRVbVrnb6s11+d+0jGvjr3k675pXNunfvROr9IQz3eVcvxfvAeH5w2g9eOP4MCi5nfFOV1jQzb39hSIb9E768/+RmNe27OU72dGyzHeSD72/n/gfJGorzR5Oc35a4etznQ/HTAeUX5lFpMPLbdTXV7iAtdux/8ZyifP0VVebLeyyueJi4pKWBKrmO3y/Z2X6mSW5+3FY3ieW4Z2SeeiiHLEdffTYM8EyzEoCEFJnDppZcCEI1G+7Rebm4uiqKgKEoiwupRLBZL2r7SOb90zg20yy/a5KP5pVU0v/0mn04+kbXXLkBvNHKBM4dJDjt6nS4ux0HOX8/r7MlgaedS/TgPVLzye8Htpcxs4kCrebfnNB75Tcy0UVBexH01bmrbw1xVUoB9D7d9DrXz164oPFTbyNfBEPPKizjAZu3z31hPUiG3/gi8+SpKawtZJ56yx+PQn/yysobWKLxCDGZSYAKPP/44M2bMoKCgoNfrKIqCx+MhPz8/KZO9q6pKLBbDYDAkZQ6tdM4vnXMDbfILNzcRWP0yzetX8934I1lz7Xx8BiNnOHM5JT8bSxzjkPPXM7fbvcefp3o7N1iOc3/FMz9/NMZrTS1cWVqIydTzM8zxzO+ArEzuGmnhj1vruH1rHb8Z5uo2+M9QPH+eSJQ/VtcTUlTuGFkatwGRUiG3/lAiYZpfXEXOqadjduz+lur+5hcIBPodmxAiuaTABOrq6ohGo31qWHU6Xdc6yZw0OVn7S+f80jm3zv0kKz+lrQ3f6pdoWv0StSPHsObqm9lsMCV0ZFg5fz3bW6/JYGnnUv04x2O/A93fam8zeSYjR2Rn7nZb8c6v0GLm9h2D/yyorNnt4D9D5fxtDrbzp621lFjMLBjuIjOObZ3WufVX4LV1EIuRc9Kpe9xOf/NLZu+4EGJgpMAUQvSZEg7T/OpafC+txJdfyKs/v46PLXaOys7kKhkZVoiEaYsprPY0c0GxM+mDyHQO/vPUEB/8593mFh7YVs8xOVlcUlKAcYgO5rMzpa0N74sryfvZDPQWGcBNiKFOCkwhRK+p0Sj+t1/Hu2oFLSYz78y5gjftOeybYeGOYiejbfEdzVIIsat13masBj2TsrV5Hk2v03Gey0mZxcwj2xvY1h7mwmInQ2EyDlVVWeH2sbzBy/kuJ6flZye1hzGVNa19Bb3JTPZxJ2gdihAiBUiBKYTYK1VRaHnvHbzPPUNbezsfn3UBa/OLyTeZ+HVRHgdZzbt9FkwIER9hReFFTxNnFuRi1Gtb2Byb66DIYuJ/qurYHgpzTXkR6dxvFVYUFtd6+Lg1yK+HFTPBYdc6pJQRa2mh6ZUXcJ5zATq5DgghkAJTCLEHqqoS/ORjPMufJuRu4KtZ5/Ny2SjQ6biwKJ/JOVno6PvIpEKIvnvdF0BV4fgd02BobYwtgztHlfHHrbUs3FLDDaUFlO1lupzBqDka5d6qOjyRCItGlDJc5vDdhe+VFzA4ssmaNFnrUIQQKSL9rgRCiLgIfvk5nmefor2qkpqfncWLYw7BE1M4w5nDqfk5XSPDJmveOyGGsqiq8kKjj9Oc8R2VeaAKzCZuH1HG/dvqWFRVx3XlRRyclT69e9XtIe6pqiXbaGBRRTFOq0XrkFJKtKmJ5rX/pvCSK9DJPJVCiB2kwBRC7KJ9y3d4lj9N2xef4Tt5Gi+ffQnfRGKc6MhkRoJGhhVC7Nk7TQFaYgonpeCgOlaDnuvLXTxZ18jvq2q5qLiAk/JTL86+2hho5a/VdRyaZeeKkgL0SZyTcrDwvbgSU1ERmYcfqXUoQogUIgWmEAKA0LZqvCuW0brxQ9qPOZ51v72b98IxjszI4N6KfFwWebZGCC0oqsqqRh8n59+E+RAAACAASURBVGdjS9FeIr1Ox9kFuQzLsHYM/hMKc1GxE8MgHARHVVX+7W3mH7WNzCjMY2ZBLgBRKTB3EWl00/z6Ooqvuh5dCvWqCyG0JwWmEENcpKEO73PPEnh3A8r4w3n7pjtZH1XZ12jijrJiRtvkeSMhtPRhoBV3OMqpg6BX8JicrK7Bf2pDYa4d5iIzRYvinkRVlb/XunnDF+DqsiKOzukYrVceBejOu2o5looR2MaN1zoUIUSKkQJTiCEq6vPifX4F/jdfw7DPfnwybxEvqSby9UZuKM9nfJZNhuAXQmOqqrLS7WNKnoPsQTKAzs6D/yzYvI0bK4opsZi1DmuvWmMx7ttax9ZQmIUjStlHPlzbrXDtdgIb3qTkNzfLdUII0c3guFoJIeImFvDje+l5mtevxlhSxuZrf8sqsx0VuKAoj8m5jkF5W5sQ6ejT1jaq2kLcUO7SOpQ++WHwn3oWbN7GdcNcHJxp0zqs3aoLhblnay1GnY67RpbhNMsjAXvife4ZMvY7ANsBB2kdihAiBaVEgdnS0sIDDzzAxx9/TEZGBrNnz+a0007rttzrr7/Ogw8+2PW9qqqEQiFuuukmjj76aD799FPmz5+PxfLDKG+zZs1i9uzZSclDiFSmtAVpWv0yvn+/hCE3l4Yrr2OFo4DGSISf5WVzqjMHqzxHI0RKWen2cUxO1qAseKwGPTcMc/F0vYfff789ZQf/+aK1jf/ZWsu+Niu/KnORYZB2cE9CW6to+eBdyubfrnUoQogUlRIF5uLFi4nFYixZsoTa2loWLlxIWVkZhxxyyC7LHXfccRx33HFd33/00Uf88Y9/ZMKECV2vZWdn88QTTyQrdCFSSsTTiP+t1wk3NtI67lCyDjsCJRym+dW1+F5cid5iIXjhz1npquCrYDsn2jOYUVgqI8MKkYK+DrbxZWsbP99nmNah9Jtep+Ncl5NSizklB/953efn0e0NnJKXw/mufPQpElcq8yx/Gvu48VhH7aN1KEKIFKV5gdne3s6GDRu47777sNlsjBo1iilTprBu3bpuBeaPrV27lkmTJu3SYynEUBWu3c62229BiURAUajf8Ab+Aw8mXLMNYjGUmWfz0sgDeCcQ5AijkXv3GYZrEDwXJcRQtcrt4whH5qB4fnFvjs114LKYuDdFBv9RVJUn6z283NjEJSUFTE3B6V9SUdt33xDctJHy23+vdShCiBSmeYFZU1MDwLBhP3xCO3LkSFauXLnH9QKBAO+//z533313t9cvvPBCTCYT48eP58ILLyQrK2uXZRobG2lsbATA7XYTi8UAUPowBHnnsn1ZZyBUVUVRFBRFScoD9emcX7rm5l76d7xmKx8dehQhs4WKmioO+GwT5lNO57WJU1jnb2V0TOG24SVdI8PG4xjI72Z8JSo/k8nU5+0m81iny3Henb7mV9Ue4uNAkLtHlPYrxlTMb7TVwh0jS7l3ax0LNm/jN+VFFPezeB5Ifu2KwoM1DXzR2s5NFcUcaM/Y63bkGtXB8+xT2A8/ClNpeb/jS3Z+Qojk07zAbG9vJyMjY5fX7HY7bW1te1zv9ddfx+Vysd9++3W9VlZWxl/+8hfKysrwer08+OCD3HfffSxYsGCXdZcvX86jjz7a9f0555wDQF1dXZ/jb2ho6PM6g0k655duuVW3BnnowquIGoyoOh3/GX8UEz/cgDczD08gyEV2MweYdOj8TdT5tY524NLt/P1YvPObO3cuIO3cj6VqbstaQuxn0mNt9lHX3P/tpGJ+V9oMLG0JM3/LNi7KtLCvqf89mX3Nr0lReDwQIqzCNVkW8gPN1AUGcIATKNXOXey7bwh98xXW6/5fv9qRH0u1/IQQ8aN5gWm1WrsVk62trd2Kzh9bt24dJ5xwwi6v5ebmkpvbMSGy0+nk8ssv58orryQUCu1yG+3MmTOZPHky0NGDuWbNGgBcrt6P0qcoCg0NDRQWFqJPwsAoqqoSjUYxGo1J+3Q/XfNL19wWH3syEaMJZafbzt4+/BjOMcT46b7DE/bMk/xuxld/89vbG74lS5Ywd+7clG3nBstx7q++5FcbivCJt5oFw0tw9XOqjFTOD+AmVWVZg49HPU1c6MrnxD7eotqf/La0hfhrdR0lFgvXlRf16RbdoX6NUlWV7Y8+QNakyRQcdPCA9tff/DrvPBNCpD7NC8zS0lIAqqurKS8vB6CyspKKiordrrNlyxa2bt3K8ccfv8dt6/V6VFXtNkGy0+nE6XQC4HA4MOy4yPSnIdfr9Um7AHTuK5lzTqVzfumUmxIO02jL3KW4BNCpKiPKyjEl8Fkn+d1MjHjnF4lEurardSw9SZfjvDt9ye8lbzNjbFYOiMO0HqmYH4AeOK/YSXmGhUdqGqgJR7mw2Imxj+e+t/m929zCg9vqmZSTxSXFBRj1fdvPUL9GtW78kFB1Fa6rr49bTMnKTwiRfJr/ZVutViZOnMjSpUsJBoNUVlayfv16pk6dutt11q1bx4QJE7p6Kztt2rSJ+vp6VFXF5/PxyCOPMG7cOKxWmSxZpC8l1E7tn/9AXpMHfvRhiqrTUWjW/HMkIUQveSJR3mjyM70gd+8Lp4FjcrJYMKKE95pb+P3322nZMSZCvKiqynMNXv5aXcfZRXlcVtL34nKoUxUFz/JlZB9/IqZ8p9bhCCEGAc0LTIArrrgCgIsvvpjbbruN888/n7FjxwIwe/ZsPv/8865lI5EIb7zxRrfbY6GjZ/O3v/0ts2fP5vrrr8fhcHDDDTckJwkhNKC0Bdl+7+8I+ptRh49Cp9NhBAyADphRkNvvQTSEEMn3YqOPcouFsXHovRws9rVlcNeoMgKxGAs2b2N7KByX7UYUlQe2NbCy0cevhxXzU2duUnvH00XL+/8h0lBP7rQztA5FCDFIpETXRmZmJjfddFOPP1u2bNku35tMJpYuXdrjstOnT2f69Olxj0+IVBRrbWH7vb+jLarwz4uupgUdd4x08VlLG26/n/EFTiZkZ2odphCil/zRGK96/fyirGjIFUJOs4nbRpTxwLZ6FmzexnXDXBw8gCK7ORrl3q11eCJRbh9RRkWGTGfWH2o0ive5Z8g56VSM2TlahyOEGCRSosAUQvRNLOBn+5/uptVo5ok5lxHW6Vk0vIQck5GRVgt10XZcWUOnB0SIdPCKp4l8k5HDHXatQ9GE1aDn+mEuljV4+d3327mo2MnJ+X0vaqrbQ9xTVYvDaOCukWXkmOStTn/5N7xJzO8n59RpWocihBhEpNUVYpCJNjex/Y930ZrpYMmZF2DQG1g4vASHUbtJy4UQAxOMKaz2NHNhsRP9EOu93Jlep+OconzKLGYW1zRQE4r0afCfTwKt/KW6jnFZdn5RWohZBpHpNyUcxrfqWXJOOx2DXe6GEUL0nhSYQgwiUZ+Xmj/cSYuzgMdOP5dMo5GbhhdjT+AosUKIxFvnbcZm0DMpJ0vrUFLCpJwsiswm/rS1lu2hMNcNc+1xWhFVVVntbeaJ2kbOLMhlVmHekLvNON78r69DjcbIOfFUrUMRQgwy8tGeEINEpNHNtrsX4S8pY/G0c8g1m7hleIkUl0IMcmFF4SVPE9OcOX2epiOd7WOzctfIjsF/5u9h8J+oqvK3WjdL6zxcVVbEWUX5UlwOkNLeju+FleROOwO9jMQvhOgj6cEUYhAI19ex/Q930DR6Px6ZMo1yq4VfD3PJ7V9CpIHXfQFQYUquQ+tQUk7X4D81HYP//Kq8iLpwhG9a29CHwpzS1s7TDT62todZOKKUfWxSDMVD09pX0JmMOI7rPmK/EELsjRSYQqS48PYaau65E++BY3lk0knsa7NyTZkLk8zlJsSgF1VVnm/0cZozRz4w2g2rQc/15S6W1Xv4fVUtOkCl4xasDZXbcZlM3DmqjAKzSeNI00OstYWml18g/5w56M0yzZUQou/kaiZECgtVV1Hzu9twj/sJD006iQMzbVxbLsWlEOninaYAwZjCiXnSe7knep2O8TtG11V3vKbs+NplMUlxGUdNr7yIweHAMfFYrUMRQgxSUmAKkaLav99Cze/voP6oY3n48OP4iSOTq8uK5BktIdKEoqqsbPRxcn42NnmWeq+8kRjGHpo/XzSa/GDSVLS5iaY1r5A3fRY6o9zkJoToH2k9hEhBbd99Q+29v2P7lFN59IAJHJ+TNeSnLxAi3Xzob6UxHOXUfsz1OBSVWkxE1V1fM+qgwmrRJqA05HtxFabCIjKPOFrrUIQQg5j0YAqRYtq++oLtf7yL6pN/xuIDJnByfjYXSXEpRFpRVZWVbh9T8xwyh20vlVstzC7MQweYdDoMQJ7RyPkup9ahpYWIp5Hm19aSN2M2OnkeWAgxANKDCbhcLoxGI6qq7n3hHVRV7VqnL+v1V+c+krGvzv2ka36pnFvw803U/fVeKs84myUVY5jhzOHMgtxer9+5XKrmF6/9SX7dGfdyO1uqt3OD5TgPZH87/7+pJUhVKMQNw1wJ2b/W+SXKmQW57G+z8m2wHdqCTCkuwm7Qp9Xvp1bnzvf8CizDKrCNG5/Q/fY3P4PcRi7EoCEFJnDppZcCEO3jcxy5ubkoioKiKIkIq0exWCxp+0rn/FIxt+B/N+J++H/5dvYF/LNkJLMLcjktz9GvY5KK+cWT5NfzOnsyWNq5VD/OA9WZ33MNXiY67GTr+n5Oeitd/05GW0yMtpggNwtI3PHrSbpeoyL1dQTefoPCa29MSo79yS8rKyuBEQkh4kkKTODxxx9nxowZFBQU9HodRVHweDzk5+ejT8KtJKqqEovFMBgMSZlAOp3zS8XcWj58H/fDf+XLOZfxZEEZFxc7OTEvu1/7S8X84kny65nb7d7jz1O9nRssx7m/ds7vm7Z2vm4LcXlp4V57nvtL/k7iK92vUe7nV2Dddz8yDx6bsvkFAoEERiWEiCcpMIG6ujqi0WifGlWdTte1TjIupjvvNxn7S+f8Ui23wH/epv6xh/h07i95JqeIK0oLmTyACddTLb9E7Efy625vvTiDpZ1L9eMcj/2uamziCEcmJQkcnEb+ThK333S7RoWqqwh+8C6lNy9KSkHb3/ySeXeDEGJg5CluITTkf+t16h99kI8uu4Znc4r4VXnRgIpLIURq+74txMZAkOkFe76lWYhk8a54hoyDx2Idva/WoQgh0oQUmEJopPnVNdQveYT//OLXvJCVzw3DijkqW54xESKdPd/YxKGZNoZnyNQaQnvtm78l+N+PyTljltahCCHSiBSYQmigafVLNPzz77x59f9jrS2HeRUlTHDYtQ5LCJFAteEI7/pbpPdSpAzP8qfJ/MmRmIdVaB2KECKNSIEpRJJ5X3iOhmVPsu6am3nLmslvh5dwSKZN67CEEAn2ktfPGJuVMfYMrUMRguAXn9H25efkTpfeSyFEfEmBKUSSqKqKZ8Uy3M8/x7+vvZkPzTbmDy9lf3mzKUTa80SivN0svZciNaiqimf502RNmoy5uETrcIQQaUYKTCGSQFVVvM/8C8+al3nx2pv51GRlwYhSRtusWocmhEiCFxubKLeYOUQ+UBIpIPjfjwl9v4W8M2ZqHYoQIg3JNCVCJJiqKPie+ge+dzew8lc3U2UwsWh4KaVWs9ahCSGSwB+N8arPz5XF+UmdUkOInqiKgmf502QffyImZwGqqmodUtxcdtllvcrnscceS0I0QgxdUmAKkUCqouB+4nG8H3/As1f9lnqjiVuHl+CySHEpxFDxiqcJp8nIBHnWWqSAlg/eJVJfR8mvb9Y6lLj75S9/CUB7ezsPP/wwJ598Mvvvv7/GUQkx9EiBKUSCqLEYDY8/jOeLz3jyynkETGYWDS/BaTZpHZoQIkmCsRirPc1cWJyPXnovhcbUWAzvc8+QfeKpGHNytA4n7g499FBCoRC33XYbo0ePZu3atUycOJGKChklV4hkkmcwhUgANRql/uH/pfGbr/nn5b8maDKzUIpLIYactV4/NoOeiTLHrUgBgQ1vEmtuIvfU07UOJSHa2tq49dZbcblcLFiwgKuuuoo77riD+vp6rUMTYkhJiR7MlpYWHnjgAT7++GMyMjKYPXs2p512Wo/L/uxnP8NisXQ9x3LAAQewaNGirp+/+OKLPPvss7S1tTFhwgSuvvpqbDa5LUkkjxqJUPfgX2is3c4/Lr0WncnEzaWF5JpS4s9NCJEkYUXh5cYmZhTmYtTpiGodkBjS1EgE76rl5Jx6OobMTK3DSYjbbruN0tJSfvWrXwFw2GGHccEFF7Bo0SIeeughjaMTYuhIiXe8ixcvJhaLsWTJEmpra1m4cCFlZWUccsghPS7/5z//mbKysm6vb9y4kaeeeorbb78dl8vFn//8ZxYvXsz111+f6BSEAEAJh6n733tp9PpYctFV2M1m/l9FMdY0GkRBCNE7r/n8AByf69A4EiGg+fX1KOEQOSeeqnUoCVNeXs5VV121y2uTJ08mEAhoFJEQQ5Pmt8i2t7ezYcMG5syZg81mY9SoUUyZMoV169b1eVuvvvoqU6dOZeTIkdhsNs4//3zefvttQqFQAiIXYldKezu1f/4D9YEWHptzBTkWC7cMLyXTYNA6NCFEkkVVlRcamzjNmYNZr/mlVgxxSqgd3wvPkTdtOvqM9J0q58fFZadp06YlORIhhjbNezBramoAGDZsWNdrI0eOZOXKlbtdZ/78+cRiMfbZZx8uvvjirnWrqqqYMGFC13IVFRUoisL27dsZMWJE1+uNjY00NjYC4Ha7icViACiK0uu4O5ftyzoDoaoqiqKgKEpShrlP5/wSkZsSDFJ73z00oOfx2ZdQarVyQ3kRFh1decm5iw/Jr39MJlOft5vMY50ux7nT200BgjGFqTlZKIqSdvn9mOQXP4nIrWnNv8FgIPO4qd22m07n7plnnuGss87a5bXly5czc6bM9ylEMmleYLa3t5Pxo0/T7HY7bW1tPS5/9913M2bMGCKRCCtWrGDhwoU8+OCD2Gw22tvbsdvtXcvqdDpsNlu3bS1fvpxHH3206/tzzjkHgLq6uj7H39DQ0Od1BpN0zi9euanBVkJ/f4S6jCyWnHEuFQY9F5jBp/GxS+dzB5JfX82dOxeQdu7HEpGboqqsaG5notmA392AP+576L10PneQ3vnF7RrV1kbbS6swnzKNBo83LtuMh0Scuy+//LJXrwkhEkvzAtNqtXYrAFtbW7sVnZ0OOuggoOPT+Dlz5vDaa6/x5ZdfMmHCBKxWK8FgcJflg8Fgt23NnDmTyZMnAx09mGvWrAHA5XL1Om5FUWhoaKCwsBB9Em5/UlWVaDSK0WhM2ieM6ZpfPHOL+f3UPvQX6vIK+NspMzk408YvSgsx7pSDnLv4kvx6trfCccmSJcydOzdl27nBcpx7431/C81N7cwcVorD2HGLfDrl1xPJL37inZt3xTIiDgelp52Oztj9bd9gOXedd57tycKFC7u9Nn/+/D7FJ4QYOM0LzNLSUgCqq6spLy8HoLKystdzFu3cGFZUVFBZWdlVPFZVVaHX6ykpKdllHafTidPpBMDhcGDY8YxcfxpyvV6ftItb576ScQHolM75DTS3aFMTtffcSVVJGUumnM7hjkwuLy3sNtednLvEkPz6JhKJdG1X61h6ki7HWVVVVjU2MyXPQc5O0xKlS367I/nFXzxyi/qbaV77CoUXXYbBbO5xmXQ/d0KI5NP8L9tqtTJx4kSWLl1KMBiksrKS9evXM3Xq1G7Lbt26lc2bNxOLxQiFQvzrX/8iHA4zZswYAKZMmcL69euprKwkGAyydOlSJk2ahMViSXZaIs1FvR5qfn8bWypG8tjxp3NMjqPH4lIIMbRsagmyNRRimjNX61CEwPfSKkzOAjKPPFrrUIQQQ4jmPZgAV1xxBffffz8XX3xx1+ivY8eOBWD27NnceuutHHjggTQ1NfHQQw/R2NiI2Wxm9OjR3HbbbWTumM/p0EMP5eyzz+a2224jGAwyYcIErrjiCi1TE2ko4m6g5p472bz/Ifz9iOM4JT+b84ryk/rJrxAiNa10+zg2x0G+zHsrNBb1evCvX0vRL65BJz2FQogkSokrYGZmJjfddFOPP1u2bFnX14cccsheJ8qdNm2aDEctEiZcV8v2e+7g60OP5B/jj+bMgjxmFORKcSmE4KvWNr4KtnN5aaHWoQiB9/kVmMuHYR9/mNahCCGGGPlIS4heCtdso+Z3t/H54cfwxKFHc3ZRPjML86S4FEIAsMrt48jsTIotPT/rJkSyhOvr8L/1Ovkzzx7S16hYLEZVVRVbt27tmpJOCJF4KdGDKUSqC22touaPd7LpuFNYNmYsFxcXcFJ+ttZhCSFSxPdtITa2BPnDqHKtQxEC78pnsY7el4wDD9Y6lKR64oknmDx5MhUVFWzdupU777wTn88HQGFhIbfccku3gR+FEPEnPZhC7EX7ls3U/OF2PjrpZywbM5bLSwuluBRC7GKl28ehWTYqMmRQOaGt0LZqWt7dQP6sc4Zc7+Xq1aspLi4G4JFHHmHq1KksW7aMZ555hilTprB48WKNIxRiaJACU4g9aPv2a7b/8U7enXYWK0fsz9VlRRyX69A6LCFECtkeCvOev4XpBTJyrNCed8UybAePI2OfMVqHknTRaLTr66qqKmbOnNlVZJ955pl88803WoUmxJAiBaYQuxH88nNq/nQ3b828gJeHjeb6YS6OzsnSOiwhRIp53u1jP5uVMbYMrUMRQ1z7ls20bvyQ/JmztQ5FE6NHj+bNN98EOuZZ37x5c9fPKisrsdvtWoUmxJAiz2AK0YPWT/9L7V//xGvnX8abBaXcOMzF2Cy5MAkhdtUYjvBWU4B5FfJcl9CeZ/lTZB52BJaKEVqHoonLLruMBQsWsHHjRkaPHs2iRYs48sgjAXj33Xe55JJLNI5QiKFBejCF+JHWjR9R85c/seaiX7KhsJTfDi+R4lII0aMXPU1UWC0ckim9l0JbwS8/p+2Lz8g78yytQ9HM8OHDeeihhyguLqaqqorc3Fy+//57AObPn8+JJ56obYBCDBHSgynETlo+eJfti+/nlUuvZVN2PrcML2Efm1XrsIQQKag5GuVVr5+ry4qG3GAqIrWoqop3+dNkTTwWc0mp1uFoKjMzkzlz5mgdhhBDmvRgCrFD4J23qVl8P89ffgOf5eQzf0SpFJdCiN16xdOM02zkMIfc4SC0Fdz0Ce2Vm8k7Y6bWoQghhPRgCgHgf+NVav75d1b+4kaq7A5uHV5KmVUmSxdC9CwYi7Ha08zcYid66b0UGlIVBc/yp8k+biqmgkKtwxFCCCkwAVwuF0ajEVVVe72Oqqpd6/Rlvf7q3Ecy9tW5n3TN78e5Na9fw/Zl/+KZX86jwZbJwopiii2muMUi5y7++5P8ujMa99ycp3o7N1iOc6fVnmbsBj1HZWf2av3Bll9/9rfz/8nYX7rm19fcWj58j0jddoqvn9ev+AbLuTMYDAmMSggRT1JgApdeeimw6/xJvZGbm4uiKCiKkoiwehSLxZK2r3TML+ZvpunFVUTra3EXFWPIyqRhzb956pfzaM6wM7+8CKdB3+ffhV7tW85d3Eh+Pa+zJ4OlnUv14wwQUhRe9jQxIz8HYjH6ckQHQ34DIfnFR29zU2MxPCuWkXX8iZCZNaBrV6qfu6wsmSZMiMFCCkzg8ccfZ8aMGRQUFPR6HUVR8Hg85Ofno9cn/lFWVVWJxWIYDIakDCaRjvnFWlqovXMhwbY2/Bl2HN99iykSZtkv5hG0Z3JrRQl5pvj/Sci5iy/Jr2dut3uPP0/1dm6wHGeA9Z5m9DodU/KzMfZy3cGUX39IfvHTl9z8725AaW4ib9oZGPZyF8PuDJZzFwgEEhiVECKepMAE6urqiEajfWpYdTpd1zrJHD0wWftLx/wCb73GuxX78MLxp6EYDOhjMX766kvs+/WnnH7u+TiMib39Rs5d/PYj+XW3t56LwdLOpfxxVlRe8DTx0/wcLP24ZS/V84vHfiW/ge+jN7mpkQi+lc+Sc8o0jFmOuOw3lc9dMntYhRADI6PIiiHjy6jKqqnTUHa8KVQMBl444XTKs7MTXlwKIdLD280B2mMKJ+Rlax2KGOKa33gVJRwi56TTtA5FCCF2IQWmGBKUUIjP9EYMP/oE1BCLUVlaoVFUQojBRFFVnnf7ODk/G5tBLp9CO0oohO+F58g97Qz0GRlahyOEELuQK6RIe62bNrJ1/o2EAwHUH9+Oo9eRUVqmTWBCiEHlfX8rnkiUU/JztA5FDHHN61eDTkf21JO0DkUIIbqRZzBF2or6vLiX/h/+Tz7ik7Mv5oPi4SiATlVRdTp0qOgMBibmDvzZFSFEelNVlZVuH1PzHHJLvdBULBjE99Iq8meeg94s8zULIVKPFJgi7aiKQvP61XiWL6Nq3GG8eMMiAjo9FxblU2w28ch2N43hCAVmM1eUFlJqkQu0EGLP/tsSpDoUYl5FsdahiCGuafVL6G12HMcer3UoQgjRIykwRVppr9yM+/8exx1sY/3Pr+Njq50Tc7KZXZhH5o5eh/8ZXU5dXR0ulyspw9sLIQa/lW4fk3McCZnKSIjeivn9NK1+iYILLkHXz2lJhBAi0aR1EmkhFgziXf40njfW88GZ57Fu+L4Mz7Dyu+IChmdYtA5PCDGIfdnaxtfBdq4sLdQ6FDHE+V5ehSnPSdZRk7QORQghdksKTDGoqapK6wfv4f7X//H1yH15+bqFhI0mfu5yMik7M6nzowkh0tMqt4+jsjNxye30QkNRn5fmdWsouvJX6OTuGyFECpMCUwxakYZ63P/4G9Xbt7P23J/zpT2bU/JzmFmYJ1MICCHiorItxCctQf4wulzrUMQQ531+BeaycuwTfqJ1KEIIsUdSYIpBR41G8b3yAvUvP8+Gk6bzxslnMcaewR+KCyizSg+DECJ+Vrl9jM+yUWGVW+2FdiIN9fjfx1b+RQAAIABJREFUfI3i6+bJnTlCiJSXEgVmS0sLDzzwAB9//DEZGRnMnj2b0047rdtyX331FU8++STfffcdAGPGjOHnP/85JSUlAHz66afMnz8fi+WHNwKzZs1i9uzZyUlEJFzb119S/3+P8WlBCa/84iZ0FgtXu5wc7rDLRVcIEVfbQ2He87dw28hSrUMRQ5x35bNYR++L7aBDtA5FCCH2KiUKzMWLFxOLxViyZAm1tbUsXLiQsrIyDjlk14a0tbWVE044gXnz5mE2m1m6dCl33nknDz74YNcy2dnZPPHEE8lOQSRYLOCncdm/2PzFF6yeOYfNjjxOL8jljIJcrPIsihAiAZ53+9jfnsG+tgytQxFDWKimmsB/3qb0t7fKB6lCiEFB83fm7e3tbNiwgTlz5mCz2Rg1ahRTpkxh3bp13ZadMGECxxxzDHa7HZPJxPTp09m2bRt+v1+DyEUyqKqK/83X+HrBTSzPLuCBub/CXlrGn/YZxtlF+VJcCiESojEc4a2mANMLcrUORQxx3hXPYDt4LBn77qd1KEII0Sua92DW1NQAMGzYsK7XRo4cycqVK/e67meffUZubi4Oh6PrtUAgwIUXXojJZGL8+PFceOGFZGVl7bJeY2MjjY2NALjdbmKxGACKovQ67s5l+7LOQKiqiqIoKIqSlE8wUyG/cM023P/4G+9bMlk99xoyrFaud+UzPss+oNhSIbdEkvziK13yM5lMfd5uMo91qh3nFxp9VFgtHJhhiUv+qZZfvEl+8bNzbqHKLbR+/AGlC+9MWK7pfu6EEMmneYHZ3t5ORsautx/Z7Xba2tr2uF5dXR2LFy/miiuu6HqtrKyMv/zlL5SVleH1ennwwQe57777WLBgwS7rLl++nEcffbTr+3POOadrm33V0NDQ53UGEy3yU8NhIq+tpfqrL3npp2dRk+fkhAwzx1mNmFoD1LUG4rIfOXeDm+TXN3PnzgWknfuxnnILKCrrm9qYk2mmvr5eg6jiJ53PHaR3fg0NDbQ/9Q8MBx6Cz5IB/fjbTWXpfO6EGOo0LzCtVmu3YrK1tbVb0bmzxsZGFi5cyKxZs5g06YfJhnNzc8nN7bidyel0cvnll3PllVcSCoV2Gfhn5syZTJ48GejowVyzZg0ALper13ErikJDQwOFhYXok3CbpqqqRKNRjEZj0j5h1CK/8JefUb3sSdaMO5L35vyCnzgyucGVj9MUv19VOXfxJfnFV3/z21vhuGTJEubOnZuy7VwqHefX670UmaOcUF6KPk6xpFJ+iSD5xU9nbo4mL8HvvqHsjnsw9+Hvtq8Gy7nrvPNMCJH6NC8wS0s7Ruerrq6mvLxjnrHKykoqKip6XN7j8XDLLbdw0kknccYZZ+xx23q9HlVVUVV1l9edTidOpxMAh8OBwWDoWr6v9Hp90i5unftK5kP+ycov4vXgXvp33o6orJt9KdkZGdxcWsjBmbaE7VPOXXxIfokR7/wikUjXdrWOpSepcpxbYzHW+vzMLXZi3HFtiIdUyS9RJL/4iPq8eFYup33bVkKNbjIPPxJrWWLnYE33cyeESD7NC0yr1crEiRNZunQp11xzDfX19axfv5558+Z1W9bj8XDzzTdz3HHHMWvWrG4/37RpE0VFRRQWFtLU1MQjjzzCuHHjsFqtyUhF9IOqKDSvX80nb73NSydMw51XwFmufE7Oz8Eoo+UJIZJsjaeZTIOeo3Oy9r6wEHEUbWqieuFNxIKtsGNsiPbvvkVpa0O/h7u6hBAi1WheYAJcccUV3H///Vx88cXYbDbOP/98xo4dC8Ds2bO59dZbOfDAA1mzZg21tbU899xzPPfcc13rP/DAAxQUFLBlyxbuu+8+AoEAdrud8ePHc9FFF2mVltiL9srNVD61lBdHH8jHZ13M0Q47vy0uIM9s0jo0IcQQFFIUXvY0cVZhvnzAJZKu+dU1xILBruISINrcROA/b5E95SQNIxNCiL5JiQIzMzOTm266qcefLVu2rOvrc889l3PPPXe325k+fTrTp0+Pe3wivmLBIO7lT7PW18z6U8+iMMPKwtIiRltMGI0p8SsphBiCXvX50et0HJcrvZci/pS2NqJeD1Gfh6jHQ8TrIebzEvF6iHo9ROrrdikuAXToiDY3axSxEEL0j7ybF0mjqiqtH7zH+6+u4/mJJ+A/NJ/zigs5Ic+BDohGo1qHKIQYoqKKygvuJqbl52CW58JEHymhUEfx2NM/n5eox4PSFgRAZzJhzMvv+Jebh3X4SIzjf0Koeiv+N9bDTtN3qEoMy7DhGmUlhBD9IwWmSIpIQz3fLvsXK10VfPrTszkuJ5NziwtxGDsG0fjxQExCCJFMbzcHCCkKJ+Rlax2KSDFKOEzE00i40Y3a3ETU6/2hJ3JHEam0tnYsbDRizM3rKiDNw4ZjHzcBY94Pr+kzs3ocTEdVFKI+L8H/bgSDHmIxso45Dvv4w5KcsRBCDIwUmCKh1GgU9ysv8lJNLa9NOoVyq4U7K0oYlSEDLwkhUoOiqqxy+zglP5sMg/Reprq2r7+kbfO3RKMxlKknobfb+70tNRLp6GHs6nHs+DrS1fvoQQnsmHvZYMCYk4sxf0fxWFKG7aCxHYVjfkdvpCHLga6fPeA6vZ7ia35N62eb8FZuJn/f/bDtd0BSR3YVQoh4kAJTJEzb11/y9up/8/z4o2mv2J+5ZS4m5zriNq+cEELEw/v+VrzRKKfk52gditgL76oVeFc+g85oRFUUtr35KuUL78LgcHRbVo1GfygeO//3eHb5Pubf8XyjXt9RPOblY8zLw+xyYTvgQIx5Tox5eRhy81Btdkxmc0ILPp1ej+2gQ/A7C8lwuaS4FEIMSlJgiriLBfx8vXIFz2bm8dXx0zgx287s0iLscZxTTggh+ktVFEKVW4htqyZiMbPSF+SE3GyyjNJGpbJQTTXe5zoG/lN3zOsa9XqofeDP2MeN/+HW1R3/Yv5mUFXQ6TDk5P5w62phIdYx+2PqfA4yPx9Dds4eex5VVZVxAoQQopekwBRxo6oqnrfe4LnvKnlj/FGMMhn43ahhVFgtWocmhBBAx/N0tffdQ9sXn4FOx1a9HsdPz+Knp0/TOjTRA1VRiLgbCNdso+W9d0Cn6ygaOykK7d9+jRoOd/U+Wkfv88MgOnn5GLNz0MkI5UIIkTTS4oq4aN+2lTdWr2HlfmNRxh/JlcNcTMzNltt7hBApxbviadq+/oqowUDIbMHWFmTWi8+QNXkS5Du1Dm/IUlWVqNdDuGYb4ZpqwtuqO77evg01HEafkYExz7lrcQmg12MfN57ia36jTeBCCCG6kQJTDIgSCvH1v1/iSb2VzT85llOyMpg1rFQGyhBCpIxYaysRdz2Rhnr8777D6qOn8PZPJqHq9WT7mzhv1b/IXrWCzPGHYSoswugsQG82ax122or6mzuKx23VOxWT1ShtbejMZswlZZhLy8g84ijMpeVYysox5OYBUPfgX2j9+IOO+SL1enRGI/mzdj8/thBCiOSTAlP0m2fTRp75/GveOmAc++lU7hkznFKLvCkTQiSXqqrEmpuJNNQRaajf6V8dkYYGlJaOUUANjmw27D+WDT+ZiLrjeTt/poO/z7qYG1f+g5b330Ftb+9YNicXU0EhpoJCjAWFmAqLMDkLMBUW7fV5PdEhFgx2FJA11YS3/dAzGQv4wWDAXFyKubQM2yGHknPaz7CUlWF0Fu7x2Lp+cQ3Nr66h7dtvCOkNFP/sTMzFJUnMSgghxN5IgSn6LOL1sHbtOlYOG41xzMFcU1rE4fk5cjusECJh1FisY/qI+p2KSHc9kfqO/9VQCHQ6jHn5HcVgkYvMw47AVNDxtamgEH1GBos//RpF98NgPqpeT8hipf3GBeyfnYXSEiDibiDS0EDEXU/U3UD7N18R2PAmUU8jqCo6owljQUHHtjsL0IJCTIWFmJwd+xlKlFCIUE017dVbidZuJ7KjqIx6vaDTYSpyYS4tx7rPGBzHn4CltBxTkatfz0Xq9HpyTjgFx5STqKurw1TkSkBGQgghBkIKTMDlcmE0GlF//GzHHqiq2rVOX9brr859JGNfnfv5cX6qovDlm6/zz5BK9X7j+GmGkTNHDcey49PmgcSWzPyG4rlL9P52/j8Z+5P8ujPu5c16qrdzqqqihMOE6mqJuus7CrwdvZDRhgYiHnfHbZH/v717D2+yvP8H/s45TdJz0zZt2gLlIIcBU0C+A0QBEVQEhFWBqiBO3W9uDjYBdUKdiofBlMnBToXLAzBAwFnHFMHN+RUFp/gFHVPAcmhKm6T03KY5PPfvj7axIS00bdI24f26rl7aO8+dfD5P0k+5ez/3/SiVTYO5FKiSUxB1xSAomweRSUafQYskBIoanDhe14DjZVU4UW+FRdbKTrFyOWTyxna5IRoaQzQ0vbP9Y3S7Gwe5TfG57Ta4bKVwnPwOLpsVUm1t43NExzTNfhobYzMmN152m5gEeZIR8i7YUTsU751wu+E8VwxncdEPayQtZ+G2WQEhoEhMgiY9A2pzJgyjx0CdbobKlN7m5cad/Z0RqXUgknNrfp2O5KfgTvREYYMDTAALFy4EgIC3II+Pj4ckSZAkKRRhtcrj8YT+NRz1qCwthTYqCh6PB5Ikobzwe2z/+hgO9B2EIc56PJNtRopGDUgS3EHMvyvyAyL3vWvG/IKvp+cXHx9/0cd7Sp2T6mrhsjbODLq9s4SlcFut8FSUAwBkUVFNM4MpUGVkIurKkY3fJ6dAERff5iWU5W4PTtbU4UR9A044nPi+vgEOIZCsUiJbq8bEWAMq3B68e74KzdnIAcQo5MhWK9t1bmQJiVAnJEI9YJDfY57aWrjttqYva+Ps5/cnUXPo08bZT48HUCgadzY1JkOZlAyl0dj0/0Yok5Kh0Os7emr9dPS9E5IEt7UUruIiOIstcFmKGr+sJYDHA0VsLFRpZqjSzIj50TCo08xQpaW3OnMrAZBCdHuPSK4DkZwb0LH8oqOjQxgREQUTB5gAXn31Vdx6660wGo3t7iNJEsrKypCYmAh5F6zFEULA4/FAoVCE9FLU48eO4fnyWpTHxgP19Rh7eB+yJDfeTsmELqM3FhtjcVVqv6C/blflB0Tue9eM+QVXuORns9ku+nggdU5yOVG1by+cJcVwRumQOm0GlHpDu+JoXA9Z4b2M1e2zJrIUUm0NgMb1kKrkFCiTU6Ab9CMoxydDkWiExmSCIjrmkufaLQTOOpw4XufA8XoHTtQ3oMTpgkYuQ7ZWg346LaYmxqGfToPYFrOaQggYVErstJajQQiYNSr8OiMV0UFYP66MjYUmNhbI7uv3mOTxwGm3QTpfBrfNCpetcQBa/9WXcNtK4amqAgDIdfofLrk1JvteipuYdMnLSoXbjbLtW1D92ScQkoTon4xFUs68VvsJIeAuszetkyzybrzjOmeBcLkg1+uhTs9oXCc5cTLU6Y2b7yiiY1p9rnD4Oeko/o4Kno7mV11dHcKoiCiYOMAEUFJSArfbHVBhlclk3j5dufYwlK9XU1WJ56qdqI2ObX4xHMjIhvXUcUwxaDBj4BVQyUOba1ecz0h87y58HeYXmtftyfldavatvXVOuFwoXvk4GorOeHfqtHz1BTLzVkIepWs8xuOBu8zuu6lO01pIl9UK4WxaD5mY1HQpayoMI0c3rY1MgcqYArlW6/u6TTeyVyqVrcZY7nLjeL2jcUBZ58D39Q1wCoE0tQp9dVrclBSH/lFamLVqKC6So0wmwy3GBNyUEIvikhKkm0xd8o94uUIBZWISlCmpkA0a4ve45HA0rv20WRsvEbbZ4CwuQu3/fQm3zQrhcvmsMVUmNQ08k5sHoymQR0fD/sYmVH3yUeN7B6Dqww8gnE4kzszx2XCnoWlQKRz1kGk0TYPHDMSMGef9f0VcfMCf+Z7+cxKM1+XvqM6/Tkfy68oZViLqHA4wL3NCCLgqK2Cz2fHvEydRY85uvJF1E0mhQGWCET8dPLAboySirlJ14GM0nD3tHaDA44HbZoXlDysh1+kaZySbLveUKVVQJidDlZwKVXIyogYP8Q4oL1wPGQiXJHDK0eCdnTxe54Dd5UaUXI6+URoM1kdhujEefaO0iFZ2bF2WTCa76EC0q8m1WmgyMqHJyPR7TEhS46yw3ea9vNhls6Lu66/ganFpMTQaoKHBt7PHg+qPPkT1Rx8CSmXjzq3mDBh+PALqaTOgTs9onBnlrrhERBQkHGBeBtyOepRZbSg5X4bS6hpYHQ2wSUCZQonzWh0qo2PgUSiRoml97U+IJy2JqAdxl9khk8ngs/WGJMFTUwXdoMFQjRwNZXIK1CmpF10P2V5CCJS53PiuzoHvautw0uFEoaMBHgGYNWr002lxqzEB/XVapGlUkPegQWFXkcnlUMYnQBmfgKh+A/wel5xOuO02OIstKFn7x1afw/z4M9CYMyDjRilERBRiHGBGAMntRnmZHefsZSitqoa13gGbxwO7XIlytRbl0TFwK1WQKQyI0ciRIHPAKCT0VymQootCSqwOpqQkGPpl4Ddf/QdVWh2kpn+EyCUPxkepujlDIuoq6tQ0/50dFUrETbwBcTfc1Onnd0oSvq/3nZ0sd3tgUMiRrdVgeLQeOSmJyI7SQMfBULvI1Wqo09KhTkuHpnc2Gs6c+mEGWqGAJqs3tFm9ujNEIiK6jHCAGQaEEKiqqICltATW6lrY6uthc7lhhxxlai3K9QY41RoAGhiUbiSqgUSPB72VClytVSPFoIYpMRHGmBioFRefbVjRrxdW//ckiqJjofK4MdVVh+kjruyaRImo2xlG/wTVn/4v6v5zFDKZHEJI0GRlIXbC5ICfSwiBUqcbJ1qsnTztaIAEIEvbODs5JyURfXVapKqU8Hg8ba7BpPYx/XIxilc/DaelCACgSjXB9MvF3RwVERFdTjjA7CAhBKQg3jOqtr4e52xWlJZXoLSmFlanG2UCsKvUKNcZ4NBoAaigk6mRIHMjSSGDSS7DcI0cyVo5UhOikZqUCG0H1zw1MyXEY9VPRsDl8cBaUgKTaQD/sUd0GZHJ5TAtWoKagwfQcK4YdSo10m64ETLVpa9kcHgknKx34Ls6R9OgsgFVHg9iFQr002lxdawBuaYk9NFqoL3gj11ddQ++SKdMSETGE8+hoeQc7HYbTIOGQNHJ3wtERESB4G+dADW4XNhw8N/4PDoeAjIM+f4z/GrElTBEaS/ez+3GObsdJefLYa2ugbWhATZJoEyhwvkoHeq0jbszatwKJEgKJMIDo1zgCoVAqlJCSowaSfHxiDUYumTAp+ji3euIqOewuT14La0PihLMiBMSFrolZF5wFw9JCJxzunC8zoETTZe7nnE4IQfQK6rxNiE/iY1GP50WRhVnJbuSTC6HOtUEOWTcvIeIiLocB5gAHA4HLBaLzzb/ZrMZcrkcZ86c8Tn29SPf4FhGbzjq6+GsrsYhjxuPvbsHD4y4Ekq9DrbKKnxz/ARs9fUo80iokCtQHRsPtykdrtoaiDI7YutrEed0IgESrkpJQZpOBVSUICkuDvpEA+TGGERFRcFoNKK2thZlZWWNG2FYrag8fx4mkwlqtRrFxcU+MWu1WiQnJ6Ourg52u90n7uTkZGi1WpSUlMDpdHrbNRoNUlJS4HA4YLVave2SJHm3BC8tLUVDi50JVSoVTCYTGhoaUFpa6vM6iYmJ0Ov1sNlsqK+v97YrlUqkpaXB6XSipKTEp09CQgL0ej3sdjucTqf3H6JyuRxmsxlutxvFxcU+feLi4hATE4Pz58+jpqbG2y6TyZCRkQGPxwOLxeLTJzY2FrGxsSgvL4fFYoHT6fTeniAjIwMAcPbsWZ8+MTExiIuLQ0VFBaqa7lPXrK3PiMFgQEJCAqqqqlBRUeFz+wWz2QyFQoGzZ8/6zNjo9XokJiaiuroa5eXlPs9nMpmgUqlgsVh8tmm/8DPSrOWNq4P5Gamvr/e716LRaIRWq0VpaSk8Ho/3vbvYZyQpKQk6nS7gz4jBYIDdbkdNTQ1sNhucTqf3nLb2GYmPj0d0dDTKyspQW1vrbW/vZ6T5nmvN71+fPn0ghAjJZ6Qlk8kEoPGz2HJQdqnPyKW0t85Vud14trQCUnQMHDXVOFVdjUVFFizOTEG1W6AyLh7fO934v8JC1Lo9iFMo0DtKjWGJCbi7TzqSXE7UVlYCrnrAVQ9HFeBux2fYbrf73KYkNTU1pHVOkiTYbDbExsZCr9eHvM7Fx8dDq9XCbrf79GGda8Q651vnWr53F/uMBKPOtXzvsrKyQl7nJEmComl9dSCfEZfLBSIKE+IyZ7FYxPjx4wUAn6/KykohhBAqlcqnvd9tc8VtR4+LYb9Z6tfnlv3/K+YePiY00TE+7ZNvvFEc/eYb8Ydnn/Xr8+233wohhMjIyPBpnzVrlhBCiDfeeMOvzxdffCGEEGLgwIE+7TfccIMQQojdu3f79fnoo4+EEEKMHDnSp33MmDFCCCE++OADvz6vv/668Hg84rrrrvNpHzZsmBBCiM8++8yvz7Zt24QQQtxyyy0+7dnZ2UIIIb7++mu/Pq+88oqQJEncfvvtPu3JyclCCCFOnTrl1+eFF14QQghx7733+rTrdDohhBB2u92vz5NPPimEEGLRokV+j0mSJBwOh1/7smXLhBBCLF++3O+xqqoqIYQQSqXSp/2BBx4QQgjx3HPP+fUpLi4WQggRFxfn0z5//nwhhBDr16/36/Pdd98JIYQwm80+7bNnzxZCCPH666/79Xn//feFx+MRV1xxhU/7lClThBBC7Nq1y6/Pv/71LyGEECNGjPBpHzt2rBBCiL179/r12bNnj5Akye9naPjw4UIIIQ4cOODXZ/v27UIIIaZNm+bT3rdvXyGEEEePHvXrs3HjRiGEEHPmzPFpT0lJEUIIUVhY6NdnzZo1Qgghfvazn/m06/V6IYQQNpvNr89TTz110c9IfX29X/vDDz8shBDiscce83usurpaCCGEQqHwaf/lL38phBDi2VZqgsViERaLRcTGxvq0L1iwQAghxLp16/z6HD9+XFgsFrFixQphsVg6Xef6z8lts879+sDn4vVim4iOi/dpv+uuu4QQQmzYsMGvT0+ucwUFBUIIEfI69/LLLwun0ynmzp3r0846xzrX/NVc5y78XdiVdU4I0SV17vDhw8Lj8QRU5woKCsSKFSvEvn37xL///e9Of+3bt6/NmklEnSMT4vJe+FJcXIw//elPmDVrFlJSUrztrf1FzuPx4LdnrVAlJMJVUw1n0wwHACysOY+Rw4chxWRCid3u89fVYPzVVrT4C2NXzmD27t0bNputS2Ywm2ML9QxmWVkZvv/+exiNxoj+y35mZiZKSkq65C/7zbF15Qym0Wjs8hlMIDSfkZZMJhNsNhtcLldAM5g2mw1//vOfce+99yItLc3n8UDq3N/sFfjQ4YQiJs5b52QAhhuiMN+UjExzOpRKJYqKioJa57prBnPQoEFdOoNZUVHRJTOYrHPhW+esVitOnz7tfe8idQYzPT0dFoul3Z8Rm82GgoICjBs3DnFxceisiooKfPzxx63WTCLqHA4wi4vb/EdZa57c9w8cM5rgUTReXSz3uJFRbscz48eEdI1Ry18AXbGWSZIklJSUIDU11fuPk1DqyvwiOTeA+QVbuOR3sVoWSJ07UlOHp08Vo+UvBjmA/2dOwdi46AAyCUy4nOeOYn7BFcn5RXJuQMfzO3LkCHbt2sUBJlEYiLjV/zU1NXj22Wdx2223Yf78+dizZ09Qn/9XI4Yj2/bDXx3N5+146Iq+3MCCiCLCUIMOP01OAAA0V7XrE2IwJtbQfUERERFR2Ii4TX7y8/Ph8XiwadMmnDt3DsuXL4fZbMbQoUOD8vwxcfF4fMI1qDh3DrYyG7LHXg1FO7bvJyIKF7cmJ+AnsQaca3AClRUYlprEP6IRERFRu0TUDKbD4cAnn3yC3Nxc6HQ6ZGdnY8KECdi3b19QX0cmlyPWZIIhKRmypp3QiIgiSapGjWEGHVIUEfVrgoiIiEIsomYwmxeyZ2Zmetv69OmDt99+2+c4u93uXfhvs9m8Gwq03LDiUpqPDaRPZwghIEkSJEnqsjUSLf8bal2ZXyTnBjC/YIuU/FRNV1r01DoXKee5LcwvuCI5v0jODej6/Iio60XUANPhcCAqKsqnTa/X++zgBgA7d+7Eyy+/7P3+9ttvBwC/Hd3ao+XOq5EokvOL5NwA5hfugp3fggULALDOXSiScwOYXziL5NyAyM+P6HIWUQNMrVbrN5isra31G3TOmjUL48ePB9A4g7l3714AQGpqartfS5IkWK1WJCcnR+wub5GaXyTnBjC/YAuX/C41cNy0aRMWLFjQY+tcuJznjmJ+wRXJ+UVybkDH87vwljNE1HNF1AAzPT0dQOM9vprv91VYWIisrCyf45KSkpCUlASg8d5OiqZ1lB0p5M33qAo1IYT3tbpys41Izi+ScwOYX7BESn4ul8v7vN0dS2si5Ty3hfmFRiTnF8m5AV2XHxF1vYj6ydZqtRgzZgw2b96Muro6FBYWYv/+/Zg4cWJ3h0ZERERERBTxImoGEwDuu+8+rF27FvPnz4dOp8O8efMwbNiw7g6LiIiIiIgo4kXcANNgMGDZsmXdHQYREREREdFlJ+IGmB3V0cXjHdmRsSOUSiXi4+Nhs9ngdru75DWByM4vknMDmF+whEt+7alhPbnOhct57ijmFxqRnF8k5wYEnl95eTkAoKamJiivH6znISJ/l/0AU6fTQaVSYdeuXQH1czgcOH36NLKysqDVakMUXfeJ5PwiOTeA+YW7zuSnUqmg0+n82lnn/EVybgDzC2eRnBvQ+fwOHz4ctFiUSmWrNZOIOkcmhBDdHUR3q6ioQF1dXUB9Tp48iUWLFuH5559HdnZ2iCLrPpGcXyTnBjC/cNeZ/HQ6HeLi4lpWjsevAAAQOElEQVR9jHXOVyTnBjC/cBbJuQGdy6/5dirBcrGaSUQdd9nPYAJAXFxcwAWmqqoKAGA0GpGWlhaKsLpVJOcXybkBzC/chSo/1jlfkZwbwPzCWSTnBkR+fkQUYbcpISIiIiIiou6jyMvLy+vuIMJVVFQURowYEbHX70dyfpGcG8D8wl1Pyq8nxRJskZwbwPzCWSTnBkR+fkSXO67BJCIiIiIioqDgJbJEREREREQUFBxgEhERERERUVBwgElERERERERBwduUdEBNTQ3WrVuHL7/8ElFRUcjJycGNN97Y3WEFrL15uFwurF69GidOnIDVasWKFStw1VVXdUPEgWlvfv/973+xdetWnDhxAgAwYMAA3HPPPT16+/T25lZSUoJVq1ahuLgYQghkZGRg/vz5GDRoUDdE3X4d+Rnbv38/1qxZg5///OeYOnVqF0XaMYHkd8stt0Cj0UAmkwEABg0ahFDvzcYaxxrXE7DO+QuXOtfTaxwRhRYHmB2Qn58Pj8eDTZs24dy5c1i+fDnMZjOGDh3a3aEFJJA8Bg4ciGnTpmH16tXdEGnHtDe/2tpaTJo0CUuWLIFarcbmzZvx5JNPYv369d0U+aW1N7eYmBgsXrwYqampkMlk+PTTT/HEE0/gjTfeCOrNqoMt0J+xqqoqvPXWW8jKyuriSDsm0Pyef/55mM3mHhtfT8Ua1ygcaxzAOnehcKpzPb3GEVFo8RLZADkcDnzyySfIzc2FTqdDdnY2JkyYgH379nV3aAEJJA+VSoXp06dj8ODBkMvD4yMTSH5XXXUVxo0bB71eD5VKhRkzZqCoqMh7M+ieJpDcdDod0tLSIJfLIYSAXC5HbW0tKisruyHy9unIz9jGjRsxc+ZMREdHd2GkHdPTa0hPj6+9WON+EG41DmCda0241LlIqSFE1HHh8Zu0B7FYLACAzMxMb1ufPn1w+vTp7gqpQyIlj7Z0Jr+vv/4a8fHxiImJCVl8ndGR3BYuXIhZs2Zh5cqVmDhxIhITE0MeZ0cFmt/Ro0dRXFyM66+/vkvi66yOvH+/+93vcMcdd+D3v/89zpw50+Pi64kiJY+2RHKNA1jnLhROda6n1zgiCr2ee+1ID+VwOBAVFeXTptfrUV9f300RdUyk5NGWjuZXUlKC/Px83HfffaEMr1M6kturr74Kp9OJjz/+ONThdVog+blcLrz00ktYvHixd/1OTxfo+7dy5UoMGDAALpcLu3btwvLly7F+/fqQ3aA8UmpDpOTRlkiucQDrXEvhVud6eo0jotDjDGaAtFqtX5Gsra31K6Y9XaTk0ZaO5Ge327F8+XLMnj0bY8eODXWIHdbR906tVmPixInYtm0bCgsLQxlipwSS386dOzF8+HBkZ2d3VXidFuj7N2TIEKhUKuh0OuTm5kKhUODYsWM9Jr6eKlLyaEsk1ziAda6lcKtzPb3GEVHocYAZoPT0dADA2bNnvW2FhYVhsei+pUjJoy2B5ldWVoZHH30UkydPxvTp07skxo7q7HsnSRJKSkpCElswBJLfkSNH8OGHH2LevHmYN28ejh07hk2bNuH555/vsngD1dn3L9QzGJFSGyIlj7ZEco0DWOdaCrc619NrHBGFHgeYAdJqtRgzZgw2b96Muro6FBYWYv/+/Zg4cWJ3hxaQQPNwuVxwOp0QQsDj8cDpdEKSpC6Ouv0Cya+srAyPPPIIrr32WsyePbsbog1MILkdPXoUx48fh8fjQUNDA/7yl7+guroa/fv374bI2yeQ/JYuXYoXX3wRa9aswZo1a9C3b1/k5ORg4cKF3RB5+wSS35kzZ3Dy5Env+7dlyxY4nU4MGDCgR8TXk7HG/SDcahzAOtdSuNW5nl7jiCj0ZEII0d1BhJuamhqsXbsWX375JXQ6XVjfI66tPHJycrBixQoMHjwYAHDPPffAarX69H/qqafwox/9qMvjbq/25rd161Zs3boVWq3Wp/+6detgNBq7I/RLam9uhw4dwmuvvQabzQaVSoVevXph3rx5YXF/uPZ+Nlt65JFHMG7cuB59fzig/fkdOXIEGzZsgN1uh1qtRt++fTF//nz07t272+ILJ6xx4VvjANa5cK5zPb3GEVFocYBJREREREREQcFLZImIiIiIiCgoOMAkIiIiIiKioOAAk4iIiIiIiIKCA0wiIiIiIiIKCg4wiYiIiIiIKCg4wCQiIiIiIqKg4ACTiIiIiIiIgoIDTCIiIiIiIgoKDjCJKCzl5eVBJpMhPT0dkiT5PX7jjTdCJpPh5ptv7oboArdq1SrIZDLv96dOnYJMJsNbb73VjVEFLlzjJiIiouDgAJOIwpZKpYLdbsc///lPn3a73Y4PPvgABoOhewILApPJhE8//RQTJkzo7lACEq5xExERUXBwgElEYUutVmPq1KnYsmWLT/v27duRlpaGK6+8spsi6zyNRoPRo0cjISGhu0MBAAgh0NDQcMnjelrcRERE1LU4wCSisDZ37lzs3LkTTqfT27ZlyxbcfvvtPpecNisqKkJubi6SkpIQFRWFa665Bl988YXPMa+//jrGjh2LhIQExMfH49prr8WhQ4d8jsnLy4PBYMCRI0cwduxY6HQ6DBkyBO+///4lY66qqsKdd96J6OhoGI1GLFmyBG632+eY1i41feeddzBixAgYDAbExcVhxIgR2LNnj/fxXr164YEHHsAf/vAHpKenQ6fTYfr06Th37pzPczc0NOCRRx5BVlYWNBoNBg4c6DdInz9/PoYMGYI9e/Zg2LBh0Gg0eOedd+ByufDQQw95+5pMJkybNg2VlZVtxi1JElauXInevXtDo9GgX79+eOGFF4J2PomIiKjn4ACTiMLatGnT4PF48Pe//x0AcPr0aRw4cABz5871O7a8vBxjx47FV199hRdffBE7d+6EXq/HhAkTYLVavcedOnUKd955J3bs2IEtW7YgIyMD11xzDb777juf53O5XMjNzcX8+fOxe/duJCUlYdasWSgrK7tozHfffTd2796NZ555Bq+99hq++eYbrF279qJ9Tp48idmzZ2Pw4MHYvXs3tm3bhpycHJSXl/sct3v3buzevRsbNmzAhg0bcOjQIdx6660+x+Tk5CA/Px+/+c1v8O6772LKlCnIzc31nsNmxcXFePDBB7F48WK89957GD58OJ5++mm89NJLWLp0Kfbu3Yu1a9ciLS3torObDz30EB577DHk5uaioKAAM2bMwKJFi/DEE08E5XwSERFRDyKIiMLQihUrhF6vF0IIcccdd4icnBwhhBArV64UgwYNEkIIMX78eHHTTTd5+yxfvlzExsaK0tJSb5vD4RBms1k89NBDrb6Ox+MRLpdLDBgwQDz88MM+rw9A/O1vf/O2HT9+XAAQb7zxRptx/+c//xEymUy8+uqr3jaXyyUyMzNFy5JcWFgoAIgdO3YIIYTYsWOHACCqqqrafO6srCwRHR0tysvLvW379u0TAMT7778vhBDiww8/9Pm+2U9/+lMxcuRI7/d33XWXACAOHjzoc9xNN90kbr311jZjuDBum80mVCqV3/m99957hV6vF9XV1UKIjp9PIiIi6lk4g0lEYW/evHkoKChATU0NtmzZgnnz5rV63N69e3HdddchISEBbrcbbrcbCoUC48aNw+eff+497tixY5g5cyZSUlKgUCigUqnw7bff+s1gyuVyTJo0yft93759oVarUVRU1Gashw4dghACM2fO9LYplUpMnz79ojkOHToUCoUCc+fORUFBgfeS1Atdd911iIuL834/ceJExMTE4LPPPvOeg4SEBEyYMMF7DtxuNyZOnIjDhw/D4/F4+yYlJWHUqFE+z3/llVdiz549yMvLw+eff97qDr4tHTx4EC6XC7fddptP+5w5c1BbW4vDhw972zpyPomIiKhn4QCTiMLepEmTEB0djSeeeAJff/015syZ0+pxdrsdb7/9NlQqlc/X1q1bcfbsWQBAdXU1Jk+ejNOnT+OPf/wjPv74Y3z++ecYNmwYHA6Hz/NFRUVBrVb7tKlUKr/jWjp37hxUKhXi4+N92lNSUi6aY//+/fHuu++isrISM2fOhNFoxC233IIzZ874HJecnOzXNzk52bsO02634/z5837n4P7774fb7fZZr9nacz366KNYunQpXnvtNYwaNQqpqal4/PHHIYRoNe7mS3hTU1N92pu/P3/+vLetI+eTiIiIehZldwdARNRZCoUCOTk5WLVqFf7nf/4HvXv3bvW4hIQETJkyxW/tH9C4+ykAfPrppygqKsK7776LYcOGeR+vrKyE2WzudKwmkwkulwvl5eU+g8zS0tJL9p0yZQqmTJmCqqoqvPfee1i0aBEWLFiA/fv3e49puZa0ZZvJZALQeA6MRqPP5kAttRxUtrZJkkajQV5eHvLy8nDixAls3LgReXl56NOnD+644w6/45t3ky0tLUV6erq3vaSkxOdxIiIiigwcYBJRRFi4cCHOnj2L3NzcNo+ZNGkS3nzzTQwcOBB6vb7VY+rr6wHAZybtwIEDOHXqFAYPHtzpOEeOHAmZTIbdu3fj7rvvBgC43W789a9/bfdzxMTEICcnBwcPHsTWrVt9HvvHP/6ByspKxMbGAgD279+PqqoqXH311QAaz8Fzzz0HtVqNoUOHdiqXvn37YuXKlcjPz8exY8daPWbUqFFQqVTYvn27z21jtm3bBr1eH9a3kiEiIiJ/HGASUUQYPnw43n777Yses3jxYmzevBnjx4/Hgw8+iMzMTNhsNhw8eBBpaWlYtGgRRo8eDYPBgF/84hdYtmwZLBYL8vLyfGbfOmPQoEGYMWMGfv3rX8PhcKBXr15Yt26dz9rH1uTn5+PAgQOYOnUqTCYTCgsL8eabb2Ly5Mk+x0VHR2Pq1KlYtmwZKioqsHTpUowaNQo33HADAOD666/HtGnTMGXKFCxZsgRDhw5FbW0tvvnmG5w4cQKvvPLKReOYMWMGrrrqKvz4xz+GXq9HQUEBzp8/jwkTJrR6fFJSEn71q19h1apV0Gg0GDNmDPbv34/8/Hw8/vjjbQ70iYiIKDxxgElEl43ExER89tln+N3vfoelS5eirKwMycnJGD16tHfTnZSUFOzYsQO//e1vMX36dPTv3x8vvfQSnn322aDFsXHjRjzwwANYsmQJtFot7rrrLowbNw4PP/xwm32GDh2KgoICLF68GGVlZUhNTcWcOXP8LvedOXMmzGYz7r//fpSXl2PSpEnIz8/3Oeatt97CM888g/Xr1+P06dOIjY3FkCFDsGDBgkvGPmbMGGzfvh2rV6+G2+3GgAEDsGXLFp/NeS703HPPIT4+Hi+//DKefvppZGZmYvXq1Vi0aNElX4+IiIjCi0y0tTMDERGFlV69euHmm2++5D01iYiIiEKFu8gSERERERFRUHCASUREREREREHBS2SJiIiIiIgoKDiDSUREREREREHBASYREREREREFBQeYREREREREFBQcYBIREREREVFQcIBJREREREREQcEBJhEREREREQUFB5hEREREREQUFBxgEhERERERUVD8f4ERE6Ed/P4BAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 800x600 with 6 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8786012259973)>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def fdist_anova(mus, ses, ns, var_eq=False):\n", " lshape = len(mus.shape)\n", " assert lshape <= 2\n", " assert mus.shape == ses.shape\n", " if len(ns.shape) == 1:\n", " ns = cvec(ns.copy())\n", " else:\n", " assert ns.shape == mus.shape\n", " if lshape == 1:\n", " mus = cvec(mus.copy())\n", " ses = cvec(ses.copy())\n", " vars = ses * 2 # variance\n", " n, k = ns.sum(0), len(ns) # Total samples and groups\n", " df1, df2 = (k - 1), (n - k)\n", " if var_eq: # classical anova\n", " xbar = np.atleast_2d(np.sum(mus * ns, 0) / n)\n", " vb = np.sum(ns(xbar - mus)2,0) / df1 # numerator is variance between\n", " vw = np.sum((vars (ns - 1)), 0) / df2 # den is variance within\n", " fstat = vb / vw\n", " pval = stats.f(dfn=df1,dfd=df2).sf(fstat)\n", " else:\n", " w = ns / vars\n", " xbar = np.sum(w * mus, 0) / np.sum(w,0)\n", " num = np.sum(w * (xbar - mus) ** 2,0) / df1\n", " v = 3np.sum((1-w/w.sum(0))2 / (ns-1),0) / (k2 - 1)\n", " den = 1 + 2((k-2)v)/3\n", " fstat = num / den\n", " pval = stats.f(dfn=df1, dfd=1/v).sf(fstat)\n", " return fstat, pval\n", "\n", "nlow, niter = 25, 5\n", "k_seq = [5, 7, 9]\n", "disp_seq = np.round(np.arange(0, 0.51, 0.1),2)\n", "dgp_seq = np.repeat(['eq', 'neq'], 2)\n", "method_seq = np.tile(['eq', 'neq'], 2)\n", "\n", "holder = []\n", "np.random.seed(1)\n", "for k in k_seq:\n", " n_seq = np.arange(nlow, nlow+k niter, niter)\n", " n_seq = np.tile(n_seq, [nsim, 1]).T\n", " nhigh = np.max(n_seq)\n", " dim_3d = [1, 1, k]\n", " for disp in disp_seq:\n", " mu_k = np.linspace(-disp, disp, num=k)\n", " se_k1 = np.repeat(1,k).reshape(dim_3d)\n", " se_k2 = np.exp(np.random.randn(k)).reshape(dim_3d)\n", " X1 = mu_k + se_k1 * np.random.randn(nhigh,nsim,k)\n", " X2 = mu_k + se_k2 * np.random.randn(nhigh, nsim, k)\n", " idx = np.tile(np.arange(nhigh),[k,nsim,1]).T <= np.atleast_3d(n_seq).T\n", " X1, X2 = np.where(idx, X1, np.nan), np.where(idx, X2, np.nan)\n", " # Calculate means and variance : (k x nsim)\n", " mu_X1, mu_X2 = np.nanmean(X1, 0).T, np.nanmean(X2, 0).T\n", " se_X1, se_X2 = np.nanstd(X1, 0, ddof=1).T, np.nanstd(X2, 0, ddof=1).T\n", " assert n_seq.shape == mu_X1.shape == se_X1.shape\n", " # Calculate significance\n", " fstat_eq1, pval_eq1 = fdist_anova(mus=mu_X1, ses=se_X1, ns=n_seq, var_eq=True)\n", " fstat_neq1, pval_neq1 = fdist_anova(mus=mu_X1, ses=se_X1, ns=n_seq, var_eq=False)\n", " fstat_eq2, pval_eq2 = fdist_anova(mus=mu_X2, ses=se_X2, ns=n_seq, var_eq=True)\n", " fstat_neq2, pval_neq2 = fdist_anova(mus=mu_X2, ses=se_X2, ns=n_seq, var_eq=False)\n", " reject_eq1, reject_neq1 = np.mean(pval_eq1 < alpha), np.mean(pval_neq1 < alpha)\n", " reject_eq2, reject_neq2 = np.mean(pval_eq2 < alpha), np.mean(pval_neq2 < alpha)\n", " reject_seq = [reject_eq1, reject_neq1, reject_eq2, reject_neq2]\n", " tmp = pd.DataFrame({'k':k,'disp':disp,'dgp':dgp_seq,'method':method_seq,'reject':reject_seq})\n", " # print(tmp)\n", " holder.append(tmp)\n", "res_f = pd.concat(holder).reset_index(None,True)\n", "res_f[['dgp','method']] = res_f[['dgp','method']].apply(lambda x: x.map(di_method),0)\n", "res_f.rename(columns={'dgp':'Variance'}, inplace=True)\n", "\n", "plotnine.options.figure_size = (8, 6)\n", "gg_fdist = (ggplot(res_f, aes(x='disp',y='reject',color='method.astype(str)')) +\n", " theme_bw() + geom_line() + geom_point() +\n", " facet_grid('k~Variance',labeller=label_both) +\n", " labs(x='Mean dispersion',y='Prob. of rejecting null') +\n", " geom_hline(yintercept=0.05,linetype='--') +\n", " scale_y_continuous(limits=[0,1]) +\n", " scale_color_discrete(name='Variance assumption'))\n", "gg_fdist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simulations in Figure 3 show a similar finding to the that of t-test: when the ground truth variances are equal, there is almost no differences between the tests, and an expected 5% false positive rate occurs when the means are equal. However, for the unequal variance situation, the assumption of homoskedasticity leads to an inflated type-I error rate (as was the case for the t-test), but also lower power when the null is false (which was not the case for the t-test). Using the Welch adjustment is better in both cases. The one surprising finding is that the power of the test is not monotonically increasing in the heteroskedastic case. I am not completely sure why this is the case. One theory could be that since a higher mean dispersion leads to a higher variance of $\\bar{x}_w$, the ratio of the degrees of freedom may be more stable for lower values of $\\mu$, leading to a more consistent rejection rate.\n", "\n", "## (4) Quick sanity checks\n", "\n", "After confirming the frequentist properties of a test statistic, it is worthwhile checking the results of any custom function to similar functions from other libraries. The `tdist_2dist` function will be compared to it's `scipy` counterpart on the [Iris](https://en.wikipedia.org/wiki/Iris_flower_data_set) dataset." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>test</th>\n", " <th>method</th>\n", " <th>pval</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>t-test</td>\n", " <td>scipy</td>\n", " <td>7.027919e-112</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>t-test</td>\n", " <td>custom</td>\n", " <td>7.027919e-112</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>t-test</td>\n", " <td>scipy</td>\n", " <td>1.459543e-96</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>t-test</td>\n", " <td>custom</td>\n", " <td>1.459543e-96</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " test method pval\n", "0 t-test scipy 7.027919e-112\n", "1 t-test custom 7.027919e-112\n", "2 t-test scipy 1.459543e-96\n", "3 t-test custom 1.459543e-96" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import datasets\n", "ix, iy = datasets.load_iris(return_X_y=True)\n", "v1, v2 = ix[:,0], ix[:,1]\n", "k = 1\n", "all_stats = [stats.ttest_ind(v1, v2, equal_var=True)[k],\n", " tdist_2dist(v1.mean(), v2.mean(), v1.std(ddof=1), v2.std(ddof=1), len(v1), len(v2), var_eq=True)[k],\n", " stats.ttest_ind(v1, v2, equal_var=False)[k],\n", " tdist_2dist(v1.mean(), v2.mean(), v1.std(ddof=1), v2.std(ddof=1), len(v1), len(v2), var_eq=False)[k]]\n", "pd.DataFrame({'test':'t-test',\n", " 'method':np.tile(['scipy','custom'],2),\n", " 'pval':all_stats})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far so good. Next, we'll use `rpy2` to get the results in `R` which supports equal and unequal variances with two different functions." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>test</th>\n", " <th>method</th>\n", " <th>pval</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>F-test</td>\n", " <td>R</td>\n", " <td>1.669669e-31</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>F-test</td>\n", " <td>custom</td>\n", " <td>1.669669e-31</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>F-test</td>\n", " <td>R</td>\n", " <td>1.505059e-28</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>F-test</td>\n", " <td>custom</td>\n", " <td>1.505059e-28</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " test method pval\n", "0 F-test R 1.669669e-31\n", "1 F-test custom 1.669669e-31\n", "2 F-test R 1.505059e-28\n", "3 F-test custom 1.505059e-28" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import rpy2.robjects as robjects\n", "\n", "moments_x = pd.DataFrame({'x':ix[:,0],'y':iy}).groupby('y').x.describe()[['mean','std','count']]\n", "\n", "all_stats = [np.array(robjects.r('summary(aov(Sepal.Length~Species,iris))[[1]][1, 5]'))[0],\n", " fdist_anova(moments_x['mean'], moments_x['std'], moments_x['count'], var_eq=True)[1][0],\n", " np.array(robjects.r('oneway.test(Sepal.Length~Species,iris)$p.value'))[0],\n", " fdist_anova(moments_x['mean'], moments_x['std'], moments_x['count'], var_eq=False)[1][0]]\n", "pd.DataFrame({'test':'F-test',\n", " 'method':np.tile(['R','custom'],2),\n", " 'pval':all_stats})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again the results are identical to the benchmark functions.\n", "\n", "## (5) Application to AUROC inference\n", "\n", "The empirical AUROC has an asymptotically normal distribution. Consequently, the difference between two AUROCs will also have an asymptotically normal distribution. For small sample sizes, the [Hanley and McNeil](http://www.med.mcgill.ca/epidemiology/hanley/software/Hanley_McNeil_Radiology_82.pdf) adjustment to the AUROC standard error will obtain slightly better coverage. For a review of the notation and meaning of the AUROC, see a previous post [here](http://www.erikdrysdale.com/auc_CI).\n", "\n", "$$\n", "\\begin{align}\n", "AUC &= \\frac{1}{n_1 n_0} \\sum_{i: y_i = 1} \\sum_{j: y_j=0} I(s_i > s_j) \\\\\n", "\\sigma_{N} &= \\sqrt{\\frac{n_1 + n_0 + 1}{12\\cdot n_1 n_0}} \\\\ \n", "\\sigma_{HM} &= \\sqrt{\\frac{AUC\\cdot (1-AUC) + q_1 + q_0}{n_1 n_0}} \\\\\n", "q_1 &= (n_1 - 1)\\cdot ( AUC / (2-AUC) - AUC^2) \\\\\n", "q_0&= (n_0- 1)\\cdot ( AUC^2 / (1+AUC) - AUC^2)\n", "\\end{align}\n", "$$\n", "\n", "The standard error from the normal approximation ($\\sigma_N$) is only a function of the positive ($n_1$) and negative ($n_0$) class sample sizes whereas the Hanley and McNeil adjustment ($\\sigma_{HM}$) uses the empirical AUROC as well. The previous t- and F-tests relied on the fact that the sample mean had a variance that $O(1/n)$ so that $\\bar x \\sim N(\\mu, \\sigma^2/n)$. As can be seen from either formula, the sample variance for the AUROC can not be nearly re-written as a function of the sample size. We can still appeal to the t-test, the only difference being that the sample size is built into the variance estimate:\n", "\n", "$$\n", "\\begin{align}\n", "\\frac{AUC_A - AUC_B}{\\sqrt{\\sigma^2_{HM_A} + \\sigma^2_{HM_B}}} &\\sim N(0,1) \\hspace{3mm} \\text{ if $H_0$ is true} \n", "\\end{align}\n", "$$\n", "\n", "In the simulation below, scores will come from one of two distributions. The negative class will have 200 samples drawn from a standard normal ($n_0$). The positive class scores will have 100 samples ($n_1$) drawn from either a standard normal (for the null distribution) and a normal with a mean at or above zero. The difference in AUROCs between these two distributions will be evaluated. Since the null distribution will have an (average) AUROC of 50%, the difference in these distribution will be above zero when the mean from the alternative is greater than zero." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 500x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (8785929525461)>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n1, n0 = 100, 200\n", "n = n1 + n0\n", "n1n0 = n1 * n0\n", "mu_seq = np.round(np.arange(0, 1.01, 0.1),2)\n", "\n", "def se_auroc_hanley(auroc, n1, n0):\n", " q1 = (n1 - 1) * ((auroc / (2 - auroc)) - auroc ** 2)\n", " q0 = (n0 - 1) * ((2 * auroc 2) / (1 + auroc) - auroc 2)\n", " se_auroc = np.sqrt((auroc * (1 - auroc) + q1 + q0) / (n1 * n0))\n", " return se_auroc\n", "\n", "def se_auroc_normal(n1, n0):\n", " return np.sqrt( (n1 + n0 + 1) / (12 * n1 * n0) )\n", "\n", "np.random.seed(1)\n", "holder = []\n", "for mu in mu_seq:\n", " x1_null, x0 = np.random.randn(n1, nsim), np.random.randn(n0, nsim)\n", " x1 = mu + np.random.randn(n1, nsim)\n", " x, x_null = np.concatenate((x1, x0)), np.concatenate((x1_null, x0))\n", " auc = (np.sum(stats.rankdata(x, axis=0)[:n1],0) - n1(n1+1)/2) / n1n0\n", " auc_null = (np.sum(stats.rankdata(x_null, axis=0)[:n1], 0) - n1 (n1 + 1) / 2) / n1n0\n", " se_HM, se_null_HM = se_auroc_hanley(auc, n1, n0), se_auroc_hanley(auc_null, n1, n0)\n", " se_N = se_auroc_normal(n1, n0)\n", " # Do pairwise t-test\n", " dauc = auc - auc_null\n", " t_score_HM = dauc / np.sqrt(se_HM2 + se_null_HM2)\n", " t_score_N = dauc / np.sqrt(2 * se_N*2)\n", " dist_null = stats.t(df=2n - 2)\n", " pval_HM = 2 * np.minimum(dist_null.sf(t_score_HM), dist_null.cdf(t_score_HM))\n", " pval_N = 2 * np.minimum(dist_null.sf(t_score_N), dist_null.cdf(t_score_N))\n", " reject_HM, reject_N = np.mean(pval_HM < alpha), np.mean(pval_N < alpha)\n", " tmp = pd.DataFrame({'method':['HM','N'],'mu':mu, 'reject':[reject_HM, reject_N]})\n", " holder.append(tmp)\n", "# Merge and analyse\n", "res_auc = pd.concat(holder).reset_index(None, True)\n", "res_auc = res_auc.assign(auc=lambda x: stats.norm.cdf(x.mu/np.sqrt(2)),\n", " nreject=lambda x: (x.reject*nsim).astype(int))\n", "res_auc = pd.concat([res_auc.drop(columns='nreject'),\n", " pd.concat(prop_CI(count=res_auc.nreject,nobs=nsim,method='beta'),1)],1)\n", "res_auc.rename(columns={0:'lb',1:'ub'},inplace=True)\n", " \n", "# plot\n", "plotnine.options.figure_size = (5, 4)\n", "gg_auc = (ggplot(res_auc,aes(x='auc',y='reject',color='method')) + theme_bw() +\n", " geom_line() +\n", " labs(x='Alternative hypothesis AUROC',y='Prob. of rejecting null') +\n", " geom_hline(yintercept=0.05,linetype='--') +\n", " geom_linerange(aes(ymin='lb',ymax='ub')) + \n", " scale_color_discrete(name='Method',labels=['Hanley-McNeil','Normal']))\n", "gg_auc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Figure 4 shows that the standard errors from both methods yield almost identical results. Furthermore, the standard errors are conservative (too large), leading to an under-rejection of the null hypothesis when the null is true (i.e. the alternative hypothesis AUROC is 50%). The alternative hypothesis AUROC needs to reach around 53% before the rejection rate reaches the expected normal level. However, between 53%-70%, the power of the test approaches 100% for this sample size combination." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
gaufung/PythonStandardLibrary	RunningFeatures/sys/MemoryManagementAndLimits.ipynb	1	27078	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Reference Counts" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "At Start : 2\n", "Second reference: 3\n", "After del: 2\n" ] } ], "source": [ "import sys\n", "one = []\n", "print('At Start :', sys.getrefcount(one))\n", "two = one\n", "print('Second reference: ', sys.getrefcount(one))\n", "del two\n", "print('After del: ', sys.getrefcount(one))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Object Size" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " list:64\n", " tuple:48\n", " dict:240\n", " str:50\n", " str:55\n", " bytes:38\n", " int:28\n", " float:24\n", " type:1056\n", " MyClass:56\n" ] } ], "source": [ "import sys\n", "\n", "class MyClass:\n", " pass\n", "\n", "objects = [\n", " [], (), {}, 'c', 'string', b'bytes', 1, 2.3, MyClass, MyClass(),\n", "]\n", "for obj in objects:\n", " print('{:>10}:{}'.format(type(obj).__name__, sys.getsizeof(obj)))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WithoutAttributes: 56\n", "WithAttributes: 56\n" ] } ], "source": [ "import sys\n", "\n", "\n", "class WithoutAttributes:\n", " pass\n", "\n", "\n", "class WithAttributes:\n", " def __init__(self):\n", " self.a = 'a'\n", " self.b = 'b'\n", " return\n", "without_attrs = WithoutAttributes()\n", "print('WithoutAttributes:', sys.getsizeof(without_attrs))\n", "\n", "with_attrs = WithAttributes()\n", "print('WithAttributes:', sys.getsizeof(with_attrs))\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "156\n" ] } ], "source": [ "import sys\n", "\n", "\n", "class WithAttributes:\n", " def __init__(self):\n", " self.a = 'a'\n", " self.b = 'b'\n", " return\n", "\n", " def __sizeof__(self):\n", " return object.__sizeof__(self) + \\\n", " sum(sys.getsizeof(k) for k,v in self.__dict__.items())\n", "\n", "\n", "my_inst = WithAttributes()\n", "print(sys.getsizeof(my_inst))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Recursion" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial limit: 1000\n", "Modified limit: 500\n", "generate_recursion_error(1)\n", "generate_recursion_error(2)\n", "generate_recursion_error(3)\n", "generate_recursion_error(4)\n", "generate_recursion_error(5)\n", "generate_recursion_error(6)\n", "generate_recursion_error(7)\n", "generate_recursion_error(8)\n", "generate_recursion_error(9)\n", "generate_recursion_error(10)\n", "generate_recursion_error(11)\n", "generate_recursion_error(12)\n", "generate_recursion_error(13)\n", "generate_recursion_error(14)\n", "generate_recursion_error(15)\n", "generate_recursion_error(16)\n", "generate_recursion_error(17)\n", "generate_recursion_error(18)\n", "generate_recursion_error(19)\n", "generate_recursion_error(20)\n", "generate_recursion_error(21)\n", "generate_recursion_error(22)\n", "generate_recursion_error(23)\n", "generate_recursion_error(24)\n", "generate_recursion_error(25)\n", "generate_recursion_error(26)\n", "generate_recursion_error(27)\n", "generate_recursion_error(28)\n", "generate_recursion_error(29)\n", "generate_recursion_error(30)\n", "generate_recursion_error(31)\n", "generate_recursion_error(32)\n", "generate_recursion_error(33)\n", "generate_recursion_error(34)\n", "generate_recursion_error(35)\n", "generate_recursion_error(36)\n", "generate_recursion_error(37)\n", "generate_recursion_error(38)\n", "generate_recursion_error(39)\n", "generate_recursion_error(40)\n", "generate_recursion_error(41)\n", "generate_recursion_error(42)\n", "generate_recursion_error(43)\n", "generate_recursion_error(44)\n", "generate_recursion_error(45)\n", "generate_recursion_error(46)\n", "generate_recursion_error(47)\n", "generate_recursion_error(48)\n", "generate_recursion_error(49)\n", "generate_recursion_error(50)\n", "generate_recursion_error(51)\n", "generate_recursion_error(52)\n", "generate_recursion_error(53)\n", "generate_recursion_error(54)\n", "generate_recursion_error(55)\n", "generate_recursion_error(56)\n", "generate_recursion_error(57)\n", "generate_recursion_error(58)\n", "generate_recursion_error(59)\n", "generate_recursion_error(60)\n", "generate_recursion_error(61)\n", "generate_recursion_error(62)\n", "generate_recursion_error(63)\n", "generate_recursion_error(64)\n", "generate_recursion_error(65)\n", "generate_recursion_error(66)\n", "generate_recursion_error(67)\n", "generate_recursion_error(68)\n", "generate_recursion_error(69)\n", "generate_recursion_error(70)\n", "generate_recursion_error(71)\n", "generate_recursion_error(72)\n", "generate_recursion_error(73)\n", "generate_recursion_error(74)\n", "generate_recursion_error(75)\n", "generate_recursion_error(76)\n", "generate_recursion_error(77)\n", "generate_recursion_error(78)\n", "generate_recursion_error(79)\n", "generate_recursion_error(80)\n", "generate_recursion_error(81)\n", "generate_recursion_error(82)\n", "generate_recursion_error(83)\n", "generate_recursion_error(84)\n", "generate_recursion_error(85)\n", "generate_recursion_error(86)\n", "generate_recursion_error(87)\n", "generate_recursion_error(88)\n", "generate_recursion_error(89)\n", "generate_recursion_error(90)\n", "generate_recursion_error(91)\n", "generate_recursion_error(92)\n", "generate_recursion_error(93)\n", "generate_recursion_error(94)\n", "generate_recursion_error(95)\n", "generate_recursion_error(96)\n", "generate_recursion_error(97)\n", "generate_recursion_error(98)\n", "generate_recursion_error(99)\n", "generate_recursion_error(100)\n", "generate_recursion_error(101)\n", "generate_recursion_error(102)\n", "generate_recursion_error(103)\n", "generate_recursion_error(104)\n", "generate_recursion_error(105)\n", "generate_recursion_error(106)\n", "generate_recursion_error(107)\n", "generate_recursion_error(108)\n", "generate_recursion_error(109)\n", "generate_recursion_error(110)\n", "generate_recursion_error(111)\n", "generate_recursion_error(112)\n", "generate_recursion_error(113)\n", "generate_recursion_error(114)\n", "generate_recursion_error(115)\n", "generate_recursion_error(116)\n", "generate_recursion_error(117)\n", "generate_recursion_error(118)\n", "generate_recursion_error(119)\n", "generate_recursion_error(120)\n", "generate_recursion_error(121)\n", "generate_recursion_error(122)\n", "generate_recursion_error(123)\n", "generate_recursion_error(124)\n", 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"generate_recursion_error(377)\n", "generate_recursion_error(378)\n", "generate_recursion_error(379)\n", "generate_recursion_error(380)\n", "generate_recursion_error(381)\n", "generate_recursion_error(382)\n", "generate_recursion_error(383)\n", "generate_recursion_error(384)\n", "generate_recursion_error(385)\n", "generate_recursion_error(386)\n", "generate_recursion_error(387)\n", "generate_recursion_error(388)\n", "generate_recursion_error(389)\n", "generate_recursion_error(390)\n", "generate_recursion_error(391)\n", "generate_recursion_error(392)\n", "generate_recursion_error(393)\n", "generate_recursion_error(394)\n", "generate_recursion_error(395)\n", "generate_recursion_error(396)\n", "generate_recursion_error(397)\n", "generate_recursion_error(398)\n", "generate_recursion_error(399)\n", "generate_recursion_error(400)\n", "generate_recursion_error(401)\n", "generate_recursion_error(402)\n", "generate_recursion_error(403)\n", "generate_recursion_error(404)\n", "generate_recursion_error(405)\n", "generate_recursion_error(406)\n", "generate_recursion_error(407)\n", "generate_recursion_error(408)\n", "generate_recursion_error(409)\n", "generate_recursion_error(410)\n", "generate_recursion_error(411)\n", "generate_recursion_error(412)\n", "generate_recursion_error(413)\n", "generate_recursion_error(414)\n", "generate_recursion_error(415)\n", "generate_recursion_error(416)\n", "generate_recursion_error(417)\n", "generate_recursion_error(418)\n", "generate_recursion_error(419)\n", "generate_recursion_error(420)\n", "generate_recursion_error(421)\n", "generate_recursion_error(422)\n", "generate_recursion_error(423)\n", "generate_recursion_error(424)\n", "generate_recursion_error(425)\n", "generate_recursion_error(426)\n", "generate_recursion_error(427)\n", "generate_recursion_error(428)\n", "generate_recursion_error(429)\n", "generate_recursion_error(430)\n", "generate_recursion_error(431)\n", "generate_recursion_error(432)\n", "generate_recursion_error(433)\n", "generate_recursion_error(434)\n", "generate_recursion_error(435)\n", "generate_recursion_error(436)\n", "generate_recursion_error(437)\n", "generate_recursion_error(438)\n", "generate_recursion_error(439)\n", "generate_recursion_error(440)\n", "generate_recursion_error(441)\n", "generate_recursion_error(442)\n", "generate_recursion_error(443)\n", "generate_recursion_error(444)\n", "generate_recursion_error(445)\n", "generate_recursion_error(446)\n", "generate_recursion_error(447)\n", "generate_recursion_error(448)\n", "generate_recursion_error(449)\n", "generate_recursion_error(450)\n", "generate_recursion_error(451)\n", "generate_recursion_error(452)\n", "generate_recursion_error(453)\n", "generate_recursion_error(454)\n", "generate_recursion_error(455)\n", "generate_recursion_error(456)\n", "generate_recursion_error(457)\n", "generate_recursion_error(458)\n", "generate_recursion_error(459)\n", "generate_recursion_error(460)\n", "generate_recursion_error(461)\n", "generate_recursion_error(462)\n", "generate_recursion_error(463)\n", "generate_recursion_error(464)\n", "generate_recursion_error(465)\n", "Caught exception: maximum recursion depth exceeded in comparison\n" ] } ], "source": [ "import sys\n", "\n", "print('Initial limit:', sys.getrecursionlimit())\n", "\n", "sys.setrecursionlimit(500)\n", "\n", "print('Modified limit:', sys.getrecursionlimit())\n", "\n", "\n", "def generate_recursion_error(i):\n", " print('generate_recursion_error({})'.format(i))\n", " generate_recursion_error(i + 1)\n", "\n", "\n", "try:\n", " generate_recursion_error(1)\n", "except RuntimeError as err:\n", " print('Caught exception:', err)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Maximum Values" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "maxsize : 9223372036854775807\n", "maxunicode: 1114111\n" ] } ], "source": [ "import sys\n", "print('maxsize :', sys.maxsize)\n", "print('maxunicode:', sys.maxunicode)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Floating Point values" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Smallest difference (epsilon): 2.220446049250313e-16\n", "\n", "Digits (dig) : 15\n", "Mantissa digits (mant_dig): 53\n", "\n", "Maximum (max): 1.7976931348623157e+308\n", "Minimum (min): 2.2250738585072014e-308\n", "\n", "Radix of exponents (radix): 2\n", "\n", "Maximum exponent for radix (max_exp): 1024\n", "Minimum exponent for radix (min_exp): -1021\n", "\n", "Max. exponent power of 10 (max_10_exp): 308\n", "Min. exponent power of 10 (min_10_exp): -307\n", "\n", "Rounding for addition (rounds): 1\n" ] } ], "source": [ "import sys\n", "\n", "print('Smallest difference (epsilon):', sys.float_info.epsilon)\n", "print()\n", "print('Digits (dig) :', sys.float_info.dig)\n", "print('Mantissa digits (mant_dig):', sys.float_info.mant_dig)\n", "print()\n", "print('Maximum (max):', sys.float_info.max)\n", "print('Minimum (min):', sys.float_info.min)\n", "print()\n", "print('Radix of exponents (radix):', sys.float_info.radix)\n", "print()\n", "print('Maximum exponent for radix (max_exp):',\n", " sys.float_info.max_exp)\n", "print('Minimum exponent for radix (min_exp):',\n", " sys.float_info.min_exp)\n", "print()\n", "print('Max. exponent power of 10 (max_10_exp):',\n", " sys.float_info.max_10_exp)\n", "print('Min. exponent power of 10 (min_10_exp):',\n", " sys.float_info.min_10_exp)\n", "print()\n", "print('Rounding for addition (rounds):', sys.float_info.rounds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Integer Values" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of bits used to hold each digit: 30\n", "Size in bytes of C type used to hold each digit: 4\n" ] } ], "source": [ "import sys\n", "\n", "print('Number of bits used to hold each digit:',\n", " sys.int_info.bits_per_digit)\n", "print('Size in bytes of C type used to hold each digit:',\n", " sys.int_info.sizeof_digit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Byte Order" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "little\n" ] } ], "source": [ "import sys\n", "\n", "print(sys.byteorder)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
tuanavu/python-cookbook-3rd	notebooks/ch01/01_unpacking_a_sequence_into_variables.ipynb	3	5146	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Unpacking a Sequence into Separate Variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem\n", "- You have an N-element tuple or sequence that you would like to unpack into a collection of N variables." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution\n", "Any sequence (or iterable) can be unpacked into variables using a simple assignment operation. The only requirement is that the number of variables and structure match the sequence." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "5\n" ] } ], "source": [ "# Example 1\n", "p = (4, 5)\n", "x, y = p\n", "print x\n", "print y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ACME\n", "(2012, 12, 21)\n", "ACME\n", "2012\n", "12\n", "21\n" ] } ], "source": [ "# Example 2\n", "data = ['ACME', 50, 91.1, (2012, 12, 21)]\n", "name, shares, price, date = data\n", "print name\n", "print date\n", "\n", "name, shares, price, (year, mon, day) = data\n", "print name\n", "print year\n", "print mon\n", "print day" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example 3\n", "- If there is a mismatch in the number of elements, you’ll get an error" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "need more than 2 values to unpack", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-3-b612f455712e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# error with mismatch in number of elements\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: need more than 2 values to unpack" ] } ], "source": [ "# Example 3\n", "# error with mismatch in number of elements\n", "p = (4, 5)\n", "x, y, z = p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 4\n", "- Unpacking actually works with any object that happens to be iterable, not just tuples or lists. This includes strings, files, iterators, and generators." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "H\n", "e\n", "o\n" ] } ], "source": [ "# Example 4: string\n", "s = 'Hello'\n", "a, b, c, d, e = s\n", "print a\n", "print b\n", "print e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 5\n", "- Discard certain values" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50\n", "91.1\n" ] } ], "source": [ "# Example 5\n", "# discard certain values\n", "data = [ 'ACME', 50, 91.1, (2012, 12, 21) ]\n", "_, shares, price, _ = data\n", "print shares\n", "print price" ] } ], "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": "2.7.12" }, "toc": { "toc_cell": false, "toc_number_sections": false, "toc_threshold": "8", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 0 }	mit
tuanavu/coursera-university-of-washington	machine_learning/4_clustering_and_retrieval/lecture/week4/quiz-EM for Gaussian mixtures.ipynb	2	6157	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EM for Gaussian mixtures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Question 1\n", "\n", "<img src=\"images/Screen Shot 2016-07-22 at 11.27.34 PM.png\">\n", "\n", "Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/QVHj1/em-for-gaussian-mixtures)\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Question 2\n", "\n", "<img src=\"images/Screen Shot 2016-07-22 at 10.56.21 PM.png\">\n", "\n", "Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/QVHj1/em-for-gaussian-mixtures)\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Question 3\n", "\n", "<img src=\"images/Screen Shot 2016-07-22 at 10.56.49 PM.png\">\n", "\n", "Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/QVHj1/em-for-gaussian-mixtures)\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Question 4\n", "\n", "<img src=\"images/Screen Shot 2016-07-22 at 11.27.39 PM.png\">\n", "\n", "Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/QVHj1/em-for-gaussian-mixtures)\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer\n", "\n", "- https://www.coursera.org/learn/ml-clustering-and-retrieval/discussions/weeks/4/threads/LbCIn0gBEear2wpBpCdaRw\n", "- Its similar to what's been explained in KMeans. The more clusters (or higher the K value), the higher log-likelihood and lesser heterogeneity. For Mixture Models, replace clusters with components and the same principles apply.\n", "- The more clusters we have or higher the value of K, lower is the heterogeneity or higher is the homogeneity. Similar thing is true for Gaussian Mixture Models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Question 5\n", "\n", "<img src=\"images/Screen Shot 2016-07-22 at 10.57.54 PM.png\">\n", "\n", "Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/QVHj1/em-for-gaussian-mixtures)\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Question 6\n", "\n", "<img src=\"images/Screen Shot 2016-07-22 at 11.10.15 PM.png\">\n", "\n", "Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/QVHj1/em-for-gaussian-mixtures)\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer\n", "\n", "- https://www.coursera.org/learn/ml-clustering-and-retrieval/discussions/weeks/4/threads/zP-sDUHmEeaswA4y58BhkQ\n", "\n", "Number of parameters:\n", " - # of parameters of pi per component = 1\n", " - # of parameters of mu per component = V = # dimensions\n", " - # of parameters of Sigma per component for full Covariance matrix = V(V+1)/2\n", " or\n", " - # of parameters of Sigma per component for just diagonal matrix = V\n", "\n", "For each components:\n", " - # of parameters of pi: 1\n", " - # of parameters of mu: 3\n", " - # of parameters of Sigma for full Covariance matrix = 3(3+1)/2 = 3 * 4/2 = 6\n", " \n", "Total # of parameters for each components: 1 + 3 + 6 = 10\n", "\n", "Total # of parameters: 10 * num of cluster (components) = $10 * 4$ = 40" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Question 7\n", "\n", "<img src=\"images/Screen Shot 2016-07-22 at 11.27.44 PM.png\">\n", "\n", "Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/QVHj1/em-for-gaussian-mixtures)\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer\n", "\n", "For each components:\n", " - # of parameters of pi: 1\n", " - # of parameters of mu: 4\n", " - # of parameters of Sigma for diagonal covariance matrices = 4\n", " \n", "Total # of parameters for each components: 1 + 4 + 4 = 9\n", "\n", "Total # of parameters: 9 * num of cluster (components) = $9 * 5$ = 45" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Question 8\n", "\n", "<img src=\"images/Screen Shot 2016-07-22 at 11.10.24 PM.png\">\n", "\n", "Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/QVHj1/em-for-gaussian-mixtures)\n", "\n", "<!--TEASER_END-->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Question 9\n", "\n", "<img src=\"images/Screen Shot 2016-07-22 at 11.31.56 PM.png\">\n", "<img src=\"images/Screen Shot 2016-07-22 at 11.32.06 PM.png\">\n", "<img src=\"images/Screen Shot 2016-07-22 at 11.32.13 PM.png\">\n", "\n", "Screenshot taken from [Coursera](https://www.coursera.org/learn/ml-clustering-and-retrieval/exam/QVHj1/em-for-gaussian-mixtures)\n", "\n", "<!--TEASER_END-->" ] } ], "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": "2.7.12" }, "toc": { "toc_cell": false, "toc_number_sections": false, "toc_threshold": "8", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 0 }	mit
makeyourowntextminingtoolkit/makeyourowntextminingtoolkit	04_index_and_relevance.ipynb	1	728218	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# notebook to illustrate text indexing and relevance scoring" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# following only used for development, reloads the modules with any code changes\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "# inline matplotlib charts\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import our text mining toolkit\n", "import text_mining_toolkit as tmt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# load Englsh dictionary\n", "dictionary_df = tmt.dictionary.get_dictionary_words(\"words.txt\")\n", "# a set is mroe efficient when checking for membership\n", "dictionary_set = set(dictionary_df['words'].values.tolist())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "content_directory = data_sets/hillsborough/txt/\n", "text_filename_pattern = .txt\n", "self.documents populated = 19217\n" ] } ], "source": [ "#cr = tmt.corpus_reader.CorpusReader(content_directory=\"data_sets/simple_test/txt/\", text_filename_pattern=\"??.txt\")\n", "#cr = tmt.corpus_reader.CorpusReader(content_directory=\"data_sets/recipes/txt/\", text_filename_pattern=\"??.txt\")\n", "#cr = tmt.corpus_reader.CorpusReader(directory_of_files=\"data_sets/mystery_corpus_01/txt/\", text_filename_pattern=\"??.txt\")\n", "#cr = tmt.corpus_reader.CorpusReader(content_directory=\"data_sets/iraq_inquiry/txt/\", text_filename_pattern=\"the-report.txt\")\n", "#cr = tmt.corpus_reader.CorpusReader(content_directory=\"data_sets/clinton_emails/txt/\", text_filename_pattern=\"C0.txt\")\n", "#cr = tmt.corpus_reader.CorpusReader(content_directory=\"data_sets/shakespeare_macbeth/txt/\", text_filename_pattern=\"macbeth_act_0?_scene_0?.txt\")\n", "#cr = tmt.corpus_reader.CorpusReader(content_directory=\"data_sets/hillsborough/txt/\", text_filename_pattern=\"HOM.txt\")\n", "cr = tmt.corpus_reader.CorpusReader(content_directory=\"data_sets/hillsborough/txt/\", text_filename_pattern=\"*.txt\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "processing AGO000000010001.txt\n", "processing AGO000000020001.txt\n", "processing AGO000000030001.txt\n", "processing AGO000000040001.txt\n", "processing AGO000000050001.txt\n", "processing AGO000000060001.txt\n", "processing AGO000000070001.txt\n", "processing AGO000000080001.txt\n", "processing AGO000000090001.txt\n", "processing AGO000000100001.txt\n", "processing AGO000000110001.txt\n", "processing AGO000000120001.txt\n", "processing AGO000000130001.txt\n", "processing AGO000000140001.txt\n", "processing AGO000000150001.txt\n", "processing AGO000000160001.txt\n", "processing AGO000000180001.txt\n", "processing AGO000000200001.txt\n", "processing AGO000000210001.txt\n", "processing AGO000000220001.txt\n", "processing AGO000000230001.txt\n", "processing AGO000000240001.txt\n", "processing AGO000000250001.txt\n", "processing AGO000000260001.txt\n", "processing AGO000000270001.txt\n", "processing AGO000000280001.txt\n", "processing AGO000000290001.txt\n", "processing AGO000000300001.txt\n", "processing AGO000000310001.txt\n", "processing AGO000000320001.txt\n", "processing AGO000000330001.txt\n", "processing AGO000000340001.txt\n", "processing AGO000000360001.txt\n", "processing AGO000000380001.txt\n", "processing AGO000000430001.txt\n", "processing AGO000000450001.txt\n", "processing AGO000000470001.txt\n", "processing AGO000000480001.txt\n", "processing 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"processing YAS000002820001.txt\n", "processing YAS000002860001.txt\n", "processing YAS000002920001.txt\n", "processing YAS000002930001.txt\n", "processing YAS000002940001.txt\n", "processing YAS000002990001.txt\n", "processing YAS000003000001.txt\n", "processing YAS000003010001.txt\n", "processing YAS000003030001.txt\n", "processing YAS000003060001.txt\n", "processing YAS000003070001.txt\n", "processing YAS000003080001.txt\n", "processing YAS000003090001.txt\n", "processing YAS000003100001.txt\n", "processing YAS000003110001.txt\n", "processing YAS000003120001.txt\n", "processing YAS000003130001.txt\n", "processing YAS000003140001.txt\n", "processing YAS000003150001.txt\n", "processing YAS000003160001.txt\n", "processing YAS000003180001.txt\n", "processing YAS000003190001.txt\n", "processing YAS000003320001.txt\n", "processing YAS000003350001.txt\n", "processing YAS000003360001.txt\n", "processing YAS000003370001.txt\n", "processing YAS000003380001.txt\n", "processing YAS000003390001.txt\n" ] } ], "source": [ "# first clear wordcount and relevance indices\n", "tmt.index_wordcount.delete_index(cr.content_directory)\n", "tmt.index_relevance.delete_index(cr.content_directory)\n", "\n", "# for all documents in corpus\n", "for document_name in cr.get_documents():\n", " print(\"processing \", document_name)\n", "\n", " # get document text\n", " document_text = cr.get_text_by_document(document_name)\n", "\n", " # simplify whitespace (remove newlines)\n", " b = tmt.text_processing.simplify_whitespace(document_text)\n", "\n", " # only keep alphanumeric characters, removes punctuation\n", " c = tmt.text_processing.keep_only_alphanumeric(b)\n", "\n", " # make lowercase\n", " d = tmt.text_processing.to_lowercase(c)\n", "\n", " # split into words list\n", " dl = tmt.text_processing.split_text_into_words(d)\n", " \n", " # build n-grams\n", " #gl = tmt.word_processing.build_ngrams_from_words(dl,2)\n", "\n", " # remove stop words\n", " el = tmt.word_processing.remove_stop_words(dl, \"./stopwords/minimal-stop.txt\")\n", " \n", " # only keep words with min length 5\n", " fl = tmt.word_processing.keep_words_min_length(el,5)\n", " \n", " # keep only words found in the Eglish dictionary\n", " gl = [word for word in fl if word in dictionary_set]\n", " \n", " # update index\n", " tmt.index_wordcount.create_wordcount_index_for_document(cr.content_directory, document_name, gl)\n", " pass\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "saving corpus word count index ... data_sets/hillsborough/txt/index_wordcount.hdf5\n" ] } ], "source": [ "# merge document indices into a corpus index\n", "tmt.index_wordcount.merge_wordcount_indices_for_corpus(cr.content_directory)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "saving corpus relevance index ... data_sets/hillsborough/txt/index_relevance.hdf5\n" ] } ], "source": [ "tmt.index_relevance.calculate_relevance_index(cr.content_directory)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>police</th>\n", " <td>83.451991</td>\n", " </tr>\n", " <tr>\n", " <th>raised</th>\n", " <td>79.235280</td>\n", " </tr>\n", " <tr>\n", " <th>ground</th>\n", " <td>66.290401</td>\n", " </tr>\n", " <tr>\n", " <th>actions</th>\n", " <td>65.596721</td>\n", " </tr>\n", " <tr>\n", " <th>document</th>\n", " <td>63.932343</td>\n", " </tr>\n", " <tr>\n", " <th>action</th>\n", " <td>61.090630</td>\n", " </tr>\n", " <tr>\n", " <th>material</th>\n", " <td>57.589417</td>\n", " </tr>\n", " <tr>\n", " <th>number</th>\n", " <td>51.046630</td>\n", " </tr>\n", " <tr>\n", " <th>indexer</th>\n", " <td>50.493850</td>\n", " </tr>\n", " <tr>\n", " <th>instructions</th>\n", " <td>48.442706</td>\n", " </tr>\n", " <tr>\n", " <th>statement</th>\n", " <td>47.873629</td>\n", " </tr>\n", " <tr>\n", " <th>other</th>\n", " <td>46.347452</td>\n", " </tr>\n", " <tr>\n", " <th>football</th>\n", " <td>46.155356</td>\n", " </tr>\n", " <tr>\n", " <th>indicated</th>\n", " <td>44.902818</td>\n", " </tr>\n", " <tr>\n", " <th>people</th>\n", " <td>43.851017</td>\n", " </tr>\n", " <tr>\n", " <th>there</th>\n", " <td>42.416433</td>\n", " </tr>\n", " <tr>\n", " <th>would</th>\n", " <td>41.164988</td>\n", " </tr>\n", " <tr>\n", " <th>sheffield</th>\n", " <td>40.631458</td>\n", " </tr>\n", " <tr>\n", " <th>supporters</th>\n", " <td>40.336179</td>\n", " </tr>\n", " <tr>\n", " <th>stand</th>\n", " <td>40.262084</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0\n", "police 83.451991\n", "raised 79.235280\n", "ground 66.290401\n", "actions 65.596721\n", "document 63.932343\n", "action 61.090630\n", "material 57.589417\n", "number 51.046630\n", "indexer 50.493850\n", "instructions 48.442706\n", "statement 47.873629\n", "other 46.347452\n", "football 46.155356\n", "indicated 44.902818\n", "people 43.851017\n", "there 42.416433\n", "would 41.164988\n", "sheffield 40.631458\n", "supporters 40.336179\n", "stand 40.262084" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "words_by_relevance = tmt.index_relevance.get_words_by_relevance(cr.content_directory)\n", "# 20 most common\n", "words_by_relevance[:20]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>scoffing</th>\n", " <td>0.000007</td>\n", " </tr>\n", " <tr>\n", " <th>probings</th>\n", " <td>0.000007</td>\n", " </tr>\n", " <tr>\n", " <th>whippets</th>\n", " <td>0.000007</td>\n", " </tr>\n", " <tr>\n", " <th>grannies</th>\n", " <td>0.000007</td>\n", " </tr>\n", " <tr>\n", " <th>sillier</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>rippled</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>impound</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>vikings</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>naivete</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>dabbing</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>dafter</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>scoffs</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>harped</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>crummy</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>cardin</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>cosily</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>boozed</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>scrawl</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>whinge</th>\n", " <td>0.000006</td>\n", " </tr>\n", " <tr>\n", " <th>talky</th>\n", " <td>0.000006</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0\n", "scoffing 0.000007\n", "probings 0.000007\n", "whippets 0.000007\n", "grannies 0.000007\n", "sillier 0.000006\n", "rippled 0.000006\n", "impound 0.000006\n", "vikings 0.000006\n", "naivete 0.000006\n", "dabbing 0.000006\n", "dafter 0.000006\n", "scoffs 0.000006\n", "harped 0.000006\n", "crummy 0.000006\n", "cardin 0.000006\n", "cosily 0.000006\n", "boozed 0.000006\n", "scrawl 0.000006\n", "whinge 0.000006\n", "talky 0.000006" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 20 least common\n", "words_by_relevance[-20:]" ] }, { "cell_type": "code", "execution_count": 6, 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IQHdfuDff3dk4rO082bgmTBvWjExg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iRJSpF/iiRpxtsxeEe8fnjvf6VevzpXB8Cx7a+Jt61qfx0AtVUtqedR5ShNbiDBP/ge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\naXPfA0yefDGdzGuKuu6ePf8j8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W2JOZJAfn4uAMOG9Yw5kvTJywv+5KW4uDTWOMLjWZll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F65bXckY9uwO8h2vzgWe/WOPeG6Hd3ITBTP2Dxz7CgAmltbAXhk6ZqwT/uKBH2x4MVlQPoz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C97jty185D4Bevdun9Z33UfJewW9+FarKrVwZKnH99EfJiloPP3JriKdx8fczonGJMumn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k7p/g2x3T/c2AjAhqoDANy6bk3YZv7oYBSzr55zETDwifTJxHtxd22QqD+5PEjNb7+vX3zoXgB+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Wz+E0FW+ese/GK0rbMzeNj6+nt/2utxHvhdcE/5953BDFDfvvuz0baqEcMA+NZlNwBw29eD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\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z2WMJYjLHvlV1q513pNVcA+alm2p50wuuCy0JVxOoa18DQEtgf9xjram+E4B9ERn589Jn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sFUdK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fr95wXTzucyf8AwBHlC4Y1TLHW/T++uix4XPZP6+7Op42nPdpZHsyq+sPtvwSgL9Ylp17wT/f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\n54Tl9uoO45z68a6ngNzvwq7Ngy+NuRXxSn2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"text/plain": [ "<matplotlib.figure.Figure at 0x1611d6fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualise most relevance words\n", "tmt.visualisation.plot_wordcloud(words_by_relevance[:100])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SYP000101810001.txt 0.234160\n", "SYP000101360001.txt 0.210744\n", "SYP000110700001.txt 0.100354\n", "SYP000134660001.txt 0.087810\n", "SYP000101660001.txt 0.083629\n", "SWF000000410001.txt 0.078945\n", "LLS000001150001.txt 0.076173\n", "SWF000003420001.txt 0.075500\n", "LLS000001180001.txt 0.075266\n", "SWF000003430001.txt 0.075266\n", "HOM000000190001.txt 0.073302\n", "HOM000001800001.txt 0.072670\n", "HOM000002280001.txt 0.072049\n", "WYC000000070001.txt 0.072049\n", "SWF000001230001.txt 0.070248\n", "HOM000031170001.txt 0.069476\n", "HOM000002410001.txt 0.067259\n", "HOM000001810001.txt 0.065565\n", "SYP000107390001.txt 0.063862\n", "HOM000001090001.txt 0.060559\n", "SYP000101720001.txt 0.056198\n", "HOM000006040001.txt 0.056198\n", "HOM000047800001.txt 0.054037\n", "SYP000134670001.txt 0.054037\n", "SWF000002620001.txt 0.050177\n", "HOM000001020001.txt 0.049670\n", "SYP000101670001.txt 0.047181\n", "LLS000001190001.txt 0.045484\n", "HOM000000480001.txt 0.043905\n", "HOM000002240001.txt 0.041673\n", " ... \n", "SFR000001040001.txt 0.000031\n", "SYP000096230001.txt 0.000031\n", "SYP000123580001.txt 0.000030\n", "SWF000001270001.txt 0.000030\n", "SYP000097020001.txt 0.000029\n", "HOM000034010001.txt 0.000026\n", "HOM000026820001.txt 0.000026\n", "SYP000047780001.txt 0.000024\n", "SYP000098100001.txt 0.000023\n", "YAS000002510001.txt 0.000023\n", "SYP000134300001.txt 0.000023\n", "HOM000019040001.txt 0.000022\n", "PRE000000520001.txt 0.000021\n", "SYP000013780001.txt 0.000021\n", "HOM000017710001.txt 0.000021\n", "SWF000001250001.txt 0.000020\n", "PRE000000140001.txt 0.000019\n", "CJB000000150001.txt 0.000019\n", "CPS000003260001.txt 0.000018\n", "SYP000096270001.txt 0.000017\n", "SWF000001300001.txt 0.000017\n", "PRE000000530001.txt 0.000017\n", "YAS000002390001.txt 0.000017\n", "SYP000096970001.txt 0.000016\n", "CJB000000050001.txt 0.000014\n", "SPA000001230001.txt 0.000012\n", "PRE000000120001.txt 0.000012\n", "SCC000002010001.txt 0.000011\n", "DOH000000060001.txt 0.000010\n", "SYP000110390001.txt 0.000009\n", "dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# search by relevance\n", "tmt.index_relevance.search_relevance_index(cr.content_directory, \"signature\")" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-2.0
YAtOff/python0-reloaded	week17/Dictionaries.ipynb	1	4734	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Речници" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Създаване" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "task = {\n", " 'id': '1',\n", " 'title': 'Do my homework',\n", " 'deadline': '2016-04-30',\n", " 'priority': 'normal',\n", " 'tags': ['school'],\n", " 'completed': False\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Извличане на стойност с ключ" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Do my homework'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "task['title']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Промяна на стойност" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "task['completed'] = True\n", "task['completed']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Преверка за сълществуването на даден ключ в речника" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'tags' in task" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'stuff' in task" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Извличане на всички ключове" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['title', 'tags', 'completed', 'id', 'priority', 'deadline'])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "task.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Извличане на всички стойности" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dict_values(['Do my homework', ['school'], True, '1', 'normal', '2016-04-30'])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "task.values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Създаване на празен речник" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "person = {}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Добавяне на двойка ключ -> стойност" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Пешо'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "person['name'] = 'Пешо'\n", "person['name']" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
jrbourbeau/cr-composition	notebooks/legacy/lightheavy/pyunfold-formatting.ipynb	1	1016394	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<a id='top'> </a>\n", "Author: [James Bourbeau](http://www.jamesbourbeau.com)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "last updated: 2017-03-14 \n", "\n", "CPython 2.7.10\n", "IPython 5.3.0\n", "\n", "numpy 1.12.0\n", "matplotlib 2.0.0\n", "scipy 0.15.1\n", "pandas 0.19.2\n", "sklearn 0.18.1\n", "mlxtend 0.5.1\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -u -d -v -p numpy,matplotlib,scipy,pandas,sklearn,mlxtend" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Formatting for PyUnfold use\n", "### Table of contents\n", "1. [Define analysis free parameters](#Define-analysis-free-parameters)\n", "1. [Data preprocessing](#Data-preprocessing)\n", "2. [Fitting random forest](#Fit-random-forest-and-run-10-fold-CV-validation)\n", "3. [Fraction correctly identified](#Fraction-correctly-identified)\n", "4. [Spectrum](#Spectrum)\n", "5. [Unfolding](#Unfolding)\n", "6. [Feature importance](#Feature-importance)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from __future__ import division, print_function\n", "from collections import defaultdict\n", "import numpy as np\n", "from scipy.sparse import block_diag\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn.apionly as sns\n", "import json\n", "\n", "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, auc, classification_report\n", "from sklearn.model_selection import cross_val_score, StratifiedShuffleSplit, KFold, StratifiedKFold\n", "\n", "import composition as comp\n", "import composition.analysis.plotting as plotting\n", " \n", "color_dict = {'light': 'C0', 'heavy': 'C1', 'total': 'C2',\n", " 'P': 'C0', 'He': 'C1', 'O': 'C3', 'Fe':'C4'}" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Define analysis free parameters\n", "[ [back to top](#top) ]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Whether or not to train on 'light' and 'heavy' composition classes, or the individual compositions" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "comp_class = True\n", "comp_list = ['light', 'heavy'] if comp_class else ['P', 'He', 'O', 'Fe']" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Get composition classifier pipeline" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "pipeline_str = 'GBDT'\n", "pipeline = comp.get_pipeline(pipeline_str)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Define energy binning for this analysis" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "energybins = comp.analysis.get_energybins()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Data preprocessing\n", "[ [back to top](#top) ]\n", "1. Load simulation/data dataframe and apply specified quality cuts\n", "2. Extract desired features from dataframe\n", "3. Get separate testing and training datasets\n", "4. Feature transformation" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sim quality cut event flow:\n", " IceTopQualityCuts: 1.0 1.0\n", " lap_InIce_containment: 0.776 0.776\n", " InIceQualityCuts: 0.786 0.75\n", " num_hits_1_60: 0.999 0.75\n", "\n", "\n", "Selecting the following features:\n", "\t$\\cos(\\theta_{\\mathrm{Lap}})$\n", "\t$\\log_{10}(S_{\\mathrm{125}})$\n", "\t$\\log_{10}$(dE/dX)\n", "\t\n", "Number training events = 208926\n", "Number testing events = 89540\n" ] } ], "source": [ "sim_train, sim_test = comp.preprocess_sim(comp_class=comp_class, return_energy=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sim quality cut event flow:\n", " IceTopQualityCuts: 1.0 1.0\n", " lap_InIce_containment: 0.776 0.776\n", " InIceQualityCuts: 0.786 0.75\n", " num_hits_1_60: 0.999 0.75\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/.local/lib/python2.7/site-packages/seaborn/matrix.py:143: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if xticklabels == []:\n", "/home/jbourbeau/.local/lib/python2.7/site-packages/seaborn/matrix.py:151: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if yticklabels == []:\n", "/home/jbourbeau/.local/lib/python2.7/site-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n", " warnings.warn(\"This figure includes Axes that are not \"\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2d8d406210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compute the correlation matrix\n", "df_sim = comp.load_dataframe(datatype='sim', config='IC79')\n", "feature_list, feature_labels = comp.analysis.get_training_features()\n", "corr = df_sim[feature_list].corr()\n", "# Generate a mask for the upper triangle\n", "mask = np.zeros_like(corr, dtype=np.bool)\n", "mask[np.triu_indices_from(mask)] = True\n", "\n", "fig, ax = plt.subplots()\n", "sns.heatmap(corr, mask=mask, cmap='RdBu_r', center=0,\n", " square=True, xticklabels=feature_labels, yticklabels=feature_labels,\n", " linewidths=.5, cbar_kws={'label': 'Covariance'}, annot=True, ax=ax)\n", "# outfile = args.outdir + '/feature_covariance.png'\n", "# plt.savefig(outfile)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data quality cut event flow:\n", " IceTopQualityCuts: 1.0 1.0\n", " lap_InIce_containment: 1.0 1.0\n", " InIceQualityCuts: 0.9 0.9\n", " num_hits_1_60: 1.0 0.9\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/cr-composition/composition/dataframe_functions.py:137: RuntimeWarning: invalid value encountered in log10\n", " df['log_dEdX'] = np.log10(df['eloss_1500_standard'])\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Selecting the following features:\n", "\t$\\cos(\\theta_{\\mathrm{Lap}})$\n", "\t$\\log_{10}(S_{\\mathrm{125}})$\n", "\t$\\log_{10}$(dE/dX)\n", "\t\n", "Number testing events = 7212805\n" ] } ], "source": [ "data = comp.preprocess_data(comp_class=comp_class, return_energy=True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "is_finite_mask = np.isfinite(data.X)\n", "not_finite_mask = np.logical_not(is_finite_mask)\n", "finite_data_mask = np.logical_not(np.any(not_finite_mask, axis=1))\n", "data = data[finite_data_mask]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Run classifier over training and testing sets to get an idea of the degree of overfitting" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==============================\n", "XGBClassifier\n", "Training accuracy = 77.79%\n", "Testing accuracy = 77.58%\n", "==============================\n" ] } ], "source": [ "clf_name = pipeline.named_steps['classifier'].__class__.__name__\n", "print('=' * 30)\n", "print(clf_name)\n", "pipeline.fit(sim_train.X, sim_train.y)\n", "train_pred = pipeline.predict(sim_train.X)\n", "train_acc = accuracy_score(sim_train.y, train_pred)\n", "print('Training accuracy = {:.2%}'.format(train_acc))\n", "test_pred = pipeline.predict(sim_test.X)\n", "test_acc = accuracy_score(sim_test.y, test_pred)\n", "print('Testing accuracy = {:.2%}'.format(test_acc))\n", "print('=' * 30)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "34" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(energybins.energy_midpoints)2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_frac_correct(train, test, pipeline, comp_list):\n", " \n", " assert isinstance(train, comp.analysis.DataSet), 'train dataset must be a DataSet'\n", " assert isinstance(test, comp.analysis.DataSet), 'test dataset must be a DataSet'\n", " assert train.y is not None, 'train must have true y values'\n", " assert test.y is not None, 'test must have true y values'\n", " \n", " pipeline.fit(train.X, train.y)\n", " test_predictions = pipeline.predict(test.X)\n", " correctly_identified_mask = (test_predictions == test.y)\n", "\n", " # Construct MC composition masks\n", " MC_comp_mask = {}\n", " for composition in comp_list:\n", " MC_comp_mask[composition] = (test.le.inverse_transform(test.y) == composition)\n", " MC_comp_mask['total'] = np.array([True]len(test))\n", " \n", " reco_frac, reco_frac_err = {}, {}\n", " for composition in comp_list+['total']:\n", " comp_mask = MC_comp_mask[composition]\n", " # Get number of MC comp in each reco energy bin\n", " num_MC_energy = np.histogram(test.log_energy[comp_mask],\n", " bins=energybins.log_energy_bins)[0]\n", " num_MC_energy_err = np.sqrt(num_MC_energy)\n", "\n", " # Get number of correctly identified comp in each reco energy bin\n", " num_reco_energy = np.histogram(test.log_energy[comp_mask & correctly_identified_mask],\n", " bins=energybins.log_energy_bins)[0]\n", " num_reco_energy_err = np.sqrt(num_reco_energy)\n", "\n", " # Calculate correctly identified fractions as a function of MC energy\n", " reco_frac[composition], reco_frac_err[composition] = comp.ratio_error(\n", " num_reco_energy, num_reco_energy_err,\n", " num_MC_energy, num_MC_energy_err)\n", " \n", " return reco_frac, reco_frac_err" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Calculate classifier generalization error via 10-fold CV" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fold 1...2...3...4...5...6...7...8...9...10..." ] } ], "source": [ "# Split training data into CV training and testing folds\n", "kf = KFold(n_splits=10)\n", "frac_correct_folds = defaultdict(list)\n", "fold_num = 0\n", "print('Fold ', end='')\n", "for train_index, test_index in kf.split(sim_train.X):\n", " fold_num += 1\n", " print('{}...'.format(fold_num), end='')\n", " \n", " reco_frac, reco_frac_err = get_frac_correct(sim_train[train_index],\n", " sim_train[test_index],\n", " pipeline, comp_list)\n", " \n", " for composition in comp_list:\n", " frac_correct_folds[composition].append(reco_frac[composition])\n", " frac_correct_folds['total'].append(reco_frac['total'])\n", "frac_correct_gen_err = {key: np.std(frac_correct_folds[key], axis=0) for key in frac_correct_folds}\n", "scores = np.array(frac_correct_folds['total'])\n", "score = scores.mean(axis=1).mean()\n", "score_std = scores.mean(axis=1).std()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "reco_frac, reco_frac_stat_err = get_frac_correct(sim_train, sim_test, pipeline, comp_list)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/.local/lib/python2.7/site-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n", " warnings.warn(\"This figure includes Axes that are not \"\n" ] }, { "data": { "image/png": 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whF6aaPRfNDiCaLu8vwM4l0Q4FJomuGlyBfOVRJMrmK802nyTstIovtfL/8u3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\n812OTCazEKSOHz++0BQ2MTF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BJaXAkyIaMBGNgNsFPEcpw3V5uYjhqJktRal2I5ZYtBrP\nET1PPSQcNQwLUIYRiii4nZcadBDVrrKSgxqi2lM9ZXM3GhgLUIZhGEZdYoMkDMMwjLrEApRhGIZR\nl1iAMgzDMOoSC1CGYRhGXWIByjAMw6hLLEAZhmEYdYkFKMMwDKMu+f8BrWZ1Q1mY+CAAAAAASUVO\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe6095fcd50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot fraction of events correctlt classified vs energy\n", "fig, ax = plt.subplots()\n", "for composition in comp_list + ['total']:\n", " err = np.sqrt(frac_correct_gen_err[composition]2 + reco_frac_stat_err[composition]2)\n", " plotting.plot_steps(energybins.log_energy_midpoints, reco_frac[composition], err, ax,\n", " color_dict[composition], composition)\n", "plt.xlabel('$\\log_{10}(E_{\\mathrm{reco}}/\\mathrm{GeV})$')\n", "ax.set_ylabel('Fraction correctly identified')\n", "ax.set_ylim([0.0, 1.0])\n", "ax.set_xlim([energybins.log_energy_min, energybins.log_energy_max])\n", "ax.grid()\n", "leg = plt.legend(loc='upper center', frameon=False,\n", " bbox_to_anchor=(0.5, # horizontal\n", " 1.1),# vertical \n", " ncol=len(comp_list)+1, fancybox=False)\n", "# set the linewidth of each legend object\n", "for legobj in leg.legendHandles:\n", " legobj.set_linewidth(3.0)\n", "\n", "cv_str = 'Accuracy: {:0.2f}\\% (+/- {:0.1f}\\%)'.format(score100, score_std100)\n", "ax.text(7.4, 0.2, cv_str,\n", " ha=\"center\", va=\"center\", size=10,\n", " bbox=dict(boxstyle='round', fc=\"white\", ec=\"gray\", lw=0.8))\n", "plt.savefig('/home/jbourbeau/public_html/figures/frac-correct.png')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Spectrum\n", "[ [back to top](#top) ]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_num_comp_reco(train, test, pipeline, comp_list):\n", " \n", " assert isinstance(train, comp.analysis.DataSet), 'train dataset must be a DataSet'\n", " assert isinstance(test, comp.analysis.DataSet), 'test dataset must be a DataSet'\n", " assert train.y is not None, 'train must have true y values'\n", " \n", " pipeline.fit(train.X, train.y)\n", " test_predictions = pipeline.predict(test.X)\n", "\n", " # Get number of correctly identified comp in each reco energy bin\n", " num_reco_energy, num_reco_energy_err = {}, {}\n", " for composition in comp_list:\n", " print('composition = {}'.format(composition))\n", " comp_mask = train.le.inverse_transform(test_predictions) == composition\n", " print('sum(comp_mask) = {}'.format(np.sum(comp_mask)))\n", " print(test.log_energy[comp_mask])\n", " num_reco_energy[composition] = np.histogram(test.log_energy[comp_mask],\n", " bins=energybins.log_energy_bins)[0]\n", " num_reco_energy_err[composition] = np.sqrt(num_reco_energy[composition])\n", "\n", " num_reco_energy['total'] = np.histogram(test.log_energy, bins=energybins.log_energy_bins)[0]\n", " num_reco_energy_err['total'] = np.sqrt(num_reco_energy['total'])\n", " \n", " return num_reco_energy, num_reco_energy_err" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "composition = light\n", "sum(comp_mask) = 3789833\n", "[ 6.084369 6.02108021 6.19372399 ..., 6.34287938 5.58828732\n", " 6.30276075]\n", "composition = heavy\n", "sum(comp_mask) = 3422971\n", "[ 6.09036488 6.04362939 6.22640939 ..., 6.23452475 6.24879068\n", " 5.93383339]\n", "{'heavy': array([333373, 247362, 171104, 111037, 71072, 46386, 31339, 20151,\n", " 12939, 8385, 5905, 3940, 2625, 1823, 1105, 760,\n", " 457, 351, 181, 96, 59, 34, 21, 17,\n", " 6, 8, 4]),\n", " 'light': array([359917, 246830, 166187, 111450, 71146, 43352, 24185, 13594,\n", " 7618, 4381, 2489, 1435, 915, 486, 300, 168,\n", " 81, 45, 26, 11, 5, 6, 3, 3,\n", " 1, 2, 1]),\n", " 'total': array([693290, 494192, 337291, 222487, 142218, 89738, 55524, 33745,\n", " 20557, 12766, 8394, 5375, 3540, 2309, 1405, 928,\n", " 538, 396, 207, 107, 64, 40, 24, 20,\n", " 7, 10, 5])}\n" ] } ], "source": [ "# Get number of events per energy bin\n", "num_reco_energy, num_reco_energy_err = get_num_comp_reco(sim_train, data, pipeline, comp_list)\n", "import pprint\n", "pprint.pprint(num_reco_energy)\n", "# Solid angle\n", "solid_angle = 2np.pi(1-np.cos(np.arccos(0.8)))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Number of events observed" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "counts_observed = [359917, 333373, 246830, 247362, 166187, 171104, 111450, 111037, 71146, 71072, 43352, 46386, 24185, 31339, 13594, 20151, 7618, 12939, 4381, 8385, 2489, 5905, 1435, 3940, 915, 2625, 486, 1823, 300, 1105, 168, 760, 81, 457, 45, 351, 26, 181, 11, 96, 5, 59, 6, 34, 3, 21, 3, 17, 1, 6, 2, 8, 1, 4]\n" ] } ], "source": [ "counts_observed = []\n", "for light_counts, heavy_counts in zip(num_reco_energy['light'], num_reco_energy['heavy']):\n", " counts_observed.extend([light_counts, heavy_counts])\n", "print('counts_observed = {}'.format(counts_observed))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Block diagonal response matrix and error" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "pipeline.fit(sim_train.X, sim_train.y)\n", "test_predictions = pipeline.predict(sim_test.X)\n", "true_comp = sim_train.le.inverse_transform(sim_test.y)\n", "pred_comp = sim_train.le.inverse_transform(test_predictions)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", " 24 25 26 27]\n", "block_response = \n", "[[ 0. 0. 0. ..., 0. 0.34194529\n", " 0.72911392]\n", " [ 0. 0. 0. ..., 0. 0.65805471\n", " 0.27088608]\n", " [ 0. 0. 0. ..., 0.67821782 0. 0. ]\n", " ..., \n", " [ 0. 0. 0.7336594 ..., 0. 0. 0. ]\n", " [ 0.27706331 0.65677749 0. ..., 0. 0. 0. ]\n", " [ 0.72293669 0.34322251 0. ..., 0. 0. 0. ]]\n", "block_response_err = \n", "[[ 0. 0. 0. ..., 0. 0.02279635\n", " 0.03037975]\n", " [ 0. 0. 0. ..., 0. 0.03162409\n", " 0.01851739]\n", " [ 0. 0. 0. ..., 0.05794406 0. 0. ]\n", " ..., \n", " [ 0. 0. 0.01806145 ..., 0. 0. 0. ]\n", " [ 0.00442467 0.00748273 0. ..., 0. 0. 0. ]\n", " [ 0.00714729 0.00540927 0. ..., 0. 0. 0. ]]\n" ] } ], "source": [ "response_list = []\n", "response_err_list = []\n", "sim_bin_idxs = np.digitize(sim_test.log_energy, energybins.log_energy_bins) - 1\n", "data_bin_idxs = np.digitize(data.log_energy, energybins.log_energy_bins) - 1\n", "energy_bin_idx = np.unique(sim_bin_idxs)\n", "# energy_bin_idx = energy_bin_idx[1:]\n", "print(energy_bin_idx)\n", "for bin_idx in energy_bin_idx:\n", " sim_bin_mask = sim_bin_idxs == bin_idx\n", " data_bin_mask = data_bin_idxs == bin_idx\n", " response_mat = confusion_matrix(true_comp[sim_bin_mask], pred_comp[sim_bin_mask],\n", " labels=comp_list)\n", " # Transpose response matrix to get MC comp on x-axis and reco comp on y-axis\n", " response_mat = response_mat.T\n", " # Get response matrix statistical error\n", " response_mat_err = np.sqrt(response_mat)/response_mat.sum(axis=0, keepdims=True)\n", " response_err_list.append(response_mat_err)\n", " # Normalize along MC comp axis to go from counts to probabilities\n", " response_mat = response_mat.astype(float)/response_mat.sum(axis=0, keepdims=True)\n", " response_list.append(response_mat)\n", "block_response = block_diag(response_list).toarray()\n", "block_response = np.flipud(block_response)\n", "print('block_response = \\n{}'.format(block_response))\n", "block_response_err = block_diag(response_err_list).toarray()\n", "block_response_err = np.flipud(block_response_err)\n", "print('block_response_err = \\n{}'.format(block_response_err))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Priors array" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from icecube.weighting.weighting import from_simprod\n", "from icecube.weighting.fluxes import GaisserH3a, GaisserH4a, Hoerandel5" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "simlist = np.unique(df_sim['sim'])\n", "for i, sim in enumerate(simlist):\n", " _, sim_files = comp.simfunctions.get_level3_sim_files(sim)\n", " num_files = len(sim_files)\n", " if i == 0:\n", " generator = num_filesfrom_simprod(int(sim))\n", " else:\n", " generator += num_filesfrom_simprod(int(sim))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "flux = GaisserH3a()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>MC_comp</th>\n", " <th>MC_type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>89387</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89388</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89389</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89390</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89391</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89392</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89393</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89394</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89395</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89396</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89397</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89398</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89399</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89400</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89401</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89402</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89403</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89404</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89406</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89421</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89422</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89423</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89424</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89426</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89428</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89431</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89432</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89433</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89454</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>89456</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>298184</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298185</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298188</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298189</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298190</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298192</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298194</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298195</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298230</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298232</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298234</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298235</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298236</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298237</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298238</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298241</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298242</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298243</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298244</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298245</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298247</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298248</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298249</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298251</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298253</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298257</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298258</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298260</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298261</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " <tr>\n", " <th>298262</th>\n", " <td>O</td>\n", " <td>1000080160</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>47678 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " MC_comp MC_type\n", "89387 O 1000080160\n", "89388 O 1000080160\n", "89389 O 1000080160\n", "89390 O 1000080160\n", "89391 O 1000080160\n", "89392 O 1000080160\n", "89393 O 1000080160\n", "89394 O 1000080160\n", "89395 O 1000080160\n", "89396 O 1000080160\n", "89397 O 1000080160\n", "89398 O 1000080160\n", "89399 O 1000080160\n", "89400 O 1000080160\n", "89401 O 1000080160\n", "89402 O 1000080160\n", "89403 O 1000080160\n", "89404 O 1000080160\n", "89406 O 1000080160\n", "89421 O 1000080160\n", "89422 O 1000080160\n", "89423 O 1000080160\n", "89424 O 1000080160\n", "89426 O 1000080160\n", "89428 O 1000080160\n", "89431 O 1000080160\n", "89432 O 1000080160\n", "89433 O 1000080160\n", "89454 O 1000080160\n", "89456 O 1000080160\n", "... ... ...\n", "298184 O 1000080160\n", "298185 O 1000080160\n", "298188 O 1000080160\n", "298189 O 1000080160\n", "298190 O 1000080160\n", "298192 O 1000080160\n", "298194 O 1000080160\n", "298195 O 1000080160\n", "298230 O 1000080160\n", "298232 O 1000080160\n", "298234 O 1000080160\n", "298235 O 1000080160\n", "298236 O 1000080160\n", "298237 O 1000080160\n", "298238 O 1000080160\n", "298241 O 1000080160\n", "298242 O 1000080160\n", "298243 O 1000080160\n", "298244 O 1000080160\n", "298245 O 1000080160\n", "298247 O 1000080160\n", "298248 O 1000080160\n", "298249 O 1000080160\n", "298251 O 1000080160\n", "298253 O 1000080160\n", "298257 O 1000080160\n", "298258 O 1000080160\n", "298260 O 1000080160\n", "298261 O 1000080160\n", "298262 O 1000080160\n", "\n", "[47678 rows x 2 columns]" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_sim[['MC_comp', 'MC_type']][df_sim.MC_comp == 'O']" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.79485424672400162, 0.2051457532759984, 0.78177507599322127, 0.21822492400677868, 0.76509234457320252, 0.23490765542679748, 0.74386528325718204, 0.25613471674281796, 0.71702794507974121, 0.28297205492025879, 0.68352718791259426, 0.31647281208740574, 0.64263221839977736, 0.35736778160022259, 0.59446276221066374, 0.40553723778933615, 0.54066937306006457, 0.45933062693993543, 0.4848140417529801, 0.51518595824701996, 0.43185878210312245, 0.56814121789687755, 0.38634654457343537, 0.61365345542656469, 0.35013964932575592, 0.64986035067424419, 0.32158482777117464, 0.6784151722288253, 0.29706559884125688, 0.70293440115874317, 0.27363011814444504, 0.72636988185555496, 0.25060502884488495, 0.74939497115511511, 0.22950895978329439, 0.77049104021670556, 0.2130490539309626, 0.78695094606903737, 0.20390734976915756, 0.79609265023084252, 0.20363846444133216, 0.79636153555866773, 0.21178653880906614, 0.78821346119093394, 0.22598373811501932, 0.77401626188498074, 0.24349899562679947, 0.75650100437320067, 0.2630930180037736, 0.7369069819962264, 0.28543971687283121, 0.71456028312716879, 0.31209084105950902, 0.68790915894049087]\n", "[0.80050169680368843, 0.19949830319631159, 0.78801561325433145, 0.21198438674566844, 0.77210739006885387, 0.22789260993114607, 0.7518989129007978, 0.2481010870992022, 0.72640531499397443, 0.27359468500602546, 0.69467065254982474, 0.30532934745017537, 0.65606328905719191, 0.3439367109428082, 0.61076868965315778, 0.38923131034684227, 0.56040788759090743, 0.43959211240909263, 0.50835876997041884, 0.4916412300295811, 0.45924854414153404, 0.54075145585846596, 0.41726961601168183, 0.58273038398831822, 0.38413056738328449, 0.61586943261671556, 0.35832875411478404, 0.64167124588521596, 0.33659354061310415, 0.6634064593868958, 0.31629831532871305, 0.68370168467128689, 0.29692987413370198, 0.70307012586629791, 0.27998152366194523, 0.72001847633805482, 0.26798247834890793, 0.73201752165109213, 0.26335767706240926, 0.73664232293759069, 0.26741899932910101, 0.73258100067089893, 0.27965790706749749, 0.72034209293250251, 0.29804730541319208, 0.70195269458680798, 0.32072080999476466, 0.67927919000523529, 0.3478559826526415, 0.6521440173473585, 0.38210284985187809, 0.61789715014812197, 0.42760988697725738, 0.57239011302274267]\n" ] } ], "source": [ "priors = defaultdict(list)\n", "for flux, name in zip([GaisserH3a(), GaisserH4a(), Hoerandel5()], ['h3a', 'h4a', 'Hoerandel5']):\n", " for energy_mid in energybins.energy_midpoints:\n", " energy = [energy_mid]4\n", " ptype = [2212, 1000020040, 1000080160, 1000260560]\n", " weights = flux(energy, ptype)/generator(energy, ptype)\n", " # print(weights)\n", " light_prior = weights[:2].sum()/weights.sum()\n", " heavy_prior = weights[2:].sum()/weights.sum()\n", " priors[name].extend([light_prior, heavy_prior])\n", "\n", "print(priors['h3a'])\n", "print(priors['h4a'])" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "54" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(counts_observed)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "with open('pyunfold_dict.json', 'w') as outfile:\n", " data = {'counts': counts_observed,\n", " 'block_response':block_response.tolist(),\n", " 'block_response_err':block_response_err.tolist()}\n", " for model in ['h3a', 'h4a', 'Hoerandel5']:\n", " data['priors_{}'.format(model)] = priors[model]\n", " json.dump(data, outfile)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>RunNum</th>\n", " <th>Good_i3</th>\n", " <th>Good_it</th>\n", " <th>Good_it_L2</th>\n", " <th>LiveTime(s)</th>\n", " <th>ActiveStrings</th>\n", " <th>ActiveDoms</th>\n", " <th>ActiveInIceDoms</th>\n", " <th>OutDir</th>\n", " <th>Comment(s)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>115978</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>26856</td>\n", " <td>-</td>\n", " <td>-</td>\n", " <td>-</td>\n", " <td>/data/exp/IceCube/2010/filtered/level2a/0531</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>115982</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>28548</td>\n", " <td>-</td>\n", " <td>-</td>\n", " <td>-</td>\n", " <td>/data/exp/IceCube/2010/filtered/level2a/0531</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>115984</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>6984</td>\n", " <td>-</td>\n", " <td>-</td>\n", " <td>-</td>\n", " <td>/data/exp/IceCube/2010/filtered/level2a/0601</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>115985</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>10476</td>\n", " <td>-</td>\n", " <td>-</td>\n", " <td>-</td>\n", " <td>/data/exp/IceCube/2010/filtered/level2a/0601</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>115986</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>28872</td>\n", " <td>-</td>\n", " <td>-</td>\n", " <td>-</td>\n", " <td>/data/exp/IceCube/2010/filtered/level2a/0601</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RunNum Good_i3 Good_it Good_it_L2 LiveTime(s) ActiveStrings ActiveDoms \\\n", "0 115978 1 1 0 26856 - - \n", "1 115982 1 1 1 28548 - - \n", "2 115984 1 1 1 6984 - - \n", "3 115985 1 1 1 10476 - - \n", "4 115986 1 1 1 28872 - - \n", "\n", " ActiveInIceDoms OutDir Comment(s) \n", "0 - /data/exp/IceCube/2010/filtered/level2a/0531 NaN \n", "1 - /data/exp/IceCube/2010/filtered/level2a/0531 NaN \n", "2 - /data/exp/IceCube/2010/filtered/level2a/0601 NaN \n", "3 - /data/exp/IceCube/2010/filtered/level2a/0601 NaN \n", "4 - /data/exp/IceCube/2010/filtered/level2a/0601 NaN " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Live-time information\n", "goodrunlist = pd.read_table('/data/ana/CosmicRay/IceTop_GRL/IC79_2010_GoodRunInfo_4IceTop.txt', skiprows=[0, 3])\n", "goodrunlist.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "livetime (seconds) = 27114012\n", "livetime (days) = 313.819583333\n" ] } ], "source": [ "livetimes = goodrunlist['LiveTime(s)']\n", "livetime = np.sum(livetimes[goodrunlist['Good_it_L2'] == 1])\n", "print('livetime (seconds) = {}'.format(livetime))\n", "print('livetime (days) = {}'.format(livetime/(246060)))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6496177790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for composition in comp_list + ['total']:\n", " # Calculate dN/dE\n", " y = num_reco_energy[composition]\n", " y_err = num_reco_energy_err[composition]\n", " # Add time duration\n", " y = y / livetime\n", " y_err = y / livetime\n", "# ax.errorbar(log_energy_midpoints, y, yerr=y_err,\n", "# color=color_dict[composition], label=composition,\n", "# marker='.', linestyle='None')\n", " plotting.plot_steps(energybins.log_energy_midpoints, y, y_err,\n", " ax, color_dict[composition], composition)\n", "ax.set_yscale(\"log\", nonposy='clip')\n", "plt.xlabel('$\\log_{10}(E_{\\mathrm{reco}}/\\mathrm{GeV})$')\n", "ax.set_ylabel('Rate [s$^{-1}$]')\n", "ax.set_xlim([6.2, 8.0])\n", "# ax.set_ylim([102, 105])\n", "ax.grid(linestyle=':')\n", "leg = plt.legend(loc='upper center', frameon=False,\n", " bbox_to_anchor=(0.5, # horizontal\n", " 1.1),# vertical \n", " ncol=len(comp_list)+1, fancybox=False)\n", "# set the linewidth of each legend object\n", "for legobj in leg.legendHandles:\n", " legobj.set_linewidth(3.0)\n", " \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating effective area...\n", "Simulation set 7006: 30000 files\n", "Simulation set 7007: 30000 files\n", "Simulation set 7241: 10000 files\n", "Simulation set 7242: 10000 files\n", "Simulation set 7262: 19999 files\n", "Simulation set 7263: 19998 files\n", "Simulation set 7579: 12000 files\n", "Simulation set 7784: 12000 files\n", "Simulation set 7791: 12000 files\n", "Simulation set 7851: 12000 files\n" ] }, { "data": { "image/png": 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iQ1ixeK7bGoZRpV8BdpWFFdegvxJ7HbyCkjbdRiaEEEEhxE2W9uwBQqGQ2wqm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\n1dXsfFCXawVj/xjABkt79gA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f64967a2250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eff_area, eff_area_error, energy_midpoints = comp.analysis.get_effective_area(df_sim,\n", " energybins.energy_bins, energy='MC')\n", "fix, ax = plt.subplots()\n", "ax.errorbar(np.log10(energy_midpoints), eff_area, yerr=eff_area_error)\n", "ax.set_ylabel('Effective area [$m^2$]')\n", "ax.set_xlabel('$\\log_{10}(E_{\\mathrm{reco}}/\\mathrm{GeV})$')\n", "ax.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4,\n", " 7.5, 7.6, 7.7, 7.8, 7.9, 8. ])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energybins.log_energy_bins" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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LRNLh72Dd/nUkO5L52ZqfMTp9NPecew8AlZ9VkuZMoyizKKzjR5MLpoL/GPEf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\nsoQBihBCSJb8A+8DKNvwbMcTAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f64f3fe2d10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot fraction of events vs energy\n", "# fig, ax = plt.subplots(figsize=(8, 6))\n", "fig = plt.figure()\n", "ax = plt.gca()\n", "for composition in comp_list + ['total']:\n", " # Calculate dN/dE\n", " y = num_reco_energy[composition]/energybins.energy_bin_widths\n", " y_err = num_reco_energy_err[composition]/energybins.energy_bin_widths\n", " # Add effective area\n", " y, y_err = comp.analysis.ratio_error(y, y_err, eff_area, eff_area_error)\n", " # Add solid angle\n", " y = y / solid_angle\n", " y_err = y_err / solid_angle\n", " # Add time duration\n", " y = y / livetime\n", " y_err = y / livetime\n", " # Add energy scaling \n", "# energy_err = get_energy_res(df_sim, energy_bins)\n", "# energy_err = np.array(energy_err)\n", "# print(10energy_err)\n", " y = energybins.energy_midpoints2.7 y\n", " y_err = energybins.energy_midpoints*2.7 y_err\n", "# print(y)\n", "# print(y_err)\n", "# ax.errorbar(log_energy_midpoints, y, yerr=y_err, label=composition, color=color_dict[composition],\n", "# marker='.', markersize=8)\n", " plotting.plot_steps(energybins.log_energy_midpoints, y, y_err, ax, color_dict[composition], composition)\n", "ax.set_yscale(\"log\", nonposy='clip')\n", "# ax.set_xscale(\"log\", nonposy='clip')\n", "plt.xlabel('$\\log_{10}(E_{\\mathrm{reco}}/\\mathrm{GeV})$')\n", "ax.set_ylabel('$\\mathrm{E}^{2.7} \\\\frac{\\mathrm{dN}}{\\mathrm{dE dA d\\Omega dt}} \\ [\\mathrm{GeV}^{1.7} \\mathrm{m}^{-2} \\mathrm{sr}^{-1} \\mathrm{s}^{-1}]$')\n", "ax.set_xlim([6.3, 8])\n", "ax.set_ylim([103, 105])\n", "ax.grid(linestyle='dotted', which=\"both\")\n", " \n", "# Add 3-year scraped flux\n", "df_proton = pd.read_csv('3yearscraped/proton', sep='\\t', header=None, names=['energy', 'flux'])\n", "df_helium = pd.read_csv('3yearscraped/helium', sep='\\t', header=None, names=['energy', 'flux'])\n", "df_light = pd.DataFrame.from_dict({'energy': df_proton.energy, \n", " 'flux': df_proton.flux + df_helium.flux})\n", "\n", "df_oxygen = pd.read_csv('3yearscraped/oxygen', sep='\\t', header=None, names=['energy', 'flux'])\n", "df_iron = pd.read_csv('3yearscraped/iron', sep='\\t', header=None, names=['energy', 'flux'])\n", "df_heavy = pd.DataFrame.from_dict({'energy': df_oxygen.energy, \n", " 'flux': df_oxygen.flux + df_iron.flux})\n", "\n", "if comp_class:\n", " ax.plot(np.log10(df_light.energy), df_light.flux, label='3 yr light',\n", " marker='.', ls=':')\n", " ax.plot(np.log10(df_heavy.energy), df_heavy.flux, label='3 yr heavy',\n", " marker='.', ls=':')\n", " ax.plot(np.log10(df_heavy.energy), df_heavy.flux+df_light.flux, label='3 yr total',\n", " marker='.', ls=':')\n", "else:\n", " ax.plot(np.log10(df_proton.energy), df_proton.flux, label='3 yr proton',\n", " marker='.', ls=':')\n", " ax.plot(np.log10(df_helium.energy), df_helium.flux, label='3 yr helium',\n", " marker='.', ls=':', color=color_dict['He'])\n", " ax.plot(np.log10(df_oxygen.energy), df_oxygen.flux, label='3 yr oxygen',\n", " marker='.', ls=':', color=color_dict['O'])\n", " ax.plot(np.log10(df_iron.energy), df_iron.flux, label='3 yr iron',\n", " marker='.', ls=':', color=color_dict['Fe'])\n", " ax.plot(np.log10(df_iron.energy), df_proton.flux+df_helium.flux+df_oxygen.flux+df_iron.flux, label='3 yr total',\n", " marker='.', ls=':', color='C2')\n", "\n", "\n", "leg = plt.legend(loc='upper center', frameon=False,\n", " bbox_to_anchor=(0.5, # horizontal\n", " 1.15),# vertical \n", " ncol=len(comp_list)+1, fancybox=False)\n", "# set the linewidth of each legend object\n", "for legobj in leg.legendHandles:\n", " legobj.set_linewidth(3.0)\n", "\n", "plt.savefig('/home/jbourbeau/public_html/figures/spectrum.png')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "if not comp_class:\n", " # Add 3-year scraped flux\n", " df_proton = pd.read_csv('3yearscraped/proton', sep='\\t', header=None, names=['energy', 'flux'])\n", " df_helium = pd.read_csv('3yearscraped/helium', sep='\\t', header=None, names=['energy', 'flux'])\n", " df_oxygen = pd.read_csv('3yearscraped/oxygen', sep='\\t', header=None, names=['energy', 'flux'])\n", " df_iron = pd.read_csv('3yearscraped/iron', sep='\\t', header=None, names=['energy', 'flux'])\n", " # Plot fraction of events vs energy\n", " fig, axarr = plt.subplots(2, 2, figsize=(8, 6))\n", " for composition, ax in zip(comp_list + ['total'], axarr.flatten()):\n", " # Calculate dN/dE\n", " y = num_reco_energy[composition]/energybins.energy_bin_widths\n", " y_err = num_reco_energy_err[composition]/energybins.energy_bin_widths\n", " # Add effective area\n", " y, y_err = comp.analysis.ratio_error(y, y_err, eff_area, eff_area_error)\n", " # Add solid angle\n", " y = y / solid_angle\n", " y_err = y_err / solid_angle\n", " # Add time duration\n", " y = y / livetime\n", " y_err = y / livetime\n", " y = energybins.energy_midpoints*2.7 y\n", " y_err = energybins.energy_midpoints*2.7 y_err\n", " plotting.plot_steps(energybins.log_energy_midpoints, y, y_err, ax, color_dict[composition], composition)\n", " # Load 3-year flux\n", " df_3yr = pd.read_csv('3yearscraped/{}'.format(composition), sep='\\t',\n", " header=None, names=['energy', 'flux'])\n", " ax.plot(np.log10(df_3yr.energy), df_3yr.flux, label='3 yr {}'.format(composition),\n", " marker='.', ls=':', color=color_dict[composition])\n", " ax.set_yscale(\"log\", nonposy='clip')\n", " # ax.set_xscale(\"log\", nonposy='clip')\n", " ax.set_xlabel('$\\log_{10}(E_{\\mathrm{reco}}/\\mathrm{GeV})$')\n", " ax.set_ylabel('$\\mathrm{E}^{2.7} \\\\frac{\\mathrm{dN}}{\\mathrm{dE dA d\\Omega dt}} \\ [\\mathrm{GeV}^{1.7} \\mathrm{m}^{-2} \\mathrm{sr}^{-1} \\mathrm{s}^{-1}]$')\n", " ax.set_xlim([6.3, 8])\n", " ax.set_ylim([103, 105])\n", " ax.grid(linestyle='dotted', which=\"both\")\n", " ax.legend()\n", "\n", " plt.savefig('/home/jbourbeau/public_html/figures/spectrum.png')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Unfolding\n", "[ [back to top](#top) ]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0.79794826, 0.76499774, 0.7639696 , 0.79415557, 0.78565179,\n", " 0.77673225, 0.77012048, 0.78905735, 0.75768668, 0.75121275,\n", " 0.77295025, 0.78764205, 0.79578947, 0.81094527, 0.800269 ,\n", " 0.75702247, 0.67834681])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reco_frac['light']" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0.6625555 , 0.69889899, 0.7124831 , 0.70190964, 0.68406072,\n", " 0.71247655, 0.71406728, 0.66709677, 0.71340206, 0.71594203,\n", " 0.71060172, 0.7338538 , 0.73440785, 0.73953824, 0.74112426,\n", " 0.79719298, 0.8142514 ])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reco_frac['heavy']" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([420632, 276894, 181297, 122976, 76838, 47431, 27133, 15700,\n", " 8685, 4821, 2978, 1670, 985, 529, 275, 145,\n", " 72])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_reco_energy['light']" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([325377, 243322, 169803, 107060, 69443, 44516, 29576, 18673,\n", " 12246, 8142, 5537, 3782, 2593, 1804, 1139, 795,\n", " 469])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_reco_energy['heavy']" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['light' 'heavy' 'heavy' ..., 'light' 'heavy' 'light']\n", "['light' 'light' 'heavy' ..., 'light' 'heavy' 'light']\n" ] } ], "source": [ "pipeline.fit(sim_train.X, sim_train.y)\n", "test_predictions = pipeline.predict(sim_test.X)\n", "true_comp = sim_train.le.inverse_transform(sim_test.y)\n", "pred_comp = sim_train.le.inverse_transform(test_predictions)\n", "print(true_comp)\n", "print(pred_comp)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['light' 'light' 'light' ..., 'light' 'light' 'light']\n" ] } ], "source": [ "data_pred = pipeline.predict(data.X)\n", "observed_comp = sim_train.le.inverse_transform(data_pred)\n", "print(observed_comp)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "['light', 'heavy']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "comp_list" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16]\n", "242546 of label light\n", "251603 of label heavy\n", "[252034.97073310346, 242114.02926689654]\n", "166316 of label light\n", "170955 of label heavy\n", "[172379.73369032133, 164891.26630967867]\n", "109767 of label light\n", "112713 of label heavy\n", "[111615.96906515925, 110864.03093484075]\n", "70235 of label light\n", "71981 of label heavy\n", "[70956.41855899457, 71259.58144100543]\n", "42535 of label light\n", "47202 of label heavy\n", "[43823.056062721218, 45913.943937278782]\n", "23757 of label light\n", "31767 of label heavy\n", "[25913.41237747125, 29610.58762252875]\n", "13505 of label light\n", "20240 of label heavy\n", "[14987.041606214123, 18757.958393785877]\n", "7546 of label light\n", "13011 of label heavy\n", "[8997.6993613439936, 11559.300638656006]\n", "4350 of label light\n", "8416 of label heavy\n", "[5322.8364595714684, 7443.1635404285316]\n", "2501 of label light\n", "5893 of label heavy\n", "[3228.194000041738, 5165.805999958262]\n", "1407 of label light\n", "3968 of label heavy\n", "[1984.0936241934473, 3390.9063758065522]\n", "889 of label light\n", "2651 of label heavy\n", "[1256.9501674307915, 2283.0498325692083]\n", "455 of label light\n", "1854 of label heavy\n", "[735.36772465599006, 1573.6322753440099]\n", "286 of label light\n", "1119 of label heavy\n", "[457.06934600699105, 947.93065399300906]\n", "162 of label light\n", "766 of label heavy\n", "[263.84585345300104, 664.15414654699896]\n", "68 of label light\n", "470 of label heavy\n", "[181.27541474817053, 356.72458525182947]\n", "defaultdict(<type 'list'>, {'heavy': array([ 242114.0292669 , 164891.26630968, 110864.03093484,\n", " 71259.58144101, 45913.94393728, 29610.58762253,\n", " 18757.95839379, 11559.30063866, 7443.16354043,\n", " 5165.80599996, 3390.90637581, 2283.04983257,\n", " 1573.63227534, 947.93065399, 664.15414655,\n", " 356.72458525]), 'light': array([ 2.52034971e+05, 1.72379734e+05, 1.11615969e+05,\n", " 7.09564186e+04, 4.38230561e+04, 2.59134124e+04,\n", " 1.49870416e+04, 8.99769936e+03, 5.32283646e+03,\n", " 3.22819400e+03, 1.98409362e+03, 1.25695017e+03,\n", " 7.35367725e+02, 4.57069346e+02, 2.63845853e+02,\n", " 1.81275415e+02]), 'total': array([ 494149., 337271., 222480., 142216., 89737., 55524.,\n", " 33745., 20557., 12766., 8394., 5375., 3540.,\n", " 2309., 1405., 928., 538.])})\n" ] } ], "source": [ "sim_bin_idxs = np.digitize(sim_test.log_energy, energybins.log_energy_bins) - 1\n", "data_bin_idxs = np.digitize(data.log_energy, energybins.log_energy_bins) - 1\n", "energy_bin_idx = np.unique(sim_bin_idxs)\n", "energy_bin_idx = energy_bin_idx[1:]\n", "print(energy_bin_idx)\n", "num_reco_energy_unfolded = defaultdict(list)\n", "for bin_idx in energy_bin_idx:\n", " sim_bin_mask = sim_bin_idxs == bin_idx\n", " data_bin_mask = data_bin_idxs == bin_idx\n", " unfolder = comp.analysis.Unfolder()\n", " unfolded_events = unfolder.unfold(true_MC_comp=true_comp[sim_bin_mask],\n", " reco_MC_comp=pred_comp[sim_bin_mask],\n", " observed_comp=observed_comp[data_bin_mask],\n", " priors=[0.5, 0.5],\n", " labels=comp_list)\n", " print(unfolded_events)\n", " for i, composition in enumerate(comp_list):\n", " num_reco_energy_unfolded[composition].append(unfolded_events[i])\n", "\n", "for composition in comp_list:\n", " num_reco_energy_unfolded[composition] = np.array(num_reco_energy_unfolded[composition])\n", "num_reco_energy_unfolded['total'] = np.sum([num_reco_energy_unfolded[composition] for composition in comp_list], axis=0)\n", "print(num_reco_energy_unfolded)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/.local/lib/python2.7/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in sqrt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[ 32964.86466106 27281.60673729 26245.51936798 25800.46976282\n", " 24367.94359577 21912.38305619 17567.46971826 13989.07475035\n", " 9781.68750813 6500.43997616 5592.42691983 3776.25783731\n", " 2593.30366087 948.219778 -677.31023813 -3104.77818648\n", " -7049.02624428]\n", "[ 1.21578705e-03 1.00618111e-03 9.67968863e-04 9.51554855e-04\n", " 8.98721428e-04 8.08157165e-04 6.47911114e-04 5.15935257e-04\n", " 3.60761348e-04 2.39744674e-04 2.06255973e-04 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"text/plain": [ "<matplotlib.figure.Figure at 0x7faf803c2c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for composition in comp_list + ['total']:\n", " # Calculate dN/dE\n", " y = num_reco_energy_unfolded[composition]/energy_bin_widths\n", " y_err = np.sqrt(y)/energy_bin_widths\n", " # Add effective area\n", " y, y_err = comp.analysis.ratio_error(y, y_err, eff_area, eff_area_error)\n", " # Add solid angle\n", " y = y / solid_angle\n", " y_err = y_err / solid_angle\n", " # Add time duration\n", " y = y / livetime\n", " y_err = y / livetime\n", " # Add energy scaling \n", "# energy_err = get_energy_res(df_sim, energy_bins)\n", "# energy_err = np.array(energy_err)\n", "# print(10energy_err)\n", " y = energy_midpoints2.7 * y\n", " y_err = energy_midpoints*2.7 y_err\n", " print(y)\n", " print(y_err)\n", "# ax.errorbar(log_energy_midpoints, y, yerr=y_err, label=composition, color=color_dict[composition],\n", "# marker='.', markersize=8)\n", " plotting.plot_steps(log_energy_midpoints, y, y_err, ax, color_dict[composition], composition)\n", "ax.set_yscale(\"log\", nonposy='clip')\n", "# ax.set_xscale(\"log\", nonposy='clip')\n", "plt.xlabel('$\\log_{10}(E_{\\mathrm{reco}}/\\mathrm{GeV})$')\n", "ax.set_ylabel('$\\mathrm{E}^{2.7} \\\\frac{\\mathrm{dN}}{\\mathrm{dE dA d\\Omega dt}} \\ [\\mathrm{GeV}^{1.7} \\mathrm{m}^{-2} \\mathrm{sr}^{-1} \\mathrm{s}^{-1}]$')\n", "ax.set_xlim([6.3, 8])\n", "ax.set_ylim([103, 105])\n", "ax.grid(linestyle='dotted', which=\"both\")\n", "leg = plt.legend(loc='upper center', frameon=False,\n", " bbox_to_anchor=(0.5, # horizontal\n", " 1.1),# vertical \n", " ncol=len(comp_list)+1, fancybox=False)\n", "# set the linewidth of each legend object\n", "for legobj in leg.legendHandles:\n", " legobj.set_linewidth(3.0)\n", " \n", "# plt.savefig('/home/jbourbeau/public_html/figures/spectrum.png')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Iterative method" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Get confusion matrix for each energy bin" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16]\n" ] } ], "source": [ "bin_idxs = np.digitize(energy_test_sim, log_energy_bins) - 1\n", "energy_bin_idx = np.unique(bin_idxs)\n", "energy_bin_idx = energy_bin_idx[1:]\n", "print(energy_bin_idx)\n", "num_reco_energy_unfolded = defaultdict(list)\n", "response_mat = []\n", "for bin_idx in energy_bin_idx:\n", " energy_bin_mask = bin_idxs == bin_idx\n", " confmat = confusion_matrix(true_comp[energy_bin_mask], pred_comp[energy_bin_mask], labels=comp_list)\n", " confmat = np.divide(confmat.T, confmat.sum(axis=1, dtype=float)).T\n", " response_mat.append(confmat)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[array([[ 0.78813559, 0.21186441],\n", " [ 0.31475086, 0.68524914]]), array([[ 0.7609382 , 0.2390618 ],\n", " [ 0.28674007, 0.71325993]]), array([[ 0.7688869 , 0.2311131 ],\n", " [ 0.28886886, 0.71113114]]), array([[ 0.7945853 , 0.2054147 ],\n", " [ 0.28737774, 0.71262226]]), array([[ 0.77821522, 0.22178478],\n", " [ 0.29981025, 0.70018975]]), array([[ 0.78485885, 0.21514115],\n", " [ 0.27861163, 0.72138837]]), array([[ 0.7826506 , 0.2173494 ],\n", " [ 0.27828746, 0.72171254]]), array([[ 0.7976269 , 0.2023731 ],\n", " [ 0.31419355, 0.68580645]]), array([[ 0.77818448, 0.22181552],\n", " [ 0.27147766, 0.72852234]]), array([[ 0.77269577, 0.22730423],\n", " [ 0.26884058, 0.73115942]]), array([[ 0.79117029, 0.20882971],\n", " [ 0.2786533 , 0.7213467 ]]), array([[ 0.80965909, 0.19034091],\n", " [ 0.24698368, 0.75301632]]), array([[ 0.82175439, 0.17824561],\n", " [ 0.25508059, 0.74491941]]), array([[ 0.8336887 , 0.1663113 ],\n", " [ 0.25829726, 0.74170274]]), array([[ 0.83120377, 0.16879623],\n", " [ 0.25887574, 0.74112426]]), array([[ 0.8005618 , 0.1994382 ],\n", " [ 0.22245614, 0.77754386]]), array([[ 0.76010782, 0.23989218],\n", " [ 0.2113691 , 0.7886309 ]])]" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response_mat" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unfolding iteration 0...\n", "Unfolding iteration 1...\n", "Unfolding iteration 2...\n", "Unfolding iteration 3...\n", "Unfolding iteration 4...\n", "Unfolding iteration 5...\n", "Unfolding iteration 6...\n", "Unfolding iteration 7...\n", "Unfolding iteration 8...\n", "Unfolding iteration 9...\n", "Unfolding iteration 10...\n", "Unfolding iteration 11...\n", "Unfolding iteration 12...\n", "Unfolding iteration 13...\n", "Unfolding iteration 14...\n", "Unfolding iteration 15...\n", "Unfolding iteration 16...\n", "Unfolding iteration 17...\n", "Unfolding iteration 18...\n", "Unfolding iteration 19...\n", "Unfolding iteration 20...\n", "Unfolding iteration 21...\n", "Unfolding iteration 22...\n", "Unfolding iteration 23...\n", "Unfolding iteration 24...\n", "Unfolding iteration 25...\n", "Unfolding iteration 26...\n", "Unfolding iteration 27...\n", "Unfolding iteration 28...\n", "Unfolding iteration 29...\n", "Unfolding iteration 30...\n", "Unfolding iteration 31...\n", "Unfolding iteration 32...\n", "Unfolding iteration 33...\n", "Unfolding iteration 34...\n", "Unfolding iteration 35...\n", "Unfolding iteration 36...\n", "Unfolding iteration 37...\n", "Unfolding iteration 38...\n", "Unfolding iteration 39...\n", "Unfolding iteration 40...\n", "Unfolding iteration 41...\n", "Unfolding iteration 42...\n", "Unfolding iteration 43...\n", "Unfolding iteration 44...\n", "Unfolding iteration 45...\n", "Unfolding iteration 46...\n", "Unfolding iteration 47...\n", "Unfolding iteration 48...\n", "Unfolding iteration 49...\n", "{'heavy': array([ 303701.43653016, 241211.27404287, 163948.17757578,\n", " 102626.3802578 , 70692.20884366, 46642.5710298 ,\n", " 32419.55282374, 22355.39549195, 15088.13580149,\n", " 10197.17390693, 7197.59084351, 4861.16140094,\n", " 3351.02448979, 2395.3345935 , 1520.2817676 ,\n", " 1027.42518063, 613.06883126]), 'light': array([ 4.29377227e+05, 2.77284802e+05, 1.85884853e+05,\n", " 1.25532190e+05, 7.52343857e+04, 4.53842925e+04,\n", " 2.47564234e+04, 1.30573195e+04, 6.28028114e+03,\n", " 3.06218741e+03, 1.70118771e+03, 8.19755039e+02,\n", " 4.49884526e+02, 1.44692843e+02, 2.81304825e+01,\n", " -6.23410099e+01, -9.21848439e+01]), 'total': array([ 7.33078663e+05, 5.18496077e+05, 3.49833031e+05,\n", " 2.28158570e+05, 1.45926595e+05, 9.20268635e+04,\n", " 5.71759763e+04, 3.54127150e+04, 2.13684169e+04,\n", " 1.32593613e+04, 8.89877856e+03, 5.68091644e+03,\n", " 3.80090902e+03, 2.54002744e+03, 1.54841225e+03,\n", " 9.65084171e+02, 5.20883987e+02])}\n" ] } ], "source": [ "r = np.dstack((np.copy(num_reco_energy['light']), np.copy(num_reco_energy['heavy'])))[0]\n", "for unfold_iter in range(50):\n", " print('Unfolding iteration {}...'.format(unfold_iter))\n", " if unfold_iter == 0:\n", " u = r\n", " fs = []\n", " for bin_idx in energy_bin_idx:\n", "# print(u)\n", " f = np.dot(response_mat[bin_idx], u[bin_idx])\n", " f[f < 0] = 0\n", " fs.append(f)\n", "# print(f)\n", " u = u + (r - fs)\n", "# u[u < 0] = 0\n", "# print(u)\n", "unfolded_counts_iter = {}\n", "unfolded_counts_iter['light'] = u[:,0]\n", "unfolded_counts_iter['heavy'] = u[:,1]\n", "unfolded_counts_iter['total'] = u.sum(axis=1)\n", "print(unfolded_counts_iter)" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/.local/lib/python2.7/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in sqrt\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[ 30608.90300256 25739.19353959 26090.26032924 24994.4368117\n", " 22855.5941019 20394.79482403 16744.52586455 12750.96672149\n", " 8963.77697026 6521.34060238 5355.5471064 3821.85911214\n", " 3190.60289416 1505.68516182 435.52646429 -1382.82112343\n", " -3470.66606373]\n", "[ 1.12889612e-03 9.49294908e-04 9.62242708e-04 9.21827312e-04\n", " 8.42944014e-04 7.52186538e-04 6.17559875e-04 4.70272224e-04\n", " 3.30595744e-04 2.40515517e-04 1.97519537e-04 1.40955131e-04\n", " 1.17673581e-04 5.55316256e-05 1.60627820e-05 -5.10002401e-05\n", " -1.28002675e-04]\n", "[ 21649.88554024 22390.63811411 23011.29201211 20433.71168285\n", " 21475.71774523 20960.24006155 21927.64402967 21830.88988231\n", " 21535.1321382 21716.25553755 22658.89678563 22663.68989536\n", " 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa79efe7b90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for composition in comp_list + ['total']:\n", " # Calculate dN/dE\n", " y = unfolded_counts_iter[composition]/energy_bin_widths\n", " y_err = np.sqrt(y)/energy_bin_widths\n", " # Add effective area\n", " y, y_err = comp.analysis.ratio_error(y, y_err, eff_area, eff_area_error)\n", " # Add solid angle\n", " y = y / solid_angle\n", " y_err = y_err / solid_angle\n", " # Add time duration\n", " y = y / livetime\n", " y_err = y / livetime\n", " # Add energy scaling \n", "# energy_err = get_energy_res(df_sim, energy_bins)\n", "# energy_err = np.array(energy_err)\n", "# print(10energy_err)\n", " y = energy_midpoints2.7 * y\n", " y_err = energy_midpoints*2.7 y_err\n", " print(y)\n", " print(y_err)\n", "# ax.errorbar(log_energy_midpoints, y, yerr=y_err, label=composition, color=color_dict[composition],\n", "# marker='.', markersize=8)\n", " plotting.plot_steps(log_energy_midpoints, y, y_err, ax, color_dict[composition], composition)\n", "ax.set_yscale(\"log\", nonposy='clip')\n", "# ax.set_xscale(\"log\", nonposy='clip')\n", "plt.xlabel('$\\log_{10}(E_{\\mathrm{reco}}/\\mathrm{GeV})$')\n", "ax.set_ylabel('$\\mathrm{E}^{2.7} \\\\frac{\\mathrm{dN}}{\\mathrm{dE dA d\\Omega dt}} \\ [\\mathrm{GeV}^{1.7} \\mathrm{m}^{-2} \\mathrm{sr}^{-1} \\mathrm{s}^{-1}]$')\n", "ax.set_xlim([6.3, 8])\n", "ax.set_ylim([103, 105])\n", "ax.grid(linestyle='dotted', which=\"both\")\n", "leg = plt.legend(loc='upper center', frameon=False,\n", " bbox_to_anchor=(0.5, # horizontal\n", " 1.1),# vertical \n", " ncol=len(comp_list)+1, fancybox=False)\n", "# set the linewidth of each legend object\n", "for legobj in leg.legendHandles:\n", " legobj.set_linewidth(3.0)\n", " \n", "# plt.savefig('/home/jbourbeau/public_html/figures/spectrum.png')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'heavy': array([343258, 251555, 170285, 109209, 72054, 46292, 30287, 19434,\n", " 12697, 8279, 5666, 3863, 2611, 1814, 1134, 785,\n", " 464]), 'light': array([402751, 268661, 180815, 120827, 74227, 45655, 26422, 14939,\n", " 8234, 4684, 2849, 1589, 967, 519, 280, 155,\n", " 77]), 'total': array([746009, 520216, 351100, 230036, 146281, 91947, 56709, 34373,\n", " 20931, 12963, 8515, 5452, 3578, 2333, 1414, 940,\n", " 541])}\n" ] } ], "source": [ "print(num_reco_energy)" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized confusion matrix\n", "[[ 0.77924806 0.22075194]\n", " [ 0.31332376 0.68667624]]\n" ] }, { "data": { "image/png": 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VM5ct3X5k8fs9+/v295dPOK969mVdWgKwXuy75Y6l26c/vb/a5KeV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\nohxBHZaQSFYbgrDr3+Zi2RkR16eUcjH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2wgIAAAAA3dVaWxykjtIJu7gb9WsrvO3CPcfd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8Dj787y2OVjcL0FJYSFbe0+uPGtr31rseYBADuMH1y3cbGnAABj7/Zvbxp+aveJxjE/69fPZM2n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\nXmuB/ZLcm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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa79561bfd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "comp_list = ['light', 'heavy']\n", "pipeline = comp.get_pipeline('RF')\n", "pipeline.fit(X_train_sim, y_train_sim)\n", "test_predictions = pipeline.predict(X_test_sim)\n", "# correctly_identified_mask = (test_predictions == y_test)\n", "# confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)/len(y_pred)\n", "true_comp = le.inverse_transform(y_test_sim)\n", "pred_comp = le.inverse_transform(test_predictions)\n", "confmat = confusion_matrix(true_comp, pred_comp, labels=comp_list)\n", "\n", "def plot_confusion_matrix(cm, classes,\n", " normalize=False,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Greens):\n", " \"\"\"\n", " This function prints and plots the confusion matrix.\n", " Normalization can be applied by setting `normalize=True`.\n", " \"\"\"\n", " if normalize:\n", " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", " print(\"Normalized confusion matrix\")\n", " else:\n", " print('Confusion matrix, without normalization')\n", "\n", " print(cm)\n", "\n", " plt.imshow(cm, interpolation='None', cmap=cmap,\n", " vmin=0, vmax=1.0)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(classes))\n", " plt.xticks(tick_marks, classes, rotation=45)\n", " plt.yticks(tick_marks, classes)\n", "\n", " thresh = cm.max() / 2.\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, '{:0.3f}'.format(cm[i, j]),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " plt.ylabel('True composition')\n", " plt.xlabel('Predicted composition')\n", "\n", "fig, ax = plt.subplots()\n", "plot_confusion_matrix(confmat, classes=['light', 'heavy'], normalize=True,\n", " title='Confusion matrix, without normalization')\n", "\n", "# # Plot normalized confusion matrix\n", "# plt.figure()\n", "# plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n", "# title='Normalized confusion matrix')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 4.65885e-05, -1.58473e-05],\n", " [ -1.80751e-05, 5.14300e-05]])" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "comp_list = ['light', 'heavy']\n", "pipeline = comp.get_pipeline('RF')\n", "pipeline.fit(X_train_sim, y_train_sim)\n", "test_predictions = pipeline.predict(X_test_sim)\n", "# correctly_identified_mask = (test_predictions == y_test)\n", "# confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)/len(y_pred)\n", "true_comp = le.inverse_transform(y_test_sim)\n", "pred_comp = le.inverse_transform(test_predictions)\n", "confmat = confusion_matrix(true_comp, pred_comp, labels=comp_list)\n", "\n", "inverse = np.linalg.inv(confmat)\n", "inverse" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[24379, 7512],\n", " [ 8568, 22084]])" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confmat" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.942477796077\n", "1.46998611753\n", "1409662.44128\n", "[[ 1.09286e+00 4.25348e-01]\n", " [ 6.78230e-01 4.21116e-01]\n", " [ 3.64535e-01 4.12274e-01]\n", " [ 2.15254e-01 3.05955e-01]\n", " [ 1.50773e-01 1.80818e-01]\n", " [ 9.30369e-02 1.16594e-01]\n", " [ 6.14838e-02 6.87016e-02]\n", " [ 2.92043e-02 5.37845e-02]\n", " [ 1.02666e-02 4.00306e-02]\n", " [ 2.43373e-03 2.84436e-02]\n", " [ 4.68133e-03 1.48927e-02]\n", " [ 1.87090e-03 1.15002e-02]\n", " [ -6.73351e-04 9.90623e-03]\n", " [ -2.40343e-04 5.57233e-03]\n", " [ -2.92290e-04 4.14347e-03]\n", " [ -1.09387e-04 2.35180e-03]\n", " [ -2.28537e-04 1.67352e-03]\n", " [ -9.98517e-05 9.89654e-04]]\n", "[[ 1.09286e+00 4.25348e-01]\n", " [ 6.78230e-01 4.21116e-01]\n", " [ 3.64535e-01 4.12274e-01]\n", " [ 2.15254e-01 3.05955e-01]\n", " [ 1.50773e-01 1.80818e-01]\n", " [ 9.30369e-02 1.16594e-01]\n", " [ 6.14838e-02 6.87016e-02]\n", " [ 2.92043e-02 5.37845e-02]\n", " [ 1.02666e-02 4.00306e-02]\n", " [ 2.43373e-03 2.84436e-02]\n", " [ 4.68133e-03 1.48927e-02]\n", " [ 1.87090e-03 1.15002e-02]\n", " [ -6.73351e-04 9.90623e-03]\n", " [ -2.40343e-04 5.57233e-03]\n", " [ -2.92290e-04 4.14347e-03]\n", " [ -1.09387e-04 2.35180e-03]\n", " [ -2.28537e-04 1.67352e-03]\n", " [ -9.98517e-05 9.89654e-04]]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f091b644e50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "comp_list = ['light', 'heavy']\n", "# Get number of events per energy bin\n", "num_reco_energy, num_reco_energy_err = get_num_comp_reco(X_train_sim, y_train_sim, X_test_data, comp_list)\n", "# Energy-related variables\n", "energy_bin_width = 0.1\n", "energy_bins = np.arange(6.2, 8.1, energy_bin_width)\n", "energy_midpoints = (energy_bins[1:] + energy_bins[:-1]) / 2\n", "energy_bin_widths = 10energy_bins[1:] - 10energy_bins[:-1]\n", "def get_energy_res(df_sim, energy_bins):\n", " reco_log_energy = df_sim['lap_log_energy'].values \n", " MC_log_energy = df_sim['MC_log_energy'].values\n", " energy_res = reco_log_energy - MC_log_energy\n", " bin_centers, bin_medians, energy_err = comp.analysis.data_functions.get_medians(reco_log_energy,\n", " energy_res,\n", " energy_bins)\n", " return np.abs(bin_medians)\n", "# Solid angle\n", "solid_angle = 2np.pi(1-np.cos(np.arccos(0.85)))\n", "# solid_angle = 2np.pi(1-np.cos(40(np.pi/180)))\n", "print(solid_angle)\n", "print(2np.pi(1-np.cos(40(np.pi/180))))\n", "# Live-time information\n", "start_time = np.amin(df_data['start_time_mjd'].values)\n", "end_time = np.amax(df_data['end_time_mjd'].values)\n", "day_to_sec = 24 * 60 * 60.\n", "dt = day_to_sec * (end_time - start_time)\n", "print(dt)\n", "# Plot fraction of events vs energy\n", "fig, ax = plt.subplots()\n", "for i, composition in enumerate(comp_list):\n", " num_reco_bin = np.array([[i, j] for i, j in zip(num_reco_energy['light'], num_reco_energy['heavy'])])\n", "# print(num_reco_bin)\n", " num_reco = np.array([np.dot(inverse, i) for i in num_reco_bin])\n", " print(num_reco)\n", " num_reco_2 = {'light': num_reco[:, 0], 'heavy': num_reco[:, 1]}\n", " # Calculate dN/dE\n", " y = num_reco_2[composition]/energy_bin_widths\n", " y_err = num_reco_energy_err[composition]/energy_bin_widths\n", " # Add effective area\n", " y, y_err = comp.analysis.ratio_error(y, y_err, eff_area, eff_area_error)\n", " # Add solid angle\n", " y = y / solid_angle\n", " y_err = y_err / solid_angle\n", " # Add time duration\n", " y = y / dt\n", " y_err = y / dt\n", " # Add energy scaling \n", " energy_err = get_energy_res(df_sim, energy_bins)\n", " energy_err = np.array(energy_err)\n", "# print(10energy_err)\n", " y = (10energy_midpoints)*2.7 y\n", " y_err = (10energy_midpoints)2.7 * y_err\n", " plotting.plot_steps(energy_midpoints, y, y_err, ax, color_dict[composition], composition)\n", "ax.set_yscale(\"log\", nonposy='clip')\n", "plt.xlabel('$\\log_{10}(E_{\\mathrm{reco}}/\\mathrm{GeV})$')\n", "ax.set_ylabel('$\\mathrm{E}^{2.7} \\\\frac{\\mathrm{dN}}{\\mathrm{dE dA d\\Omega dt}} \\ [\\mathrm{GeV}^{1.7} \\mathrm{m}^{-2} \\mathrm{sr}^{-1} \\mathrm{s}^{-1}]$')\n", "ax.set_xlim([6.2, 8.0])\n", "# ax.set_ylim([102, 105])\n", "ax.grid()\n", "leg = plt.legend(loc='upper center', \n", " bbox_to_anchor=(0.5, # horizontal\n", " 1.1),# vertical \n", " ncol=len(comp_list)+1, fancybox=False)\n", "# set the linewidth of each legend object\n", "for legobj in leg.legendHandles:\n", " legobj.set_linewidth(3.0)\n", " \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipeline.get_params()['classifier__max_depth']" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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/G16iS1avCC4CR1/5w9rmj9vczKTGGuJ3ffrCiBXGFmy+Ovr5S00szWK3ra8+40YwQAAACASkZe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\nhLwKwFj28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9X5wgtSe1s0oK0tKsQNpKZWVVOnKjd1mqpOPmXIWXYoPXdvktQUP0C4TL1n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\nur4t5MnOnr9gN5YylsMdeeSRr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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa269751f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "energy_bin_width = 0.1\n", "energy_bins = np.arange(6.2, 8.1, energy_bin_width)\n", "fig, axarr = plt.subplots(1, 2)\n", "for composition, ax in zip(comp_list, axarr.flatten()):\n", " MC_comp_mask = (df_sim['MC_comp_class'] == composition)\n", " MC_log_energy = df_sim['MC_log_energy'][MC_comp_mask].values\n", " reco_log_energy = df_sim['lap_log_energy'][MC_comp_mask].values\n", " plotting.histogram_2D(MC_log_energy, reco_log_energy, energy_bins, log_counts=True, ax=ax)\n", " ax.plot([0,10], [0,10], marker='None', linestyle='-.')\n", " ax.set_xlim([6.2, 8])\n", " ax.set_ylim([6.2, 8])\n", " ax.set_xlabel('$\\log_{10}(E_{\\mathrm{MC}}/\\mathrm{GeV})$')\n", " ax.set_ylabel('$\\log_{10}(E_{\\mathrm{reco}}/\\mathrm{GeV})$')\n", " ax.set_title('{} response matrix'.format(composition))\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 410369.12250776, 516624.1165407 , 650391.2286588 ,\n", " 818794.04536659, 1030800.63073774, 1297701.1085292 ,\n", " 1633708.90244087, 2056717.65275717, 2589254.11794165,\n", " 3259677.80666943, 4103691.22507761, 5166241.16540694,\n", " 6503912.28658791, 8187940.45366581, 10308006.30737735,\n", " 12977011.08529189, 16337089.02440856, 20567176.52757148])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energy_bins = np.arange(6.2, 8.1, energy_bin_width)\n", "10energy_bins[1:] - 10energy_bins[:-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "probs = pipeline.named_steps['classifier'].predict_proba(X_test)\n", "prob_1 = probs[:, 0][MC_iron_mask]\n", "prob_2 = probs[:, 1][MC_iron_mask]\n", "# print(min(prob_1-prob_2))\n", "# print(max(prob_1-prob_2))\n", "# plt.hist(prob_1-prob_2, bins=30, log=True)\n", "plt.hist(prob_1, bins=np.linspace(0, 1, 50), log=True)\n", "plt.hist(prob_2, bins=np.linspace(0, 1, 50), log=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "probs = pipeline.named_steps['classifier'].predict_proba(X_test)\n", "dp1 = (probs[:, 0]-probs[:, 1])[MC_proton_mask]\n", "print(min(dp1))\n", "print(max(dp1))\n", "dp2 = (probs[:, 0]-probs[:, 1])[MC_iron_mask]\n", "print(min(dp2))\n", "print(max(dp2))\n", "fig, ax = plt.subplots()\n", "# plt.hist(prob_1-prob_2, bins=30, log=True)\n", "counts, edges, pathes = plt.hist(dp1, bins=np.linspace(-1, 1, 100), log=True, label='Proton', alpha=0.75)\n", "counts, edges, pathes = plt.hist(dp2, bins=np.linspace(-1, 1, 100), log=True, label='Iron', alpha=0.75)\n", "plt.legend(loc=2)\n", "plt.show()\n", "pipeline.named_steps['classifier'].classes_" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "print(pipeline.named_steps['classifier'].classes_)\n", "le.inverse_transform(pipeline.named_steps['classifier'].classes_)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "pipeline.named_steps['classifier'].decision_path(X_test)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa26695eb50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "comp_list = ['light', 'heavy']\n", "pipeline = comp.get_pipeline('RF')\n", "pipeline.fit(X_train_sim, y_train_sim)\n", "# test_probs = defaultdict(list)\n", "fig, ax = plt.subplots()\n", "test_predictions = pipeline.predict(X_test_data)\n", "test_probs = pipeline.predict_proba(X_test_data)\n", "for class_ in pipeline.classes_:\n", " test_predictions == le.inverse_transform(class_)\n", " plt.hist(test_probs[:, class_], bins=np.linspace(0, 1, 50),\n", " histtype='step', label=composition,\n", " color=color_dict[composition], alpha=0.8, log=True)\n", "plt.ylabel('Counts')\n", "plt.xlabel('Testing set class probabilities')\n", "plt.legend()\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "probs = (15883, 4)\n", "min = 1.38011981318\n", "max = 3.56902754108\n", "probs = (15920, 4)\n", "min = 1.40518894839\n", "max = 3.5562126663\n", "probs = (15507, 4)\n", "min = 1.45593414673\n", "max = 3.57278805564\n", "probs = (15066, 4)\n", "min = 1.50426220527\n", "max = 3.60566706999\n" ] }, { "data": { "image/png": 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FnlRrfTzJ7/Y/HGDYTR0/nPs/864kybk/+/sNR8MwOn5k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\nbd68mUYdAACoBGpNAAAAAJ7i1UbdKUn7JXVZa3v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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc71eefcc10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pipeline = comp.get_pipeline('RF')\n", "pipeline.fit(X_train, y_train)\n", "test_predictions = pipeline.predict(X_test)\n", "\n", "comp_list = ['P', 'He', 'O', 'Fe']\n", "fig, ax = plt.subplots()\n", "test_probs = pipeline.predict_proba(X_test)\n", "fig, axarr = plt.subplots(2, 2, sharex=True, sharey=True)\n", "for composition, ax in zip(comp_list, axarr.flatten()):\n", " comp_mask = (le.inverse_transform(y_test) == composition)\n", " probs = np.copy(test_probs[comp_mask])\n", " print('probs = {}'.format(probs.shape))\n", " weighted_mass = np.zeros(len(probs))\n", " for class_ in pipeline.classes_:\n", " c = le.inverse_transform(class_)\n", " weighted_mass += comp.simfunctions.comp2mass(c) * probs[:, class_]\n", " print('min = {}'.format(min(weighted_mass)))\n", " print('max = {}'.format(max(weighted_mass)))\n", " ax.hist(weighted_mass, bins=np.linspace(0, 5, 100),\n", " histtype='step', label=None, color='darkgray',\n", " alpha=1.0, log=False)\n", " for c in comp_list:\n", " ax.axvline(comp.simfunctions.comp2mass(c), color=color_dict[c],\n", " marker='None', linestyle='-')\n", " ax.set_ylabel('Counts')\n", " ax.set_xlabel('Weighted atomic number')\n", " ax.set_title('MC {}'.format(composition))\n", " ax.grid()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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HnCg5OZFEXhQvBuowKMaYGZI2SVou6Slr7f7A8SslPSWpXNJia+1D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\nC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jzOSNexEydO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\nwiKQHXWVGGPOkLRc0oC19lmfw0HApNNpdXR0zFmeSqV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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbb0bca0810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pipeline = comp.get_pipeline('RF')\n", "pipeline.fit(X_train, y_train)\n", "test_predictions = pipeline.predict(X_test)\n", "\n", "comp_list = ['P', 'He', 'O', 'Fe']\n", "fig, ax = plt.subplots()\n", "test_probs = pipeline.predict_proba(X_test)\n", "fig, axarr = plt.subplots(2, 2, sharex=True, sharey=True)\n", "for composition, ax in zip(comp_list, axarr.flatten()):\n", " comp_mask = (le.inverse_transform(y_test) == composition)\n", " probs = np.copy(test_probs[comp_mask])\n", " weighted_mass = np.zeros(len(probs))\n", " for class_ in pipeline.classes_:\n", " c = le.inverse_transform(class_)\n", " ax.hist(probs[:, class_], bins=np.linspace(0, 1, 50),\n", " histtype='step', label=c, color=color_dict[c],\n", " alpha=1.0, log=True)\n", " ax.legend(title='Reco comp', framealpha=0.5)\n", " ax.set_ylabel('Counts')\n", " ax.set_xlabel('Testing set class probabilities')\n", " ax.set_title('MC {}'.format(composition))\n", " ax.grid()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "ename": "NotFittedError", "evalue": "This StandardScaler instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.", "output_type": "error", "traceback": [ "\u001b[0;31m\u001b[0m", "\u001b[0;31mNotFittedError\u001b[0mTraceback (most recent call last)", "\u001b[0;32m<ipython-input-25-56957a68d00e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# test_probs = pipeline.predict_proba(X_test)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32min\u001b[0m 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"\u001b[0;32m/home/jbourbeau/.local/lib/python2.7/site-packages/sklearn/utils/metaestimators.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(args, kwargs)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;31m# lambda, but not partial, allows help() to work with update_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0;34m\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 55\u001b[0m \u001b[0;31m# update the docstring of the returned function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0mupdate_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/jbourbeau/.local/lib/python2.7/site-packages/sklearn/pipeline.pyc\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 377\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 378\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m 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Call 'fit' with appropriate arguments before using this method." ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa264977f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "comp_list = ['light', 'heavy']\n", "test_probs = defaultdict(list)\n", "fig, ax = plt.subplots()\n", "# test_probs = pipeline.predict_proba(X_test)\n", "for event in pipeline.predict_proba(X_test_data):\n", " composition = le.inverse_transform(np.argmax(event))\n", " test_probs[composition].append(np.amax(event))\n", "for composition in comp_list:\n", " plt.hist(test_probs[composition], bins=np.linspace(0, 1, 100),\n", " histtype='step', label=composition,\n", " color=color_dict[composition], alpha=0.8, log=False)\n", "plt.ylabel('Counts')\n", "plt.xlabel('Testing set class probabilities')\n", "plt.legend(title='Reco comp')\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
kunalj101/scipy2015-blaze-bokeh	1.5 Glyphs - Legend.ipynb	5	2340	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <img src=images/continuum_analytics_b&w.png align=\"left\" width=\"15%\" style=\"margin-right:15%\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 align='center'>Bokeh Tutorial</h1>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.5 Glyphs - Legend" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise: Create a legend plot for the temperature map with the Glyph interface\n", "\n", "Tips:\n", "\n", "- Glyphs: Text, Rect" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Output option" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Set ranges" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# For each color in your palette, add a Rect glyph to the plot with the appropriate properties" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Add text labels and add them to the plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Show plot" ] } ], "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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
camm/SHUG2015	mantid/basic_tasks.ipynb	1	6242	{ "metadata": { "name": "", "signature": "sha256:61c0fbfd656da0101a745e69b0bd7a5f1e2631a7f05f20bfd277773d1996e736" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Mantid</h1>\n", "\n", "For an introduction to Mantid, you can go over at the [Basic course](http://www.mantidproject.org/Mantid_Basic_Course)\n", "\n", "<a id='Table of Contents'></a><h3>Table of Contents</h3>\n", " \n", "<a href='#starting'>Starting Mantid</a> \n", "<a href='#adding_directory'>Adding a directory to the list of \"Data Search Directories\"</a> \n", "\n", "\n", "<a href='#Section'><h4>Section</h4></a>\n", "\n", "* <a href='#Section.subsection'>subsection</a> \n", "\n", "<a href='#Syntax'>Examples of HTML and Markdown syntax</a></br>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='starting'></a><h3>Starting Mantid</h3>\n", "Bring up a terminal and enter the following commands: \n", "\n", "<center><a href=\"files/supporting/starting.1.png\"><img src=\"files/supporting/starting.1.png\" width=\"400\" height=\"400\" alt=\"supporting/starting.1.png\"></a></center>\n", "\n", "The Mantid session will start. Select <i>SNS</i> as \"Default Facility\" and <i>BASIS</i> as \"Default Instrument\": \n", "\n", "<center><a href=\"files/supporting/starting.2.png\"><img src=\"files/supporting/starting.2.png\" width=\"500\" height=\"500\" alt=\"supporting/starting.2.png\"></a></center>\n", "\n", "* Click on the \"Manage User Directories\" button. \n", "* Click \"Browse To Directory\" and navigate to the location of the data files (/SNS/users/<b>yourusername</b>/SHUG2015/mantid/me8t8). \n", "* Do the same for the default save directory. \n", "\n", "<center><a href=\"files/supporting/starting.3.png\"><img src=\"files/supporting/starting.3.png\" width=\"400\" height=\"400\" alt=\"supporting/starting.3.png\"></a></center>\n", "\n", "* Click \"OK\". \n", "* Click \"Set\". \n", "\n", "The initial Mantid session should look like the picture below, but don't worry if some of the panels are not showing up. Panels can be brought to view from the <b>View</b> menu. \n", "\n", "<center><a href=\"files/supporting/starting.4.png\"><img src=\"files/supporting/starting.4.png\" width=\"800\" height=\"800\" alt=\"supporting/starting.4.png\"></a></center>\n", "\n", "Mantid can plot data with the intensity normalized by the bin width, and this is the default behaviour for all histogram and event data. As we are not going to use this normalization in this course we need to change the default settings. To do that, go to \"View\"->\"Preferences...\" and in the new window select \"2D Plots\" and untick \"Normalize histograms to bin width\". \n", "\n", "<center><a href=\"files/supporting/starting.5.png\"><img src=\"files/supporting/starting.5.png\" width=\"600\" height=\"600\" alt=\"supporting/starting.5.png\"></a></center>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='adding_directory'></a><h3>Adding a directory to the list of \"Data Search Directories\"</h3>\n", "We will add directory <i>/SNS/users/shared/MantidTrainingCourseData</i> to the list. This directory contains all data that is used in the Mantid courses. \n", "\n", "First, click in the \"Manage User Directories\" icon. This will bring about the dialog window: \n", "\n", "<center><a href=\"files/supporting/adding_directory.1.png\"><img src=\"files/supporting/adding_directory.1.png\" width=\"600\" height=\"400\" alt=\"supporting/adding_directory.1.png\"></a></center>\n", "\n", "Write down the path of the directory and then click in \"Add Directory\": \n", "\n", "<center><a href=\"files/supporting/adding_directory.2.png\"><img src=\"files/supporting/adding_directory.2.png\" width=\"300\" height=\"300\" alt=\"supporting/adding_directory.2.png\"></a></center>\n", "\n", "Now the directory has been included in the list: \n", "\n", "<center><a href=\"files/supporting/adding_directory.3.png\"><img src=\"files/supporting/adding_directory.3.png\" width=\"300\" height=\"300\" alt=\"supporting/adding_directory.3.png\"></a></center>\n", "\n", "Finally, click the \"OK\" button.\n", "\n", "Every time you load a file with the <i>Load</i> Algorithm, these directories will be searched. \n", "\n", "<center><a href=\"files/supporting/adding_directory.4.png\"><img src=\"files/supporting/adding_directory.4.png\" width=\"300\" height=\"300\" alt=\"supporting/adding_directory.4.png\"></a></center>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='Section'></a><h2>Section</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='Section.subsection'></a><h3>Subsection</h3>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(<a href='#Table of Contents'>Top</a>)<a id='Syntax'></a><h3>Markdown Syntax Examples</h3>\n", "local link: [link](files/link)</br>\n", "remote link: <a href=\"http://ambermd.org/\">http://ambermd.org</a>\n", "<font face=\"courier new\"> font face=\"courier new\" </font><br/>\n", "$$S_{model}(Q,E)=A(Q)\\cdot S_{elastic}(E) + B(Q)\\cdot S_{simulation}(Q,E)\\otimes S_{elastic}(E) + C(Q)+D(Q)\\cdot E$$\n", "<pre> Quoted text </pre>\n", "<center><table><tr>\n", "<td><a href=\"files/image.png\"><img src=\"files/image.png\" width=\"300\" height=\"250\" alt=\"image here\"></a> <br/>\n", " <i>image caption</i></td>\n", "<td>some text</td>\n", "</tr></table></center>" ] } ], "metadata": {} } ] }	mit
proinsias/gilbert-shannon-reeds	Gilbert-Shannon-Reeds.ipynb	1	277585	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import multiprocessing as mp\n", "import typing" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import matplotlib\n", "matplotlib.use('nbagg')\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker\n", "import numpy as np\n", "import scipy as sp\n", "import sklearn.utils\n", "\n", "from IPython import get_ipython # For automatically-generated python file." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From Wikipedia:\n", "> In the mathematics of shuffling playing cards, the Gilbert–Shannon–Reeds model is a probability distribution on riffle shuffle permutations that has been reported to be a good match for experimentally observed outcomes of human shuffling, and that forms the basis for a recommendation that a deck of cards should be riffled seven times in order to thoroughly randomize it. ... The deck of cards is cut into two packets... [t]hen, one card at a time is repeatedly moved from the bottom of one of the packets to the top of the shuffled deck." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we implement the Gilbert–Shannon–Reeds model, and verify this recommendation of seven shuffles." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the functions below have `doctest` examples.\n", "To test the functions, just run `pytest` in the top level of the repository." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, define a function to determine how many cards to split into our right hand." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_random_number_for_right_deck(n: int, seed: int=None, ) -> int:\n", " \"\"\"\n", " Return the number of cards to split into the right sub-deck.\n", "\n", " :param n: one above the highest number that could be returned by this\n", " function.\n", " :param seed: optional seed for the random number generator to enable\n", " deterministic behavior.\n", " :return: a random integer (between 1 and n-1) that represents the\n", " desired number of cards.\n", "\n", " Examples:\n", "\n", " >>> get_random_number_for_right_deck(n=5, seed=0, )\n", " 1\n", " \"\"\"\n", " random = sklearn.utils.check_random_state(seed=seed, )\n", " \n", " return random.randint(low=1, high=n, )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, define a function to determine which hand to drop a card from." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def should_drop_from_right_deck(n_left: int, n_right:int, seed: int=None, ) -> bool:\n", " \"\"\"\n", " Determine whether we drop a card from the right or left sub-deck.\n", " \n", " Either `n_left` or `n_right` (or both) must be greater than zero.\n", " \n", " :param n_left: the number of cards in the left sub-deck.\n", " :param n_right: the number of cards in the right sub-deck.\n", " :param seed: optional seed for the random number generator to\n", " enable deterministic behavior.\n", " :return: True if we should drop a card from the right sub-deck,\n", " False otherwise.\n", " \n", " Examples:\n", "\n", " >>> should_drop_from_right_deck(n_left=32, n_right=5, seed=0, )\n", " True\n", "\n", " >>> should_drop_from_right_deck(n_left=0, n_right=5, )\n", " True\n", "\n", " >>> should_drop_from_right_deck(n_left=7, n_right=0, )\n", " False\n", "\n", " >>> should_drop_from_right_deck(n_left=0, n_right=0, )\n", " Traceback (most recent call last):\n", " ...\n", " ValueError: Either `n_left` or `n_right` (or both) must be greater than zero.\n", " \"\"\"\n", " if n_left > 0 and n_right > 0:\n", " # There are cards left in both sub-decks, so pick a\n", " # sub-deck at random.\n", " random = sklearn.utils.check_random_state(seed=seed, )\n", " num = random.randint(low=0, high=2, )\n", " boolean = (num == 0)\n", " return boolean\n", " elif n_left == 0 and n_right > 0:\n", " # There are no more cards in the left sub-deck, only\n", " # the right sub-deck, so we drop from the right sub-deck.\n", " return True\n", " elif n_left > 0 and n_right == 0:\n", " # There are no more cards in the right sub-deck, only\n", " # the left sub-deck, so we drop from the left sub-deck.\n", " return False\n", " else:\n", " # There are no more cards in either sub-deck.\n", " raise ValueError ('Either `n_left` or `n_right` '\\\n", " '(or both) must be greater than zero.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can implement the 'Gilbert–Shannon–Reeds' shuffle." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def shuffle(deck: np.array, seed: int=None, ) -> np.array:\n", " \"\"\"\n", " Shuffle the input 'deck' using the Gilbert–Shannon–Reeds method.\n", "\n", " :param seq: the input sequence of integers.\n", " :param seed: optional seed for the random number generator\n", " to enable deterministic behavior.\n", " :return: A new deck containing shuffled integers from the\n", " input deck.\n", "\n", " Examples:\n", "\n", " >>> shuffle(deck=np.array([0, 7, 3, 8, 4, 9, ]), seed=0, )\n", " array([4, 8, 3, 7, 0, 9])\n", " \"\"\"\n", " \n", " # First randomly divide the 'deck' into 'left' and 'right'\n", " # 'sub-decks'.\n", " num_cards_in_deck = len(deck)\n", " orig_num_cards_right_deck = get_random_number_for_right_deck(\n", " n=num_cards_in_deck,\n", " seed=seed,\n", " )\n", "\n", " # By definition of get_random_number_for_right_deck():\n", " n_right = orig_num_cards_right_deck\n", " \n", " n_left = num_cards_in_deck - orig_num_cards_right_deck\n", " \n", " shuffled_deck = np.empty(num_cards_in_deck, dtype=int)\n", " \n", " # We will drop a card n times.\n", " for index in range(num_cards_in_deck):\n", " drop_from_right_deck = should_drop_from_right_deck(\n", " n_left=n_left,\n", " n_right=n_right,\n", " seed=seed,\n", " )\n", " \n", " if drop_from_right_deck is True:\n", " # Drop from the bottom of right sub-deck\n", " # onto the shuffled pile.\n", " shuffled_deck[index] = deck[n_right - 1]\n", " n_right = n_right - 1\n", " else:\n", " # Drop from the bottom of left sub-deck\n", " # onto the shuffled pile.\n", " shuffled_deck[index] = deck[\n", " orig_num_cards_right_deck + n_left - 1\n", " ]\n", " n_left = n_left - 1\n", " \n", " return shuffled_deck" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we run some experiments to confirm the recommendation of seven shuffles for a deck of 52 cards." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "num_cards = 52\n", "max_num_shuffles = 20\n", "num_decks = 10000\n", "\n", "# Shuffling the cards using a uniform probability\n", "# distribution results in the same expected frequency\n", "# for each card in each deck position.\n", "uniform_rel_freqs = np.full(\n", " shape=[num_cards, num_cards],\n", " fill_value=1./num_cards,\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def calculate_differences(\n", " num_shuffles: int\n", " ) -> typing.Tuple[np.float64, np.float64, np.float64,]:\n", " \"\"\"\n", " Calculate differences between observed and uniform distributions.\n", " \n", " :param The number of times to shuffle the deck each time.\n", " :return Three metrics for differences between the\n", " observed and uniform relative frequencies.\n", " \"\"\"\n", " shuffled_decks = np.empty(shape=[num_decks, num_cards], )\n", "\n", " # First create a random deck.\n", " orig_deck = np.array(range(num_cards))\n", " np.random.shuffle(orig_deck)\n", "\n", " for i in range(num_decks):\n", " # Now shuffle this deck using the Gilbert–Shannon–Reeds method.\n", " new_deck = orig_deck\n", " for j in range(num_shuffles):\n", " new_deck = shuffle(new_deck)\n", " \n", " shuffled_decks[i] = new_deck\n", "\n", " # Calculate the relative frequencies of each card in each position.\n", " rel_freqs = np.empty(shape=[num_cards, num_cards], )\n", "\n", " for i in range(num_cards):\n", " col = shuffled_decks[:, i]\n", " \n", " # Make sure that each card appears at least once in this\n", " # position, by first adding the entire deck, and then\n", " # subtracting 1 from the total counts of each card in\n", " # this position.\n", " col = np.append(col, orig_deck)\n", " col_freqs = sp.stats.itemfreq(col)[:, 1]\n", " col_freqs = col_freqs - 1\n", " rel_freqs[i] = col_freqs / num_decks\n", " \n", " # Here I use three metrics for differences between the\n", " # observed and uniform relative frequencies:\n", " # * The sum of the squared element-wise differences,\n", " # * The relative information entropy, and\n", " # * The Kolmogorov-Smirnov statistic.\n", " sum_squared = np.sum(np.square(np.subtract(uniform_rel_freqs, rel_freqs)))\n", " entropy = sp.stats.entropy(rel_freqs.flatten(), uniform_rel_freqs.flatten())\n", " kstest = sp.stats.kstest(rel_freqs.flatten(), 'uniform').statistic\n", " \n", " return sum_squared, entropy, kstest" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Now run the experiment using all our CPUs!\n", "\n", "num_cpus = max(mp.cpu_count() - 2, 1)\n", "\n", "with mp.Pool(num_cpus) as p:\n", " results = p.map(calculate_differences, range(1, max_num_shuffles+1))\n", " results = np.array(results)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sums_squared = results[:, 0]\n", "entropies = results[:, 1]\n", "kstests = results[:, 2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The KS statistics are of most use here. You can see how the statistic approaches its maximum value around num_shuffles = 7." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fs = 14" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x112a01dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6), dpi=300)\n", "ax.scatter(range(1, max_num_shuffles + 1), kstests, );\n", "ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=True))\n", "ax.set_xlabel('Number of Shuffles', fontsize=fs, )\n", "ax.set_ylabel('Kolmogorov-Smirnov Statistic', fontsize=fs, )\n", "ax.set_xlim([0, max_num_shuffles + 1])\n", "plt.show();" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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iRJkiRJkiRJkvpkSo6Pzhh0AJqWflPbfilwxSACkTRhbcHQ5VYOAP40oFgk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\nxCJJkiTJYgNJkiRJU9PRwNIO7R/sdyAT2DnjOPeqY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"text/plain": [ "<matplotlib.figure.Figure at 0x11865e4e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6), dpi=300)\n", "ax.scatter(range(1, max_num_shuffles + 1), sums_squared, );\n", "ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=True))\n", "ax.set_xlabel('Number of Shuffles', fontsize=fs, )\n", "ax.set_ylabel('Sum of the Squared Differences', fontsize=fs, )\n", "ax.set_xlim([0, max_num_shuffles + 1])\n", "plt.show();" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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DwzLst8yzPjssy3PDMuy3zLM+PyzLizli5dyu677944tHtmLu1UPaXYDDHO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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1186c6c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8, 6), dpi=300)\n", "ax.scatter(range(1, max_num_shuffles + 1), entropies, );\n", "ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=True))\n", "ax.set_xlabel('Number of Shuffles', fontsize=fs, )\n", "ax.set_ylabel('Relative Information Entropy', fontsize=fs, )\n", "ax.set_xlim([0, max_num_shuffles + 1])\n", "plt.show();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
wcmckee/wcmckee	twithash.ipynb	1	339181	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Tweet Hash Create \n", "\n", "Script to create hashtag for twitter tweeting\n", "\n", "Code for twitter bot. I want to feed it an api of twitter hash tags to tweet. This list could have when to start/stop tweeting the hashtag.\n", "\n", "Example\n", "\n", "{'Hashtag' : 'kiwipycon', 'description' : 'NZ Python lang conference',\n", "'starttweet' : 'datetostarttweeting', 'stoptweet' : 'datetostoptweeting', 'active' : True, 'similarhashtags' : 'pythonic'}\n", "\n", "Anything missing?\n", "\n", "When was the last tweet with that hash tag sent. \n", "\n", "Get recent tweets/users with that hashtag and filter OTHER hashtags out of them " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import twitter" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from TwitterFollowBot import TwitterBot" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning: Your Twitter follower sync files are more than a day old. It is highly recommended that you sync them by calling sync_follows() before continuing.\n" ] } ], "source": [ "wcm_bot = TwitterBot('/home/wcmckee/Downloads/wcmtweet/config.txt')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "import arrow" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "curtim = arrow.now()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'2015-10-25 22:35:26.881629+13:00'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str(curtim.datetime)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Add what hashtag? pyconie\n" ] } ], "source": [ "whathashtag = input('Add what hashtag? ')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "if '#' in whathashtag:\n", " pass\n", "else:\n", " whathashtag = ('#' + whathashtag)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'#pyconie'" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "whathashtag" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "wcmbotsea = wcm_bot.search_tweets(whathashtag, count= 50)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "botsatl = len(wcmbotsea['statuses'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { 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phoebe-project/phoebe2-docs	2.3/examples/eccentric_ellipsoidal.ipynb	1	83149	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Eccentric Ellipsoidal (Heartbeat)\n", "============================\n", "\n", "Setup\n", "-----------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#!pip install -I \"phoebe>=2.3,<2.4\"" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "As always, let's do imports and initialize a logger and a new bundle." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import phoebe\n", "import numpy as np\n", "\n", "b = phoebe.default_binary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need a highly eccentric system that nearly overflows at periastron and is slightly eclipsing." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "b.set_value('q', value=0.7)\n", "b.set_value('period', component='binary', value=10)\n", "b.set_value('sma', component='binary', value=25)\n", "b.set_value('incl', component='binary', value=0)\n", "b.set_value('ecc', component='binary', value=0.9)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ParameterSet: 4 parameters\n", " requiv@primary@component: 1.0 solRad\n", "C requiv_max@primary@component: 1.1005323225331147 solRad\n", " requiv@secondary@component: 1.0 solRad\n", "C requiv_max@secondary@component: 0.928057544397582 solRad\n" ] } ], "source": [ "print(b.filter(qualifier='requiv', context='component'))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "b.set_value('requiv', component='primary', value=1.1)\n", "b.set_value('requiv', component='secondary', value=0.9)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Adding Datasets\n", "-------------------\n", "\n", "We'll add light curve, orbit, and mesh datasets." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<ParameterSet: 73 parameters \| contexts: compute, dataset, constraint, figure>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.add_dataset('lc', \n", " compute_times=phoebe.linspace(-2, 2, 201),\n", " dataset='lc01')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<ParameterSet: 45 parameters \| contexts: compute, dataset, constraint, figure>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.add_dataset('orb', compute_times=phoebe.linspace(-2, 2, 201))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "anim_times = phoebe.linspace(-2, 2, 101)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<ParameterSet: 83 parameters \| contexts: compute, dataset, constraint, figure>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.add_dataset('mesh', \n", " compute_times=anim_times,\n", " coordinates='uvw',\n", " dataset='mesh01')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Running Compute\n", "--------------------" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%\|██████████\| 201/201 [00:09<00:00, 20.78it/s]\n" ] }, { "data": { "text/plain": [ "<ParameterSet: 423 parameters \| kinds: orb, lc, mesh>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b.run_compute(irrad_method='none')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting \n", "---------------" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "afig, mplfig = b.plot(kind='lc', x='phases', t0='t0_perpass', show=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's make a nice figure.\n", "\n", "Let's go through these options:\n", " `time`: make the plot at this single time\n", "* `z`: by default, orbits plot in 2d, but since we're overplotting with a mesh, we want the z-ordering to be correct, so we'll have them plot with w-coordinates in the z-direction.\n", "* `c`: (will be ignored by the mesh): set the color to blue for the primary and red for the secondary (will only affect the orbits as the light curve is not tagged with any component).\n", "* `fc`: (will be ignored by everything but the mesh): set the facecolor to be blue for the primary and red for the secondary.\n", "* `ec`: disable drawing the edges of the triangles in a separate color. We could also set this to 'none', but then we'd be able to \"see-through\" the triangle edges.\n", "* `uncover`: for the orbit, uncover based on the current time.\n", "* `trail`: for the orbit, let's show a \"trail\" behind the current position.\n", "* `highlight`: disable highlighting for the orbit, since the mesh will be in the same position.\n", "* `tight_layout`: use matplotlib's tight layout to ensure we have enough padding between axes to see the labels." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x864 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "afig, mplfig = b.plot(time=0.0, \n", " z={'orb': 'ws'},\n", " c={'primary': 'blue', 'secondary': 'red'},\n", " fc={'primary': 'blue', 'secondary': 'red'}, \n", " ec='face', \n", " uncover={'orb': True},\n", " trail={'orb': 0.1},\n", " highlight={'orb': False},\n", " tight_layout=True,\n", " show=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's animate the same figure in time. We'll use the same arguments as the static plot above, with the following exceptions:\n", "\n", "* `times`: pass our array of times that we want the animation to loop over.\n", "* `pad_aspect`: pad_aspect doesn't work with animations, so we'll disable to avoid the warning messages.\n", "* `animate`: self-explanatory.\n", "* `save`: we could use `show=True`, but that doesn't always play nice with jupyter notebooks\n", "* `save_kwargs`: may need to change these for your setup, to create a gif, passing {'writer': 'imagemagick'} is often useful." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/kyle/.local/lib/python3.8/site-packages/phoebe/dependencies/nparray/nparray.py:408: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " return getattr(self.array, operator)(other)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING: tight_layout with fixed limits may cause jittering in the animation\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "afig, mplfig = b.plot(times=anim_times, \n", " z={'orb': 'ws'},\n", " c={'primary': 'blue', 'secondary': 'red'},\n", " fc={'primary': 'blue', 'secondary': 'red'}, \n", " ec='face', \n", " uncover={'orb': True},\n", " trail={'orb': 0.1},\n", " highlight={'orb': False},\n", " tight_layout=True, pad_aspect=False,\n", " animate=True, \n", " save='eccentric_ellipsoidal.gif',\n", " save_kwargs={'writer': 'imagemagick'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![gif](eccentric_ellipsoidal.gif)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
dvrp/pattern-recognition-deliveries	seminar-1/seminar-1.ipynb	1	390982	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Seminar 1 - Pattern Recognition\n", "=================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 1\n", "\n", "Find an example in order to explain all of these concepts:\n", "\n", "### Feature\n", "\n", "Let's imagine we want to construct a handwriting digit recognizer. Somehow using advanced pattern recognition techniques we are able to indentify the number of straight horizontal lines in each sample containing a number. These straight horizontal lines are features.\n", "\n", "### Supervised Learning\n", "\n", "An example of supervised learning is a prediction model. Imagine a dataset where you want to predict if a house is going be sold based on its price and the average income of a household of the zone where the house is, such as:\n", "\n", "\| House Price \| Average Income \| Sold? \|\n", "\|--------------------------------------\|\n", "\| 120000 \| 24000 \| Yes \|\n", "\| 135000 \| 30000 \| Yes \|\n", "\| 180000 \| 24000 \| No \|\n", "\n", "Well, you could feed a learning algorithm to learn about the $n$ features that contribute to a house being sold and try to predict if it's going get sold.\n", "\n", "### Classification\n", "\n", "Try to predict if a picture contains a cat or a tiger.\n", "\n", "### Regression\n", "\n", "Try to predict stock prices or estimate the remaining battery left in a phone based on several features.\n", "\n", "### Unsupervised Learning\n", "\n", "Try to classify $n$ amount of people into $m$ groups. For example, you have a dataset of height and weight of your clients and you want to create the best fit for S, M, and L kind of clothes for them. Clustering would be a good approach.\n", "\n", "### Clustering\n", "\n", "Clustering is an unsupervised learning technique used to automatically classify unlabeled data into groups, also known as clusters. After that, this new data can be used to improve the algorithm." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. (Bayes Theorem) Walking through the jungle, you see a large feline shadow. However, you can not identify what animal it is. You know that, in this area of the jungle, 90% of felines are not dangerous cats whereas the other 10 % are pumas . In addition, you know that 80% of cats are smaller than the animal you are seeing whereas 85% of pumas has a similar size. Would you run?\n", "\n", "Reminder: We define Bayes Theorem as follows $p(A\|B) = \\frac{p(B\|A)p(A)}{p(B)}$, where $A, B$ are events and $p(B) \\neq 0$\n", "\n", "### Procedure:\n", "\n", "Let $A$ denote the event of feline being dangerous. Let $B$ denote the event of watching a big feline with similar size to pumas and bigger than 80% of cats. We know that the probability of any random given feline of being a puma is 0.1 and any random feline of being a cat is 0.9. Also, we know that 85% of pumas can be a big feline like the one watched and that at most, 20% of cats are like that big feline:\n", "\n", "$p(A) = 0.1$\n", "\n", "$p(B) = 0.1 \\cdot 0.85 + 0.9 \\cdot 0.2 = 0.265$\n", "\n", "Then, the probability of watching a big feline ($B$) knowing that the feline is a puma ($A$) is defined as:\n", "\n", "$p(B\|A) = 0.85$\n", "\n", "Then, using Bayes Theorem we can calculate the probability of a feline being a puma given the event that we see a big feline: $p(A\|B)$\n", "\n", "$p(A\|B) = \\frac{p(B\|A)p(A)}{p(B)} = \\frac{0.85 \\cdot 0.1}{0.265} = 0.32$\n", "\n", "*Indeed, I would run." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gaussian Model\n", "## 3. Following the last example, assume that the variable size is continuous:\n", "### Draw a figure (approximated) of the distribution functions p(size \| cat) and p(size \| puma). To do so, assume that the size of cats and pumas follow a normal distribution where cat’s size tend to be smaller than pumas. Moreover, you know that the size of different cat species use to be more variable than in the case of pumas.\n", "\n", "### Over the same figure, and following the Bayesian Decision Theory, draw the 2 decision regions which classify cats and pumas with respect to their size. Draw also the region which corresponds to the decision error." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matlab Demo" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The decision error is around = 57.4788\n", "\n" ] }, { "data": { "image/png": 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ZvEGTP1I5QQ8JQL44G+c31cmTJ6dMmfLCCy+kpaVNmTLl3XffJfc2iNuxY4dO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\ngD5GTOIRApKQbP3TNxmK4p/0RyEJqZB6NGJKVJq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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "size_var = linspace(0,120, 200);\n", "mu_cat = 40;\n", "mu_puma = 70;\n", "sigma_cat = 12;\n", "sigma_puma = 7;\n", "p_cat = normpdf(size_var, mu_cat, sigma_cat);\n", "p_puma = normpdf(size_var, mu_puma, sigma_puma);\n", "plot(size_var, p_cat, 'lineWidth', 2);\n", "hold on;\n", "plot(size_var, p_puma, 'lineWidth', 2);\n", "title('PDFs of two distributons');\n", "ylabel('probability');\n", "xlabel('Size [cm]');\n", "\n", "p_cat_func = @(x) exp(-(x-mu_cat).^2 / (2sigma_cat^2)) / sqrt(2sigma_cat^2pi);\n", "p_puma_func = @(x) exp(-(x-mu_puma).^2 / (2sigma_puma^2)) / sqrt(2sigma_puma^2pi);\n", "intersection = fzero(@(x) p_cat_func(x) - p_puma_func(x), (mu_cat + mu_puma)/2);\n", "disp(['The decision error is around = ', num2str(intersection)]);\n", "line([intersection, intersection], [0, p_cat_func(intersection)], 'Color','red','LineStyle','--', 'lineWidth', 1.5);\n", "plot(intersection, p_cat_func(intersection), 'r', 'lineWidth', 10);\n", "legend('Cat Prob. Dist.', 'Puma Prob. Dist.', 'Decision boundary', 'Intersection of distributions');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Assume that probabilities p(size\|cat) and p(size\|puma) follow two normal distributions $N(μ_1 , σ )$ and $N(μ_2 , σ )$ respectively. Given that p(cat) = p(puma) = 0.5 and σ1 = σ2 , find the size value x\\* which corresponds to the decision boundary classifying cats or pumas. Define x\\* as a function of $μ_1$ and $μ_2$ . " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matlab Demo" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Assuming the variance is the same for both distributions...\n", "The decision error is around = 55\n", "\n" ] }, { "data": { "image/png": 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Id2NiYkhAeuyxx3S7N9HOTGFhYQMGDDhw4MCAAQOuX7+ud1IRWY7/rYOQCFi7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\n9xpnSCQStVqte1ifRZtsOCiO0Tt0Fky/qUfIEveyR6Hm/aD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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "disp('Assuming the variance is the same for both distributions...')\n", "size_var = linspace(0,120, 200);\n", "mu_cat = 40;\n", "mu_puma = 70;\n", "sigma_cat = 12;\n", "sigma_puma = 12;\n", "p_cat = normpdf(size_var, mu_cat, sigma_cat);\n", "p_puma = normpdf(size_var, mu_puma, sigma_puma);\n", "plot(size_var, p_cat, 'lineWidth', 2);\n", "hold on;\n", "plot(size_var, p_puma, 'lineWidth', 2);\n", "title('PDFs of two distributons');\n", "ylabel('probability');\n", "xlabel('Size [cm]');\n", "\n", "p_cat_func = @(x) exp(-(x-mu_cat).^2 / (2sigma_cat^2)) / sqrt(2sigma_cat^2pi);\n", "p_puma_func = @(x) exp(-(x-mu_puma).^2 / (2sigma_puma^2)) / sqrt(2sigma_puma^2pi);\n", "intersection = fzero(@(x) p_cat_func(x) - p_puma_func(x), (mu_cat + mu_puma)/2);\n", "disp(['The decision error is 0.5 at size = ', num2str(intersection)]);\n", "line([intersection, intersection], [0, p_cat_func(intersection)], 'Color','red','LineStyle','--', 'lineWidth', 1.5);\n", "plot(intersection, p_cat_func(intersection), 'ro', 'lineWidth', 2);\n", "legend('Cat Prob. Dist.', 'Puma Prob. Dist.', 'Decision boundary', 'Intersection of distributions');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Imagine that you have a hen and a goose in your home. One day, you find an egg but you do not know who have laid it. You measure the weight and the height of the egg, which are 60g and 5cm respectively. Assuming that the laying frequency of the chickens is the double than the gooses one, and the following mean and covariance matrices:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataset:\n", "\n", "$\\mu_1 = (54, 5)$\n", "\n", "$\\mu_2 = (65, 6)$\n", "\n", "$\\sigma_1 =\n", "\\left(\\begin{array}{cc} \n", "5 & \\frac{1}{10}\\\\\n", "\\frac{1}{10} & \\frac{1}{2}\n", "\\end{array}\\right)$\n", "\n", "$\\sigma_2 =\n", "\\left(\\begin{array}{cc} \n", "8 & \\frac{1}{5}\\\\\n", "\\frac{1}{5} & 1\n", "\\end{array}\\right)$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we have to do is calculate the probability density function (pdf) of the two distributions. One pdf for the chicken eggs and another one for the goose eggs. However, this time we have two variables, weight and size, so we have to use the multivariate gaussian pdf, given by the formula: \n", "\n", "$$\\operatorname{det}(2\\pi\\boldsymbol\\Sigma)^{-\\frac{1}{2}} \\, e^{ -\\frac{1}{2}(\\mathbf{x} - \\boldsymbol\\mu)'\\boldsymbol\\Sigma^{-1}(\\mathbf{x} - \\boldsymbol\\mu) }$$\n", "\n", "So we just have to compute the pdf of the sample in both pdf, compare the probabilities and predict the one with a higher pdf. The probability of predicting correctly given distribution A, as it was explained on the last exercise, is done by taking the pdf of distribution A and dividing it by the sum of the pdfs of all the distributions. Using Matlab we can easily visualize the results:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matlab demo" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Probability of being a goose is 0.68397\n", "\n" ] }, { "data": { "image/png": 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JsDAbtjCclSawvJ122VnaPy/Iu4ki2D4hdnoCQJvlo8IiKtPJ7dqtTSrRJqE+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\nStlD/xxMOgjG6IR0+PDhV199tampKS8vDwAuu+yyd9999+qrq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sZjKZZJU5CcEhBYnEIsnLy3N0dKxPHTYJ0h7jFBTUnDogU1AkxIAUJBLLAJ8jKS0tlUql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eD999/vZPzVnIfhBgQjJIbfiKKiIvT8dOJpWbZsGbplJk6cGB0dDQB33HFH5y+VlpaK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\nQ9h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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scale = 3;\n", "s = scale;\n", "res = 40;\n", "\n", "figure(1)\n", "\n", "% Chicken?\n", "meanWeight_chicken = 54;\n", "meanHeight_chicken = 5;\n", "mu = [meanWeight_chicken, meanHeight_chicken]; \n", "SIGMA = [5 .1; .1 .5]; \n", "[X1,X2] = meshgrid(linspace(meanWeight_chicken - ssqrt(SIGMA(1, 1)), meanWeight_chicken+ssqrt(SIGMA(1, 1)),res)',linspace(meanHeight_chicken-ssqrt(SIGMA(2, 2)),meanHeight_chicken+ssqrt(SIGMA(2, 2)),res)');\n", "X = [X1(:) X2(:)];\n", "p = mvnpdf(X,mu,SIGMA);\n", "subplot(2, 1, 1);\n", "surf(X1,X2,reshape(p,res,res));\n", "hold on;\n", "X = [60, 5];\n", "p_chicken = mvnpdf(X,mu,SIGMA);\n", "plot3(X(1), X(2), p_chicken, 'r');\n", "title(['Chicken pdf at point = ' num2str(mvnpdf(X,mu,SIGMA)100)])\n", "axis tight\n", "view(45, 45)\n", "\n", "% Goose?\n", "meanWeight_goose = 65;\n", "meanHeight_goose = 6;\n", "mu = [meanWeight_chicken, meanHeight_chicken]; \n", "SIGMA = [8 .2; .2 1]; \n", "[X1,X2] = meshgrid(linspace(meanWeight_chicken - ssqrt(SIGMA(1, 1)), meanWeight_chicken+ssqrt(SIGMA(1, 1)),res)',linspace(meanHeight_chicken-ssqrt(SIGMA(2, 2)),meanHeight_chicken+ssqrt(SIGMA(2, 2)),res)');\n", "X = [X1(:) X2(:)];\n", "p = mvnpdf(X,mu,SIGMA);\n", "subplot(2, 1, 2);\n", "surf(X1,X2,reshape(p,res,res));\n", "hold on;\n", "X = [60, 5];\n", "p_goose = mvnpdf(X,mu,SIGMA);\n", "plot3(X(1), X(2), p_goose, 'r', 'lineWidth', 2);\n", "title(['Goose pdf at point = ' num2str(mvnpdf(X,mu,SIGMA)100)])\n", "axis tight\n", "view(45, 45)\n", "disp(['Probability of being a goose is ' num2str(p_goose/(p_goose+p_chicken))]);\n", "\n", "% Top Views\n", "\n", "figure(2)\n", "\n", "\n", "% Chicken?\n", "meanWeight_chicken = 54;\n", "meanHeight_chicken = 5;\n", "mu = [meanWeight_chicken, meanHeight_chicken]; \n", "SIGMA = [5 .1; .1 .5]; \n", "[X1,X2] = meshgrid(linspace(meanWeight_chicken - ssqrt(SIGMA(1, 1)), meanWeight_chicken+ssqrt(SIGMA(1, 1)),res)',linspace(meanHeight_chicken-ssqrt(SIGMA(2, 2)),meanHeight_chicken+ssqrt(SIGMA(2, 2)),res)');\n", "X = [X1(:) X2(:)];\n", "p = mvnpdf(X,mu,SIGMA);\n", "subplot(2, 1, 1);\n", "surf(X1,X2,reshape(p,res,res));\n", "hold on;\n", "X = [60, 5];\n", "p_chicken = mvnpdf(X,mu,SIGMA);\n", "plot3(X(1), X(2), p_chicken, 'r');\n", "title(['Chicken pdf at point = ' num2str(mvnpdf(X,mu,SIGMA)100)])\n", "view(0, 90)\n", "axis tight\n", "\n", "% Goose?\n", "meanWeight_goose = 65;\n", "meanHeight_goose = 6;\n", "mu = [meanWeight_chicken, meanHeight_chicken]; \n", "SIGMA = [8 .2; .2 1]; \n", "[X1,X2] = meshgrid(linspace(meanWeight_chicken - ssqrt(SIGMA(1, 1)), meanWeight_chicken+ssqrt(SIGMA(1, 1)),res)',linspace(meanHeight_chicken-ssqrt(SIGMA(2, 2)),meanHeight_chicken+ssqrt(SIGMA(2, 2)),res)');\n", "X = [X1(:) X2(:)];\n", "p = mvnpdf(X,mu,SIGMA);\n", "subplot(2, 1, 2);\n", "surf(X1,X2,reshape(p,res,res));\n", "hold on;\n", "X = [60, 5];\n", "p_goose = mvnpdf(X,mu,SIGMA);\n", "plot3(X(1), X(2), p_goose, 'r');\n", "title(['Goose pdf at point = ' num2str(mvnpdf(X,mu,SIGMA)100)])\n", "view(0, 90)\n", "axis tight" ] }, { "cell_type": "markdown", "metadata": { "lang": "en" }, "source": [ "## 6. Download the data from the dataset that you will find in https://archive.ics.uci.edu/ml/datasets/Iris and build a Multivariate Gaussian Mixture Model. Use it to classify the following unlabeled samples:\n", "\n", "\n", "\| Sepal length \| Petal length \| Sepal width \| Petal width \|\n", "\|--------------\|--------------\|-------------\|-------------\|\n", "\| 4,9 \| 3,2 \| 1,7 \| 0,2 \|\n", "\| 5 \| 3,2 \| 1,6 \| 0,5 \|\n", "\| 5,5 \| 2,8 \| 3,6 \| 1,3 \|\n", "\| 7,1 \| 3,1 \| 6,1 \| 1,7 \|\n", "\n", "\n", "\n", "Gaussian Distribution Formula:\n", "\n", "$$\n", "\\mathcal{N}(\\boldsymbol\\mu,\\,\\boldsymbol\\Sigma) \\sim \\operatorname{det}(2\\pi\\boldsymbol\\Sigma)^{-\\frac{1}{2}} \\, e^{ -\\frac{1}{2}(\\mathbf{x} - \\boldsymbol\\mu)'\\boldsymbol\\Sigma^{-1}(\\mathbf{x} - \\boldsymbol\\mu) }\n", "$$" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "lang": "en", "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading samples...\n", "The sample 1 is a setosa. Confidence: 100 %\n", "The sample 2 is a setosa. Confidence: 100 %\n", "The sample 3 is a versicolor. Confidence: 99.9972 %\n", "The sample 4 is a virginica. Confidence: 99.9289 %\n", "\n" ] } ], "source": [ "clearvars;\n", "format short;\n", "disp('Reading samples...');\n", "samples = [4.9, 3.2, 1.7, 0.2; 5, 3.2, 1.6, 0.5; 5.5, 2.8, 3.6, 1.3; 7.1, 3.1, 6.1, 1.7]; % Imaginary Samples\n", "\n", "% Labels\n", "% 1: Setosa\n", "% 2: Versicolor\n", "% 3: Virginica\n", "\n", "addpath('dataset');\n", "dataset = csvread('data.csv');\n", "labels = [\"setosa\", \"versicolor\", \"virginica\"];\n", "% Clean the dataset\n", "\n", "sepal_length = dataset(:, 1);\n", "petal_length = dataset(:, 2);\n", "sepal_width = dataset(:, 3);\n", "petal_width = dataset(:, 4);\n", "classId = dataset(:, 5);\n", "X = [sepal_length, petal_length, sepal_width, petal_width]; % Features\n", "y = classId; % Variable we want to predict\n", "\n", "% Extract features from each class\n", "\n", "X_setosa = X(find(y(:) == 1), :);\n", "X_versicolor = X(find(y(:) == 2), :);\n", "X_virginica = X(find(y(:) == 3), :);\n", "\n", "% Calculate cov. matrices\n", "\n", "setosa_covMat = cov(X_setosa);\n", "versicolor_covMat = cov(X_versicolor);\n", "virginica_covMat = cov(X_virginica);\n", "\n", "% Extract the means\n", "\n", "setosa_mean = mean(X_setosa, 1);\n", "versicolor_mean = mean(X_versicolor, 1);\n", "virginica_mean = mean(X_virginica, 1);\n", "\n", "% Calculate the pdf of the samples\n", "\n", "setosa_pdf = mvnpdf(samples, setosa_mean, setosa_covMat);\n", "versicolor_pdf = mvnpdf(samples, versicolor_mean, versicolor_covMat);\n", "virginica_pdf = mvnpdf(samples, virginica_mean, virginica_covMat);\n", "\n", "% Probability of the samples being setosa\n", "prob_setosa = (setosa_pdf)./(setosa_pdf + versicolor_pdf + virginica_pdf);\n", "\n", "% Probability of the samples being setosa\n", "prob_versicolor = (versicolor_pdf)./(setosa_pdf + versicolor_pdf + virginica_pdf);\n", "\n", "% Probability of the samples being setosa\n", "prob_virginica = (virginica_pdf)./(setosa_pdf + versicolor_pdf + virginica_pdf);\n", "\n", "matProbabilities = [prob_setosa, prob_versicolor, prob_virginica];\n", "\n", "for i=1:size(matProbabilities, 1)\n", " [value, idx] = max(matProbabilities(i, :));\n", " disp(['The sample ', num2str(i), ' is a ', char(labels(idx)), '. Confidence: ', num2str(value100), ' %']);\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Suppose that we have a set of 2D samples: [0, 0], [1, 1], [2, 3], [3, 2] and [4, 4].\n", "### (a) Draw the data and compute the covariance matrix.\n", "### (b) Apply PCA over these samples and find the basis where the data have the maximum variance. To do so, find the eigenvalues and eigenvectors of the data covariance matrix. Draw these basis in the same figure than the previous exercise.\n", "\n", "### (c) Project the data over the obtained basis with PCA. Discuss the relation existing between the covariance of the projected data and the eigenvalues computed in b).\n", "### (d) Project and re-project the data using only the basis of maximum variance. Draw the results over the original data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Covariance Formula:\n", "\n", "$C_v = \\frac{1}{N-1}\\left(X-\\mu\\right)\\left(X-\\mu\\right)^{T}$" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. Draw the data\n", "2. Compute the covariance matrix\n", "\n", "covarMat =\n", "\n", " 2.5000 2.2500\n", " 2.2500 2.5000\n", "\n", "3. Apply PCA and find the basis:\n", "\n", "eigenVector =\n", "\n", " -0.7071 0.7071\n", " 0.7071 0.7071\n", "\n", "\n", "eigenValue =\n", "\n", " 0.2500 0\n", " 0 4.7500\n", "\n", "We take the vector with the highest eigenValue.\n", "Highest Eigenvalue: 4.75\n", "Max. var. Eigenvector / new basis: [0.70711 0.70711]\n", "\n", "dimReduction =\n", "\n", " 0 1.4142 3.5355 3.5355 5.6569\n", "\n", "\n", "projected_X =\n", "\n", " 0 1.0000 2.5000 2.5000 4.0000\n", " 0 1.0000 2.5000 2.5000 4.0000\n", "\n", "\n" ] }, { "data": { "image/png": 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jfH19p0+fvn79eg8Pj02bNhnuCggIWLNmTUBAABH5+fkNGzYsJyeHpzB5Y1jT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\nhh49epTfmBmFQiGoWXaMUVSCvYxXrlwJDg7OzMy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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "clearvars;\n", "dataset = [0,0; 1,1; 2,3;3,2; 4,4]';\n", "disp('1. Draw the data');\n", "plot(dataset(1, :), dataset(2, :),'bo','MarkerSize', 10, 'lineWidth', 2);\n", "\n", "disp('2. Compute the covariance matrix');\n", "mu = mean(dataset, 2);\n", "covarMat = ((dataset - mu) (dataset - mu)')./(size(dataset, 2)-1)\n", "\n", "% apply PCA and find the basis where the data has the maximum variance\n", "\n", "% Find the eigenvalues and vectors to do so\n", "disp('3. Apply PCA and find the basis:');\n", "[eigenVector, eigenValue] = eig(covarMat)\n", "\n", "disp('We take the vector with the highest eigenValue.');\n", "[value, idx] = max(eigenValue);\n", "[value, idx] = max(value);\n", "\n", "disp(['Highest Eigenvalue: ', num2str(value)]);\n", "eigenVector_reduced = eigenVector(:, idx);\n", "disp(['Max. var. Eigenvector / new basis: [' num2str(eigenVector_reduced'), ']']);\n", "\n", "dimReduction = eigenVector_reduced' * dataset\n", "\n", "projected_X = eigenVector_reduced * dimReduction\n", "hold on;\n", "plot(projected_X(1, :), projected_X(2, :), 'r+', 'MarkerSize', 14, 'lineWidth', 2);\n", "hor_axis_helper = min(dataset(1, :)):0.1:max(dataset(2, :));\n", "quiver(0, 0, eigenVector_reduced(1), eigenVector_reduced(2), 1, 'maxHeadSize', 1)\n", "plot(hor_axis_helper, hor_axis_helpereigenVector_reduced(2)/eigenVector_reduced(1));\n", "\n", "title('Projection of datapoints using PCA');\n", "legend('Datapoints', 'Projected Datapoints', 'New Basis', 'Projection Axis', 'location', 'northwest');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. Repeat the previous exercise with the Cartesian samples [0, 1], [0, -1], [1, 0], [-1, 0] but using cylindrical coordinates (you first have to transform the data to cylindrical coordinates)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Matlab Demo" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "dataset =\n", "\n", " 0 0 1 -1\n", " 1 -1 0 0\n", "\n", "We go from cartesian to polar...\n", "\n", "dataset =\n", "\n", " 1.5708 -1.5708 0 3.1416\n", " 1.0000 1.0000 1.0000 1.0000\n", "\n", "\n", "covariance_mat =\n", "\n", " 4.1123 0\n", " 0 0\n", "\n", "\n", "eigenVector =\n", "\n", " 0 1\n", " 1 0\n", "\n", "\n", "eigenValue =\n", "\n", " 0 0\n", " 0 4.1123\n", "\n", "We take the vector (in our new polar space) with the highest eigenValue.\n", "Highest Eigenvalue: 4.1123\n", "Max. var. Eigenvector / new basis: [1 0]\n", "\n", "z =\n", "\n", " 1.5708 -1.5708 0 3.1416\n", "\n", "\n", "projected_data =\n", "\n", " 1.5708 -1.5708 0 3.1416\n", " 0 0 0 0\n", "\n", "\n" ] }, { "data": { "image/png": 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7/ab6XXCAhY5oo9tCEKchQeoC7ERMspQEQR8dVsJOni7CQm2SJBnStjwHbdaE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\nsCzgemSTQy+ugTckkkUq8aFDhz766KM33nhjZWXlJ598Mmf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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "clearvars;\n", "% Load the data\n", "dataset = [0, 0, 1, -1; 1, -1, 0, 0]\n", "\n", "% Convert to polar coordinates\n", "disp('We go from cartesian to polar...');\n", "[theta radius] = cart2pol(dataset(1, :), dataset(2, :));\n", "dataset = [theta; radius]\n", "\n", "% Plotting\n", "figure(1);\n", "polarplot(dataset(1, :), dataset(2, :),'bo','MarkerSize', 10, 'lineWidth', 2);\n", "hold on;\n", "rlim([0, 1.25]);\n", "title('PCA with polar coordinates using raw dataset')\n", "\n", "% Calculate covariance matrix\n", "covariance_mat = cov(dataset')\n", "\n", "% Calculate eigenvectors\n", "[eigenVector eigenValue] = eig(covariance_mat)\n", "\n", "% Take the most important eigenvectors\n", "disp('We take the vector (in our new polar space) with the highest eigenValue.');\n", "[value, idx] = max(eigenValue);\n", "[value, idx] = max(value);\n", "disp(['Highest Eigenvalue: ', num2str(value)]);\n", "eigenVector_reduced = eigenVector(:, idx);\n", "disp(['Max. var. Eigenvector / new basis: [' num2str(eigenVector_reduced'), ']']);\n", "\n", "% Calculate the new values in the new basis\n", "z = eigenVector_reduced' dataset\n", "\n", "% Reproject the data\n", "projected_data = eigenVector_reduced * z\n", "polarplot(projected_data(1, :), projected_data(2, :),'rx','MarkerSize', 14, 'lineWidth', 2);\n", "legend('2D data', '1D data');" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "dataset =\n", "\n", " 0 0 1 -1\n", " 1 -1 0 0\n", "\n", "We go from cartesian to polar...SUBSTRACTING MEAN!\n", "\n", "dataset =\n", "\n", " 0.7854 -2.3562 -0.7854 2.3562\n", " 0 0 0 0\n", "\n", "\n", "covariance_mat =\n", "\n", " 4.1123 0\n", " 0 0\n", "\n", "\n", "eigenVector =\n", "\n", " 0 1\n", " 1 0\n", "\n", "\n", "eigenValue =\n", "\n", " 0 0\n", " 0 4.1123\n", "\n", "We take the vector (in our new polar space) with the highest eigenValue.\n", "Highest Eigenvalue: 4.1123\n", "Max. var. Eigenvector / new basis: [1 0]\n", "\n", "z =\n", "\n", " 0.7854 -2.3562 -0.7854 2.3562\n", "\n", "# We add the mean to the projected data!\n", "\n", "projected_data =\n", "\n", " 1.5708 -1.5708 0 3.1416\n", " 1.0000 1.0000 1.0000 1.0000\n", "\n", "\n" ] }, { "data": { "image/png": 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QGMxgyjb2IalCOU+YlBp1fxJEVpiuP7IWmF3NwLwJ6kPAWT49l8tl2/qzesIS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\nJEmanp7+8eOHxWIRRVGSJH+9ckCJosg+Pc5JkiSKosVisVgs09PT/vq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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "clearvars;\n", "dataset = [0, 0, 1, -1; 1, -1, 0, 0]\n", "disp('We go from cartesian to polar...SUBSTRACTING MEAN!');\n", "[theta radius] = cart2pol(dataset(1, :), dataset(2, :));\n", "dataset = [theta; radius];\n", "mu = mean(dataset, 2);\n", "dataset = dataset - mu\n", "\n", "% Calculate cov. mat.\n", "covariance_mat = cov(dataset')\n", "\n", "% Calculate Eigens\n", "\n", "[eigenVector eigenValue] = eig(covariance_mat)\n", "\n", "disp('We take the vector (in our new polar space) with the highest eigenValue.');\n", "[value, idx] = max(eigenValue);\n", "[value, idx] = max(value);\n", "\n", "disp(['Highest Eigenvalue: ', num2str(value)]);\n", "eigenVector_reduced = eigenVector(:, idx);\n", "disp(['Max. var. Eigenvector / new basis: [' num2str(eigenVector_reduced'), ']']);\n", "\n", "% Find new values on new basis\n", "z = eigenVector_reduced' * dataset\n", "\n", "% Add the mean again...\n", "disp('# We add the mean to the projected data!');\n", "projected_data = eigenVector_reduced * z;\n", "projected_data = projected_data + mu\n", "\n", "\n", "% Plots\n", "figure(1);\n", "polarplot(dataset(1, :) + mu(1), dataset(2, :) + mu(2),'bo','MarkerSize', 10, 'lineWidth', 2);\n", "rlim([0, 1.25]);\n", "title('PCA with polar coordinates substracting \\mu');\n", "hold on;\n", "polarplot(projected_data(1, :), projected_data(2, :),'rx','MarkerSize', 14, 'lineWidth', 2);\n", "legend('2D data', '1D data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 9. Apply PCA to the data from exercise 6 and classify again the unlabeled samples using the Multivariate Gaussian Mixture Model with the two more significant “new attributes” found with the PCA." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Samples:\n", "\n", "\| Sepal length \| Petal length \| Sepal width \| Petal width \|\n", "\|--------------\|--------------\|-------------\|-------------\|\n", "\| 4,9 \| 3,2 \| 1,7 \| 0,2 \|\n", "\| 5 \| 3,2 \| 1,6 \| 0,5 \|\n", "\| 5,5 \| 2,8 \| 3,6 \| 1,3 \|\n", "\| 7,1 \| 3,1 \| 6,1 \| 1,7 \|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dimensionality Reduction using PCA\n", "\n", "DATASET: `data`\n", "\n", "Dimension: $M$, integer.\n", "\n", "Features: $X$, subset of `data`\n", "\n", "Class: $y$, subset of `data`\n", "\n", "Steps to follow:\n", "1. Normalize features from `data` using mean normalization, obtaining Z.\n", "2. Calculate the covariance matrix of the newly obtained Z values.\n", "3. Using single values decomposition or eigen decomposition, extract the eigenvectors and eigenvalues, obtaining U and V respectively.\n", "4. Keep the N most significant eigenvalues and eigenvectors. Reminder, the eigenvalues represent how significant an eigenvector or direction is.\n", "5. Reproject the data onto the new basis. Your dataset dimension is now N." ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading samples...\n", "Applying PCA...\n", "Using 2 out of 4 features...\n", "97.7632% variability retained\n", "The sample 1 is a setosa. Confidence: 100 %\n", "The sample 2 is a setosa. Confidence: 100 %\n", "The sample 3 is a versicolor. Confidence: 99.9823 %\n", "The sample 4 is a virginica. Confidence: 99.9457 %\n", "\n" ] } ], "source": [ "clearvars;\n", "format short;\n", "disp('Reading samples...');\n", "samples = [4.9, 3.2, 1.7, 0.2; 5, 3.2, 1.6, 0.5; 5.5, 2.8, 3.6, 1.3; 7.1, 3.1, 6.1, 1.7]; % Imaginary Samples\n", "\n", "% Labels\n", "% 1: Setosa\n", "% 2: Versicolor\n", "% 3: Virginica\n", "\n", "addpath('dataset');\n", "dataset = csvread('data.csv');\n", "labels = [\"setosa\", \"versicolor\", \"virginica\"];\n", "% Clean the dataset\n", "\n", "sepal_length = dataset(:, 1);\n", "petal_length = dataset(:, 2);\n", "sepal_width = dataset(:, 3);\n", "petal_width = dataset(:, 4);\n", "classId = dataset(:, 5);\n", "X = [sepal_length, petal_length, sepal_width, petal_width]; % Features\n", "y = classId; % Variable we want to predict\n", "\n", "disp('Applying PCA...');\n", "%% Apply PCA to matrix X before extraction of features for each class\n", "%====================================================================\n", "\n", "% Generating covariance matrix\n", "X_cov = cov(X);\n", "\n", "% Generating the means for each feature\n", "X_mu = mean(X, 1);\n", "\n", "%% Extracting eigens\n", "% U: Eigenvectors\n", "% V: Eigenvalues\n", "% Find the highest eigenvalues using Matlab's implementation of the singular value decomposition function\n", "%[U, V, W] = svd(X_cov)\n", "% For learning purposes, I decided to use eig\n", "[U, V] = eig(X_cov);\n", "\n", "% Find the highest N features.\n", "NUMBER_FEATURES = 2;\n", "V = sum(V, 2); % Transform matrix into vector of eigenvalues\n", "totalVar = sum(V);\n", "[V, idx] = sort(V, 'descend'); % Sort the matrix\n", "disp(['Using ' num2str(NUMBER_FEATURES) ' out of ' num2str(size(X, 2)) ' features...']);\n", "V = V(1:NUMBER_FEATURES);\n", "pickedVar= sum(V); % Store the variability\n", "disp([num2str(100pickedVar/totalVar), '% variability retained']);\n", "U = U(:, idx(1:NUMBER_FEATURES));\n", "\n", "%% Reproject the data onto X_proj\n", "% Find the values projection on the new basis\n", "T = X U;\n", "\n", "% Dimension-reduced data: X_proj\n", "X_proj = T * U';\n", "\n", "% PCA Finished! -> New dataset -> T\n", "%====================================================================\n", "\n", "% Extract features from each class\n", "\n", "X_setosa = T(find(y(:) == 1), :);\n", "X_versicolor = T(find(y(:) == 2), :);\n", "X_virginica = T(find(y(:) == 3), :);\n", "\n", "% Calculate cov. matrices\n", "\n", "setosa_covMat = cov(X_setosa);\n", "versicolor_covMat = cov(X_versicolor);\n", "virginica_covMat = cov(X_virginica);\n", "\n", "% Extract the means\n", "\n", "setosa_mean = mean(X_setosa, 1);\n", "versicolor_mean = mean(X_versicolor, 1);\n", "virginica_mean = mean(X_virginica, 1);\n", "\n", "% Calculate the pdf of the samples\n", "% But convert samples to new space before\n", "samples = samples * U;\n", "\n", "setosa_pdf = mvnpdf(samples, setosa_mean, setosa_covMat);\n", "versicolor_pdf = mvnpdf(samples, versicolor_mean, versicolor_covMat);\n", "virginica_pdf = mvnpdf(samples, virginica_mean, virginica_covMat);\n", "\n", "% Probability of the samples being setosa\n", "prob_setosa = (setosa_pdf)./(setosa_pdf + versicolor_pdf + virginica_pdf);\n", "\n", "% Probability of the samples being setosa\n", "prob_versicolor = (versicolor_pdf)./(setosa_pdf + versicolor_pdf + virginica_pdf);\n", "\n", "% Probability of the samples being setosa\n", "prob_virginica = (virginica_pdf)./(setosa_pdf + versicolor_pdf + virginica_pdf);\n", "\n", "matProbabilities = [prob_setosa, prob_versicolor, prob_virginica];\n", "\n", "for i=1:size(matProbabilities, 1)\n", " [value, idx] = max(matProbabilities(i, :));\n", " disp(['The sample ', num2str(i), ' is a ', char(labels(idx)), '. Confidence: ', num2str(value*100), ' %']);\n", "end" ] } ], "metadata": { "kernelspec": { "display_name": "Matlab", "language": "matlab", "name": "matlab" }, "language_info": { "codemirror_mode": "octave", "file_extension": ".m", "help_links": [ { "text": "MetaKernel Magics", "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md" } ], "mimetype": "text/x-matlab", "name": "matlab", "version": "0.14.3" }, "nbTranslate": { "displayLangs": [ "es" ], "hotkey": "alt-t", "langInMainMenu": true, "sourceLang": "en", "targetLang": "es", "useGoogleTranslate": true }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	mit
jrbourbeau/cr-composition	notebooks/legacy/fraction-correct-SGD.ipynb	1	412129	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SGD fraction correct analysis\n", "### Table of contents\n", "1. [Data preprocessing](#Data-preprocessing)\n", "2. [Fitting classifier](#Fit-random-forest-and-run-10-fold-CV-validation)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Added to PYTHONPATH\n" ] } ], "source": [ "import sys\n", "sys.path.append('/home/jbourbeau/cr-composition')\n", "print('Added to PYTHONPATH')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/.local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "import argparse\n", "from collections import defaultdict\n", "import itertools\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import ListedColormap\n", "import seaborn.apionly as sns\n", "\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.model_selection import cross_val_score, StratifiedShuffleSplit, KFold, GridSearchCV\n", "\n", "import composition as comp\n", "import composition.analysis.plotting as plotting\n", "\n", "# Plotting-related\n", "sns.set_palette('muted')\n", "sns.set_color_codes()\n", "color_dict = defaultdict()\n", "for i, composition in enumerate(['P', 'He', 'O', 'Fe', 'total']):\n", " color_dict[composition] = sns.color_palette('muted').as_hex()[i]\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data preprocessing\n", "1. Load simulation dataframe and apply specified quality cuts\n", "2. Extract desired features from dataframe\n", "3. Get separate testing and training datasets" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/cr-composition/composition/load_sim.py:109: RuntimeWarning: divide by zero encountered in log10\n", " df['log_NChannels_1_30'] = np.nan_to_num(np.log10(df['NChannels_1_30']))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "training features = ['lap_log_energy', 'InIce_log_charge_1_30', 'lap_cos_zenith', 'NChannels_1_30', 'log_s125', 'StationDensity']\n", "number training events = 145543\n", "number testing events = 62376\n" ] } ], "source": [ "df, cut_dict = comp.load_sim(return_cut_dict=True)\n", "selection_mask = np.array([True] * len(df))\n", "standard_cut_keys = ['lap_reco_success', 'lap_zenith', 'num_hits_1_30', 'IT_signal',\n", " 'max_qfrac_1_30', 'lap_containment', 'energy_range_lap']\n", "for key in standard_cut_keys:\n", " selection_mask = cut_dict[key]\n", "\n", "df = df[selection_mask]\n", "\n", "feature_list, feature_labels = comp.get_training_features()\n", "print('training features = {}'.format(feature_list))\n", "X_train, X_test, y_train, y_test, le = comp.get_train_test_sets(\n", " df, feature_list, train_he=True, test_he=True)\n", "\n", "print('number training events = ' + str(y_train.shape[0]))\n", "print('number testing events = ' + str(y_test.shape[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grid search to find optimal hyperparameters" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 10 folds for each of 20 candidates, totalling 200 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=20)]: Done 160 tasks \| elapsed: 11.8s\n", "[Parallel(n_jobs=20)]: Done 200 out of 200 \| elapsed: 13.0s finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "best GS CV score = 0.400410875137\n", "best GS CV depths = {'classifier__loss': 'log', 'classifier__penalty': 'none'}\n", "Grid scores on development set:\n", "0.391 (+/-0.015) for {'classifier__loss': 'hinge', 'classifier__penalty': 'none'}\n", "0.392 (+/-0.014) for {'classifier__loss': 'hinge', 'classifier__penalty': 'l2'}\n", "0.393 (+/-0.014) for {'classifier__loss': 'hinge', 'classifier__penalty': 'l1'}\n", "0.394 (+/-0.016) for {'classifier__loss': 'hinge', 'classifier__penalty': 'elasticnet'}\n", "0.400 (+/-0.012) for {'classifier__loss': 'log', 'classifier__penalty': 'none'}\n", "0.400 (+/-0.010) for {'classifier__loss': 'log', 'classifier__penalty': 'l2'}\n", "0.400 (+/-0.008) for {'classifier__loss': 'log', 'classifier__penalty': 'l1'}\n", "0.400 (+/-0.008) for {'classifier__loss': 'log', 'classifier__penalty': 'elasticnet'}\n", "0.374 (+/-0.021) for {'classifier__loss': 'modified_huber', 'classifier__penalty': 'none'}\n", "0.374 (+/-0.020) for {'classifier__loss': 'modified_huber', 'classifier__penalty': 'l2'}\n", "0.383 (+/-0.023) for {'classifier__loss': 'modified_huber', 'classifier__penalty': 'l1'}\n", "0.379 (+/-0.027) for {'classifier__loss': 'modified_huber', 'classifier__penalty': 'elasticnet'}\n", "0.312 (+/-0.034) for {'classifier__loss': 'squared_hinge', 'classifier__penalty': 'none'}\n", "0.300 (+/-0.020) for {'classifier__loss': 'squared_hinge', 'classifier__penalty': 'l2'}\n", "0.307 (+/-0.031) for {'classifier__loss': 'squared_hinge', 'classifier__penalty': 'l1'}\n", "0.298 (+/-0.020) for {'classifier__loss': 'squared_hinge', 'classifier__penalty': 'elasticnet'}\n", "0.320 (+/-0.035) for {'classifier__loss': 'perceptron', 'classifier__penalty': 'none'}\n", "0.318 (+/-0.043) for {'classifier__loss': 'perceptron', 'classifier__penalty': 'l2'}\n", "0.324 (+/-0.054) for {'classifier__loss': 'perceptron', 'classifier__penalty': 'l1'}\n", "0.321 (+/-0.031) for {'classifier__loss': 'perceptron', 'classifier__penalty': 'elasticnet'}\n" ] } ], "source": [ "pipeline = comp.get_pipeline('SGD')\n", "param_grid = {'classifier__loss': ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron'],\n", " 'classifier__penalty': ['none', 'l2', 'l1', 'elasticnet']}\n", "gs = GridSearchCV(estimator=pipeline,\n", " param_grid=param_grid,\n", " scoring='accuracy',\n", " cv=10,\n", " verbose=1,\n", " n_jobs=20)\n", "gs = gs.fit(X_train, y_train)\n", "print('best GS CV score = {}'.format(gs.best_score_))\n", "print('best GS CV depths = {}'.format(gs.best_params_))\n", "print('Grid scores on development set:')\n", "means = gs.cv_results_['mean_test_score']\n", "stds = gs.cv_results_['std_test_score']\n", "for mean, std, params in zip(means, stds, gs.cv_results_['params']):\n", " print(\"%0.3f (+/-%0.03f) for %r\"\n", " % (mean, std 2, params))\n", "pipeline.set_params(*gs.best_params_)\n", "pipeline.fit(X_train, y_train)\n", "scaler = pipeline.named_steps['scaler']\n", "clf = pipeline.named_steps['classifier']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit random forest and run 10-fold CV validation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==============================\n", "SGDClassifier\n", "CV score: 40.04% (+/- 0.58%)\n", "==============================\n" ] } ], "source": [ "pipeline = comp.get_pipeline('SGD')\n", "clf_name = pipeline.named_steps['classifier'].__class__.__name__\n", "print('=' 30)\n", "print(clf_name)\n", "scores = cross_val_score(\n", " estimator=pipeline, X=X_train, y=y_train, cv=10, n_jobs=20)\n", "print('CV score: {:.2%} (+/- {:.2%})'.format(scores.mean(), scores.std()))\n", "print('=' * 30)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_frac_correct(X_train, X_test, y_train, y_test, comp_list):\n", " \n", " pipeline = comp.get_pipeline('SGD')\n", " pipeline.fit(X_train, y_train)\n", " test_predictions = pipeline.predict(X_test)\n", " correctly_identified_mask = (test_predictions == y_test)\n", "\n", " # Energy-related variables\n", " energy_bin_width = 0.1\n", " energy_bins = np.arange(6.2, 8.1, energy_bin_width)\n", " energy_midpoints = (energy_bins[1:] + energy_bins[:-1]) / 2\n", " log_energy = X_test[:, 0]\n", "\n", " # Construct MC composition masks\n", " MC_comp_mask = {}\n", " for composition in comp_list:\n", " MC_comp_mask[composition] = (le.inverse_transform(y_test) == composition)\n", "\n", " # Get number of MC comp in each reco energy bin\n", " num_MC_energy, num_MC_energy_err = {}, {}\n", " for composition in comp_list:\n", " num_MC_energy[composition] = np.histogram(log_energy[MC_comp_mask[composition]],\n", " bins=energy_bins)[0]\n", " num_MC_energy_err[composition] = np.sqrt(num_MC_energy[composition])\n", "\n", " num_MC_energy['total'] = np.histogram(log_energy, bins=energy_bins)[0]\n", " num_MC_energy_err['total'] = np.sqrt(num_MC_energy['total'])\n", "\n", "\n", " # Get number of correctly identified comp in each reco energy bin\n", " num_reco_energy, num_reco_energy_err = {}, {}\n", " for composition in comp_list:\n", " num_reco_energy[composition] = np.histogram(\n", " log_energy[MC_comp_mask[composition] & correctly_identified_mask],\n", " bins=energy_bins)[0]\n", " num_reco_energy_err[composition] = np.sqrt(num_reco_energy[composition])\n", "\n", " num_reco_energy['total'] = np.histogram(log_energy[correctly_identified_mask], bins=energy_bins)[0]\n", " num_reco_energy_err['total'] = np.sqrt(num_reco_energy['total'])\n", "\n", " # Calculate correctly identified fractions as a function of MC energy\n", " reco_frac, reco_frac_err = {}, {}\n", " for composition in comp_list:\n", "# print(composition)\n", " reco_frac[composition], reco_frac_err[composition] = comp.ratio_error(\n", " num_reco_energy[composition], num_reco_energy_err[composition],\n", " num_MC_energy[composition], num_MC_energy_err[composition])\n", " frac_correct_folds[composition].append(reco_frac[composition])\n", "\n", " reco_frac['total'], reco_frac_err['total'] = comp.ratio_error(\n", " num_reco_energy['total'], num_reco_energy_err['total'],\n", " num_MC_energy['total'], num_MC_energy_err['total'])\n", " \n", " return reco_frac, reco_frac_err" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute systematic in fraction correct via CV" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/cr-composition/composition/analysis/data_functions.py:11: RuntimeWarning: invalid value encountered in true_divide\n", " ratio_err = ratio * np.sqrt((num_err / num)2 + (den_err / den)2)\n" ] } ], "source": [ "comp_list = ['P', 'He', 'O', 'Fe']\n", "# Split data into training and test samples\n", "kf = KFold(n_splits=10)\n", "frac_correct_folds = defaultdict(list)\n", "for train_index, test_index in kf.split(X_train):\n", " X_train_fold, X_test_fold = X_train[train_index], X_train[test_index]\n", " y_train_fold, y_test_fold = y_train[train_index], y_train[test_index]\n", " \n", " reco_frac, reco_frac_err = get_frac_correct(X_train_fold, X_test_fold,\n", " y_train_fold, y_test_fold,\n", " comp_list)\n", " for composition in comp_list:\n", " frac_correct_folds[composition].append(reco_frac[composition])\n", " frac_correct_folds['total'].append(reco_frac['total'])\n", "frac_correct_sys_err = {key: np.std(frac_correct_folds[key], axis=0) for key in frac_correct_folds}" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "$\\mathrm{\\underline{CV \\ score}}$:\n", "40.04\\% (+/- 0.58\\%)\n" ] }, { "data": { "image/png": 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LU1Inktfa2irDMOIaPnwh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odPeaS0tFTrEAjr1dUTExNoaWlBWVlZxuvJsryLUeWviooKmM1mGAwGNXFeUVGB7u5uvP322xgd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"text/plain": [ "<matplotlib.figure.Figure at 0x7fdd8f0f18d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reco_frac, reco_frac_sterr = get_frac_correct(X_train, X_test,\n", " y_train, y_test,\n", " comp_list)\n", "# Energy-related variables\n", "energy_bin_width = 0.1\n", "energy_bins = np.arange(6.2, 8.1, energy_bin_width)\n", "energy_midpoints = (energy_bins[1:] + energy_bins[:-1]) / 2\n", "step_x = energy_midpoints\n", "step_x = np.append(step_x[0]-energy_bin_width/2, step_x)\n", "step_x = np.append(step_x, step_x[-1]+energy_bin_width/2)\n", "# Plot fraction of events vs energy\n", "def plot_steps(x, y, y_err, ax, color, label):\n", " step_x = x\n", " x_widths = x[1:]-x[:-1]\n", " if len(np.unique(x_widths)) != 1:\n", " raise('Unequal bins...')\n", " x_width = np.unique(x_widths)[0]\n", " step_x = np.append(step_x[0]-x_width/2, step_x)\n", " step_x = np.append(step_x, step_x[-1]+x_width/2)\n", " \n", " step_y = y\n", " step_y = np.append(step_y[0], step_y)\n", " step_y = np.append(step_y, step_y[-1])\n", " \n", " err_upper = y + y_err\n", " err_upper = np.append(err_upper[0], err_upper)\n", " err_upper = np.append(err_upper, err_upper[-1])\n", " err_lower = y - y_err\n", " err_lower = np.append(err_lower[0], err_lower)\n", " err_lower = np.append(err_lower, err_lower[-1])\n", " \n", " ax.step(step_x, step_y, where='mid',\n", " marker=None, color=color, linewidth=1,\n", " linestyle='-', label=label, alpha=0.8)\n", " ax.fill_between(step_x, err_upper, err_lower,\n", " alpha=0.15, color=color,\n", " step='mid', linewidth=1)\n", " \n", " return step_x, step_y\n", "\n", "fig, ax = plt.subplots()\n", "for composition in comp_list + ['total']:\n", " err = np.sqrt(frac_correct_sys_err[composition]2+reco_frac_sterr[composition]2)\n", " plot_steps(energy_midpoints, reco_frac[composition], err, ax, color_dict[composition], composition)\n", "plt.xlabel('$\\log_{10}(E_{\\mathrm{reco}}/\\mathrm{GeV})$')\n", "ax.set_ylabel('Fraction correctly identified')\n", "ax.set_ylim([0.0, 1.0])\n", "ax.set_xlim([6.2, 8.0])\n", "ax.grid()\n", "leg = plt.legend(loc='upper center', \n", " bbox_to_anchor=(0.5, # horizontal\n", " 1.1),# vertical \n", " ncol=len(comp_list)+1, fancybox=False)\n", "# set the linewidth of each legend object\n", "for legobj in leg.legendHandles:\n", " legobj.set_linewidth(3.0)\n", "\n", "# place a text box in upper left in axes coords\n", "textstr = '$\\mathrm{\\underline{Training \\ features}}$: \\n'\n", "for i, label in enumerate(feature_labels):\n", " if (i == len(feature_labels)-1):\n", " textstr += '{}) '.format(i+1) + label\n", " else:\n", " textstr += '{}) '.format(i+1) + label + '\\n'\n", "props = dict(facecolor='white', linewidth=0)\n", "ax.text(1.025, 0.855, textstr, transform=ax.transAxes, fontsize=8,\n", " verticalalignment='top', bbox=props)\n", "cvstr = '$\\mathrm{\\underline{CV \\ score}}$:\\n' + '{:0.2f}\\% (+/- {:.2}\\%)'.format(scores.mean()100, scores.std()100)\n", "print(cvstr)\n", "props = dict(facecolor='white', linewidth=0)\n", "ax.text(1.025, 0.9825, cvstr, transform=ax.transAxes, fontsize=8,\n", " verticalalignment='top', bbox=props)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " MC Compositions\n", "P 52941\n", "He 53066\n", "O 51691\n", "Fe 50221\n" ] }, { "ename": "NameError", "evalue": "name 'test_predictions' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m\u001b[0m", "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)", "\u001b[0;32m<ipython-input-15-c2e202b3b33e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mautopct\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%.2f'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_predictions\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mcomposition\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcomposition\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcomp_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcomp_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'after'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'test_predictions' is not defined" ] }, { "data": { "image/png": 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u+y0luxIk6Xgzu3niSwLyFyECAIzBzF4o6c+SDvPO1fTU6OgPHaE5583KONYO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\nMNfV1eGa6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+CAsbCqqgq6zpN2aHSeLC1FcWsrzszNxfPnn4/sMAKEbTYbmlwuXK9wmOB7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\nzjlcNGNGwWuTSs2lc+fS0eU+5qrXfYZXPK/XDE8aE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"text/plain": [ "<matplotlib.figure.Figure at 0x7f92a6aba190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a = pd.DataFrame([np.sum(df.MC_comp == composition) for composition in comp_list],\n", " index=comp_list, columns=['MC Compositions'])\n", "print(a)\n", "a.plot.pie(subplots=True, figsize=(4,4), legend=False, autopct='%.2f')\n", "a = pd.DataFrame([np.sum(le.inverse_transform(test_predictions) == composition) for composition in comp_list], index=comp_list, columns=['after'])\n", "print(a)\n", "a.plot.pie(subplots=True, figsize=(2,2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature importance" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1) 0.406473947902\n", "2) 0.283826242805\n", "3) 0.141256788312\n", "4) 0.125846138943\n", "5) 0.0309333112274\n", "6) 0.0116635708116\n" ] }, { "data": { "image/png": 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F/G7Wuvu9cdvr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\nsgos1ByeVIuI+AR4npm/H+T7ZuYd4DNK+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"text/plain": [ "<matplotlib.figure.Figure at 0x7f482ac4fc90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "num_features = len(feature_list)\n", "pipeline = comp.get_pipeline('RF')\n", "pipeline.fit(X_train, y_train)\n", "importances = pipeline.named_steps['classifier'].feature_importances_\n", "indices = np.argsort(importances)[::-1]\n", "\n", "fig, ax = plt.subplots()\n", "for f in range(num_features):\n", " print('{}) {}'.format(f + 1, importances[indices[f]]))\n", "\n", "plt.ylabel('Feature Importances')\n", "plt.bar(range(num_features),\n", " importances[indices],\n", " align='center')\n", "\n", "plt.xticks(range(num_features),\n", " feature_labels[indices], rotation=90)\n", "plt.xlim([-1, len(feature_list)])\n", "# plt.ylim([0, .40])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "probs = pipeline.named_steps['classifier'].predict_proba(X_test)\n", "prob_1 = probs[:, 0][MC_iron_mask]\n", "prob_2 = probs[:, 1][MC_iron_mask]\n", "# print(min(prob_1-prob_2))\n", "# print(max(prob_1-prob_2))\n", "# plt.hist(prob_1-prob_2, bins=30, log=True)\n", "plt.hist(prob_1, bins=np.linspace(0, 1, 50), log=True)\n", "plt.hist(prob_2, bins=np.linspace(0, 1, 50), log=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "probs = pipeline.named_steps['classifier'].predict_proba(X_test)\n", "dp1 = (probs[:, 0]-probs[:, 1])[MC_proton_mask]\n", "print(min(dp1))\n", "print(max(dp1))\n", "dp2 = (probs[:, 0]-probs[:, 1])[MC_iron_mask]\n", "print(min(dp2))\n", "print(max(dp2))\n", "fig, ax = plt.subplots()\n", "# plt.hist(prob_1-prob_2, bins=30, log=True)\n", "counts, edges, pathes = plt.hist(dp1, bins=np.linspace(-1, 1, 100), log=True, label='Proton', alpha=0.75)\n", "counts, edges, pathes = plt.hist(dp2, bins=np.linspace(-1, 1, 100), log=True, label='Iron', alpha=0.75)\n", "plt.legend(loc=2)\n", "plt.show()\n", "pipeline.named_steps['classifier'].classes_" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(pipeline.named_steps['classifier'].classes_)\n", "le.inverse_transform(pipeline.named_steps['classifier'].classes_)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pipeline.named_steps['classifier'].decision_path(X_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "comp_list = np.unique(df['MC_comp'])\n", "# test_probs = defaultdict(list)\n", "fig, ax = plt.subplots()\n", "test_probs = pipeline.predict_proba(X_test)\n", "for class_ in pipeline.classes_:\n", " composition = le.inverse_transform(class_)\n", " plt.hist(test_probs[:, class_], bins=np.linspace(0, 1, 50),\n", " histtype='step', label=composition,\n", " color=color_dict[composition], alpha=0.8, log=True)\n", "plt.ylabel('Counts')\n", "plt.xlabel('Testing set class probabilities')\n", "plt.legend()\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd10bcdc050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "comp_list = np.unique(df['MC_comp'])\n", "test_probs = defaultdict(list)\n", "fig, ax = plt.subplots()\n", "# test_probs = pipeline.predict_proba(X_test)\n", "for event in pipeline.predict_proba(X_test):\n", " composition = le.inverse_transform(np.argmax(event))\n", "# print(composition)\n", " test_probs[composition].append(np.amax(event))\n", "for composition in comp_list:\n", " plt.hist(test_probs[composition], bins=np.linspace(0, 1, 75),\n", " histtype='step', label=composition,\n", " color=color_dict[composition], alpha=0.8, log=True)\n", "plt.ylabel('Counts')\n", "plt.xlabel('Testing set class probabilities')\n", "plt.legend(title='Reco comp')\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
xclxxl414/rqalpha	ipynbs/strategys/small_market_value.ipynb	1	251815	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from rqalpha.api import *\n", "\n", "# 在这个方法中编写任何的初始化逻辑。context对象将会在你的算法策略的任何方法之间做传递。\n", "def init(context):\n", " logger.info(\"init\")\n", "\n", "def before_trading(context):\n", " pass\n", "\n", "# 你选择的证券的数据更新将会触发此段逻辑，例如日或分钟历史数据切片或者是实时数据切片更新\n", "def handle_bar(context, bar_dict):\n", " _preDt = context.prev_trading_dt\n", " _fvalue = get_factors(\"pe\",_preDt.date(),_preDt.date()).iloc[0]\n", " _fvalue = _fvalue[_fvalue>0]\n", "# print(context.now,_fvalue.sort_values())\n", " #买入低估值排名前10的票\n", " buy_codes = list(_fvalue.sort_values().index[:10])\n", "# holdings = [code for code in context.portfolio.positions]\n", " print(buy_codes)\n", " # equalWeight_order(buy_codes,context)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2017-01-03 INFO init\n", "2017-01-03 INFO 000001.XSHE 6.7407\n", "000002.XSHE 9.5646\n", "000004.XSHE 48.2571\n", "000005.XSHE 26.5261\n", "000006.XSHE 11.4471\n", "000007.XSHE 42.4395\n", "000008.XSHE 30.5768\n", "000009.XSHE -79.2387\n", "000010.XSHE 137.9092\n", "000011.XSHE 20.9009\n", "000012.XSHE 22.2610\n", "000014.XSHE 1003.7368\n", "000016.XSHE 7.5821\n", "000017.XSHE 1321.2224\n", "000018.XSHE 29.6576\n", "000019.XSHE 32.2956\n", "000020.XSHE 420.9129\n", "000021.XSHE 56.7905\n", "000022.XSHE 23.2090\n", "000023.XSHE -644.1486\n", "000025.XSHE 1425.9790\n", "000026.XSHE 60.6876\n", "000027.XSHE 30.3558\n", "000028.XSHE 16.9075\n", "000029.XSHE 35.0933\n", "000030.XSHE 13.1728\n", "000031.XSHE 20.6855\n", "000032.XSHE 94.0107\n", "000033.XSHE -58.4716\n", "000034.XSHE 18.5765\n", " ... \n", "603936.XSHG 117.3741\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 39.6121\n", "603955.XSHG NaN\n", "603958.XSHG 157.8029\n", "603959.XSHG 47.7746\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 26.9517\n", "603969.XSHG 35.8462\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 59.7196\n", "603978.XSHG NaN\n", "603979.XSHG 59.5519\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 65.4678\n", "603988.XSHG 1136.0236\n", "603989.XSHG 36.9102\n", "603990.XSHG 85.8684\n", "603991.XSHG NaN\n", "603993.XSHG 51.2801\n", "603996.XSHG 47.7668\n", "603997.XSHG 25.6374\n", "603998.XSHG 158.9808\n", "603999.XSHG 129.2768\n", "Name: 2017-01-03 00:00:00, Length: 3490, dtype: float64\n", "2017-01-03 WARN 600725.XSHG 在 2017-01-03 15:00:00 时停牌。\n", "2017-01-04 INFO 000001.XSHE NaN\n", "000002.XSHE 9.6200\n", "000004.XSHE 48.5285\n", "000005.XSHE 27.2252\n", "000006.XSHE 11.6010\n", "000007.XSHE 43.0412\n", "000008.XSHE 30.7405\n", "000009.XSHE -80.6780\n", "000010.XSHE 138.6173\n", "000011.XSHE 21.1596\n", "000012.XSHE 22.5344\n", "000014.XSHE 1016.6114\n", "000016.XSHE 7.6486\n", "000017.XSHE 1453.2415\n", "000018.XSHE 29.6858\n", "000019.XSHE 32.7700\n", "000020.XSHE 422.5988\n", "000021.XSHE 57.4426\n", "000022.XSHE 23.3651\n", "000023.XSHE -643.7421\n", "000025.XSHE 1438.5168\n", "000026.XSHE 66.7652\n", "000027.XSHE 30.5313\n", "000028.XSHE 20.1359\n", "000029.XSHE 35.0933\n", "000030.XSHE 13.2184\n", "000031.XSHE 20.7772\n", "000032.XSHE 95.7797\n", "000033.XSHE -58.4716\n", "000034.XSHE 18.9010\n", " ... \n", "603936.XSHG 119.7114\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 39.6521\n", "603955.XSHG NaN\n", "603958.XSHG 160.7627\n", "603959.XSHG 48.0668\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 26.9410\n", "603969.XSHG 35.7470\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 59.2980\n", "603978.XSHG NaN\n", "603979.XSHG 60.2448\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 64.7987\n", "603988.XSHG 1249.6898\n", "603989.XSHG 37.0290\n", "603990.XSHG 86.1638\n", "603991.XSHG NaN\n", "603993.XSHG 51.9639\n", "603996.XSHG 48.5919\n", "603997.XSHG 25.8974\n", "603998.XSHG 159.2468\n", "603999.XSHG 131.2987\n", "Name: 2017-01-04 00:00:00, Length: 3490, dtype: float64\n", "2017-01-04 WARN 600725.XSHG 在 2017-01-04 15:00:00 时停牌。\n", "2017-01-05 INFO 000001.XSHE NaN\n", "000002.XSHE 9.6569\n", "000004.XSHE 48.2463\n", "000005.XSHE 27.0698\n", "000006.XSHE 11.6247\n", "000007.XSHE 42.6672\n", "000008.XSHE 30.8060\n", "000009.XSHE -79.8447\n", "000010.XSHE 138.2633\n", "000011.XSHE 20.9440\n", "000012.XSHE 22.6515\n", "000014.XSHE 1018.0419\n", "000016.XSHE 7.6819\n", "000017.XSHE 1423.3310\n", "000018.XSHE 29.4597\n", "000019.XSHE 33.1455\n", "000020.XSHE 429.5298\n", "000021.XSHE 58.0947\n", "000022.XSHE 23.8573\n", "000023.XSHE -644.7584\n", "000025.XSHE 1453.3574\n", "000026.XSHE 72.2702\n", "000027.XSHE 30.6190\n", "000028.XSHE 20.0796\n", "000029.XSHE 35.0933\n", "000030.XSHE 13.0359\n", "000031.XSHE 20.8919\n", "000032.XSHE 105.3829\n", "000033.XSHE -58.4716\n", "000034.XSHE 18.6431\n", " ... \n", "603936.XSHG 117.9949\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 39.7321\n", "603955.XSHG NaN\n", "603958.XSHG 159.1530\n", "603959.XSHG 48.0668\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 26.9304\n", "603969.XSHG 35.7470\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 58.0965\n", "603978.XSHG NaN\n", "603979.XSHG 60.4427\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 62.7326\n", "603988.XSHG 1240.2708\n", "603989.XSHG 36.0587\n", "603990.XSHG 86.7079\n", "603991.XSHG NaN\n", "603993.XSHG 51.9639\n", "603996.XSHG 49.2757\n", "603997.XSHG 25.8064\n", "603998.XSHG 157.2961\n", "603999.XSHG 129.7070\n", "Name: 2017-01-05 00:00:00, Length: 3490, dtype: float64\n", "2017-01-05 WARN 600725.XSHG 在 2017-01-05 15:00:00 时停牌。\n", "2017-01-06 INFO 000001.XSHE NaN\n", "000002.XSHE 9.5231\n", "000004.XSHE 47.7251\n", "000005.XSHE 27.3028\n", "000006.XSHE 11.8496\n", "000007.XSHE 41.3826\n", "000008.XSHE 31.0351\n", "000009.XSHE -79.6174\n", "000010.XSHE 136.4929\n", "000011.XSHE 21.8279\n", "000012.XSHE 22.2219\n", "000014.XSHE 1029.4859\n", "000016.XSHE 7.6653\n", "000017.XSHE 1433.6449\n", "000018.XSHE 28.3288\n", "000019.XSHE 33.9361\n", "000020.XSHE 425.0340\n", "000021.XSHE 58.2132\n", "000022.XSHE 23.8453\n", "000023.XSHE -651.4662\n", "000025.XSHE 1416.2559\n", "000026.XSHE 67.7341\n", "000027.XSHE 31.3647\n", "000028.XSHE 20.1596\n", "000029.XSHE 35.0933\n", "000030.XSHE 13.2336\n", "000031.XSHE 20.7084\n", "000032.XSHE 107.4047\n", "000033.XSHE -58.4716\n", "000034.XSHE 18.4850\n", " ... \n", "603936.XSHG 114.6716\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 39.2252\n", "603955.XSHG NaN\n", "603958.XSHG 155.3624\n", "603959.XSHG 48.7810\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 26.8343\n", "603969.XSHG 35.4989\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 54.8712\n", "603978.XSHG NaN\n", "603979.XSHG 60.5747\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 58.8757\n", "603988.XSHG 1209.1403\n", "603989.XSHG 35.6132\n", "603990.XSHG 82.4480\n", "603991.XSHG NaN\n", "603993.XSHG 51.5536\n", "603996.XSHG 49.1814\n", "603997.XSHG 25.5074\n", "603998.XSHG 155.7887\n", "603999.XSHG 130.8255\n", "Name: 2017-01-06 00:00:00, Length: 3490, dtype: float64\n", "2017-01-06 WARN 600725.XSHG 在 2017-01-06 15:00:00 时停牌。\n", "2017-01-09 INFO 000001.XSHE NaN\n", "000002.XSHE 9.5323\n", "000004.XSHE 46.6938\n", "000005.XSHE 27.4582\n", "000006.XSHE 11.7786\n", "000007.XSHE 40.7972\n", "000008.XSHE 31.1988\n", "000009.XSHE -80.0720\n", "000010.XSHE 137.7322\n", "000011.XSHE 21.9573\n", "000012.XSHE 22.3196\n", "000014.XSHE 1031.3932\n", "000016.XSHE 7.7318\n", "000017.XSHE 1435.7077\n", "000018.XSHE 28.3005\n", "000019.XSHE 33.9361\n", "000020.XSHE 422.7862\n", "000021.XSHE 58.8060\n", "000022.XSHE 23.8573\n", "000023.XSHE -632.1559\n", "000025.XSHE 1443.3784\n", "000026.XSHE 69.1434\n", "000027.XSHE 31.4525\n", "000028.XSHE 20.5831\n", "000029.XSHE 35.0933\n", "000030.XSHE 13.1576\n", "000031.XSHE 20.7772\n", "000032.XSHE 106.1411\n", "000033.XSHE -58.4716\n", "000034.XSHE 17.8444\n", " ... \n", "603936.XSHG 115.5116\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 39.6388\n", "603955.XSHG NaN\n", "603958.XSHG 157.1279\n", "603959.XSHG 47.8233\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 26.7169\n", "603969.XSHG 35.3997\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 55.9674\n", "603978.XSHG NaN\n", "603979.XSHG 61.5975\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 58.6199\n", "603988.XSHG 1290.0796\n", "603989.XSHG 35.7914\n", "603990.XSHG 82.2614\n", "603991.XSHG NaN\n", "603993.XSHG 51.8271\n", "603996.XSHG 49.2521\n", "603997.XSHG 25.5854\n", "603998.XSHG 156.9414\n", "603999.XSHG 135.3427\n", "Name: 2017-01-09 00:00:00, Length: 3490, dtype: float64\n", "2017-01-09 WARN 600725.XSHG 在 2017-01-09 15:00:00 时停牌。\n", "2017-01-10 INFO 000001.XSHE NaN\n", "000002.XSHE 9.4954\n", "000004.XSHE 46.9543\n", "000005.XSHE 27.2252\n", "000006.XSHE 11.5537\n", "000007.XSHE 40.3094\n", "000008.XSHE 31.0024\n", "000009.XSHE -80.4507\n", "000010.XSHE 136.4929\n", "000011.XSHE 20.9333\n", "000012.XSHE 23.0030\n", "000014.XSHE 1013.2735\n", "000016.XSHE 7.7318\n", "000017.XSHE 1416.1112\n", "000018.XSHE 28.6115\n", "000019.XSHE 33.1060\n", "000020.XSHE 422.5988\n", "000021.XSHE 57.9761\n", "000022.XSHE 23.0889\n", "000023.XSHE -624.8384\n", "000025.XSHE 1423.1644\n", "000026.XSHE 68.8351\n", "000027.XSHE 31.1454\n", "000028.XSHE 20.4646\n", "000029.XSHE 35.0933\n", "000030.XSHE 13.0359\n", "000031.XSHE 20.6167\n", "000032.XSHE 102.9189\n", "000033.XSHE -58.4716\n", "000034.XSHE 18.0690\n", " ... \n", "603936.XSHG 114.2334\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 38.9050\n", "603955.XSHG NaN\n", "603958.XSHG 156.0894\n", "603959.XSHG 49.6414\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 27.4427\n", "603969.XSHG 35.2756\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 54.2599\n", "603978.XSHG NaN\n", "603979.XSHG 61.5645\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 58.1673\n", "603988.XSHG 1292.6339\n", "603989.XSHG 36.0884\n", "603990.XSHG 82.6501\n", "603991.XSHG NaN\n", "603993.XSHG 52.3741\n", "603996.XSHG 48.8749\n", "603997.XSHG 25.5464\n", "603998.XSHG 157.4734\n", "603999.XSHG 133.0626\n", "Name: 2017-01-10 00:00:00, Length: 3490, dtype: float64\n", "2017-01-10 WARN 600725.XSHG 在 2017-01-10 15:00:00 时停牌。\n", "2017-01-11 INFO 000001.XSHE NaN\n", "000002.XSHE 9.4123\n", "000004.XSHE 46.0858\n", "000005.XSHE 26.6426\n", "000006.XSHE 11.4590\n", "000007.XSHE 40.4070\n", "000008.XSHE 30.6423\n", "000009.XSHE -78.1781\n", "000010.XSHE 131.1819\n", "000011.XSHE 19.9955\n", "000012.XSHE 23.2569\n", "000014.XSHE 987.0476\n", "000016.XSHE 7.6819\n", "000017.XSHE 1342.8818\n", "000018.XSHE 27.6785\n", "000019.XSHE 32.2363\n", "000020.XSHE 410.7975\n", "000021.XSHE 59.2210\n", "000022.XSHE 22.5126\n", "000023.XSHE -613.2522\n", "000025.XSHE 1423.9320\n", "000026.XSHE 67.3817\n", "000027.XSHE 30.8383\n", "000028.XSHE 20.2603\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.7925\n", "000031.XSHE 20.4332\n", "000032.XSHE 103.1717\n", "000033.XSHE -58.4716\n", "000034.XSHE 17.7612\n", " ... \n", "603936.XSHG 111.5675\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 38.6381\n", "603955.XSHG NaN\n", "603958.XSHG 148.8717\n", "603959.XSHG 48.7486\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 27.0051\n", "603969.XSHG 35.1764\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 52.8475\n", "603978.XSHG NaN\n", "603979.XSHG 61.5645\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 57.6360\n", "603988.XSHG 1303.1704\n", "603989.XSHG 35.9498\n", "603990.XSHG 81.5928\n", "603991.XSHG NaN\n", "603993.XSHG 54.2886\n", "603996.XSHG 47.4367\n", "603997.XSHG 25.0263\n", "603998.XSHG 166.5175\n", "603999.XSHG 128.9326\n", "Name: 2017-01-11 00:00:00, Length: 3490, dtype: float64\n", "2017-01-11 WARN 600725.XSHG 在 2017-01-11 15:00:00 时停牌。\n", "2017-01-12 INFO 000001.XSHE NaN\n", "000002.XSHE 9.4123\n", "000004.XSHE 45.6515\n", "000005.XSHE 26.3707\n", "000006.XSHE 11.0091\n", "000007.XSHE 40.0167\n", "000008.XSHE 30.6423\n", "000009.XSHE -77.0418\n", "000010.XSHE 128.7034\n", "000011.XSHE 19.8769\n", "000012.XSHE 22.8468\n", "000014.XSHE 987.0476\n", "000016.XSHE 7.7151\n", "000017.XSHE 1315.0340\n", "000018.XSHE 27.9047\n", "000019.XSHE 32.0584\n", "000020.XSHE 395.6244\n", "000021.XSHE 57.7390\n", "000022.XSHE 22.2244\n", "000023.XSHE -602.4792\n", "000025.XSHE 1409.0914\n", "000026.XSHE 65.3118\n", "000027.XSHE 30.6629\n", "000028.XSHE 20.1892\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.6252\n", "000031.XSHE 19.9287\n", "000032.XSHE 102.2871\n", "000033.XSHE -58.4716\n", "000034.XSHE 17.6198\n", " ... \n", "603936.XSHG 110.4354\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 38.1445\n", "603955.XSHG NaN\n", "603958.XSHG 143.2117\n", "603959.XSHG 47.0765\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 26.5461\n", "603969.XSHG 34.2089\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 53.4167\n", "603978.XSHG NaN\n", "603979.XSHG 60.7067\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 57.0260\n", "603988.XSHG 1344.5181\n", "603989.XSHG 35.9795\n", "603990.XSHG 81.6706\n", "603991.XSHG NaN\n", "603993.XSHG 53.1946\n", "603996.XSHG 47.1302\n", "603997.XSHG 24.9093\n", "603998.XSHG 162.2615\n", "603999.XSHG 129.1477\n", "Name: 2017-01-12 00:00:00, Length: 3490, dtype: float64\n", "2017-01-12 WARN 600725.XSHG 在 2017-01-12 15:00:00 时停牌。\n", "2017-01-13 INFO 000001.XSHE NaN\n", "000002.XSHE 10.0629\n", "000004.XSHE 44.5116\n", "000005.XSHE 25.9435\n", "000006.XSHE 10.4527\n", "000007.XSHE 38.7159\n", "000008.XSHE 30.1512\n", "000009.XSHE -76.2843\n", "000010.XSHE 124.9857\n", "000011.XSHE 18.4433\n", "000012.XSHE 23.3741\n", "000014.XSHE 979.8951\n", "000016.XSHE 7.5655\n", "000017.XSHE 1250.0559\n", "000018.XSHE 27.8764\n", "000019.XSHE 30.8132\n", "000020.XSHE 386.4457\n", "000021.XSHE 56.6127\n", "000022.XSHE 21.6121\n", "000023.XSHE -590.4865\n", "000025.XSHE 1355.8699\n", "000026.XSHE 62.6254\n", "000027.XSHE 30.4874\n", "000028.XSHE 20.1033\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.2905\n", "000031.XSHE 19.5159\n", "000032.XSHE 98.5596\n", "000033.XSHE -58.4716\n", "000034.XSHE 16.7546\n", " ... \n", "603936.XSHG 107.0390\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 36.9437\n", "603955.XSHG NaN\n", "603958.XSHG 138.0191\n", "603959.XSHG 45.0474\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 25.7882\n", "603969.XSHG 33.7624\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 53.3113\n", "603978.XSHG NaN\n", "603979.XSHG 59.5849\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 54.0940\n", "603988.XSHG 1297.7425\n", "603989.XSHG 34.3558\n", "603990.XSHG 77.1618\n", "603991.XSHG NaN\n", "603993.XSHG 53.6048\n", "603996.XSHG 48.0968\n", "603997.XSHG 23.6613\n", "603998.XSHG 160.6654\n", "603999.XSHG 125.9642\n", "Name: 2017-01-13 00:00:00, Length: 3490, dtype: float64\n", "2017-01-13 WARN 000617.XSHE 在 2017-01-13 15:00:00 时停牌。\n", "2017-01-13 WARN 600725.XSHG 在 2017-01-13 15:00:00 时停牌。\n", "2017-01-16 INFO 000001.XSHE NaN\n", "000002.XSHE 9.6892\n", "000004.XSHE 41.5369\n", "000005.XSHE 25.0114\n", "000006.XSHE 10.0029\n", "000007.XSHE 38.7159\n", "000008.XSHE 28.6453\n", "000009.XSHE -72.5723\n", "000010.XSHE 120.5599\n", "000011.XSHE 17.4947\n", "000012.XSHE 22.2610\n", "000014.XSHE 901.6942\n", "000016.XSHE 7.1997\n", "000017.XSHE 1162.3869\n", "000018.XSHE 26.4911\n", "000019.XSHE 28.2043\n", "000020.XSHE 371.0852\n", "000021.XSHE 54.5378\n", "000022.XSHE 21.0238\n", "000023.XSHE -537.0276\n", "000025.XSHE 1284.2255\n", "000026.XSHE 57.9131\n", "000027.XSHE 29.6101\n", "000028.XSHE 20.4231\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.2297\n", "000031.XSHE 18.6215\n", "000032.XSHE 91.4203\n", "000033.XSHE -58.4716\n", "000034.XSHE 16.6964\n", " ... \n", "603936.XSHG 100.2099\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 35.9564\n", "603955.XSHG NaN\n", "603958.XSHG 128.5167\n", "603959.XSHG 41.4436\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 24.6141\n", "603969.XSHG 33.1422\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 51.0136\n", "603978.XSHG NaN\n", "603979.XSHG 57.4074\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 51.0243\n", "603988.XSHG 1281.2992\n", "603989.XSHG 32.6528\n", "603990.XSHG 72.9796\n", "603991.XSHG NaN\n", "603993.XSHG 54.5621\n", "603996.XSHG 49.1106\n", "603997.XSHG 22.3612\n", "603998.XSHG 144.7053\n", "603999.XSHG 115.6392\n", "Name: 2017-01-16 00:00:00, Length: 3490, dtype: float64\n", "2017-01-16 WARN 600725.XSHG 在 2017-01-16 15:00:00 时停牌。\n", "2017-01-16 WARN 订单被拒单: [000037.XSHE] 已跌停。\n", "2017-01-17 INFO 000001.XSHE NaN\n", "000002.XSHE 9.5969\n", "000004.XSHE 40.5707\n", "000005.XSHE 25.1668\n", "000006.XSHE 10.1094\n", "000007.XSHE 38.7159\n", "000008.XSHE 28.4489\n", "000009.XSHE -72.7238\n", "000010.XSHE 121.7991\n", "000011.XSHE 17.8720\n", "000012.XSHE 21.9095\n", "000014.XSHE 895.0185\n", "000016.XSHE 7.2828\n", "000017.XSHE 1191.2661\n", "000018.XSHE 27.1696\n", "000019.XSHE 28.6589\n", "000020.XSHE 374.6444\n", "000021.XSHE 55.6049\n", "000022.XSHE 21.2279\n", "000023.XSHE -566.0946\n", "000025.XSHE 1299.8337\n", "000026.XSHE 59.4545\n", "000027.XSHE 29.8294\n", "000028.XSHE 20.4498\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.1993\n", "000031.XSHE 18.7591\n", "000032.XSHE 93.6316\n", "000033.XSHE -58.4716\n", "000034.XSHE 16.8461\n", " ... \n", "603936.XSHG 103.5697\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 36.1032\n", "603955.XSHG NaN\n", "603958.XSHG 131.9957\n", "603959.XSHG 42.0929\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 24.5394\n", "603969.XSHG 33.5888\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 56.1149\n", "603978.XSHG NaN\n", "603979.XSHG 57.1105\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 53.4053\n", "603988.XSHG 1153.1054\n", "603989.XSHG 32.1578\n", "603990.XSHG 74.9385\n", "603991.XSHG NaN\n", "603993.XSHG 54.6988\n", "603996.XSHG 48.7334\n", "603997.XSHG 22.8942\n", "603998.XSHG 145.8580\n", "603999.XSHG 117.8763\n", "Name: 2017-01-17 00:00:00, Length: 3490, dtype: float64\n", "2017-01-17 WARN 600725.XSHG 在 2017-01-17 15:00:00 时停牌。\n", "2017-01-18 INFO 000001.XSHE NaN\n", "000002.XSHE 9.6523\n", "000004.XSHE 40.3319\n", "000005.XSHE 25.4386\n", "000006.XSHE 10.1450\n", "000007.XSHE 38.7159\n", "000008.XSHE 28.1542\n", "000009.XSHE -71.6633\n", "000010.XSHE 120.9140\n", "000011.XSHE 18.2169\n", "000012.XSHE 21.7338\n", "000014.XSHE 911.2309\n", "000016.XSHE 7.2329\n", "000017.XSHE 1185.0777\n", "000018.XSHE 27.3392\n", "000019.XSHE 28.8170\n", "000020.XSHE 382.6992\n", "000021.XSHE 55.3678\n", "000022.XSHE 21.2399\n", "000023.XSHE -573.4122\n", "000025.XSHE 1316.4655\n", "000026.XSHE 59.5426\n", "000027.XSHE 29.7417\n", "000028.XSHE 20.3639\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.3362\n", "000031.XSHE 18.5986\n", "000032.XSHE 92.2416\n", "000033.XSHE -58.4716\n", "000034.XSHE 16.5051\n", " ... \n", "603936.XSHG 100.7211\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 36.0231\n", "603955.XSHG NaN\n", "603958.XSHG 132.3073\n", "603959.XSHG 41.3137\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 24.4433\n", "603969.XSHG 33.3655\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 56.0306\n", "603978.XSHG NaN\n", "603979.XSHG 57.0115\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 51.4769\n", "603988.XSHG 1086.3744\n", "603989.XSHG 33.3855\n", "603990.XSHG 71.5026\n", "603991.XSHG NaN\n", "603993.XSHG 53.4681\n", "603996.XSHG 47.2245\n", "603997.XSHG 22.8292\n", "603998.XSHG 145.0600\n", "603999.XSHG 117.1019\n", "Name: 2017-01-18 00:00:00, Length: 3490, dtype: float64\n", "2017-01-18 WARN 600725.XSHG 在 2017-01-18 15:00:00 时停牌。\n", "2017-01-19 INFO 000001.XSHE NaN\n", "000002.XSHE 9.5046\n", "000004.XSHE 38.7468\n", "000005.XSHE 25.0114\n", "000006.XSHE 10.0029\n", "000007.XSHE 38.7159\n", "000008.XSHE 27.9905\n", "000009.XSHE -70.6027\n", "000010.XSHE 120.2058\n", "000011.XSHE 17.8935\n", "000012.XSHE 21.6752\n", "000014.XSHE 927.4433\n", "000016.XSHE 7.1831\n", "000017.XSHE 1165.4811\n", "000018.XSHE 26.6042\n", "000019.XSHE 28.1252\n", "000020.XSHE 371.0852\n", "000021.XSHE 55.6049\n", "000022.XSHE 20.8917\n", "000023.XSHE -569.3469\n", "000025.XSHE 1303.4160\n", "000026.XSHE 59.6747\n", "000027.XSHE 29.8294\n", "000028.XSHE 20.1359\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.1993\n", "000031.XSHE 18.4610\n", "000032.XSHE 92.9366\n", "000033.XSHE -58.4716\n", "000034.XSHE 16.4801\n", " ... \n", "603936.XSHG 100.8307\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 35.7029\n", "603955.XSHG NaN\n", "603958.XSHG 136.8248\n", "603959.XSHG 41.6221\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 24.4220\n", "603969.XSHG 33.7128\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 55.3561\n", "603978.XSHG NaN\n", "603979.XSHG 56.4836\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 52.0476\n", "603988.XSHG 1116.0681\n", "603989.XSHG 32.3459\n", "603990.XSHG 70.8807\n", "603991.XSHG NaN\n", "603993.XSHG 53.7416\n", "603996.XSHG 45.8806\n", "603997.XSHG 22.6342\n", "603998.XSHG 144.1733\n", "603999.XSHG 117.6612\n", "Name: 2017-01-19 00:00:00, Length: 3490, dtype: float64\n", "2017-01-19 WARN 600725.XSHG 在 2017-01-19 15:00:00 时停牌。\n", "2017-01-20 INFO 000001.XSHE NaN\n", "000002.XSHE 9.5415\n", "000004.XSHE 39.6045\n", "000005.XSHE 25.2444\n", "000006.XSHE 10.1094\n", "000007.XSHE 38.7159\n", "000008.XSHE 28.2852\n", "000009.XSHE -71.3603\n", "000010.XSHE 121.7991\n", "000011.XSHE 18.0121\n", "000012.XSHE 21.8314\n", "000014.XSHE 934.1190\n", "000016.XSHE 7.2828\n", "000017.XSHE 1173.7323\n", "000018.XSHE 27.3392\n", "000019.XSHE 28.4415\n", "000020.XSHE 374.4570\n", "000021.XSHE 55.9013\n", "000022.XSHE 21.0598\n", "000023.XSHE -572.8024\n", "000025.XSHE 1311.0921\n", "000026.XSHE 60.2912\n", "000027.XSHE 30.0049\n", "000028.XSHE 20.5653\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.3362\n", "000031.XSHE 18.7821\n", "000032.XSHE 93.3157\n", "000033.XSHE -58.4716\n", "000034.XSHE 16.9543\n", " ... \n", "603936.XSHG 103.1314\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 36.7169\n", "603955.XSHG NaN\n", "603958.XSHG 138.1230\n", "603959.XSHG 42.3039\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 24.7529\n", "603969.XSHG 33.9361\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 55.7144\n", "603978.XSHG NaN\n", "603979.XSHG 56.8465\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 53.6808\n", "603988.XSHG 1117.5049\n", "603989.XSHG 33.1578\n", "603990.XSHG 73.4304\n", "603991.XSHG NaN\n", "603993.XSHG 53.6048\n", "603996.XSHG 46.2814\n", "603997.XSHG 22.9722\n", "603998.XSHG 146.0353\n", "603999.XSHG 119.3820\n", "Name: 2017-01-20 00:00:00, Length: 3490, dtype: float64\n", "2017-01-20 WARN 600725.XSHG 在 2017-01-20 15:00:00 时停牌。\n", "2017-01-23 INFO 000001.XSHE NaN\n", "000002.XSHE 9.5692\n", "000004.XSHE 40.7770\n", "000005.XSHE 25.3610\n", "000006.XSHE 10.1805\n", "000007.XSHE 38.7159\n", "000008.XSHE 28.5471\n", "000009.XSHE -72.1178\n", "000010.XSHE 123.3924\n", "000011.XSHE 18.0660\n", "000012.XSHE 22.3391\n", "000014.XSHE 932.2116\n", "000016.XSHE 7.4158\n", "000017.XSHE 1187.1405\n", "000018.XSHE 27.7633\n", "000019.XSHE 28.4612\n", "000020.XSHE 378.3908\n", "000021.XSHE 56.1384\n", "000022.XSHE 21.3719\n", "000023.XSHE -596.9910\n", "000025.XSHE 1315.1861\n", "000026.XSHE 60.6436\n", "000027.XSHE 30.0926\n", "000028.XSHE 20.4350\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.3970\n", "000031.XSHE 18.8050\n", "000032.XSHE 93.5684\n", "000033.XSHE -58.4716\n", "000034.XSHE 18.0940\n", " ... \n", "603936.XSHG 104.2270\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 37.4107\n", "603955.XSHG NaN\n", "603958.XSHG 137.5518\n", "603959.XSHG 42.7909\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 25.1265\n", "603969.XSHG 33.8616\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 57.4008\n", "603978.XSHG NaN\n", "603979.XSHG 57.4404\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 53.8185\n", "603988.XSHG 1120.8574\n", "603989.XSHG 33.3063\n", "603990.XSHG 75.6382\n", "603991.XSHG NaN\n", "603993.XSHG 54.9723\n", "603996.XSHG 46.4229\n", "603997.XSHG 23.2062\n", "603998.XSHG 149.7593\n", "603999.XSHG 120.9308\n", "Name: 2017-01-23 00:00:00, Length: 3490, dtype: float64\n", "2017-01-23 WARN 600725.XSHG 在 2017-01-23 15:00:00 时停牌。\n", "2017-01-24 INFO 000001.XSHE NaN\n", "000002.XSHE 9.5461\n", "000004.XSHE 41.9386\n", "000005.XSHE 25.2833\n", "000006.XSHE 10.1213\n", "000007.XSHE 38.7159\n", "000008.XSHE 28.4161\n", "000009.XSHE -71.5875\n", "000010.XSHE 124.6317\n", "000011.XSHE 17.8612\n", "000012.XSHE 22.1048\n", "000014.XSHE 931.2580\n", "000016.XSHE 7.3992\n", "000017.XSHE 1181.9835\n", "000018.XSHE 27.7351\n", "000019.XSHE 28.5205\n", "000020.XSHE 372.5838\n", "000021.XSHE 56.0791\n", "000022.XSHE 21.4320\n", "000023.XSHE -624.2286\n", "000025.XSHE 1298.0426\n", "000026.XSHE 59.8949\n", "000027.XSHE 29.9610\n", "000028.XSHE 20.2721\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.5795\n", "000031.XSHE 18.7133\n", "000032.XSHE 92.0521\n", "000033.XSHE -58.4716\n", "000034.XSHE 17.9692\n", " ... \n", "603936.XSHG 104.8844\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 36.8636\n", "603955.XSHG NaN\n", "603958.XSHG 134.0728\n", "603959.XSHG 42.2877\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG NaN\n", "603968.XSHG 24.8276\n", "603969.XSHG 33.5888\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 55.2507\n", "603978.XSHG NaN\n", "603979.XSHG 57.5724\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 53.0314\n", "603988.XSHG 1115.5892\n", "603989.XSHG 32.7023\n", "603990.XSHG 76.0269\n", "603991.XSHG NaN\n", "603993.XSHG 55.2458\n", "603996.XSHG 45.8806\n", "603997.XSHG 23.2322\n", "603998.XSHG 149.6707\n", "603999.XSHG 118.0914\n", "Name: 2017-01-24 00:00:00, Length: 3490, dtype: float64\n", "2017-01-24 WARN 600725.XSHG 在 2017-01-24 15:00:00 时停牌。\n", "2017-01-25 INFO 000001.XSHE NaN\n", "000002.XSHE 9.5092\n", "000004.XSHE 41.5261\n", "000005.XSHE 25.3221\n", "000006.XSHE 10.2160\n", "000007.XSHE 38.7159\n", "000008.XSHE 28.2524\n", "000009.XSHE -71.8148\n", "000010.XSHE 124.8087\n", "000011.XSHE 17.9582\n", "000012.XSHE 22.0657\n", "000014.XSHE 851.6266\n", "000016.XSHE 7.4491\n", "000017.XSHE 1175.7951\n", "000018.XSHE 27.6502\n", "000019.XSHE 28.7182\n", "000020.XSHE 372.3965\n", "000021.XSHE 56.1977\n", "000022.XSHE 21.3840\n", "000023.XSHE -618.1306\n", "000025.XSHE 1296.7633\n", "000026.XSHE 60.0270\n", "000027.XSHE 30.0049\n", "000028.XSHE 20.8230\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.5035\n", "000031.XSHE 18.7133\n", "000032.XSHE 91.9257\n", "000033.XSHE -58.4716\n", "000034.XSHE 18.1356\n", " ... \n", "603936.XSHG 105.1765\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 37.3973\n", "603955.XSHG NaN\n", "603958.XSHG 133.9689\n", "603959.XSHG 42.4338\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG 28.0971\n", "603968.XSHG 24.7956\n", "603969.XSHG 33.4151\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 55.7777\n", "603978.XSHG NaN\n", "603979.XSHG 57.9683\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 53.7595\n", "603988.XSHG 1118.6224\n", "603989.XSHG 32.6924\n", "603990.XSHG 78.8565\n", "603991.XSHG NaN\n", "603993.XSHG 55.3826\n", "603996.XSHG 45.9749\n", "603997.XSHG 23.2712\n", "603998.XSHG 151.6214\n", "603999.XSHG 118.0914\n", "Name: 2017-01-25 00:00:00, Length: 3490, dtype: float64\n", "2017-01-25 WARN 600725.XSHG 在 2017-01-25 15:00:00 时停牌。\n", "2017-01-26 INFO 000001.XSHE NaN\n", "000002.XSHE 9.5415\n", "000004.XSHE 41.5695\n", "000005.XSHE 25.4775\n", "000006.XSHE 10.2633\n", "000007.XSHE 38.7159\n", "000008.XSHE 28.5143\n", "000009.XSHE -72.0420\n", "000010.XSHE 125.8709\n", "000011.XSHE 17.9043\n", "000012.XSHE 22.2024\n", "000014.XSHE 845.4277\n", "000016.XSHE 7.5156\n", "000017.XSHE 1189.2033\n", "000018.XSHE 27.8764\n", "000019.XSHE 29.0542\n", "000020.XSHE 379.5147\n", "000021.XSHE 56.1977\n", "000022.XSHE 21.7201\n", "000023.XSHE -626.0580\n", "000025.XSHE 1301.6248\n", "000026.XSHE 60.4674\n", "000027.XSHE 30.6629\n", "000028.XSHE 20.8259\n", "000029.XSHE 35.0933\n", "000030.XSHE 12.5187\n", "000031.XSHE 18.7821\n", "000032.XSHE 92.9366\n", "000033.XSHE -58.4716\n", "000034.XSHE 18.1356\n", " ... \n", "603936.XSHG 106.6373\n", "603937.XSHG NaN\n", "603938.XSHG NaN\n", "603939.XSHG 37.8243\n", "603955.XSHG NaN\n", "603958.XSHG 135.9421\n", "603959.XSHG 42.5312\n", "603960.XSHG NaN\n", "603963.XSHG NaN\n", "603966.XSHG 30.8962\n", "603968.XSHG 24.9343\n", "603969.XSHG 33.7376\n", "603970.XSHG NaN\n", "603976.XSHG NaN\n", "603977.XSHG 55.9674\n", "603978.XSHG NaN\n", "603979.XSHG 58.2982\n", "603980.XSHG NaN\n", "603985.XSHG NaN\n", "603986.XSHG 91.2327\n", "603987.XSHG 53.7202\n", "603988.XSHG 1122.4539\n", "603989.XSHG 33.2271\n", "603990.XSHG 80.2091\n", "603991.XSHG NaN\n", "603993.XSHG 56.4765\n", "603996.XSHG 46.4464\n", "603997.XSHG 23.8563\n", "603998.XSHG 152.2420\n", "603999.XSHG 118.4356\n", "Name: 2017-01-26 00:00:00, Length: 3490, dtype: float64\n", "2017-01-26 WARN 600725.XSHG 在 2017-01-26 15:00:00 时停牌。\n" ] }, { "data": { "image/png": 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3N/j4+NRjicmnxMXFISAgAJ6eniLhu3fvxsyZM7n68ClPnz4VWxfE1Z927dqh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cOdaCOjPB3WCchkMgKiowmIjibq/BkA/PXmABQRLkLUzTjaJRxdnTisVgBUOh2BiYnIZBAcLHHk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\nEEJUhZMnrZk0qRZ5eSomTarFp5+mmXyRIzc3F1tbWwDy8/Oxtq76P98VR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"text/plain": [ "<matplotlib.figure.Figure at 0x16a194b67f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'sys_analyser': {'benchmark_portfolio': cash market_value static_unit_net_value total_value \\\n", " date \n", " 2017-01-03 673.23 999326.77 1.000 1000000.00 \n", " 2017-01-04 673.23 1007124.69 0.992 1007797.92 \n", " 2017-01-05 673.23 1006969.21 1.000 1007642.44 \n", " 2017-01-06 673.23 1000953.33 1.008 1001626.56 \n", " 2017-01-09 673.23 1005806.10 1.007 1006479.33 \n", " 2017-01-10 673.23 1004122.73 1.001 1004795.96 \n", " 2017-01-11 673.23 997015.50 1.006 997688.73 \n", " 2017-01-12 673.23 991968.38 1.004 992641.61 \n", " 2017-01-13 673.23 992653.09 0.997 993326.32 \n", " 2017-01-16 673.23 992515.55 0.993 993188.78 \n", " 2017-01-17 673.23 994581.64 0.992 995254.87 \n", " 2017-01-18 673.23 998471.63 0.989 999144.86 \n", " 2017-01-19 673.23 995457.71 0.994 996130.94 \n", " 2017-01-20 673.23 1003112.11 0.997 1003785.34 \n", " 2017-01-23 673.23 1005859.92 0.995 1006533.15 \n", " 2017-01-24 673.23 1005970.55 1.005 1006643.78 \n", " 2017-01-25 673.23 1009394.10 1.006 1010067.33 \n", " 2017-01-26 673.23 1013000.04 1.006 1013673.27 \n", " \n", " unit_net_value units \n", " date \n", " 2017-01-03 1.000000 1000000.0 \n", " 2017-01-04 1.007798 1000000.0 \n", " 2017-01-05 1.007642 1000000.0 \n", " 2017-01-06 1.001627 1000000.0 \n", " 2017-01-09 1.006479 1000000.0 \n", " 2017-01-10 1.004796 1000000.0 \n", " 2017-01-11 0.997689 1000000.0 \n", " 2017-01-12 0.992642 1000000.0 \n", " 2017-01-13 0.993326 1000000.0 \n", " 2017-01-16 0.993189 1000000.0 \n", " 2017-01-17 0.995255 1000000.0 \n", " 2017-01-18 0.999145 1000000.0 \n", " 2017-01-19 0.996131 1000000.0 \n", " 2017-01-20 1.003785 1000000.0 \n", " 2017-01-23 1.006533 1000000.0 \n", " 2017-01-24 1.006644 1000000.0 \n", " 2017-01-25 1.010067 1000000.0 \n", " 2017-01-26 1.013673 1000000.0 ,\n", " 'portfolio': cash market_value static_unit_net_value total_value \\\n", " date \n", " 2017-01-03 104254.977 895029.0 1.000 999283.977 \n", " 2017-01-04 104443.551 908899.0 0.993 1013342.551 \n", " 2017-01-05 104760.297 916325.0 0.997 1021085.297 \n", " 2017-01-06 103844.463 924233.0 1.018 1028077.463 \n", " 2017-01-09 106441.267 928718.0 1.026 1035159.267 \n", " 2017-01-10 105904.609 929508.0 1.032 1035412.609 \n", " 2017-01-11 107331.440 928507.0 1.040 1035838.440 \n", " 2017-01-12 104772.751 912431.0 1.034 1017203.751 \n", " 2017-01-13 1026.709 1007706.0 1.032 1008732.709 \n", " 2017-01-16 110665.150 867968.0 1.027 978633.150 \n", " 2017-01-17 100143.612 884830.0 0.996 984973.612 \n", " 2017-01-18 99545.955 895418.0 0.973 994963.955 \n", " 2017-01-19 99110.048 888076.0 0.982 987186.048 \n", " 2017-01-20 100860.305 903039.0 0.992 1003899.305 \n", " 2017-01-23 102120.582 911864.0 0.988 1013984.582 \n", " 2017-01-24 102948.606 922267.0 1.002 1025215.606 \n", " 2017-01-25 102145.478 919848.0 1.012 1021993.478 \n", " 2017-01-26 104010.400 934436.0 1.024 1038446.400 \n", " \n", " unit_net_value units \n", " date \n", " 2017-01-03 0.999284 1000000.0 \n", " 2017-01-04 1.013343 1000000.0 \n", " 2017-01-05 1.021085 1000000.0 \n", " 2017-01-06 1.028077 1000000.0 \n", " 2017-01-09 1.035159 1000000.0 \n", " 2017-01-10 1.035413 1000000.0 \n", " 2017-01-11 1.035838 1000000.0 \n", " 2017-01-12 1.017204 1000000.0 \n", " 2017-01-13 1.008733 1000000.0 \n", " 2017-01-16 0.978633 1000000.0 \n", " 2017-01-17 0.984974 1000000.0 \n", " 2017-01-18 0.994964 1000000.0 \n", " 2017-01-19 0.987186 1000000.0 \n", " 2017-01-20 1.003899 1000000.0 \n", " 2017-01-23 1.013985 1000000.0 \n", " 2017-01-24 1.025216 1000000.0 \n", " 2017-01-25 1.021993 1000000.0 \n", " 2017-01-26 1.038446 1000000.0 ,\n", " 'stock_account': cash dividend_receivable market_value total_value \\\n", " date \n", " 2017-01-03 104254.977 0 895029.0 999283.977 \n", " 2017-01-04 104443.551 0 908899.0 1013342.551 \n", " 2017-01-05 104760.297 0 916325.0 1021085.297 \n", " 2017-01-06 103844.463 0 924233.0 1028077.463 \n", " 2017-01-09 106441.267 0 928718.0 1035159.267 \n", " 2017-01-10 105904.609 0 929508.0 1035412.609 \n", " 2017-01-11 107331.440 0 928507.0 1035838.440 \n", " 2017-01-12 104772.751 0 912431.0 1017203.751 \n", " 2017-01-13 1026.709 0 1007706.0 1008732.709 \n", " 2017-01-16 110665.150 0 867968.0 978633.150 \n", " 2017-01-17 100143.612 0 884830.0 984973.612 \n", " 2017-01-18 99545.955 0 895418.0 994963.955 \n", " 2017-01-19 99110.048 0 888076.0 987186.048 \n", " 2017-01-20 100860.305 0 903039.0 1003899.305 \n", " 2017-01-23 102120.582 0 911864.0 1013984.582 \n", " 2017-01-24 102948.606 0 922267.0 1025215.606 \n", " 2017-01-25 102145.478 0 919848.0 1021993.478 \n", " 2017-01-26 104010.400 0 934436.0 1038446.400 \n", " \n", " transaction_cost \n", " date \n", " 2017-01-03 716.023 \n", " 2017-01-04 33.426 \n", " 2017-01-05 308.254 \n", " 2017-01-06 41.834 \n", " 2017-01-09 25.196 \n", " 2017-01-10 36.658 \n", " 2017-01-11 30.169 \n", " 2017-01-12 27.689 \n", " 2017-01-13 116.042 \n", " 2017-01-16 220.558 \n", " 2017-01-17 33.538 \n", " 2017-01-18 288.657 \n", " 2017-01-19 32.907 \n", " 2017-01-20 273.742 \n", " 2017-01-23 276.723 \n", " 2017-01-24 21.976 \n", " 2017-01-25 32.128 \n", " 2017-01-26 36.078 ,\n", " 'stock_positions': avg_price last_price market_value order_book_id quantity symbol\n", " date \n", " 2017-01-03 6.930 6.93 99792.0 600581.XSHG 14400 八一钢铁\n", " 2017-01-03 6.440 6.44 99820.0 600675.XSHG 15500 中华企业\n", " 2017-01-03 11.220 11.22 99858.0 000037.XSHE 8900 深南电A\n", " 2017-01-03 2.860 2.86 99814.0 600808.XSHG 34900 马钢股份\n", " 2017-01-03 2.740 2.74 99736.0 600307.XSHG 36400 酒钢宏兴\n", " 2017-01-03 3.010 3.01 99932.0 600569.XSHG 33200 安阳钢铁\n", " 2017-01-03 19.030 19.03 98956.0 000617.XSHE 5200 中油资本\n", " 2017-01-03 42.250 42.25 97175.0 002352.XSHE 2300 顺丰控股\n", " 2017-01-03 4.130 4.13 99946.0 600546.XSHG 24200 山煤国际\n", " 2017-01-04 6.930 7.14 101388.0 600581.XSHG 14200 八一钢铁\n", " 2017-01-04 6.440 6.54 101370.0 600675.XSHG 15500 中华企业\n", " 2017-01-04 11.216 11.03 100373.0 000037.XSHE 9100 深南电A\n", " 2017-01-04 2.860 2.90 101210.0 600808.XSHG 34900 马钢股份\n", " 2017-01-04 2.740 2.77 101105.0 600307.XSHG 36500 酒钢宏兴\n", " 2017-01-04 3.010 3.04 101232.0 600569.XSHG 33300 安阳钢铁\n", " 2017-01-04 19.030 19.98 101898.0 000617.XSHE 5100 中油资本\n", " 2017-01-04 42.250 43.04 98992.0 002352.XSHE 2300 顺丰控股\n", " 2017-01-04 4.130 4.17 101331.0 600546.XSHG 24300 山煤国际\n", " 2017-01-05 6.932 7.03 101935.0 600581.XSHG 14500 八一钢铁\n", " 2017-01-05 6.440 6.67 0.0 600675.XSHG 0 中华企业\n", " 2017-01-05 11.214 11.03 101476.0 000037.XSHE 9200 深南电A\n", " 2017-01-05 2.860 2.90 102080.0 600808.XSHG 35200 马钢股份\n", " 2017-01-05 2.740 2.77 101936.0 600307.XSHG 36800 酒钢宏兴\n", " 2017-01-05 3.010 3.03 102111.0 600569.XSHG 33700 安阳钢铁\n", " 2017-01-05 19.030 20.98 102802.0 000617.XSHE 4900 中油资本\n", " 2017-01-05 5.030 5.03 102109.0 601666.XSHG 20300 平煤股份\n", " 2017-01-05 42.250 43.32 99636.0 002352.XSHE 2300 顺丰控股\n", " 2017-01-05 4.130 4.26 102240.0 600546.XSHG 24000 山煤国际\n", " 2017-01-06 6.932 7.09 102805.0 600581.XSHG 14500 八一钢铁\n", " 2017-01-06 11.208 10.92 102648.0 000037.XSHE 9400 深南电A\n", " ... ... ... ... ... ... ...\n", " 2017-01-23 5.400 5.49 101565.0 600117.XSHG 18500 西宁特钢\n", " 2017-01-23 3.015 3.22 101108.0 600569.XSHG 31400 安阳钢铁\n", " 2017-01-23 5.029 5.02 101404.0 601666.XSHG 20200 平煤股份\n", " 2017-01-24 6.875 6.34 102708.0 600581.XSHG 16200 八一钢铁\n", " 2017-01-24 11.167 11.06 102858.0 000037.XSHE 9300 深南电A\n", " 2017-01-24 2.863 3.15 102375.0 600808.XSHG 32500 马钢股份\n", " 2017-01-24 6.890 6.89 101972.0 600375.XSHG 14800 华菱星马\n", " 2017-01-24 2.742 2.90 102660.0 600307.XSHG 35400 酒钢宏兴\n", " 2017-01-24 4.133 4.37 102258.0 600546.XSHG 23400 山煤国际\n", " 2017-01-24 5.400 5.56 102860.0 600117.XSHG 18500 西宁特钢\n", " 2017-01-24 3.015 3.26 102364.0 600569.XSHG 31400 安阳钢铁\n", " 2017-01-24 5.029 5.06 102212.0 601666.XSHG 20200 平煤股份\n", " 2017-01-25 6.871 6.26 102038.0 600581.XSHG 16300 八一钢铁\n", " 2017-01-25 11.167 11.10 103230.0 000037.XSHE 9300 深南电A\n", " 2017-01-25 2.864 3.12 102024.0 600808.XSHG 32700 马钢股份\n", " 2017-01-25 6.890 6.83 101767.0 600375.XSHG 14900 华菱星马\n", " 2017-01-25 2.742 2.88 101952.0 600307.XSHG 35400 酒钢宏兴\n", " 2017-01-25 4.133 4.39 102287.0 600546.XSHG 23300 山煤国际\n", " 2017-01-25 5.400 5.63 102466.0 600117.XSHG 18200 西宁特钢\n", " 2017-01-25 3.017 3.22 102074.0 600569.XSHG 31700 安阳钢铁\n", " 2017-01-25 5.029 5.05 102010.0 601666.XSHG 20200 平煤股份\n", " 2017-01-26 6.871 6.34 103342.0 600581.XSHG 16300 八一钢铁\n", " 2017-01-26 11.167 11.22 104346.0 000037.XSHE 9300 深南电A\n", " 2017-01-26 2.864 3.17 103659.0 600808.XSHG 32700 马钢股份\n", " 2017-01-26 6.889 6.87 103737.0 600375.XSHG 15100 华菱星马\n", " 2017-01-26 2.744 2.90 103820.0 600307.XSHG 35800 酒钢宏兴\n", " 2017-01-26 4.136 4.39 103604.0 600546.XSHG 23600 山煤国际\n", " 2017-01-26 5.400 5.85 104130.0 600117.XSHG 17800 西宁特钢\n", " 2017-01-26 3.018 3.26 103668.0 600569.XSHG 31800 安阳钢铁\n", " 2017-01-26 5.029 5.34 104130.0 601666.XSHG 19500 平煤股份\n", " \n", " [168 rows x 6 columns],\n", " 'summary': {'STOCK': 1000000.0,\n", " 'alpha': 0.115,\n", " 'annualized_returns': 0.775,\n", " 'benchmark': '000300.XSHG',\n", " 'benchmark_annualized_returns': 0.229,\n", " 'benchmark_total_returns': 0.014,\n", " 'beta': 1.55,\n", " 'cash': 104010.4,\n", " 'downside_risk': 0.204,\n", " 'end_date': '2017-01-26',\n", " 'information_ratio': 1.763,\n", " 'max_drawdown': 0.098,\n", " 'run_type': 'BACKTEST',\n", " 'sharpe': 3.486,\n", " 'sortino': 5.12,\n", " 'start_date': '2017-01-03',\n", " 'strategy_file': './testStrategy.ipynb',\n", " 'strategy_name': 'testStrategy',\n", " 'total_returns': 0.038,\n", " 'total_value': 1038446.4,\n", " 'tracking_error': 0.252,\n", " 'unit_net_value': 1.038,\n", " 'units': 1000000.0,\n", " 'volatility': 0.299},\n", " 'trades': commission exec_id last_price last_quantity \\\n", " datetime \n", " 2017-01-03 15:00:00 79.1648 1524487211 19.03 5200 \n", " 2017-01-03 15:00:00 77.7400 1524487212 42.25 2300 \n", " 2017-01-03 15:00:00 79.8336 1524487213 6.93 14400 \n", " 2017-01-03 15:00:00 79.8864 1524487214 11.22 8900 \n", " 2017-01-03 15:00:00 79.7888 1524487215 2.74 36400 \n", " 2017-01-03 15:00:00 79.9456 1524487216 3.01 33200 \n", " 2017-01-03 15:00:00 79.9568 1524487217 4.13 24200 \n", " 2017-01-03 15:00:00 79.8512 1524487218 2.86 34900 \n", " 2017-01-03 15:00:00 79.8560 1524487219 6.44 15500 \n", " 2017-01-04 15:00:00 5.0000 1524487220 19.98 100 \n", " 2017-01-04 15:00:00 5.0000 1524487221 11.03 200 \n", " 2017-01-04 15:00:00 5.0000 1524487222 7.14 200 \n", " 2017-01-04 15:00:00 5.0000 1524487223 2.77 100 \n", " 2017-01-04 15:00:00 5.0000 1524487224 3.04 100 \n", " 2017-01-04 15:00:00 5.0000 1524487225 4.17 100 \n", " 2017-01-05 15:00:00 82.7080 1524487226 6.67 15500 \n", " 2017-01-05 15:00:00 5.0000 1524487227 20.98 200 \n", " 2017-01-05 15:00:00 5.0000 1524487228 11.03 100 \n", " 2017-01-05 15:00:00 5.0000 1524487229 7.03 300 \n", " 2017-01-05 15:00:00 5.0000 1524487230 2.77 300 \n", " 2017-01-05 15:00:00 5.0000 1524487231 3.03 400 \n", " 2017-01-05 15:00:00 5.0000 1524487232 4.26 300 \n", " 2017-01-05 15:00:00 5.0000 1524487233 2.90 300 \n", " 2017-01-05 15:00:00 81.6872 1524487234 5.03 20300 \n", " 2017-01-06 15:00:00 5.0000 1524487235 22.03 200 \n", " 2017-01-06 15:00:00 5.0000 1524487236 42.31 100 \n", " 2017-01-06 15:00:00 5.0000 1524487237 10.92 200 \n", " 2017-01-06 15:00:00 5.0000 1524487238 2.81 200 \n", " 2017-01-06 15:00:00 5.0000 1524487239 3.11 600 \n", " 2017-01-06 15:00:00 5.0000 1524487240 2.91 100 \n", " ... ... ... ... ... \n", " 2017-01-19 15:00:00 5.0000 1524487290 6.07 100 \n", " 2017-01-19 15:00:00 5.0000 1524487291 10.49 200 \n", " 2017-01-19 15:00:00 5.0000 1524487292 2.76 100 \n", " 2017-01-19 15:00:00 5.0000 1524487293 3.12 300 \n", " 2017-01-19 15:00:00 5.0000 1524487294 4.22 400 \n", " 2017-01-19 15:00:00 5.0000 1524487295 3.06 200 \n", " 2017-01-20 15:00:00 81.0560 1524487296 6.80 14900 \n", " 2017-01-20 15:00:00 5.0000 1524487297 3.20 200 \n", " 2017-01-20 15:00:00 5.0000 1524487298 4.32 100 \n", " 2017-01-20 15:00:00 80.2944 1524487299 6.97 14400 \n", " 2017-01-23 15:00:00 82.2528 1524487300 7.14 14400 \n", " 2017-01-23 15:00:00 5.0000 1524487301 6.28 100 \n", " 2017-01-23 15:00:00 5.0000 1524487302 3.12 200 \n", " 2017-01-23 15:00:00 81.0264 1524487303 6.89 14700 \n", " 2017-01-24 15:00:00 5.0000 1524487304 11.06 100 \n", " 2017-01-24 15:00:00 5.0000 1524487305 2.90 300 \n", " 2017-01-24 15:00:00 5.0000 1524487306 4.37 100 \n", " 2017-01-24 15:00:00 5.0000 1524487307 6.89 100 \n", " 2017-01-25 15:00:00 5.0000 1524487308 6.26 100 \n", " 2017-01-25 15:00:00 5.0000 1524487309 3.22 300 \n", " 2017-01-25 15:00:00 5.0000 1524487310 4.39 100 \n", " 2017-01-25 15:00:00 5.0000 1524487311 3.12 200 \n", " 2017-01-25 15:00:00 5.0000 1524487312 5.63 300 \n", " 2017-01-25 15:00:00 5.0000 1524487313 6.83 100 \n", " 2017-01-26 15:00:00 5.0000 1524487314 2.90 400 \n", " 2017-01-26 15:00:00 5.0000 1524487315 3.26 100 \n", " 2017-01-26 15:00:00 5.0000 1524487316 4.39 300 \n", " 2017-01-26 15:00:00 5.0000 1524487317 5.34 700 \n", " 2017-01-26 15:00:00 5.0000 1524487318 5.85 400 \n", " 2017-01-26 15:00:00 5.0000 1524487319 6.87 200 \n", " \n", " order_book_id order_id position_effect side symbol \\\n", " datetime \n", " 2017-01-03 15:00:00 000617.XSHE 1524487252 None BUY 中油资本 \n", " 2017-01-03 15:00:00 002352.XSHE 1524487253 None BUY 顺丰控股 \n", " 2017-01-03 15:00:00 600581.XSHG 1524487254 None BUY 八一钢铁 \n", " 2017-01-03 15:00:00 000037.XSHE 1524487255 None BUY 深南电A \n", " 2017-01-03 15:00:00 600307.XSHG 1524487256 None BUY 酒钢宏兴 \n", " 2017-01-03 15:00:00 600569.XSHG 1524487257 None BUY 安阳钢铁 \n", " 2017-01-03 15:00:00 600546.XSHG 1524487258 None BUY 山煤国际 \n", " 2017-01-03 15:00:00 600808.XSHG 1524487259 None BUY 马钢股份 \n", " 2017-01-03 15:00:00 600675.XSHG 1524487260 None BUY 中华企业 \n", " 2017-01-04 15:00:00 000617.XSHE 1524487262 None SELL 中油资本 \n", " 2017-01-04 15:00:00 000037.XSHE 1524487263 None BUY 深南电A \n", " 2017-01-04 15:00:00 600581.XSHG 1524487264 None SELL 八一钢铁 \n", " 2017-01-04 15:00:00 600307.XSHG 1524487265 None BUY 酒钢宏兴 \n", " 2017-01-04 15:00:00 600569.XSHG 1524487266 None BUY 安阳钢铁 \n", " 2017-01-04 15:00:00 600546.XSHG 1524487267 None BUY 山煤国际 \n", " 2017-01-05 15:00:00 600675.XSHG 1524487268 None SELL 中华企业 \n", " 2017-01-05 15:00:00 000617.XSHE 1524487270 None SELL 中油资本 \n", " 2017-01-05 15:00:00 000037.XSHE 1524487271 None BUY 深南电A \n", " 2017-01-05 15:00:00 600581.XSHG 1524487272 None BUY 八一钢铁 \n", " 2017-01-05 15:00:00 600307.XSHG 1524487273 None BUY 酒钢宏兴 \n", " 2017-01-05 15:00:00 600569.XSHG 1524487274 None BUY 安阳钢铁 \n", " 2017-01-05 15:00:00 600546.XSHG 1524487275 None SELL 山煤国际 \n", " 2017-01-05 15:00:00 600808.XSHG 1524487276 None BUY 马钢股份 \n", " 2017-01-05 15:00:00 601666.XSHG 1524487277 None BUY 平煤股份 \n", " 2017-01-06 15:00:00 000617.XSHE 1524487279 None SELL 中油资本 \n", " 2017-01-06 15:00:00 002352.XSHE 1524487280 None BUY 顺丰控股 \n", " 2017-01-06 15:00:00 000037.XSHE 1524487281 None BUY 深南电A \n", " 2017-01-06 15:00:00 600307.XSHG 1524487282 None SELL 酒钢宏兴 \n", " 2017-01-06 15:00:00 600569.XSHG 1524487283 None SELL 安阳钢铁 \n", " 2017-01-06 15:00:00 600808.XSHG 1524487284 None BUY 马钢股份 \n", " ... ... ... ... ... ... \n", " 2017-01-19 15:00:00 600581.XSHG 1524487345 None SELL 八一钢铁 \n", " 2017-01-19 15:00:00 000037.XSHE 1524487346 None BUY 深南电A \n", " 2017-01-19 15:00:00 600307.XSHG 1524487347 None BUY 酒钢宏兴 \n", " 2017-01-19 15:00:00 600569.XSHG 1524487348 None BUY 安阳钢铁 \n", " 2017-01-19 15:00:00 600546.XSHG 1524487349 None SELL 山煤国际 \n", " 2017-01-19 15:00:00 600808.XSHG 1524487350 None SELL 马钢股份 \n", " 2017-01-20 15:00:00 600375.XSHG 1524487351 None SELL 华菱星马 \n", " 2017-01-20 15:00:00 600569.XSHG 1524487353 None SELL 安阳钢铁 \n", " 2017-01-20 15:00:00 600546.XSHG 1524487354 None SELL 山煤国际 \n", " 2017-01-20 15:00:00 600675.XSHG 1524487355 None BUY 中华企业 \n", " 2017-01-23 15:00:00 600675.XSHG 1524487356 None SELL 中华企业 \n", " 2017-01-23 15:00:00 600581.XSHG 1524487358 None SELL 八一钢铁 \n", " 2017-01-23 15:00:00 600808.XSHG 1524487359 None BUY 马钢股份 \n", " 2017-01-23 15:00:00 600375.XSHG 1524487360 None BUY 华菱星马 \n", " 2017-01-24 15:00:00 000037.XSHE 1524487362 None SELL 深南电A \n", " 2017-01-24 15:00:00 600307.XSHG 1524487363 None SELL 酒钢宏兴 \n", " 2017-01-24 15:00:00 600546.XSHG 1524487364 None BUY 山煤国际 \n", " 2017-01-24 15:00:00 600375.XSHG 1524487365 None BUY 华菱星马 \n", " 2017-01-25 15:00:00 600581.XSHG 1524487367 None BUY 八一钢铁 \n", " 2017-01-25 15:00:00 600569.XSHG 1524487368 None BUY 安阳钢铁 \n", " 2017-01-25 15:00:00 600546.XSHG 1524487369 None SELL 山煤国际 \n", " 2017-01-25 15:00:00 600808.XSHG 1524487370 None BUY 马钢股份 \n", " 2017-01-25 15:00:00 600117.XSHG 1524487371 None SELL 西宁特钢 \n", " 2017-01-25 15:00:00 600375.XSHG 1524487372 None BUY 华菱星马 \n", " 2017-01-26 15:00:00 600307.XSHG 1524487374 None BUY 酒钢宏兴 \n", " 2017-01-26 15:00:00 600569.XSHG 1524487375 None BUY 安阳钢铁 \n", " 2017-01-26 15:00:00 600546.XSHG 1524487376 None BUY 山煤国际 \n", " 2017-01-26 15:00:00 601666.XSHG 1524487377 None SELL 平煤股份 \n", " 2017-01-26 15:00:00 600117.XSHG 1524487378 None SELL 西宁特钢 \n", " 2017-01-26 15:00:00 600375.XSHG 1524487379 None BUY 华菱星马 \n", " \n", " tax trading_datetime transaction_cost \n", " datetime \n", " 2017-01-03 15:00:00 0.000 2017-01-03 15:00:00 79.1648 \n", " 2017-01-03 15:00:00 0.000 2017-01-03 15:00:00 77.7400 \n", " 2017-01-03 15:00:00 0.000 2017-01-03 15:00:00 79.8336 \n", " 2017-01-03 15:00:00 0.000 2017-01-03 15:00:00 79.8864 \n", " 2017-01-03 15:00:00 0.000 2017-01-03 15:00:00 79.7888 \n", " 2017-01-03 15:00:00 0.000 2017-01-03 15:00:00 79.9456 \n", " 2017-01-03 15:00:00 0.000 2017-01-03 15:00:00 79.9568 \n", " 2017-01-03 15:00:00 0.000 2017-01-03 15:00:00 79.8512 \n", " 2017-01-03 15:00:00 0.000 2017-01-03 15:00:00 79.8560 \n", " 2017-01-04 15:00:00 1.998 2017-01-04 15:00:00 6.9980 \n", " 2017-01-04 15:00:00 0.000 2017-01-04 15:00:00 5.0000 \n", " 2017-01-04 15:00:00 1.428 2017-01-04 15:00:00 6.4280 \n", " 2017-01-04 15:00:00 0.000 2017-01-04 15:00:00 5.0000 \n", " 2017-01-04 15:00:00 0.000 2017-01-04 15:00:00 5.0000 \n", " 2017-01-04 15:00:00 0.000 2017-01-04 15:00:00 5.0000 \n", " 2017-01-05 15:00:00 103.385 2017-01-05 15:00:00 186.0930 \n", " 2017-01-05 15:00:00 4.196 2017-01-05 15:00:00 9.1960 \n", " 2017-01-05 15:00:00 0.000 2017-01-05 15:00:00 5.0000 \n", " 2017-01-05 15:00:00 0.000 2017-01-05 15:00:00 5.0000 \n", " 2017-01-05 15:00:00 0.000 2017-01-05 15:00:00 5.0000 \n", " 2017-01-05 15:00:00 0.000 2017-01-05 15:00:00 5.0000 \n", " 2017-01-05 15:00:00 1.278 2017-01-05 15:00:00 6.2780 \n", " 2017-01-05 15:00:00 0.000 2017-01-05 15:00:00 5.0000 \n", " 2017-01-05 15:00:00 0.000 2017-01-05 15:00:00 81.6872 \n", " 2017-01-06 15:00:00 4.406 2017-01-06 15:00:00 9.4060 \n", " 2017-01-06 15:00:00 0.000 2017-01-06 15:00:00 5.0000 \n", " 2017-01-06 15:00:00 0.000 2017-01-06 15:00:00 5.0000 \n", " 2017-01-06 15:00:00 0.562 2017-01-06 15:00:00 5.5620 \n", " 2017-01-06 15:00:00 1.866 2017-01-06 15:00:00 6.8660 \n", " 2017-01-06 15:00:00 0.000 2017-01-06 15:00:00 5.0000 \n", " ... ... ... ... \n", " 2017-01-19 15:00:00 0.607 2017-01-19 15:00:00 5.6070 \n", " 2017-01-19 15:00:00 0.000 2017-01-19 15:00:00 5.0000 \n", " 2017-01-19 15:00:00 0.000 2017-01-19 15:00:00 5.0000 \n", " 2017-01-19 15:00:00 0.000 2017-01-19 15:00:00 5.0000 \n", " 2017-01-19 15:00:00 1.688 2017-01-19 15:00:00 6.6880 \n", " 2017-01-19 15:00:00 0.612 2017-01-19 15:00:00 5.6120 \n", " 2017-01-20 15:00:00 101.320 2017-01-20 15:00:00 182.3760 \n", " 2017-01-20 15:00:00 0.640 2017-01-20 15:00:00 5.6400 \n", " 2017-01-20 15:00:00 0.432 2017-01-20 15:00:00 5.4320 \n", " 2017-01-20 15:00:00 0.000 2017-01-20 15:00:00 80.2944 \n", " 2017-01-23 15:00:00 102.816 2017-01-23 15:00:00 185.0688 \n", " 2017-01-23 15:00:00 0.628 2017-01-23 15:00:00 5.6280 \n", " 2017-01-23 15:00:00 0.000 2017-01-23 15:00:00 5.0000 \n", " 2017-01-23 15:00:00 0.000 2017-01-23 15:00:00 81.0264 \n", " 2017-01-24 15:00:00 1.106 2017-01-24 15:00:00 6.1060 \n", " 2017-01-24 15:00:00 0.870 2017-01-24 15:00:00 5.8700 \n", " 2017-01-24 15:00:00 0.000 2017-01-24 15:00:00 5.0000 \n", " 2017-01-24 15:00:00 0.000 2017-01-24 15:00:00 5.0000 \n", " 2017-01-25 15:00:00 0.000 2017-01-25 15:00:00 5.0000 \n", " 2017-01-25 15:00:00 0.000 2017-01-25 15:00:00 5.0000 \n", " 2017-01-25 15:00:00 0.439 2017-01-25 15:00:00 5.4390 \n", " 2017-01-25 15:00:00 0.000 2017-01-25 15:00:00 5.0000 \n", " 2017-01-25 15:00:00 1.689 2017-01-25 15:00:00 6.6890 \n", " 2017-01-25 15:00:00 0.000 2017-01-25 15:00:00 5.0000 \n", " 2017-01-26 15:00:00 0.000 2017-01-26 15:00:00 5.0000 \n", " 2017-01-26 15:00:00 0.000 2017-01-26 15:00:00 5.0000 \n", " 2017-01-26 15:00:00 0.000 2017-01-26 15:00:00 5.0000 \n", " 2017-01-26 15:00:00 3.738 2017-01-26 15:00:00 8.7380 \n", " 2017-01-26 15:00:00 2.340 2017-01-26 15:00:00 7.3400 \n", " 2017-01-26 15:00:00 0.000 2017-01-26 15:00:00 5.0000 \n", " \n", " [109 rows x 12 columns]}}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from rqalpha import run_file\n", "\n", "config = {\n", " \"base\": {\n", " \"start_date\": \"2017-01-01\",\n", " \"end_date\": \"2017-01-31\",\n", " },\n", " \"mod\": {\n", " \"sys_analyser\": {\n", " \"enabled\": True,\n", " \"plot\": True\n", " }\n", " }\n", "}\n", "\n", "file_path = \"./testStrategy.ipynb\"\n", "run_file(file_path, config,config_file = \"../config.yml\")\n", "# run_func(,config,config_file=)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: better_exceptions will only inspect code from the command line\n", " when using: `python -m better_exceptions'. Otherwise, only code\n", " loaded from files will be inspected!\n" ] } ], "source": [ "%load_ext rqalpha" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Zn5WVhdOnT2PUqFH1XPqvS10+a6Rpys3NxfPnz7F+/XrMmzcPQOnQPzc3N7x5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ZXx+uuv09bWxu7du7n//vspLy/n0Ucf7fH5E0KI/tbU1kVDSxdGswWTyYzxZHHBaLJgNJl/LDaY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\nYuvWrTh27Bh8fX2RkJCAESNGdP1maYyWsrVardi8e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"text/plain": [ "<matplotlib.figure.Figure at 0x24393376c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%rqalpha -s 20170101 -e 20170131 --config ../config.yml -f ./testStrategy.ipynb -p" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:rqalpha]", "language": "python", "name": "conda-env-rqalpha-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
ga-students/DAT_SF_11_homework	michaelhousman/hw1_soln_michaelhousman.ipynb	1	180140	{ "metadata": { "name": "", "signature": "sha256:ba68c5a38f90ebeb6e8c148f83bdf50a5ba7b2981ea72883fbef374b3943b4e4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Homework 1 - Data Analysis and Regression\n", "In this assignment your challenge is to do some basic analysis for Airbnb. Provided in hw/data/ there are 2 data files, <a href=../data/bookings.csv>bookings.csv</a> and <a href=../data/listings.csv>listings.csv</a>. The objective is to practice data munging and begin our exploration of regression." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Standard imports for data analysis packages in Python\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "# This enables inline Plots\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Part 1 - Data exploration\n", "###First, create 2 data frames: `listings` and `bookings` from their respective data files" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a data frame from the listings dataset\n", "\n", "listings = pd.read_csv('../data/listings.csv')\n", "\n", "# Create a data frame from the bookings dataset\n", "\n", "bookings = pd.read_csv('../data/bookings.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 59 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###What is the mean, median and standard deviation of price, person capacity, picture count, description length and tenure of the properties?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings[['price', 'person_capacity', 'picture_count', 'description_length', 'tenure_months']].describe()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>price</th>\n", " <th>person_capacity</th>\n", " <th>picture_count</th>\n", " <th>description_length</th>\n", " <th>tenure_months</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 408.000000</td>\n", " <td> 408.000000</td>\n", " <td> 408.000000</td>\n", " <td> 408.000000</td>\n", " <td> 408.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 187.806373</td>\n", " <td> 2.997549</td>\n", " <td> 14.389706</td>\n", " <td> 309.159314</td>\n", " <td> 8.487745</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 353.050858</td>\n", " <td> 1.594676</td>\n", " <td> 10.477428</td>\n", " <td> 228.021684</td>\n", " <td> 5.872088</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 39.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.000000</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 90.000000</td>\n", " <td> 2.000000</td>\n", " <td> 6.000000</td>\n", " <td> 179.000000</td>\n", " <td> 4.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 125.000000</td>\n", " <td> 2.000000</td>\n", " <td> 12.000000</td>\n", " <td> 250.000000</td>\n", " <td> 7.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 199.000000</td>\n", " <td> 4.000000</td>\n", " <td> 20.000000</td>\n", " <td> 389.500000</td>\n", " <td> 13.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 5000.000000</td>\n", " <td> 10.000000</td>\n", " <td> 71.000000</td>\n", " <td> 1969.000000</td>\n", " <td> 30.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 82, "text": [ " price person_capacity picture_count description_length \\\n", "count 408.000000 408.000000 408.000000 408.000000 \n", "mean 187.806373 2.997549 14.389706 309.159314 \n", "std 353.050858 1.594676 10.477428 228.021684 \n", "min 39.000000 1.000000 1.000000 0.000000 \n", "25% 90.000000 2.000000 6.000000 179.000000 \n", "50% 125.000000 2.000000 12.000000 250.000000 \n", "75% 199.000000 4.000000 20.000000 389.500000 \n", "max 5000.000000 10.000000 71.000000 1969.000000 \n", "\n", " tenure_months \n", "count 408.000000 \n", "mean 8.487745 \n", "std 5.872088 \n", "min 1.000000 \n", "25% 4.000000 \n", "50% 7.000000 \n", "75% 13.000000 \n", "max 30.000000 " ] } ], "prompt_number": 82 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###What what are the mean price, person capacity, picture count, description length and tenure of the properties grouped by property type?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings.info()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 408 entries, 0 to 407\n", "Data columns (total 8 columns):\n", "prop_id 408 non-null int64\n", "prop_type 408 non-null object\n", "neighborhood 408 non-null object\n", "price 408 non-null int64\n", "person_capacity 408 non-null int64\n", "picture_count 408 non-null int64\n", "description_length 408 non-null int64\n", "tenure_months 408 non-null int64\n", "dtypes: int64(6), object(2)\n", "memory usage: 28.7+ KB\n" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "listings.groupby(['prop_type'])['price', 'person_capacity', 'picture_count', 'description_length', 'tenure_months'].agg(['mean'])" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th>price</th>\n", " <th>person_capacity</th>\n", " <th>picture_count</th>\n", " <th>description_length</th>\n", " <th>tenure_months</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>mean</th>\n", " <th>mean</th>\n", " <th>mean</th>\n", " <th>mean</th>\n", " <th>mean</th>\n", " </tr>\n", " <tr>\n", " <th>prop_type</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Property type 1</th>\n", " <td> 237.085502</td>\n", " <td> 3.516729</td>\n", " <td> 14.695167</td>\n", " <td> 313.171004</td>\n", " <td> 8.464684</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 93.288889</td>\n", " <td> 2.000000</td>\n", " <td> 13.948148</td>\n", " <td> 304.851852</td>\n", " <td> 8.377778</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 3</th>\n", " <td> 63.750000</td>\n", " <td> 1.750000</td>\n", " <td> 8.750000</td>\n", " <td> 184.750000</td>\n", " <td> 13.750000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 62, "text": [ " price person_capacity picture_count description_length \\\n", " mean mean mean mean \n", "prop_type \n", "Property type 1 237.085502 3.516729 14.695167 313.171004 \n", "Property type 2 93.288889 2.000000 13.948148 304.851852 \n", "Property type 3 63.750000 1.750000 8.750000 184.750000 \n", "\n", " tenure_months \n", " mean \n", "prop_type \n", "Property type 1 8.464684 \n", "Property type 2 8.377778 \n", "Property type 3 13.750000 " ] } ], "prompt_number": 62 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Same, but by property type per neighborhood? " ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings.groupby(['prop_type', 'neighborhood'])['price', 'person_capacity', 'picture_count', 'description_length', 'tenure_months'].agg(['mean'])" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th></th>\n", " <th>price</th>\n", " <th>person_capacity</th>\n", " <th>picture_count</th>\n", " <th>description_length</th>\n", " <th>tenure_months</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th></th>\n", " <th>mean</th>\n", " <th>mean</th>\n", " <th>mean</th>\n", " <th>mean</th>\n", " <th>mean</th>\n", " </tr>\n", " <tr>\n", " <th>prop_type</th>\n", " <th>neighborhood</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"20\" valign=\"top\">Property type 1</th>\n", " <th>Neighborhood 1</th>\n", " <td> 85.000000</td>\n", " <td> 2.000000</td>\n", " <td> 26.000000</td>\n", " <td> 209.000000</td>\n", " <td> 6.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 10</th>\n", " <td> 142.500000</td>\n", " <td> 3.500000</td>\n", " <td> 13.333333</td>\n", " <td> 391.000000</td>\n", " <td> 3.833333</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 11</th>\n", " <td> 159.428571</td>\n", " <td> 3.214286</td>\n", " <td> 9.928571</td>\n", " <td> 379.000000</td>\n", " <td> 9.642857</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 12</th>\n", " <td> 365.615385</td>\n", " <td> 3.435897</td>\n", " <td> 10.820513</td>\n", " <td> 267.205128</td>\n", " <td> 7.897436</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 13</th>\n", " <td> 241.897959</td>\n", " <td> 4.061224</td>\n", " <td> 15.653061</td>\n", " <td> 290.408163</td>\n", " <td> 9.122449</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 14</th>\n", " <td> 164.676471</td>\n", " <td> 3.205882</td>\n", " <td> 14.764706</td>\n", " <td> 317.205882</td>\n", " <td> 8.441176</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 15</th>\n", " <td> 178.880000</td>\n", " <td> 3.720000</td>\n", " <td> 14.320000</td>\n", " <td> 321.760000</td>\n", " <td> 9.320000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 16</th>\n", " <td> 158.928571</td>\n", " <td> 2.928571</td>\n", " <td> 21.642857</td>\n", " <td> 310.714286</td>\n", " <td> 7.071429</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 17</th>\n", " <td> 189.869565</td>\n", " <td> 3.521739</td>\n", " <td> 16.086957</td>\n", " <td> 317.347826</td>\n", " <td> 9.869565</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 18</th>\n", " <td> 173.590909</td>\n", " <td> 2.954545</td>\n", " <td> 16.090909</td>\n", " <td> 369.227273</td>\n", " <td> 8.227273</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 19</th>\n", " <td> 222.375000</td>\n", " <td> 3.625000</td>\n", " <td> 11.000000</td>\n", " <td> 254.500000</td>\n", " <td> 6.500000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 2</th>\n", " <td> 250.000000</td>\n", " <td> 6.000000</td>\n", " <td> 8.000000</td>\n", " <td> 423.000000</td>\n", " <td> 6.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 20</th>\n", " <td> 804.333333</td>\n", " <td> 2.777778</td>\n", " <td> 9.444444</td>\n", " <td> 223.555556</td>\n", " <td> 9.666667</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 21</th>\n", " <td> 362.500000</td>\n", " <td> 4.250000</td>\n", " <td> 49.000000</td>\n", " <td> 306.250000</td>\n", " <td> 14.750000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 22</th>\n", " <td> 225.000000</td>\n", " <td> 3.000000</td>\n", " <td> 19.000000</td>\n", " <td> 500.000000</td>\n", " <td> 9.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 5</th>\n", " <td> 194.500000</td>\n", " <td> 2.500000</td>\n", " <td> 8.500000</td>\n", " <td> 266.500000</td>\n", " <td> 11.500000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 6</th>\n", " <td> 146.000000</td>\n", " <td> 3.333333</td>\n", " <td> 12.666667</td>\n", " <td> 290.666667</td>\n", " <td> 4.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 7</th>\n", " <td> 161.000000</td>\n", " <td> 3.666667</td>\n", " <td> 14.333333</td>\n", " <td> 343.000000</td>\n", " <td> 5.333333</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 8</th>\n", " <td> 174.750000</td>\n", " <td> 5.000000</td>\n", " <td> 11.000000</td>\n", " <td> 300.000000</td>\n", " <td> 6.750000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 9</th>\n", " <td> 151.142857</td>\n", " <td> 4.285714</td>\n", " <td> 13.428571</td>\n", " <td> 471.428571</td>\n", " <td> 5.714286</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"16\" valign=\"top\">Property type 2</th>\n", " <th>Neighborhood 10</th>\n", " <td> 137.500000</td>\n", " <td> 2.000000</td>\n", " <td> 20.000000</td>\n", " <td> 126.000000</td>\n", " <td> 3.500000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 11</th>\n", " <td> 78.750000</td>\n", " <td> 2.000000</td>\n", " <td> 16.750000</td>\n", " <td> 161.250000</td>\n", " <td> 11.250000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 12</th>\n", " <td> 96.894737</td>\n", " <td> 1.947368</td>\n", " <td> 10.473684</td>\n", " <td> 244.526316</td>\n", " <td> 9.842105</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 13</th>\n", " <td> 81.130435</td>\n", " <td> 1.826087</td>\n", " <td> 16.695652</td>\n", " <td> 418.565217</td>\n", " <td> 9.739130</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 14</th>\n", " <td> 83.809524</td>\n", " <td> 1.857143</td>\n", " <td> 15.904762</td>\n", " <td> 348.619048</td>\n", " <td> 8.714286</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 15</th>\n", " <td> 95.000000</td>\n", " <td> 2.266667</td>\n", " <td> 11.733333</td>\n", " <td> 301.733333</td>\n", " <td> 8.200000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 16</th>\n", " <td> 83.625000</td>\n", " <td> 2.062500</td>\n", " <td> 15.375000</td>\n", " <td> 246.250000</td>\n", " <td> 6.687500</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 17</th>\n", " <td> 102.454545</td>\n", " <td> 2.000000</td>\n", " <td> 15.454545</td>\n", " <td> 308.272727</td>\n", " <td> 7.181818</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 18</th>\n", " <td> 120.666667</td>\n", " <td> 2.222222</td>\n", " <td> 12.333333</td>\n", " <td> 297.777778</td>\n", " <td> 9.222222</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 19</th>\n", " <td> 88.875000</td>\n", " <td> 2.000000</td>\n", " <td> 15.125000</td>\n", " <td> 383.375000</td>\n", " <td> 5.500000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 20</th>\n", " <td> 60.000000</td>\n", " <td> 1.000000</td>\n", " <td> 3.000000</td>\n", " <td> 101.000000</td>\n", " <td> 6.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 3</th>\n", " <td> 60.000000</td>\n", " <td> 2.000000</td>\n", " <td> 7.000000</td>\n", " <td> 264.000000</td>\n", " <td> 9.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 4</th>\n", " <td> 60.000000</td>\n", " <td> 2.000000</td>\n", " <td> 10.000000</td>\n", " <td> 95.000000</td>\n", " <td> 11.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 7</th>\n", " <td> 100.000000</td>\n", " <td> 2.000000</td>\n", " <td> 3.000000</td>\n", " <td> 148.000000</td>\n", " <td> 2.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 8</th>\n", " <td> 350.000000</td>\n", " <td> 4.000000</td>\n", " <td> 5.000000</td>\n", " <td> 223.000000</td>\n", " <td> 3.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 9</th>\n", " <td> 110.000000</td>\n", " <td> 2.000000</td>\n", " <td> 3.500000</td>\n", " <td> 114.500000</td>\n", " <td> 9.000000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">Property type 3</th>\n", " <th>Neighborhood 11</th>\n", " <td> 75.000000</td>\n", " <td> 2.000000</td>\n", " <td> 15.000000</td>\n", " <td> 196.000000</td>\n", " <td> 8.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 14</th>\n", " <td> 75.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 113.000000</td>\n", " <td> 5.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 17</th>\n", " <td> 65.000000</td>\n", " <td> 2.000000</td>\n", " <td> 15.000000</td>\n", " <td> 189.000000</td>\n", " <td> 23.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 4</th>\n", " <td> 40.000000</td>\n", " <td> 2.000000</td>\n", " <td> 4.000000</td>\n", " <td> 241.000000</td>\n", " <td> 19.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 63, "text": [ " price person_capacity picture_count \\\n", " mean mean mean \n", "prop_type neighborhood \n", "Property type 1 Neighborhood 1 85.000000 2.000000 26.000000 \n", " Neighborhood 10 142.500000 3.500000 13.333333 \n", " Neighborhood 11 159.428571 3.214286 9.928571 \n", " Neighborhood 12 365.615385 3.435897 10.820513 \n", " Neighborhood 13 241.897959 4.061224 15.653061 \n", " Neighborhood 14 164.676471 3.205882 14.764706 \n", " Neighborhood 15 178.880000 3.720000 14.320000 \n", " Neighborhood 16 158.928571 2.928571 21.642857 \n", " Neighborhood 17 189.869565 3.521739 16.086957 \n", " Neighborhood 18 173.590909 2.954545 16.090909 \n", " Neighborhood 19 222.375000 3.625000 11.000000 \n", " Neighborhood 2 250.000000 6.000000 8.000000 \n", " Neighborhood 20 804.333333 2.777778 9.444444 \n", " Neighborhood 21 362.500000 4.250000 49.000000 \n", " Neighborhood 22 225.000000 3.000000 19.000000 \n", " Neighborhood 5 194.500000 2.500000 8.500000 \n", " Neighborhood 6 146.000000 3.333333 12.666667 \n", " Neighborhood 7 161.000000 3.666667 14.333333 \n", " Neighborhood 8 174.750000 5.000000 11.000000 \n", " Neighborhood 9 151.142857 4.285714 13.428571 \n", "Property type 2 Neighborhood 10 137.500000 2.000000 20.000000 \n", " Neighborhood 11 78.750000 2.000000 16.750000 \n", " Neighborhood 12 96.894737 1.947368 10.473684 \n", " Neighborhood 13 81.130435 1.826087 16.695652 \n", " Neighborhood 14 83.809524 1.857143 15.904762 \n", " Neighborhood 15 95.000000 2.266667 11.733333 \n", " Neighborhood 16 83.625000 2.062500 15.375000 \n", " Neighborhood 17 102.454545 2.000000 15.454545 \n", " Neighborhood 18 120.666667 2.222222 12.333333 \n", " Neighborhood 19 88.875000 2.000000 15.125000 \n", " Neighborhood 20 60.000000 1.000000 3.000000 \n", " Neighborhood 3 60.000000 2.000000 7.000000 \n", " Neighborhood 4 60.000000 2.000000 10.000000 \n", " Neighborhood 7 100.000000 2.000000 3.000000 \n", " Neighborhood 8 350.000000 4.000000 5.000000 \n", " Neighborhood 9 110.000000 2.000000 3.500000 \n", "Property type 3 Neighborhood 11 75.000000 2.000000 15.000000 \n", " Neighborhood 14 75.000000 1.000000 1.000000 \n", " Neighborhood 17 65.000000 2.000000 15.000000 \n", " Neighborhood 4 40.000000 2.000000 4.000000 \n", "\n", " description_length tenure_months \n", " mean mean \n", "prop_type neighborhood \n", "Property type 1 Neighborhood 1 209.000000 6.000000 \n", " Neighborhood 10 391.000000 3.833333 \n", " Neighborhood 11 379.000000 9.642857 \n", " Neighborhood 12 267.205128 7.897436 \n", " Neighborhood 13 290.408163 9.122449 \n", " Neighborhood 14 317.205882 8.441176 \n", " Neighborhood 15 321.760000 9.320000 \n", " Neighborhood 16 310.714286 7.071429 \n", " Neighborhood 17 317.347826 9.869565 \n", " Neighborhood 18 369.227273 8.227273 \n", " Neighborhood 19 254.500000 6.500000 \n", " Neighborhood 2 423.000000 6.000000 \n", " Neighborhood 20 223.555556 9.666667 \n", " Neighborhood 21 306.250000 14.750000 \n", " Neighborhood 22 500.000000 9.000000 \n", " Neighborhood 5 266.500000 11.500000 \n", " Neighborhood 6 290.666667 4.000000 \n", " Neighborhood 7 343.000000 5.333333 \n", " Neighborhood 8 300.000000 6.750000 \n", " Neighborhood 9 471.428571 5.714286 \n", "Property type 2 Neighborhood 10 126.000000 3.500000 \n", " Neighborhood 11 161.250000 11.250000 \n", " Neighborhood 12 244.526316 9.842105 \n", " Neighborhood 13 418.565217 9.739130 \n", " Neighborhood 14 348.619048 8.714286 \n", " Neighborhood 15 301.733333 8.200000 \n", " Neighborhood 16 246.250000 6.687500 \n", " Neighborhood 17 308.272727 7.181818 \n", " Neighborhood 18 297.777778 9.222222 \n", " Neighborhood 19 383.375000 5.500000 \n", " Neighborhood 20 101.000000 6.000000 \n", " Neighborhood 3 264.000000 9.000000 \n", " Neighborhood 4 95.000000 11.000000 \n", " Neighborhood 7 148.000000 2.000000 \n", " Neighborhood 8 223.000000 3.000000 \n", " Neighborhood 9 114.500000 9.000000 \n", "Property type 3 Neighborhood 11 196.000000 8.000000 \n", " Neighborhood 14 113.000000 5.000000 \n", " Neighborhood 17 189.000000 23.000000 \n", " Neighborhood 4 241.000000 19.000000 " ] } ], "prompt_number": 63 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Plot daily bookings:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Generate a count of bookings by date\n", "cross_tab = bookings.groupby(['booking_date'])['prop_id'].agg(['count']).unstack(0)\n", "\n", "# Plot the table\n", "cross_tab.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10bc97b90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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CSewDKRh464PHlG2ofE4dz8/1bGSaU5CS+DVm6wHNVkyiyG3W64dJLkiaIlkQocnS4zU2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\nd9iPDDjnlNxEMHpYhWJIlbsFnbGSPLc9p5zK6nPWlZ5TdyvrnhLdJbv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M+2sZvzVuJ0Pp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\n", "text": [ "<matplotlib.figure.Figure at 0x10b6a2bd0>" ] } ], "prompt_number": 64 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Plot the daily bookings per neighborhood (provide a legend)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a smaller dataset with just the prop_id and neighborhood\n", "neighborhoods = listings[['prop_id', 'neighborhood']]\n", "\n", "# Merge bookings_by_prop into the listings data\n", "bookings_neighborhoods = bookings.merge(neighborhoods, on='prop_id', how='left')\n", "\n", "# Generate a count of bookings by date and neighborhood\n", "cross_tab = bookings_neighborhoods.groupby(['neighborhood','booking_date'])['prop_id'].agg(['count']).unstack(0)\n", "\n", "# Plot the table\n", "cross_tab.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 65, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10bd99750>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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+dTG3G+35VpRRc3MHXm9oWas1J6Gz78MPP4annw64RNzu2J/L/mwJTJ8uz5dj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\nukD+AvQVkY+A84BoovYhagvOj4A3AXyNPhohb8c2XGUwXIjeAZzv472GK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"text": [ "<matplotlib.figure.Figure at 0x10bfbe290>" ] } ], "prompt_number": 65 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Part 2 - Develop a data set" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Count the number of bookings by property\n", "bookings_by_prop = bookings.groupby('prop_id')[['prop_id']].count()\n", "bookings_by_prop\n", "\n", "# Rename columns and reset the index\n", "bookings_by_prop.rename(columns={'prop_id': 'number_of_bookings'}, inplace=True)\n", "bookings_by_prop = bookings_by_prop.reset_index()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 66 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Add the columns `number_of_bookings` and `booking_rate` (number_of_bookings/tenure_months) to your `listings` data frame" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Merge bookings_by_prop into the listings data\n", "listings = listings.merge(bookings_by_prop, on='prop_id', how='left')\n", "\n", "# Replace null values with 0's\n", "listings.number_of_bookings.replace(np.nan, 0, inplace = True)\n", "\n", "# Divide number_of_bookings by tenure_months\n", "listings['booking_rate'] = listings.number_of_bookings / listings.tenure_months\n", "\n", "# Describe the resulting dataset\n", "listings.info()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 408 entries, 0 to 407\n", "Data columns (total 10 columns):\n", "prop_id 408 non-null int64\n", "prop_type 408 non-null object\n", "neighborhood 408 non-null object\n", "price 408 non-null int64\n", "person_capacity 408 non-null int64\n", "picture_count 408 non-null int64\n", "description_length 408 non-null int64\n", "tenure_months 408 non-null int64\n", "number_of_bookings 408 non-null float64\n", "booking_rate 408 non-null float64\n", "dtypes: float64(2), int64(6), object(2)\n", "memory usage: 35.1+ KB\n" ] } ], "prompt_number": 67 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###We only want to analyze well established properties, so let's filter out any properties that have a tenure less than 10 months " ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings_filtered = listings[listings.tenure_months > 10]\n", "listings_filtered.info()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 120 entries, 0 to 119\n", "Data columns (total 10 columns):\n", "prop_id 120 non-null int64\n", "prop_type 120 non-null object\n", "neighborhood 120 non-null object\n", "price 120 non-null int64\n", "person_capacity 120 non-null int64\n", "picture_count 120 non-null int64\n", "description_length 120 non-null int64\n", "tenure_months 120 non-null int64\n", "number_of_bookings 120 non-null float64\n", "booking_rate 120 non-null float64\n", "dtypes: float64(2), int64(6), object(2)\n", "memory usage: 10.3+ KB\n" ] } ], "prompt_number": 68 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###`prop_type` and `neighborhood` are categorical variables, use `get_dummies()` (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.reshape.get_dummies.html) to transform this column of categorical data to many columns of boolean values (after applying this function correctly there should be 1 column for every prop_type and 1 column for every neighborhood category." ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings_filtered = pd.get_dummies(listings_filtered)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 69 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###create test and training sets for your regressors and predictors\n", "predictor (y) is `booking_rate`, regressors (X) are everything else, except `prop_id`,`booking_rate`,`prop_type`,`neighborhood` and `number_of_bookings`<br>\n", "http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html<br>\n", "http://pandas.pydata.org/pandas-docs/stable/basics.html#dropping-labels-from-an-axis" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a reduced data from of features from the original data frame\n", "cols = list(listings_filtered.columns)\n", "cols.remove('prop_id')\n", "cols.remove('booking_rate')\n", "cols.remove('number_of_bookings')\n", "iv = listings_filtered[cols]\n", "\n", "# Create a data frame with the target variable\n", "dv = listings_filtered['booking_rate'].values\n", "\n", "# Split those two data frames into training and test datasets\n", "iv_train, iv_test, dv_train, dv_test = train_test_split(iv, dv, test_size=0.2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 71 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Part 3 - Model `booking_rate`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Create a linear regression model of your listings" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.linear_model import LinearRegression\n", "lr = LinearRegression()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 72 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###fit your model with your test sets" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lr.fit(iv_train, dv_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 73, "text": [ "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" ] } ], "prompt_number": 73 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###report the score\n", "http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.score" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lr.score(iv_train, dv_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 74, "text": [ "0.31484836732897703" ] } ], "prompt_number": 74 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Interpret the results of the above model:\n", "* What does the `score` method do?\n", "* What does this tell us about our model?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# The score tells us the amount of variance in the outcome variable that is explained by the predictor variables.\n", "# A score of 0.41 tells us that the model explains about 40% of the variance in the outcome variable." ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 75 }, { "cell_type": "markdown", "metadata": {}, "source": [ "...type here..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Optional - Iterate\n", "Create an alternative predictor (e.g. monthly revenue) and use the same modeling pattern in Part 3 to " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Create a reduced data from of features from the original data frame\n", "listings_filtered['monthly_revenue'] = listings_filtered.price * listings_filtered.booking_rate\n", "\n", "# Create a reduced data from of features from the original data frame\n", "cols = list(listings_filtered.columns)\n", "cols.remove('prop_id')\n", "cols.remove('booking_rate')\n", "cols.remove('number_of_bookings')\n", "iv = listings_filtered[cols]\n", "\n", "# Create a data frame with the target variable\n", "dv = listings_filtered['booking_rate'].values\n", "\n", "# Split those two data frames into training and test datasets\n", "iv_train, iv_test, dv_train, dv_test = train_test_split(iv, dv, test_size=0.2)\n", "\n", "# Fit model with the test sets\n", "lr.fit(iv_train, dv_train)\n", "\n", "# Report the score\n", "lr.score(iv_train, dv_train)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 76, "text": [ "0.92979467316975706" ] } ], "prompt_number": 76 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
shengshuyang/PCLCombinedObjectDetection	TheanoLearning/TheanoLearning/theano_demo.ipynb	2	17238	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basics about Theano" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First let's do the standard import" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "import numpy as np\n", "#import matplotlib.pyplot as plt\n", "import theano\n", "# By convention, the tensor submodule is loaded as T\n", "import theano.tensor as T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following are all Theano defined types:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorType(float32, matrix)\n", "TensorType(float32, scalar)\n", "TensorType(float32, vector)\n" ] } ], "source": [ "A = T.matrix('A')\n", "b = T.scalar('b')\n", "v = T.vector('v')\n", "\n", "print A.type\n", "print b.type\n", "print v.type" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All those types are symbolic, meaning they don't have values at all. Theano variables can be defined with simple relations, such as " ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorType(float32, scalar)\n", "TensorType(float32, scalar)\n" ] } ], "source": [ "a = T.scalar('a')\n", "c = a2\n", "\n", "print a.type\n", "print c.type" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "note that `c` is also a symbolic scalar here.\n", "\n", "We can also define a function:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<theano.compile.function_module.Function object at 0x0000000029FEBC88>\n" ] } ], "source": [ "f = theano.function([a],a2)\n", "print f" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Again, Theano functions are symbolic as well. We must evaluate the function with some input to check its output. For example:" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.0\n" ] } ], "source": [ "print f(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Shared variable is also a Theano type" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "variable type:\n", "TensorType(float32, matrix)\n", "\n", "variable value:\n", "[[ 1. 2.]\n", " [ 3. 4.]]\n", "\n", "values changed:\n", "[[ 4. 5.]\n", " [ 6. 7.]]\n" ] } ], "source": [ "shared_var = theano.shared(np.array([[1, 2], [3, 4]], \n", " dtype=theano.config.floatX))\n", "print 'variable type:'\n", "print shared_var.type\n", "print '\\nvariable value:'\n", "print shared_var.get_value()\n", "\n", "shared_var.set_value(np.array([[4, 5], [6, 7]], \n", " dtype=theano.config.floatX))\n", "print '\\nvalues changed:'\n", "print shared_var.get_value()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "They have a fixed value, but are still treated as symbolic (can be input to functions etc.).\n", "\n", "Shared variables are perfect to use as state variables or parameters. Fore example, in CNN each layer has a parameter matrix 'W', we need to store its value so we can perform testing against thousands of images, yet we also need to update their values during training.\n", "\n", "As a side note, since they have fixed value, they don't need to be explicitly specified as input to a function:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 17. 26.]\n", " [ 37. 50.]]\n", "\n", "\n", "[[ 17. 26.]\n", " [ 37. 50.]]\n" ] } ], "source": [ "bias = T.matrix('bias')\n", "shared_squared = shared_var2 + bias\n", "\n", "bias_value = np.array([[1,1],[1,1]], \n", " dtype=theano.config.floatX)\n", "\n", "f1 = theano.function([bias],shared_squared)\n", "print f1(bias_value)\n", "print '\\n'\n", "print shared_squared.eval({bias:bias_value})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The example above defines a function that takes square of a shared_var and add by a bias. When evaluating the function we only provide value for bias because we know that the shared variable is fixed value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Gradients" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To calculate gradient we can use a `T.grad()` function to return a tensor variable. We first define some variable:" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def square(a):\n", " return a2\n", "\n", "a = T.scalar('a')\n", "b = square(a)\n", "c = square(b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we define two ways to evaluate gradient:" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": true }, "outputs": [], "source": [ "grad = T.grad(c,a)\n", "f_grad = theano.function([a],grad)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The TensorVariable grad calculates gradient of b w.r.t. a. \n", "The function f_grad takes a as input and grad as output, so it should be equivalent. However, evaluating them have different formats:" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4000.0\n", "4000.0\n" ] } ], "source": [ "print grad.eval({a:10})\n", "print f_grad(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# MLP Demo with Theano" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 7.5 7.5 7.5]\n", " [ 9.5 9.5 9.5]\n", " [ 9. 9. 9. ]]\n", "time: 1.14300012589\n" ] } ], "source": [ "class layer(object):\n", " def __init__(self, W_init, b_init, activation):\n", "\n", " [n_output, n_input] = W_init.shape\n", " assert b_init.shape == (n_output,1) or b_init.shape == (n_output,)\n", " self.W = theano.shared(value = W_init.astype(theano.config.floatX),\n", " name = 'W',\n", " borrow = True)\n", " self.b = theano.shared(value = b_init.reshape(n_output,1).astype(theano.config.floatX),\n", " name = 'b',\n", " borrow = True,\n", " broadcastable=(False, True))\n", " self.activation = activation\n", " self.params = [self.W, self.b]\n", " #return super(layer, self).__init__(args, kwargs)\n", " def output(self, x):\n", " lin_output = T.dot(self.W, x) + self.b\n", " if self.activation is not None:\n", " non_lin_output = self.activation(lin_output)\n", " return ( lin_output if self.activation is None else non_lin_output )\n", "\n", "t1 = time.time()\n", "W_init = np.ones([3,3])\n", "b_init = np.array([1,3,2.5]).transpose()\n", "activation = None #T.nnet.sigmoid\n", "L = layer(W_init,b_init,activation)\n", "x = T.vector('x')\n", "out = L.output(x)\n", "print out.eval({x:np.array([1.0,2,3.5]).astype(theano.config.floatX)})\n", "t2 = time.time()\n", "print 'time:', t2-t1" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Plotting Flowchart (or Theano Graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code snippet below can plot a flowchart that shows what happens inside our mlp layer. Note that the input `out`* is the output of layer, as defined above." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<svg height=\"392pt\" viewBox=\"0.00 0.00 464.00 392.00\" width=\"464pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 388)\">\n", "<title>G</title>\n", "<polygon fill=\"white\" points=\"-4,4 -4,-388 460,-388 460,4 -4,4\" stroke=\"none\"/>\n", "<!-- dot -->\n", "<g class=\"node\" id=\"node1\"><title>dot</title>\n", "<ellipse cx=\"202\" cy=\"-279\" fill=\"none\" rx=\"27\" ry=\"18\" stroke=\"black\"/>\n", "<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"202\" y=\"-275.3\">dot</text>\n", "</g>\n", "<!-- DimShuffle{x,0} -->\n", "<g class=\"node\" id=\"node4\"><title>DimShuffle{x,0}</title>\n", "<ellipse cx=\"202\" cy=\"-192\" 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font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"104\" y=\"-144.8\">1 TensorType(float32, col)</text>\n", "</g>\n", "</g>\n", "</svg>" ], "text/plain": [ "<IPython.core.display.SVG object>" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import SVG\n", "SVG(theano.printing.pydotprint(out, return_image=True,\n", " compact = True, \n", " var_with_name_simple = True,\n", " format='svg'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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 }	gpl-2.0
erichseamon/siglearn	QuickDemo.ipynb	2	36352	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstrating SigLearn" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn; seaborn.set()\n", "\n", "from sklearn.linear_model import LinearRegression as skLinearRegression\n", "from siglearn.linear_model import LinearRegression" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Q3RFupLcX3P0T+Ljjbz3QDkxVVXVTx31fAMuA9X3rnhBCnN+MRiPTp89Ap9PJ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\nSf8UdpudHdnbqCqoYJ1zNTNdnXj/4weMHG+YHWOnV+7HWV1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"text/plain": [ "<matplotlib.figure.Figure at 0x10673be50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate Data\n", "rng = np.random.RandomState(6)\n", "N = 20\n", "x = 10 * rng.rand(N)\n", "dy = np.ones(N)\n", "dy[-6:] = 10\n", "y = 2 * x - 4 + dy * rng.randn(N)\n", "\n", "# Fit the scikit-learn model\n", "model1 = skLinearRegression(fit_intercept=True)\n", "model1.fit(x[:, None], y)\n", "\n", "# Fit the siglearn model\n", "model2 = LinearRegression(fit_intercept=True)\n", "model2.fit(x[:, None], y, dy)\n", "\n", "# Compute the predicted results\n", "xfit = np.linspace(-2, 12)\n", "yfit1 = model1.predict(xfit[:, None])\n", "yfit2 = model2.predict(xfit[:, None])\n", "\n", "# Plot data and fits\n", "plt.errorbar(x, y, dy, fmt='dk', ecolor='gray', alpha=0.5);\n", "plt.plot(xfit, yfit1, label='scikit-learn model (ignoring errors)');\n", "plt.plot(xfit, yfit2, label='siglearn model (accounting for errors)');\n", "plt.plot(xfit, 2 * xfit - 4, label='generating model')\n", "plt.legend(loc='upper left');" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.3.5" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-2-clause
tylere/earthengine-api	python/examples/ipynb/EarthEngineColabInstall.ipynb	1	4521	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2j_Ok5BKJiXU" }, "source": [ "Copyright 2018 Google LLC.\n", "\n", "SPDX-License-Identifier: Apache-2.0" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "GwrEeAOB_MXY" }, "source": [ "# Earth Engine Colab installation\n", "\n", "This notebook demonstrates a simple installation of Earth Engine to a Colab notebook." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "b1EUdVhC3SCp" }, "source": [ "# Colab setup\n", "\n", "This notebook section installs the Earth Engine Python API on your Colab virtual machine (VM) and will need to be executed each time a new Colab notebook is created. Colab VMs are recycled after they are idle for a while." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pkke7LnZBuqI" }, "source": [ "## Install Earth Engine\n", "\n", "The Earth Engine Python API and command line tools can be installed using [Python's `pip` package installation tool](https://pypi.org/project/pip/). The following notebook cell line is starts with `!` to indicate that a shell command should be invoked." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "JFv3jrBIG2av" }, "outputs": [], "source": [ "!pip install earthengine-api" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "GYUppo1t39B7" }, "source": [ "## Authenticate to Earth Engine\n", "\n", "In order to access Earth Engine, signup at [signup.earthengine.google.com](https://signup.earthengine.google.com).\n", "\n", "Once you have signed up and the Earth Engine package is installed, use the `earthengine authenticate` shell command to create and store authentication credentials on the Colab VM. These credentials are used by the Earth Engine Python API and command line tools to access Earth Engine servers.\n", "\n", "You will need to follow the link to the permissions page and give this notebook access to your Earth Engine account. Once you have authorized access, paste the authorization code into the input box displayed in the cell output." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Bgbfo1Ap37Bj" }, "outputs": [], "source": [ "import ee\n", "\n", "# Check if the server is authenticated. If not, display instructions that\n", "# explain how to complete the process.\n", "try:\n", " ee.Initialize()\n", "except ee.EEException:\n", " !earthengine authenticate" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5skJZQXSCdgd" }, "source": [ "# Test the installation\n", "\n", "Import the Earth Engine library and initialize it with the authorization token stored on the notebook VM. Also import a display widget and display a thumbnail image of an Earth Engine dataset." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "BxAjoaeNHeOG" }, "outputs": [], "source": [ "import ee\n", "from IPython.display import Image\n", "\n", "# Initialize the Earth Engine module.\n", "ee.Initialize()\n", "\n", "# Display a thumbnail of a sample image asset.\n", "Image(url=ee.Image('CGIAR/SRTM90_V4').getThumbUrl({'min': 0, 'max': 3000}))" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "Earth Engine Colab Install.ipynb", "private_outputs": true, "provenance": [], "toc_visible": true, "version": "0.3.2" }, "kernelspec": { "display_name": "Python 2", "name": "python2" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
atulsingh0/MachineLearning	Tips_N_Tricks/Missing_Value_in_Pandas_Dataframe.ipynb	1	20785	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Missing Value in Pandas datafrmae" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# reading the file\n", "data = pd.read_csv(\"data/train.csv\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Loan_ID</th>\n", " <th>Gender</th>\n", " <th>Married</th>\n", " <th>Dependents</th>\n", " <th>Education</th>\n", " <th>Self_Employed</th>\n", " <th>ApplicantIncome</th>\n", " <th>CoapplicantIncome</th>\n", " <th>LoanAmount</th>\n", " <th>Loan_Amount_Term</th>\n", " <th>Credit_History</th>\n", " <th>Property_Area</th>\n", " <th>Loan_Status</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>LP001002</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>5849</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>LP001003</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>1</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>4583</td>\n", " <td>1508.0</td>\n", " <td>128.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Rural</td>\n", " <td>N</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>LP001005</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>Yes</td>\n", " <td>3000</td>\n", " <td>0.0</td>\n", " <td>66.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>LP001006</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>0</td>\n", " <td>Not Graduate</td>\n", " <td>No</td>\n", " <td>2583</td>\n", " <td>2358.0</td>\n", " <td>120.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>LP001008</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>0</td>\n", " <td>Graduate</td>\n", " <td>No</td>\n", " <td>6000</td>\n", " <td>0.0</td>\n", " <td>141.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>LP001011</td>\n", " <td>Male</td>\n", " <td>Yes</td>\n", " <td>2</td>\n", " <td>Graduate</td>\n", " <td>Yes</td>\n", " <td>5417</td>\n", " <td>4196.0</td>\n", " <td>267.0</td>\n", " <td>360.0</td>\n", " <td>1.0</td>\n", " <td>Urban</td>\n", " <td>Y</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Loan_ID Gender Married Dependents Education Self_Employed \\\n", "0 LP001002 Male No 0 Graduate No \n", "1 LP001003 Male Yes 1 Graduate No \n", "2 LP001005 Male Yes 0 Graduate Yes \n", "3 LP001006 Male Yes 0 Not Graduate No \n", "4 LP001008 Male No 0 Graduate No \n", "5 LP001011 Male Yes 2 Graduate Yes \n", "\n", " ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term \\\n", "0 5849 0.0 NaN 360.0 \n", "1 4583 1508.0 128.0 360.0 \n", "2 3000 0.0 66.0 360.0 \n", "3 2583 2358.0 120.0 360.0 \n", "4 6000 0.0 141.0 360.0 \n", "5 5417 4196.0 267.0 360.0 \n", "\n", " Credit_History Property_Area Loan_Status \n", "0 1.0 Urban Y \n", "1 1.0 Rural N \n", "2 1.0 Urban Y \n", "3 1.0 Urban Y \n", "4 1.0 Urban Y \n", "5 1.0 Urban Y " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Looking the data\n", "data.head(6)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Loan_ID</th>\n", " <th>Gender</th>\n", " <th>Married</th>\n", " <th>Dependents</th>\n", " <th>Education</th>\n", " <th>Self_Employed</th>\n", " <th>ApplicantIncome</th>\n", " <th>CoapplicantIncome</th>\n", " <th>LoanAmount</th>\n", " <th>Loan_Amount_Term</th>\n", " <th>Credit_History</th>\n", " <th>Property_Area</th>\n", " <th>Loan_Status</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome \\\n", "0 False False False False False False False \n", "1 False False False False False False False \n", "2 False False False False False False False \n", "3 False False False False False False False \n", "4 False False False False False False False \n", "5 False False False False False False False \n", "\n", " CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area \\\n", "0 False True False False False \n", "1 False False False False False \n", "2 False False False False False \n", "3 False False False False False \n", "4 False False False False False \n", "5 False False False False False \n", "\n", " Loan_Status \n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "5 False " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# isnull or notnull\n", "data.isnull().head(6)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Loan_ID</th>\n", " <th>Gender</th>\n", " <th>Married</th>\n", " <th>Dependents</th>\n", " <th>Education</th>\n", " <th>Self_Employed</th>\n", " <th>ApplicantIncome</th>\n", " <th>CoapplicantIncome</th>\n", " <th>LoanAmount</th>\n", " <th>Loan_Amount_Term</th>\n", " <th>Credit_History</th>\n", " <th>Property_Area</th>\n", " <th>Loan_Status</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome \\\n", "0 True True True True True True True \n", "1 True True True True True True True \n", "2 True True True True True True True \n", "3 True True True True True True True \n", "4 True True True True True True True \n", "5 True True True True True True True \n", "\n", " CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area \\\n", "0 True False True True True \n", "1 True True True True True \n", "2 True True True True True \n", "3 True True True True True \n", "4 True True True True True \n", "5 True True True True True \n", "\n", " Loan_Status \n", "0 True \n", "1 True \n", "2 True \n", "3 True \n", "4 True \n", "5 True " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.notnull().head(6)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# use of any\n", "data.isnull().values.any()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# use of all\n", "data.isnull().values.all()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Loan_ID 0\n", "Gender 13\n", "Married 3\n", "Dependents 15\n", "Education 0\n", "Self_Employed 32\n", "ApplicantIncome 0\n", "CoapplicantIncome 0\n", "LoanAmount 22\n", "Loan_Amount_Term 14\n", "Credit_History 50\n", "Property_Area 0\n", "Loan_Status 0\n", "dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# taking the count of Null/NaN in each column of dataframe\n", "data.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "149" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# if want to know the total\n", "data.isnull().sum().sum()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# if want to check in any particular column\n", "data['Dependents'].isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Atul Singh_ \n", "Follow me - \n", "http://www.datagenx.net \n", "https://twitter.com/datagenx \n", "https://www.facebook.com/datastage4you" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
ForestClaw/forestclaw	applications/clawpack/advection/2d/disk/swirl.ipynb	1	3086	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Swirl\n", "\n", "---\n", "\n", "Scalar advection problem with swirling velocity field.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run code in serial mode (will work, even if code is compiled with MPI)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!swirl " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Or, run code in parallel mode (command may need to be customized, depending your on MPI installation.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!mpirun -n 4 swirl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "Create PNG files for web-browser viewing, or animation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%run make_plots.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "View PNG files in browser, using URL above, or create an animation of all PNG files, using code below. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import glob\n", "from matplotlib import image\n", "from clawpack.visclaw.JSAnimation import IPython_display\n", "from matplotlib import animation\n", "\n", "figno = 0\n", "fname = '_plots/*fig' + str(figno) + '.png'\n", "filenames=sorted(glob.glob(fname))\n", "\n", "fig = plt.figure()\n", "im = plt.imshow(image.imread(filenames[0]))\n", "def init():\n", " im.set_data(image.imread(filenames[0]))\n", " return im,\n", "\n", "def animate(i):\n", " image_i=image.imread(filenames[i])\n", " im.set_data(image_i)\n", " return im,\n", "\n", "animation.FuncAnimation(fig, animate, init_func=init,\n", " frames=len(filenames), interval=500, blit=True)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "---" ] } ], "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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-2-clause
patrick-s-h-lewis/BathSim	BathAuthorsCorpusGenerator.ipynb	2	9344	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#CamAuthorsCorpus Generator\n", "Take the titles for an author in the CamAuthors collection and generate a representative corpus for that author" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pymongo\n", "from pymongo import MongoClient\n", "from nltk.corpus import stopwords\n", "import string\n", "punct_filter = dict((ord(char), u' ') for char in '\"#$%&\\'()*+,./-:;<=>?@[\\\\]^_`{\|}')\n", "stop = stopwords.words('english')\n", "mongo_url = 'mongodb://localhost:27017/'\n", "db = 'CamSim'\n", "coll = 'CamAuthors'\n", "client = MongoClient(mongo_url)\n", "ca = client[db][coll]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "updated 0 records\n", "updated 100 records\n", "updated 200 records\n", "updated 300 records\n", "updated 400 records\n", "updated 500 records\n", "updated 600 records\n", "updated 700 records\n", "updated 800 records\n", "updated 900 records\n", "updated 1000 records\n", "updated 1100 records\n", "updated 1200 records\n", "updated 1300 records\n", "updated 1400 records\n", "updated 1500 records\n", "updated 1600 records\n", "updated 1700 records\n", "updated 1800 records\n", "updated 1900 records\n", "updated 2000 records\n", "updated 2100 records\n", "updated 2200 records\n", "updated 2300 records\n", "updated 2400 records\n", "updated 2500 records\n", "updated 2600 records\n", "updated 2700 records\n", "updated 2800 records\n", "updated 2900 records\n", "updated 3000 records\n", "updated 3100 records\n", "updated 3200 records\n", "updated 3300 records\n", "updated 3400 records\n", "updated 3500 records\n", "updated 3600 records\n", "updated 3700 records\n", "updated 3800 records\n", "updated 3900 records\n", "updated 4000 records\n", "updated 4100 records\n", "updated 4200 records\n", "updated 4300 records\n", "updated 4400 records\n", "updated 4500 records\n", "updated 4600 records\n", "updated 4700 records\n", "updated 4800 records\n", "updated 4900 records\n", "updated 5000 records\n", "updated 5100 records\n", "updated 5200 records\n", "updated 5300 records\n", "updated 5400 records\n", "updated 5500 records\n", "updated 5600 records\n", "updated 5700 records\n", "updated 5800 records\n", "updated 5900 records\n", "updated 6000 records\n", "updated 6100 records\n", "updated 6200 records\n", "updated 6300 records\n", "updated 6400 records\n", "updated 6500 records\n", "updated 6600 records\n", "updated 6700 records\n", "updated 6800 records\n", "updated 6900 records\n", "updated 7000 records\n", "updated 7100 records\n", "updated 7200 records\n", "updated 7300 records\n", "updated 7400 records\n", "updated 7500 records\n", "updated 7600 records\n", "updated 7700 records\n", "updated 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"updated 11600 records\n", "updated 11700 records\n", "updated 11800 records\n", "updated 11900 records\n", "updated 12000 records\n", "updated 12100 records\n", "updated 12200 records\n", "updated 12300 records\n", "updated 12400 records\n", "updated 12500 records\n", "updated 12600 records\n", "updated 12700 records\n", "updated 12800 records\n", "updated 12900 records\n", "updated 13000 records\n", "updated 13100 records\n", "updated 13200 records\n", "updated 13300 records\n", "updated 13400 records\n", "updated 13500 records\n", "updated 13600 records\n", "updated 13700 records\n", "updated 13800 records\n", "updated 13900 records\n", "updated 14000 records\n", "updated 14100 records\n", "updated 14200 records\n", "updated 14300 records\n", "updated 14400 records\n", "updated 14500 records\n", "updated 14600 records\n", "updated 14700 records\n", "updated 14800 records\n", "updated 14900 records\n", "updated 15000 records\n", "updated 15100 records\n", "updated 15200 records\n", "updated 15300 records\n", "updated 15400 records\n", "updated 15500 records\n", "updated 15600 records\n", "updated 15700 records\n", "updated 15800 records\n", "updated 15900 records\n", "updated 16000 records\n", "updated 16100 records\n", "updated 16200 records\n", "updated 16300 records\n", "updated 16400 records\n", "updated 16500 records\n", "updated 16600 records\n", "updated 16700 records\n", "updated 16800 records\n", "updated 16900 records\n", "updated 17000 records\n", "updated 17100 records\n", "updated 17200 records\n", "updated 17300 records\n", "updated 17400 records\n", "updated 17500 records\n", "updated 17600 records\n", "updated 17700 records\n", "updated 17800 records\n", "updated 17900 records\n", "updated 18000 records\n", "updated 18100 records\n", "updated 18200 records\n", "updated 18300 records\n", "updated 18400 records\n", "updated 18500 records\n", "updated 18600 records\n", "updated 18700 records\n", "updated 18800 records\n", "updated 18900 records\n", "updated 19000 records\n", "updated 19100 records\n", "updated 19200 records\n", "updated 19300 records\n", "updated 19400 records\n", "updated 19500 records\n", "updated 19600 records\n", "updated 19700 records\n", "updated 19800 records\n", "updated 19900 records\n", "updated 20000 records\n", "updated 20100 records\n", "updated 20200 records\n", "updated 20300 records\n", "updated 20400 records\n", "updated 20500 records\n", "updated 20600 records\n", "updated 20700 records\n", "updated 20800 records\n", "updated 20900 records\n", "updated 21000 records\n", "updated 21100 records\n", "updated 21200 records\n", "updated 21300 records\n", "updated 21400 records\n", "updated 21500 records\n", "21552\n" ] } ], "source": [ "cursor = ca.find()\n", "ind=0\n", "for rec in cursor:\n", " name = rec['name']\n", " corp=u''\n", " for pub in rec['pubs']:\n", " title = pub['title']\n", " title = title.lower()\n", " title = title.translate(punct_filter)\n", " stop_filtered = [i for i in title.split() if i not in stop]\n", " export = u' '.join(stop_filtered)+' '\n", " corp+=export\n", " if ind%100==0: print('updated ' +str(ind)+' records')\n", " ind+=1\n", " ca.update_one({'name':name},\n", " {'$set':{'corpus':corp}})\n", "print(ind)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
leoferres/prograUDD	labs/ejercicios_for.ipynb	1	7161	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Una contraseña es valida si cumple con lo siguiente:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ " a) Debe tener un largo de al menos 8 caracteres\n", " b) Al menos uno debe ser numerico\n", " c) Debe contener exactamente un caracter que no sea letra ni numero.\n", "Cree un programa que solicite la contraseña e imprima si esta es valida." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ingrese su contraseña: abcd123,\n" ] } ], "source": [ "passwd = input(\"Ingrese su contraseña: \")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Contraseña valida!!!\n" ] } ], "source": [ "if len(passwd) < 8:\n", " print(\"Contraseña no valida, faltan caracteres\")\n", "else:\n", " cantnum = 0\n", " cantsimb = 0\n", " for i in passwd:\n", " if i.isdigit():\n", " cantnum += 1\n", " elif not i.isalpha():\n", " cantsimb += 1\n", " if cantsimb != 1:\n", " print(\"Contraseña no valida, debe tener solo un caracter no letra ni numero\")\n", " elif cantnum == 0:\n", " print(\"Contraseña no valida, no tiene numeros\")\n", " else:\n", " print(\"Contraseña valida!!!\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a False True\n", "b False True\n", "c False True\n", "d False True\n", "1 True False\n", "2 True False\n", "3 True False\n", ", False False\n" ] } ], "source": [ "# aqui probamos primero para conocer las funciones con is.\n", "for i in passwd:\n", " print(i, i.isdigit(), i.isalpha())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imprima todos los numeros primos positivos menores que n, solicite el n." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ingrese el n: 20\n" ] } ], "source": [ "n = int(input(\"Ingrese el n: \"))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 es primo\n", "3 es primo\n", "5 es primo\n", "7 es primo\n", "11 es primo\n", "13 es primo\n", "17 es primo\n", "19 es primo\n" ] } ], "source": [ "#ojo... podriamos saltar de a 2, desde el 3.\n", "for i in range(2,n):\n", " cantdiv = 0\n", " for j in range(2,int(i0.5)+1):\n", " if i%j == 0:\n", " cantdiv += 1\n", " if cantdiv == 0:\n", " print(i, \"es primo\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solicite n y k y calcule $\\binom{n}{k}$" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "* Realice la menor cantidad de calculos posibles" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ingrese el n: 10\n" ] } ], "source": [ "n = int(input(\"Ingrese el n: \"))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ingrese el k: 4\n" ] } ], "source": [ "k = int(input(\"Ingrese el k: \"))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "El resultado es = 210.0\n" ] } ], "source": [ "if n < 0 or k < 0:\n", " print(\"No se puede calcular, hay un elemento negativo\")\n", "elif n < k:\n", " print(\"No se puede calcular, n debe ser mayor o igual a k\")\n", "else:\n", " menor = k\n", " if (n-k) < menor:\n", " menor = (n-k)\n", " resultado = 1\n", " for i in range(menor):\n", " resultado *= (n-i)/(i+1)\n", " print(\"El resultado es =\", resultado)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Se define como numero perfecto, aquél natural positivo en que la suma de sus divisores, no incluido si mismo, es el mismo número. " ] }, { "cell_type": "raw", "metadata": { "collapsed": true }, "source": [ "Solicite un n e imprima los perfectos menores que n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ingrese su n: 10000\n" ] } ], "source": [ "# obviaremos chequeo que sea positivo\n", "n = int(input(\"Ingrese su n: \"))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6 es perfecto\n", "28 es perfecto\n", "496 es perfecto\n", "8128 es perfecto\n" ] } ], "source": [ "for i in range(1, n+1):\n", " suma = 0\n", " for j in range(1,i):\n", " if i%j == 0:\n", " suma += j\n", " if suma == i:\n", " print(i, \"es perfecto\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "4\n", "8\n", "16\n", "31\n", "62\n", "124\n", "248\n" ] } ], "source": [ "for i in range(1, 496):\n", " if 496%i == 0:\n", " print(i)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
michaelaye/iuvs	notebooks/2015-03-24 APP1 mode 4.ipynb	1	11828	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from iuvs import io\n", "from iuvs import io, scaling, plotting" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.DataFrame(io.l1b_filenames('APP1mode14', iterator=False), columns=['fname'])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['mode'] = df.fname.map(lambda x: io.Filename(x).mode)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "mode1444 14\n", "mode1442 14\n", "mode1443 14\n", "mode1441 14\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['mode'].value_counts()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>fname</th>\n", " <th>mode</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>/maven_iuvs/stage/products/level1b/mvn_iuv_l1b...</td>\n", " <td>mode1441</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>/maven_iuvs/stage/products/level1b/mvn_iuv_l1b...</td>\n", " <td>mode1441</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>/maven_iuvs/stage/products/level1b/mvn_iuv_l1b...</td>\n", " <td>mode1443</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>/maven_iuvs/stage/products/level1b/mvn_iuv_l1b...</td>\n", " <td>mode1442</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>/maven_iuvs/stage/products/level1b/mvn_iuv_l1b...</td>\n", " <td>mode1442</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " fname mode\n", "0 /maven_iuvs/stage/products/level1b/mvn_iuv_l1b... mode1441\n", "1 /maven_iuvs/stage/products/level1b/mvn_iuv_l1b... mode1441\n", "2 /maven_iuvs/stage/products/level1b/mvn_iuv_l1b... mode1443\n", "3 /maven_iuvs/stage/products/level1b/mvn_iuv_l1b... mode1442\n", "4 /maven_iuvs/stage/products/level1b/mvn_iuv_l1b... mode1442" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def calc_4_to_3(width):\n", " height = width 3 / 4\n", " return (width, height)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.style.use('bmh')\n", "plt.rcParams['figure.figsize']= calc_4_to_3(9)\n", "plt.rcParams['image.aspect'] = 'auto'\n", "plt.rcParams['image.interpolation'] = 'none'\n", "plt.rcParams['lines.linewidth'] = 1\n", "plt.ioff()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "def process_fname(fname):\n", " import os\n", " l1b = io.L1BReader(fname)\n", " fig = l1b.plot_raw_overview(-1, save_token='1', imglog=True, \n", " proflog=False, prof_plot_hist=True)\n", " scaling.do_all(l1b, -1, log=False)\n", " plt.close('all')\n", " return \"{} done.\".format(os.path.basename(fname))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.parallel import Client\n", "c = Client()\n", "dview = c.direct_view()\n", "lbview = c.load_balanced_view()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%px\n", "def calc_4_to_3(width):\n", " height = width * 3 / 4\n", " return (width, height)\n", "from iuvs import io, scaling, plotting\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('bmh')\n", "plt.rcParams['figure.figsize']= calc_4_to_3(9)\n", "plt.rcParams['image.aspect'] = 'auto'\n", "plt.rcParams['image.interpolation'] = 'none'\n", "plt.rcParams['lines.linewidth'] = 1\n", "plt.ioff()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ret = lbview.map_async(process_fname, df.fname)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mvn_iuv_l1b_APP1-orbit00087-mode1441-fuv_20141014T141802_v01_r01.fits.gz done.\n", "mvn_iuv_l1b_APP1-orbit00087-mode1441-muv_20141014T141914_v01_r01.fits.gz done.\n", "mvn_iuv_l1b_APP1-orbit00087-mode1443-muv_20141014T142730_v01_r01.fits.gz done.\n", "mvn_iuv_l1b_APP1-orbit00087-mode1442-muv_20141014T142258_v01_r01.fits.gz done.\n", 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"mvn_iuv_l1b_APP1-orbit00087-mode1443-fuv_20141014T142722_v01_r01.fits.gz done.\n", "mvn_iuv_l1b_APP1-orbit00087-mode1444-muv_20141014T142826_v01_r01.fits.gz done.\n", "mvn_iuv_l1b_APP1-orbit00087-mode1441-muv_20141014T141950_v01_r01.fits.gz done.\n", "mvn_iuv_l1b_APP1-orbit00087-mode1441-fuv_20141014T142026_v01_r01.fits.gz done.\n", "mvn_iuv_l1b_APP1-orbit00087-mode1441-fuv_20141014T141650_v01_r01.fits.gz done.\n", "mvn_iuv_l1b_APP1-orbit00087-mode1444-fuv_20141014T142832_v01_r01.fits.gz done.\n" ] } ], "source": [ "for res in ret:\n", " print(res)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from iuvs.multitools import progress_display" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "progress_display(ret, df.fname, sleep=20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }	isc
alexji/gaia-hacks	Initial Look.ipynb	1	393026	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib notebook\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from astropy.coordinates import SkyCoord\n", "from astropy import units as u\n", "from astropy import table\n", "\n", "import pandas as pd\n", "\n", "#https://github.com/jobovy/gaia_tools\n", "import gaia_tools.load as gload\n", "from gaia_tools import xmatch\n", "import read_data as rd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load catalogs with Jo Bovy's code" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tgas_cat= gload.tgas()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tgas_tab = table.Table(tgas_cat)\n", "df = tgas_tab.to_pandas()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'hip', u'tycho2_id', u'solution_id', u'source_id', u'random_index',\n", " u'ref_epoch', u'ra', u'ra_error', u'dec', u'dec_error', u'parallax',\n", " u'parallax_error', u'pmra', u'pmra_error', u'pmdec', u'pmdec_error',\n", " u'ra_dec_corr', u'ra_parallax_corr', u'ra_pmra_corr', u'ra_pmdec_corr',\n", " u'dec_parallax_corr', u'dec_pmra_corr', u'dec_pmdec_corr',\n", " u'parallax_pmra_corr', u'parallax_pmdec_corr', u'pmra_pmdec_corr',\n", " u'astrometric_n_obs_al', u'astrometric_n_obs_ac',\n", " u'astrometric_n_good_obs_al', u'astrometric_n_good_obs_ac',\n", " u'astrometric_n_bad_obs_al', u'astrometric_n_bad_obs_ac',\n", " u'astrometric_delta_q', u'astrometric_excess_noise',\n", " u'astrometric_excess_noise_sig', u'astrometric_primary_flag',\n", " u'astrometric_relegation_factor', u'astrometric_weight_al',\n", " u'astrometric_weight_ac', u'astrometric_priors_used',\n", " u'matched_observations', u'duplicated_source',\n", " u'scan_direction_strength_k1', u'scan_direction_strength_k2',\n", " u'scan_direction_strength_k3', u'scan_direction_strength_k4',\n", " u'scan_direction_mean_k1', u'scan_direction_mean_k2',\n", " u'scan_direction_mean_k3', u'scan_direction_mean_k4', u'phot_g_n_obs',\n", " u'phot_g_mean_flux', u'phot_g_mean_flux_error', u'phot_g_mean_mag',\n", " u'phot_variable_flag', u'l', u'b', u'ecl_lon', u'ecl_lat'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>parallax_pmdec_corr</th>\n", " <th>parallax_pmra_corr</th>\n", " <th>parallax</th>\n", " <th>parallax_error</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>2.057050e+06</td>\n", " <td>2.057050e+06</td>\n", " <td>2.057050e+06</td>\n", " <td>2.057050e+06</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>-1.596101e-01</td>\n", " <td>2.463325e-02</td>\n", " <td>2.477627e+00</td>\n", " <td>3.850052e-01</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>4.844652e-01</td>\n", " <td>6.398656e-01</td>\n", " <td>2.910183e+00</td>\n", " <td>1.699272e-01</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>-9.968984e-01</td>\n", " <td>-9.985727e-01</td>\n", " <td>-2.481995e+01</td>\n", " <td>2.039740e-01</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>-5.673988e-01</td>\n", " <td>-6.121245e-01</td>\n", " <td>1.030510e+00</td>\n", " <td>2.681603e-01</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>-1.608538e-01</td>\n", " <td>4.522673e-02</td>\n", " <td>1.780198e+00</td>\n", " <td>3.220124e-01</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>1.813491e-01</td>\n", " <td>6.500711e-01</td>\n", " <td>3.015706e+00</td>\n", " <td>4.360010e-01</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>9.962620e-01</td>\n", " <td>9.987622e-01</td>\n", " <td>2.958036e+02</td>\n", " <td>9.999985e-01</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " parallax_pmdec_corr parallax_pmra_corr parallax parallax_error\n", "count 2.057050e+06 2.057050e+06 2.057050e+06 2.057050e+06\n", "mean -1.596101e-01 2.463325e-02 2.477627e+00 3.850052e-01\n", "std 4.844652e-01 6.398656e-01 2.910183e+00 1.699272e-01\n", "min -9.968984e-01 -9.985727e-01 -2.481995e+01 2.039740e-01\n", "25% -5.673988e-01 -6.121245e-01 1.030510e+00 2.681603e-01\n", "50% -1.608538e-01 4.522673e-02 1.780198e+00 3.220124e-01\n", "75% 1.813491e-01 6.500711e-01 3.015706e+00 4.360010e-01\n", "max 9.962620e-01 9.987622e-01 2.958036e+02 9.999985e-01" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['parallax_pmdec_corr','parallax_pmra_corr','parallax','parallax_error']].describe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x122be1650>]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.figure()\n", "plt.plot(df['parallax'],df['parallax_error'],'k.')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['distance'] = 1./df['parallax'] #in kpc" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.text.Text at 0x122c08cd0>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.figure()\n", "plt.hist(df['phot_g_mean_mag'],bins=np.arange(4,20,.25))\n", "plt.xlabel('Gaia G (330-1050nm)')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def contourbin(x,y,binx,biny,ax=None,kwargs):\n", " h,xe,ye = np.histogram2d(np.array(x),np.array(y),bins=[binx,biny])\n", " xmid = (xe[1:]+xe[:-1])/2.0\n", " ymid = (ye[1:]+ye[:-1])/2.0\n", " X, Y = np.meshgrid(xmid,ymid)\n", " ax.contour(X,Y,h.T,kwargs)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots()\n", "ii = np.logical_and(df['distance'] < 100, df['distance'] >= 0)\n", "plt.plot(df[ii]['distance'],df[ii]['b'],'k.')\n", "binx = np.arange(0,100,10)\n", "biny = np.arange(-90,90,10)\n", "contourbin(df[ii]['distance'],df[ii]['b'],binx,biny,ax,colors='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# With other catalogs" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "galah_cat = gload.galah()\n", "rave_cat = gload.rave()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Name',\n", " 'RAVE',\n", " 'RAdeg',\n", " 'DEdeg',\n", " 'GLON',\n", " 'GLAT',\n", " 'HRV',\n", " 'e_HRV',\n", " 'R',\n", " 'hcp',\n", " 'wcp',\n", " 'HRVsky',\n", " 'e_HRVsky',\n", " 'Rsky',\n", " 'cRV',\n", " 'ZPFLAG',\n", " 'SNRS',\n", " 'TYCHO2',\n", " 'distT2',\n", " 'XT2',\n", " 'pmRAT2',\n", " 'e_pmRAT2',\n", " 'pmDET2',\n", " 'e_pmDET2',\n", " 'UCAC2',\n", " 'distU2',\n", " 'XU2',\n", " 'pmRAU2',\n", " 'e_pmRAU2',\n", " 'pmDEU2',\n", " 'e_pmDEU2',\n", " 'UCAC3',\n", " 'distU3',\n", " 'XU3',\n", " 'pmRAU3',\n", " 'e_pmRAU3',\n", " 'pmDEU3',\n", " 'e_pmDEU3',\n", " 'UCAC4',\n", " 'distU4',\n", " 'XU4',\n", " 'pmRAU4',\n", " 'e_pmRAU4',\n", " 'pmDEU4',\n", " 'e_pmDEU4',\n", " 'PPMXL',\n", " 'distP',\n", " 'XP',\n", " 'pmRAP',\n", " 'e_pmRAP',\n", " 'pmDEP',\n", " 'e_pmDEP',\n", " 'Obsdate',\n", " 'Field',\n", " 'Plate',\n", " 'Fiber',\n", " 'TeffK',\n", " 'e_TeffK',\n", " 'loggK',\n", " 'e_loggK',\n", " '[M/H]K',\n", " 'c[M/H]K',\n", " 'e_[M/H]K',\n", " 'SNRK',\n", " 'QK',\n", " '[Al/H]',\n", " 'o_Al',\n", " '[Si/H]',\n", " 'o_Si',\n", " '[Fe/H]',\n", " 'o_Fe',\n", " '[Ti/H]',\n", " 'o_Ti',\n", " '[Ni/H]',\n", " 'o_Ni',\n", " '[Mg/H]',\n", " 'o_Mg',\n", " 'CHISQc',\n", " 'TeffS',\n", " 'loggS',\n", " '[a/Fe]S',\n", " '2MASS',\n", " 'dist2',\n", " 'X2',\n", " 'Jmag2',\n", " 'e_Jmag2',\n", " 'Hmag2',\n", " 'e_Hmag2',\n", " 'Kmag2',\n", " 'e_Kmag2',\n", " 'DENIS',\n", " 'distD',\n", " 'XD',\n", " 'ImagD',\n", " 'e_ImagD',\n", " 'JmagD',\n", " 'e_JmagD',\n", " 'KmagD',\n", " 'e_KmagD',\n", " 'USNOB1',\n", " 'distUB1',\n", " 'XUB1',\n", " 'B1magUB1',\n", " 'R1magUB1',\n", " 'B2magUB1',\n", " 'R2magUB1',\n", " 'ImagUB1',\n", " 'plx',\n", " 'e_plx',\n", " 'Dist',\n", " 'e_Dist',\n", " 'MOD',\n", " 'e_MOD',\n", " 'Av',\n", " 'e_Av',\n", " 'Age',\n", " 'e_Age',\n", " 'Mass',\n", " 'e_Mass',\n", " 'c1',\n", " 'c2',\n", " 'c3',\n", " 'c4',\n", " 'c5',\n", " 'c6',\n", " 'c7',\n", " 'c8',\n", " 'c9',\n", " 'c10',\n", " 'c11',\n", " 'c12',\n", " 'c13',\n", " 'c14',\n", " 'c15',\n", " 'c16',\n", " 'c17',\n", " 'c18',\n", " 'c19',\n", " 'c20']" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rave_cat.colnames" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: AstropyDeprecationWarning: Initializing frame classes like \"ICRS\" using string or other non-Quantity arguments is deprecated, and will be removed in the next version of Astropy. Instead, you probably want to use the SkyCoord class with the \"frame=icrs\" keyword, or if you really want to use the low-level frame classes, create it with an Angle or Quantity. [astropy.coordinates.baseframe]\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "482194 203833\n", "2057050 0.0990899589218\n" ] } ], "source": [ "m1,m2,sep = xmatch.xmatch(tgas_cat,rave_cat,colRA1='ra',colDec1='dec',colRA2='RAdeg',colDec2='DEdeg')\n", "tgas_rave = tgas_cat[m1]\n", "rave_tgas = rave_cat[m2]\n", "plt.figure()\n", "plt.hist(sep)\n", "print len(rave_cat), len(rave_tgas)\n", "print len(tgas_cat), len(tgas_rave)/float(len(tgas_cat))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " / FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " / This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "10680 7919\n", "2057050 0.00384968765951\n" ] } ], "source": [ "m1,m2,sep = xmatch.xmatch(tgas_cat,galah_cat,colRA1='ra',colDec1='dec',colRA2='RA',colDec2='dec')\n", "tgas_galah= tgas_cat[m1]\n", "galah_tgas= galah_cat[m2]\n", "plt.figure()\n", "plt.hist(sep)\n", "print len(galah_cat), len(galah_tgas)\n", "print len(tgas_cat), len(tgas_galah)/float(len(tgas_cat))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['galah_id',\n", " 'tycho2_id',\n", " 'tmass_ID',\n", " 'RA',\n", " 'dec',\n", " 'Teff',\n", " 'logg',\n", " '[Fe/H]',\n", " '[alpha/Fe]',\n", " 'vrad',\n", " '(m-M)v',\n", " 'E(B-V)']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "galah_cat.colnames" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-25-27b9433abde5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtycho2_cat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mm2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxmatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtgas_cat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxcat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'vizier:Tycho2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolRA\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ra'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolDec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'dec'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msavefilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"tycho2.dat\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtgas_tycho2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtgas_cat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/alexji/lib/python/gaia_tools/gaia_tools/xmatch/__init__.pyc\u001b[0m in \u001b[0;36mcds\u001b[0;34m(cat, xcat, maxdist, colRA, colDec, savefilename)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mwr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriterow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RA'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'DEC'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mii\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0mwr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriterow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mii\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolRA\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mii\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolDec\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0;31m# Send to CDS for matching\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresultfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "tycho2_cat,m2 = xmatch.cds(tgas_cat, xcat='vizier:Tycho2',colRA='ra',colDec='dec',savefilename=\"tycho2.dat\")\n", "tgas_tycho2 = tgas_cat[m2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "apass_cat,m2 = xmatch.cds(tgas_cat, xcat='vizier:')" ] } ], "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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
xMyrst/BigData	python/howto/005_Estructuras de control.ipynb	1	13655	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![logo_python3](https://www.python.org/static/img/python-logo.png \"Logo Python3\")\n", "# ``ESTRUCTURAS DE CONTROL``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br />\n", " ``INSTRUCCIONES IF, ELIF, ELSE``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``El intérprete de Python ejecuta un programa ejecutando una instrucción cada vez.``\n", "\n", " if <condition>:\n", " <do something>\n", " elif <condition2>:\n", " <do other thing>\n", " else:\n", " <do other thing>\n", "\n", "``Recordar que en Python los bloques se delimitan por sangrado.``\n", "\n", " + ``Cuando ponemos los dos puntos al final de la primera línea del condicional, todo lo que vaya a continuación con un nivel de sangrado superior se considera dentro del condicional.``\n", "\n", " + ``En cuanto escribimos la primera línea con un nivel de sangrado inferior, hemos cerrado el condicional.``\n", " \n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x es mayor que y\n", "x sigue siendo mayor que y\n" ] } ], "source": [ "x, y = 2, 0\n", "if x > y:\n", " print(\"x es mayor que y\")\n", " print(\"x sigue siendo mayor que y\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Esto se ejecuta siempre\n" ] } ], "source": [ "if 1 < 0:\n", " print(\"1 es mayor que 0\") # esto está dentro de bucle, pero no se escribe por que no cumple que 1 sea menor que 0\n", "print(\"Esto se ejecuta siempre\") # esto no está dentro del bloque y SE ejecuta siempre; ya que no pertenece al IF" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IndentationError", "evalue": "unexpected indent (<ipython-input-4-8231ed33204f>, line 3)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-4-8231ed33204f>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m print(\"1 sigue siendo menor que 0\") # error de sangrado, porque el sangrado es superior\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" ] } ], "source": [ "if 1 < 0:\n", " print(\"1 es menor que 0\")\n", " print(\"1 sigue siendo menor que 0\") # error de sangrado, porque el sangrado es superior" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``Si queremos añadir ramas adicionales al condicional, podemos emplear la instrucción elif (abreviatura de else if). Para la parte final, que debe ejecutarse si ninguna de las condiciones anteriores se ha cumplido, usamos la instrucción else.``" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x es mayor que y\n" ] } ], "source": [ "x, y = 2, 0\n", "if x > y:\n", " print(\"x es mayor que y\")\n", "else:\n", " print(\"x es menor que y\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x es mayor que y\n" ] } ], "source": [ "# Uso de ELIF (contracción de else if)\n", "x, y = 2 , 0\n", "if x < y:\n", " print(\"x es menor que y\")\n", "elif x == y:\n", " print(\"x es igual a y\")\n", "else:\n", " print(\"x es mayor que y\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br />\n", " ``EXPRESIONES TERNARIAS``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``Las expresiones ternarias en Python tienen la siguiente forma:``\n", "\n", " + ``e = valorSiTrue if <condicion> else valorSiFalse``\n", " \n", "``Permite definir la instrucción if-else en una sola línea. La expresión anterior es equivalente a:``\n", "\n", " + ``if <condicion>:\n", " e = valorSiTrue\n", "else: \n", " e = valorSiFalse``" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'x es igual a 8'" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Una expresión ternaria es una contracción de un bucle FOR\n", "# Se define la variable para poder comparar\n", "x = 8\n", "# Se puede escrbir la expresión de esta manera o almacenando el resultado dentro de una variable \n", "# para trabajar posteriormente con ella\n", "\"Hola CICE\" if x == 8 else \"Adios CICE\"\n", "a = 'x es igual a 8' if x == 8 else 'x es distinto de 8'\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br />\n", " ``BUCLES FOR Y WHILE``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``El bucle FOR``\n", "\n", "``Permite realizar una tarea un número fijo de veces. Se utiliza para recorrer una colección completa de elementos (una tupla, una lista, un diccionario, etc ):``\n", "\n", " for <element> in <iterable_object>:\n", " <hacer algo...>\n", "\n", "+ ``Aquí el objeto <iterable_object> puede ser una lista, tupla, array, diccionario, etc.``\n", "+ ``El bucle se repite un número fijo de veces, que es la longitud de la colección de elementos.``\n", " " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "# itera soble los elementos de la tupla\n", "for elemento in (1, 2, 3, 4, 5):\n", " print(elemento)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n" ] } ], "source": [ "# Suma todos los elementos de la tupla\n", "suma = 0\n", "for elemento in (1, 2, 3, 4, 5):\n", " suma = suma + elemento\n", "print(suma)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Lunes\n", "Martes\n", "Miércoles\n", "Jueves\n", "Viernes\n", "Sábado\n", "Domingo\n" ] } ], "source": [ "# Muestra todos los elemntos de una lista\n", "dias = [\"Lunes\", \"Martes\", \"Miércoles\", \"Jueves\", 'Viernes', 'Sábado', 'Domingo']\n", "for nombre in dias:\n", " print(nombre)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "3\n", "5\n", "7\n", "9\n" ] } ], "source": [ "# La instrucción CONTINUE permite saltar de una iteración a otra\n", "# Crea la lista de enteros en el intervalo [0,10)\n", "# No se ejecuta si j es un número par, gracias a la sentencia continue\n", "for j in range(10):\n", " if j % 2 == 0:\n", " continue \n", " print(j)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 'Lunes')\n", "(2, 'Martes')\n", "(3, 'Miércoles')\n" ] } ], "source": [ "# También es posible recorrer un diccionario mediante el bucle FOR\n", "# dic.items\n", "# dic.keys\n", "# dic.values\n", "dic = {1:'Lunes', 2:'Martes', 3:'Miércoles' }\n", "# Python 3.5\n", "for i in dic.items():\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La clave es: 1 y el valor es: Lunes\n", "La clave es: 2 y el valor es: Martes\n", "La clave es: 3 y el valor es: Miércoles\n" ] } ], "source": [ "dic = {1:'Lunes', 2:'Martes', 3:'Miércoles' }\n", "# Python 3.5\n", "for (clave, valor) in dic.items():\n", " print(\"La clave es: \" + str(clave) + \" y el valor es: \" + valor)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(dict_items([(1, 'Lunes'), (2, 'Martes'), (3, 'Miércoles')]),\n", " dict_keys([1, 2, 3]),\n", " dict_values(['Lunes', 'Martes', 'Miércoles']))" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dic.items(), dic.keys(), dic.values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<br />\n", "``El bucle WHILE``\n", "\n", "``Los bucles while repetirán las instrucciones anidadas en él mientras se cumpla una condición:``\n", "\n", " while <condition>:\n", " <things to do>\n", "\n", "+ ``El número de iteraciones es variable, depende de la condición.``\n", "+``Como en el caso de los condicionales, los bloques se separan por sangrado sin necesidad de instrucciones del tipo end.``" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-2\n", "-1\n", "0\n", "1\n", "2\n", "3\n", "4\n", "Estoy fuera del while\n" ] } ], "source": [ "i = -2\n", "while i < 5:\n", " print(i)\n", " i = i + 1\n", "print(\"Estoy fuera del while\")" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n" ] } ], "source": [ "# Para interrumpir un bucle WHILE se utiliza la instrucción BREAK\n", "# Se pueden usar expresiones de tipo condicional (AND, OR, NOT)\n", "i = 0\n", "j = 1\n", "while i < 5 and j == 1:\n", " print(i)\n", " i = i + 1\n", " if i == 3:\n", " break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<BR />\n", "``LA FUNCIÓN ENUMERATE``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``Cuando trabajamos con secuencias de elementos puede resultar útil conocer el índice de cada elemento. La función enumerate devuelve una secuencia de tuplas de la forma (i, valor).``\n", "\n", "``Mediante un bucle es posible recorrerse dicha secuencia:``\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: Madrid\n", "1: Sevilla\n", "2: Segovia\n", "3: Valencia\n" ] } ], "source": [ "# Creamos una lista llamada ciudades\n", "# Recorremos dicha lista mediante la instrucción FOR asignando una secuencia de números (i) a cada valor de la lista\n", "ciudades = [\"Madrid\", \"Sevilla\", \"Segovia\", \"Valencia\" ]\n", "for (i, valor) in enumerate(ciudades):\n", " print('%d: %s' % (i, valor))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: Valencia\n", "1: Segovia\n", "2: Sevilla\n", "3: Madrid\n" ] } ], "source": [ "# Uso de la función reversed \n", "# la función reversed devuelve un iterador inverso de una lista\n", "for (i, valor) in enumerate(reversed(ciudades)):\n", " print('%d: %s' % (i, valor))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
atulsingh0/MachineLearning	HandsOnML/code/08_dimensionality_reduction.ipynb	2	3722584	null	gpl-3.0
crocha700/UpperOceanSeasonality	notebooks/Figure4.ipynb	1	288705	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "__depends__ = [\"../outputs/eta_anomaly_var_wavenumber_freq_october.npz\",\n", " \"../outputs/eta_anomaly_var_wavenumber_freq_april.npz\",\n", " \"../outputs/eta_anomaly_var_wavenumber_freq_all.npz\",\n", " \"../outputs/eta_anomaly_var_wavenumber_freq_4320_october.npz\",\n", " \"../outputs/eta_anomaly_var_wavenumber_freq_4320_april.npz\",\n", " \"../outputs/ke__wavenumber_freq_4320_october.npz\",\n", " \"../outputs/ke__wavenumber_freq_4320_april.npz\",\n", " \"../outputs/ke__wavenumber_freq_4320_all.npz\",\n", " \"../WOA/radii_min.npz\",\n", " \"../WOA/radii_max.npz\",\n", " \"../WOA/radii.npz\",\n", " '../outputs/llc_kuroshio_spectra.nc']\n", "__dest__ = [\"../writeup/figs/fig4.pdf\",\"../writeup/figs/figS7.pdf\"]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "__figpath__ = 'figs/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plots wavenumber-frequency KE and SSH variance spectra" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import LogNorm\n", "from matplotlib.mlab import bivariate_normal\n", "\n", "from netCDF4 import Dataset\n", "\n", "import cmocean\n", "import seawater as sw\n", "\n", "from pyspec import spectrum\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "speco = np.load(__depends__[0])\n", "speca = np.load(__depends__[1])\n", "spec = np.load(__depends__[2])\n", "speco4320 = np.load(__depends__[3])\n", "speca4320 = np.load(__depends__[4])\n", "kespeco4320 = np.load(__depends__[5])\n", "kespeca4320 = np.load(__depends__[6])\n", "kespec4320 = np.load(__depends__[7])\n", "radii = np.load(__depends__[-2])['radii']\n", "radii_min = np.load(__depends__[-4])['radii']\n", "radii_max = np.load(__depends__[-3])['radii']\n", "llc = Dataset(__depends__[-1])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "c1 = 'slateblue'\n", "c2 = 'tomato'\n", "c3 = 'k'\n", "c4 = 'indigo'\n", "plt.rcParams['lines.linewidth'] = 2.5\n", "\n", "def leg_width(lg,fs):\n", " \"\"\"\" Sets the linewidth of each legend object \"\"\"\n", " for legobj in lg.legendHandles:\n", " legobj.set_linewidth(fs)\n", " \n", "def add_second_axis(ax1):\n", " \"\"\" Add a x-axis at the top of the spectra figures \"\"\"\n", " ax2 = ax1.twiny() \n", " ax2.set_xscale('log')\n", " ax2.set_xlim(ax1.axis()[0], ax1.axis()[1])\n", " kp = 1./np.array([500.,250.,100.,50.,25.,10.])\n", " lp=np.array([500,250,100,50,25,10])\n", " ax2.set_xticks(kp)\n", " ax2.set_xticklabels(lp)\n", " plt.xlabel('Wavelength [km]')\n", " \n", "def set_axes(type='ke'):\n", " if type=='ke':\n", " plt.loglog(kr,12.e2,'.5',linewidth=2)\n", " plt.loglog(kr,35e3,'.5',linewidth=2)\n", " plt.xlim(.75e-3,1/3.)\n", " plt.ylim(1.e-3,1.e2)\n", " plt.ylabel(r'KE density [m$^2$ s$^{-2}$/cpkm]')\n", "\n", " elif type=='ssha':\n", " plt.loglog(kr,e2/.5e1,'.5',linewidth=2)\n", " plt.loglog(kr,3e5/1.5e2,'.5',linewidth=2)\n", " plt.xlim(.75e-3,1/3.)\n", " plt.ylim(1.e-6,1.e2)\n", " plt.ylabel(r'SSH variance density [m$^2$/cpkm]') \n", " \n", " plt.xlabel(r'Wavenumber [cpkm]')\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f,ki = speco['f'],spec['ki']\n", "Eetao = speco['iEeta']\n", "Eetaa = speca['iEeta']\n", "Eeta = spec['iEeta']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " if __name__ == '__main__':\n" ] } ], "source": [ "f, Eetao, Eetaa, Eeta = f[1:f.size/2], Eetao[:,1:f.size/2],Eetaa[:,1:f.size/2], Eeta[:,1:f.size/2]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f0 = sw.f(32.5)\n", "fmin, fmax = sw.f(25), sw.f(40.)\n", "\n", "\n", "#f0 = sw.f(32.69)\n", "N2 = (276.32f0)2\n", "\n", "m = (1./150)\n", "m2 = (1./500)\n", "m3 = (1./550)\n", "m4 = (1./1500)\n", "\n", "k = np.logspace(-3,-1,100)\n", "\n", "omg = np.sqrt(f02 + N2((k/m)2))3600/2/np.pi\n", "omg2 = np.sqrt(f0*2 + N2((k/m2)*2))3600/2/np.pi\n", "omg3 = np.sqrt(f0*2 + N2((k/m3)*2))3600/2/np.pi\n", "omg4 = np.sqrt(f0*2 + N2((k/m4)*2))3600/2/np.pi\n", "\n", "rd1 = 2np.piradii[0]\n", "rd2 = 2np.piradii[1]\n", "rd3 = 2np.piradii[2]\n", "rd4 = 2np.piradii[3]\n", "rd5 = 2np.piradii[4]\n", "\n", "rd1_min = 2np.piradii_min[0]\n", "rd4_min = 2np.piradii_min[3]\n", "rd1_max = 2np.piradii_max[0]\n", "rd4_max = 2np.piradii_max[3]\n", "\n", "rd0 = 2000 # external deformation radius (km)\n", "omg_0 = f0np.sqrt(1 + (krd0)*2)3600/2/np.pi\n", "\n", "omg_1 = f0np.sqrt(1 + (krd1)*2)3600/2/np.pi\n", "omg_2 = f0np.sqrt(1 + (krd2)*2)3600/2/np.pi\n", "omg_3 = f0np.sqrt(1 + (krd3)*2)3600/2/np.pi\n", "omg_4 = f0np.sqrt(1 + (krd4)*2)3600/2/np.pi\n", "omg_5 = f0np.sqrt(1 + (krd5)*2)3600/2/np.pi\n", "\n", "omg_1_min = fminnp.sqrt(1 + (krd1_min)*2)3600/2/np.pi\n", "omg_4_min = fminnp.sqrt(1 + (krd4_min)*2)3600/2/np.pi\n", "\n", "omg_1_max = fmaxnp.sqrt(1 + (krd1_max)*2)3600/2/np.pi\n", "omg_4_max = fmaxnp.sqrt(1 + (krd4_max)*2)3600/2/np.pi\n", "\n", "m2 = 1./12.4\n", "f0 = f03600/(2np.pi)\n", "fmin = fmax3600/(2np.pi)\n", "fmax = fmin3600/(2np.pi)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RatioEeta = Eetao.T/Eetaa.T\n", "RatioEeta = np.ma.masked_array(RatioEeta,Eetao.T<1.e-2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plt_freqs():\n", " plt.fill_between(k, omg_4_min, omg_1_max, facecolor='.5',alpha=.35)\n", " plt.text(1./150,.425,r'mode 1, 40$^\\circ$N',fontsize=11,rotation=34.5)\n", " plt.text(1./37.5,.28,r'mode 4, 25$^\\circ$N',fontsize=11,rotation=36)\n", " plt.plot([1.e-3,1.e-1],[f0,f0],'k--',linewidth=1)\n", " plt.plot([1.e-3,1.e-1],[m2,m2],'k--',linewidth=1)\n", " #plt.plot([1.e-3,1.e-1],[2f0,2f0],'k--',linewidth=1)\n", " plt.text(1/15.,.75m2,'$M_2$',fontsize=14)\n", " plt.text(1/16.5,.75f0,'$f_{32.5}$',fontsize=14)\n", " #plt.text(1./550,2.1f0,'2$f_{32.5}$',fontsize=14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SSH variance" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.66610942100452908" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L, T = 1./ki, 1./f\n", "\n", "fLsub = L < 100\n", "fTsub = T < 1./f0\n", "\n", "\n", "SSHvaro = Eetao[fLsub][...,fTsub].sum()\n", "SSHvara = Eetaa[fLsub][...,fTsub].sum()\n", "\n", "(SSHvaro-SSHvara)/SSHvaro" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LLC4320, 1/48$^\\circ$" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f4320,ki4320 = speco4320['f'],speco4320['ki']\n", "Eetao4320 = speco4320['iEeta']\n", "Eetaa4320 = speca4320['iEeta']" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " if __name__ == '__main__':\n" ] } ], "source": [ "f4320, Eetao4320, Eetaa4320 = f4320[1:f4320.size/2], Eetao4320[:,1:f4320.size/2],Eetaa4320[:,1:f4320.size/2]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RatioEeta4320 = Eetao4320.T/Eetaa4320.T\n", "RatioEeta4320 = np.ma.masked_array(RatioEeta4320,Eetao4320.T<1.e-2)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.65027354684741756" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L, T = 1./ki4320, 1./f4320\n", "\n", "fLsub = L < 100\n", "fTsub = T < 1.2/f0\n", "fTnsub = T > 0.8/f0\n", "\n", "Tm2 = 12.4\n", "Tf = 1./f0\n", "\n", "fTm2 = (T>.9Tm2) & (T<1.1Tm2) \n", "fTf = (T>.9Tf) & (T<1.1Tf) \n", "\n", "SSHvaro = Eetao4320[fLsub][...,fTsub].sum()\n", "SSHvara = Eetaa4320[fLsub][...,fTsub].sum()\n", "SSHvaro_si = Eetao4320[fLsub][...,fTnsub].sum()\n", "SSHvara_si = Eetaa4320[fLsub][...,fTnsub].sum()\n", "\n", "SSHvaro_m2 = Eetao4320[fLsub][...,fTm2].sum()\n", "SSHvara_m2 = Eetaa4320[fLsub][...,fTm2].sum()\n", "\n", "SSHvaro_f = Eetao4320[fLsub][...,fTf].sum()\n", "SSHvara_f = Eetaa4320[fLsub][...,fTf].sum()\n", "\n", "(SSHvaro-SSHvara)/SSHvaro" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.049271001056777453,\n", " 0.029137724170601832,\n", " 0.020402961157148479,\n", " 0.051702318621115559,\n", " 0.031153752883761997,\n", " 0.014923084764656985,\n", " 0.002484947799126629,\n", " 0.003764396644105784)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dk, df = ki4320[1],f4320[1]\n", "np.sqrt(SSHvarodkdf), np.sqrt(SSHvaradkdf), np.sqrt(SSHvaro_sidkdf), np.sqrt(SSHvara_sidkdf),np.sqrt(SSHvaro_m2dkdf),np.sqrt(SSHvara_m2dkdf), np.sqrt(SSHvaro_fdkdf),np.sqrt(SSHvara_fdkdf) " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "N = 10 # there are about 15 independent realizations of the wavenumber-frequency\n", " # spectrum at the 2 x f --- less at low frequencies/low wavenumbers,\n", " # more at high frequencies/high wavenumbers\n", "\n", "Ns = np.ones_like(ki4320)N\n", "\n", "ssh_k_s = np.ones_like(ki4320)1.e-5\n", "ke_k_s = np.ones_like(ki4320)3.e-3\n", "\n", "Ns[1./ki4320 > 200] = N/8\n", "Ns[(1./ki4320 < 200) & (1./ki4320 > 100)] = N/4\n", "Ns[(1./ki4320 < 100) & (1./ki4320 > 50)] = N/2\n", "Ns[(1./ki4320 < 25)] = 2N\n", "\n", "ssh_k_l, ssh_k_u = spectrum.spec_error(ssh_k_s,sn=Ns)\n", "ke_k_l, ke_k_u = spectrum.spec_error(ke_k_s,sn=Ns)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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LDAgbXD4DfpPtfcmlqZ7HVddpmo+r6lVN+TLla2NLqvr27VvpF8H6+vOf/+y7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\nEakjFVtEREREREREROrovwHPa8RjABAryAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1151fe470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vmin, vmax = 1.e-2, 1.e2\n", "\n", "\n", "# SWOT threeshold requirements\n", "ESWOT = 2.e-4 + 1.25e-7(ki4320-2)\n", "\n", "\n", "fig = plt.figure(figsize=(21,4.))\n", "\n", "ax = plt.subplot(131)\n", "plt.pcolormesh(ki4320,f4320,Eetaa4320.T,norm=LogNorm(vmin=vmin,vmax=vmax),cmap=cmocean.cm.amp) #cmocean.cm.ice_r)\n", "\n", "ax.set_xscale(\"log\", nonposx='clip')\n", "ax.set_yscale(\"log\", nonposx='clip')\n", "plt.xlabel(\"Horizontal wavenumber [cpkm]\")\n", "plt.ylabel(\"Frequency [cph]\")\n", "plt_freqs()\n", "plt.xlim(1.e-3,1/5./2.)\n", "plt.ylim(0,.5)\n", "add_second_axis(ax)\n", "\n", "plt.text(1/800,.35,'April',color='k',fontsize=16)\n", "plt.text(.75e-3,1/1.4,'(a)',fontsize=14)\n", "\n", "#add_second_axis(ax)\n", "\n", "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)\n", "\n", "ax = plt.subplot(132)\n", "cdensity = plt.pcolormesh(ki4320,f4320,Eetao4320.T,norm=LogNorm(vmin=vmin,vmax=vmax),cmap=cmocean.cm.amp)#cmocean.cm.ice_r)\n", "\n", "ax.set_xscale(\"log\", nonposx='clip')\n", "ax.set_yscale(\"log\", nonposx='clip')\n", "plt.xlabel(\"Horizontal wavenumber [cpkm]\")\n", "plt_freqs()\n", "\n", "plt.xlim(1.e-3,1/5./2)\n", "#plt.xlim(1.e-3,1/3.)\n", "\n", "plt.ylim(0,.5)\n", "#plt.yticks([])\n", "plt.ylabel(\"Frequency [cph]\")\n", "\n", "plt.text(1/800,.35,'October',color='k',fontsize=16)\n", "add_second_axis(ax)\n", "plt.text(.75e-3,1/1.4,'(b)',fontsize=14)\n", "\n", "\n", "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.35, hspace=None)\n", "\n", "\n", "ax = plt.subplot(133)\n", "\n", "kr = np.array([1.e-4,1.])\n", "e2 = kr-2/1.e4\n", "e3 = kr-3/1.e7\n", "e5 = kr-5/1.e9\n", "df = f[1]\n", "\n", "plt.loglog(ki4320,Eetaa4320[:,:].sum(axis=1)df,color=c1,label='April')\n", "plt.loglog(ki4320,Eetaa4320[:,fTnsub].sum(axis=1)df,'--',color=c1,label='April, $< 0.8$ $f_{32.5}$')\n", "plt.loglog(ki4320,Eetao4320[:,:].sum(axis=1)df,color='g',label='October')\n", "plt.loglog(ki4320,Eetao4320[:,fTnsub].sum(axis=1)df,'--',color='g',label='October, $< 0.8$ $f_{32.5}$')\n", "plt.loglog(ki4320,ESWOT,'.5',linewidth=2)\n", "\n", "\n", "plt.fill_between(ki4320,ssh_k_l, ssh_k_u, color='.25', alpha=0.25)\n", "plt.text(1.15e-3,1.4e-5,'95%',fontsize=14)\n", "\n", "plt.text(1.15e-1,7.e-4,'-2',fontsize=14)\n", "plt.text(1.15e-1,.3e-5,'-5',fontsize=14)\n", "plt.text(1./960,2.1e-2,'SWOT baseline',fontsize=14,rotation=-20)\n", "plt.text(1./170,.9e-3,'requirement',fontsize=14,rotation=-15)\n", "\n", "plt.loglog(kr,e2/.6e1,'.5',linewidth=2)\n", "plt.loglog(kr,8.5e5/1.5e2,'.5',linewidth=2)\n", "plt.xlim(1.e-3,1e-1)\n", "plt.ylim(1.e-5,1.e2)\n", "plt.xlabel(r'Horizontal wavenumber [cpkm]')\n", "plt.ylabel(r'SSH variance density [m$^2$/cpkm]')\n", "#plt.text(3.e-2, 14, \"SSH variance\", size=16, rotation=0.,\n", "# ha=\"center\", va=\"center\",\n", "# bbox = dict(boxstyle=\"round\",ec='k',fc='w'))\n", "plt.ylim(1.e-6,1.e2)\n", "plt.text(.65e-3,700.,'(c)',fontsize=14)\n", "plt.yticks([1.e-6,1.e-4,1.e-2,1.e0,1e2])\n", "add_second_axis(ax)\n", "\n", "fig.subplots_adjust(right=0.8)\n", "cbar_ax = fig.add_axes([0.225, 1.25, 0.25, 0.04])\n", "fig.colorbar(cdensity, cax=cbar_ax,label=r'SSH variance density [m$^2$/(cpkm $\\times$ cph)]'\n", " ,extend='both',orientation='horizontal')\n", "\n", "lg = ax.legend(loc=(-0.2,1.35), ncol=2, fancybox=True,frameon=True, shadow=True)\n", "leg_width(lg,fs=6)\n", "\n", "plt.savefig(__dest__[0],dpi=100,bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# KE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LLC4320, 1/48$^\\circ$" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f4320,ki4320 = kespeco4320['f'],kespeco4320['ki']\n", "Eo4320 = kespeco4320['iE']\n", "Ea4320 = kespeca4320['iE']\n", "E4320 = kespec4320['iE']" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " if __name__ == '__main__':\n" ] } ], "source": [ "f4320, Eo4320, Ea4320, E4320 = f4320[1:f4320.size/2], Eo4320[:,1:f4320.size/2],Ea4320[:,1:f4320.size/2], E4320[:,1:f4320.size/2]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vmin, vmax = 1e-1, 1.e4" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "label_KEspec = r'KE density [(m$^2$ s$^{-2}$)/(cpkm $\\times$ cph)]'" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:7: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 234 but corresponding boolean dimension is 117\n", "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:8: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 234 but corresponding boolean dimension is 117\n", "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:9: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 234 but corresponding boolean dimension is 117\n", "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:10: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 234 but corresponding boolean dimension is 117\n", "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:16: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 234 but corresponding boolean dimension is 117\n", "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:17: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 234 but corresponding boolean dimension is 117\n", "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:19: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 234 but corresponding boolean dimension is 117\n", "/Users/crocha/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:20: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 234 but corresponding boolean dimension is 117\n" ] }, { "data": { "text/plain": [ "(0.32707724983024805,\n", " 0.19892509394993646,\n", " 0.1709941074117029,\n", " 0.45712162237150744,\n", " 0.25068416794364562,\n", " 0.12138063568479114,\n", " 0.037978114942184865,\n", " 0.081813264451539219)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "L, T = 1./ki, 1./f\n", "\n", "fLsub = L < 100\n", "fTsub = T < 1./f0\n", "fTnsub = T > 0.8/f0\n", "\n", "KEo = Eo4320[fLsub][...,fTsub].sum()\n", "KEa = Ea4320[fLsub][...,fTsub].sum()\n", "KEo_si = Eo4320[fLsub][...,fTnsub].sum()\n", "KEa_si = Ea4320[fLsub][...,fTnsub].sum()\n", "Tm2 = 12.4\n", "\n", "fTm2 = (T>.9Tm2) & (T<1.1Tm2) \n", "fTf = (T>.9Tf) & (T<1.1Tf) \n", "\n", "KEo_m2 = Eo4320[fLsub][...,fTm2].sum()\n", "KEa_m2 = Ea4320[fLsub][...,fTm2].sum()\n", "\n", "KEo_f = Eo4320[fLsub][...,fTf].sum()\n", "KEa_f = Ea4320[fLsub][...,fTf].sum()\n", "\n", "\n", "(KEo-KEa)/KEo\n", "\n", "np.sqrt(KEodkdf), np.sqrt(KEadkdf), np.sqrt(KEo_sidkdf), np.sqrt(KEa_sidkdf),np.sqrt(KEo_m2dkdf),np.sqrt(KEa_m2dkdf), np.sqrt(KEo_fdkdf),np.sqrt(KEa_fdkdf) " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0aeOjR492d/evvvrKDz/8cK9Xr55nZGTknZPbt2/34cOH+2677eZ16tTxHj16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gX7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\nmq5Khd/4exX38v+OeV76Z1bZePDasuuubLq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"text/plain": [ "<matplotlib.figure.Figure at 0x1188af438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vmin, vmax = 1.e-1, 1.e4\n", "\n", "\n", "fig = plt.figure(figsize=(21,4.))\n", "\n", "ax = plt.subplot(131)\n", "plt.pcolormesh(ki4320,f4320,Ea4320.T,norm=LogNorm(vmin=vmin,vmax=vmax),cmap=cmocean.cm.amp)\n", "ax.set_xscale(\"log\", nonposx='clip')\n", "ax.set_yscale(\"log\", nonposx='clip')\n", "plt.xlabel(\"Horizontal wavenumber [cpkm]\")\n", "plt.ylabel(\"Frequency [cph]\")\n", "plt_freqs()\n", "plt.xlim(1.e-3,1/5./2.)\n", "plt.ylim(0,.5)\n", "add_second_axis(ax)\n", "\n", "plt.text(1/800,.35,'April',color='k',fontsize=16)\n", "plt.text(.75e-3,1/1.4,'(a)',fontsize=14)\n", "\n", "#add_second_axis(ax)\n", "\n", "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)\n", "\n", "ax = plt.subplot(132)\n", "cdensity = plt.pcolormesh(ki4320,f4320,Eo4320.T,norm=LogNorm(vmin=vmin,vmax=vmax),cmap=cmocean.cm.amp)\n", "ax.set_xscale(\"log\", nonposx='clip')\n", "ax.set_yscale(\"log\", nonposx='clip')\n", "plt.xlabel(\"Horizontal wavenumber [cpkm]\")\n", "plt_freqs()\n", "\n", "plt.xlim(1.e-3,1/5./2)\n", "#plt.xlim(1.e-3,1/3.)\n", "\n", "plt.ylim(0,.5)\n", "#plt.yticks([])\n", "plt.ylabel(\"Frequency [cph]\")\n", "\n", "plt.text(1/800,.35,'October',color='k',fontsize=16)\n", "add_second_axis(ax)\n", "plt.text(.75e-3,1/1.4,'(b)',fontsize=14)\n", "\n", "\n", "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.35, hspace=None)\n", "\n", "\n", "ax = plt.subplot(133)\n", "\n", "kr = np.array([1.e-4,1.])\n", "e2 = kr-2/1.e4\n", "e3 = kr-3/1.e7\n", "e5 = kr-5/1.e9\n", "df = f[1]\n", "\n", "plt.loglog(ki4320,Ea4320[:,:].sum(axis=1)df,color=c1,label='April')\n", "plt.loglog(ki4320,Ea4320[:,fTnsub].sum(axis=1)df,'--',color=c1,label='April, $< 0.8$ $f_{32.5}$')\n", "plt.loglog(ki4320,Eo4320[:,:].sum(axis=1)df,color='g',label='October')\n", "plt.loglog(ki4320,Eo4320[:,fTnsub].sum(axis=1)df,'--',color='g',label='October, $< 0.8$ $f_{32.5}$')\n", "\n", "\n", "plt.fill_between(ki4320,ke_k_l, ke_k_u, color='.25', alpha=0.25)\n", "plt.text(1.15e-3,2.4e-3,'95%',fontsize=14)\n", "\n", "\n", "plt.loglog(kr,20.e2,'.5',linewidth=2)\n", "plt.loglog(kr,85*e3,'.5',linewidth=2)\n", "\n", "\n", "plt.text(1.15e-1,1.5e-1,'-2',fontsize=14)\n", "plt.text(1.15e-1,.5e-2,'-3',fontsize=14)\n", "\n", "plt.xlim(1.e-3,1e-1)\n", "plt.ylim(1.e-5,1.e2)\n", "plt.xlabel(r'Horizontal wavenumber [cpkm]')\n", "plt.ylabel(r'KE density [m$^2$ s$^{-2}$/cpkm]')\n", "#plt.text(3.e-2, 14, \"SSH variance\", size=16, rotation=0.,\n", "# ha=\"center\", va=\"center\",\n", "# bbox = dict(boxstyle=\"round\",ec='k',fc='w'))\n", "plt.ylim(1.e-3,1.e2)\n", "plt.text(.65e-3,340.,'(c)',fontsize=14)\n", "plt.yticks([1.e-3,1.e-1,1.e1])\n", "add_second_axis(ax)\n", "\n", "fig.subplots_adjust(right=0.8)\n", "cbar_ax = fig.add_axes([0.225, 1.25, 0.25, 0.04])\n", "fig.colorbar(cdensity, cax=cbar_ax,label=r'KE density [m$^2$ s$^{-2}$/(cpkm $\\times$ cph)]'\n", " ,extend='both',orientation='horizontal')\n", "\n", "\n", "lg = ax.legend(loc=(-0.2,1.35), ncol=2, fancybox=True,frameon=True, shadow=True)\n", "leg_width(lg,fs=6)\n", "\n", "plt.savefig(__dest__[1],dpi=100,bbox_inches='tight')" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	cc0-1.0
mikecroucher/notebook	GPy/basic_classification.ipynb	1	338127	{ "metadata": { "name": "", "signature": "sha256:b2fbc2d005e8c1d7ec8ba706ae8026bf171fb7066da1ba4b8bd88b3f7620b7e6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "#configure plotting\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg'\n", "import matplotlib;matplotlib.rcParams['figure.figsize'] = (8,5)\n", "import GPy\n", "import numpy as np\n", "from matplotlib import pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 60 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#A brief guide to GP classification with GPy\n", "\n", "###James Hensman, November 2014\n", "\n", "Gaussian Process classification is perhaps the most popular case of a GP model where the likelihood is not conjugate to the prior. this means that we have to approximate inference over the values of the function. There are many approaches to doing this: here we'll start with the populat EP method. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##The generative model\n", "To illustate GP classification, we'll consider the generative model of the data. The idea is that there is a hidden function, drawn from a GP, which takescontinuous values. Those values are then squashed through a probit function, and Bernoulli draws are made (taking the values 0 or 1) conditioned on the squashed values. \n", "\n", "First we'll set up a kernel from which to draw some latent function values, as well as the positions ($\\mathbf X$) at which we get observations." ] }, { "cell_type": "code", "collapsed": false, "input": [ "k = GPy.kern.RBF(1, variance=2., lengthscale=0.3)\n", "X = np.random.rand(200,1)\n", "\n", "#draw the latent function value\n", "f = np.random.multivariate_normal(np.zeros(200), k.K(X))\n", "\n", "plt.plot(X, f, 'bo')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "[<matplotlib.lines.Line2D at 0xa239a90>]" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", 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Value \| Constraint \| Prior \| Tied to\n", " \u001b[1mrbf.variance \u001b[0;0m \| 2.0 \| +ve \| \| \n", " \u001b[1mrbf.lengthscale\u001b[0;0m \| 0.3 \| +ve \| \| \n" ] } ], "prompt_number": 53 }, { "cell_type": "markdown", "metadata": {}, "source": [ "There's a simpler way to build GP classifiers, with some default options like so:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "m = GPy.models.GPClassification(X,Y)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "m.plot()\n", "plt.plot(X, p, 'ro')\n", "m.plot_f()\n", "plt.plot(X, f, 'bo')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 56, "text": [ "[<matplotlib.lines.Line2D at 0xa20a890>]" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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ComputationalModeling/spring-2017-danielak	past-semesters/fall_2016/day-by-day/day12-exploratory-data-analysis-day1/dataset_file_creation.ipynb	1	65284	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook creates one of the datesets used for this week's homework, and also has some miscellaneous messing around." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "import numpy as np\n", "\n", "# interesting function; semi-randomly chosen.\n", "x = np.arange(-4.0,14.0,0.5)\n", "y = 3.0np.exp(-(x-2.0)2/4.0) + 0.5x\n", "z = np.sin(x)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# now add noise\n", "\n", "xnew = x + 0.05np.random.randn(x.size)\n", "ynew = y + 0.2np.random.randn(y.size)\n", "znew = z + 0.2np.random.randn(z.size)\n", "\n", "xerror = np.abs(0.05np.random.randn(x.size) + 0.25)\n", "yerror = np.abs(0.2np.random.randn(y.size) + 0.1)\n", "zerror = np.abs(0.2np.random.randn(z.size) + 0.2)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x107ebb5c0>,\n", " <matplotlib.lines.Line2D at 0x107ebb780>]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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tNa7tOJIVex92N0jxJK3YBYi20U3tOpPlDON9Tqn8bGJH6kZni+dTe6yvZXGf7hwyejRk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\nGCV2ERGPUWIXEfEYJXYREY/5fyc2X+w+CxzmAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1065eb208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot just so we can see what's going on\n", "\n", "plt.plot(xnew,ynew,'bo',x,y,'r-')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x107e84d68>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x107e84dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# now with error bars!\n", "\n", "plt.errorbar(xnew,ynew,yerror,xerror,'b.',ecolor='r')\n", "plt.plot(x,y,'g-')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DEfeJ+I7MRsvtMXPmBMrK6mPebZ9u+mjTotuuXTueeuop7rjjDsNZZ4kW3NRi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\nxNIlIlLRT7cWKhhePSu3AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10803cdd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 3d plot\n", "\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import numpy as np\n", "\n", "fig = plt.figure()\n", "ax = fig.gca(projection='3d')\n", "ax.plot(xnew, ynew, znew,'bo')\n", "ax.plot(xnew, ynew, znew,'r-')\n", "\n", "ax.view_init(elev=30., azim=10)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# creates the data file we use in the in-class assignment\n", "\n", "import csv\n", "cvsfile = open('datafile_1.csv','w',newline='')\n", "\n", "cvswriter = csv.writer(cvsfile,delimiter=',')\n", "\n", "cvswriter.writerow(['# content of each column:'])\n", "cvswriter.writerow([\"# 1. x value\"])\n", "cvswriter.writerow([\"# 2. x error\"])\n", "cvswriter.writerow([\"# 3. y value\"])\n", "cvswriter.writerow([\"# 4. y error\"])\n", "cvswriter.writerow([\"# 5. z value\"])\n", "cvswriter.writerow([\"# 6. z error\"])\n", "\n", "for i in range(xnew.size):\n", " cvswriter.writerow([xnew[i],xerror[i],ynew[i],yerror[i],znew[i],zerror[i]])\n", "cvsfile.close()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# example simple python file to write a CSV file\n", "\n", "f = open('file_of_arrays.csv','w')\n", "\n", "f.write(\"xval,yval,zval\\n\")\n", "for i in range(x.size):\n", " line = str(x[i]) + ',' + str(y[i]) + ',' + str(z[i]) + '\\n'\n", " f.write(line)\n", "\n", "f.close()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['xval', 'yval', 'zval']\n", "['-4.0', '-1.99962977059', '0.756802495308']\n", "['-3.5', '-1.74844127595', '0.35078322769']\n", "['-3.0', '-1.49420863759', '-0.14112000806']\n", "['-2.5', '-1.23101085372', '-0.598472144104']\n", "['-2.0', '-0.945053083334', '-0.909297426826']\n", "['-1.5', '-0.609688132848', '-0.997494986604']\n", "['-1.0', '-0.183802326314', '-0.841470984808']\n", "['-0.5', '0.378834161453', '-0.479425538604']\n", "['0.0', '1.10363832351', '0.0']\n", "['0.5', '1.95934847419', '0.479425538604']\n", "['1.0', '2.83640234921', '0.841470984808']\n", "['1.5', '3.56823918844', '0.997494986604']\n", "['2.0', '4.0', '0.909297426826']\n", "['2.5', '4.06823918844', '0.598472144104']\n", "['3.0', '3.83640234921', '0.14112000806']\n", "['3.5', '3.45934847419', '-0.35078322769']\n", "['4.0', '3.10363832351', '-0.756802495308']\n", "['4.5', '2.87883416145', '-0.977530117665']\n", "['5.0', '2.81619767369', '-0.958924274663']\n", "['5.5', '2.89031186715', '-0.70554032557']\n", "['6.0', '3.05494691667', '-0.279415498199']\n", "['6.5', '3.26898914628', '0.215119988088']\n", "['7.0', '3.50579136241', '0.656986598719']\n", "['7.5', '3.75155872405', '0.937999976775']\n", "['8.0', '4.00037022941', '0.989358246623']\n", "['8.5', '4.2500776043', '0.798487112623']\n", "['9.0', '4.50001435535', '0.412118485242']\n", "['9.5', '4.75000234345', '-0.0751511204618']\n", "['10.0', '5.00000033761', '-0.544021110889']\n", "['10.5', '5.25000004292', '-0.879695759972']\n", "['11.0', '5.50000000482', '-0.999990206551']\n", "['11.5', '5.75000000048', '-0.875452174688']\n", "['12.0', '6.00000000004', '-0.536572918']\n", "['12.5', '6.25', '-0.0663218973512']\n", "['13.0', '6.5', '0.420167036827']\n", "['13.5', '6.75', '0.803784426552']\n" ] } ], "source": [ "# example to read the CSV file written above!\n", "\n", "csvfile = open('file_of_arrays.csv','r')\n", "csvreader = csv.reader(csvfile,delimiter=',')\n", "for row in csvreader:\n", " print(row)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# saves a couple of numpy arrays with keywords\n", "np.savez('thisfile',xarray=x,yarray=y)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = np.load('thisfile.npz')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['yarray', 'xarray']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.files" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ -4. -3.5 -3. -2.5 -2. -1.5 -1. -0.5 0. 0.5 1. 1.5\n", " 2. 2.5 3. 3.5 4. 4.5 5. 5.5 6. 6.5 7. 7.5\n", " 8. 8.5 9. 9.5 10. 10.5 11. 11.5 12. 12.5 13. 13.5]\n" ] } ], "source": [ "print(data['xarray'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "numpy.lib.npyio.NpzFile" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(data)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['yarray', 'xarray']\n" ] } ], "source": [ "print(data.files)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	agpl-3.0
wtsi-medical-genomics/team-code	python-club/notebooks/python-club-11.ipynb	1	7434	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "All exercises from Downey, Allen. Think Python. Green Tea Press, 2014. http://www.greenteapress.com/thinkpython/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise 11.1 (Masa)\n", "Write a function that reads the words in words.txt and stores them as keys in a dictionary. It doesn’t matter what the values are. Then you can use the in operator as a fast way to check whether a string is in the dictionary." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise 11.2 (Wendy)\n", "Dictionaries have a method called get that takes a key and a default value. If the key appears in the dictionary, get returns the corresponding value; otherwise it returns the default value. For example:\n", "\n", "```\n", ">>> h = histogram('a')\n", ">>> print h\n", "{'a': 1}\n", ">>> h.get('a', 0)\n", "1\n", ">>> h.get('b', 0)\n", "0\n", "```\n", "Use get to write histogram more concisely. You should be able to eliminate the `if` statement." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise 11.3 (Dan)\n", "Dictionaries have a method called keys that returns the keys of the dictionary, in no particular order, as a list.\n", "Modify print_hist to print the keys and their values in alphabetical order.\n", "\n", "```\n", "def print_hist(h):\n", " for c in h:\n", " print c, h[c]\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise 11.4 (Sarah)\n", "Modify reverse_lookup so that it builds and returns a list of all keys that map to v, or an empty list if there are none.\n", "\n", "```\n", "def reverse_lookup(d, v):\n", " for k in d:\n", " if d[k] == v:\n", " return k\n", " raise ValueError\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise 11.5 (Sarah)\n", "Exercise 5 Read the documentation of the dictionary method setdefault and use it to write a more concise version of invert_dict.\n", "\n", "```\n", "def invert_dict(d):\n", " inverse = dict()\n", " for key in d:\n", " val = d[key]\n", " if val not in inverse:\n", " inverse[val] = [key]\n", " else:\n", " inverse[val].append(key)\n", " return inverse\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise 11.6 (Liu)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run this version of fibonacci and the original with a range of parameters and compare their run times.\n", "\n", "```\n", "known = {0:0, 1:1}\n", "\n", "def fibonacci(n):\n", " if n in known:\n", " return known[n]\n", "\n", " res = fibonacci(n-1) + fibonacci(n-2)\n", " known[n] = res\n", " return res\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise 11.7 (Wendy)\n", "Memoize the Ackermann function from Exercise 5 and see if memoization makes it possible to evaluate the function with bigger arguments. Hint: no.\n", "\n", "```\n", "def ack(m, n):\n", " if m < 0 or n < 0:\n", " print \"m and n must be nonnegative\"\n", " return None\n", " elif m == 0:\n", " return n + 1\n", " elif m > 0 and n == 0:\n", " return ack(m - 1, 1)\n", " elif m > 0 and n > 0:\n", " return ack(m - 1, ack(m, n - 1))\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise 11.9 (Masa)\n", "If you did Exercise 8, you already have a function named has_duplicates that takes a list as a parameter and returns True if there is any object that appears more than once in the list.\n", "Use a dictionary to write a faster, simpler version of has_duplicates.\n", "\n", "```\n", "def has_duplicates(L):\n", " seen = []\n", " for el in L:\n", " if el in seen:\n", " return True\n", " seen.append(el)\n", " return False\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise 11.10 (Dan)\n", "Two words are “rotate pairs” if you can rotate one of them and get the other (see rotate_word in Exercise 12). Write a program that reads a wordlist and finds all the rotate pairs.\n", "\n", "```\n", "def rotate_word(word, r):\n", " return word[r:] + word[0:r]\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercise 11.11 (Liu)\n", "Here’s another Puzzler from Car Talk (http://www.cartalk.com/content/puzzlers):\n", "\n", ">This was sent in by a fellow named Dan O’Leary. He came upon a common one-syllable, five-letter word recently that has the following unique property. When you remove the first letter, the remaining letters form a homophone of the original word, that is a word that sounds exactly the same. Replace the first letter, that is, put it back and remove the second letter and the result is yet another homophone of the original word. And the question is, what’s the word?\n", "\n", ">Now I’m going to give you an example that doesn’t work. Let’s look at the five-letter word, ‘wrack.’ W-R-A-C-K, you know like to ‘wrack with pain.’ If I remove the first letter, I am left with a four-letter word, ’R-A-C-K.’ As in, ‘Holy cow, did you see the rack on that buck! It must have been a nine-pointer!’ It’s a perfect homophone. If you put the ‘w’ back, and remove the ‘r,’ instead, you’re left with the word, ‘wack,’ which is a real word, it’s just not a homophone of the other two words.\n", "\n", ">But there is, however, at least one word that Dan and we know of, which will yield two \n", "homophones if you remove either of the first two letters to make two, new four-letter words. The question is, what’s the word?\n", "\n", "You can use the dictionary from Exercise 1 to check whether a string is in the word list.\n", "\n", "To check whether two words are homophones, you can use the CMU Pronouncing Dictionary. You can download it from http://www.speech.cs.cmu.edu/cgi-bin/cmudict or from http://thinkpython.com/code/c06d and you can also download http://thinkpython.com/code/pronounce.py, which provides a function named read_dictionary that reads the pronouncing dictionary and returns a Python dictionary that maps from each word to a string that describes its primary pronunciation.\n", "\n", "Write a program that lists all the words that solve the Puzzler." ] } ], "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 }	gpl-2.0
Upward-Spiral-Science/team1	code/Assignment11_Group.ipynb	1	224084	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Group" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end setup\n" ] } ], "source": [ "from mpl_toolkits.mplot3d import axes3d\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline \n", "import numpy as np\n", "import urllib2\n", "import scipy.stats as stats\n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "url = ('https://raw.githubusercontent.com/Upward-Spiral-Science'\n", " '/data/master/syn-density/output.csv')\n", "data = urllib2.urlopen(url)\n", "csv = np.genfromtxt(data, delimiter=\",\")[1:] # don't want first row (labels)\n", "\n", "# chopping data based on thresholds on x and y coordinates\n", "x_bounds = (409, 3529)\n", "y_bounds = (1564, 3124)\n", "\n", "def check_in_bounds(row, x_bounds, y_bounds):\n", " if row[0] < x_bounds[0] or row[0] > x_bounds[1]:\n", " return False\n", " if row[1] < y_bounds[0] or row[1] > y_bounds[1]:\n", " return False\n", " if row[3] == 0:\n", " return False\n", " \n", " return True\n", "\n", "indices_in_bound, = np.where(np.apply_along_axis(check_in_bounds, 1, csv,\n", " x_bounds, y_bounds))\n", "data_thresholded = csv[indices_in_bound]\n", "n = data_thresholded.shape[0]\n", "\n", "\n", "def synapses_over_unmasked(row):\n", " s = (row[4]/row[3])(643)\n", " return [row[0], row[1], row[2], s]\n", "\n", "syn_unmasked = np.apply_along_axis(synapses_over_unmasked, 1, data_thresholded)\n", "syn_normalized = syn_unmasked\n", "print 'end setup'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1) Boxplot of General Density" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1064544d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# syn_unmasked_T = syn_unmasked.values.T.tolist()\n", "# columns = [syn_unmasked[i] for i in [4]]\n", "\n", "plt.boxplot(syn_unmasked[:,3], 0, 'gD')\n", "plt.xticks([1], ['Set'])\n", "plt.ylabel('Density Distribution')\n", "plt.title('Density Distrubution Boxplot')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 2) Is the spike noise? More evidence.\n", "We saw from Emily's analysis that there is strong evidence against the spike being noise. If we see that the spike is noticeable in the histogram of synapses as well as the histogram of synapse density, we will gain even more evidence that the spike is noise." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x111fc41d0>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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34J8l7dzcVJmNTnnvqd7eXubOnUt/f//ql37BmhJJ+d9W1slP7e3kvNXEWF4I\nXo8B2BO4Ijc9GziubJ3w4KGdh56enujp6QkgIvtRR19f3+rpVtbX19fsJNRNJ+ctIkq/r6rPz+No\nPVsDS3LTjwIzm5QWs7rIl0DK7xjv7e2lt7d39dXuwMDAWtVZpb8nnXQqEydOZNmyR1bvq/SMq/w8\ns5oZS7SpxwB8BPhRbvrjwP8tW6fpV4gePDRqyEoiE2LjjSetLpWUL8+XWCArrfT19UVPT09ExFrT\npXnlRrq6Li1v1lV4I45bfoxOK3HA2EoYLdfoLWlPoD8i9k3Ts8kyOS+3Tmsl2sysTUQnvUBJ0vrA\nA8B7gceBW4F/joiFTU2YmVmXa7k2jIh4RdK/AAtY063WwcLMrMlaroRhZmatqeXuwxiJpH0l/VHS\ng5KOa3Z6qiHpDEmDku7JzZssaYGkByRdKWlSbtkcSYskLZS0T3NSXYykbSRdI+k+SfdKOibN75T8\nTZB0i6S7Uv760vyOyB9k90JJulPS/DTdMXkDkPSIpN+n7/DWNK8j8ihpkqQLU1rvk7RHTfM2lhbz\nRg9kAe5PwDRgA+BuYOdmp6uKfPwjsDtwT27ePOCrafw44MQ0vitwF1n14fSUfzU7D8PkbSqwexqf\nSNYetXOn5C+leaP0d33gZrJu352Uvy8BPwXmd9JvM5e/h4DJZfM6Io/AWcAn0/g4YFIt89ZuJYyZ\nwKKIWBwRK4DzgFlNTtOoRcQNwDNls2cBZ6fxs4ED0/gBwHkRsTIiHgEW0cL3pUTEsoi4O43/DVgI\nbEOH5A8gIl5MoxPI/tmCDsmfpG2ADwKn52Z3RN5yxLq1K22fR0mbAntHxJkAKc3PUsO8tVvAqHRT\n39ZNSkutbRURg5CddIGt0vzyPC+lTfIsaTpZSepmYEqn5C9V2dwFLAOuiojb6Jz8fQ/4ClkQLOmU\nvJUEcJWk2yR9Ks3rhDzOAJ6UdGaqUvyRpI2oYd7aLWB0k7bujSBpInARcGwqaZTnp23zFxGrIuIt\nZCWnmZJ2owPyJ+lDwGAqIQ7XV7/t8lZmr4h4K1lJ6guS9qYDvj+y0u5bgX9P+XuB7NFKNctbuwWM\npcB2uelt0rxOMChpCoCkqcATaf5SYNvcei2fZ0njyILFORFxWZrdMfkriYjngAFgXzojf3sBB0h6\nCPg58B5J5wDLOiBvq0XE4+nvX4FLyaphOuH7exRYEhG3p+mLyQJIzfLWbgHjNmAHSdMkjQcOBuY3\nOU3VEms8IMOzAAADyklEQVRfxc0HjkjjhwOX5eYfLGm8pBnADmQ3M7ayHwP3R8T3c/M6In+SXl3q\nZSJpQ+D9ZO00bZ+/iDg+IraLiO3J/reuiYjDgF/S5nkrkbRRKv0iaWNgH+BeOuP7GwSWSNopzXov\ncB+1zFuzW/Wr6AWwL1nPm0XA7Ganp8o8/Ax4jOytO38BPglMBq5OeVsAbJZbfw5ZD4aFwD7NTv8I\nedsLeIWsB9tdwJ3pO9u8Q/L3xpSnu4F7gBPS/I7IXy7NPazpJdUxeSOr5y/9Nu8tnUM6JY/Am8ku\nrO8GfkHWS6pmefONe2ZmVki7VUmZmVmTOGCYmVkhDhhmZlaIA4aZmRXigGFmZoU4YJiZWSEOGNa1\nJJ0g6Q/pUdd3SnpHs9Nk1spa7o17Zo2g7N3xHyR7FPtKSZsD45ucLLOW5hKGdavXAE9GxEqAiHga\n2EXSJaUVJL1P0sVp/HlJ/1vS3ZJukrRlmv9hSTdLuiO9pKY0v0/ST9K6D5SeiippqqTrUonmHkl7\npfnvT+veLun89JRRJJ2YSkF3S/p2Iz8gs3K+09u6UnqO0A3AhsBvgfMj4npJ95O9U+ApSecC50bE\n5ZJWAR9O4/OAZyPi3yRNiuydA0g6iuyFXl9R9ia+A4E9gE3IHkUxEzgEmBAR35IkYCPgVWSPcdg3\nIl6S9FWy0s5pwE0RsXPa/6aRPfDQrClcJWVdKSJekPRWYG/gPcB5kuYA5wAfl3QWsCdwWNrk5Yi4\nPI3fAbwvjW8r6QKyEssGwMO5w1wWEX8HnpJ0DVnAuA34saQN0vLfS+ole/vZjSmIbADcBDwLvCTp\ndODXwK9q/TmYjYYDhnWtyIrX1wPXS7oX+ARwNNmJ+WXgwohYlVZfkdv0Fdb875wCnBQRv5bUA/Tl\nD5EbVzrkf6b3L3wIOFPSycByYEFEHFqeRkkzyZ46+lHgX9K4WVO4DcO6kqSdJO2Qm7U7sDiyN5I9\nBpwAnJnfZIhdbZrWh+zR0Xmz0qOjtyB7+uttkrYDnoiIM4AzyN5XcDOwl6TXpbRtJGnHVG22WUT8\nBvgy8KZq82tWCy5hWLeaCJyS3m2xkuwRz59Jy84FXh0RD+TWH6qxby5wkaSngWuA6bll95C9YGkL\n4OsRsUzSJ4CvSFoBPA98IiKelHQE8HNJE9KxvpaWXybpVWl/XxpDfs3GzI3eZmUknQLcGRFnjrjy\n0PvoA56PiJNrlzKz5nIJwyxH0u3A38iqgMwsxyUMMzMrxI3eZmZWiAOGmZkV4oBhZmaFOGCYmVkh\nDhhmZlaIA4aZmRXy/wFZO5U0G9rOtQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x106454c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure = plt.figure()\n", "plt.hist(data_thresholded[:,4],5000)\n", "plt.title('Histogram of Synapses in Brain Sample')\n", "plt.xlabel('Synapses')\n", "plt.ylabel('frequency')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we don't see the spike in the histogram of synapses, the spike may be some artifact of the unmasked value. Let's take a look!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3) What is the spike? We still don't know." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x112a562d0>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1128f6910>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(data_thresholded[:,3],5000)\n", "plt.title('Histogram of Unmasked Values')\n", "plt.xlabel('unmasked')\n", "plt.ylabel('frequency')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4) Synapses and unmasked: Spike vs Whole Data Set" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average Density: 0.00134070207006\n", "Std Deviation: 8.46720771375e-05\n", "maxbin 0.00131489435301\n", "There are 136 points in the 'spike'\n" ] }, { "data": { "image/png": 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SbsBXgPWAy4FDIuK8nhYs/QPwVmA5cCXwllTu8cAi4Ebg9RFxT8G8USdumz6S8D5gVk/6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\n/FF12Y8gbZimBzigbdzektZ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"text/plain": [ "<matplotlib.figure.Figure at 0x11b9d7b90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Spike\n", "a = np.apply_along_axis(lambda x:x[4]/x[3], 1, data_thresholded)\n", "spike = a[np.logical_and(a <= 0.0015, a >= 0.0012)]\n", "print \"Average Density: \", np.mean(spike)\n", "print \"Std Deviation: \", np.std(spike)\n", "\n", "# Histogram\n", "n, bins, _ = plt.hist(spike, 2000)\n", "plt.title('Histogram of Synaptic Density')\n", "plt.xlabel('Synaptic Density (syn/voxel)')\n", "plt.ylabel('frequency')\n", "\n", "bin_max = np.where(n == n.max())\n", "\n", "print 'maxbin', bins[bin_max][0]\n", "\n", "bin_width = bins[1]-bins[0]\n", "syn_normalized[:,3] = syn_normalized[:,3]/(64*3)\n", "spike = syn_normalized[np.logical_and(syn_normalized[:,3] <= 0.00131489435301+bin_width, syn_normalized[:,3] >= 0.00131489435301-bin_width)]\n", "print \"There are \", len(spike), \" points in the 'spike'\"" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 409. 2149. 166. 133848. 176.]\n", " [ 409. 2539. 388. 129275. 170.]\n", " [ 448. 1876. 1165. 149046. 196.]\n", " [ 487. 2422. 943. 136890. 180.]\n", " [ 526. 2383. 943. 136890. 180.]\n", " [ 565. 3007. 610. 114075. 150.]\n", " [ 643. 1993. 610. 138411. 182.]\n", " [ 643. 2344. 277. 156663. 206.]\n", " [ 760. 1876. 1165. 158176. 208.]\n", " [ 760. 1915. 55. 153621. 202.]\n", " [ 760. 2851. 499. 156663. 206.]\n", " [ 799. 2422. 1054. 159705. 210.]\n", " [ 838. 2071. 1054. 159705. 210.]\n", " [ 877. 1564. 721. 144501. 190.]\n", " [ 877. 2305. 55. 155142. 204.]\n", " [ 916. 1759. 166. 138411. 182.]\n", " [ 916. 2110. 832. 147537. 194.]\n", " [ 916. 2422. 388. 149058. 196.]\n", " [ 955. 2227. 277. 156663. 206.]\n", " [ 955. 2461. 499. 158184. 208.]\n", " [ 994. 2890. 1054. 158184. 208.]\n", " [ 1033. 2773. 943. 153621. 202.]\n", " [ 1072. 1564. 943. 153620. 202.]\n", " [ 1072. 2656. 166. 133848. 176.]\n", " [ 1072. 2773. 832. 146016. 192.]\n", " [ 1111. 1993. 610. 139932. 184.]\n", " [ 1111. 2344. 1165. 158182. 208.]\n", " [ 1150. 1642. 1165. 159705. 210.]\n", " [ 1150. 1954. 277. 158184. 208.]\n", " [ 1150. 2617. 832. 146016. 192.]\n", " [ 1150. 2812. 832. 146016. 192.]\n", " [ 1189. 1876. 721. 149058. 196.]\n", " [ 1189. 2539. 166. 133848. 176.]\n", " [ 1189. 3007. 832. 144495. 190.]\n", " [ 1228. 2071. 166. 135369. 178.]\n", " [ 1228. 2227. 1054. 161226. 212.]\n", " [ 1228. 2500. 943. 156663. 206.]\n", " [ 1228. 2695. 499. 158184. 208.]\n", " [ 1267. 1798. 721. 147554. 194.]\n", " [ 1267. 2032. 721. 149058. 196.]\n", " [ 1267. 2188. 943. 156663. 206.]\n", " [ 1306. 2227. 1054. 161226. 212.]\n", " [ 1345. 1603. 943. 156663. 206.]\n", " [ 1345. 2773. 943. 156663. 206.]\n", " [ 1384. 2383. 721. 147537. 194.]\n", " [ 1384. 2500. 277. 156663. 206.]\n", " [ 1423. 2110. 55. 156663. 206.]\n", " [ 1423. 2110. 943. 156663. 206.]\n", " [ 1423. 2383. 721. 147537. 194.]\n", " [ 1462. 1681. 943. 156663. 206.]\n", " [ 1540. 1603. 832. 147537. 194.]\n", " [ 1540. 2032. 943. 156663. 206.]\n", " [ 1540. 2149. 1165. 158184. 208.]\n", " [ 1579. 1720. 1165. 159705. 210.]\n", " [ 1579. 1915. 832. 147537. 194.]\n", " [ 1657. 2812. 721. 149058. 196.]\n", " [ 1696. 2032. 721. 150579. 198.]\n", " [ 1735. 2110. 943. 155142. 204.]\n", " [ 1735. 2383. 943. 156663. 206.]\n", " [ 1735. 2812. 721. 149058. 196.]\n", " [ 1735. 2968. 610. 117117. 154.]\n", " [ 1813. 1564. 277. 158184. 208.]\n", " [ 1813. 1720. 721. 147537. 194.]\n", " [ 1813. 2110. 55. 156663. 206.]\n", " [ 1813. 2422. 1165. 158184. 208.]\n", " [ 1852. 1876. 610. 139932. 184.]\n", " [ 1891. 1720. 1054. 164268. 216.]\n", " [ 1891. 2110. 166. 135369. 178.]\n", " [ 1891. 2539. 1054. 162747. 214.]\n", " [ 1891. 2734. 832. 147537. 194.]\n", " [ 1930. 2110. 832. 147537. 194.]\n", " [ 1930. 2344. 277. 156663. 206.]\n", " [ 1969. 2305. 277. 156660. 206.]\n", " [ 2008. 2188. 388. 149058. 196.]\n", " [ 2047. 1681. 1054. 164268. 216.]\n", " [ 2047. 2188. 388. 149058. 196.]\n", " [ 2125. 2188. 943. 156663. 206.]\n", " [ 2164. 2032. 1054. 162747. 214.]\n", " [ 2164. 2266. 943. 156663. 206.]\n", " [ 2164. 2656. 943. 156663. 206.]\n", " [ 2203. 1798. 277. 155142. 204.]\n", " [ 2203. 2032. 832. 147537. 194.]\n", " [ 2203. 2695. 721. 149058. 196.]\n", " [ 2203. 2773. 721. 149058. 196.]\n", " [ 2242. 1759. 721. 146016. 192.]\n", " [ 2242. 1798. 1054. 164268. 216.]\n", " [ 2242. 2110. 610. 135369. 178.]\n", " [ 2242. 2227. 1054. 162733. 214.]\n", " [ 2242. 2383. 1054. 162747. 214.]\n", " [ 2281. 1759. 388. 146791. 193.]\n", " [ 2281. 2656. 166. 135369. 178.]\n", " [ 2359. 1603. 610. 142974. 188.]\n", " [ 2359. 2734. 610. 121672. 160.]\n", " [ 2398. 1759. 1054. 164268. 216.]\n", " [ 2398. 1798. 1054. 164268. 216.]\n", " [ 2398. 2227. 832. 147537. 194.]\n", " [ 2437. 2266. 388. 147537. 194.]\n", " [ 2437. 2305. 721. 149054. 196.]\n", " [ 2476. 2188. 721. 147537. 194.]\n", " [ 2515. 1837. 388. 149058. 196.]\n", " [ 2515. 2929. 499. 155142. 204.]\n", " [ 2554. 1759. 1165. 158184. 208.]\n", " [ 2554. 1915. 277. 156663. 206.]\n", " [ 2554. 1915. 388. 149050. 196.]\n", " [ 2554. 2773. 277. 153621. 202.]\n", " [ 2593. 1837. 943. 156663. 206.]\n", " [ 2593. 2188. 1054. 161226. 212.]\n", " [ 2632. 1642. 943. 156663. 206.]\n", " [ 2632. 1954. 1165. 158184. 208.]\n", " [ 2671. 1759. 499. 155142. 204.]\n", " [ 2710. 1798. 55. 155142. 204.]\n", " [ 2710. 2695. 388. 146016. 192.]\n", " [ 2710. 2734. 721. 147537. 194.]\n", " [ 2749. 2110. 277. 156663. 206.]\n", " [ 2749. 2188. 277. 155146. 204.]\n", " [ 2749. 2344. 166. 136890. 180.]\n", " [ 2788. 1954. 166. 138411. 182.]\n", " [ 2788. 1954. 388. 147537. 194.]\n", " [ 2788. 2071. 277. 156663. 206.]\n", " [ 2788. 2110. 388. 147537. 194.]\n", " [ 2866. 2383. 721. 149058. 196.]\n", " [ 2866. 2617. 166. 136882. 180.]\n", " [ 2905. 2227. 610. 130806. 172.]\n", " [ 2944. 2383. 499. 156663. 206.]\n", " [ 3061. 2578. 166. 136890. 180.]\n", " [ 3139. 1798. 55. 153621. 202.]\n", " [ 3139. 2032. 1165. 158184. 208.]\n", " [ 3178. 2695. 610. 123201. 162.]\n", " [ 3217. 2032. 1165. 158184. 208.]\n", " [ 3217. 2461. 166. 136890. 180.]\n", " [ 3217. 2773. 499. 155142. 204.]\n", " [ 3256. 2461. 55. 149813. 197.]\n", " [ 3373. 2110. 1165. 158182. 208.]\n", " [ 3373. 2383. 721. 147537. 194.]\n", " [ 3412. 2656. 721. 147537. 194.]\n", " [ 3451. 3007. 1054. 155142. 204.]]\n" ] } ], "source": [ "spike_thres = data_thresholded[np.logical_and(syn_normalized[:,3] <= 0.00131489435301+bin_width, syn_normalized[:,3] >= 0.00131489435301-bin_width)]\n", "print spike_thres" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x3ce1b630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import math\n", "fig, ax = plt.subplots(1,2,sharey = True, figsize=(20,5))\n", "weights = np.ones_like(spike_thres[:,3])/len(spike_thres[:,3])\n", "weights2 = np.ones_like(data_thresholded[:,3])/len(data_thresholded[:,3])\n", "\n", "ax[0].hist(data_thresholded[:,3], bins = 100, alpha = 0.5, weights = weights2, label = 'all data')\n", "ax[0].hist(spike_thres[:,3], bins = 100, alpha = 0.5, weights = weights, label = 'spike')\n", "ax[0].legend(loc='upper right')\n", "ax[0].set_title('Histogram of Unmasked values in the Spike vs All Data')\n", "\n", "weights = np.ones_like(spike_thres[:,4])/len(spike_thres[:,4])\n", "weights2 = np.ones_like(data_thresholded[:,4])/len(data_thresholded[:,4])\n", "\n", "ax[1].hist(data_thresholded[:,4], bins = 100, alpha = 0.5, weights = weights2, label = 'all data')\n", "ax[1].hist(spike_thres[:,4], bins = 100, alpha = 0.5, weights = weights, label = 'spike')\n", "ax[1].legend(loc='upper right')\n", "ax[1].set_title('Histogram of Synapses in the Spike vs All Data')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5) Boxplot of different clusters by coordinates and densities" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cluster 4 has relatively high density" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "(1L, 10509L, 4L)\n", "\n", "Working on cluster: 1\n" ] }, { "data": { "image/png": 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0iKQFkn6dn5+VdL6kJklzJS2RdI+kfUrOmSZpqaTFko6tZnxmfcGjiWwg6Lc+A0mDgD8B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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x9311cf8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Done with cluster\n", "\n", "Working on cluster: 2\n" ] }, { "data": { "image/png": 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iItozmugs4BpJ91CNJroUWADMlLSKKiAuA7C9AlgMrAC+AZyRS4AYTwYHB5k9+xwGBwc7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\n0As+AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10a31518>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Done with cluster\n", "\n", "Working on cluster: 3\n" ] }, { "data": { "image/png": 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LEfEe4G+Bz0t6NzADWBQRBwG/AGYCSBoHTAbGAscAc1Sr7hxm3TRt2seA04HW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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x8540240>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Done with cluster\n", "\n", "Working on cluster: 4\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x8349d30>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Done with cluster\n" ] } ], "source": [ "import sklearn.mixture as mixture\n", "\n", "n_clusters = 4\n", "gmm = mixture.GMM(n_components=n_clusters, n_iter=1000, covariance_type='diag')\n", "labels = gmm.fit_predict(syn_unmasked)\n", "clusters = []\n", "for l in range(n_clusters):\n", " a = np.where(labels == l)\n", " clusters.append(syn_unmasked[a,:])\n", "\n", "print len(clusters)\n", "print clusters[0].shape\n", "\n", "counter = 0\n", "indx = 0\n", "indy = 0\n", "for cluster in clusters:\n", " s = cluster.shape\n", " cluster = cluster.reshape((s[1], s[2]))\n", " counter += 1\n", " print \n", " print'Working on cluster: ' + str(counter)\n", " plt.boxplot(cluster[:,-1], 0, 'gD', showmeans=True)\n", " plt.xticks([1])\n", " plt.ylabel('Density')\n", " plt.title('Boxplot of density \\n at cluster = ' + str(int(counter)))\n", " plt.show()\n", " \n", " \n", " print \"Done with cluster\"\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 5 OLD ) Boxplot distrubutions of each Z layer" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Density Distrubtion Boxplots:\n" ] }, { "data": { "image/png": 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g3MzcNM/7NirHu8ckv5dqefXzgauB9zIxcVUjZ7noc5G85DluWoa1N4tklc4i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Z6XWl9voXzKKVdbIA/BIjkTV1zxPnbd9UzgSeBWit6LW4En2XXXM+1JVstbsOCzpPRRivW9q5fO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\njFOG3jinW0iSJEktLJIlSXqdFizodA8kj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"text/plain": [ "<matplotlib.figure.Figure at 0x8f1d048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "data_uniques, UIndex, UCounts = np.unique(syn_unmasked[:,2], return_index = True, return_counts = True)\n", "'''\n", "print 'uniques'\n", "print 'index: ' + str(UIndex)\n", "print 'counts: ' + str(UCounts)\n", "print 'values: ' + str(data_uniques)\n", "'''\n", "fig, ax = plt.subplots(3,4,figsize=(10,20))\n", "counter = 0\n", "\n", "for i in np.unique(syn_unmasked[:,2]):\n", " # print 'calcuating for z: ' + str(int(i))\n", " \n", " def check_z(row):\n", " if row[2] == i:\n", " return True\n", " return False\n", " \n", " counter += 1\n", " xind = (counter%3) - 1\n", " yind = (counter%4) - 1\n", " \n", " index_true = np.where(np.apply_along_axis(check_z, 1, syn_unmasked))\n", " syn_uniqueZ = syn_unmasked[index_true]\n", " \n", " ax[xind,yind].boxplot(syn_uniqueZ[:,3], 0, 'gD')\n", " ax[xind,yind].set_xticks([1], i)\n", " ax[xind,yind].set_ylabel('Density')\n", " ax[xind,yind].set_title('Boxplot at \\n z = ' + str(int(i)))\n", "\n", "#print 'yind = %d, xind = %d' %(yind,xind)\n", "#print i\n", "\n", "ax[xind+1,yind+1].boxplot(syn_uniqueZ[:,3], 0, 'gD',showmeans=True)\n", "ax[xind+1,yind+1].set_xticks([1], 'set')\n", "ax[xind+1,yind+1].set_ylabel('Density')\n", "ax[xind+1,yind+1].set_title('Boxplot for \\n All Densities')\n", "\n", "print \"Density Distrubtion Boxplots:\"\n", "plt.tight_layout()\n", "\n", "plt.show()\n", " \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
AWS-Spot-Analysis/spot-analysis	Grab_Actual_Pricing.ipynb	1	23137	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Grab Actual Pricing\n", "### Here is the code that scrapes http://www.ec2instances.info/ for data on actual pricing" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import urllib\n", "from bs4 import BeautifulSoup\n", "import urllib3\n", "import requests\n", "import time\n", "import csv\n", "import ast\n", "import pandas as pd\n", "from pandas.stats.api import ols\n", "import statsmodels.formula.api as sm" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#url = \"http://www.ec2instances.info/?region=ap-southeast-2&reserved_term=yrTerm3Standard.allUpfront\"\n", "urlBase=\"http://www.ec2instances.info/?region=\"\n", "\n", "term = [\"yrTerm3Standard.allUpfront\", \"yrTerm1Standard.partialUpfront\", \"yrTerm1Standard.allUpfront\",\"yrTerm3Standard.partialUpfront\"]\n", "instance_types = [ 't1.micro', 't2.nano', 't2.micro', 't2.small', 't2.medium', 't2.large', 't2.xlarge', 't2.2xlarge', \n", " 'm1.small', 'm1.medium', 'm1.large', 'm1.xlarge', 'm3.medium', 'm3.large', 'm3.xlarge', 'm3.2xlarge', \n", " 'm4.large', 'm4.xlarge', 'm4.2xlarge', 'm4.4xlarge', 'm4.10xlarge', 'm4.16xlarge', 'm2.xlarge', \n", " 'm2.2xlarge', 'm2.4xlarge', 'cr1.8xlarge', 'r3.large', 'r3.xlarge', 'r3.2xlarge', 'r3.4xlarge',\n", " 'r3.8xlarge', 'r4.large', 'r4.xlarge', 'r4.2xlarge', 'r4.4xlarge', 'r4.8xlarge', 'r4.16xlarge',\n", " 'x1.16xlarge', 'x1.32xlarge', 'i2.xlarge', 'i2.2xlarge', 'i2.4xlarge', 'i2.8xlarge', 'hi1.4xlarge',\n", " 'hs1.8xlarge', 'c1.medium', 'c1.xlarge', 'c3.large', 'c3.xlarge', 'c3.2xlarge', 'c3.4xlarge', \n", " 'c3.8xlarge', 'c4.large', 'c4.xlarge', 'c4.2xlarge', 'c4.4xlarge', 'c4.8xlarge', 'cc1.4xlarge',\n", " 'cc2.8xlarge', 'g2.2xlarge', 'g2.8xlarge', 'cg1.4xlarge', 'p2.xlarge', 'p2.8xlarge', 'p2.16xlarge',\n", " 'd2.xlarge', 'd2.2xlarge', 'd2.4xlarge', 'd2.8xlarge', 'f1.2xlarge', 'f1.16xlarge']\n", " \n", "regions = ['us-east-1', 'us-west-2', 'us-west-1', 'eu-west-1','eu-central-1', 'ap-southeast-1', 'ap-northeast-1',\n", " 'ap-northeast-2', 'ap-southeast-2', 'sa-east-1']" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "\n", " \n", "dataHolder =[]\n", "response = requests.get(\"http://www.ec2instances.info/?region=ap-southeast-2&reserved_term=yrTerm3Standard.allUpfront\")\n", "soup = BeautifulSoup(response.content, \"html.parser\")\n", " \n", "for intype in instance_types:\n", " #Find each types data and store it\n", " try:\n", " trhold = soup.find('tr', {'id':intype})\n", " resCost = trhold.find('td', {'class':\"cost-reserved cost-reserved-linux\"})\n", " resCost = resCost.get('data-pricing')\n", " demandCost = trhold.find('td', {'class':\"cost-ondemand cost-ondemand-linux\"})\n", " demandCost = demandCost.get('data-pricing')\n", " dC = ast.literal_eval(demandCost)\n", " rC = ast.literal_eval(resCost)\n", "\n", " for region in regions:\n", " try:\n", " dh = {}\n", " if dC[region] != \"N/A\":\n", " noNA = True\n", " dh['OnDemand'] = dC[region]\n", " dh['InstanceType'] = intype \n", " dh['AvailabilityZone']= region \n", " for term in rC[region]:\n", " if rC[region][term]:\n", " dh[term] = rC[region][term] \n", " else:\n", " noNA = False\n", " if noNA == True:\n", " dataHolder.append(dh) \n", " except KeyError:\n", " #Triggered because that region doesnt exist in dC\n", " #print(\"ERROR \"+ region)\n", " #print (dC)\n", " pass\n", " except AttributeError:\n", " #Triggered because Instance Type doesnt have pricing data on site\n", " #print(\"ATRIBUTE: \"+intype)\n", " pass" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>AvailabilityZone</th>\n", " <th>InstanceType</th>\n", " <th>OnDemand</th>\n", " <th>yrTerm1Standard.allUpfront</th>\n", " <th>yrTerm1Standard.noUpfront</th>\n", " <th>yrTerm1Standard.partialUpfront</th>\n", " <th>yrTerm3Convertible.allUpfront</th>\n", " <th>yrTerm3Convertible.noUpfront</th>\n", " <th>yrTerm3Convertible.partialUpfront</th>\n", " <th>yrTerm3Standard.allUpfront</th>\n", " <th>yrTerm3Standard.partialUpfront</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>us-east-1</td>\n", " <td>t1.micro</td>\n", " <td>0.02</td>\n", " <td>0.012</td>\n", " <td>0.014</td>\n", " <td>0.012</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.008</td>\n", " <td>0.009</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>us-west-2</td>\n", " <td>t1.micro</td>\n", " <td>0.02</td>\n", " <td>0.012</td>\n", " <td>0.014</td>\n", " <td>0.012</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.008</td>\n", " <td>0.009</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>us-west-1</td>\n", " <td>t1.micro</td>\n", " <td>0.025</td>\n", " <td>0.015</td>\n", " <td>0.017</td>\n", " <td>0.015</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.011</td>\n", " <td>0.012</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>eu-west-1</td>\n", " <td>t1.micro</td>\n", " <td>0.02</td>\n", " <td>0.015</td>\n", " <td>0.016</td>\n", " <td>0.015</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.011</td>\n", " <td>0.012</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>ap-southeast-1</td>\n", " <td>t1.micro</td>\n", " <td>0.02</td>\n", " <td>0.015</td>\n", " <td>0.016</td>\n", " <td>0.015</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.011</td>\n", " <td>0.012</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>ap-northeast-1</td>\n", " <td>t1.micro</td>\n", " <td>0.026</td>\n", " <td>0.016</td>\n", " <td>0.018</td>\n", " <td>0.016</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.012</td>\n", " <td>0.013</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>ap-southeast-2</td>\n", " <td>t1.micro</td>\n", " <td>0.02</td>\n", " <td>0.015</td>\n", " <td>0.016</td>\n", " <td>0.015</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.011</td>\n", " <td>0.012</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>sa-east-1</td>\n", " <td>t1.micro</td>\n", " <td>0.027</td>\n", " <td>0.016</td>\n", " <td>0.019</td>\n", " <td>0.017</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.011</td>\n", " <td>0.012</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>us-east-1</td>\n", " <td>t2.nano</td>\n", " <td>0.0059</td>\n", " <td>0.004</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " <td>0.003</td>\n", " <td>0.003</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>us-west-2</td>\n", " <td>t2.nano</td>\n", " <td>0.0059</td>\n", " <td>0.004</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " <td>0.003</td>\n", " <td>0.003</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>us-west-1</td>\n", " <td>t2.nano</td>\n", " <td>0.0077</td>\n", " <td>0.005</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.005</td>\n", " <td>0.005</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>eu-west-1</td>\n", " <td>t2.nano</td>\n", " <td>0.0063</td>\n", " <td>0.004</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.003</td>\n", " <td>0.003</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>eu-central-1</td>\n", " <td>t2.nano</td>\n", " <td>0.0068</td>\n", " <td>0.005</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.003</td>\n", " <td>0.003</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>ap-southeast-1</td>\n", " <td>t2.nano</td>\n", " <td>0.0075</td>\n", " <td>0.006</td>\n", " <td>0.007</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>ap-northeast-1</td>\n", " <td>t2.nano</td>\n", " <td>0.008</td>\n", " <td>0.006</td>\n", " <td>0.007</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>ap-northeast-2</td>\n", " <td>t2.nano</td>\n", " <td>0.008</td>\n", " <td>0.006</td>\n", " <td>0.007</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.004</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>ap-southeast-2</td>\n", " <td>t2.nano</td>\n", " <td>0.008</td>\n", " <td>0.006</td>\n", " <td>0.007</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.006</td>\n", " <td>0.006</td>\n", " <td>0.004</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>sa-east-1</td>\n", " <td>t2.nano</td>\n", " <td>0.0101</td>\n", " <td>0.006</td>\n", " <td>0.007</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.006</td>\n", " <td>0.005</td>\n", " <td>0.004</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>us-east-1</td>\n", " <td>t2.micro</td>\n", " <td>0.012</td>\n", " <td>0.008</td>\n", " <td>0.009</td>\n", " <td>0.008</td>\n", " <td>0.007</td>\n", " <td>0.008</td>\n", " <td>0.007</td>\n", " <td>0.005</td>\n", " <td>0.005</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>us-west-2</td>\n", " <td>t2.micro</td>\n", " <td>0.012</td>\n", " <td>0.008</td>\n", " <td>0.009</td>\n", " <td>0.008</td>\n", " <td>0.007</td>\n", " <td>0.008</td>\n", " <td>0.007</td>\n", " <td>0.005</td>\n", " <td>0.005</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AvailabilityZone InstanceType OnDemand yrTerm1Standard.allUpfront \\\n", "0 us-east-1 t1.micro 0.02 0.012 \n", "1 us-west-2 t1.micro 0.02 0.012 \n", "2 us-west-1 t1.micro 0.025 0.015 \n", "3 eu-west-1 t1.micro 0.02 0.015 \n", "4 ap-southeast-1 t1.micro 0.02 0.015 \n", "5 ap-northeast-1 t1.micro 0.026 0.016 \n", "6 ap-southeast-2 t1.micro 0.02 0.015 \n", "7 sa-east-1 t1.micro 0.027 0.016 \n", "8 us-east-1 t2.nano 0.0059 0.004 \n", "9 us-west-2 t2.nano 0.0059 0.004 \n", "10 us-west-1 t2.nano 0.0077 0.005 \n", "11 eu-west-1 t2.nano 0.0063 0.004 \n", "12 eu-central-1 t2.nano 0.0068 0.005 \n", "13 ap-southeast-1 t2.nano 0.0075 0.006 \n", "14 ap-northeast-1 t2.nano 0.008 0.006 \n", "15 ap-northeast-2 t2.nano 0.008 0.006 \n", "16 ap-southeast-2 t2.nano 0.008 0.006 \n", "17 sa-east-1 t2.nano 0.0101 0.006 \n", "18 us-east-1 t2.micro 0.012 0.008 \n", "19 us-west-2 t2.micro 0.012 0.008 \n", "\n", " yrTerm1Standard.noUpfront yrTerm1Standard.partialUpfront \\\n", "0 0.014 0.012 \n", "1 0.014 0.012 \n", "2 0.017 0.015 \n", "3 0.016 0.015 \n", "4 0.016 0.015 \n", "5 0.018 0.016 \n", "6 0.016 0.015 \n", "7 0.019 0.017 \n", "8 0.005 0.004 \n", "9 0.005 0.004 \n", "10 0.006 0.005 \n", "11 0.005 0.004 \n", "12 0.006 0.005 \n", "13 0.007 0.006 \n", "14 0.007 0.006 \n", "15 0.007 0.006 \n", "16 0.007 0.006 \n", "17 0.007 0.006 \n", "18 0.009 0.008 \n", "19 0.009 0.008 \n", "\n", " yrTerm3Convertible.allUpfront yrTerm3Convertible.noUpfront \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 NaN NaN \n", "8 0.004 0.004 \n", "9 0.004 0.004 \n", "10 0.005 0.005 \n", "11 0.004 0.005 \n", "12 0.004 0.005 \n", "13 0.005 0.006 \n", "14 0.005 0.006 \n", "15 0.005 0.006 \n", "16 0.005 0.006 \n", "17 0.005 0.006 \n", "18 0.007 0.008 \n", "19 0.007 0.008 \n", "\n", " yrTerm3Convertible.partialUpfront yrTerm3Standard.allUpfront \\\n", "0 NaN 0.008 \n", "1 NaN 0.008 \n", "2 NaN 0.011 \n", "3 NaN 0.011 \n", "4 NaN 0.011 \n", "5 NaN 0.012 \n", "6 NaN 0.011 \n", "7 NaN 0.011 \n", "8 0.004 0.003 \n", "9 0.004 0.003 \n", "10 0.005 0.004 \n", "11 0.004 0.003 \n", "12 0.004 0.003 \n", "13 0.005 0.004 \n", "14 0.005 0.004 \n", "15 0.005 0.004 \n", "16 0.006 0.004 \n", "17 0.005 0.004 \n", "18 0.007 0.005 \n", "19 0.007 0.005 \n", "\n", " yrTerm3Standard.partialUpfront \n", "0 0.009 \n", "1 0.009 \n", "2 0.012 \n", "3 0.012 \n", "4 0.012 \n", "5 0.013 \n", "6 0.012 \n", "7 0.012 \n", "8 0.003 \n", "9 0.003 \n", "10 0.004 \n", "11 0.003 \n", "12 0.003 \n", "13 0.004 \n", "14 0.004 \n", "15 0.004 \n", "16 0.005 \n", "17 0.005 \n", "18 0.005 \n", "19 0.005 " ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(dataHolder)\n", "df.head(20)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.to_csv('pricing-data.csv')" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [py35]", "language": "python", "name": "Python [py35]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
leriomaggio/python-in-a-notebook	08 Classes and OOP.ipynb	1	97026	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Classes\n", "===" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "So far you have learned about Python's core data types: strings, numbers, lists, tuples, and dictionaries. In this section you will learn about the last major data structure, classes. Classes are quite unlike the other data types, in that they are much more flexible. Classes allow you to define the information and behavior that characterize anything you want to model in your program. Classes are a rich topic, so you will learn just enough here to dive into the projects you'd like to get started on." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "There is a lot of new language that comes into play when you start learning about classes. If you are familiar with object-oriented programming from your work in another language, this will be a quick read about how Python approaches OOP. If you are new to programming in general, there will be a lot of new ideas here. Just start reading, try out the examples on your own machine, and trust that it will start to make sense as you work your way through the examples and exercises." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name=\"top\"></a>Contents\n", "===\n", "- [What are classes?](#what)\n", "- [Object-Oriented Terminology](#oop_terminology)\n", " - [General terminology](#general_terminology)\n", " - [A closer look at the Rocket class](#closer_look)\n", " - [The \\_\\_init()\\_\\_ method](#init_method)\n", " - [A simple method](#simple_method)\n", " - [Making multiple objects from a class](#multiple_objects)\n", " - [A quick check-in](#check_in)\n", " - [Exercises](#exercises_closer_look)\n", "- [Refining the Rocket class](#refining_rocket)\n", " - [Accepting parameters for the \\_\\_init\\_\\_() method](#init_parameters)\n", " - [Accepting parameters in a method](#method_parameters)\n", " - [Adding a new method](#adding_method)\n", " - [Exercises](#exercises_refining_rocket)\n", "- [Inheritance](#inheritance)\n", " - [The Shuttle class](#shuttle)\n", " - [Exercises](#exercises_inheritance)\n", "- [Modules and classes](#modules_classes)\n", " - [Storing a single class in a module](#single_class_module)\n", " - [Storing multiple classes in a module](#multiple_classes_module)\n", " - [A number of ways to import modules and classes](#multiple_ways_import)\n", " - [A module of functions](#module_functions)\n", " - [Exercises](#exercises_importing)\n", "- [Method Resolution Order (example)](#mro)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name=\"what\"></a>What are classes?\n", "===\n", "Classes are a way of combining information and behavior. For example, let's consider what you'd need to do if you were creating a rocket ship in a game, or in a physics simulation. One of the first things you'd want to track are the x and y coordinates of the rocket. Here is what a simple rocket ship class looks like in code:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "One of the first things you do with a class is to define the \\__init\\__() method. The \\_\\_init\\_\\_() method sets the values for any parameters that need to be defined when an object is first created. The self part will be explained later; basically, it's a syntax that allows you to access a variable from anywhere else in the class.\n", "\n", "The Rocket class stores two pieces of information so far, but it can't do anything. The first behavior to define is a core behavior of a rocket: moving up. Here is what that might look like in code:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The Rocket class can now store some information, and it can do something. But this code has not actually created a rocket yet. Here is how you actually make a rocket:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Rocket object at 0x7f6f50c39190>\n" ] } ], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1\n", "\n", "# Create a Rocket object.\n", "my_rocket = Rocket()\n", "print(my_rocket)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "To actually use a class, you create a variable such as my\\_rocket. Then you set that equal to the name of the class, with an empty set of parentheses. Python creates an object from the class. An object is a single instance of the Rocket class; it has a copy of each of the class's variables, and it can do any action that is defined for the class. In this case, you can see that the variable my\\_rocket is a Rocket object from the \\_\\_main\\_\\_ program file, which is stored at a particular location in memory." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Once you have a class, you can define an object and use its methods. Here is how you might define a rocket and have it start to move up:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rocket altitude: 0\n", "Rocket altitude: 1\n", "Rocket altitude: 2\n" ] } ], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1\n", "\n", "# Create a Rocket object, and have it start to move up.\n", "my_rocket = Rocket()\n", "print(\"Rocket altitude:\", my_rocket.y)\n", "\n", "my_rocket.move_up()\n", "print(\"Rocket altitude:\", my_rocket.y)\n", "\n", "my_rocket.move_up()\n", "print(\"Rocket altitude:\", my_rocket.y)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "To access an object's variables or methods, you give the name of the object and then use dot notation to access the variables and methods. So to get the y-value of my\\_rocket, you use my\\_rocket.y. To use the move_up() method on my_rocket, you write my\\_rocket.move\\_up()." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Once you have a class defined, you can create as many objects from that class as you want. Each object is its own instance of that class, with its own separate variables. All of the objects are capable of the same behavior, but each object's particular actions do not affect any of the other objects." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Once you have a class, you can define an object and use its methods. Here is how you might define a rocket and have it start to move up:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Rocket object at 0x7f6f5073cd10>\n", "<__main__.Rocket object at 0x7f6f5073cc90>\n", "<__main__.Rocket object at 0x7f6f5073cbd0>\n", "<__main__.Rocket object at 0x7f6f5077e410>\n", "<__main__.Rocket object at 0x7f6f5077ead0>\n" ] } ], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1\n", " \n", "# Create a fleet of 5 rockets, and store them in a list.\n", "my_rockets = []\n", "for x in range(0,5):\n", " new_rocket = Rocket()\n", " my_rockets.append(new_rocket)\n", "\n", "# Show that each rocket is a separate object.\n", "for rocket in my_rockets:\n", " print(rocket)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "You can see that each rocket is at a separate place in memory. By the way, if you understand [list comprehensions](03_lists_tuples.html#comprehensions), you can make the fleet of rockets in one line:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Rocket object at 0x7f6f50789190>\n", "<__main__.Rocket object at 0x7f6f50763450>\n", "<__main__.Rocket object at 0x7f6f507634d0>\n", "<__main__.Rocket object at 0x7f6f50763510>\n", "<__main__.Rocket object at 0x7f6f50763550>\n" ] } ], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1\n", " \n", "# Create a fleet of 5 rockets, and store them in a list.\n", "my_rockets = [Rocket() for x in range(0,5)]\n", "\n", "# Show that each rocket is a separate object.\n", "for rocket in my_rockets:\n", " print(rocket)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "You can prove that each rocket has its own x and y values by moving just one of the rockets:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rocket altitude: 1\n", "Rocket altitude: 0\n", "Rocket altitude: 0\n", "Rocket altitude: 0\n", "Rocket altitude: 0\n" ] } ], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1\n", " \n", "# Create a fleet of 5 rockets, and store them in a list.\n", "my_rockets = [Rocket() for x in range(0,5)]\n", "\n", "# Move the first rocket up.\n", "my_rockets[0].move_up()\n", "\n", "# Show that only the first rocket has moved.\n", "for rocket in my_rockets:\n", " print(\"Rocket altitude:\", rocket.y)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The syntax for classes may not be very clear at this point, but consider for a moment how you might create a rocket without using classes. You might store the x and y values in a dictionary, but you would have to write a lot of ugly, hard-to-maintain code to manage even a small set of rockets. As more features become incorporated into the Rocket class, you will see how much more efficiently real-world objects can be modeled with classes than they could be using just lists and dictionaries." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name='exercises_what'></a>Exercises\n", "---\n", "\n", "#### Rocket With No Class\n", "- Using just what you already know, try to write a program that simulates the [above example](#what) about rockets.\n", " - Store an x and y value for a rocket.\n", " - Store an x and y value for each rocket in a set of 5 rockets. Store these 5 rockets in a list.\n", "- Don't take this exercise too far; it's really just a quick exercise to help you understand how useful the class structure is, especially as you start to see more capability added to the Rocket class." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.0 : Rocket with no Class\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='oop_terminology'></a>Object-Oriented terminology\n", "===" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Classes are part of a programming paradigm called object-oriented programming. Object-oriented programming, or OOP for short, focuses on building reusable blocks of code called classes. When you want to use a class in one of your programs, you make an object from that class, which is where the phrase \"object-oriented\" comes from. Python itself is not tied to object-oriented programming, but you will be using objects in most or all of your Python projects. In order to understand classes, you have to understand some of the language that is used in OOP." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='general_terminology'></a>General terminology\n", "---" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "A class is a body of code that defines the attributes and behaviors required to accurately model something you need for your program. You can model something from the real world, such as a rocket ship or a guitar string, or you can model something from a virtual world such as a rocket in a game, or a set of physical laws for a game engine." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "An attribute is a piece of information. In code, an attribute is just a variable that is part of a class." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "A behavior is an action that is defined within a class. These are made up of methods, which are just functions that are defined for the class." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "An object is a particular instance of a class. An object has a certain set of values for all of the attributes (variables) in the class. You can have as many objects as you want for any one class." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "There is much more to know, but these words will help you get started. They will make more sense as you see more examples, and start to use classes on your own." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='closer_look'></a>A closer look at the Rocket class\n", "---\n", "Now that you have seen a simple example of a class, and have learned some basic OOP terminology, it will be helpful to take a closer look at the Rocket class." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name=\"init_method\"></a>The \\_\\_init()\\_\\_ method\n", "---\n", "Here is the initial code block that defined the Rocket class:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The first line shows how a class is created in Python. The keyword class tells Python that you are about to define a class. The rules for naming a class are the same rules you learned about [naming variables](var_string_num.html#naming_rules), but there is a strong convention among Python programmers that classes should be named using CamelCase. If you are unfamiliar with CamelCase, it is a convention where each letter that starts a word is capitalized, with no underscores in the name. The name of the class is followed by a set of parentheses. These parentheses will be empty for now, but later they may contain a class upon which the new class is based." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "It is good practice to write a comment at the beginning of your class, describing the class. There is a [more formal syntax](http://www.python.org/dev/peps/pep-0257/) for documenting your classes, but you can wait a little bit to get that formal. For now, just write a comment at the beginning of your class summarizing what you intend the class to do. Writing more formal documentation for your classes will be easy later if you start by writing simple comments now." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Function names that start and end with two underscores are special built-in functions that Python uses in certain ways. The \\_\\_init()\\_\\_ method is one of these special functions. It is called automatically when you create an object from your class. The \\_\\_init()\\_\\_ method lets you make sure that all relevant attributes are set to their proper values when an object is created from the class, before the object is used. In this case, The \\_\\_init\\_\\_() method initializes the x and y values of the Rocket to 0." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The self keyword often takes people a little while to understand. The word \"self\" refers to the current object that you are working with. When you are writing a class, it lets you refer to certain attributes from any other part of the class. Basically, all methods in a class need the self object as their first argument, so they can access any attribute that is part of the class." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Now let's take a closer look at a method." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='simple_method'></a>A simple method\n", "---\n", "Here is the method that was defined for the Rocket class:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "A method is just a function that is part of a class. Since it is just a function, you can do anything with a method that you learned about with functions. You can accept [positional](08_more_functions.html#positional_arguments) arguments, [keyword](08_more_functions.html#keyword_arguments) arguments, an arbitrary [list of argument values](08_more_functions.html#arbitrary_sequence), an arbitrary [dictionary of arguments](08_more_functions.html#arbitrary_keyword_arguments), or any combination of these. Your arguments can return a value or a set of values if you want, or they can just do some work without returning any values." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Each method has to accept one argument by default, the value self. This is a reference to the particular object that is calling the method. This self argument gives you access to the calling object's attributes. In this example, the self argument is used to access a Rocket object's y-value. That value is increased by 1, every time the method move_up() is called by a particular Rocket object. This is probably still somewhat confusing, but it should start to make sense as you work through your own examples." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "If you take a second look at what happens when a method is called, things might make a little more sense:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rocket altitude: 0\n", "Rocket altitude: 1\n", "Rocket altitude: 2\n" ] } ], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1\n", "\n", "# Create a Rocket object, and have it start to move up.\n", "my_rocket = Rocket()\n", "print(\"Rocket altitude:\", my_rocket.y)\n", "\n", "my_rocket.move_up()\n", "print(\"Rocket altitude:\", my_rocket.y)\n", "\n", "my_rocket.move_up()\n", "print(\"Rocket altitude:\", my_rocket.y)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In this example, a Rocket object is created and stored in the variable my_rocket. After this object is created, its y value is printed. The value of the attribute y is accessed using dot notation. The phrase my\\_rocket.y asks Python to return \"the value of the variable y attached to the object my_rocket\".\n", "\n", "After the object my_rocket is created and its initial y-value is printed, the method move_up() is called. This tells Python to apply the method move_up() to the object my_rocket. Python finds the y-value associated with my_rocket and adds 1 to that value. This process is repeated several times, and you can see from the output that the y-value is in fact increasing." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='multiple_objects'></a>Making multiple objects from a class\n", "---" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "One of the goals of object-oriented programming is to create reusable code. Once you have written the code for a class, you can create as many objects from that class as you need. It is worth mentioning at this point that classes are usually saved in a separate file, and then imported into the program you are working on. So you can build a library of classes, and use those classes over and over again in different programs. Once you know a class works well, you can leave it alone and know that the objects you create in a new program are going to work as they always have." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "You can see this \"code reusability\" already when the Rocket class is used to make more than one Rocket object. Here is the code that made a fleet of Rocket objects:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Rocket object at 0x7f6f50763090>\n", "<__main__.Rocket object at 0x7f6f5077ead0>\n", "<__main__.Rocket object at 0x7f6f50763990>\n", "<__main__.Rocket object at 0x7f6f50763a10>\n", "<__main__.Rocket object at 0x7f6f50763a50>\n" ] } ], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1\n", " \n", "# Create a fleet of 5 rockets, and store them in a list.\n", "my_rockets = []\n", "for x in range(0,5):\n", " new_rocket = Rocket()\n", " my_rockets.append(new_rocket)\n", "\n", "# Show that each rocket is a separate object.\n", "for rocket in my_rockets:\n", " print(rocket)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "If you are comfortable using list comprehensions, go ahead and use those as much as you can. I'd rather not assume at this point that everyone is comfortable with comprehensions, so I will use the slightly longer approach of declaring an empty list, and then using a for loop to fill that list. That can be done slightly more efficiently than the previous example, by eliminating the temporary variable new\\_rocket:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Rocket object at 0x7f6f50763990>\n", "<__main__.Rocket object at 0x7f6f50763a10>\n", "<__main__.Rocket object at 0x7f6f50763750>\n", "<__main__.Rocket object at 0x7f6f506fd8d0>\n", "<__main__.Rocket object at 0x7f6f506fd6d0>\n" ] } ], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1\n", " \n", "# Create a fleet of 5 rockets, and store them in a list.\n", "my_rockets = []\n", "for x in range(0,5):\n", " my_rockets.append(Rocket())\n", "\n", "# Show that each rocket is a separate object.\n", "for rocket in my_rockets:\n", " print(rocket)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "What exactly happens in this for loop? The line my\\_rockets.append(Rocket()) is executed 5 times. Each time, a new Rocket object is created and then added to the list my\\_rockets. The \\_\\_init\\_\\_() method is executed once for each of these objects, so each object gets its own x and y value. When a method is called on one of these objects, the self variable allows access to just that object's attributes, and ensures that modifying one object does not affect any of the other objecs that have been created from the class.\n", "\n", "Each of these objects can be worked with individually. At this point we are ready to move on and see how to add more functionality to the Rocket class. We will work slowly, and give you the chance to start writing your own simple classes." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='check_in'></a>A quick check-in\n", "---\n", "If all of this makes sense, then the rest of your work with classes will involve learning a lot of details about how classes can be used in more flexible and powerful ways. If this does not make any sense, you could try a few different things:\n", "\n", "- Reread the previous sections, and see if things start to make any more sense.\n", "- Type out these examples in your own editor, and run them. Try making some changes, and see what happens.\n", "- Try the next exercise, and see if it helps solidify some of the concepts you have been reading about.\n", "- Read on. The next sections are going to add more functionality to the Rocket class. These steps will involve rehashing some of what has already been covered, in a slightly different way.\n", "\n", "Classes are a huge topic, and once you understand them you will probably use them for the rest of your life as a programmer. If you are brand new to this, be patient and trust that things will start to sink in." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name='exercises_closer_look'></a>Exercises\n", "---\n", "#### Your Own Rocket\n", "- Without looking back at the previous examples, try to recreate the [Rocket class](#multiple_objects) as it has been shown so far.\n", " - Define the Rocket() class.\n", " - Define the \\_\\_init\\_\\_() method, which sets an x and a y value for each Rocket object.\n", " - Define the move_up() method.\n", " - Create a Rocket object.\n", " - Print the object.\n", " - Print the object's y-value.\n", " - Move the rocket up, and print its y-value again.\n", " - Create a fleet of rockets, and prove that they are indeed separate Rocket objects." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.1 : Your Own Rocket\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='refining_rocket'></a>Refining the Rocket class\n", "===\n", "The Rocket class so far is very simple. It can be made a little more interesting with some refinements to the \\_\\_init\\_\\_() method, and by the addition of some methods." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='init_parameters'></a>Accepting paremeters for the \\_\\_init\\_\\_() method\n", "---\n", "The \\_\\_init\\_\\_() method is run automatically one time when you create a new object from a class. The \\_\\_init\\_\\_() method for the Rocket class so far is pretty simple:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self):\n", " # Each rocket has an (x,y) position.\n", " self.x = 0\n", " self.y = 0\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "All the \\_\\_init\\_\\_() method does so far is set the x and y values for the rocket to 0. We can easily add a couple keyword arguments so that new rockets can be initialized at any position:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self, x=0, y=0):\n", " # Each rocket has an (x,y) position.\n", " self.x = x\n", " self.y = y\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Now when you create a new Rocket object you have the choice of passing in arbitrary initial values for x and y:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rocket 0 is at (0, 0).\n", "Rocket 1 is at (0, 10).\n", "Rocket 2 is at (100, 0).\n" ] } ], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self, x=0, y=0):\n", " # Each rocket has an (x,y) position.\n", " self.x = x\n", " self.y = y\n", " \n", " def move_up(self):\n", " # Increment the y-position of the rocket.\n", " self.y += 1\n", " \n", "# Make a series of rockets at different starting places.\n", "rockets = []\n", "rockets.append(Rocket())\n", "rockets.append(Rocket(0,10))\n", "rockets.append(Rocket(100,0))\n", "\n", "# Show where each rocket is.\n", "for index, rocket in enumerate(rockets):\n", " print(\"Rocket %d is at (%d, %d).\" % (index, rocket.x, rocket.y))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='method_parameters'></a>Accepting paremeters in a method\n", "---\n", "The \\_\\_init\\_\\_ method is just a special method that serves a particular purpose, which is to help create new objects from a class. Any method in a class can accept parameters of any kind. With this in mind, the move_up() method can be made much more flexible. By accepting keyword arguments, the move_up() method can be rewritten as a more general move_rocket() method." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This new method will allow the rocket to be moved any amount, in any direction:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self, x=0, y=0):\n", " # Each rocket has an (x,y) position.\n", " self.x = x\n", " self.y = y\n", " \n", " def move_rocket(self, x_increment=0, y_increment=1):\n", " # Move the rocket according to the paremeters given.\n", " # Default behavior is to move the rocket up one unit.\n", " self.x += x_increment\n", " self.y += y_increment" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The paremeters for the move() method are named x_increment and y_increment rather than x and y. It's good to emphasize that these are changes in the x and y position, not new values for the actual position of the rocket. By carefully choosing the right default values, we can define a meaningful default behavior. If someone calls the method move_rocket() with no parameters, the rocket will simply move up one unit in the y-direciton. Note that this method can be given negative values to move the rocket left or right:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rocket 0 is at (0, 1).\n", "Rocket 1 is at (10, 10).\n", "Rocket 2 is at (-10, 0).\n" ] } ], "source": [ "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self, x=0, y=0):\n", " # Each rocket has an (x,y) position.\n", " self.x = x\n", " self.y = y\n", " \n", " def move_rocket(self, x_increment=0, y_increment=1):\n", " # Move the rocket according to the paremeters given.\n", " # Default behavior is to move the rocket up one unit.\n", " self.x += x_increment\n", " self.y += y_increment\n", " \n", "# Create three rockets.\n", "rockets = [Rocket() for x in range(0,3)]\n", "\n", "# Move each rocket a different amount.\n", "rockets[0].move_rocket()\n", "rockets[1].move_rocket(10,10)\n", "rockets[2].move_rocket(-10,0)\n", " \n", "# Show where each rocket is.\n", "for index, rocket in enumerate(rockets):\n", " print(\"Rocket %d is at (%d, %d).\" % (index, rocket.x, rocket.y))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='adding_method'></a>Adding a new method\n", "---\n", "One of the strengths of object-oriented programming is the ability to closely model real-world phenomena by adding appropriate attributes and behaviors to classes. One of the jobs of a team piloting a rocket is to make sure the rocket does not get too close to any other rockets. Let's add a method that will report the distance from one rocket to any other rocket.\n", "\n", "If you are not familiar with distance calculations, there is a fairly simple formula to tell the distance between two points if you know the x and y values of each point." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This new method performs that calculation, and then returns the resulting distance." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The rockets are 11.180340 units apart.\n" ] } ], "source": [ "from math import sqrt\n", "\n", "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self, x=0, y=0):\n", " # Each rocket has an (x,y) position.\n", " self.x = x\n", " self.y = y\n", " \n", " def move_rocket(self, x_increment=0, y_increment=1):\n", " # Move the rocket according to the paremeters given.\n", " # Default behavior is to move the rocket up one unit.\n", " self.x += x_increment\n", " self.y += y_increment\n", " \n", " def get_distance(self, other_rocket):\n", " # Calculates the distance from this rocket to another rocket,\n", " # and returns that value.\n", " distance = sqrt((self.x-other_rocket.x)2+(self.y-other_rocket.y)2)\n", " return distance\n", " \n", "# Make two rockets, at different places.\n", "rocket_0 = Rocket()\n", "rocket_1 = Rocket(10,5)\n", "\n", "# Show the distance between them.\n", "distance = rocket_0.get_distance(rocket_1)\n", "print(\"The rockets are %f units apart.\" % distance)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Hopefully these short refinements show that you can extend a class' attributes and behavior to model the phenomena you are interested in as closely as you want. The rocket could have a name, a crew capacity, a payload, a certain amount of fuel, and any number of other attributes. You could define any behavior you want for the rocket, including interactions with other rockets and launch facilities, gravitational fields, and whatever you need it to! There are techniques for managing these more complex interactions, but what you have just seen is the core of object-oriented programming.\n", "\n", "At this point you should try your hand at writing some classes of your own. After trying some exercises, we will look at object inheritance, and then you will be ready to move on for now." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name='exercises_refining_rocket'></a>Exercises\n", "---\n", "#### Your Own Rocket 2\n", "- There are enough new concepts here that you might want to try re-creating the [Rocket class](#adding_method) as it has been developed so far, looking at the examples as little as possible. Once you have your own version, regardless of how much you needed to look at the example, you can modify the class and explore the possibilities of what you have already learned.\n", " - Re-create the Rocket class as it has been developed so far:\n", " - Define the Rocket() class.\n", " - Define the \\_\\_init\\_\\_() method. Let your \\_\\_init\\_\\_() method accept x and y values for the initial position of the rocket. Make sure the default behavior is to position the rocket at (0,0).\n", " - Define the move_rocket() method. The method should accept an amount to move left or right, and an amount to move up or down.\n", " - Create a Rocket object. Move the rocket around, printing its position after each move.\n", " - Create a small fleet of rockets. Move several of them around, and print their final positions to prove that each rocket can move independently of the other rockets.\n", " - Define the get_distance() method. The method should accept a Rocket object, and calculate the distance between the current rocket and the rocket that is passed into the method.\n", " - Use the get_distance() method to print the distances between several of the rockets in your fleet.\n", "\n", "#### Rocket Attributes\n", "- Start with a copy of the Rocket class, either one you made from a previous exercise or the latest version from the [last section](#adding_method).\n", "- Add several of your own attributes to the \\_\\_init\\_\\_() function. The values of your attributes can be set automatically by the \\_\\_init\\_\\_ function, or they can be set by paremeters passed into \\_\\_init\\_\\_().\n", "- Create a rocket and print the values for the attributes you have created, to show they have been set correctly.\n", "- Create a small fleet of rockets, and set different values for one of the attributes you have created. Print the values of these attributes for each rocket in your fleet, to show that they have been set properly for each rocket.\n", "- If you are not sure what kind of attributes to add, you could consider storing the height of the rocket, the crew size, the name of the rocket, the speed of the rocket, or many other possible characteristics of a rocket.\n", "\n", "#### Rocket Methods\n", "- Start with a copy of the Rocket class, either one you made from a previous exercise or the latest version from the [last section](#adding_method).\n", "- Add a new method to the class. This is probably a little more challenging than adding attributes, but give it a try.\n", " - Think of what rockets do, and make a very simple version of that behavior using print statements. For example, rockets lift off when they are launched. You could make a method called launch(), and all it would do is print a statement such as \"The rocket has lifted off!\" If your rocket has a name, this sentence could be more descriptive.\n", " - You could make a very simple land_rocket() method that simply sets the x and y values of the rocket back to 0. Print the position before and after calling the land_rocket() method to make sure your method is doing what it's supposed to.\n", " - If you enjoy working with math, you could implement a safety_check() method. This method would take in another rocket object, and call the get_distance() method on that rocket. Then it would check if that rocket is too close, and print a warning message if the rocket is too close. If there is zero distance between the two rockets, your method could print a message such as, \"The rockets have crashed!\" (Be careful; getting a zero distance could mean that you accidentally found the distance between a rocket and itself, rather than a second rocket.)\n", " \n", "#### <a name='exercise_person_class'></a>Person Class\n", "- Modeling a person is a classic exercise for people who are trying to learn how to write classes. We are all familiar with characteristics and behaviors of people, so it is a good exercise to try.\n", " - Define a Person() class.\n", " - In the \\_\\_init()\\_\\_ function, define several attributes of a person. Good attributes to consider are name, age, place of birth, and anything else you like to know about the people in your life.\n", " - Write one method. This could be as simple as introduce_yourself(). This method would print out a statement such as, \"Hello, my name is Eric.\"\n", " - You could also make a method such as age_person(). A simple version of this method would just add 1 to the person's age.\n", " - A more complicated version of this method would involve storing the person's birthdate rather than their age, and then calculating the age whenever the age is requested. But dealing with dates and times is not particularly easy if you've never done it in any other programming language before.\n", " - Create a person, set the attribute values appropriately, and print out information about the person.\n", " - Call your method on the person you created. Make sure your method executed properly; if the method does not print anything out directly, print something before and after calling the method to make sure it did what it was supposed to.\n", " \n", "#### <a name='exercise_car_class'></a>Car Class\n", "- Modeling a car is another classic exercise.\n", " - Define a Car() class.\n", " - In the \\_\\_init\\_\\_() function, define several attributes of a car. Some good attributes to consider are make (Subaru, Audi, Volvo...), model (Outback, allroad, C30), year, num_doors, owner, or any other aspect of a car you care to include in your class.\n", " - Write one method. This could be something such as describe_car(). This method could print a series of statements that describe the car, using the information that is stored in the attributes. You could also write a method that adjusts the mileage of the car or tracks its position.\n", " - Create a car object, and use your method.\n", " - Create several car objects with different values for the attributes. Use your method on several of your cars." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.2 : Your Own Rocket 2\n", "\n", "# put your code here" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.3 : Rocket Attributes\n", "\n", "# put your code here" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.4 : Rocket Methods\n", "\n", "# put your code here" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.5 : Person Class\n", "\n", "# put your code here" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.6 : Car Class\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='inheritance'></a>Inheritance\n", "===" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "One of the most important goals of the object-oriented approach to programming is the creation of stable, reliable, reusable code. If you had to create a new class for every kind of object you wanted to model, you would hardly have any reusable code. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "In Python and any other language that supports OOP, one class can inherit from another class. This means you can base a new class on an existing class; the new class inherits all of the attributes and behavior of the class it is based on. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "A new class can override any undesirable attributes or behavior of the class it inherits from, and it can add any new attributes or behavior that are appropriate. The original class is called the parent class, and the new class is a child of the parent class. The parent class is also called a superclass, and the child class is also called a subclass." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The child class inherits all attributes and behavior from the parent class, but any attributes that are defined in the child class are not available to the parent class. This may be obvious to many people, but it is worth stating. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This also means a child class can override behavior of the parent class. If a child class defines a method that also appears in the parent class, objects of the child class will use the new method rather than the parent class method.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "To better understand inheritance, let's look at an example of a class that can be based on the Rocket class." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='shuttle'></a>The SpaceShuttle class\n", "---\n", "If you wanted to model a space shuttle, you could write an entirely new class. But a space shuttle is just a special kind of rocket. Instead of writing an entirely new class, you can inherit all of the attributes and behavior of a Rocket, and then add a few appropriate attributes and behavior for a Shuttle.\n", "\n", "One of the most significant characteristics of a space shuttle is that it can be reused. So the only difference we will add at this point is to record the number of flights the shutttle has completed. Everything else you need to know about a shuttle has already been coded into the Rocket class." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Here is what the Shuttle class looks like:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Shuttle object at 0x7f1e62ba6cd0>\n" ] } ], "source": [ "from math import sqrt\n", "\n", "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self, x=0, y=0):\n", " # Each rocket has an (x,y) position.\n", " self.x = x\n", " self.y = y\n", " \n", " def move_rocket(self, x_increment=0, y_increment=1):\n", " # Move the rocket according to the paremeters given.\n", " # Default behavior is to move the rocket up one unit.\n", " self.x += x_increment\n", " self.y += y_increment\n", " \n", " def get_distance(self, other_rocket):\n", " # Calculates the distance from this rocket to another rocket,\n", " # and returns that value.\n", " distance = sqrt((self.x-other_rocket.x)2+(self.y-other_rocket.y)2)\n", " return distance\n", " \n", "class Shuttle(Rocket):\n", " # Shuttle simulates a space shuttle, which is really\n", " # just a reusable rocket.\n", " \n", " def __init__(self, x=0, y=0, flights_completed=0):\n", " super().__init__(x, y)\n", " self.flights_completed = flights_completed\n", " \n", "shuttle = Shuttle(10,0,3)\n", "print(shuttle)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "When a new class is based on an existing class, you write the name of the parent class in parentheses when you define the new class:\n", "\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "class NewClass(ParentClass):" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The \\_\\_init\\_\\_() function of the new class needs to call the \\_\\_init\\_\\_() function of the parent class. The \\_\\_init\\_\\_() function of the new class needs to accept all of the parameters required to build an object from the parent class, and these parameters need to be passed to the \\_\\_init\\_\\_() function of the parent class. The super().\\_\\_init\\_\\_() function takes care of this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "class NewClass(ParentClass):\n", " \n", " def __init__(self, arguments_new_class, arguments_parent_class):\n", " super().__init__(arguments_parent_class)\n", " # Code for initializing an object of the new class." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The super() function passes the self argument to the parent class automatically. You could also do this by explicitly naming the parent class when you call the \\_\\_init\\_\\_() function, but you then have to include the self argument manually:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "class Shuttle(Rocket):\n", " # Shuttle simulates a space shuttle, which is really\n", " # just a reusable rocket.\n", " \n", " def __init__(self, x=0, y=0, flights_completed=0):\n", " Rocket.__init__(self, x, y)\n", " self.flights_completed = flights_completed" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This might seem a little easier to read, but it is preferable to use the super() syntax. When you use super(), you don't need to explicitly name the parent class, so your code is more resilient to later changes. As you learn more about classes, you will be able to write child classes that inherit from multiple parent classes, and the super() function will call the parent classes' \\_\\_init\\_\\_() functions for you, in one line. This explicit approach to calling the parent class' \\_\\_init\\_\\_() function is included so that you will be less confused if you see it in someone else's code." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The output above shows that a new Shuttle object was created. This new Shuttle object can store the number of flights completed, but it also has all of the functionality of the Rocket class: it has a position that can be changed, and it can calculate the distance between itself and other rockets or shuttles. This can be demonstrated by creating several rockets and shuttles, and then finding the distance between one shuttle and all the other shuttles and rockets." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This example uses a simple function called [randint](https://docs.python.org/3/library/random.html#random.randint), which generates a random integer between a lower and upper bound, to determine the position of each rocket and shuttle:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from math import sqrt\n", "from random import randint\n", "\n", "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self, x=0, y=0):\n", " # Each rocket has an (x,y) position.\n", " self.x = x\n", " self.y = y\n", " \n", " def move_rocket(self, x_increment=0, y_increment=1):\n", " # Move the rocket according to the paremeters given.\n", " # Default behavior is to move the rocket up one unit.\n", " self.x += x_increment\n", " self.y += y_increment\n", " \n", " def get_distance(self, other_rocket):\n", " # Calculates the distance from this rocket to another rocket,\n", " # and returns that value.\n", " distance = sqrt((self.x-other_rocket.x)2+(self.y-other_rocket.y)2)\n", " return distance" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "class Shuttle(Rocket):\n", " # Shuttle simulates a space shuttle, which is really\n", " # just a reusable rocket.\n", " \n", " def __init__(self, x=0, y=0, flights_completed=0):\n", " super().__init__(x, y)\n", " self.flights_completed = flights_completed" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Create several shuttles and rockets, with random positions.\n", "# Shuttles have a random number of flights completed.\n", "shuttles = []\n", "for x in range(0,3):\n", " x = randint(0,100)\n", " y = randint(1,100)\n", " flights_completed = randint(0,10)\n", " shuttles.append(Shuttle(x, y, flights_completed))\n", "\n", "rockets = []\n", "for x in range(0,3):\n", " x = randint(0,100)\n", " y = randint(1,100)\n", " rockets.append(Rocket(x, y))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shuttle 0 has completed 8 flights.\n", "Shuttle 1 has completed 6 flights.\n", "Shuttle 2 has completed 9 flights.\n", "\n", "\n", "The first shuttle is 0.000000 units away from shuttle 0.\n", "The first shuttle is 40.199502 units away from shuttle 1.\n", "The first shuttle is 30.886890 units away from shuttle 2.\n" ] } ], "source": [ "# Show the number of flights completed for each shuttle.\n", "for index, shuttle in enumerate(shuttles):\n", " print(\"Shuttle %d has completed %d flights.\" % (index, shuttle.flights_completed))\n", " \n", "print(\"\\n\") \n", "# Show the distance from the first shuttle to all other shuttles.\n", "first_shuttle = shuttles[0]\n", "for index, shuttle in enumerate(shuttles):\n", " distance = first_shuttle.get_distance(shuttle)\n", " print(\"The first shuttle is %f units away from shuttle %d.\" % (distance, index))\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "The first shuttle is 40.311289 units away from rocket 0.\n", "The first shuttle is 15.556349 units away from rocket 1.\n", "The first shuttle is 76.694198 units away from rocket 2.\n" ] } ], "source": [ "print(\"\\n\")\n", "# Show the distance from the first shuttle to all other rockets.\n", "for index, rocket in enumerate(rockets):\n", " distance = first_shuttle.get_distance(rocket)\n", " print(\"The first shuttle is %f units away from rocket %d.\" % (distance, index))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Inheritance is a powerful feature of object-oriented programming. Using just what you have seen so far about classes, you can model an incredible variety of real-world and virtual phenomena with a high degree of accuracy. The code you write has the potential to be stable and reusable in a variety of applications." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name='exercises_inheritance'></a>Exercises\n", "---\n", "#### <a name='exercise_student_class'></a>Student Class\n", "- Start with your program from [Person Class](#exercise_person_class).\n", "- Make a new class called Student that inherits from Person.\n", "- Define some attributes that a student has, which other people don't have.\n", " - A student has a school they are associated with, a graduation year, a gpa, and other particular attributes.\n", "- Create a Student object, and prove that you have used inheritance correctly.\n", " - Set some attribute values for the student, that are only coded in the Person class.\n", " - Set some attribute values for the student, that are only coded in the Student class.\n", " - Print the values for all of these attributes.\n", "\n", "#### Refining Shuttle\n", "- Take the latest version of the [Shuttle class](#shuttle). Extend it.\n", " - Add more attributes that are particular to shuttles such as maximum number of flights, capability of supporting spacewalks, and capability of docking with the ISS.\n", " - Add one more method to the class, that relates to shuttle behavior. This method could simply print a statement, such as \"Docking with the ISS,\" for a dock_ISS() method.\n", " - Prove that your refinements work by creating a Shuttle object with these attributes, and then call your new method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.7 : Student Class\n", "\n", "# put your code here" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.8 : Refining Shuttle\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<a name='modules_classes'></a>Modules and classes\n", "===\n", "Now that you are starting to work with classes, your files are going to grow longer. This is good, because it means your programs are probably doing more interesting things. But it is bad, because longer files can be more difficult to work with. Python allows you to save your classes in another file and then import them into the program you are working on. This has the added advantage of isolating your classes into files that can be used in any number of different programs. As you use your classes repeatedly, the classes become more reliable and complete overall." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='single_class_module'></a>Storing a single class in a module\n", "---\n", "\n", "When you save a class into a separate file, that file is called a module. You can have any number of classes in a single module. There are a number of ways you can then import the class you are interested in.\n", "\n", "Start out by saving just the Rocket class into a file called rocket.py. Notice the naming convention being used here: the module is saved with a lowercase name, and the class starts with an uppercase letter." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This convention is pretty important for a number of reasons, and it is a really good idea to follow the convention." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Save as rocket.py\n", "from math import sqrt\n", "\n", "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self, x=0, y=0):\n", " # Each rocket has an (x,y) position.\n", " self.x = x\n", " self.y = y\n", " \n", " def move_rocket(self, x_increment=0, y_increment=1):\n", " # Move the rocket according to the paremeters given.\n", " # Default behavior is to move the rocket up one unit.\n", " self.x += x_increment\n", " self.y += y_increment\n", " \n", " def get_distance(self, other_rocket):\n", " # Calculates the distance from this rocket to another rocket,\n", " # and returns that value.\n", " distance = sqrt((self.x-other_rocket.x)2+(self.y-other_rocket.y)2)\n", " return distance" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Make a separate file called rocket_game.py. If you are more interested in science than games, feel free to call this file something like rocket_simulation.py. Again, to use standard naming conventions, make sure you are using a lowercase_underscore name for this file." ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The rocket is at (0, 0).\n" ] } ], "source": [ "# Save as rocket_game.py\n", "from rocket import Rocket\n", "\n", "rocket = Rocket()\n", "print(\"The rocket is at (%d, %d).\" % (rocket.x, rocket.y))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This is a really clean and uncluttered file. A rocket is now something you can define in your programs, without the details of the rocket's implementation cluttering up your file. You don't have to include all the class code for a rocket in each of your files that deals with rockets; the code defining rocket attributes and behavior lives in one file, and can be used anywhere." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The first line tells Python to look for a file called rocket.py. It looks for that file in the same directory as your current program. You can put your classes in other directories, but we will get to that convention a bit later. Notice that you do not." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "When Python finds the file rocket.py, it looks for a class called Rocket. When it finds that class, it imports that code into the current file, without you ever seeing that code. You are then free to use the class Rocket as you have seen it used in previous examples." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='multiple_classes_module'></a>Storing multiple classes in a module\n", "---\n", "\n", "A module is simply a file that contains one or more classes or functions, so the Shuttle class actually belongs in the rocket module as well:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# Save as rocket.py\n", "from math import sqrt\n", "\n", "class Rocket():\n", " # Rocket simulates a rocket ship for a game,\n", " # or a physics simulation.\n", " \n", " def __init__(self, x=0, y=0):\n", " # Each rocket has an (x,y) position.\n", " self.x = x\n", " self.y = y\n", " \n", " def move_rocket(self, x_increment=0, y_increment=1):\n", " # Move the rocket according to the paremeters given.\n", " # Default behavior is to move the rocket up one unit.\n", " self.x += x_increment\n", " self.y += y_increment\n", " \n", " def get_distance(self, other_rocket):\n", " # Calculates the distance from this rocket to another rocket,\n", " # and returns that value.\n", " distance = sqrt((self.x-other_rocket.x)2+(self.y-other_rocket.y)2)\n", " return distance\n", " \n", "\n", "class Shuttle(Rocket):\n", " # Shuttle simulates a space shuttle, which is really\n", " # just a reusable rocket.\n", " \n", " def __init__(self, x=0, y=0, flights_completed=0):\n", " super().__init__(x, y)\n", " self.flights_completed = flights_completed" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Now you can import the Rocket and the Shuttle class, and use them both in a clean uncluttered program file:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The rocket is at (0, 0).\n", "\n", "The shuttle is at (0, 0).\n", "The shuttle has completed 0 flights.\n" ] } ], "source": [ "# Save as rocket_game.py\n", "from rocket import Rocket, Shuttle\n", "\n", "rocket = Rocket()\n", "print(\"The rocket is at (%d, %d).\" % (rocket.x, rocket.y))\n", "\n", "shuttle = Shuttle()\n", "print(\"\\nThe shuttle is at (%d, %d).\" % (shuttle.x, shuttle.y))\n", "print(\"The shuttle has completed %d flights.\" % shuttle.flights_completed)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The first line tells Python to import both the Rocket and the Shuttle classes from the rocket module. You don't have to import every class in a module; you can pick and choose the classes you care to use, and Python will only spend time processing those particular classes." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='multiple_ways_import'></a>A number of ways to import modules and classes\n", "---\n", "There are several ways to import modules and classes, and each has its own merits." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### import module_name\n", "\n", "The syntax for importing classes that was just shown:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from module_name import ClassName" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "is straightforward, and is used quite commonly. It allows you to use the class names directly in your program, so you have very clean and readable code. This can be a problem, however, if the names of the classes you are importing conflict with names that have already been used in the program you are working on. This is unlikely to happen in the short programs you have been seeing here, but if you were working on a larger program it is quite possible that the class you want to import from someone else's work would happen to have a name you have already used in your program." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In this case, you can use simply import the module itself:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The rocket is at (0, 0).\n", "\n", "The shuttle is at (0, 0).\n", "The shuttle has completed 0 flights.\n" ] } ], "source": [ "# Save as rocket_game.py\n", "import rocket\n", "\n", "rocket_0 = rocket.Rocket()\n", "print(\"The rocket is at (%d, %d).\" % (rocket_0.x, rocket_0.y))\n", "\n", "shuttle_0 = rocket.Shuttle()\n", "print(\"\\nThe shuttle is at (%d, %d).\" % (shuttle_0.x, shuttle_0.y))\n", "print(\"The shuttle has completed %d flights.\" % shuttle_0.flights_completed)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The general syntax for this kind of import is:\n", "\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import module_name" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "After this, classes are accessed using dot notation:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "module_name.ClassName" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This prevents some name conflicts. If you were reading carefully however, you might have noticed that the variable name rocket in the previous example had to be changed because it has the same name as the module itself. This is not good, because in a longer program that could mean a lot of renaming." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### import module_name as local_module_name\n", "\n", "There is another syntax for imports that is quite useful:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import module_name as local_module_name" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "When you are importing a module into one of your projects, you are free to choose any name you want for the module in your project. So the last example could be rewritten in a way that the variable name rocket would not need to be changed:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The rocket is at (0, 0).\n", "\n", "The shuttle is at (0, 0).\n", "The shuttle has completed 0 flights.\n" ] } ], "source": [ "# Save as rocket_game.py\n", "import rocket as rocket_module\n", "\n", "rocket = rocket_module.Rocket()\n", "print(\"The rocket is at (%d, %d).\" % (rocket.x, rocket.y))\n", "\n", "shuttle = rocket_module.Shuttle()\n", "print(\"\\nThe shuttle is at (%d, %d).\" % (shuttle.x, shuttle.y))\n", "print(\"The shuttle has completed %d flights.\" % shuttle.flights_completed)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "This approach is often used to shorten the name of the module, so you don't have to type a long module name before each class name that you want to use. But it is easy to shorten a name so much that you force people reading your code to scroll to the top of your file and see what the shortened name stands for. In this example," ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import rocket as rocket_module" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "leads to much more readable code than something like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import rocket as r" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### from module_name import \n", "There is one more import syntax that you should be aware of, but you should probably avoid using. This syntax imports all of the available classes and functions in a module:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from module_name import " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This is not recommended, for a couple reasons. First of all, you may have no idea what all the names of the classes and functions in a module are. If you accidentally give one of your variables the same name as a name from the module, you will have naming conflicts. Also, you may be importing way more code into your program than you need.\n", "\n", "If you really need all the functions and classes from a module, just import the module and use the `module_name.ClassName` syntax in your program." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "You will get a sense of how to write your imports as you read more Python code, and as you write and share some of your own code." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<a name='module_functions'></a>A module of functions\n", "---\n", "You can use modules to store a set of functions you want available in different programs as well, even if those functions are not attached to any one class. To do this, you save the functions into a file, and then import that file just as you saw in the last section. Here is a really simple example; save this is multiplying.py:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Save as multiplying.py\n", "def double(x):\n", " return 2x\n", "\n", "def triple(x):\n", " return 3x\n", "\n", "def quadruple(x):\n", " return 4x" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Now you can import the file multiplying.py, and use these functions. Using the `from module_name import function_name` syntax:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "15\n", "20\n" ] } ], "source": [ "from multiplying import double, triple, quadruple\n", "\n", "print(double(5))\n", "print(triple(5))\n", "print(quadruple(5))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Using the `import module_name` syntax:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "15\n", "20\n" ] } ], "source": [ "import multiplying\n", "\n", "print(multiplying.double(5))\n", "print(multiplying.triple(5))\n", "print(multiplying.quadruple(5))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Using the `import module_name as local_module_name` syntax:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "15\n", "20\n" ] } ], "source": [ "import multiplying as m\n", "\n", "print(m.double(5))\n", "print(m.triple(5))\n", "print(m.quadruple(5))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Using the `from module_name import ` syntax:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "15\n", "20\n" ] } ], "source": [ "from multiplying import \n", "\n", "print(double(5))\n", "print(triple(5))\n", "print(quadruple(5))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<a name='exercises_importing'></a>Exercises\n", "---\n", "#### Importing Student\n", "- Take your program from [Student Class](#exercise_student_class)\n", " - Save your Person and Student classes in a separate file called person.py.\n", " - Save the code that uses these classes in four separate files.\n", " - In the first file, use the `from module_name import ClassName` syntax to make your program run.\n", " - In the second file, use the `import module_name` syntax.\n", " - In the third file, use the `import module_name as different_local_module_name` syntax.\n", " - In the fourth file, use the `import ` syntax.\n", " \n", "#### Importing Car\n", "- Take your program from [Car Class](#exercise_car_class)\n", " - Save your Car class in a separate file called car.py.\n", " - Save the code that uses the car class into four separate files.\n", " - In the first file, use the `from module_name import ClassName` syntax to make your program run.\n", " - In the second file, use the `import module_name` syntax.\n", " - In the third file, use the `import module_name as different_local_module_name` syntax.\n", " - In the fourth file, use the `import *` syntax." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.9 : Importing Student\n", "\n", "# put your code here" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Ex 9.10 : Importing Car\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "[top](#top)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"mro\"></a>Method Resolution Order (`mro`)\n", "---" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MRO: [<class '__main__.C'>, <class '__main__.A'>, <class '__main__.B'>, <class '__main__.GreatB'>, <class 'object'>]\n", "c.a: A\n", "c.b: B\n", "Greetings from Type: <class '__main__.C'>\n", "Greetings from Type: <class '__main__.C'>\n", "Greetings from Type: <class '__main__.C'>\n" ] } ], "source": [ "class A:\n", " def __init__(self, a):\n", " self.a = a\n", " \n", "\n", "class GreatB:\n", " \n", " def greetings(self):\n", " print('Greetings from Type: ', self.__class__)\n", " \n", "class B(GreatB):\n", " def __init__(self, b):\n", " self.b = b\n", " \n", " \n", "class C(A,B):\n", " def __init__(self, a, b):\n", " A.__init__(self, a)\n", " B.__init__(self, b)\n", " \n", "print('MRO: ', C.mro()) \n", "c = C('A', 'B')\n", "print('c.a: ', c.a)\n", "print('c.b: ', c.b)\n", "\n", "c.greetings()\n", "super(C, c).greetings()\n", "super(B, c).greetings()\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
WneleiGao/Seismic.jl	test/IJulia/TeapotDome.ipynb	6	343005	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Loading help data...\n" ] } ], "source": [ "using PyPlot,Seismic" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 5824M 100 5824M 0 0 2283k 0 0:43:31 0:43:31 --:--:-- 2236k\n" ] }, { "data": { "text/plain": [ "\"npr3_gathers.sgy\"" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "download(\"http://s3.amazonaws.com/teapot/npr3_gathers.sgy\",\"npr3_gathers.sgy\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of traces: 723991\n", "number of samples per trace: 2049\n" ] } ], "source": [ "SegyToSeis(\"npr3_gathers.sgy\",\"npr3_gathers.seis\");" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "SeisGeometry(\"npr3_gathers.seis\",ang=90,omx=788937,omy=938845,dmx=110,dmy=110,oh=0,oaz=0,dh=420,daz=45)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x11b03ba10>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x11b03ba10>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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XschZ6N4GqbaQ6xBnRvgQEgGQyMly004hHgqkmlEWoRVMO3/FVkup5kFNOWVc8dfq7joPsgDSPswDUBH4q6DkIu3FlaFRmARadak5JsU0ZLGZV41xEkRd8nxECgcHh8cTnMNwRUFi2mJRD+IydJWRJHEv+y+/76YkeqC8RcPWGDprQQMWqQ3iOAj1Qf52t8l4Uh3RdQq6HAfZNsBE2g9Sm+5e0wRIQR2vUJsaPfXQmdc6GnKdPW31HFQbqZDzD4ENUNQov4ZIH6hireceetbpISE2WCbv4ihId2vhKkgpp+zXRA9otShkLOmuLQUQYw/GEVQR5H3w1g7mPjQlqnXnBDWt8+fREh2l3jTjbG1uOLvctjumg8PjFy7C4PA4hMIk6TcO2R6wWE4Z2veSOO9qIst2ib2r1nB3uZAiigTtpFUupVtNGQNPwMz4tzCT2K5+Uw9TaSBtD8SGSdS9b7dvYiochFewbKvkmoYahhq/n+H7OeW/96jOxG1X6oFejNSP9GKEYazMtWxq1KAk6M/xgoP7MJTzhHKWwESZRcaRyII4SGKHu+etWCzRXM4AdB2ILvdB0iVwuEDnAoTYGGLCJl1xqD1MOmOX1sM4qLxSnh/5WRFVLdefwuHxDSfc5PA4g5Q8HtQUSvpAyPQ+oK0bFMVHid0HEAcwCEH7kHlQq3aYLolRZrRScdl9361oECqEzJBlm/wvlDJEmeGL0V+xr0PdBk5Qi7xOgcgPSLuE4wqugmoSU6sQHfhwtR2jBM6oNkOzLHQp/L+R+Sw6DSjHfVSm2zR/B/XcNxGKUrURBmnlII5Al6i5cDCtANRBk3UZT5wFWXIWiaXd/buVkbXkRST/IYSKKYansmX/Ktp6VG1fF7ThEGhLViT8IeM6ODg8HuAchisGMjOc0lrebjllV41RygYkeS9Wyyoa6cCw8HVlFztc5ZtFILNhya8HnaEL2qyGVO/t0ypGCzVCjhXDKOkAcTxEy6GrdJwuvZcxustDQKrQiQ89j3A9I7jGXrBt36ALRVkFqEoTVzmq1GRVRJFH6LmPxoNMoTXoKmydALmtYqilxFGuRYSd9jDRgK6qZJdOIJ9Z03a/FNSdsSSqsI+x9XLfu6WrZQ1ZZaS1lQ91BVW3+6U4Dl3G5R4tX2HZC5STK84Oe0gqw3EcHK4MuJSEw+MMy+Vvkm+WdshiXbpNp4QUAKZe0DoUeQB513p1BZw8o1I47WwWvqQQ8sUZOGpPLZc2PuDShHcZsNhbSWxaCATqcNskOX4JsAw0qqeNHTwFakPj9yr6G3v0rx8vHFrXPmkWo2oYRWMUmr1sFSYDqq0YnbaiEGpF4w00XlJDCHqg0JZ/oece9dRDz5RZJgoiJYceLIIpPpoEd4a0zoA4WPPOa2kFIY6Z/IJJdGdaQZ2BF0LgQ1FBkVutCAV1CdqGOvSctqazj6mqgJZwIqUr4rUcJDLRpy35WO6sVXN2qMTBweFSh3MYrkhIolx+/KW8UvgIUk4p73sY3sNhctDdZkodnYUul0GqM7tSyMttpumcrluQISWNy+8lSn7NIWNhP0Kim/SH388I+pm1WZq4nxP3UvrxnHi5S6MHw9ijqEKmRZ+0Sqi0T9DP6D9pgq9MuD1QJYmXMWDCSjVGK0Xmx9T2BkzqIfv1iCxLyOYx5ZmY6nQC/wNDlDwMjfSBalMd0sdLov1bwH/QVr12JRUUrTNRBSb6k8lYoYkUbWOkq9ME5mtQpFBmwFNpnw0ZVMJAc0xJiYRrlj+DtO0UHkS37APaEItzGhweH3AcBofHIRQm8T6gjX93lRuX34uuwoBFsqOF+BZiyfvKzITFuC+XO3aHkW3dEsYBrTOwXC4pNstKQahBhT8qYFQbp2Hp0gK/IggKwqgkDAsCVRJS0A/HDIKJvRs1G8E2I3+fGQPm9DB7BZQEVPjgQaV8ElIq3yOkJPBKAr8k8jJ6zInsEQpNbS9UoanwKQgJbVhg7GsKzycOK/wjM+Ms2OvWWlGWAWUZUucBdR5Y3oVqswYpLTVAUhEzzD1f7i4uWQaJNmjrQcxpe1XkHecurW3qRkIx3QZZkh+RLytisbOlkDwkMiFttyXsIemOLmvUwcHhcoNzGK4YiGVYwcwOu+WTUhUhLHex2j1aFaKlobphb7EtMrSsk1JJMfprtLy55fEiTAGHCA92fZdupZ51GPz1kujYDC8xRkvXirr20HbHMCyIwpzET+l5KREZETmDeka/nhF4JaEqWM3G9CZzKiJmekhVB5RVRF7HlDpoJtgBmsjLiaKcMMzx/ZJYZ/TrKTE5gVdSqJAZfWo8PGoqfEoCyjykSkOqKqSuQsIoJxwu5v21VuhaU9WeIZPWmMjCPi0/QWSnJR0xxuwnJNIuRA1axJwE4mh0NR9SIKtNmqL5sgTCIO32Bw86A4uS5ByTtjpFG4Vw1REOVwYch8HhcYYebQMESUEIoWAZIa1G8gHbY0xau1vyOKQ17CIIKbPdFUxG46BTSQqiy1cQGoWU+ss2OfdQ46+V9IZzgsgY3qIISbMEbSs2iiKkLEOqyKeKfZQy9Z1ZEZNlMevxLqNgzNbJq9k9cRSNMsfObCp/poy9Wyjt1HAMos05w/VdvKAkTRNiL2M92aWvZsRkzOmxyyql/e813V5l8sN16sRDD86meABm8j1R6JQ28yOtH05jnAPp/L1l/3YlsQ+DCENJNkAEH8VhkCBAW2aydFHdVuRCmhUmZoZxECZ2uxBLhFzh4ODweIJzGK4YSPnbhMWuT0lnEc2FAjNbHC+NEQMjyEKzWVIKwvjvNrrsDi12pwvV2V8msF31aaFQRPYypNOkD0wURR0zLo/gUUMKtfKoIg+UsZ5eVODHOXkRUdV+E3HohXOG3gR8RaoSnnn021wzOsE2R9jVaxRViK4Ug2pKXGcQQOgVjBgT+AV5HJHFEWkYk3oJ8yQBBZEqKAlISSgJiMnJd3vsn15HRTWjJ29TEFDo0JpSRZ2FVGlkqyWU1YYACtWWRko1iNAHpEeFRHXEWZCKiondTxQipYmVVEsKF6IoYFZCHYBenhsJk7JbXiLPj1TUnLTPiEQXdu36dQyb9SBIbethzkRXttzB4fKB4zA4PM7QbSQlj3fY+dttTS0GQiBiCDZk0G0t3S1VxK6TELmQHAsWfQ85ZdjZR5SHJfLdnTl3hY2sralnAfkkQEUVXlTh9UqiKMf3TSIgCjOiaI6vSnwqQgpCCiKvMOkDQmo8wkGKGpSM2CVmhkbhU7HODiPGzbGJNXRzehSWcVkQkgYJBSElPjMG7LNCTkSFz8ibMgpmxIM5gyP7lLVPWZh9KwLyKKGIEmIywrCAFdA9xSzrk2YJDEFveGR5RJGGbQlmB7pWVFVAlfnomY+eeG2qQppxdbteCg9ipswiN/msdnvKKHcGNieT1jAJ4HQM49gMrANTmqnntLmkLqFEPMFua23xhLo8hhLHa3BwuPThHIbLHt02igdBLPIAK3FIyz6U0oWQtkqiO7SySwKsm7/d1hHdxk8ip+ybXdnkbOVgEWqSqon+0t9ud8lI43k1KtSLjZ8sz06VGpVq/I2cYDMlXpmTxIavkJDSY05CSkxGQooySYdmAajxeJDj3MNTOM5JjvEQPeb0mbLKLivsk5DhUZPa0UoCeqRcxSliMrs+YU7CnB4z+qT0yIjRKwq1oonIjcPhgQ5Uc0zWiykIWT22x4gxGkVOyEmOs8UmFT45IfusMF+Qf6T5JGUZME97FGlMncbURUCtleE/7KlWFEochh1MVQQBqM5//260p1Ym6qDCtqHXmT7cB3zbilOxBlUJOrV6DotX1zY6k0qJ2dLDINEL2W+Gg8PlCsdhcLhMsEwYWEa3ptGnpdTL+w3a7oVLY4QejCKIbQxcTiUljdLPQdj5IxYbOEnQQrCOIT52q/WEozAA+roJZARxQX9ln6ifHqgBlJCywpgwyFFhTeTnJF5qzbaY5BSfCoVu3sdkxHaaXuFzjIc4wxGu5cHGYYjJ8KmaYxWaIRMCSoZMCCmo8ajxGDEmJ2qchpQeJQEaRUhBbB0OhaYgJCO2+yUm4kGBT2WdkpgZfSaMGDNii032GdnzLrIeKvzGgQGIkoxwOKFMY2Z7K1SzwGQNpJpxF1NCKToOki4S7GHoCDu0XTJFilqyDvMaZnlHg6MALRGDLpbLKqUGVCIMEp6SPIkjRzo4XA5wDsNlD6ltPMxh6HZpko5M0jlJSix7i2OI1kHiQeJD7LV8goM6RAqfUiIF3c7J3apM2d5VNJRyyr6Rd/biiqg3J+6nREmKHy6Gqj1qQgqGTFljl9gaq9DO4qUoUqHJiG2BZElORI1i1mluoVGUBCRkbLHJGTYIKA0vwm4vGuNf0mfOGjsElA1XASAjZsyIkIIR+1YHMcCjJqBsyiubUk2LwCYnxM2ZkzDTfXbKdfaqNSYMms8g10TtUeUhVR5Q5gFlHVD6gSnprErqnQB9woMHFTxIa68lMyD0AbHp8h0K+VGqVYYY+34/xoGQSopqBrWkq7o9tbsQaUopz5TwRrf7lrQLdakIh8sfZ2X0LuK4lxIutetxeNiQxL9Mwbv189D+qIsGc9XZ36etj5fyyhA8f1ExWnoYaBYr62SdCAD6tF0VYTEbIo5EySJDqMt/K41qcRCU+GFuAtal2dFTNYFXEqnChvizxkGQMkaFpiQgJ7JuQkBA2UQUAHIiclsN4FFzlNMcYZt9VpgyoCBENQ6DR0GA0qDqjIyaHW+dWnk29ZCQNwWbEX1mlPhWLDmyMQqfjJiUxDIXShubqCkJKGzKwaQdbDKlTMjzCN+rUJ6JTGhtykarIqScJtRpgMpqlGdSNjpV5Ft9OGUdhYcw1RVCeJS+Yaq5EeZ7ku9ZSjClrYh8P2ENnrbRoBLqLmFFwkyWzCIpLGyDLS3PWkgrHiHPpGg4dCszug+Pg4PDpQbnMFz2aOriLGRKL5ZBeiDvsqiOtE2bohjQRhw2oBiY3ZcEHBuqgxASV4BjtHly4TJ0gx3KbktZ7E4plydllH0FfagGAdN8ldlwSNCf40dmBhr7Gf14RhJkDQcBaCoTRDDJGHAzKxfuwmEIKbie+7iFu6zJ3icmJbL306cyHIg6pzcvKHTEyWSTrfAIu6yxxSYnuZqAgqs5RUrMQxwjs1wQEznoI6yKFfZYZ5s9VtnBjCFOyjI8ryaOLRkSqGuPNE2oCrOvF1cE6yleUIKC6mRIudVDn/EXJ+0SzZFoj9x3oQ2IZIIQWE/bZReYaKhz26AKjNb1KiYE0YUVbvIURLHZLwf0BPOcRfa4nLbkZbm1OrTsTAeHywuOw+BwGUHTph66mszd7WuYH23hMEAbKohpVZWiNsshjoJ0lJSqhsQOJc6DDCFPk3SSFMdhA1Np1w1+CFmyZ4iL3moBPnhhTdQrCJIC5VeEXs6QKStqnzV/l5CCgpCAkgFT+szoMaPGIydGoQkomgiEOAwFITkhESZCIamLOT2+zgu5nvu4jvs5zgmO8hADpgzmc3oP5BRZxINXH2d7ZY3Uj8nsrDgmZciEo+kWz9r/36RBzD0rT8ILatbZYcKQLTZJmFtnxMQivsdP8W88k6s5yZBJE4mYk5CphCryqEOf0vOplVWO9DR+UpGHMWd6R1FojvsPMPEG/BvPZHztKv1RSpkGTKsBZe63vSVo731RhhRFSDWLqKYRbCvYAf9YjrdeUN0XUT8YtuJQp0MY++a7qhWc9k0JKLQCj5WNKswU7NheIiGg7ANSeVDOQduy3KZ80mk1ODw+4MoqHS4ziJrSMpdBSI+rNP2YG0q8DQcrH7wh+MkiHSK0h4h4kTy9q5j+DQEmcjDATDqliaGQH0d2EadC2ldAE832ejW9o1Pi1Sl5FZvGycke/dD0ew4o6TNjlT022aKqfU7UVxNScK1/PyM1ocKzZEJTAtljzpAJQyasscsqu5zmKCe5mhXGrLLHCvv0mPMNnsv3+YmGb7DKHn5Vk0xyepMcXXrkQUgZKnRopuHCi0hIWWeHo2xztXeCwgstK6IkOJ+KAAAgAElEQVRglV3m9NhjjVE2YSPboYw8siRs0hUDpvSYU+FT4zXpipkv6Y6QkpAKH60g8gtq32M/WqHCyFVvscnVnCIOM4JRRUZMZVMzAD41PmXjQk6zPnU2ICpzglKTbvVJt3rGj7TVkszt95gqKH3w/TY6IUEp7L57mP4UBC23sapBCclxH1OWsUMrIqHtYOIwCLdBQiHirTpdBgeHSwnOYbjsIeUGCcY6L/ukx+wikJDBCGP5VZtukDy3j/mtT4DrMMEHOVRhnIHrMcbhjB3mOrtNOkP27bonajOL3cIYmqOdWaWn8cKSwWDKMN5jPx9R1X5j1IWXADR6CFkdcyI7zjXqQZ4Q30fip9zLDcwYNIZYoxruwgr7PIX/QKM4ydXEpKyyx1WcZI1dfsgTeIBrqPDYZ0ROiC4V5ZmEySxk79oB2XpASMY620wYkRPZyos5q+wRJTmTxEQ3jnKq4ShE5KywT5hqojOa/dWYWTxgQ21zC3exxyp7rFLjUeGzwRlW2OcUVzX8i5qMDFN+KRoRm2wxZsS9+ga29CaxzhjqGXt6hRkDSmU0JtAQ1DWhLvG0Bg1ZXqMzj148Zm1ljzPlMdK6R3UyojoZwT3Af9OmmTJMJEEiTAJpLyGpDdF80ICuIU8xZRf/Rav+KKUzwp4VEszMDiZa4uKhPoRzGBwuB7iUhMNlgiEm5t8RVlpAipndQdsqUiQA57Q1kB105ZkzzCQR2iiBbQ3dkBilDE98EME+8APV8thSYMfOdT1NcCTDH2ZMdI889emHU3pxCspUHowYW8EkQ1DcZ4XK97mh9wN6zLlbPY2AkoyYmIxNThNSoqiZ0+MMT+YUV/E9nm5Nk2KHdSaMOMlV9JnhUfOTfJ8f8ETu5ieY0+f/hDexfe0G03pIEJYUBJxhgwqfdXYYMiEmayIN4tRIpcY6O2yyBSgyYraHR9hKjlL4IZXymqJPSUWIqNR/ciMeNROGpDYUI3oLCt3ITv83N3CquopxvsI8HVCkCeU8opr51J5HPVBQmhSBmteouUZZwaaq8qi0z05vwLh/lHIrgC0FPwQeoKW8aMwv4KZ9tPZopRIq2lbbUiGTYOgJwqEtgDrC5KaGdgcRgpAHQsIZXaKj6DK46IKDw8PF7//+7/OBD3yApz/96Xz3u99t1r/xjW/kM5/5zFn733TTTXz/+9+/4PGdw3DZI8dY5mVnQWZxsl3Yb1OMBZjQOA86hnIN9MD8dksKoTKbG3K79Kvq0XLXhGUv4WhpPSBGY5+2DFM4bwOgr6jHAVRQxTFF2KMKYspwzqA/JoiMGqNvjYlHjUbhqZpEGW7CNkcIbH9JhWbGgNomBYRoOGHIHquW1LjXyDaZ6Hpt9irnHH/wIeLdguHRMdFazpgRqUrQqqaqfbKih8YjDmf4XrcqpUWFR0lA33awlIqM0g/QPnhUeFRMGLLPCn1mDCzJT1vlyBpFTtQQIaWMtEaxxSZbbPIA17Kj1imDgNxLSMsVylLaXrLIXdCqrXLxwBuUeMOCei8gPdEzjsCuPcajJT92q2QyWr0MOUdsv98c40icBvYKmM+s+qPkLp5gD/IwTsARWqKuKD7KsyldLkX8QSoplpUhHRwuLVwKEYb777+fD33oQwwGA5Q6+/cpjmM+9alPLaxbXV09a7+LdT0OlyRE61cgakjCXZBfe1FREs1gUfTxQOdQDlqFXmHwiOEQSKq5opWDHtGSJIXD0O1mXNCIPSlVo1RtBaA0ntJ4uQJfo3xFlYcUdQUxjZiRwKdaEE1S1DbDb0iMsm9tXYiEjBXG1mTnjJoqiJyYjDV2WWHPOBm1z9PG93DD6fvQfVADbcZTUFQe1BDPK6g1ed8n80Py2qQ/uhmgtOoxLkcQQtDPmXs9+kxZYY9jPNSIPW1zhG2OsMI+I8ZNWSYYxyElobDvFRrf6j5UBIwZcZQtht6E2vPIk4RZPqLwYupB261To6jx0AMFQ9U4EPWqplqvqe6LqcqYwosowsh8j8c1/qTEm9dUQUCt/MVyS5Hvlu9fKiW3MIGDsoIgNY9aFUA1hPoIqAq8AvSKDSZIZEF0vhPQPUzY6jRtnkN+nuRkDg4Oh+G9730vz3/+8ynLkq2trbO2h2HIa1/72kd1DucwPO6QsFhWKcQEML/612EICPK+U/7gsZihkG7XQnJbLqscYULW0lly1b6X7eKzWNEm/1hGsJlaGoUmiVPiKAdlyI1r7DJQ0yb8vsYuPQz5cciEa3iQhDkpCREFa+w2JZCSFhCDucEZruIUgZ2ldiWhFdr2ithnlzUm0Qj9tJKTN62b26BM5CHJM5IzFeFuhbL+ld8v6VGh52n7GeV+TXbRZ06SH/OY3xRQJ+asosOQWg3Kp3E3EVlzTXopWtF9XxKyyyp7rPEk7mWfFVo9y5gyDtGbkOuIOX0qezE5YVtu2qGNbKlNTqmrqG/y4CmKrZ2rOLNzFWjj0PXX9kkGUya7a8zHozZicdpUUzQKkDNap/BJwE9g+kzcd9TwWia2amIGhAH0/E7nzD4tMQbTbCsHk74o7TZRmtrFweFSx4+7SuLrX/86X/jCF/j2t7/N2972tgMjDFpr6rpmMpmwsrLyiK7HOQyPGwwxP7TSUEq0fyW/0Cmr9AIYhJBISYRNZ3RLKFftskLbFVt8kW6hhfgjK3KMNvnzFOLNGb2rJqaksQ4ZjfYZDtsuVJXvg6dZZ5c1dlhnl2HTKtnAqDpOGNmqhpCcwBIgI/LGIRCsscMxHmKNPUbsk0xKonHVigtagcFolBP0CwbjkjzbZ35tSHrUVDBoPEp8at8n6M3wvZrc96GCaFrhZxolfRckl58DsUZfC9FKjZ9V6NL8p90NI/aihFhlpgrDKjxGeU2YV+SxTxGa70DblERKwi7rjC3JMiNuUhU5IZUVq/KoyVTEXPXZZwWPmgFTS/sMmmoJEbSS6oypP2Dq9ynLwBj3nkZHHulDQ/I0ocwjU/2QYaIIe/YziszHGBNZOI1xEM4A+6ptCy4aHp49ble10SkPCBWUJZQF1Kcw0QX5gvbsMqElRDo4OByEqqq47bbbePOb38zTn/70Q/ebzWasrKwwm81YX1/nl3/5l/nDP/xDBoPBoccswzkMlz0izDRXGIcSIhALv1RW6dsyOT+AIGj9CclZC0l9hbakUuScxTGQ1tNdJ0JKKIegdIVXVkQbc+JjU/wywC9D+sGUQdgK80i4fY29hii4YhmWhqTo4VMxYEKPlIACz5pzSTWAEWuKyRgyYZMzHOU0MZmJoCtltAyUbrM1NUy9AZXnU/k+pR8wVTFjBjzEMcaMTDzAmzPoZYRJjo40flXRi3LizJzbryrIoc48qtxHDTT+RkUdeQ2hscJnqnqM6VsZp1kT6ehREKuCAo8Sz0Yd4ARXc5pjTBgyt70pUhJ2WGdG3zIh/M4SMGXALmtoFBOGTeRiUozYyTZIi4SiCI1Bn8BM95gyIN23PIZSQaEpZjGkVlRJOClCOSgxTmVFy18UW3/G7iMpLOFRCAdihlWJrMC3PSSqCZT7GK/jIdpc1j5nt1Z3cLh0EfjGB77o44o47znwF3/xF/zwhz/kH/7hHw7d55prruE3fuM3uPnmm6nrmjvuuIM///M/5zvf+Q5f+9rX8P0Li2U4h+GyRx+jiiSkA0Wr01x3tnvmvR9CGJowcKXbkHpPGZ9iAxOs6Co7SspBog8xrfDSkc52q/jrDQqCYYqKS+rKw/crI/dMRaV9PGp8VbHGLiNrGFrBpbl9XdqIwmLuWioGjLNRNca0x5xNthgyadblxCaHv+RAS58JSRXkOsKvKoos4v9wEw9wLbFOCbU5d0BFL5uZNEUvZdibsqr3iLwMfCi9kFQleKomUhkFIXPbsTIj7rTHXkQcZURRjsJwJkaM8aj5N57Jf/Jk2ymzYsqAqSVvFoREmGOkkiK2pZcz+szokxMxZMIGZ9hL17h756eY7/eN4NIDmK6T8qiI8/cAcFK13297s1pF8TGmmmKKKa31MM6ACIhK+mqOiT5IHwupuKgrqDPQ25BZSUn1kBlcV7RdLWFRMlrKLw/D8vb6sB0dHB5XOHPmDB/84Af54Ac/yMbGxqH7fehDH1p4/+pXv5qnPvWpvO997+Pzn/88r3nNay7ofM5huOwxxsSMu1rLHuYXfQMzFbzfvj5iHIXSkiJVYhsGKogi6Afmh36fNjhR0gouSWm9ZD/mmI6IEmmYAGcU9XpEcTSg9PvMPY2fZPhJxow+HjUr8ZhROCYjxqNuOjqe4iojdMTYKiAGhEudEI2+Qs4eqzzINTYCMWWXNWb0G7Jkt3qii8iSHiVgDxCUJU++9z6OnzjF/803yesIL61RheU8VBovrfF0jUqMo+KnFd6KhidCva6o+4o0iZjECfveCnu2vjS03SgPkqiWhlRyPTuss8cqPeY8iXuZMGDMiMz2rBDnSo7rMackaByfGo8BU67iFBOG/Ac3shMdgVFGkHuUkwRGCq7tfGeSTlkHnkGbCZBHKcE4C/9N22hybvcTmoH0mRIl0D5G2EuqM0SEdBLAtgf7V8P+Jvj/A8ICypNQnKRlVHYhOg2HlVjKBYljKRKVLo3h8KOD70NwWP+/RzPuQf8lOnj/+9/P5uYmt91228Me+13vehcf+MAH+OpXv+ochisH0nlSnlaZ6nVbRPZpJKE9wLcyiyqwTSwVFJ4xCBFtbyApsfRpJ3ESyEhpjYH0GLIER5176F0PYk0VQ1V7eEXIoDdm0JsReubHXcSY2u6Qnm3bnDRG37P/W0zEIW2MvE9FRM6AKUfYJrEpC+MolI1DEJQVYVFDDqoA3a/R/ZpekROVJZOwT1aHbO6f4cipXejvms+7jzGc3dJBIXaGnfUa47MdhbwfsBJEHKnHpMUZVFETFBVKHzw7LouAsvQJ+iVeX7O6NmY8HBAWNbpW7ISrjH3Ta6KyOt1SNrr0KZvFKF722GbdCE0FQ+b9PlM1ZNYb4V1VExYlZeBThmFb1WI7mBsJBE1IiVawE66zX61QPjGgOuPDDPSOR31/gJ76raxH2bknEpmSFibYdbsKBj7s+TCOW7kFYlBrUJWmJBPsQyZllhLe6kLyJdL9rKItv3QRBofLD5+vzNLF3jkCa/fccw9/+Zd/yZ/8yZ9w//33N+vTNCXPc37wgx+wsrLC+vr6gccnScKRI0fY3t6+4Gt0DsNlC0nIDzClCaKgt0abJwhomIvdlELS+SuHYYeT9SLSJEuI8TmO0HYttjNHL6zwwxJGGtbA82xZYqghhkCVBHXFir/HSrzTmLeBDbZ71I0DIETGoGMWFTWxdQ4kDRGTsc5OR+Z5RkxOv54yrKf41mhEk4p4v0LNgBSyVZ9s1SeuK8KqZhz2mOuYYTlvHaNud84c40jt07beGNCKVp2mmdxGcUlUl6ykM+NgdEWQDoKULB4FfQyO6G0K38OrARRTPyHzg0YcSkiPtdV7yIkpCRqlyJKALTa4jyeYHhectuTGIbO4z3yt1zhpohEhpZ5yx6vap9I+MRm6VpSZIs1C9GaMLoxuht730f/to8fm0aLGRBwkBSHVNiL/IUGwSpspU682pZa1PQYf/FWTsmiiDAUHRxVEKEIiCdJ2U4TIXGTB4UePwIPwUTZ++GUffnlp3bcq+H/OzmYC8MADD1DXNbfffju33377Wduf9KQn8c53vpOPfvSjBx4/Ho/Z2tri6NGjF3yNzmG4bCEsRLFe0jpSKh/EutuZmYgn9THkxCcCxzlbf0goEF0uQ9++X+2ccqib8sloOGe4vmucBgWRyulJ4ycFK+yxxh6ZMgZPuAuSu09sh0iFJqTgCNuW6a+bng3CO6jxGoLjgCkJqW0VHVIRkBQF/XSPsLbllA+Cuo/GCYgeqIi8Co6YSe3KfM4om5uqhycC/4lJswgNBIxtOkNbMSDET1nmGM5eZbdL2WHN+fsr2aiFqiHeKQlKn92NAbOVmEiZBlpdaegaj9QSITWqichMGNpaE9M9wxAm++yxyi5rDcdDohDSgEtUJycM2dVr7OarjPORubbMI/9Bn+KhBLQyNAPJDlTK3JdtWp6COJlngP8P4yjUGH7EPcC8BJ2BntpF2/szA6agJRo2odUJWYZ4GeKJlfa9RBscHK4MPOMZz+CLX/ziQgml1pr3v//9TCYT/uf//J/ceOONZFlGnueMRqOF43/v934PgJe+9KUXfM5LymH43ve+x+/+7u/yzW9+k5MnT5IkCTfddBNve9vbeN3rXrew7/e//33e9a538c///M9EUcTLX/5yPvrRj7K5uXnWuJ/61Kf4yEc+wr333sv111/P7bffztvf/vYf1cd6hOhWOnRbQQqkPEEiCQFtA4jYvq5omGgy8ZKmlAWGr9DtERDQllHKTFsqJWRS13TNVkY1cCWHXkmmI0INYVBY+eO4UTI08s0RCWmjnSAcA8nxS/mkLD3mzX5SCyD9GRJSRoxZZ4eY1By/XxFt1SR1TuhXxgGwTTh1CEXokUc+hagoxqBCzWAnpb+XQQU1HpOfTEj/rwgCqJVHUYWUedDezhiiMKfvTyn9gN1gncr3Cf0CT9dQwTCdsT7fx6/PTW+eRgnTKKHoRxS9EHyoQp+deJWJ6uNTo6ibSoiFZlTWEZzS5zRH2bbtsk1ZqGKfEWfYbBQjt+zrtEyYF8YBAKNOWWmf9P4h6YMD8jCiUCHsgx6DngboiWeM/5Q2nypK5DK5l2xAhXEY7sNEGDKM/S8xDkKpQQtLVjywIcYbnWJyGKIQJiGakrbcQppbyHYp33DOgsOPD0FgKiUu+rjnqLzY2NjgF37hF85a/7GPfQyAV7ziFQDce++9PPvZz+a1r30tN910EwBf/vKXueOOO3jZy1524BiHXs/DuPbHHD/84Q+ZTCa88Y1v5JprrmE2m/H5z3+eN7zhDdx77728733vA4wE5gtf+ELW19f58Ic/zHg85iMf+Qjf/e53ufPOOwnDVp7wE5/4BL/2a7/Gq171Kt773vfy9a9/ndtvv53ZbMav//qv/7g+6jkgsokRrTUXiT1o9ZnlB7ey7+UHVKy9jCWlar55L0rRoiYthDQppezZIcVACodBTi2S0XF7fFUHVHlAGUSUYUEY51RRblIRnSqH5YoHSUO06YisSUt0W1DL9oS0cUJMa+tpw2nwlPmnUAF1lLS3LwHWWodBQvD+RBNMa+P/hPbWTU0baYVGl6AKjcq04SDIVzEHNQB1xFBAVKZRqcbrCkMpW31yGGyUJu8FTHo9pgyZ0W8+y9i+B5oogrmThrcg3S0B9lnhQa7hDBuNZgPAjD4ThuazUjEnYcaAuRWQsv4CeRWRl5GRcvZ8yklENQuN3d63j5VIeqed50EcUOEuSOmkpCbk2RJuSwiUHpQhlAEUnSZUeWyWpiVqRRuuEVXIlJbQK0xLaZ8phBsw/0/EwXBw+NEg8I0+2UUf9xEco5RaiDqsr6/z8z//83zlK1/h05/+NFVV8ZSnPIUPf/jDvPe97314Y2t9CCPrEkFd19xyyy1sb2/zgx/8AIC3vvWtfOYzn+Huu+/muuuuA+CrX/0qL37xi/nEJz7Bm9/8ZgDm8znXX389z3/+8/nSl77UjPmGN7yBv/3bv+W+++5jbW3trHN+85vf5JZbbgHegonb/yghSXIhFhS09HSpZRzQhmIjGgGEhdq2ozTllAqIYgijltkunSeFCX8N5qNu2OHkUoYYR2ID41RItkP6XUkKY6BRgUZ5mt7KPsO1PTzPRAVW2GfNdp/0qRbaS/eZ0WNObEPMIQVr7DWNoaS8smdN3ZAJq+ya/bXtoqyh0BGpTmwVRWpm+wc82bHOSHRKcl9JcqJqy0q3gBPA9zANFicYg7mHsUXdlMw1wE9hjt3m7FS7tPcWR0uuw/psXAf6etjtD9jrDdnTa4wZkqjMpAm0aW2Nghq/SUmgoFImBVGoAK0U+8o4DLusNamFPVZRaCLyJmVRW5dGRKHk/V62yl62QpTkBF7J9J41Zv+9Qj330DOFP69RmenjoX2PuqfQnqKeeUbTYQ66wEQsJhhOh7bPSkEbmehmGGZ2vfBERKdJUjg6M3LlWsJapzCiD90UhKQkRD5SUhLiuTgpaQfBCeCT3HXXXdx8880XdWSxFXetws2PgcPwzRJu2eMxufZHgksqwnAQPM/juuuuYzxuhVy+8IUvcOuttzbOAsCLXvQinvrUp/K5z32ucRj+8R//ke3tbd761rcujPm2t72Nz372s/zd3/3dWamOHz9EFUdY4aJ+E9A0ZTBSebTyikdpnQZxHGzYN7S6C8o6DtI4UBT7JEUhkQbZjv17xK4T8cg12hljCPR1c0lxf05vZZ8gKlCeURwcMSa009EjtouCpCNMP4f9xpGIyZqoQkjBiDEjxqxaBUjhOvhURGVJnFZ4Y43aAT1T1KmHqi3hUgSHlpwGL61NRGCs29tcYSpPT3VuufDnrmZRn0JmzlIFKOH2Lnzaia9Uk4Dhph4DxqAegNF6Sn8t52i6T537qKRGhRqdKurSa5p71XhoX0ECOjDMjzQJ2V/rs5us8AR+wIQRKQnSAluiEhKNKAgbDkiNZxtgjSjDgCoIGHsjE5G4wae4OmKr3mRW97mmfpC1etd8E2rE2BuxX48YpyPSIjEkyMKnTGP0rg8PqVb+YwvDCZmyOOGf0zoRKW2GYRvjF6QhpB4Ue1CKxGRNS6AQYmSX9FjSOg4uuuDwI4Zw0C82LrFH+ZJ0GGazGbPZjL29Pb70pS/x5S9/mT/7sz8DDDP09OnTPOc5zznruOc+97nccccdzftvfetbAGfte/PNN+N5Ht/+9rcvQYdByAMSYpXGUdJ9UhyJHq0gwrr9uwZ+DH7UkhN9m1wTAx9iIgPHMAZMlByXt3cqMtWgxhsUqEFtekLERogpCEvCqCD2DDkvjudEvZShGjekRo3HiLFt/JRa/sGkkYD2qJsIg6QfQPQWMhtAn9uySWOZK3wy5VGHNUG/ItQFdV9RFj7hTkm8VeKVuu2x1elz1KRj5FbPaQWJTrFIDl3r3IeEdgI7xjgMkj6XrM/yf26Jpg8wTodEy6fABILTFUFQEVO0Qlld1c3lJYW6pyhWPYJehPZL60769G3PDYE4DNKXQt4LyXFu+1rse+bbETGscW9E0fPxKQiqnN3ddfan6+YbqBPSIiHNEop5jzoLoAA99WDPRhzEfu/Y+5TTEkE70twLixV+JLX3p6xNeWUtjrFE2bpkiYNey8kdHBweC1ySDsO73/1uPvnJTwIQBAF/+qd/ylve8hYATpw4AcDx42enCo4fP8729jZFURCGISdOnMD3/bOIkFEUsbGxwYMPPvgYf5JHA8nPivSzFMpLbkrkntftYps/KB98OztdpeVDSglljDGAV2Mchq4kdNx5vYbxR0LrMBwp8XolyqsJo5wkTun7MwbMbK+HcRMdWGebo5y26oMDjnKaqzhlZ7uKTc6whpm1zuk1aQnjMBir69vqiNB2opRcfm3deKU1VaUJ/BK9XlNVvsnFA8msMAQ7uWVimLrGXT63tCqQ/hkSyLkG9IaiDHxqX5l5eVHjTTRKOjCLBIA4AjLRFedEXvdoUz1yj0XzILfrItrGoqv2mI4Wlw4Upe+Txz7z1ZDZKGFGn8ymFwwtsqTGayIJAim7rGx6Y8qgYYpMGLHFJpN6yFgPOcMmu3oNXXuUWcTOziaT7dW20lGMu5SN5pgoy7b9vIndLlLR2O27GAdiQutYyfch6/0afA1FAaU4AxKmkTCFCDUJf0fKK10KwuHHiMeq+5SLMJwf73rXu3j1q1/Ngw8+yGc/+1ne/va30+v1+NVf/VXmczOTiuP4rOOSxMTS5/M5YRgyn8+Jouis/eR4GevSRJfLINPPAe1XpjC/stL0wU5BywiqCE4pk0+WJlJgHuonYjIYMmHbtEPMMb+/mxhjJcZhpKl7PnXWw9cFfq8tChZ+gSHVmRp/oz4YcoqrWWGPqzlB38r9HmG7SUEoNCPGDJg2SoWr7DEwhflE5Kyy2/RekJSFRBnCSU3vvhK/rFE90DNge4aKQR3RbcRaKB7btO0JRvazSUpiA7iRVsLCRhbKFZ+djQHZMCAhI5nn9LZKgjO1MYjdx0fT9lcQ2yW+nqR/jthzSXGAHNdlQgu3RDqU28e8iH12N4eMh31Sry2jLAmIyG1lxIqtoFCmIsJGEzLipr2XbH+IY5ziqsaZOFlcxUPZMVO6qX3KWWLSDMqDFQ3TTqpBCnNG9ho1rQaDoo3W7HU+44CWrys/gmcw0Z4a42gUBaS5cfaaPNH9tGk58VJke5fL4ODg8FjjknQYbrrppqb84/Wvfz0veclLeOc738lrXvMaej1DP8+ys0Vd0tT8Css+vV6PPM/P2k/2lf0Ox9/TJvQFP4XR0H2sICkISTmILrPEq4V40O1BLT+iNm4uM9+AtiriKMYZqGnlnKX6UnpXiQhPat97wJYls8WKuheiS0VMSRJmBH5FTtTIM3vUjdyzaUutmNGn32m6lLHo6Cl0k3KQ6ocBU3rMzBy4LgnzirA0c2SVaRiDP9fW9GlT3ZDbz1vSaibM7GffpZ0NSyOlRmXQ6AVNvgXZPgxXoWerRfy4YsWbU9QKnVforCbLNJU2tBBvWXywtOOv00ojd3spPIRx4iTfKVmmxRvSFgvkUPuKfMMjXQ0pEo/CD5p+EX1mDSehwicib6opRHK7JGBOj4icNXYbxwFgRdp7MyQJMq7xTphumDoiCxPyUacJVacQoa48inloKiq2I6P4aFuVNAJXT2n3b6oopFpyQqOOyQ20vaZmAcw8mBYw15hW7Ec5O0SUdQYUnWrxwLqtNR2uPHwX+N9L6w5RPrqYkEjuxYaLMDx8vPKVr+QrX/kKd999d5OKkNREFydOnGBjY6Mpqzx+/DhVVbG1tbWQlsjznO3tba655prznFkMVpwAACAASURBVPml/OirJARSdy7df8QLgEWPQMrLbL7X802NoTzA3aaVCe0MNqENkfdoAxWr7WvVq1FBhRdX+L2KsFcQRTk9f0aiFksgD1tiS1LsQrZJGL3PlIGeMahmDOsZfabEdQYl+KkmHFf4qYkaqDntrFR8j64WgITLxSjtsljaL9tzQ8TPS8hSKGS2Kz6aNj0kwlmOlxmnQtO5pyOaCsC6NNtVAt6qETFsSKQBbeRBJsZRZ5EKYIkqyFcq8sorZr2vapIsR/kz/EpR6RlhUIBnekvUno8OoPJ8Y/S9kNwPmKkeqwxsnUmv4YvIe+GI9Pw5sZ81apLzqEduG135VE2L7Rl9pvWA/WKF6XhIoWpKFaEnAbq2Yk5D4Am0HBJxNiRls2fvh1RN7thlz8pG9zCOQzqATNijpQ001J0Du7Wc4nXUnC0j7XDl4BmcPaEzVRIOjx6XhcMgqQPP87j22ms5evQo3/jGN87a78477+RZz3pW8/7Zz342AN/4xjd42cte1qz/13/9V+q6Xtj30oGEWqUNtFiSEYapOMNMUyUCsQnqmHmvYohUq5Hgm81s0pavb9hF1m9gjJKUV27qpqxShRXBICXqzenFc0bBuCmPlJbSa+w2TZ56zFllz/aAqBkyYZ0dPOpmlttnxhq7rLFLYo2XQhPqkrVsyiBPUbWGEpRIMp/B2ISDlH+FczHDGJ1J5/V2571EFISdr0zkezKFcQ5HjsDKdXYszxyrJ5DPDP8u6oHfCfbUQJ0bB6ZKIZ+Ddwzi643Ppk7ROmNC/JMUhThtXUjTppjWT7wOvGOaZKciGVWwnkE8gfmWOUaoLTYqoW3GSnuKaZSw2xuY8ksUZ9jgFFezyRY1Hg9xlFNcxdWcQKOIre6FtvTHlIQSn5EeE1Gwyypn2OCkPs5JfRwdeFQrAYSge5rqhwo9C1qC5/XaSmfbDxrTymwLoXTbfreiEunZ16MQqtA85jsY5S0ddgogevYmiczkur0J27QCIg4OP0I8VhyGS+xRvqQchtOnT5+la10UBZ/5zGfY2Njg6U9/OmAiDp/+9Ke5//77F3QY7rnnHt7znvc0x/7sz/4sR44c4eMf//iCw/Dxj3+cwWDAy1/+8h/Bp3q0GGFCsyu01lEIj0cgSCAIIfEgUW11xFUYYqOw/tdoSX3Sr2pAS/5bBwbadLPcB3q6mf0KNyGkJCNeUGz0qRrlxcgSFAdMWWWvcSRCCquzABOGDTlSSIwjxiTenLKnmXkB8W5FOK0X/7P4GEOznK4WfZ/KbpcognzGhLYKr6Z1JHrg9WB1BMMIggcwKYNd2ui3RVHBeAzlGDOjV9bn0KYtQg/Tvys4A+nUfCVRYvinjRGUdtLdfh4CIaQO7Ne9SduzQzpBRrScAOEQzDqPRAFqDMXAI1vzKWNN4s2p6TNmBZ+ao5xmmyOcYYOQgqt4qIkcbLHBDkfIiG2PCUWuY07lx9gp1k2qo46Yz0dkZd+oTs4D9BmPet9DZ16r21EA/6nalMy2vbdje82SjpGIw/0Y+egux1H6bOjOeg1tiELqWWd2gIMEMRwcHC4mLimH4S1veQvj8ZgXvvCFXPP/s/dmsZIlZ73vLyLWlNPeufeuqq5qd7dtbAx44ABmuugcBNcC6Qq/IWHOI5J9JWTZBsODhYxfEObB2DISEugiBDxYSCALiYeLdSXgGC6gY8tmMNh4ALu7q2vcc05rjLgPEd+KtXdV281xuekL+UlLVZm5cmXmirVXfPF9/+Hxx7lz5w4f+chH+MIXvsDv/M7vYIwvyf/CL/wCf/iHf8gP//AP8653vYvFYsEHPvABvv3bv52f+qmf6o9XFAW/9Eu/xNvf/nZ+4id+gh/90R/lL//yL/nIRz7C+9///oeKNr10QtyedomAA5kBZamagU2hM17bX8q+UqQ4wicKYlFcEsWXRO9GQGwl+GU3/qpwoLUlS2vStMFp3aPxpcUgfXJRcIyJRd03UYDeVdKXvz2pT5IGcZhMbEe66kjWFl06PyP3vW2eHwQvk5JUI1ZE5oNM2BArDCm+qhLaAYkJ8quvxPfdByFeU4UDbaGrgDPolpAHIaIs9dWHbAI60FDNUBLgYarFQp2UECqrVHxk2yVqYCgu4iEkEQz/b9KETZJRjwxNplnrcSCvippF3JZMe9VIwTzc5yqH+LadrzCMWDHmVM85MzuUXUHV5ViVYtsEe5Lilsb/RgPMQWUdOm1hpbD3Ep9ECBAyJ0JtpIsg7aIlETQpLMkh66QjtCKkVCRmHaKuJSwJef0ltizbxn/8+EbpMLzEumsvqYThJ3/yJ/nt3/5tfuM3foOjoyN2dnb4vu/7Pn7913+dN73pTf1+TzzxBB//+Md597vfzXve8x7yPOfNb34zH/zgBy/IQgP89E//NGma8sEPfpA//uM/5qmnnuLDH/7wQ929Xhohy9ARfpk5I9ZrBXIvjxuwCmwGTnvpXVHLk97wY/jnTvEX9AE+iZA++Zy4UBP9gSn9JKcDjL8mQ4dqglAcRSRI4cLjKP9ckQduhxdhEhEn0WfwlQkPdkxtQ1Jb8lVHurARPCjyxAL8GWpZDamLIl18Dz8pDYFCgl04D+dD2goiztR4kcL2Kehu0FMcO7wMQFf46ngD2DWYu5AewfzMm3EmE9A7+AqN4ECEVijaA5cTBn1pE1zEk3jIjLAq9sAVoTVTE5O98B6XeFsGmymq3LDMc0rjVSHPmHPIlT5BOOQK97nau1uKELcNwk4bRj17xQs9JdQqY5RuSEzLqvY4iDb1zh/tSU6nMtqZweYGVyj0uCWbrnHnmpoJ7kz78RiF62wRxuBe2NZ4cSdhhIgCulBPxfmyxy5U4MpwnQvdQmQlxe5aMsXLJ90N/h0mFJZtgrGNbbyweEklDG95y1t4y1ve8oL2fe1rX8vHPvaxF7TvW9/6Vt761rd+PV/tRQyxrB7KK14LzwlacZ+45AxLsDaFLmAYIApA1ngjIBFoEpS6iBGd4hOFA/ykJ7LRE2iblMXxHtloxHR+Sl10HLHPDudc5y4JLSUFho4pSzTOI+4pGQUqJfgKgwg1rZiQU/VVCuUgLzsmVeNVDqegDsP33g3fU4SXhNQyxDKc4Ccd8d0SoJ0kDUv85C1zgpS7RQNhH+wEzv4Szp/17x+iSFYKzgJDtXQw6eCag1dYuJ7D/gxyEXeSRE06RpeloSUkIRDFzT18oiGbjE0DSnB9Hb07ppv4x+oI2kxTPpHQjhS5KinJOWafkhE5FasAeExpuM4dykBhjVgFD358gptMWXKH69zjWgCjrikp6LThsfwuLlecsct6PIZ9qJqc03rOupzQlQWJ6RgVG6wxtHaEveqi9PN95S9pUc7Mw///C7FNJGWpIXtS1DTLxJ+oZu6PtwbPEV7gWxKn+Oy4IkqpPyyGmg5yMa0eMkjb2Ma/Ib5RGIZvRNXi64iXVMKwDYhLX7lbygwv7Qmp1Qef6iKDcQojEzEMwo8Xrvz40jYlaj3l+Jp7AWbcke+uSKcVjLzz5IQ1uSlJ0yqYP63Ig4tkTsWYda/CmNKQUTEK05AoPGbUbBgF4+Wz0NZog0pAic0cJYZ0YUk3Nq44RbhPcqMzLnoRCZhQ+t3CtHP4+UO0Eh62yg999OYu1CvPkuh8ToMjql8UwL6BV48gGUM6hWIMsxxGmYeP9BYH4XO6U2juBTmBQSSZt/PoWxLXwibvh+js3BCxCvKbgvGTEoZtAqZwFLdb7FzBPuT5GTNqVqOC1axg15xxnTs9b0UEnBpS6mAJ3mGYsmTMmjVj1ow5Z6dnUQAcKy/sfcIeJ2qPUz1naSZM0iVtkeJaQ+VylnaE1nDlym2qcsxiOccmYPYa7Imhu5/BVRX1G8DP+eJsKUUCqRxt8MnEMXA/uGZqILPe+dIm+My4IBpSJA8ZcOmDCMNIwBJbp8ttbOOFxjZheMmGlFilXj0sqTrvDWESyFOYpDBXEa8glD6h/4nGwtAzQp4X8GMBjBxqAnrckWY1eeIrBQUbMhpGbHq7arhIj4ToPimuk0l4vsNQk7FhREIbnChrQNEpQ5PUNCohqzvSzmKKDtN1mLVDr12kJi6JgkuCVRhS8kWsacVFQyP/5eKpFBZJ6P6oBvI5qAy6NdgynunWRb+tHCK7QUFnYV1B10JbenHCtvKti57hN/joNIU09z5guVAu9eA3LIgOj5aYMAzDENkXU9DTcI5CcpXOSvJJRe4yMgp2Qx+nCenc5YShuTBiOQ6vapnS0LqEyuZs7IgjDjhinyXTXi1jY8e4zuBaj1WwraGux6AhTbsoUCUJ3jG+VAOREgsREFpd2iRZvMAoGSbS4kqZEpkTUnWTpZkAIfoRJWYjQ6OPbWzj64hvlA7DS2yGfol9nf9scVniD6Kio8xqu3hE3g7RPWrP+0VkKo6g6D3J4xGRTiliQOPwWkeU6h0DMwXXHJ1JWDU7VJuC6d4pauxoVEahCuac9nTKERvmnKJwvfNhQRk64N4cyaJZBdFnYUzc5yq3eLx3qxQswy6nTM0SNXfouRd9Krqa0aYhXQUp5jMugjXllAm7zoVTdoqvUAvkQ3APwh4R242Aj0tvQNrB+BjcEVS3giYDwfahg8rBmagZSvW6d1aM6sgr5V9O8YQTg399FD5ahblsb+wTFFp8L1/aJXfD7xPAo0yc8lul5LFDxGEoYB/cFFynqI2hvJLQjRRZEC/ytEl/oIqcipxc3CqZs2HEszzJ07ycU/Y4Y9fjGtyIz9ffwrPNE70nhYRD4VqvCOlWif/xjR956zQrt+d1GSzwLNgvpLFwJkQfiaHfR4evDAklVs75Bl+yKSxUlRfQ6BMAAXmIfarogTtiC0J6cgKi2FYWtvEIYwt63MY3NmRGk6WW1OGzwf/FgECUHmX/JrAeMg96FCrhPXq2JWv8BHQd39qVvm8x2OQCF778Eth3dDplebJHtRkx3lmg845j9vs2g8b21Lw8TEGycu1IWDNBY/uWREPKkmmvy5DQ9vbLGTUWw5pJIPhdosZ1RFB8RxS7lBaAQDxOiUC6odaBUBilHVMBt4gTkVQnDkC9GrLXh7YBnvo/3gF3WRB0FT7r2J9Xa8EWHkLSFV6bwZyBCkmJluRhB5j71kRPh8yIysfi7fGw1UpKxHQIgeYx+qpRNc1YzEdU4wSbq1BByC9UDoALbpULpnSBJSFjJiZhh1yl0SnX8zvMs1MqMtpA7RC/CucU2aRFH3gK7MStOOCI480VPnv+Bs7v78Id5c/xNWL7SECq8lgYH1IVkgqLiDh2QOe8YMa69eZU/YVx2a2yIVJn1oODSX+jI2qhD13JtrGNbXyt2CYML3po4nJXZo7s0ibJgcgOSh9hBDoJIHB9UT1PaJPSmg3mlZzg75W9HTXRgVEOnYd9HV5QwDiccZ6L36Qo1WGSEqs9uh64wJJIaGlJqMjpMMELcUOHoSTvaZTevVL1k5fGBmJf2ycfhSspmoa8adGdixOH4NhkUamJLRbxdEh5EPAup1dUhS/TL2U+acEtAks1/FVYDXXhmQiZAqWgDmD9bAE6HMM4MMFFnBSMCjZhYgEydP+c0IMX+zGRKrokC8OQ3FHGaxp+d0gcbKpo9jX1rsaOnVd7DMwHH6p3qhy6VQ4dK0sKGhJyKuoAijR07HDOjj4PR/HXQ0tKF1gIDSmLZMaaCSUFq/Mx7e2MFRPSeck0dzCBrjK0bYJrvBKkPUuwp+mD6pxCGV0TdcvycA2fA5n2mIWGMEgaXBsoLR0+uRaUaEVUi6qJf0PDMpEevL6NbXwdsQU9buPRh1xVgjwcPhZTAWFGpMReq0Dw8LQCMzicTEhDa2Q55D7+5ioYSUVs4YZNKQuJg0TjclDiTJBV6Ny7PqrOkRnf8RbfAtFRMHQ9HQ8Em9cG98kGh0YH8SZxnRSsg8c++NeEYlm4iqztSBqLlu/ZcRHCYYm9fAEFziBYJFwM2f+cqCgc7KVZ+eqA6/DKjhXUJTQ12OBfVcu8kvoEYC199TToNwwpnKFS4ILUts686mOvByFjIZWPYcsBLiY7vQjEYFwlJNHbgE0UbaLBWEZNSdJ6GWerWjAlTdC+2OgRaz3qgY8KR04VKgemtx1vg2lVwUlksuDIqILdmddwSGjYMOZmqA5V5KzLCc2dETY1qLkln69hB5oqxZUZVmi/Zwo7S3x1DHzF5xZRbVQRJbRH+PnfKi96MXT5ZBSrMz2uYR1OmIAgqsEbJDsU9ag8PJb2xTa2sY2vFtuE4UULRRQBEHMBQRue8CCeQbAMQrEM/3ZJcA2cwu4+TJPIehiK/gxL9rJCHVYXxqBmluRKidpp6aoC6wzJuETnNUo7Ml2zm58xSkqUEu2E8oFNtBi87PNJLw09Yc0upxgsS6Y8xl2ucS9IQtdU5DSXLkGrFeUoodGa0VFLWjlfis/wLYCO6LYpqoyjcEqlNT0MmS8EcyC21JWvKDQbnyQIHsHhk4VV5+eovSmMJ6BCRWAKkPjHCi5SPAOOri2hvAfJFPI9r8DIajAW53hlw68WCVG2+xQ/t+0RtQtmwA1IrMU87XogpDMNjg0U4MaA9qncIh9zMppyRd3HO1vucsYuV7nfV3wUjm/iX2lJOGOXFRM/JqEFsWZMiXfKvMc1NoxwKHY490nflZtc+/773D+9zt/e/W5Oz/dgDW6tAmbE/+s6YrIAEdB6hP9TkI7DJjx/GM4ZRIuV4Tg7OfnHxDaDlJMkGVjiEZfV4CBCr9wmC9v4OmMLetzGow0h6YtH8D4+KZBygSyvhm5RUy72DlKYePoc4xRyfbGyIAum83BYqUBI2yEjTpoJuI2iO8xQywQ78eI7XZOhNOSTFVlR0xlDrTJS6gALcP0mE4k4TXq3ygIXOuceELnHmDUT1hxyhVPmvQGSxvbqkKLNkNIwViuKtGKzZ0mm/jnTdfA4mJUjW3TohfNtFFk8Vni8hggADnUXhq3qx+j9vJSDZB1aC8f0k5ILi1kCCcV0RC8LiDTPHJ+8yNCGfrzZg3wMuvD2Hn2X6WE3FTV4XS6DC8yVwWWQ07NpXdinKxRNZrCJB8C2ytCQYY1/XKqCNRM6o3AKFsw4Zd63kuTxc7yMWzzeK0CKONeKMatuyno1Y72aUlU5dePlo9s6gQW42u9rjlue/vKGUo1ZXZ1hE+MndhFsEkajXKuSM0jCJsJUi8EYOnzCKDgSgS5IZaZX1RQVsowoGz0mUmZkwASiKsYU22RhG9t4obFNGP5dQmqtgVgPxBKptCuksiD7hVlf4XWKrYNN59WEtBnqMMeWxHhwaPD3UKEVOqBR2GXiH+8CU4ctEjoHXdLQKEuXJLSmoTAKrRw6tCCkqqAHNfk26DrWZGRU1NTkpLhA1fC4Bx3bD2GryAPVsunZFq1JMOMu0DBN3/7INh3JqUXPXJykV0Qw3YoIqKvDKeyICZTAREKbwCzALIml7TBJZRmR8rjCr3JX/Q8lsEIjZlUErxKfKOgJEeEsSdtlwykJSSpkMp2GbUiBFeLMGNwOPrlLFE2iqTNDa0xQaEwD4DELVNYxKyY9puGMXU6Z97gRb3c95x7XOORKGJG8H68lE1ZMWbkdKjcCq3DWv4YFYzu6zrBhhGk7aBRtnqDGLXpksG3Q3pYugeBPFDHRu8wEGZ4nMeUS0TGBGwT77wf1meTNAnQcIi23ycE2vkGxZUls49HFUO55ROyjBnvB/iYnFQg32MqAngtViZXx5H8XJuo0VBqOlS/Jy2Qj7Qc5pLDNDojGRRUR23CEp8ZddXRJwup0F51MerfKLtfYxPSAOsEiSKVgM+DJKcYoXE+n9JPOuBd08vu4HgTZkOJQfaXBYqgoKNjgUNRWwJQl6M6X3uXKrYi0y2AqRUF0uZRVujwvp1WAdgIUfT2evSoVC5lvjvCJwmuITuNDMSGhdO7hqw0vC0N9WaJ6eCkQ3nO5CzW0IDf4JG5G36Fy+2FsFXRGURYJbaJxygMdK/K+OlBScMa8P7dSSRCw6jF7nDLv2xHXuMsT3KQkZ8OYE/ZYMMOhsEZT7hY0u74M4sGsIywqiHIV3OU6mat4uXuGZTvl89VrOC0PYF3gOu3LObcVPK1wZ8BS+XN3SCyqLcL5lPMhMtgQwbxSLZLKhYAlafBlDNmhxP9BDPmasrPod6+4CJDZxja28dVimzC8KCEzgSxz9/HNaQE9DmHw2WA/DTrA73WoaacKiiKyIqYKDpR/m6zGhliFCq9LMCf2w6WfnuMn1gJfzh8BlQogSffA1dFiWAfxpTmnvTS0unTDHbNmxgKABTthzVr2r4sZlVQoZiyYsOoBlEKvLCgp6sq7V24CIbCx6I1DiYLjEGEvFt6SJE2Jq1mZjM6I/hQCntTA/wT+Br/CF6XIc3z1uiFO5LJYFUBmw0WA6SGxOHR5xSHVHVlVy2OIbY2OSJ8Uuuyu/3wVVuKuAKMso3VDmxqq3OBM2yduS2Z0JKFao1kw8zRIFGvGLJj12BFhRRxz0PtMWBQGy5h1X6koKHtdhw6DoWPJlLtc64GQy2rG4fnjtM5QZQadNKhpR5q0ZFlNt5NQPZ7R3srpnsvhumdNcD9s8rsFnDojJgo1/voV3Q1LFCpbAlWGz9hE0XEMPE5kTQh6Vi4aeXxKzPq2sY3/xdiyJLbx6EJmFvBXgOjgChl/eDOTIRHQY+EpY64FMq/wWAETA1dCuVfKuMLGFLqhPJZDSYtCD/abDF4LpXOVW3TRYMY1eVGSpjVaC96gJKfCoWjDd5WEIaPu2wxDuqQ8Pw3+iULDLMKxDF2/0nUo7IAG2OkWl3fgLKa16NpFKWhpCWhiK0JK1KLquMIvPMUF8RhfNRBnRIkBtdF10N0Bew7JKLQXZMW7IFppiz24WFPL0ImW0OUWxIDC2bcwpAohRSRRNZbvJm2Wjp6GqcLYmtSh0g6VOppC0440NqmxAU9SUoSKjQ7nVQclxy60jxIc5z0qReG8GRgNS6Z91chXFCIVc80Yi/Z4Eza9omdtcla5T05UaqjLnHqR0pYGVWu6pcGeJ7gTAycq/vbLTqRy85Xn5U9D2C1n+HYcG6/JYEV4QzKxYWlH/jCkyiD0k5qo47CNbWzjhcQ2YXhRQihdMmMLOluwCnI3NINt4v91Kvgqi5pQGDID7JpotjQkVchElhD1GIblXSlyHIR/Jw5KFVT4HGpqMeOadFSSZDVJ0g5YEpu+stBhyKl6fIEYFhk6LBoVqgV+jRppmC0JNVlPr9wwCuqDft+ajITcO1MkOZvdimJaU5QNadKhO4vuHLqwaOdQtUPdw+dh4rkgQE+h3cscIhgB0XWQoZEepA3UfuXnIf0YaGkFiJokxIrNNL6vD0lOZNEqyYZ0nwRHISGgVWlByCWQew0IpxS2Uz0w0tYa14UEyynaOqFxhjI3A8Ci7scoCxJO3nE0Vg4q8l5uy39Nx9iuSF3Dbf04lctJyg7qlsZmWJtQ2jELt8OCHSqXk9qO1Pk2U+4aMluzYsJCz2jORnS3C7q7CdVdfOImhCCRxe7dKInVoiHGYehkfUQUIKvlDdafJBSxx6QGB5YLXh4PRZ6O2VIqt/FIYlth2MajDbHhq/GSfgf42UZq3aKqJF7JATWn1YBMobyM8xX8vfJzylddXxfeMiaaMM3CR0iSIOqOhOdEzOgQOFVRDXEQXWcoy4I2SSjyktpkHHFAFsCJAnycsGKXs37il4qC1Bj8ZjjkgJq8N6FaMelxEBk1Kmgy7HLGmDULZj3WITEtxbikKEqKg5LcVRSqpDhpKG62mLGDNxArCrKYnOLPkcwJu2ELKoy9fPTtcC7w2ljZrh8CJcDHO/jJaj8cR4Uh2g/vFxFBiGBFOff54PHlqkOQdu6TOhPokOGysCbQTFPtsQvKr/ZbElAErMIulcpBw5oxZ+xSk10QyZL/V5fcKs/Y5YQ9nuVJbronOK9nrJoJSd6irWP1L3PKZye4tcZuFHatcY2vVtCqWO134BrPvHFa4UYKq7XHL9wFniEu/nfDtTlU3xRIwYbICBlGgr/uZ/j2xXngnVoHdRAx4yoxWxOVr6F7pQsD+RUuKj5u3Sq3sY0XEtuE4UULqZ8LniEZbBI1sV0RGtw2CR2LHJj6G6WA8lr8IumzRKCjVBaO8Fx/MZiSVawg7ksumjEFy2SswjWa9rxAZRkU0KaWxkzQaYvJaoxpMUyZmhXz9LSfuNKA0Qd6dUEB450zCwVxx2nwL/BnxYUit2wbTpmzZtwfT47ZqoTaZH2JfMkUs2NJnrIUrmScrDGNNz1KlpZs0aE0OANNpmlS08sxpxuPi1Ad2E6xfkXBWuWeVqgT796ZdIxZk7cVrEFvHFndYTbOtyakFe7CORY2hAxrNnhseDBZyMHJ+R9OkCm4RFFnmibVdImm1aanPMo59boIo97YS3QS6r6mE8+gJA4i4S37eC2MlAkrXqaeYzedsjQTlmbK2k3IHt+gd1poQbeWtG1Q1qs8YhVp24AlPHakbUNlc07dnOp8hD1McS/X8P0O1ipe3g3RSEywNxArMZfneqG2LsPzKJ+wdAyEm0QWGiLwUfoaVTiAAHiG9MptsrCNrzO2OgzbePQhoAJBHMqSc0iv7Ih91QDLd5oe3i8gsIy4KhUaoUz80/D4jGgwJT13qcoKhW9IvQw9dVdpnNH9SrnLHE0CKuswee3bFFlDrqt+pSorWvBCP4JLEICcaCx47wiv3+A/tiMLOAYdWBMtCUOviSKwJ4ZtjV5ZMlXYHU2rNLXRJM5hOhclmKXVYBROqHhSDRD1xQ7cHti5wnbaKxJWoBqHawMhJVR5XE0EVwrAksGQCkVy+O/lRCE87+Q9MiFeDkVgyNCflyb0JRw6nM08eH9mF1gSQrEUIGMdEgdJOOrgAiIJhLBexmaNMV0o6js4WPpqAD65k/ZFTUbnDHTQdgld5zEQaVLSbhTq1MKxizhecWY/XqmPWQAAIABJREFUI7YmzomCTHIOBYYgCW3KReKQXKsC8pWug1zXnfJJtpPelJSaRPJTE0s+0r54oXVfoWc8bLC2sY3/+LFNGF70kErDsP4vNzBJHoSEr0J9PPf/yq4joj21l/WPIYQLKYdv8EnDk8SEYUHEOQwnM0ukGcpkuwIaBVOHaw1dV5AoSzFdkmSdB7gFsJxMRtKSkAlKgI3yuoAdwctIH3DMde70ssU5VVCNPGXGoveikP0FVAmQtpaibLFGURWGpLUUmxbtHEzBbbzSYrb2Gg4c4yerwapeG8dss2F2uok+BtIv3xABlpeHUUIwdTL/TImti/rSexRw4LUUAOi8WRVr+ta7m4DSjnzTkdaWTZHQZg6l/Ab0qpsNKStGvWOooaOkCBZTwfmTkiUTTpkH4OK4BzmKv8RdHuM+V/sWUEbdO4qqQSLnfSd8xaKxKafVLqtyRrseeV2mUYU7M3TP5NjW4Ea+lcKZisBORWTmuHCuj8L17PAth4bomXHkzxNX8Emy2JwPr/tGwzqPjIquCAMh9JihW+VdIn7ohbYkJOEYqoJtYxshtjoM2/jGRAM8i2/qSrUhmBVcoFUGrp4dQaUDNH4EVQ6rFIz2qoVCP5d7mIjjCGbhGvFGfM6DWAZJPuSCH4oIJcAoyA4bSPOK8c6CtCgx6QOKOX0FQSoAUiEQcacZC2ac95NZHl5fMeZZnmSPk94NsyXpWxPCvMioSWhZMiOhIafGpYpWp0zakr3NGVnne+9UYSIWmr34bYzCOTkkcv4dvn1TEo0ORVVY5hXxrYDYyhmKdAouRFoLogo5jBl+EnSgTomraSmrh3aJEuweoJVjpFvS3JHMHfWoDof3WAWF5Qa3aUlYMqMmY8aib1P4r2f7selCxcG7Vc6YssSieZzn0DhmLMipuM0NztnhZTzHDuccsR9aSWM2oQ3S6JS2SNhkI86nu5wsrnDv/nWU7rjybc9R25zT5QHt/czP0ZIolER84rBrICGsk7thjCZ4fQvRYRAdkfVD3tvHIhxAVB/lzeJuKcCJF6LDIOIbcoFsYxuXYgt63MajD1luik2hpI/S8B7qMYTegspCXzeBNPGOfZnyF9IhkR0xPKQA8gJwjwlRKEjaEyLPC9EpUZhnQvlz+D6xdqDAWU1TZ17yoGtxiUIl/jWRivbGRG1PnZTHwpyQBMAbHzV9idvQ0qEDQ6Ltn7/saCn/l1ZFoSrypCRRLSsKrFMUtvZJQ0ssWQviXuikljjxCJPiPnEOEaxHSoSWiL22nGup9gz/iuS8DWMSPlP0BWQfoXLKMcXqedgPTRTdrsYmkK8adO1YjMc0acKEJSN0r2cxZt2fdzlH0iYSWmtJQUVOQktOxYIpS2ZU4flT5li818TGjjkv55jScV7PWDcTmiaj6fxI2EZ7ZmOXskkLVmdT2mcylHKUV8a0NsWd6Sh1INhe6bqJqFZDTLDE9mFB/HOR8ySKnnIcaUkIRfaCUJb4oDM4uFTwhgMoNJmHJQ2OaGsq+21jG/95Y5swPNIYchcvx1Dib0oEIIiOsyQLU6KCjfYw/UR7NcdCwUj5lzb4QsU+8BRRRVqogsJ6kDbueLCJKKMimvzIvVQKB0IVrMOOiaPrDPV6RNcZUrfBqI7apBjl5ZsFyyCVBaFPSoVgwYwWw06YpqQnngehpiVTLLrHK8hEOPxXKgs+4ciBM6YsULpjk2YYZ8nbh0wAFX4SErto0aA4DcOwBldBlxisUiRPWvSehRLcAlypcBvf11dSnc7DccQHQuQ2DH214EJCJ4ZZQ9afIypRSjIh2IYEXArtRNGODeYMujqlzEZ0qWHKkg7TKzpKm8Zi+kRL8AxpSM4axJ6a8LrHk9zhOveCCNOakVfwdHBUXmOxmIcVvfLno/EYClcqOIWu1bRFilm37Nw+p2s067tTbGdIygZjw/foDN0yUIUhgkJFhEucpsVLgnAepJogC/wxEXMjiZcUDnp7iKHR2zGRoiGVBskmN1w0G5EQVPGSB7UdtrGNS7GtMGzj3x6COrzc8Ia4vBdwgDwe+vnKauYQfyedQDcGO4G6gGXuQXBDDJf0xYXjL3nHAk8V3A/b0NZaFkrSkkguPc4Gx5y4/nGa10zmp4zzFYUqMapDKceEJXPO+gnL/1rXJwLSRxfFR2lJ+Md1SAS8eqQoQEqPXhQf80AITGj7vrokEhrLqKnZLVekXcAvXA6pGEg+J8A6jaek7oD9Vs3ZjTHVfsZus2LcleCgU4oyS3BLxehLLck9G6sF0lIgnLs94mR2BU/pFACqSG/IXCYTpVwGw0vFeClotesoVEfZGO7NDyjTlKleBCXlGSsmrIMUt+BCAFqSS2eu6EGODsVdHuMO15lxzoxzGtKgsunhkQqHNZrNfEy1k19QK+9ckKF23jl16Sbc04+xa0/5L90/sGLMP6hvZ+0mFK6kDayW9WLG6nSO7cIgfEXBv4QvvINXJL0dzuHLiO6VEGmVcm2KgONAsoQzBqQHKWNUxKxiSK+UAzxfS0ISCdHolvLGNrbxnze2CcMjCWleD90nHxbSS5V9pcIgj6FfkupRaEOk3jrRhFkoIfZ4Z/j73jlxZSoJg9Apd4ngMak4DD9yyO4U118bflIHbJTnOuzUJOMNJm3RRrD1fqZ0oRUxXLmmNAGdn11Qc2xJmQR3iVFIIoT8J5WJyI4oe0yEoe1zo5Smx0UI+yKnIrEdunZeQlmKDLL6lG2Jv/dLMUhWpvugdxyTuxXF7YZs3aBCSVzXkC86WHhqZW+gJJvkh+J1NMcD+hK8nYEIE0HUbBh+B/muQ9dKB6qBttKsZwVVljK1a6a1RWUtrTGk1Exx7HAe8kfPnFChruBBjd4YStpDorsAsMMZLQlHXGHJtAedCtWyJUFrC9rTJqVSYdFsKFg0OyxWvrphJjUbN+ZvV2+kdAUn+Q6tNlThfR2Gpk28YGmJFwq7BsycTxy+Eq7XVxCTOpF+XhFFUEXQCTx7Y0JsNxkG3QMNbRbYEgI0FjAPRO9syfzkeckCh+5jYqUpfaNta2Ibl0LE1r4Rx30JxTZheCQhfhAPqywMQ8oCslwTlSV5n9ArBcOQ+IqCxgs4QbwwZ/hVmFDLh5UGAYiL/pNUDS5PckJPk0lKvqK7+Fjrjmy0oRityUzVr/Jlda/wvPxhEuHfqnpq5RDbkIW2wjA5uEyXBPr3ybELNgEQmYYpqCOxHeOupGgbTwOUCUko+ZIoyDwgrWjBsEll+tQzE4rTOopuholL1w596vz7hqxYSdAu/xWZwXeQds9QxFO+09A5U5I4eSzCWp0XQ9KtZaJKdGJZJTloxbRbh89zNCq5oOwYnDd69oq0JhLavrozYs0qCHaLn4e0LlZMEOvylIaaDIvGW5jnlIw8O8YKIHXD2k141j6FpmPujrBOsWaCJzh2dLqhzRq6JsE5431RxM57Ojg/Mk4BINo/luqYiG7lRLdSiH9aHV4y+oKdpeAWJESXe5i1CUglXoFxX0nwhyW6bWzjP1dsE4ZHEjLLPl8rAi769EoITBxis3sKtGA3UE+gyUJCIMBIE0uyoj8DERgmvP8Gv4AassEgJhAQy7xCr5RcJR+8PnWY3ZZiWjLJ14zYhIk9UhwlaRB7amkXyGPwCoRzTnmMuxeSAtFkaEj794ow0zA8wG8daIN5TxlMWsuobMnaLg6FVEqCcnD/W9Lwe3PgM0SiigL39/jyuNAbnwz7yntH+GrNyOMKZMhUDkoeT72eA/dB/VP4LgUxcch5eF45Hjwvl8mOP5bRlt3bK2jAXYVqN6Vqc1ynmG+WoOBkNGWTjPuEAQVaWQyWsVr3qpmJ65iqJU/ybO8NscsCQjtjxIYdzhkNxLNyqr6q0DlD6Qruu6toZdk1pzy+ewuAW+px7pjrNAeaXXfGt7gvULoRX+HlbNwIHB6joqFmTOsK34J4JqiMfgsRq7AzODdyqXSD11f4qtohEX4gm+QJtbhUVTw8pMIgWYaoRAlC9d6l1y9/oW1sYxAvEQzDL//yL/OLv/iLvO51r+Mzn/nMhdc+97nP8bM/+7P81V/9FVmW8WM/9mN86EMf4sqVK89ztAdjmzA8khAu49CfGB70Vh66U8qsrC/uozNIDeiw3Ep0oEIqGKtIi1wB/4zPL67jcQq7xFXq+NIGF1kSAsqTRZR8PUGb7znUboeZ1KTjDdrEyoEg8KUiIIh7eRwpfIYVEw444oBDMipWTIK/RNMLDUnfvAoqhQ+LkqJ3vpywYhrof22qWesU20BWdehDB58jMj9EUVB0F6QlMcZPUhrsGs7/Bpb/SEwwngGTwciA1rBxXo5CFJE3QGZgL4XJBNJdKFs4X0LW+aFwHdQNJMZ3ltSgatGHwleCpPw+xdNl90B9Lnyf0GZSM8jSlv3GO4EmaQe54mC0YJ6svFzzWHk8xkRjC01daKrcoBqNqRSrrGCRi9F4TF495iHnJk9wmxt9C2PFhHO7w+3mBkfLKzS3R1SbEau9MZ02ZCc1NFAXOdm8ZP7YIVUz5q/v/hDrxZiyKrCHBu5CNzW0VxPsbQ1f1v53PxbG6pwoDS3XpnQL5F9JBI/w4NEFvsJ2SGSYCDVYB05xZy+RIAQVvIPPCIXCURApMeJ2OQZu4ZMHiOWLbWzjpRc3b97k/e9/P5PJBKXUA6/94A/+IHt7e/zKr/wKi8WCX/3VX+Uzn/kMn/jEJ0jT9HmOejG2CcMjCWlSXz7pwvkSlKLI1qnB/iITHd7rHFjl9YylLi5VBEfsk8sqSz5SVqcPm5DUQ/4vIHCJDlTi0PMGPW1JdlvSaU2WlqRpRWpaRmyYsuyFk55vE/qjVBKuc5vHud3/CMEuPOy90QjJktChhV7ZOpK1I9/UjDYbRtWGUbsha2uSpsOcO9QxqPt4+v3wvi4CfQ0Rr9GCa2GzhmoBdgXFK4iYuFFwFu9AWcgtmDDpdBWYc0g6yDKPcVArMB3kZSjYhNK6WYPOQT1fZUHsnBfEivcpUWPA4CfVAjgCbR2Za6N0x9iRd9bvJyKEU6hVQplkaG1IfF8JlWiUtgHuEsWzgGB5nTJmzQFH4ZIwnLCHU4oTvccoXXNldkyadbTjhFpnbOyItksghWRUU5g1tcvpJilKdxT1BmUcSdayMDuc5Ack11qyYkmjc2pT+N9/TmSvyFh1g38tF7UXBCMs7SNJDiV3d8bjfqTw5y5f8NLXGLrIDvtFQ2SxuFs+qD2yjW0AL4kKw8///M/zAz/wA7Rty+Hh4YXX3v/+97PZbPjbv/1bnnjiCQC+93u/lx/5kR/hd3/3d3nb2972gj5jmzA8kpCZ+rKSj9C4hDwulYYV/g63Q7xJhXApNAM/ZWk/SEhiIAZH0usWsTrpB3fEHr6sbAXkJ1guKX8XYV8s+kpNdq0kyyuKpKRQJZny1YQRJTucBzEmoThWPQCxCOtWeTxmzYQlVznkCoeIo+WIDWPWF4SdNNGEylhL4jpSXZO4FlU70rUlP+lIjq1Hzg/xajX+ufv0BRtnFbYOEBDjoiTxDh7/8S/Al6E6g1UF81fD9NvCMS3YneDzELAIOf78dnhBKHPiEwV0PM95Bvncy0e7jZevyCFS/iVhkJuLGGEN7cWHc5PgGoLLOSfhNfGtkGqIAFgDndZNoJ4kLMYFrfbXZJkUrJNJ34oQoKhgG8S6es4pVzjsKbJTlkzUir30hDZN2Ns5oaDsvSvO2ellvr1nyA5VljMfH9OQUpHjrEK3cLi5Rr3MUOOWbLZh/cyM+l9yVO2gcvCYwl3RfhyPwrUtbR1JhqSSIG008Udp8JWGiostNpnjrSQMzg9a10B3RszKhshUiKwIR6TXlA/Zbxvb+PePv/iLv+CjH/0of/d3f8fb3/72ByoMH/3oR3nzm9/cJwsAb3rTm3jNa17DH/zBH2wThhc3ROpZ+HRyl78cl5rUXCPqMUgMKQwqriQlFxH/B5FskE2eOyCix0UiWnIVWeVehlUIXCB8bGd171LZZQkkS3Y5C22DvNdKUJie0ud/nesxCwAZNVNWNKTc5TF2OWOXs176WbAOvk/u6ZXGWuabFbmtqQqNqxWj51rSU4sKCo69F8aY2IaRUzYOE2ahKfOE7H5HcatFCbjx74F/ABpQc9h9BcxGoJ8FPgE4P7fUFVyWc2gsrDowc9h7NRQ5vmItuhcdcArtBuq1J7lkhWc7XFiczvGTnLAlvhv4NuBTwL/i3Uef9MfijKhQKfgKEQcVQKUITc3psbJiNd6Fa8uh2QzkyIUFIZTLTWhRiCdFSc6CGV/hlZyxy6v5EnuccJsbvcT0UN/Bf4Yip6IL14X4ixyvr3Lr9EmmxTmvvPoFzu2M+5sr1EUGT4F5dY3JS9q6oKtyqJU/XwJ6hJ7JwjHwxfBY7n2iHJkTEwwRZxRr7E4AOuGASw1nY9+P6unMgp6E2DYUb3RBDg9kOLexDYl/R5ZE13W84x3v4G1vexuve93rHnj9ueee4/79+3z3d3/3A699z/d8D3/yJ3/ygr/ONmF4JCHLmeHM9bDegEDrhYxf4uvnDN4v2UFYlvZulYGC2Wq/KDoAXh8+5oxY4BDVW9GkESR+TbwfKiLOYQ/Y8zfRJGvZmS4YjTxCMlEthfbaCSKkNLRMFslhUXhsSPtVqvhATFn2tMkpS0ZBda8i7+mRnpLpz1ejOlQG406TNw1Z1ZIoi8oHM7do75yE3zwn4taUByEmK0tx1mKs8/5NQq+8x4WFpq69ENHRIZzfiSOY2IA9mIAJcJMESG0oVD/jMQssQ+siBdtAu4DNBtah+mAqr7mV60FHIrTJk8cheQK/mv5roubAafj3nKgyKcmfzFsieGT9uHZ7ivqqQVtHesuSJh167nAjXynJW8esKmnThC431KmhyTQdSSCtGqwz6EbhWsNRNufMzHhj/fcYa8nTNTZR3OcqS6Z9FUnGfs2YjJor3MdiOGHOszzFl3g1Nk9wc8UoWbGXHmNsS6cNypxgpo4FExZu4n0yOjDXa/TVlq5OsU24RW2AL3rhKG6E61+ugzWx6iBOrmt8ciEtDBGL6qzPBOvSl4H6RGEZNmlRXO7xiQHc12JCbWMbL2785m/+Js888wx/9md/9tDXb9/27eAbN2488NqNGzc4Pj6maZoXhGPYJgyPNITTePmmIjw8WebvhOfWxN6AtCB0kGIOjVtlA/88HFOKGaLqKLmF0Cil3SCvSf9XWhQyI8rKNHRM9E6HnrYo5XvxWVaSGgE2+n62DTdP6/UOsURZYqHrQTRG8oZTphdfcqh+FSv6CWIq1YMlVU2bJtQmZVqVWFOR7tTosaNDozeQJi3KODqjfKo2sSjpZwfgnDlymPtdrNCcE1UdB+BOewTVCm4fwXPrMEIKJgE/mAJG5hqgNFDX0BwDtR8aXfhh6xpoVlA1UFqwAV+XayhMvCrGm+ADNvatj+Q+JCeQ5x4T0ZY+T0wqPxbJCEzY1ARfspfV9K6/7FynvCJlo+AYTOEwB513xVRQdBtoNr5dM4FmZKhHOrZHMrCZZtNNWLkpzllGrBm5CmXhLlc4YU5L0tNnpW0hrYmCkhnnYL0x1cYVLJhRNhm66WirlLPVPlWWoQtLkrakaU1tDWWXQZrgco0yHWgL56GVdIpPplqiLpq0ogRiUHCR8CD5uWg0SIWncVC7oM8gGAWhVwr3WJZ1An6RfsgW8LiN54l/JwzD0dER73vf+3jf+97HwcHBQ/fZbPwiLc/zB14riqLfZ5swvCjxtVYcCr8EvkKsROwTVZdEYzio0khfVkYmVVFwiXCYV+BXT/8DuIovYV/Hl7GXRI8JcQZ0RChFQVQ9zPy+6tRiXltC3nG62mO1nnF17w6MLQumAIwoSfobpl/ti7jSDufMOWXMuscwrBlRBifLljMUrlcbLML/hscSpccdznHOCzwt8h2yrGI+PSWlpnQFaWOZHyzRqqMcJejOUWwc5sShDvEmUjfxM//LgDvh8S0ijXIEtgCbQnUPlrfhvoVnQ7vGABMLuWg1hK+5JtoZQSzS2AZWi1i4GI48+CF2NqaFLw9DeP/zcPh5n+vNFFx1/kqRRfEEmCqYJjDOIB9DIiSSEX6VfcO/IfmMJXnW+udfTjTYKkEdc0GBXI0gG3Vko663PXdXoT5I+efi5fzz6NUUylNcbxc3eI7H+TLfxBFXeqXOTQ9dLUJLo8ChyFxD2RWcVTseDAnY04z2/gjXaK/JsF+RXNvgtMI6xbhYszs+Y6VanOlo1yPa8xHcU348Px3G8b/iWzefC+MsmAW5ziGCWvfC83eAf8KDSi/sYC79KyMnNJowcP0+SwYyktvYxksi3vve93LlyhXe8Y53PO8+o5FnQ1XVgxTjsiwv7PO1YpswfF0xdB96vsTB4WerW/i72Ax/F3M8qPg4ApcFcaGwbCpTP4MkBpIUVtrfLAt83nGAv5/dwc9mQ/ylJhY3pJixG7+qyhzmiQq122LrhOQeXDt4jtnuOS4RQyOvuyBaC/5XV0xZ9oBFoTpK4uBbDR3XuMcN7gSKvO6ZECL4BL6ykFGTUzJmzbjdMDptyE46uA96bUmyBlU7ZvdrVOUrDBgYmxa19qJKStQXV/hVqQAihTWxCad+BK6Az9+HLx1CVsLIwvU5fOuOP1cOsPeBJaS5J62cr+G89vmZ5BBLYkPpevi4E2JRW1yul+H5b9bwOgP3LDzd+YTglQoeH8N+Dv+6hs+V8AoDT2qoO19YykZQjEELMBJ8ZvGqcPndCR+ahi/y6nAuTsL4v5Kov5FwUX9oF9wNWEzHrEYFTx7e5MnzW+jdjnYn4VZ+jTOzw7j6vzHWsspzNolPEJog5iStqM9Wr+f/Of8/aPSIV06+wtXiHlc4JMk73J6mdl6mmtShMsuXlq/hnxev53Xq0/y34uP8xen/zl/f/W84G7irOlzf34pPgHT4rUK9fGpwna/Dn5hgGGSel+qbVNP6qMKF8nzGU0N3SgFEnLGtMmzjofEIKgy//0X4/S9dfO6sfvi+AF/84hf5rd/6LT784Q9z8+bN/vmyLKnrmqeffpqdnZ2+FSGtiWHcvn2bg4ODLa3yxYkhbVLU4oa+xtJYHXL65AYkM/sQs2DA6UCpDGWGRIeSsfa1cVGhFsaDiNdI8iBV1L2wCWUtLI70QYP+5i72wXOHMY79ySGzbEExXaEyy7Kdoqzlijki1Q1LJrigHDjEJAguQbYx655WOWZDFsCMDtVT+c7YZcmUx5s7PNbeI21a0q4hSVuSpiW71WFuu0gvbMNvPO2ivkIXZJql9Ayx/SIVmilRyOfAP39+12+2gisGOgV1MuJTr/2vnL/qVXzrl/5fnnr6Hz22oYPz1n+FUxsVimXKEPibItoZDace8fVK4sezcrB0PomY4K062g7OGg+qVGGfI+ur5gZfwahq2EmhkJb6Bo/HEAC/jGdK1C64Q1RQlNxUSvaCAQx3AGccXQqb0YiqG1PkJdp4r5CR2nDFHDFTS2qlsVaTtI6KnKNkj1LnHvioFZ/NXs+hPmA/PWFuztjhnCKpSArfE3AoKnI2FFzP77DpJuynxxg6rhZ3+ebdz3P//DFOqgPSeUlyraHOC9qjUFpp8VnblKiKKYMhBl7iiCnwhN3B34vgHqoUynFoTQxDsAoCPpaRFPnNU2KNaRvbCPEIEob//m1+G8an78Eb/+Dh+z/33HNYa3nnO9/JO9/5zgdef+UrX8nP/MzP8KEPfYirV6/yyU9+8oF9PvGJT/Ad3/EdL/g7bhOGryvEV1c0FuSxhNgUyt0cIi9uPHheqAqixyAw+BqMhXHum+AiBrmLv2fdwrcmZDX1FDFBuBa2W+GxAUYOvddhXlaBBucU3SpDrRX7N47Y2z9kqaYs3JRlOyV1NUq7vp3gUfF1v6pUOKYs2OGcCeu+aiAKkC0Jp8z7wjX4CWPFhGO3zxPtHfbLc8ymwzSdV01sHeoEf18W9prIX0NcHa/DPjP8qhqiBbW0dTJoWmjWYFJIR7B4Fu58Ba7sw8vmcNzBzSbnr65/P3/zyh/ixz57h//tmX/sU7uzJmoKCYtScrIhue5huPmSKBG0B5xZuGNjeinGjHdLKMt4FRxbnwcWQOZAb2DW+kJB3oY5buk1J9Q1vADVnJgcPYefFI/DB4hux2awz5VwfVT+OHnb0HWKO/pxnps9wY47Z7TaUNkMh2ZhJjgDeV0ydiU77ZrKFZwlu7hMk+Q119K7vHH3f3JkD0jb1mNEEkOj0p4NkweqYknB9dEt9kZHpDQ8w1OkexWv3v0c9rZicTSluLok3a2wRtPm4e+nC79Vunky37f4rEwYKWf4hConJk2SwS2B8wKqYpDhCUZBhJ2GJVoxV5FsbJswbOPfP97whjfwR3/0RxcolM453vve97JcLvm1X/s1XvWqVwHw4z/+4/ze7/0eN2/e7KmVf/qnf8oXv/hFfu7nfu4Ff6Zy7mHWfv+549Of/jRvfOMbgf8T3yT+WiEcx8ttiQmRXqmIXEch0h/i7+jClbtKNIAogae9JLR+DezOvfOhxZeaZSW9h7/5C3pexOhm+JlKwN/BtVLtO8+KGIHasZhXVZgnalLTkKQNWd6QJjWZ87LOE7UioQ0sBhXaFGvmnDJl2f9/jxPErVJAjqLLUFzodpe9QJNzCu0cu/aMmV1QVB3ppkOdESe6BZ5FEGwTeuVGEe+RTVbZd4Avx/ffuQ/P3YU9DVcTL7rUVgHW4GDRwQpNN56jsxFX16fk5Ypn8YnCHhfJdePw0fe5WEt6WIhHmHxlmd+Gek3DeU8aUwdE1qW4OV9N4bUz/xvqIA+QjcDcwCcM03BAqRxIUiVYFimGSYXqm/CtiwAedFphNbSktC5BbyyqdN7C2oLes6gdiyoceuI8A0MrmtOEk/EuN1/1GMv5FAfsrFZcOz7ifnaVf9z/Flapp942pL3BlQBnBThZkvMZ3sA/8TqaNqNR5Bd1AAAgAElEQVTuMpo2pW4ymuWYdh1W+x2wUDEBkvxcMIzCjriNx6wcDf4GhuzJy55TnBLlIy8nBE24qG4RAZDb+P9P3Ab+Lz71qU/xXd/1XY/0yDJXfOq/w3dde6SH9se/B2/8ff5N3/2HfuiHODo6uiANffPmTb7zO7+T+XzOu971LhaLBR/4wAd46qmn+OQnP7ltSby4IWvFoZrS0IFSYti6kOlHRBAK/My/499X5LD3Ci8JzSgyMjP8jAJx4SOO2UOuf4u/IR7gl6bBtdKNFUyUB7pNFNgMe5jQ5A5ddGRtSV6U2GSDNf5m7qyiaRJGquRKckiqm/7mn1P1NsszFsw5pSVhw6ifEMQIacVkMFHAVK2YqJV3NXQZM7VkYjZk2pJk1t/cx/iVsCz8hiZE0gHa4O/1R/iV5ZoeXT8+gysb3wb4rIvK2c7CgYOrGrS2dOtj6qXPN6QoI5N5EIbsO9ky936tED0tWbfK3CSeV5flGQjPHeGvJml2VUDXeTbHSEPXBhmKFqYd7CwhCyWRRHsBKRPKICb3LAtbe5KE2YP0avghInSlQT3mMI+B2a/J5jW1MlQq5az17aMmT+nyJFbmC9gw4v7+VU6SOeejaRDIXEOmaOYFSz3hzOywDBbcLiScl5VARQdiyRSDxSYdRneUmzH1aowylmS8wR5m2FUCIwc7KpZ5sviduE9MGCdEYdVRuI4EUEIYWFFMdSJSErxcajybol1Cd44va0hDWTSqn0/5Uf74tonFNl78UEo9INz0xBNP8PGPf5x3v/vdvOc97yHPc9785jfzwQ9+8AUnC7BNGF5gyNrvqzEihtQsMXy4HHIjkUa8IBKTwfuCdKDSkFzxhgVoP9ss8BPoPBxuuEIaKjtWxP692FuPiN2RHNh3sAu2TGDtHSltCk2bQmNRxtI5Q2eNxwtYS66rB6SfxehagIzymvhKSNLgUH2NQWiYaXfOrFuRtS26U9ApXK1wS3ArULIsTzw1UMlQCPsNIg//EL+iPCUuAusgoFj4BOG883gB3Xn2Qa78aZaRWYW3J/iCjOPielIIeJexChJylQiLVQh5l+Ny42oYQVbB41zDT7V4mubtAcg5BaYN7GxgcxS77an2OIdUBRht4b0unAqtjOGXlIRMrqmgWO46P3/aQtGSeD1Ok/WqkQKq3WQFi9mEUuVkNNhWU5oRy3TKcbqPwgUbc92rS05Cb8miWTJlwaxnXHQkTFiF9pevPsnAK+XAugFGxQVYkLp44oTMII6ufcZF7PjJSYVIkHCBb9xYv2k8pVnJiZpyUa9auLkPizZ8sGS32yLuf/h4CUhDS/z5n//5Q59/7Wtfy8c+9rGv6+tsE4YXFAK1/lqnK3DUHhqyPpXZ3OBvRDURuXZMn5RsUnhWBJ5yepdKmUVkwryOp9FJOfYQv8o6wHsQNPgVlHRDZHJIw79jUMZiRiUmr+NXddB1hrIqKCh5rLjL3Jz1cs57YZlWkTPnlG/iX3u3yYyaEZsLrApxtUxpuM5trru77FULdtbrqKGwxidF0nuHvrSuLt+b5R58G/gCsW0hqoghksxvrwBe7qDeQLWJnZu7LZx0Udrim8MmHz2U9JH15QrPjlhzMTJiC0IqC/+rISKc0tS6nH4KnEW0B/v2SMhqxCB9Lw3Yxj1IhFED/pKacfGSPQX+FfQ5FKOOIt8wV8+Bfi5CbkJnzT0BXFU4gekAR+mcL4+fZGmmQVB5xS6nVOScMWfEhjknHHHAbW7wz3wbn+W1QGTLTFh5Vo6yNKOUWhnaVUFbFTAHNfczvcN/PhsHd5UvCz0dToQong4HSHwo5MSd4DEOQwdrB6waOJXMzOIvxAFquC9pfbUQxK3gHbYeFNv4jxHbhOGrRoK/qw7h2F8tvlqZUnAKItgkVAbxkxh6Swu7IlglpjoWIGQmkK9T46VyhXDxGPCd+PLrVS42zif4pfMefnl9BowcLlW0qxGdyz22wTjqakaS1ORX12STmlpnrJj0/eeWhF3O2OeInJIT9noviIKSDtMnDGfs0pL0CpAutCemVPR3dVGmFFrB5HlOp7QmpJRcht80JsoAL8P5uR7+fw6LtadGSvX6Vgd3rdfIcvic4/jSR0lSIZssSqWQczlqIgfmUbkNyPq24aIidIef86QjddkXtdcalQlyh+iIKUDAGf56uTb40tfwsB3JoKQA1oBNFNVjhmqeUBcZdZZRm5xa+TRypSec6VkvzvXlICs9Yck17mPRfIFv5i7XucXjHLPPghkjNuxyxpd5BXe5xoQ1U7VEZ5ZxssGNNNaa3t0UoG0SNosZzSbUVgxR1EmsIYYhOX9B9O0oBvuJ10iSeovSpoG2JmbYot8gf5sCaX1YCK1Hroht/IePl1CF4RsZ24Thq4bIIr7QUXs+NTi5yQxlZkWSUSwnh/bX8pkmKD8O3goX/SCywdsy/L3tgNiGEElkUQ2SOvsZfjKdKxgrb5jUGahB5x0mb8jGFeN0zdhsSEMrQhwrvezzojeR8r9eX/CelMRCKg47nLPPcehYl5B0VLkhXVhMFWZuQwSwyakSAooA+eReLfdj+V011OewuR/mAbmvr8K9v4W1CnlGWImLXcNh+PfriUdpSyTsRznekDkpzTGxRpKCijA1DJApj3dILLQhkUqGaog5sUMmGYlgQmpi6V4qU0FyRDeO5MRC0aFGDaYAPXG4HUfx/7H3ZjG3ZWW992+M2a/+bXZTmyoaAcujByOlxubmS7wwVpB4oxAQI/kkJFiGRCFyQUxMICiJJsYQiCRcoHJjuBFi+IgaiFyoR6NyzuHA+T44VVRRVbt529XNfozvYoxnzvm+tasBS6gq15Os7P2uNddcc8051xjPeJ5/E7ieRkjNmikKy4hNR8NdMuOYQza+pBFRe7+LoMPB5GS0TUBkMmygnKInigaFwWJai9lGtOsYu9RwpnpFZ6GwrOgrCkv/r7QypHUh24sPxbnftqqgzb1stFQEm7s87nalhYIyvDl3sYuXTuwShmeMu3lCfDshM72s+0RRSSgNQgqf0I9ew+zAF6KHzXQZr0SAZ4arJMhqULjo5/QDv/DSay7K5Usbdrgys6CjlmR/w2ixcm6VVCSU3UpwwRlzzrzGgqNKioS0JAnOHyKgIWTGkqvcZsqKxDMnMpVjY0OuQ2zQEqsWPbaoociUqGwLTnRDt9rtBvwVvXTwMVRHsLrj7ALsYExPtFs4HrfuMcIVIJbKvVXZXnvzGbRS/kNCWLHDkK8tyBhwWkYhMLH+7lHukh8AgYbcw12Mds7Okwgib0WiSgjW7nt2WYalF7mSyfYYOAOjFSbQlFlMPYux1xRmprCPa4xS2IXGLBR2oVBXNCoDHbtkIfXazCkFC05ZMucJbnDOnDMWtASM2dASUBFzzAHHHLBh7FQk24xlHRElNVobqiamqSNaq2nLkPY4pT2NsUvVu5YO8Yi+qtQllyKrEOPumy19KegUh3Qt/WumALsEKxmH1JkkY5WdyQmDvjwzRKfIVRtyP3fxko3vofnUdzN2CcMzhows3+lpkkKxLO+FFSGJwxX6XoGgtGVbH1ILl2qoNzzqkoM1LucQveF4sBv8R7yMvioxfK3B4R1EMnkO3GcxE0VhUmxhaJOAJnDARjGaKkipudJZJAvAUZIK7f+esGbGCoVlw5gz70Uw962Jq9xmPzihuBFQX9GkhadVCu0N973tyB2rOsZNaGI7bXD8yNt0q8hRAumewypUea/LUxg485IZ4g+qgB/W8CMBbFuHZfiqPx3frVD0XaJhiBlnBz1RMA6cdlfhJ7ssdJWEAAhHEC0gmDjp5yCDQDQ7Fi5RUKL0uY/LUaXtU+Ku/wj4v8C8TFFkAat4zNf1a3hM30cZJJQ6obDO3bIIMgqdUeiEMkzIA5fcOgOqI+7lWzSEHHHIY9zHI7zSVQ86R1e8fmivAJpQMWVNHJeEUctSTdmqEfNkiQot59WMrRkRXCnQWU3TpJg8umgNLiJVcy66e4rSoyQKkqtD3+OxQD6DfOw8J2qpGIiQRUGvdiXKHNBnIWJusaGvWQ2NLHaxixd37BKGZwyZnWWG9Q6Sz1p1GNIqhymi0BqkMSwjnHTFh/sW6lYI1rtUSsk+pFeuFTMqcWGUSoPUrzc4YODQP0J62HIYKa6XfWh9sUNhraKqEuo8w4QbxqPcKSUy65wmhQ0xZE1YFFNWzDmj9lbJLUG3etQYZmgCDDkjTpQiCivCoCEPDWFqiKY1unUulqqFUVESl02PBPSCQx3vMKSjVOoSdOMeUQPWt2QSAyMveDSk2UfaA+5XcKWCg31YjunZJtLSGVSY8waOSzhuXL7VcLFxVXOxnXA5pNElUeH2g7/6wqCVRbHcHYV3ytTWUz1b13aYafddgxUEDUTVwFJbKBtify39ixW9y6lkTxp4DNSpJbpimMwLXlE9zr5Z0kYhTRzQxCFt5P6tsoh8EjvdhjNFYAxB0hLGFWFUUumIDWNexcP8V/6nTzQjVkw5Z84xh9zh0Cs/Zt4OfY1RGqsUMRUN52zUmE0wIowbEltRrkY05wlWB+5+GHYBpPI0obfHlgpdOthug6suDC3CK6DUUHvualdZEARJNjihQkWTKy1LTHnfXZL/Xbx0Y4dh2EU/aQsX7dmungwad6NVWnpaVjD4W1ZcQwOc4XvsxT+H45Wg2xSuBLuknxgE4CYiRzKLybLVU+jEwkJ8FmjArhSmDSFRKK1oVUhrXflYhHZiqs4XIqDt/u/Q7g5lXhH5RMFBJYd2186UKuwUJI2qaCJDELVUaLSxUGnCdetKzwJUl4lc6JTgqihTLlATtHH0Sdk+lpb0kTvlVeMeKyA30IRgvA/YTIo9EpfACYHtAffDuVZSvediRzZMOaVTjjs0MaDsnitxSULtb4VO0dl6qIqCpHVy11IoV0Psh1QWhJwz5H1KpiNV9q2rRESRIVQVYXvMvj0liFswUNvIPVREG2mMddc8bCza308bk3LKjJqIOUsOOe60FgpS7nDIba4BdAZlLcEFwKxTFa27bSpirHI6DrQKa3VflFsOrhOD7yIVOXGvFHnNMxzC9Yw+T5fkqvvJ+R+b9ifTZC5x7xSfVL9N90YZL54pXdzFLl68sUsYnlM8V1plQj8bP1sI/r31+5745y396CYlAPnTOiekCldWvh/3f6kuyCHMcG2Il9PXtIV7JwA2MWm6gksslriGPmDDgHaUog9Kxi8/IZnnrBjToFlwhrgVygAvLpR7nHKdm0x8G6L2cDfndOnAklf8WnrLiAWnjDwvX1oaAS0lKaaGvaM1o6Oyp0xKz/1buNXhlh4SIg+ZiSWpEAzHmX/fMdgTWC3hzhr+D/AIfWenc5PyIVfSXvp3eIUl9bvb++4WYhx6txCNIVkc1/7QW9ylTOgLJAI7aYAgdG6WgZyHPdy1PcSBHIZdL+hbEMMPFTriPtgxNGPNMhuxTWIn7a3gjD2v67nAoDuFz+Jq2jEYpP1UeTl0Jwjdu1qesscJ+xS+3DFm0+kvbBl1CqHgEhSLwhpNWaZUdQSLmnBiaLYZ9ixySFapPona6WVba/lbFCClmyDlHlkPbHH9mzICG0E0dq9J9aIjzh7T+23H9GCKy7HDL/yniF2FYRfPnVY5LCI/3VQho7Is6QQMKbOZyM8Fg+cHQlDGegGBGOwMzkL437iJ4RquojCnHwAVzlNAPkpWlTKzvQwnNS1GBxmotCVIK7J0w356wiRdE2Ru2hdnSgE4xlRYFDkZYzZc4Q5zzpmy7GyuhVZ5yDGHHJFQdtiGERusF++Rs7NhQmwqFuWKab4latrOrtnmoG7jEh2Z6Gb0INCcvgohRaGIvvQuctOV+6zZyCkihluYF33HWeCo4PABe9o5Rj7RwontpwS52tALPskcNPGnNPdX9dvBygvT/4S+8jDE501xrMd9fwwj61wtNzhFx0DaVdLPwB+YTIhiTCVYBrlfJJH8H7gqwx6EM8M82DCJcoKZgTFMkprr+pS6DEErwlmNmlvaeUAxjlnHGefBlISS21zlJtdZMiVn1LUiKmJqIlIKrnLbq4YmXgKs5ZQF52ZOvppSrMeUeUJVxhgTYBrlIQUKe+JZEiKLKYJU0koSAyphBEnSLFUHaevJOSlwqo7tGmzqHsPWDhVO8OGU/ocFfetS9Kfl/zu1x128tGKXMDxjDJn4d+tHDqXj4ru8LiHGNcng//KegB4sFQ1eF/yDb18oC8rD3iPVv000FoYz3bD3LmAw6AFgsr3GjW9Smp6Buq+FRUsTKqowJNR4eWeF9hgFsGwZda0GcajUGAyanBEFGTOWzDknosbiVANFKnooHQ2gMSSUjNSWSpeUcUM5BTUxhElDmBvCxglLd5o4Um4XvNnQjVAmADkPAT3Q7ypEOYQ5XC9gWsO1CHIFydIlEbJ6H7XQGoiNc5Y8pScVSP5l+tPXASql2zPi4tw9jEh70GLkWA0SYe0ud5uCkbI67pqlCRxEMAv83eO7ZckY9B4XPczE10wkkoVdE+IShj16Ma852ImiWEeURUw5SsizjHPmrIIpxSilyULCqAEFbRpgtIYxmFTTRCG5TlipMSVRp7VxzpwlU0/EdY+EslN8XDNh6CmxZcSGCSs1pQxH1HEMFkJd07QWWwaYTYgtgosZmtzfQ5yi3AeKXtBLMDvCT5VCn2wzDp3SVx06LAP0GIgmgDYGI2IW8iNkcGJl0fB0Op+7eEnGrsKwi36J0tAP/RJCr5LnhmS8IZJMrKoF09DS10Cl/ym4B6mpQi/aL1LRAYTetXJYzFD0BgerSx/dNbQHz1/B8QmFqriiV6reB2aaappypFOCsCWJS8KwBm0dkt3UWAV1EJGqnAXnlKQsmVMTdYlA6+/0BWdsGJGTdcJO0qqIqYioO+DknHO0MhwnsExGqInt5IRHUUHauFWvstaB+nJcwrOiz+lksriNWwwK3UBOtfS3T0GdOYriBLgm47+UGlrnv1BtoMjhWguZgZFxVtfb1tlQVzj/hnkA1yzc28LK9sSTQ//xqX1qOjnRbn4KEtCJLwJ5NcrWQDqFJIOwBK1A7YOaDW4TRa/cNJj4O8pFTp80yKpabs8FfZkiAw7AHkCRRCyTMctwxmmwx6PqPp5Q93DOgoKUzBRoayiNE1EygaLRIaX3oix9pSAjJ6EkoqYhoiTpcAkjtsw572iWgoPZMGbFFIMmVQXJuMSMNY0NadqQosio1inWaqz1vwM7+MkMtZLWuK6B9t9RqgSiuyBFAPkdyG8kHbnH0N2zxSlK5hHYG2Cu0tMoZUdDeS/x2B4CI3bxko4drXIXfcjMejkuw9ckRoNHRr/UkTijnz5kRku5IA3dZQMj4BDaSb96WtOvIs9wTfgDXK1aetUyEYg2lPS2pSEu26S4CeZeiz3QNE0K30pgDCq2lMqio4YgK9yqN0+I4orRYkUU1WwZURNx4pUeUwr2OeEKd9gw5uu8puteKywa4xOFxjsXar8GXXVsCoUloCWhZMrS0TeTiu1VRVgb0rwlaDxl4MR7TsjEMKX30LiHizS6AodRuEUPAiwGjxZ4nT8fZ6BPITmCeO02PWzgFTlUayhOneulAaIRJHugKzBncLV28BFJF6sW8vbu04a1cHMLJ+ueuGet+zrj244BcUXDPIK4hvCcfjU8lHcUDekzHOVCwI3DdkMyeI/QNAble72xzFTOJCy5tn9Ks/gW35/+f2xjN9mrFrKiISpa7FbRaE2xH9JkYijm/j1ljye4wYopFTG5U97oWlm3ucrjvIwFp9zHY9zhkDtc7dxPpQpVkJDbEbfLq5yXE+ptRruJsbV233/us7Bc9Qrryv+MBB4kFTZpQUiIX9ywxzSm7yPJPF8AhYWygkqs7GUhMWw/bAb/Cv3So0h3lYZdvERilzA8p5BB4tlCOItSLZDTexkxLUyJGW6Uuky/hJ5QLqjFGKzucxRZTUr92+IEaNLBLoUBIWwKsayQVoUwPEc4VaBzsLVy+1s6zBcxGB1gbIBKWjdIRwaUpSbEkhEREeHtsHF22MKoML5xUZL4RKEi9yCEMWsmrMnIO7XIhtBjJkr2OGHPnDGttmR1DaVTGtSNrzBUUOqQ7atTQlpGtiSoTS/6NKTRlVykzksLWnyCpI995l/3lQZlcA7jGyjWcLqBcgth5VgYGlgWcHrq9BHayqkrDoUUa9s7S8f0zuN3jFu4nhk4txebXiEwNt5bKfSsv41jQoQx6Ije20jgMMLQXdIXqGQhPB0cgLirQ9/9SsDGimISk48T6iyiSQJU0JLZnFFVErUtVRCTj1LOoxm5ymgiTUNATURAy4gtBSnnzDllj61HWmoMBSkn7FMTseCMgoQjDjlnTk7W0XN70S9XiWptgDEa2zpASbCoUBhaG2FN4GgruccyiDCjQIaGgEXpFGzo4QWS1zP4PSjcvjobbAVt4DM+mfyl9DB8wEXgiJT35AbcMSdesrFrSeziuYfUo8b0gvV3c6uEft0po7eM3O1dXhfbIcHLW0fxUh6JpwebLHEl2Bw32MkhHeDq4gU9ZEL621LSTuj7tAoILVq1rpGuQSuLbloCVRNkFWFWobQb/ER4x4k0uUqBCDwJPkESB4dhwFcQDCmlE+rxgEp3JlvGbJh5RcmJ3RBYi20DdNOiazeJG+NwFU0S0MwDVGux0l4ZgtjW/nwc45IAKcsP3T2Fux/hVqen9G5Tid9uA3YFden0fILYJRIY10bYFE55kcRpQBSlW5iu6aeJkb87ZKF7ZHqsvcgEiEC46HIlOMXKCgiM+78OncVIl+xJVUnaVNKekflKBrOhuJHcUiNc9uIrEG2kqVVAtQlpVyExhtBC3BhCZSiniiqLKJKUbZDRBoqSmC1jGkI0hnPm3OIqp+yx9i0GTUtFTEGGczytOGOPYw5oCGkJyE2KaTVlm1G1KVUZU1YJeTXG1BG20WAsKjIOVVOAlYRwZeF4oPwoJ1USRVF/7CSg6buBAhTd4Li1xri/xTeqGao9DnsagqIUJItk80KVlpO/02PYxUsjdgnD8xKihCR14WcaHKTNkOBGrks8vo7CKXa6smLxn9Fm0CZu1XOMEzG6l37pKj196WuLTLSUssd+uwhXehcxqEPrKhYKdNASjnN0VIOCMGhI04IwvFhaDX2v+pAjrnOzqxJItUFYEjEVI7YdoO0GT3Cdm4BLJkqSDjXfy0+fYlEsgwlkEGaNa23UFVneYJSiGIUEW8v+0ZLgyLqEQHrPoux7gjtPl/sBFf0kUdKDDR7F7Wfo4uhzuSyBV4jTJ3QCfzda+C/Q4QWWSzi+Bav6YsIg6eRtnLGiUDnHOEsP6ItHE/oCwcZAaWHi8a7ddXy5P27RE8rpO1zB4EMP/A6HQFeJhb8fANVaZt/cMj3ZuhX7EPPgtToWBxvmexvuG9+iGWnyLGQVjTljjye5h4d5FSfsUxHTELJlxCl7nHVulWcde0JwLqknXd6srnOruEazzWjWqTtRJ5dOTAjNNuuPrcC1YM7pPSLk+so9sMHdA7cHr8tJXnHRe6KsoRr4iINcBfr2gzwu0yilfDFUgNy5Vf6niF2FYRffecha8XLiIP0BWXUIL1C0aWWWnwy280vDIIRQlpaqJ10kuAFPBGcO/W5lJTkE42gclfKq/yixPFwB31BuhroKZhrQ1JlzK5pDPbVU4xYVuVlXhw06rRjHa5KoJNcZRxyS4nwntow6IGNMxTkzWgLfyS444pCVn6VEf2HEhkOOSCm4xTXOWBBREfrZ2nXIWwg1zSgkpGGkt0yyFfPrK8L9Bl4JTRtSmZjsTsXkZo4ubF8RLgfnTBaHsiqXpGpGP8YX/jmDm3S9sUObQ30ExgtFBROIroD2l2wUQ5TAoXa5nfW3QVBBUMJhCa+poInBJA70GEgnK3XY1iDyl85XElQCQerknvUeFxOArjI0uLXEoXIGduqqMpJ/2hSqJKBKAtbphE3qMiNrFfU9MWWdsG4nrI1rGq3UlJWesg3HVFFMEwUQQBMEFEFK6a3Jlsy4zRWmrPg+/o+7RmyZsuJ+/jdbRpwz55AjDjj2dErHjMjJmMZrCBTLYMYmmjrr9XstcVwRBC1VG9MY15+xhaY9SzDnofveC/oigOAVRGvk1F/zQ3pvN2GNSELRJRcRbP0PxjIwF0ndiaxP3UVlzkX/UslSI/+hZ4Pf9S528dKIXcLwvISMPFKSlNF5GNLLlIK0bCNeFdIjUPQNV+khBGADp8UgS2VR6tv4h1RCxZFvSNAIcZPHFIeMH7r1yr5EAOBxIFSYIOxBk1s/RkYWItDjmkhZCpWiraEJQ/IgI1IOzCjeAKqDwvVKjzVrlsw6YGNEzYyl55y01MSEg30Ii8IdZoBViiBsSSkcUDJU6LAlzQq3XQth06JD02MYai72sUWkT0r2BrdKPaNvzUhJWsZ96DAO6tyxGrBACjoDJRYhOKnp0A4+a6hMmcM48cfi8QNtC23t/R+E7neZGil21FN6eExD16nqbhsG7xOlcdnG0omW6olFzwyR93BwoQhCQ5C5iVApSxNqyiAkQ6FoiQdXuCbC+JMZev+QyFeTJqyJqZmw6WiTNREiH9563MOQXVHZmNpEWA06rglHDUHQopR13bimQTVg6xBrNLZSPSFBvqfwXaEnKQT0VTXpDohNupAaJNGKtcMKif6CxmEk2gqMPCFZp/yGK3pnKzvY+eUxYBcv2dixJHbx3EMmfnADxvoZtoWe4ycCAVdwM7lA26WhLnxA5Qj5RpaRGirlJiFrwLqVN3HQuzrKBDOcaIROKR8rBA9hGLS4DkmBW1Ff9thJldtPoLC1pgxSqjZmY2qSuER7XIO4W7YqpFbOrXLBeWdcJWyJMRvnHeAnkDMWHU9fdB2ELaExHRVzzjkaQ0NIzsj/PyJjS9rWJGVFmLSY62Bzd560AWVsj22QREomcpmExchIKhDSI6jH4RMAACAASURBVG9wycPDoOcQyyQunaNTepybrGKlf35KbzEgNECxmjbQbJwFtaogkCq3lODFIErsCdrB/kShWBQcxXn0gJ5aGDpGrs3AjhVtqzFNgA1ccjJVOTM2XXLnzrimJaRSESuVsQ165wvjX8t9A0kUHIU2GdCyZcQJ+xg0MRVnLHiSe9gyoiHsBJyEPdGakLYNWDUT1o3zHImCmiisCcKWqoqpmwjTBtg6pNkkmLOoF22S5E08ota4Rf4JfRIhCZTQceV6yDWSKoMUDSS3t9YlCvUGmjU9RcUOdiIYBtmhSL5LU+mC5vQudvGijV3C8LyEsBmeC7hJlGIsDnxwHe8IwMVEQTQYhnoOUnFQffWg3foy6QiKPWees6QHfoW4Se0WbgCVroisfqEfbK0/lAl9wUQ0GvCHMraYNKAuRmhbE2YFTeMGfAkh2E3iNdNk1a1gpa8trpYzzrnCEdr7FjqdhrpLKFKKrlKRUHKFO06TgW33fEnC47yMhIoFZ4RRgw4tY+tAmHpsUBbioiUtmk7yX53iEgApRQ85/JJINPRSw97ciiluQt7zr4tTplR8JCHJ6QU8h7pcQ3qjVxmMXw7RFOcmOWRxCN5k3/8rxaucix5myl+3a/Ry0KIp7WWi64OA4iBgGcxY6hlWu3so8cLNkqQ5qa05t7nKuZoTKsdcAOcuWfrkQLwhclIaX2lIKZizZMuIW1y7QKvcMO48IkqSDhALsFztcXJ+hXS+ZjJdk1IS2ZplPWWbj4jjiiwo2J7PqM9H2LV2CNM5fRVMqkBDGIFobEv7TRKskB4AKXAF0WKSxAFcslBX3is99hd9KLZW+4uT40pUBb2EvCwI5OYaGJ3s4qUXOwzDdz/+6Z/+iU9+8pN84Qtf4Jvf/CYHBwf85E/+JB/84Ad57Wtf22339re/nT/90z99yvvvv/9+vvrVrz7l+U984hP8wR/8AY888gj33Xcf7373u/mN3/iN5/HIpSVx2YfwbiEVBZm5K9zIldMrQUqNVLYfOl/6gdbUbjDTEcRjD4ZUfW4Bbnw6x41rQ838odCkrLxkUprjJipZJY9BjVv0tIYRmCyCyBJENTpqUMqitSGKalc6hq6VMA42Hc1y+LyYUIHinHlHpxMEfUxFQNu1LiIqMgpKElZMn9KuEIZFQ0igWpRqKIk5Zt9312us0thIcBQ5KgFmlvjMEJ6bXipjyJYT7IMkDtcG58YzJzqGgZxvqUTc4aIGAvStBknQUpz1tHaElAvXTqodgqEVMH57aTsBQEpLpMQlhgxeNxAYQwzM2BKbFgqFKiFIGoK4ISxbgqpFsybRLfGkZi85IVtVRFVDPdFUadT5QUh1ISel8qBVaTMA7HNCQMsxB0TUTFh3bYz+0HzakB4574i0BW2o6piinRCqllm4osjHbPMR9TLBroO+4jNU+lz58y7USqmaSVVmqMchjAlhS9jBPuQ+qBtoGtcvMlIWEnqJ9PwsF7XHJZOTTFsyGXGp3cUuXtzxgkoYPvzhD/P3f//3/NIv/RI//MM/zJNPPslHPvIRHnjgAf7hH/6BH/qhH+q2TZKET3ziExfeP5/Pn7LPP/mTP+Fd73oXv/iLv8h73/te/u7v/o53v/vdbLdbfvu3f/t5OnLhcD0Tz3pYJfBotAutDOirCTVPrSow2L4F4x/RAqKZA0QaehGfoXrt8OOlDSETo7Akxjhsg2hIDbl9U1AL4yD6DajGohJPq6xDtAWUIYhqgqgmVW46kYlf+tfSqpDnh7bGklRo34IIabCejjcidyVyj2+QNoUA6mYsOy0HJx6VY01A00ZYrWkDjbEhTRsRUbm+u6dFGlRf/t/SKz2Wg8dwNS9CWKf0DAupUg8LTFJREBepy+dcJrQRfUXi8m0i85OAMCVZuKxELo6lklQs6ZMV/5lBadGrlsRumZotyi+QrSf2bKuMTZ1hCCBQZG1OYGvaJqGoR+QmYkvCljFbMgqyrmIkyp2OBTMmpuoSBldRiDFei+OyaVlByV5yQpA4pcctIwqbUJmYKK5RGk7LkLpNCXSOTQsnS91oTO6aVrYOsKEHAotbqyRnssgvB+doaNQmyZ9Udjr4kXWCGqnyBh2RwxGVtRPWMNr93WUjoj0tZaBi8JpcpKcLyTR38aKNXYXhux/vec97+PEf/3HCsD+sN7/5zbzuda/j93//9/mzP/uz7vkoinjrW9/6jPvL85z3v//9/PzP/zx/8Rd/AcCv/dqvYYzhAx/4AO985ztZLBbP4zeQGeZuoegVc7a4gUWomMKKGIbQKy+7EEijdoKrjfuBSAgWV3ArYUkMhsI0Q0bnlp4VVuGcHG8OthdhqBpsGdDcHnUAPDsOME3Y3T0yJo9mKyb7p91NLhOJhBhWrZiyZkLqS+ECepNYATEl+5wwZUVGgVgdB/SAxz1OGbPp6Jiy9k0pyOqabNtQJgFFFpIsG0ZHzd0XeooeU3AEfOMu24BLEr6GczyUyZnBpRh6eWzpZZsvO2ge+OskKP7LxyLqjYbeOGloVynJhQAgA/qcVUCS/Ul3x6dASatqS5+s4KitTy6u8M303g6P4A434Rv7r+ZbuOfFhXLFtJMATymIaC6c1pfxOD/EV5ix5Ap3fFsi7fxHRv6ElP6KyTU7ZY8lMxbxGeN4w02uc5urRPOaxfwIcMDXgpS8TSmKlHKZ0dzOsOto8H2tO3dr1bcYTnDXdggNgh7Dso9jEN3CVYeIQEX9TxR/vW/FcBL7KoeYUlzxG2zoe39D4YtnSwZE12FXhdjFCzteUAnDT/3UTz3lude85jX84A/+IF/72tcuPG+txRjDer1mNpvddX9f+MIXODk54dd//dcvPP/QQw/xqU99ir/6q7/il3/5l5+/L/CMITQEaUHISC4Jw7BuLQPRUEJaoPuybI3d380GTA5mBO3UYRikHD607ZVDkEKIACGlBSGvw0W16g7weJevVAG5Qmc1wV5JkJZeATJyK1W/wz3O2Oekm/Rjqk6sSWG7ioPYIbuWxconA87yWmSFR2yZsezwDSmFd8IsuvaExrCNElaTMWlTMdqUhKW5OB6LuJPw8CXXa3AJ15Kehmn93wbn8rmgn7Slg3SGmydERXPs3yf99BKXBEz8vkS+WRwkpW0U4Sy4aycSZVqIAr/IHYpujXAToFSR5DYROM3cH6fcSlv/uZ4lIQmmugZcs9zI7rCXrmhLjWk0JGASzY/HX6ZIU9o0oI1cy6FpQ0yhwYJO3TVUBTSRopoFVHHUUSYr4q6y4MCONStmrJh2wNccZ3IlOJUVU+5whRVTtoz8FXXmVKVJKOqUcptRHWe0qxhbBv47DaSiJa8WHIm4lQqRQa7JZVqlpvfigL6FJDCEDVA2YES8YegVITeQuFcKuOXpQm7Chl2y8CIPYan9R+z3BRQvqIThbmGt5datW7zuda+78Px2u2U2m7Hdbtnb2+Mtb3kLH/7whxmPx902//qv/wrAj/3Yj1147wMPPIDWmn/7t3/7LiYM0NeVZeUhIEdwA0Z06bXLntRSdRAHohBU63AMLbCtYetnjihwrIlhG0IAXwK+k1KslG+H2Ifi0vaS68jrG9VjMkOFbQLqMoFWo6MGHTaMwi2jYHshOXDfVHVGRDFV97y0FWacM/YOmNJmyMg7r4ERW59kuPaEVCzcoTkUf6gbIl0xZQtWoUYWFUDS1CRN3bMf1rgJRRKEfVwFIKUvYw+xAyO/jSRfkixs6PrlxtMkrWfXBdrrKVT0NM4hZmF0aX++7a2MzwOGjFsGt0cz+FuAlBN6n4yMfrITmQ+5njO/3dxRQkc2JysKyjCijCOaOKSNAkbxhiQqaIIQGygCGvc1owCsIggaJ9AUjajCkJGy3OGQx7nBknkn3rRm4q9c1gEnBQTZej7MlBUK54S6YtopR1r/5VsCahNT5iPKTUZbR6A1alqjNZg2dD4TUvKSZFBUO4fncIhJFCyDJHsZF80mRWZBKjTtkLMs7YSani0hF3XIW5bXLv9dDZ7fxS5e2PGCTxg+9alP8cQTT/DBD36we+7GjRu8733v44EHHsAYw+c+9zk++tGP8uUvf5kvfvGLBN4r+MknnyQIAg4PDy/sM45jDg4OeOKJJ76r36VPQwX+fkAns9c9L3VtAVdBD064Rt+m8JmATiCc9iBI5VdZbQxFcNFxV/QYDuh1hwWvKUUL6cXLJCPlbVkoBfTJw8iiDi1WKdoypm1iR4tPC+LRlogle8EZGtO5VSaUXe9bdBikbbHgtKseBLS+rhAQU3VYBVGQ1L7isGXEEfd2HH/RCFhwxhXuYCJNEUXokUtY9vI18dYlDCqnW2mbWvUujns4R0wp6mz9ZSroQXIBbjJ63J/XOd3EbU6huuUTBiCcgPIsCLX0nzHss0sl6BhXpfAK4NFl81LxMrvsVintI2FviBql9ONLeraFXF/vTimh/HfLFzGr2YjCJJQ27XUSbIJpAxJdorShTBOsViRhSRGk3FLXKFRCSM0xh3yDV7P0gl1rJiyZOQMrbAdYzclYM2bMljEbp8XgKxJSLQppyUmpibBWY9qAtopomwg9sqhZRZCWzgPlKMVUIbZRUKseuwC9nbVAC8zg/EhrTgy9hC0h+X1FT9esoVfGkvKS9PcEjRn5iwF9UiA7FOEHUdsKBtvt4kUbQ12b53u/L6B4gR3Oxfja177GQw89xE//9E/zq7/6q93zH/rQhy5s96Y3vYnv//7v5/3vfz+f/vSnefOb3ww4DEMcX8YAuEiShDzP7/raf0xIc1oI/AmuTno82GaImhN+mPhOz3EDjcwUHtXWzsEsXM1arK8z+v1I1VOeF5pggRv8RJRyH6cAKb4EQ8vkETC27pBOPbhsz6JnNeGkREXmQulskqyYp+dEumbD+CmJglD4nuAGJUnHmnCr0XGXMMjEsc8JIzY+wWhIKLxQkCHwygFSiZix9KJBldduiGmIOvpgdykWTriPe8EaRRkFVJFLsLSyJEVDVBhUZyHpLo/N3N/qSVy7eoRbeW7pVAWDsZORlhVqs4b8FkQRxKLMucX1zA/oS+ZSGhfKvqddAj1OQaS+ZXIT62rRGqj9LSUToYBXR7g2iNhft6CEPojbRsWW2c0tk0cLbKEwlSfIGoUtFLYFlVpU5KpEdgzqhiXfTzhJvsl56NK6l/MYP8nfs2HMHa5SkHYVpYSyqyAsfKvqlAUnHPB1XsPDvKq7P9xd4yZUYzRFlZBXCcQNabwkSZz2R1nHtDpEXd9iziKab6XYbfDMbpUCDpX23NDdVc65wSVwJ4NrYaEXw1hyUTJaDKZC/0MSCrX0QbyWeJc4SA9KstJdW+JFGzvQ4/c2bt68yRve8Ab29vb49Kc/jVLPrG/wm7/5m/zO7/wOf/u3f9slDFmWUVXVXbcvioIsy573437mkAGjpq8rP933Er3bKW6EF0WhoSNm5hV5pNaq+tK21FRt6OgAGf3KU1D9id/1FDcGHuEmlchvOxt8rBynMAoCha0C2vO4o1OqWUNwUFPbkHU1IQprwqjuSs9Cn9wwxqI6rQWZIMRXoiUgpOkmeelfOxql8eyJ+oIipDAmJt4BU1/qHU82ObPNhqSpnNOlPACwhMa452+DOrEEuevLd2N95Jwim1tQH0N5Bq1PJprKt7UlOZPx3y8aTQlN4QwPmwKupXA9gc0TsL3j3C8r38q2TQ9pkNOd4HS5CF1rI8F1m5IYojmEV5y1dicRPlR/lMRPWhBScZCJVBgXc7BzaCeK6jBgWc/YtAPJ6CaiNlGn6nnOnCqOiaYVNoJSR2wYsWbKIUf8AF9lnxP2Pcczou6YLlHTklQ1myhjHY09JTPklTzCjGUHli1I2ZKxZMZaTbka3qZRzs1U6xYbKBoVUuiMPErJGVHEGWWSYl4WQg11EVNuR5hNcFEqeorDKso5kCRNMA1n9FLh1/w1FWO33P8I2gDyCVT77sTq1ncqtMsODZDX0AgAQrQbRHBDnNGeDii9i128sOIFmTCcn5/z4IMPslwu+dKXvsT169ef9T1pmrK/v8/JSU9Cv+eee2jblqOjowttiaqqODk54caNG8+y1/+Hp6L9/ivwurts+2whSCtJYMTR5+4VEDfyiEzj0CZbWhfiWQ1QDEqsLeTScFcQKojCvs0q/wrgTnrqS/qFT0yvdDeEU2S4SoQGTsEGrmWgEoPKDEHqgIWNDdkaTWoVCtO1FkQWWER75px31QABRDrdhZqM3DMkcrRvXfS6gkVHxRR8g6hGJv41wUUEpUXnhsm6YLoeLDFFT6cF1UC0NkRnxrlC3eSisq9MwqdgH4b2GOra0fRpna31snFsO4AwcBASY6Bqnf4PQKtd+ztt3KR/egpneU/9l862+GdJlVO6R0PTyYmC/QDSNdjKy3AAqnZ+FUHgPCmiFJKRa4l0K+kEdOBtSeQWO8S1KRbQTDWFidmYzKmSa+0AjDqmimI24ZgjDiiLlGRZEKgGAmhV5Aodo4Zsr2IWLVmwJNAtga6pbMq2HRO2hqStKYIbnLDXsTL2fEtKRKHWjH2FqmKiNujQEIRtJxUuSpHbyFUsAgw6MYSzmrqNaOsQs9aoU+smf2nNKVyiPJROlyRvOfi5yfmRTkOON8TSUI+hHPf7G9KWdQNR7gSfVEOv4Co7lh+XgCN38fzF/wD+56XnLpuE/QfErsLwvYmiKHjjG9/I17/+df7mb/6GH/iBH3hO71utVhwdHXHlypXuude//vWAE4R68MEHu+f/+Z//GWMMP/IjP/Ise/054J5v9ys8xxAHvM3TvC61cEkoxO76ADfQPJ1AlCyRDoFrPXgrx9EBhep3lV5kSKoGM/+2Bb1gkPjpHHJR1GkQelETvipHpW5JHYU1aVqQqWJAeSz8inHEgjMWnHXVhWHEVFznJnucIn4THVVyQJu0KApS5pzzSh5mxLZLEoaRHrdkjzao2F6kG8JFOWEpPVv//YdxDnwN9AkkB+4xGyhErjcQLqHyLYhpAgdT2BZwvHL21wCLGRxchceP4H/ddlPFhn5xK8VtkXcYkm1FsFPMJkcWXt1AeAa3zntZCEkzhXtzGMD1wOWNXShIMvfggI7mqRaQHLUkJznz7eNQP9FnK9AbOE2leO7FiwQoKJWo11jUTwMHbiuhts7qNVfzMx6OX8F/n/wQazWmIO0SSGd/7ZKFLRknHHCO01YRJdCAdpA2pqyZDKieKY2/OesyYX2yR30eO3rlCa56JkmgtHNK+hbRBldlOxrcH2f+MXQAFUE0+elKO0jh2RcbWH/T7+hubpVyn+bs2hDPd7yOpy7ongQ+/j04lpdevKAShrZtefOb38w//uM/8pd/+Zf8xE/8xFO2KcuSqqqYTqcXnv/ABz4AwM/93M91z/3Mz/wM+/v7fOxjH7uQMHzsYx9jPB7zhje84T/om/x7IqCvPEgDeg+3tJdZe+LdKwGzhWYFVoBXI+A+t82FUc5HiptNGtygJ0JPgluQHvgt+mKGSEWLsY/cNXPgmsXMQupiTEBFkFxsASksa8acM+sqATFPbROFNBxw3CUSQ1xCOpgihH4Z0rDgDICH+b6OxifvO+SIa9yk2re0WUTctMRtixJRPik5i3DPTeAJnFK3FJ4a3IqzBu6nZ8qVuKRi6d6X1XB1AZsCVjncKeGRFkrjqg5iHfHwEo5LMJWbJi6rSQsWT7CpUtwZanCNFbzaQ1WqFgoLqXVpbQRkGiYaRiNIZy4pSFPHhOgolr7C0GUXE/99LS6RvOa6WES2E+7ikA5KY1NfmApcMtCGGgKodMyaCeejBaf7e1QeP2Q0tEqzimac6T1O9B4nao+GkHZwfzoabc45C5bMqIjR3rQsI7+g6yG+JDkZLQEBrVMDbRPW9ZiiSGnbAbBmRH+SUutKPdIhHCp3Xrx5e5qqQIokURCFzaHBV3dbj4BXuhPZ9aiGmuFLellK8eTeJQ4v6tjRKr/78Z73vIfPfvazvPGNb+To6Ig///M/v/D62972Np588kle//rX89a3vpX7778fgM9//vN87nOf48EHH+QXfuEXuu3TNOUDH/gADz30EG9605v42Z/9Wb70pS/xqU99ig996EPPs2jT8xnSX5CegdyJ0qjOnaOexde7DR2mQU8gmNHXWn2IW6Ug7QWd76l1HQ6zGezOSxd3xAwZWEWscs+696YWFRiCoCGOK5KwIlEOIVaSdGj3lJIxmwt4hYZwQJMUVciw85OIqDFox//3NLvhw6DJyTBotwptKybNljQoUYED6GllUVt7UVJYcjIRMprixveX41bcgmODrsdP6Z+T8d/DS4IcshL0BtTasS9sDolvAdW1e5xW8K2qh4+I2LckD0NCrShDy2GmCkb+McZVLaQrLgt8mfvHGrIQkgi07FDUI4e4W/GnALcCl579lIuaHcIUyOlX0V4uRGcWO7VO4npksaElKGuydU68csv5NlM0E00VRIR6zJwzRqw9YLXxaaHTAJX2hLSlRIMDetGmDWOPVJl0CYMxmqqNyauMIp9QbT3wsVT9/TylbxuIYvOQuSyV63RwjaWNJ4lmx5Tw+5HzVON6VFZukiV922Eo+iC4BeFo7mSjd/HiiRdUwvDlL38ZpRSf/exn+exnP3vhNaUUb3vb29jb2+ONb3wjf/3Xf80nP/lJ2rblta99Lb/3e7/He9/73qfs813vehdRFPGHf/iHfOYzn+HlL385f/RHf8S73/3u79bX+jajpQdGTnADjgCjjumAkkb5FY3w5XxtOQh8adg33pVvVl/DrZyH3P8JvYeEzFriZLmHmySH1D4ZWDPguu3EibQ1BFFJFFfEYUVka6KypdIR2yBmrs6Zq/OupCwGRI1PDITRIAqCGVtG5DQENISUJCjsBbfKgJY1kw70OGbNHqcs2hWLck0bQRUEhI0hKRo3mUu1IAY7w/lkPO6/86vpAQOKnrsvoLjan6KRx5iWYCfQ3ut0FzgFtYFxAeNzuH7sij9tATc38JifZERyoaWHroozgZzqIaN1Ti9EeaAhUHCnhTPT+4PJPruug3VVjGYN0dannQnoFNTYPbgHlxwImO82vVGWAEItXaHKPK6wocKkun9MlLtPbrjzF4Qw0lvmyxXTbzxMmBsIoboSUNwbUEYuMTD+SKOwJgorzplzxh5f59U8zPd1egyJvy/OfMVBWk6tvy+kTRH5i7OtM9oyJCgtaVlDWTuWcWwhcTLYttLYrcLUGmM0rXEaEB3teKKwiXI/NSEuiNLzlt4qG3qGpJwr3brfangLgm+CXV2kcFrpfw3losWoyg7+HUpK7+JFETta5Xc/vvCFLzzrNvP5/K7GU88U73jHO3jHO97xnR7W9zgEGCX6w2K/J5dujaul+853E1wca6K585t4EjcISutBOhbf8tsJql9YE7IiHdNjK8UiWwH/r+qKGm0WYcYBzXhEPjGo0q3o9bxCH1SYVFPEKdpLNoHrSR9yzB6n5GQd3TKh7CaEhoDcC/0EtMxYdj1smTAE+AhuVUqkKIOIRJVEqqKKNU0Yk6xa0lXjSurXQN0CHqW3PxZdhP8OfJ2+WiyLwhOozuFsA4W3m94AR4FfmLaQGBgZCPx4f2bg2Lh/z+1FLSBwa8yhjtP54G9w87fILITWtSBqoLH9PAS9fpYGUuOUkcetS172cN2ESQijEJIFxCLM9Rj94vZe4LX+uqe4Ssu9TqvB7CnKNHDqmXrOUjnHy0InF9Qng7AlVhWHZyckX3mCsC7gOkRtS7AxjHSDHXAb19dSljfGVCpGYXk5j3EPN4m930hFTE1MS0BFzDnzTv2xIu58KpbMWOspm3TMNhlRTlNa47xVQk/lbFVAqZ2eRGUSVmbC0s5YFu5hGwUtNI+ntI8nfYVJsA3y/2GIoNPKX6zzGLaHEM7h4FUukzym7z7UUsaQKydZyOXHMRfp1rvYxQsjXlAJwy6GIaOR9AAq3AAjloRDyboAN9A85pa/FrqpogFMAaWnV+a4CUHQ4KIfI6BH6W1DL+o0oq84SPKg6EvdkcLGAbaxmNMQHTfoaU04bkiSCgK63rPCMmPJlBUjtl3JWcR8xCciI/fSB6Yzl5qy6nAM4lC5157zsvpJpmblVpt1TVg1hFVLUDdOa2ALYWM66qi6Ta8CKK0WcSc+8adXAAYDeeFg4+QoEo/1SyoIaqj8+C8MBqnya+MeIsAoFH8pSj9TyKk1ftsWh4d4tnXnBlhZBz+ITA9RiVuIauenFEhrYRinuAQCV5RK9lyuaUdOe0GHhjCoiVkTUrOn17QqojSwtVIDa2lpodlyT9W4BHMLagRBaJ1089h0stWjvETfNkySkiZeOTGtGszYYkbW0zAjVsw4Z96xaYYsGCcKNiJQDZFybYw4qDqBKGHmVMSep+TIuUWbsKlHVFmIigzmNMKcR9g8cL+fmD5xFoYz9BUYadEIFdPgqn5B6DK626XT9t6WUPh7D8NFGeih3rRkKMKR2cWLKnYsiV18b0PqoDLACLfxzL8utXJRnBl6TQi5fuYSCGMgsBdNL2WhI66KwtSU1ad8hMxc0kyXpa083zHFrPusFogMetKgEgcuMyaiIiIKS+KoJKNgyhpxqJREYeT9CoU2aVBE1MxZdmBJ6WeLDfbMLhm1WyLTYggpm5CqhKisScoSu1K0S004aYgWDeFZQ7Rq+56ADPiP4ICPAvKT7yVqkAsIM7d6FwGltoRF6UQ28RTKoukn9lj1lhLQI1KeieQlHSNZuMv2d1cTeWpImilTjogWKl/pNltothfzQoDwyH9m6DQeUiDxiEudQBYZxmFFStXVuJR1DN6VdfnGOoQihfgVCvM6RXtFoUeWNtI0OqSNNSYLMDNNe6DdPVRBpBuyoHAOmpWiTqFGd3WjkqSrKog7pohBCfCx9FUn1+IqfRLa0hCSe0LuhjFLZpyyYGWmbMyIuoxpi4j2KHaVhS0X52vBHYsa/H1/8wAAIABJREFUp1ScLmd/kmRaHNr1vIaicGAWeznNE9CD7GxJD5DY0Sx38dziK1/5Cr/7u7/Lv/zLv3Dz5k3SNOX+++/noYceumB58Pa3v/2uVfn777+fr371q9/WZ+4Shhd8CIbh8kAyxwETRNtBSgRXcH0Dz4cLVV/Xlv68CNBV9By8FDczHdPPaDMc2DsCHvbbCVFD6uuCa8hVh6Y3VUS1iqg2sBkAuq7s3WS8d0JJwm2ussfpBf0FcZwUQJtTeMw7meicXmhrxpI9TinDmP8WPtC7XfqPO+CYe7hJQ0BhU2YsWahT9vI1i+3aKTiu6F06/wn4X8BPAT9Az9mv3Gnklf68PIob20cQjFwe5r4/bNfweAVnzcX1ovwrhehngrgNsQxrnjm5eC5xAHwfPTNSpiiBsEhMcNoOByNYiB1LgmtNeEVKJb37xqtE1u6SX/VP2wVwL9T/RVG8PiQfW7K8IQ8TTrMJ28DZYhcqvUCnXHDGQp35A7KkyhmLHXPIba8UKaqfI7ZdOyL3yULTsSRyD5bcYtFsfavLHbJ4WjjApIk0o2jL5iyieHSMXd8Fji7gEeG2DpXahewg2MUUlwCtcRlWfAjmsJcSvxBLXClLynwiO7mLF218D1gSjz76KOv1mre//e3cuHGD7XbLpz/9aX7lV36FRx55hPe///3dtkmS8IlPfOLC++fz+bd9OLuE4UUT4mIpd2VEz7xf4KYGT5JnQjeqNRGYyI1Ja+hg3yqCOOytlgU9PqV37BWBSeH1JVxE2it/CInffgbMbc9JtwCK2eSU2eSUJHbTn7hVGjRbRoQ4A6PbXPPqjzkjcgpSwLJh3GEfMnL2vUbDGYuuZTFmwx4nZNuKZNmisxo1awi3lvDcEpUNUVkRha2jDIqccgrmumL9f2eUOmJympMtqx4AeS+9HoXIOV+m1S+B2zDbOG2E+gzaO3BnDU/mcGR6Z+WUntU5nItkbgrp15z/Htib1J4KnCO3dCCk0yJs2ev+sTeCWQplBTdP/cYal1R9g751g2NoFC1Y324ZT2B6FWKfv0ZHFv3PDeoA9DVLNqsIsyVtsqZNAoxyTSVZnUf7NdFhjU3Bpoo8jtnEYypfY7EoGkJO2OOMBRsmbBj7RMJhHLQX/LIocjJvqZ111QWpLEhlojwdkd+Z0JzG2DPdYxXudtLFZlz0Gs5w7Zvb9Pl6gassFDVUpUO7mst26j6z7N50Sg9yOGcgPepjeIc8XUhJ8PJ7d/FSjwcffPCCXAA4J+Yf/dEf5eMf//iFhCGKIt761rf+uz9zlzC8aEJQ1ALHFfK3tCGk473GDUDe+dJk7tEIPyx0TepYud1s6LNYkcCXZEAwDENfHGFSiAyxwSUK0WBbmZ08dc+YgKaNMLWitDFpkNMGjhgJsGJKQdrRKJ1stKVFE/p1cETtKZlNx5ooSTptB9fO2JDpijhs0bpB0ZCamqxtUMJqk46N0OJxx68XhiAyqI3t2XAtfalZgGviGwSULawrMGsIT508s8Ix64oGWuPAihmuKCOTtWh8Sg42DGmPP9cWxOWQfE6wFAV+0UuPyZd/O+NS5WiYYz8/tQICnQ02lISyAaUgSsBG/s6bgXoZNFcDykWEXluSR2t07pQ/w21LmLU9l3TocxH5c1u6zzNzRTvTlHHLolmStTVn4YwgaKm8hHRIS0LpDKlQHaZFY2gIO9WOjJwNY1aeBhRSe8fMEaG2qEjTzGuarOjUmgVTI8Dbtg1oywDzRIi1QU+lFDvyITiywAllGL/cFH6s9IkqC3UMTQiNl5ZuRXWjpC9Z1IMPeDqBtqEG+I6W+T2PFwiGQWvNvffey2p1ESVlrcUYw3q9ZjabfceHs0sYXjTRDB5inSu6f2ucbSL0y/9rOO6coLRkZSPqOzi03resA2kp7VR/bvjNZEk8HjykrQE95VDMj/B/oyC0qNDCDafTsC3H5NWIKMsJs4IsiamCmDlLImqWzLAoFpx1E4HYVQt4bcK6o1NuGFMRM2JLQklIgyFgyZxN1qIyy8SuWdgzZ5Q0dZYaxArVeBfKFd0yX0eWySMFHBe9MI+lZ7Ke0dfyz+n8g4oCbq0d/X4EBB4Tcm7gtr1oSr5Pb0aZ4go7km8JFO5y+/w7icR/FlzM9eRSXfAT889vPEBSe5XL5BDUq0Hd50SabEZf3PK256M9nOwHbmfFPmynAWdpRnqr5VrSkpaOVtl9UcH4iQzBvcDL6BUWLajIEqcV07ZlUa2xTUCsKnTgWg6HHFOSdO6kgmUR5U+HaUh9dWHEkhlnLDqPkhP2ucU1kllFNt14rdEEFChlSWxFbCtym1GYlLJIKNcJTDLaSdDrM4hQ62M4wOwWKDVYwQ9Neq2T3F/8JV7HwzqkaFU5QLIVTMO3cNrkQs95uhDxiKEd5y7+s8Z2u2W73XJ+fs5nPvMZPv/5z/ORj3zkKdvMZjO22y17e3u85S1v4cMf/jDj8fhp9nr32CUML7oQ9aEh2HE4NZzTL4My3OgseoGpe7/d+s6EcaAs1TjqZTh2g+GSgekUveKjVB5G9IJOsp30/IHgak1wb4FKnWxwnJQkcUWqc1JdkKpetTGg7TwlRmw7u2vpPacUXOU2M59ciJKjJBCH3OE6twhoqIkI/OpzVJeMigptLMpCOQ0oD0LSmw3Jk22vlyOVhzu4QX2CW/DJ0lyW+jN6064t8CRM1vB93itC4/v6Z3C4hXsKV2EAV77ftk5/c6jsOCTRnXOxkPOdhshKQ5+MiMGy5ElCcBk+wq3zoOAU1GMw+grEMWy10z7qqlADBGeo/eX3C+oiqFmrJS9/Jfz06w2pWMDUuPlNil8ionTiv7y0xbyIWEKL3sDj8XVuja+w0a6GVJB6toPyOg0lFuUTyKQTDs9JWTHljD0KUloCzlhwxCFLZmwYkzcpRZnRerJvHFckccm6HlOVe9RFRpOn2LXCrDTc0e7kSXZ36r/LMMO73B0QHLLk9J2kZ+vlPrdgN7hk/zF6eOsz3QkCQCqeZbtdfFfje6jD8Fu/9Vt8/ONO+joMQ/74j/+Yd77znd3rN27c4H3vex8PPPAAxhg+97nP8dGPfpQvf/nLfPGLXyQInnsZY5cwvOhChF0uy9TJcmZM7zJ0Rl/WlK61F4oR4sVeApMUgqi/6WXz4TJUkOHi7Cc4AJHJhU750JYB7VGM8piHahRgRjFtFFJHEZt6hmpARzVhXDEJ14yCbSfCFHvWhCQJNRFbRkRUnvFWorAYNLe4zjd5RdfGiHEsjDQoyeKefpeFG7JwTbZfkYUl2bYk3VROuOkmDtR5CvwYTsRJKgozeslgP6FSAVdcKT6owZxDcxu257DauudGONnmlYW1dbuSuVbwCjLsy2T+nURC7zEheoKivSWqxcOQu+CStQR7Y5iPcVpfNUQNBI3PK8W9NKX/IgvQC4gXoH2boc0s9ZWWxSshu58eSiNih4dOoLSZaJqZpjYhTR1RTyKaaUg2ykmygiUzlsw4j6ZsgxGFF/kCvDh0jcZgUV3roBo4XC6ZsWVMS0BJ0u1vzYR1O2HVTNDWMIo23XlwwqmK1vz/7L3Jrz3bVef52XtHe/p7bvNrn99zT9qQAlIpalCiBkgFDBAlIRkKIVEjRgywxAwxQoJ/AIkRQiC5aoJqVkKUhVSzkjJLUJnKBAzY5rW/+7vduaeNPnYN9l4RcX+2K3n4Pb9n+ywpdO/p4pwTEWfvtdf6Nsa5clYhdRHCVrmfkUiiS6IgmAaxfJHHp/QOYpJcy+9qKydaO/plo6Cp3Zt3iOThGZPmlFA05AzL7/4YH5v4CKWhv/zlL/OlL32J9957j6985Sv85m/+Jmma8uu//usA/P7v//6D53/pS1/ic5/7HL/zO7/Dn//5n3fuzv+SOCYM37fxqgyQNEyFFyh6tuIiJd7LMnonTvovDWEW9Dy7oYRwTI9lEEtk6PWMhXEhAk8+mWgPBgrTJRxN01DZljIJMDbCliHUhlBlRCYnNymBX5aFVJxw7xkSBypC1szJSJh4OeGanIy0a11ccYGhYcqG1FMyQ1MRmLoDWJ6gMR7W304h2NYkG2ALbarYn4wow4jx/EAyLdz3meEmPZHDGEpFy/i9cRWGZg3lGPZjSAqYeBZKVfXmUxJN7TR9BFWi6Bmr32kaGJohyikYGp1LJ1v2J8KFr443ooAtaWeKk5t+nsCTGVSHXmJAKhUqAjPtWSEBEC3BPMKZVs1c0qDOgNeBC7ACwM7oW/JjYOGAjXWsqSJDGYb+DEVkJgQ95YZzbjntKJRCp3SajE4pVGSiBdRY+NbCjgl3LDkwoiZgbedct2ccrGtjZWXKIZuQRhnj5ICyYBtDUYWUTUxdRrRliN36isKangmxwwEdpQUhHQGpKIjolRzc2ro+lS6de2XQ9CUdgHZDn4HGDCQh/RPkbApcVqQlhVLN4M2O1YYf1vj85z/f2ST82q/9Gj/7sz/Lb/3Wb/GlL32JNE2/7Wu+/OUv87u/+7v81V/91TFh+OEMWVuucdw/KQ/IaL3CjVRjXO33E1Cf9IZ60tgW3wj87gQ/KSKTw1aFlPOfAp+hb1lEg31NLWZUEoxyLwYA6WxLEuWk5N5zgq4SIHiFkKqTih6KOQm90olHJ8QUPOU9zrnmCS86YytxPjzjhse8QNN276GsJcxr1A7sG1B9IeQb8etcNhf8m//8j3zi6++67yvS2VJanvvvaen70u+AOYP0i5BEcJ7iNB3+X5jewUXGt4zlX9vB33lMkghMhvQ2Fd8uxCdKTs0YR2csgW/Qk/JkqpG606sxbKvvcZTLp7gkp93CegvXe7dPQcUYYPyyr07MFTz6Oky9sETwGOIfB/PMHQ8uHAiyw7vE/v+dk8+OaIhUg12W2PMDKLe6//v0M/xD8qmO3bDyLpTgbNJTcgx1lyhkHsAoyp+Af51jU6xYsK/HZEVK07jlX1PEVPuUOo7JmpimiKkPiXO03ClnzWKV+12Ie2mF+0mJoKpADL4TfEBAwZsG7gsoL91ZsrtX8IlyFoZJwjAeCH77EGtZObiSyR6xDB9pfACgx//t/3TbMNb/ClmOX/qlX+KrX/0qX/va176jI3OSJCyXS+7u7t7Xvo8Jww9U2MFfcUwKBvfLwFQBL8Cu3ODWBJAvYDuFdQiBV7uTFdPEv+Seh8tcmVBLXElf6JdDVN1Y0TQRbW1ciyKBZpmSLxq2HtkOYEzNfHLHON2yYUZGSkTZceudAI8T72kwhNSkHIi96t8VF3yTTzJmz5I7x5jwfhQZqbc2Kok2DdGmwZQWqxS7JGE/Tri4u+Lx+orp6R67BCVVX1lBig3ANW6cX9EbEnkRKAV9m+YCZ8g0LBncutc/T2EeOmfLXQahP6bvNHDV9sLfOY6R8ETDiX4otmSt0wPa2N48E/q1qDxP6JWTGKYjJzZVZs7BcpTCOIJxDNHYKTJODcQRnJzBFzxTV51AEIGJIUhAJwoVG/IwZs+YIk6pFxE2cR9yttnx6MUNSVXg53s3waa4pNNfPypyX2QVzVhFM/bGMRvE1nzC1tMlSwLPXGg8J6ImJCPtxJhEh2PLtHM4nbJ12g9JSmvdm5qkJZg0tFpTGUOZxhSTFLsEGsW+HLOvxvDYwXuat2Lad0NXlvHKlV3FCfq+z4A9Q4ZXDDVOk0E9di++qV3iIWybqoG66cs2de3ZTAJ2kB+UlPhu/IWXD7bhGx/j+zn+5//RbcP467+Hf/e/vL/9ZJlbdmj9nfsZ2+2Wm5sbzs/P39e+jwnDD2xII9XTKzsbwqFsHY5iqULvd6z6Ja5UCqRSKiQL/H1Teoqi0A7l9pSeSnYHtjXY1nidBu8sXFv/0Sw6aAijgtgmGEpyYgwtKRktupMCzki7SkHqdXmNd6yUEnWD6VwONS0b5tQEJE4yiFSXJEFJEwTUylCPHD1wFOYkSUU+DcniiLgsiA4VwaZFt9aN3UOhKlHnXtKzAKSCfIqbYKTHIN0gT2CZVjCtYbdzYk9xCUENM+V4LUHhqtgFzjfscQRz7VoZdeOUJA+2ZyMKZRI65iPQu1ie4Kceb7dtgUkLC58U1TVsMgfO7DAqB3oapPVtiRhM5BQv9dhixi3NpIW2xrynaFoNgasSKW174Suh4ooltI9au9bEPk5Yh1NqTHeuBasQUHd4BQE9Gg9VLAkx1MTkNGgOXpuhIqRFu/3oGnTeJSGy3107Jq/nEFjCOKMuYqoiorUaq7xiKarv4Ilhmzh4yk/p1cRShFlDHFq0CKD1DaO8dAlBW4OtXNYHrzAjh5rrgsDd0fOVh9ahw37IMT7S+AhAj9fX198y4VdVxZ/92Z9xenrKF7/4RYqioCxLptPpg+f93u/9HgA/93M/90F9nGN8f4eQw2WpL46W4AakibMtDFIHh0+9dHTWurEp1d5PGTf4SZtUYBIiJikQ/CHWQdquclsqDgKzyHCD6dSipqCjCh2VFG2ILaeooCXQdacEaGg6zEJIxYJ771Y55eBnoAk7FtyTkNOivSrDuOt7d/j5SUE8KbpKxQl3LOyaw0nCYZ5wMGNqY1hwz3S6Iwlrp+uQeIvsPb2PtMEVcSpc9WBo/TGilw72XhQkuIwgc6dmsnebaDyME/iEgfzGGV1pb00QJy6fKzLHhN03sGpcsWNNLxth6dkXETD11ewY2NSQ5e4jjCKv2uE/h7VwreBW5qohjUI5h8yxgpF21tnT2LKYNUzOMubPMrezG/8hYuDfAD/tj8E9tI2mURrbKmytaK1TTchNQDYO2apxh1No0Z2viIAape1QEHcqj2JGdfD21ltmvOAxaxZeKjruRJokAdG0HSZiVS+5Ki9QQUukSg77ObvVoocEXCmHV5CWnczVa3+9i3z4LT22RcTR1v7+l/g2RuVALlaUn3xPQzV9te5bhDcEICQ7FYcr4fh+txqgx/h+j9/4jd9gu93y0z/90zx9+pTLy0u+8pWv8A//8A/8yZ/8CcYY3n77bX7iJ36CX/3VX+1wDn/5l3/JX/zFX/DzP//z/OIv/uL7es9jwvADGwn9LG1xI9eNf8xjGewUynOop5D5Aaz1VpbZAu7TfgKB3q1S1P9ksBtaWiS4wfIf/fNlQpWCx4vBRzhV2KWiGSW0SUSpLEFaMXqyJli4Ubgg5gWPPahx582GnOTvlmmHS7jhrAPE1QRdsrHkjjNufIvi4AV+LBO2zMRGuSk5f7Fisj5w9TRgsxyTE2ND2J1aomnNLDuQbEvUjftO9guAwfkfbHAT5RVukpAq8cp/36HPADyYMzozMF+6tzUc7pxHw3wMaeIkMhQQj6AKINu5x+UMiwWI4AtbYKbhwkDi9bmCU4c10OYhnVKq2o8SqOUcCm125nauPD5Wjxwzwpw43IYVVdAalMyFolJ+5i+xT8EmHnOdnHAIUkoTs1Xu6GdBQqaSjh0jq3+xrBa9hRbdWVmLDXpB7CsQEXecsuKEciByJDLSQ50GAVcWxJiw4Ty4ZleN2R0mlJVHh2TA3jMjNvTJsHipzOgrRzseLvBFGmFPL6f5BrBN4PYCylMekF1j2zGdO7rMg4LBm7hen/AyNzwUGT/GxyY+AuGmX/mVX+GP//iP+aM/+iNub2+ZzWb81E/9FH/4h3/Iz/zMzwBwcnLCL/zCL/DVr36VP/3TP6VpGj772c/yB3/wB/z2b//2+/44x4ThBzYEqfcqaAp/3wTYgl1D4yUZuz5D4Orfos4U6F4+UFZaGT1aTyD8QvQP6Tsf0uNteNhYl02DrTVUCjWpsKOWyobsDxOycopWLWZUEYcZAXVXmhbsg/SqI0ov4NRPLiMOjNn7qoPj64cee39gTOvxDS/1I86md8zDLbtkRE7kJjFVE4QVkalIwgqTtFTjEE1LmmakOiOtMoK8hifQ7AzVNiS9L5it9pi0cYdUxvdXXYwlRN6xAlXAyED4yIEoNbjJa+8qDkkI52NHbtnnjm0hXYR08FYTBSfKWYmAE/aMlZPceDApKXeO4qHZlk9wbAZ16SoQZu6YEp09uthgjv15lUxFLq0Cx6A4gzgoWURbRjp3So1hTR0K1cZ2yZ20ltbMOy2OnikReqDrqDOj2jJly5TdQCpaKhVSVVBYdkwom4i8Tqis229TBbRVSHGIyQ8J7XXgSjal6rHDIrooXQC5fkW0STQXCnowpK/auAJfBtkKDmWfNAId9NTW0JQOWFKVvnUBPZ1SwA5t/5pv4dEMVTaO8cMUv/zLv/zfZDjM5/Nvazz1r41jwvADG7Ks/3Yh5U5ZHo9wskLCqhDAgles01GfGBxwgyrKzUIpD5ldDXR2hrLikhjmJJJg7Pz/pUWlTgWpXMUUTUrTRujQkjZb2tQJR+91jdItRjcEumamNp3aoyQKESULT81UWJoeKuhJeLm/pbhnwZU+53px1jllyv6clbLzPihNjE00RRJjmoZpFdC0ChU2hJGCGVRnAWUVo29a7IvB4Zfe9r3fZHwXyeU7v9WgKxg9psdKiOqkzwSiFs4imObuzBUNfblA7DCBSDtBJePLEFpKETKxy/mSyX8YUlWqoS2gbjX5JHbg1Z12GxpiS7CoIXHmTspAEDtL86TMUZWltYqoKTkrCioTUOqQRiuKIGSkDp2fiNAgewZM2iWFe0bsmLBm4Yyj0GSkXHPOhplLADpkg/JfwSUjFQE5KYWNKJuYyvokpEyoDint3mB3xlXFLgfHRbQVJPkVXW/Bqsg5lbbbZnCty3W9raE8uMwrpQcM28g5yFaVQ6DaDLTP/ro3z3nocypw1k45YvChjsnCRx4foQ7D9zKOCcMPZQiqrcAtB0NcPV30x0UT+gR45AQGVrhNyeMDJ0xptUope4orzYsggMSZ34bOmV6zxk419VUKY+s+mlJwCu0J7IoTdvEJYNFhTTDKmKQ7FvE9ezP25lRucF1yx2MufSm68CZEIx7xkie88I6YfcIgcsLSI3/CC57zDnPuGbN3YLnWkmQ1YeVn7QOoaycxrUa2Hyh2YG9BxaCWtv913eMmJAGEiuim+FDLKlaqOML5v/OnSEzA8I/dQdzAmXh4DFf9r/yi1WvA5/x+7uiJMjm9dbckDAYn1fzE3afGEC+hPY148/wZl7MLf6zirsqzUM5ufc0C5W8/rV7yRv42YVCSjwKCuiXJazbhhMv0jDu9ZMWiw530TpITNsyIKZiwI8G5VjqTMev9RHZkjDp9jQX33LPAopiz7s5t5W2xhYLZBhobKHZ2wooFWVCjgoYqSKjDxF3LJ6pvEwmjceQu9QeVIYEPiHT4yp9bSRCvcK0oPYHXxj0dWbYKOCi4te65jbsa2ZSOhmkFBfkNHEdXLpQh9XKo+Hj0kjjG9yaOCcMPdQzh2TJCCo/yDDfSDWyjtYEghMi4+rYwNMeDpwopY7jrqX98Rl/Clvun9I14i+sfCyJ9BiQWm+tu9WbTFhtpKhtw8EBIgJiciZ/gM0a8R8QLnnQUuwMj3uITHXsCQNN6Weqth0Zqbjjjr/lJRuwZkTk5IF2QJgVplDFhT5LkROOS2gbszZhUHXjEFXGdwzOoTUAZRySqYMyOeFkTnTc9y8KvNNUlbsKY009OCocPKXF4kUf0i0gBx3u8gBIVYWkJiJGYsGnxz/2P9G2PoVCDfBbxP5rhEj3B1xXApyA6q3i+veSsWtHMDI0xtBvtFMVjh9arCqcUGs0q1Gca9j+Z0M4SGqModEJuRqz0Cbd6SUTBkruuZRBReojqnme826k3bphxzTl3LNkwo8FQEjkVSOZsmVISdclDg2HHpEsWBOvSYMjqlH01dpUGIqqbhPJFSlsGUCs3ge8Hx0WcWaVQJ27UsqgXDRIRd1rRCzwJNrEo4bD1Xb4ETAMmB9v253M439ete6wrRYkBRT14klQgXgXGHOMjjY+J+dSHHceE4Yc6ZMbP6Oul0oiWGXzNw2WsdoNaW0FrHFKu1g9h+hJSphMvCm/a1AEkh6abguUyuIkrwisZDQbyxFUiWh1S1iPsKCRMSoK4cPlJo8jyEXf5ObV2MtSLaMUyvqFUzu7Y0BBQdy6XDZqQklNuGbPnkifcsfSrVpdUjNSBi+CaGRtWzDE4gyvXsiixNNwzISFwaUdj3Va3qMp9F7scFFvEgCnF6TXIKnZF7051zkNnqmG1OvbHTKo0Bf0iNKS3KBcp40v6NogAVMvBvhJ/zKU6NKIHRowdgHBmdszYOXhLoClGoaNRxg6YmoWJo6qOAw5pzCqa0QTaMVxUylbP2DJlw5QZ2w6fIJUKcSoVKXAn6TzlngUbj1MQqed735pwehwVKQ7fIgmF2FsLI6IgIlMpB5VSZgnVLqa5i2g2oWuvyeQtCuseWwP0YMThX8EpyCZmWmt/zUriFuP6QhaXkNTabcO5vxn+IxwXL0HaVQHFrEWcr4521sf4aOKYMPxQh7gvSQ9U/srMFdPzwS+gXTheX9k6xB0JEEKm3UskpNUgOg6yapME4gxXZheLbOgThRl9i+OWnuMeutdZa6irlHqXkk8t8fzA5OSenQq5LU6pVgnNTYIOLWZSs1tM2ccjchuR2ZSRylwCwBURJS2aipBlu+K5fZuRzhirfTdJ5aRU1IzZM2HHmjl7xlSEnHDHZ/lHRhw4eAOklIxZs+Mk3xIeGq9nYChHxiUrtsFuFO27GpZgnyl04iZM9XXgazjpxU/5U3BP72IsSdbG//2EP8biXCUAU9HtSv0+RvSuVBNcBUJA92c4tq1MlOfuVNvXgCWoK/daK8qXFmqj2SUJeRiBhTVz3laf4GBGJGHuKI/NHAqIg4JaG3KVUNiYwsasVMBejbtKwYwNc9Yd6FGYLgURBQk14QOFz5wEhWXJHSkZFkXlnyOW6QpLYGtKQkoV0ShDaGrqDOrLlLYyfUVHKgrSKhq2IIbdgJBerEvuFwLDcMEv130Uwyh29wsocj/ttcIr67AMVpKALX3fSH57avDhJOsGtG4bAAAgAElEQVQTfrJ9ZTvGRxYfofnU9zI+Zh/nGN/bEJlZ0WpI/P0y48e4GUU4dgY3iopWtAxqr4S0EwRFL6pCc/pVbEpfWcU/5xluwpIVG7jB94S+fTFc/QFNHXDIUpRpaVuNnlTopOEiuuZZ8i5VEJCphKqM2JUTRlHGONpTEnHJY97hOXUb8H+s/ydsZng8f4fT8fUDql+L5m/5AgAHRmhapmzYM+KGM7QXjxpxYM6ak3DNwtwTpI377J5F8ri44nl+ye5Zyua1EYWJqU3I2XrF6WbtKHiP6QGmJ/T0y8rfZ3H41A2uclDRa0LI8ZQyt6ZvYYgq+A7XXz/FJSZPcZiFt/z9I6AFtfLPfQe4BSUjRQFR3bBUO9pWQQ7nbPhEckX7RKM+32KVork0MAf1hqWcG7I4xNSWsLBkYcQuTlGq7RQcaww3nHPDGdecsWFKSMWIAxtcdWLNnIKYkQenGv/akrjDVVSEWBSHesR1eQYaxtGBep2wfnFGeYhp62+DJBvOyQJgHNJfh0qfW3rRpgMuWb72z5EKjmBOhGI5LCLUnnZhM7DiV3pD72glmYoooYEbqj+H++GI4qNkLcJnPcYxPtw4Jgw/1CEtiZyHE79YGwnZvMaNorJ0EmTeq5TNKbCAyriBsqKXBBa9Yo1bNQtkIqDHNbyLGwvFrXtorSjI88Tf75X4mizAXvegx2SxZ3yxRgc1d3ZBWUfkdcyEHY/DSyZmx9jbTyovO2zR/K35Im+pN3h5d87Z/Q1vLL7Op8f/xI/wd0SU/A0/yZo5z3iXE1YE1CyaNc+qF5Qq5N3wCfPdlk9dv4VNYXM+Zaa2XLQ3BNbhF6bZHnWA9WzGW7OnLKt7luWaZFM67AD+e135w7vwh1vYdaI0eOWPz+foZbnFt9p9MW6u4O1vOt+H16YQ+hZFcw31CzCfgeCEfmEqTAuZv4QeaehAkW2oqJ5p6rGhIqBsY7LKyTNvwynNzBCc11z+1zP+6//+aZ6+ccl//yv/kenplkTVRKYmDQumVnOaBWzDMZtw7BsSBk3r8QwH7w3hhJtE7nvkLSAX3HfGZOIZIhLRNQEhFRO9JQwqWqVJVM4mrsiXCYxr6jrsRBYDGgJbuYQohGKTUm4StKrRtqZdhbTroKdPnvufgrQexKHynIcYEehpx5KfSPKWayeoUSZQadhr2I2gyqARhpLQKvc40bU5fUVQknkpGx11GT7yOLIkjvGDHzIACUxfAAgBbmbOcbO7xBg3Mhr6Oq0YKUtGMO/HO4mF34YTv5g5zemBkt/0bymFj0f0ZWCxmB6IBbF14tBNY7o+tFYHRk8ysjrh5fYx1oCKWiZmz8JsMNYpCLatBmtZqi1jdeCd8DkqrtnkM2xpeDp+h9DbbCsstyy554RP8Q3mrKkISW3OWXNHpUO2TJiXa87vb1m3U9anM0JdEzYNaet67FUb8p59zNs84y2eM25zZvU70EKuQoKmwdQtba6wmULPLTqwPc3R0FsKjMD+iDsk6o5ebdATWDIDN14Z/OkcQn962wyaBpRUwG/ocRCywl759xKAn6GTAmiXimqhKauQLIjZzsasowW37ZKqComKkm+885z/57/8KJ9VY36s+TtSlTldBxR1oNG1wtSWykRsmHethIagMx8zNKw48bbmFUtu/ZVZM2VHROnxCi5ZCKhQtNSEDqOiM+Ko7LQZolHOeLSGsibPU1AWrVqCoCYIelpiO1bUkwCtKzQNNjUPfZ7WuK6BsIBGuEqQPD7EOAztraG3h8g1lJFr5WUGmgSyJTSV27qsQxyvTv0P6I6BR/YxPk5xBD0e44cnpLY6FE34doJPMa4OLjSIGJdEPPObtC3+BSG4SkkAxK9ijxtkhSkh7twSKfDYutdcK/dRnlj32itFTcieEcV2RPn2FD2vCM4z3so+yT9nP8J4cc/k5J68TKjKkE8m3+RReMWaOSaoWTy94TS8w6iKr/Mp/i/+h85qWSayc64d5S+o+Q/m3zNiz1ytuT+dc3VyxkGNuNMnvM2BN4PXWHDPgntup2dc2kc0ymBRLOM75tEtTEF/2nKS7ZiUGXkcUGtNelcTbRs3X4gO1wrXupCy9wQnw/werkLjvUCe/Sg8+YITgjIFHeg+vIAgxoExZZX8Jm7Sk4mvxrH5JOnzQl26tCSqJilruCmwsw38d1fYxy7Bsbeg3oF2o/mNL/yv6C9aAluht8Nlt+UmPuFyfEaga8658vTH1FtLOV6DRbHihDd5nRPumHrKb03APfNubzkJK29nbVGdAqiofe4Yc8ljcpJOowEgCGqSOKeqQvaH3uDCaks43dFcp5Q3qWPtiL9KgZvDr+i1MV7tBgy/6h3wD/R5+J4e6xMB9QGKWwcitvTeEl0o+jKTpreMfelP0jnuB3RF38M7xjE+vDgmDMfw8epg9e1AVILskjqZwPVF0vGWhwmDh97vQ6hCN1gOr7gc1/s9xU2A7+EG4E/gJsN3/ePQy/KWwEb1DoEjnAmC75oU9ynN1TnttcG+qWneCLFfcCVv2xj29ZyiTCFo0GHFxs7RylJHhnm44Q39Juf6mg0zbjklY8SEPRdcccKKMXs0LTmJK32rnTNbwmKVpjHO/OoZGQkZE7XvnDMn7FiomDOuueCKhByrFJEqiVRJYGrQlrhtCJuGoPVOUYLOF/jIvwUbgNrQK36LKqGfE7V1IlDdajjGMTIKUEPziTFuzhGoylDaWACn0GlBKKAeGXbPEtozxWSaU6YRV+aUoo4IDxV1E7BnTDwvWDxa0USGPRMiCsbs2ZgpW+2sy4csiVtOuWPpWChYTlgxwbWQYnLPSQm7vyURITUL7okp2DHBoogpqAlYM+euOWVTzdC6ZRLuSYOMabqj0YZaG8KwQuuWqgo7eWjlkJzwAsyTAv2spvmniPYy7H2g8D8DYfwIpmHj/wqGQWA/UsGZ0bNkohTGFw78KMDeIfgyP4O8cgDjw4Ferzr3J0gwD0dfiY88jhWGYxzj1RB1SPl1CN0S+j4D9KOooMG9qp3WYEyvcCiSuzJYiniRPDZ0yPQWF5S4CVKA4uDyFI8Pa+qA5ipAVQ36tMKONE0dEqQV4fjgPpq2mKDGhDXbakZejSFuicKim/wrT/JLyTow44hDh8jXvoge+sb1gZFH6FsmXqzYSQZl/uhMMNRccMUZN5xx6303lXdRrNE0KAumbjGNt9iWCOgwBXbsNRhu6M2PpJUtNEzRCRAzMDld8BBUf+E3Ud+UAUrEQIVOKIlL4O5XN67iwAhUYjFhi4kbTNxgR5ogaZwCwmULjcLUDUa36KAlNQWnwT0HU3IIKtSkRY1rfyzEdEx1NEvRzRhiFgTg6BKHmIyEmII9467aoGm9i6UTejrjhkYbMp1iUU6zQydkOsWohliVFE1EaSPUqMYsFSp2n59a9ZTgXji0T7xE/lyEuaBvQYgPnGg2iFKktmBaJwltcWwJm7nbDc7Vsq2dMiQpPUtCWhJDt7BjHOPDj2PCcIz3ESLd6CWjWeLKojEPB60xbhZKcJeYn8GC2PHSZcYKtZOXvsdNTp/BIfahp6fJ4CvASRmwxRHSDB5f+v8L0I9rgtdzJ2eca5LFnunyzn/K3lY4W03J9jPGiw06PLBl2k3iZ9ySkBF6zrsoSkq1wKkRZlg0mf+gCbmfrBpfYm842BFbO2PJHY94icayZkHc5iRtjrYWbS2qoJ+ch5bJuENsx+44qWv6ZEGwp2IlLQB7z5iwxp+KCtS9n3vGOOEoDVZaEfegbgenUOQ5xCjxPTogamAbZlcHt+/PQLhseN2+23lp2yWwVFBZlPfB6BgEovIp7aYx5M8C8ucGqxWtUWzaObt2CgpapclVTKGcgJjYmIspVUHMmjkZKSfcs2XKJY8ZsWfGhgtzRmgc4+Wca/aMeckjYgrmrLljyUsuCIKaKKhYFzPW7Rz9Wkv7pKJ+K6F+O3HH/bzFtgqs6umYQzaF8tegYD729Ll15G+Lq6nCJQj5ypm+hUB7C/tr2EkCsQdbQvMF/+OQ39w/+53N3YXhTDA4xkcYR1rlMY7xagT0NEyRlBYWhThjgpvp1vRcswWwdAY7tZ+JVAH5HIJpv8sdbiwElxic4cbElF7PZoRLKjS97TC4dsc3cJPfBbTbiPKtAFIFiSKi7JQgAafgqAqWs79lNtlQmwALnWHVnDVTtiTkXcIQUTJly4QdKZm3Sw6IvGSx9r4VykExfWc+Z1QVjPKCyFYenOdCr1r0fYvKLbpoUa2bOzpqpJgZ+eqB8pRHbgabCDiJTPHWvyYGptDsoLxyFtVRBHUNZQHhDKIFVAcHzo8CCIejwdBIaoYDoC7d8W/OFcUXA6pnyimEY4mLGtM6Oew60RRjg/4vluQ/NOh72+tCSMIgFuljiG4agk1L8dhQXBjO7u95tL2HFNpUkcUheeQqCaWnTZZEzvGSlBlbdoy54QyAGRsOjLhjyYg9P87feCVPw5QtZ9x0wlEpGRdcsWfMjglplHEW3HJdnnHXLjFPC/RZ6bVBNPVdQrsPQSTBd6rXWBBaq2AVWlw+La2eM+BHGJAaxu5ESRti8whWVQ+czFtnN39p4KbGcVzF/rTG9ZukZHGMY3z4cUwYjvE+QloSQscUy8kRnRQjEW5Au8eNhBM6KUNpI4TWz05RL+Qk+AR4sPrsNAnE3kL0GKTy4ENNG8xJBQvrkozIvSYY1QSjiiApnQNiHVDXhmmgCIOKLEg8ut5JCN+zIKBi6fvn4JQOE3JGHLqeekLetSs0OxoCFLWrKHgnxTF7FqxodEAVhShf2Eh89z6YVoRBC5WCyhDuW/Te9qVuwZaKf7VoAtT+0J7wUFxIjp1UXgJQqaNP6gaUdY6XoQK7g+IOas/kuzMOvC8CkVug0DBNQY/h8gVkE3gSwHgK+6+1NAvlikCtxbbQtLBrYa8t+6BlXsOzDOK5P8fCyBXbc69UqUOLGlknsZFZwrZFB06vIQ8jCuMwC4qWgJrcKzmCY17svXmVyH6LuJNYXIdUlEQcGGN9G0jaHjkJOyYAjNmzak64rxcUTQwKVNyiRB29sqjCyzqbQUVNDbahYOqOHlg61HMQw0lj3KYb0LVLDg5NL0ddFa6Vlw/7RPKjkL7ezD8m/b1X3SxFxrL9DreP8UGE1b6a9yHs9+MUx4ThGO8jBFgwxCpM6Pl3Jf1yV1DdJ3QIReEqRwnEU7cLMeIb/l0MdjsoyXfJgpSDZXA2oOYt6vXa95zpysBBXBElBbUybPI5TatprMYYJxG995NIL9RU+gkmRvtEwtB0ALygW5vWzFjziCtvr114uekDOQlr5tQEJOTUQcgmANO2BG3DpFGotiGaWJi7z6sqMHf2ITxEjoH1h1wOscg3T3BVlyt/fNLBa32lwozALOjko3Xh/q/voN6CbbyidwObGoLGzV+bwCUMNodgA5f3sIu8OLi2FP+pwXrnc2ug0U5leQdslSXTDfEnof1xnNCUdKhCaBeK9kR1EiDWC3rp1pLkNW2gqCNNSUBRRVRNRGMCdNhgjZvkD4w7qWinyJkQ+IRN2hWpp/46y3NL4EW4al8FAvuKPHVD3sbs6jGldQmJ1i1KWWyraJRGRS06qbGNdrbsggcZGkfKhL+nB6TKz2YomyAJRtk629Gq9FoM/jnVAaocWkkQQv/jyOhRwPLm0kOSH4zcP9S1ljc+ykof418Xx4ThGB9AiBC/uF2m9FbZ0tz1N2sN2WugnvYvf4G7EkWXQcB1O1xCIKI4okEgMAopcY+g3YW03wx6sN8F8BpUjyz7C9t1S8aLNePFmkg5PYaCmIqABfecccNP8Dc8412+ySe55bRzTlxyR0zRKQpmjCiJuWXJBpf8TNiz4J45607g6YQV2q/65uWeRbZHH1rU3rpDlFqUX33KXw64sX7pv/8/4disQ+JKhmORbPz9gT8eQk8VV8qS3qfDQnUHxbsQhTCaOnflNoNHKTyNYbWDXQafHsPMsyIYwSc/C/apFyuOPU5h5ioYhL2J6dK3G2wKKnR+ZZ0o1ALsGZRjTT4KsEr1Al4KkqwmLmqK2FAGhvSdgvn1AVJFPTGsTsdsZmMS8q4aVBMwZUtKRuGZK2fcOHlqL+OdkRBSEuC4pBbFhhkrlpxzzTPe5et8mq/xOWbRlk+F3+SyeMRNeUYcFYRhRV4ktFYTnOXYRUl9SGnKsE/mrpTrwInk9gZ3sN7wfy0uwTulB5/KtXoI4RDAbeocLOUauAJuWijvoFrx0EpTQsASxSv31/TqaVIRFPrLUUb6g47GQPMhzKbNkSVxjB+ckCRByrOCUhx6KEv93IMS7Bkw7rX5g8HDM/oCRY2b9KX9IFpRBb38g2AcOvCj8rmKdXV3jQOp3YOelZh5SVVFrK/O2dFgbEuTG9pKkc0nHKZjorBgE8ywKEYcMDTUBFxz3tH/UjIec+nliWsqQvaMfUFcd+JDATUFcVe1yMOQOz2hikOqacRJtmGx3vbSwYK2F1G/rd/EERR/SIUZIS6fcipEx0JK36V/zhmu3f0eBBmoxFXClYJwCnrukoY6g3EIo8gxNIqda6PnWygyKP/RvVWjoYgdW/beuCrDIyDSrno+X8DzJ54/sx2cq5F773DWohf1t4D7zahFpRCZhsBYz6qw0FrCyrJY7Ynzis20JkhrUg7eZCrqWkoiAiW01xkbGnSnwbBiyUsekZDzBv/MihOuOSek4hO87T6IgifhJefmhtwkFDpmFGU0gXGsjDamCBrKbUJ7FdEeApj5pHTnLd/P/DkZzs8rnI6DnJ/OLrt29MkigCLsKa35Foo9tJFLsIPaOV6C026oa2gKfzEItVLUo0r/g8roUcUTeoDMsSVxjPcfx4ThGN9FDD2Sw1ful2atZARCcxDP4Bqs9xCWFajIPLT+qYJ5kDKvwCSkHC/kjJI+VxnjVua+JK9Mg44bwlFGNMlQpYbSdG2Fqd4x0jusshhVUhKz4oQFK6bsMNTorqzdkpAz9gh88TOQcnZMwZgDM9YsWBOVFSa3hEVFWFSo0EIC+mDRe4vKbN9qkNzrQC9k9TauoiI4Dq9uyXsOg9AYB9gHaHLXYmh9UmVKCCuHO9hkUK6gfknX4q5rX5j2WI+qcFiGIAITQO3xqU3r/JHUzp2OEJds2NS1O4KxSxhM7lmCAegVsIL2AM1LaGKoTsCE7rlNaqlHTffZ4wTilM6yJAitk6Zc4JKnKaipJR5VBJMGnVvSNHfKk1pTEtF6ukyjNVUQkgUJu2DkjKcwnoIZYzyZtcU5aQqAcsSec66oiByQ1VRo49QmN8ypTUBjXMKQ24SNmnFgjMoNTRCSJyl1HEJuYewomMq2KN2gPD/W3mvalwGsVS+fIN4Va9XDEmROD5WjstQJNKNXEiwBtQyZS9KzGj5HEoOjQdWHGe2HVGFojxWGY/zghAxWsuQXhI4srUa4KUbKBzVw7ZB3Sru6dhU9NM30pWtiHABcFk7S0xfG5hQ3Hq78cyYWdQ4Y6xgFvvccnOYEz3PiNCeNM6KRw9oLG+IN/pknvOdhiCkFMRblbx2IKUg8PiHxCgAAJVEnEvSIl7zOm97rgG41G2cN6V2NurPYO4WaWfTSwguLes+iUksbOwMndQBurDN9kuPxjv9+3lWcPW5yuYd2A+W+VxIuW9g3UPv5IdEw1nD1Er7597C2sLc95V9MFuUMioSDrnrDSzmzE1xXaOnP5DSEkxMYPQKe+3MjzqLe56M9wKEEW0B+D/v3IFYwNg4vsW98sqPh5Azic3r2xNS/qXS2zvw5f+I0KmbrQ9+CkTxVgdUKG4EdKQ6jmLUeszPjDoTaYDjjhqe8xy2nvOAJJ6w45da3mXokrThnTthzz46CmBKXfBQqRkctQdgQzSrqIuR29Yg6COHzQGlhr1BRQ7DMUVHjjkkeYvcau9NOQVIqSy8CeDvomS93/njux27zuBM62q114JOupCRYhT09KPngfmvs+daWxDGO8a+LY8JwjO8iQnqx/OHyRyoK4h4l4k6J4wZOIpj4fkRL3369wK0oJ/RtCNntBKc+LW0IMfFL3W19WhM8yiF2peFxuGcWbTFxDZEl0W6yd/S5MSMOpGTcs+hEl6wXCzI0XPKYd3nWiTTNWXdUSie0VHHKLa/xNnvGvM1rHf2yInStiElFmhTUFwFFHTM2e6bBlvhxTvzFgkiXxKro7LyTdxqiy6an532e3rBoyJAwoEtIVq7SQAFpDZPBAlKVbtUfb+DiHvID5AVktQPiS8IgdZ9bXH4m4sPytu/hph0R6TS46oV5CeYO+Dp99QP6nLGBT03h3z2HRej2qUonU522MLf+PPrKQ2fSZHC02Uf+0poOzntDLyn+qmtprMkXhiINKXRErUNqrcmJO2lvh0FJuOGMlzziBU9IyZizZhgWxZYZG2YcGJGRdpbbzklzhqYlrGtWmzMOhylVPaiwhcDU0uaG6psj1I2FF2CvNfZOQaHcd9kMDrwAI+Uc10Bbga3gPILHoT9pFrYlHERhtcaVom7pfeGldyeJu9idHtsQH1Y0RlEb9d9+4vve78erMnRMGI7xXYTQLAV1LXa8IiYgyO41btCagJ1ANYJD7F5ra1dlaCPYxFAHPWAMIFCQBM6oR+FmMSlaiI32HuxK07wVojx3sZyM2M00KrYQ4v0Pa/KTmHwRY6OAQ5gSBDWhqXxFIe+EmmoCWnRnQBVREvhZ0fj2g8Kyx7kt3rH01QjXpgBojKE2AUmcM2fFpD4wqQ6EQUUQ1NiwpTaa8L4lPLRoa/v2g3z/ir7jM6ebExQecCir1BiCEW6uEMdPDxTVFWwaeK9xbYMIWGp4rB0Nctf2fl9nGi4UXLZQWDdXP8HhE6LQsy4C/7k0fUXAtw4YgV0AZ3DRQrKGUJKdkXuekYleEgVpyQw1GoTxYenlluX5AoqFThpExZbAttDWmBZ2JmCvprTKVRUyUrZMu9ZSSsaSOyJKUn/OE3Iv6J125EuDY9NknsoZUzJlR0ZCoQ06bggosHVDUwe0lUscdFqBVrTbkNYY11ZJG4I3SuxO06xCWCmXCAvtUjp8lfysvKtlo/skosEhScMamr0rNXWWmSV9j05qR5qeLAtHWuUxvps4JgzH+C5CRjFZGopggnDBRYZQLCg93yzbQyYjf03Xa8gbuPWkd6VcspAamBuXf+x4yOVf4pa+EVjrXSv9GFlPUvbTef98sbt4DjyzZOGIm+iUKC1J4pxZtGEabBjpA4nKO6tlaV1ElN4YqSYhZ8qOmIKSaNDGyJix6aoX4niw8GXvpK2Iq6YTj2pa5eb6W19ZEDCc5GFD0CP0CoLC7X+VQSdOoC/ofMTqHA57uKrhm03vxBwo1xkqlDuEsnhvlXdctm4xuwBmClINiXGMWCNSGyIDcAo8BnPqBKF4As3rwA2s/zNst94AawqcQBh6LISvOKgQtFeJZMFDrwZJEn2+2RWrZC5M3fnXjSU6NJgaVKhQkaI0EWCZssWiuGNJg8HQMGVLTNG1j9oOieIAk5FnxwiAUh6PKLzWQ0qpQ8yoJI4aVBlRFTG1AmqFbmqs1RAH7vic+jZZUMOdQb0IsBPlvq8kVNIXEgxDFbjtJa7M0/jfhQ68pLScdOnV7ejrQBE9/VIyEaHQSJZ5jA8qGmNogg9eNKExovX+8YhjwnCMDyBEb1iShpiHyo+CRhQRp6Fs4wn9TDAo6QUKFjHMApiqHuCY4Abgud+lOAaKHba8hVRmRfnwKW6mbIE7RZPFtEVMObLsJ5bN+YJ4ueckuWcWbjq8QkXIihMKYgwNc9acc01MwYgDCXknUVwTdD4G0i8/44aQyrlKRClqULle3O6Y3+0dnVJC/BuEUi8eEuB621KyjujZFAdcVeFt3Oxf9LsrWlh7fYXX6AsCbzfwpjfCXNLPWe80btvipp83cS15aq9Cmb+Cvbukm9gnGs6Vsww5aKj965LWEVe0B7ee4rpPE+MwDfGPQvxj9DIfT3EqkNIvGR4XwfRJSUQqEbUDZmbjhMvJKUUSMlEbdkz4Jz7TTfoVYedcOXSvlPsr3+uKcb4i15xzwxkxBRElL3nEHSfOIt1qijymqkOsVaigITQZ7TqgfjOhzbzSqK8WtX8f0v7fgVeB9JnaFX0CKLbwUoGe4xKyEe6gCU32XWAbgX2Ou6hX0LVV5IIRWo1IT8oZPsaHEa0xNOaDTxhaM+z3ffRxTBiO8V2E9AZEcUkGYCl5yu0DfRdcVkBS1xa+uN+XSiGYQJi4pe5GuZd7rAIxPVvzih5GoegxXiN6tcQz4HOgn5aYJyXj9MA42RM2FVFTEpoKEzZkSUwZhUSm7FaTHRbB4xWW3LHkzjMkCgxNp/YoEtLCmhB1yJKYesggUTAvt5yUG6JtjVrTizPJ2C6mUdKKkA6PPM/vp6s+3/ljcU+3Us1Kp6ewqWDjF5MLv4utPzvp4Law/24Ht4dVcKAD2k9whqKPYpjMIPbFIlVA4LtMNbBq4WXrEochubb1HzVOIYnBvg3ZnbfePge+5r/3kh6vIrAYwa6I/XnsL6UZ8BQSXfBodUueRGTTkCp0nhFjD0gVMKMkCzXBA0MrMdh+ySOuOWfJLWN2bJizZcqSW0bsHZVTBTSRoQ11t++SEEKNSVt044S4duWU290F9Y8azGsVbaZpiwi70e5AyDlf+wMj3zf3273fBOtQApH1tEqpFsgBCQcvFKEHyUjEpexVNchjHONfFseE4RjfZUg9/F8yCAmmQXSfZckEvcSjBqbQBr3mjLgoSitWZjEZJ4egOSnne2dt9bxBf6JBz50OgwlLdFChKdGqIjQ5kS5R1Og2odinZOWELMkJowJlYKzdZCPKjwdGHWoe7x/hrJYrjHe7BLwuQNBNTpJcNIQEyhIkDWbREFEQtRUmatGppWk0rVUY02Ks7T0llvQCVpJriePxnAdaFWoPauttwgJHsUxyVy0QKYulP8x8lvYAACAASURBVCXWQLt3woKRdcrd0JNQRI5reMYtjnHRAo2/Q4h+be/txQTnUzEOvYRABZFxScYogTB2ZpDtsCIk7QbB60klQczHpL0kH8Qnklbj5KNVjUGjCAhoSDl0tEsJSRzyATPC0LBl2iUOgmEQQ+2I0icbmoDGCU8FdBWlmoCIkDbU6HFLu9fUd7FLendAoRwzxKqedSyGr5JDy//aHws7+L4JfRdPaMZdXyagZ0uIUpT0rAQUIwet5Zg0fLDR+Ovpg9/vxyuOCcMxvosQDdx/aUjbQpBdQyVIL91oW6j8yinSkChY+F7vaPByMcxM6QfT4WD7BPiCRc8rgpETCbC5Ylc4lgSe2ZkkOXGcu4pB3XJ/dcpuM8Oc5ySLAyfJikTnbJhRE6CwHUBuwo575igsOSkBTkzI4HAKBTEZI8AlG4+55CnvsY/G3EczkrHzo5jbe+Z2Q5LXBFVLkRhqo0gy0IfGUS6lzSDj/QZHu3zpv/8TerGnO0h2kBzoJ6QNrm0hI5Av/ogURvEuFBXsvaVB6/OUvwPe8sdKOkwW16p40+JWvap/rIOx4ECPb9TwWgTPJ1DUsMpgksLJ1LcogOAZBM8Hr5fKgnxWWThLb18Eq4y7BuwncclhBlUbsD4dUSQhVjmL68C2bleKrhXRYMg8jVYShBbNNee8yeu+3pBxyZNO8TOm4J4FG2bEFB0rRqoUlf/ilQ05NCP2NzN2/2lBfeupwzfQvhu4atAtD12qpd2W0LMlRMxLZNHHuA7eWzha5rd4uUhFYbhTOTlS2xEt8Y/bVHSM74c4JgzH+B5GhlsBrelnmCHUPva376FNoTyFegqHCN41PQgOej2ogF44oBns6jHwTNFOI6pR4BKOBQ9XqgnUozH7kUWnLcGoYnKyZv74jjIMUKZlrPekZN1Kc8qWsbc7alGsmfsVZ9WxKEIqYkrGHGhZd7LTS2455bbzPEjJSWzGqChJy4oq0hxGIVHVEh9q9B7Uq6J8OS5JuKKXIq79/VKyllX6hB4I+aqisBe6qm6guoa2dC9JvXLjXQOVhR/T8O9TiB5BMH/ldMqKeIKb4EV8y09+wUtI/h70BuqDSwwefRaCxEtGS6In8x7+tbJYvsS1WwT+Ih4ad7hc83V3v7p039leQJRUnBZbSmsoYoNqISwsjdEUsaHQkW9BpF1LqUVzyWNe8giF5Qkv2DJlx4SYgnOuqQipCLngilNuUdhO9KnwRle1H063+zn7ywW71ZxmHrjvtKLXkxCsopwPOQ5CYJAWxb1/nZzvl7ik7wHWQaiUYm8qrQfZxLziQA+COXpJfNDhKkzHCsMxjvEBhsDAoa8qSKlgTj9TeJOINnJSZ/UAZieEDCm/y0Mprg0hqP0EKMAajUX3bQsBGPrbrTBBJ1BNI/RpS7s06EVFEDXkxCim3YRRE/jKQYLGdqvK0LcfCm9aBc4qe8rGf1tXhbjl1NM3cwecU4rSJOyiFoIWZRpaShILQdXQYtiGY2oM0/2e5FD2rZiZ/x4HesyGgAFFeUmIKoJFFRiJX8Wa2ukAEYINICugLF3rorJO1qKt4LCFtu7PYonXuQ+hSaC8dVWIyLEJIYTJGpZ7SANnpR1MHDNCCR1WQH1ebbLrWMmbnPo3GtNV3W2iqOYaO4dg1tKkhp0dUyQRdg5t7PQSamOolEZrSxC0VDogV1En0OTOZciOSZcc7BmzYdrZZmekA6uxoCs5W9QD7IMAXqssorhOOaymHNZT6kPYF9IkF5YOXDD4vtJ6GW57XAIojGSpqgl7UtjMta8g2Mi36QTUs6Cv4N0P3nBNn3Ed4xjvL44JwzE+gpAZX/yqT+iXqL7O3M2GMj1JH1b5SoNoFmtHM5soRwM4w42VgmkQgyoBD9pXdiml9jtoY8P2fsFhOyEttsTLA0WcsA8rJ/ykJqyZE9qKsK2IVEmqMgJVo2m73EXEos+4wVCzpqJF+XSjZsaGOeuuL04IKrR+KsvJw5LIlITaYhvNJh5jrSK2JUlRusMl1EaZWCRhkiWJd6fssG/QV2gUTpFpD2bc6yrYyrM6G6e5MPZmjHsLhzWU9/77BZCNofK6QdXeJRrKOvpl4A/EsoKwgHAB4xMnKc1qcOrLVz6bVIvw/5/Re4lE7nPbCJoLRTNTaGtpjGEfJ+zDlFYrauWSthYBIiqs0V3rQbAn0kK483UfhUXTsGLJFRcAXQVBmBPu0hHsQ0ruGxWlEGirhPx+Qr2OMNSkUQ0ttIGmngfYTME9qMaidUurnPcFO+XmcKksDP1AZBMVzZSHiUXhs7NWek/+g1ppUazpgT7SEvz/Y0sc8Q3/mmj9guGD3+/HK44JwzG+x6HoVSDHuNm8xg1sY9zMIPaK8nzoBrw29Ks0X7s1MURLqEJXga1wpWwRdpJNJsrnODUiCUkm7nDl/AgaHbC/m5ONJwSvZ4QXOU0cUAQxCkvTGIoiRilLEuekxlUMpLIwZv9APVDUAmdsWLCmJGLHpANHum9pO+0GhcVqRZEkKFrO1RVn9R1m5CylOXOfVb3tD90532oVUPKtffAE18Z44Y+TGrzuBtSdk7yYerdJvCQGLdgMrKyOl2D/LR1P02Zg38OBLRNQflRRt2DeceBLfeM/9/PBKb6ld0pXr/wN/P4f0V8ynwZ9YUn+sXb7eA5mUfIku6auDHkaUL3ChRe6pNvSrkpwyykvecQFVzznHa455yUXjNnxaHAgh9RL0WQoBlUKi+JAyksewRSmX9gQtIMJ18KhTlmXc8omhBaisCKJc/KbMft3Tmi3yrXQhsnCsGr01zgq5WCf3xKiz9SVf/Y4kEtGry3+EtevGvJVXw2Rm/74qAse4+MTx4ThGN/DEPCAJAtDSemGXp5wKOcoM52UBvyyV4cQzGASw4nprSwmuIlJ9BheZXy2uAkT+qKFCEEtcGPqm2CfaZoLhX1zRHMVU5kJ2psItVrRBAZiOIwbwqQgijPCqCQKK1qlCKh5h+f/H3vvGitbVtb9/saY96pVq2pd9n130w1NNy+eRkURNSckvrzqS4BPKO0b8aARSbAJChhj5IsJBDWCMYaESEIUtfMmiB+EGPGoIUo8KhiRg5eWhrYv+77Xra7zPsf5MMYz51y7u6X30aZ3d9eTVPau26yqMeaa4xnP879whdMkrBg6AagNFs5T8Tj63gpD1SgaCrdDHqspY6Z41NRewOFgmyjOGbIkHmSEoxJdHN8N+lVDWDSoytIWKl9ThB7+YUN4tUZJ73sLiwFYuOF2CZaqbZuCyvpAlAdQTiErLGgxA+YZXM1g8c90IFQpmwvoFDjfwMsb2BhiQZkKmyRIG8jnyTEMG3RSAvLeHfdcCWobjHMpNRrKUFMFCqMbvB5DpZ8sVAQoDCkJR0woCInJWLDBHjukbnduKw3H93UG5cyrQgd9DFvg5CETlgwBhW8qVvWAoggtbXLpwQyqzKdoQhrjqh7zmvJwSJ37mEp1a7QU2FI6xc49Nz8bHNehuDEkuahKaMS9ckSXnRksmjThyd0qpdwT0cmMwvES1TqeKp45lsStVWNYJwzr+CaHXNnSb/A6EaARSHxfCzjGru5D23iva+sSWGinEEmnDOnfcJP+b79K66SN1cTgezleU1Of9mgmx3erQrPTqgEfKs+jqEOq2icwqmVJiGy0IO9Dd5UvCSxDw0XYLkP2ZuWovVbDYZMZPpWtSKghoV9Qo/Ap0GGJGXZkQd0YvNrY1kLhqH6+oq61rSZI9UWSKlmkpTWzwXECiyxgY45LbUDXcz9uwWDD5XRte0Gmccfd7xuYyk2+i9wXvIlMd/86LCwModc6HxJVGQJM+5mlr6g80KohoKLGbzEo/aqBTSxCNx92MHxXkjcImM3KhPelogXTYnqGFo3xSKuYNN2gmsY006DTTehFPbNg0xbG01drFils8ZnYd2+a0K3dQiuO6QCUmXvPE7oJYoOa0f3tyETRO4CE/KFUNzy+jnWsE4Z1fFNDGrJPttL0Q6DyQ7q6uPQURsAp15oYWnTekbJ6w1EIl1X3cuh8Cfrid4YOeS/yhxpU2JD8nwvC8yvyPKZwvgCeVxNHObFv96s+NRjI6pijchPfq5hER0y8aYtN0DRssGDCEQOWDEh7LQhzrB0hu1UrMa05yyVOc6UVHBIq55gpG8zdd6ja9ypsshCnlRULQlEGlhUQ7DUkj1XoPdMxKgz2//tYTMERHQZCQJKlAzHeaTs+Q7ejp8EmExftNBq3yBk35i37YYjNCffpJABOYlUc5aqjbrg5OqWZKszcTqAxYC4ouKTgJQa1RSds5SpIAS5ZwN5vhhqVNNRJifIaInJKp5+hMOyyxwHbXOUUBsWYaYtxiHqru7QgUmKMmJOZAp+YhdqgMCGbzZzEZFzhFKt6QFX7FlcwV92Gva8ZIYpZp+i0RJy8OfvY1tgeXaKw0RsfoZpKIU6KAStj57BQkAXY7EJ4t3s80aVSykCi1SAVB58ua5Gscd2aeDrRuLrhf/1x1xWGdazjPwiPTlyhv8OPsNvUXWzDdwQEFrEXBLb5vqO7namjDbbXRsFZSs4CneSgu2ibfc2yGZPORzRGYxq7aJXKUGjDQlkNSFUZe4ykwZyuYGxxChU+R0wYM2WXvWMiT1MLv3NfrWirDgA5ETUeO452ucUhMVkrLDRi7qidlVvEEuRC7lNZ3INvWA0DamMTj3BVsXE1Jdir0VOnsDSk271KlXkXu3jJGMnuXswlZMG6hBVfEBXiOTRTKKaQpVa7wd+A8VkIz9L6J/Ci3rENxwtLI2ySIp0nH4ynmN2bsDiduCnSzJtN6++QFASBQ/cLYPWYTjXkOmKuR+Q6pNGKOSP22HGuo4YZm+yx2xPeov2cpne+yfOpwyw0aAoTslfsMKs3UaGhqTzyKxvkM2tMVRkP02iaWmPK3rkra7KzJm/X7kvAI3T2DnKOLunUssTufUQnGY6bv4vAtLEe53UDhUOGtmWjk9gsrC8RKiEgF1GKlOePsFmL+McfYCd9HetYJwzruOVCWhYSUtPexF78JnTYh9A5F/lQWgT6MZR92DuMlF43fdj0OjCkuEMaMKmi/reA+pHeMQb2OA10lHdX9lXjGp1VmElAncQEo5xwnNouCR4zRiRkbaLQEfTsTah6suiHFETkVPjM2GTAigErciJWDPCp0O7DNU17nIAKoxSVZ4WlfCpG0YJm4BHt5vjDiqCo8csGNceOk9D9pLwvypHQlf1vVFvccI9t0jpJeisIHXbBi0EPOc5QEQVKmQJR78S9ruw9lwC+IbxWM5jlcBqaHY1mzkDn+EGF9o/X3ButqAJNpT2ng9Dgk1Pgs2JETti2JDLiNkkIXFPhxqhc60IwECElCliwQUaMr2sSlZKphMILKTc8Gm3wyduLaegcLS1mIqasQ8rSsSRWUKYhRZqgJg36XEVz3aO57kC7K+xaPep9KZkjqTT0k2GUtf9UyipIKmOThwa68oYMcHHDTeTbhTnRl4/uv24d3yiEPfNff9ynZqz88z//M7/0S7/EP/zDP3DlyhXiOOaee+7h/vvv50d/9EePvfZf//Vfefe7381f//VfE4Yhr3/96/n1X/91dnd3b+r7rBOGddxiIS5DsoUULtkmnUFVRsvNM5418llgN0J940w5XAP4jbUE9jXseh3wTjCY4pX1b1gmgcZea0XsSdhmdfe4WXrUK496HJGPITpht391FDBjQtCURE1hLbc1hMomBHKsIgqoIo8tfchEHVHjMWfUJhATjthk5kh73eImyULknBM7p0WPASv7vmjKPBoy3LYiU0lRkOQlSgB1pjuWpkFnDSo3LbBOe8babV+z90mwbIzKje0QdGiHKfBgEFhrg2JhNRmC6zaZ0DKOUmEQjIOmlbFuca1YhkWyn5PkOdQWomL0HOOBioDQMkgapazfh+eReQGZtumWtHtqPJYMickZsmLutDQkCevONttq6EtFp06ISbAK0q7wVM1mMGPAkj12KbyAeHfhgJK259CgieucrfwIGlvhsIqSIU2p7VjOoDyKUKcadFjCBWge9uGKsuPdN90Qsw+xv5Zz2rjzd8NldwILauob8IySZfTBKYvewURnWioLApCQE2VNsbxV47HHHmOxWPDjP/7jnD17ltVqxac+9Sl+7Md+jEceeYT3ve99AFy4cIHXvOY1bG1t8cu//MvM53M+9KEP8ZWvfIUvfOELBEHwDT6pi3XCsI5bNISsP8FC+mO6XusQeyHLXB/YtS8MHR5Skn257m37cMK3Z/yM7rrYr0IUwEPAv9AZGyk6oTwPW8K/HbgHu4hedccaQDGLKReR3eVhv55KTaveqxSAaUWT/Bdl+LelFFFIFsZc5RQ+FWOmbDJjyrilWXajUrcYBoVphaRCZ6M94YiciEO2juEokiAlCTLU0ByrTkfCIzAZobF9Cl1BfFAR7dXHq9El9r5UqWXddaC7xRFcuAY6h5MKNnYgPA9anEVFxVAwDm44eDE46QNIwbzMjbEPJlBksUcZaOKsxm8astgnC3t0SRW3AlpzRlzjZDsmth00piA81nLoflJA5iS801ZSK25xJdadcrudh312WDA8dixNQ0zmUtmI2XLM1Qu3US6iXiqhLJzgApgjhVlqSA1NFmBKrKGGCDNJspvROZEKzkTRSYL3OwlSQOgrSLozpkOVilKUqEXRm9Aam5QLp1P6RGuXy6cTFfoZUXqsnuSclXjd617H6173umOP3X///XzHd3wHH/vYx9qE4YMf/CBpmvKlL32J8+etBvt3fdd38f3f//38zu/8Dj/1Uz/1tL/POmFYxy0cDn3HPt1KE2Kb6D3qgxESeuwMC3LQDlJebUN9Cg5GsNroDivuh5vYUrtcS3eB19H5VszdV5Dy+gn3r0hJjE1LAzSe8PbtFV9vVGivaL9mUwTURUC0vSKOlkQbGVGcEjmHTJGcHrJsMQ4iN6xpWjdM64pomxiahoSUETO2OWTAqm1LDFkyZElEjqdqanS7GAWOoRGREZGRErs2yJKBWnUg+fNgzmEtuAVs10frg124Koi0Ysv38PYMg681BJ5BnaSzaB44fQdXYi9DTRl6BKohVDXFyKMcaYK4JggbmINaGMKmwcdYB8jEENY1qgFChfZqIjLXthkSUrDDfivS1KDZZPaEZEFaD0JltWEIsbTVQ7aYM2LAioDS0SmjY3MjYSsTEWkTk5UxWZaQFzG1A82ywC76pT1F2XLnX6kwFV21a8BxQStZu2/HJplX3Z/C7VgYj4hzzeg0N7SG2ElnZkBqrEFIJQcUXm1KJyNZ9T5UekYy0WumxNOJBv8ZEm66uQqP1prz588zn3eWcX/4h3/IG97whjZZAHjta1/L3XffzSc/+cl1wrCO53IIQyKkQ3LLRU5ABP3weq+PbV28EkMDD7Sxt8yV20UKQg4ngDPoTH5EdBI6uuEWtnKx7V4nIDXBZvY3YQGdDHOEdXIqsJWHGIKtgmR70dIqhVqZkDJkiU/Vyg+3VE63qInttlADI3I2XUUidsJPIQUxWZssSIm+dpRNr3cREkGjPuXQKE0Z14TjmmSQEQalzdcELCq4BnFKduttOITtXVAzRfgil7P1KZMCRZGxFcMwqQKN3Gsae2zlAKp+07S9e4OdO2UMvimpnUaCuIJKYiUOooLzkOj3msV23J5xNRE5qRuLukejVE4CXGGIyInJ2seF3VLhY4yiqqx3xGC8oPYD8mVCrT07BgLAleRAqlzCoBA8jUiRSMIgX1Ju8joBph4TZ3R/P8pYUY223CB/V4JLEPRvH1SyjudirFYrVqsV0+mUT3/60/zpn/4pH/nIRwC4ePEi169f5zu/8zuf8L5XvepV/Mmf/MlNfdY6YVjHLRaClovpcAwJXe+gf3Hr0y+lSS6Vh9PA7ZZFEcpxDewqW/4WumX/UCLq9HXsrhB3mG3sDlF2gP3bvjpOE4TWz6LxAxqC1mVR7RjUyaZtWZT41GgSt7gHlK0ccUHYigiJiqS4KspCOOGIDeeCEFJSEdA4J01JFPoeCGATDDFKKgmYscmcERMzZbOZWg8Fk8A2BOOK0+keYVoeNyZdYRH+N3gZeYkh2arsoj9xz+3TsWiVa8u4Wxg1hHHTVh/Co5qQuquGO80IIzAWBY2nyBOPMvQwSlEQcZ2TpCSOPhm0LQg7611DX9oM1iM07loaRFjJJ8URE65yisglXVPGzB0CUY6ladrxzYjbRKIkIFMx3iBjvDWlmofsXzhNFiVwhxuvR53F9UjZcUuNHaM91SVfor+wwuES3A8QoOO13tjLmi8JgyQfprGOYm7Wuz6QcQcXrWnN8eqdqEjJJAgXdx3/UTxzoMenbklIvOc97+FjH/sYAL7v85u/+Zu8/e1vB+DyZatSd+bMmSe878yZMxwcHFCW5dPGMawThnXcYiFE/4JO0/nJ+qiinV9ia7Il9mInCpG1fV/luQvuBNiBvRBWQQeOxH2EXIyndKJFQ7qLsUj1iobUkK7U3sHjO12c/tfdAHYN8c6CZGeOF1Tu4QUjFi0Qr49V0K48HlC2wMYcK01t7ZFmbHPQViQsMr9z0pT3iZqkHFuWyMI1JE5xlTvMvzM8KhhM867MPZV2QImSioJ4U4ig08iNmawnBba/LovYFAsgFWlnqd5IlUfwdiLjjRtj0Rk6ZadMSaI2Br1hSKYVXgTzyYAmUZzkKiUBSye3vc3BMXEmoJXIyhyg0WsdQ61b5R677LHLBktGfI0FI5YMGTE/BpIEWkDlkqEDSVrsQ65Dgqikrj2mxRgTQHLHlLBZktchzYa2EiJFQF1EaK/CC3PqVUC9iKyGyGX3u4XtWGGlzkXVeYE9xXewVS/oqlm4cTsEphrmsSM5KPfmK3QoU+UmVN6cPcX9ddzq8e53v5s3v/nNXLp0iQceeIB3vvOdJEnCW9/6VtLUzmEURU94Xxzbi1Waps/NhOGLX/win/jEJ/jc5z7Ho48+ys7ODt/93d/NBz7wAV760pcee+3N0EQ+/vGP86EPfYhHHnmE2267jXe96128853v/Gb9rHXcVIhPtSyeT0Xr6oO5pAmcYK+iu7RViVA5+eEBhL5lVMhOruE4HkycHqU1ISVyKWQItU2Yn5LHSIm5b2y1oisfe8BIUa8CCpOghyV6WBF4Jb5fUeKzdCum/SihS5ZtlcAgSpJpS72cM6LCJ3GiUFKhyIjb3bC0J+S+PbLnpKlrJhyCAj80BMOSWBXETdmV0OU3dppTnThgiM3DBFS/wK5JovcjAllusafC5nbL3liCXZukogOdAqdsjEUq2oOm1mRRSB4HGM/u9oWoKi2JPsagr9TYJ7XKeIpbZU6IT+kUIcN2t9hvaUibSKo28n+w4FFtGubNBmUdUNU+ptDoXGEyTZ36mNruFk3lYUqFMZbvYlLPjrGMjTh5HmGTLhknYZwIvbXsncOGjklRYymWvuraFUb0z0VkQ6gqfepk//6aTnkz8cxJQ3/jCsM999zDPffcA8Bb3vIWfvAHf5Cf/dmf5b777iNJbBk1z58o751ltscor3k6cUslDL/6q7/K3/zN3/DDP/zDvOIVr+Dy5ct85CMf4ZWvfCV/+7d/y7d8y7cAN0cT+a3f+i3e8Y538EM/9EP83M/9HH/1V3/Fu971LlarFT//8z//bP3UdXzDEHrlNwq5gu5iewenseYDrqbte9ZCUcraKZDKBVNB3EDiVkFlYNeHgWfzDukzi5zykK4S0b+uhnTaBHLBrugUJgtgAWURUi18/CYjCFOWatii+8Eufp4rdweULV2ywsOnZotDQgoKQre7jdrWhQVDKjyXGEg7I6C0VE5seTMlYcWAMVMmHCEOjOXQpxpottIFUVo6E1FFfV3TzBQqNihtaHINlTt+0KA3ja1AXKNLqqQis4k1m9rCHu8Iu4OW3r3IPksSJonZCXcTjMPYtiWUAeMr8kFAPvDx/YIA43AH4TF8hlAlpVRc9ezJu8f81lvCo2bAihlj5w1BO24ydjU+hUs8BFgaUqCMZZ6YWtOUmqoMqAuPZhFSTSPrKZGBagzoxi7mDTQrn2bud7IH0mJIaAtk7NGpOkqbRqpcgiupeu8V8KOAeCVxq2Ns6ULsTbtfZjMPAeRUvZtkJOv4ZsRn//eUz/7v2bHHFtObp7W+6U1v4s/+7M948MEH21aEtCb6cfnyZXZ2dp67tMr3vve9vOpVr8L3u6913333ce+99/Irv/Ir/N7v/R7w9GkiaZryvve9jze84Q188pOfBOAnf/InaZqG97///bz97W9nMpl8k3/lOp7ZOKJz5BvCagjZwPaIZWEy0JoWqBKU84mOSrhrC06MOn3+bTqBSesx1D2nsDts4+4fYROSgXvfrnuvEzLS4wJ/NyMeZMTJ8XKv7YMXxxb3jMhJF8/aBV6kooVGaUvmaUuzlPCp2sqCwrTl+CFLTnGVCUdscei6+emxXr+0EOpEcXRuSBqERCrDoyE3lnkQkRPXBXFe4R80qNIN6UuB/xf4Izdeu9j16Zobq3vprjpTbPlcEgNpVQgcRariV0B5YO4C71TNZLagmmvyHY9i2LDF4RNaEJUTaupaEDZFSEgxKA7YbsWxXsLXmTNiyiYbzBkeK3eAcYlWStK2MsQ8DGDRbPBYfjszMyKISqKyYHltQpnHzl7dzosOS7wkw1Qe1SrG7GvwVGfxkNnTkMtYaq/ktS/CVhz68yNU3zkWknCETVx33NhNgcfoFQqEd2noVBwPe4+JV7rcFu62BkQ+nZAm138m/sf/2uZ//K/tY489+A8p/9d3fP2mjiNtCK01586d48SJE3zxi198wuu+8IUv8G3f9m03dexbKmH4nu/5nic8dtddd/Hyl7+cBx98sH3s6dJEPve5z3FwcMBP//RPHzvm/fffzwMPPMAf//EfP0ERax3PlZCrrEMYttxI6TU4rrlJrXRuu6DItndvdgAAIABJREFUVk3ACdpKFG77sNnAIrKgx013k2upuCxCJ44nBI0Se/EWfGZfX2rffbURNFVAlSnSMqRYdEIEXlSiw5KcGN001EWIaTTBMCOOLbbBoFgypMZjyIqa3GEaIHUeCXZUKkJyfCqWDKjxyQnRrgoROtBjScAREzJiYhLL1lAVR+E2lRe0v9N4BryOLVDjETUFp4trRKsK76qxlEupwNRQvVJT3qXRB4bgWoOemw7xP6Vbu0RHQKas7/sx6L3GUV/V14BHQMWGIG5QVwxhXHftE2g7VU2kqaOcPFyRhSGl9lphrCkTtjlgk1lLl0xImXDUmkqBOCpY2Syf0t1sVWGbA6xb5RZGK14cPmwFnryIIomozu5TNgGlH1BohyQpQ4ostu2JpbbeD9At+HJeiaeEMCXO0YEa697jYr4lrQxpDQnN0gls2U6Dhip0rQn5+xEOp/TXFr1/RfrzGhaIso5bLa5fv86JEyeOPVaWJb/7u7/Lzs5OW5F/05vexCc+8QkuXLjQrpl/8Rd/wUMPPcR73/vem/rMWypheLIwxnD16lXuvfde4OZoIl/60pcAnvDaV77ylWit+cd//Md1wvCcDSmhioyg4+K1dXG5msoWSx6XEoOgylyDXMVWz0FEnaQaazje4hW/hRtdgKFTr3Y6DWrQoIYV3rjBm9R4SY3nu9aHAQ9rWaNNgTYlYmCjTYNvSrY4YMyhAzKmhOQkpGwysxbXPRMq7d5tlyZbpajxME4RMiJjwIptDtlhj6Bo8PKGoCoJ64KgKvHrimXtU9TadmgaQ+BV+F7V7vqDtCEpcmIqwmVj/QwWHJORrnxYboA/Bf9haA6gWVmGhCdTVVvfsKIBrwS/Ai8BnUDt9Br00ElNL+m0ulx7SA0MgWc6+IpgRURfY1hjhiVxVJBEKUXoU0Q+ShvHSLSTl5KwZIg4hRYuYZCahAAaRfkxci2hgoCMBJ+SWGXEfta+vgk1XtjpZQh5Ns02WDablHVIbTy036CHDVXsU+iAvEzI68QWAJwYWItDFNHFAlsYEJq9YGbk337CoLGUXmpHT+lLoMpgCcpVsjYB3kiFQUDETycEpfnCa2M07gx5Jo77VPH2t7+d+XzOa17zGs6ePcuVK1d44IEH+OpXv8pv//Zv43n2vb/4i7/IH/zBH/B93/d9/MzP/Azz+Zxf+7Vf4xWveAU/8RM/cVPf55ZPGB544AEuXbrEBz7wAeDmaCKXL1/G87wnACHDMGRnZ4dLly498z9gHc9QyMIv8P2+JaUAu5a91w/p/Jxd+VU5KL7xYS+Cfd3J8orOzbB3CKkgyCZN+u4jbM9dWhAOhK5Pl/gvSok2MuI4bxkKSlk8QERGZGw2YowiVxGV8omHGSMWnOIKWxy2LQjZFfdJgZIwBI5OKT38gtAJDa2YcEjCqn1PQkqyqEj2RCraoKQCILeUjsEgtxqbIKywiZHG7oSvAo/T6mvVhw3p9YZQw3BkxynPwPdBRzY3I4LVPuxfhYEPGz5ECYSJldEogfBbIbwLu8kFu9PepttRy1wI9mFAJ+PdWBkCz2+IlaHeDMh3YkJdcJaLLNngiEk7liL/bLGrqqVeChbE4kEqGlasGPAId7DHLpGr5kwZUxIQkTNwFuctnsIojFGUQUg2ici2YnIV4zclcZOzYIMjNWb/0mn2Ho+t54S0tiKs6dcF95tlTvrtNYH77NNhHApsNSevQTszKiXsCMDU3d9Ba2LRN6O6jnXGGnC8H/JU0UcCPxFgt47/+viRH/kRPv7xj/PRj36U/f19Njc3efWrX81HPvIRXvva17avO3/+PH/5l3/Je97zHn7hF36BKIp4wxvewIc//OGbwi/ALZ4wPPjgg9x///187/d+L29961sBboomkqYpYRg+4XXyfjnWOp6LIZD7ER0z4gSddKOgi0UKV3oITlAg2LEmCHEIkQMZeHR0NWc61W68oMtB+gZXAkDbcB8jEtIT24IoL3pUwwHpsEHnDd6qITm5IDk146iYUBY+dRbRlD7x5pJkaGmSJT7XOMmMzZYuKXS+AavWQtunbLEPCRmN03HosyYCJ5Esmg4xGfFmRpJkhHVB2BREq5pwWaMuY9eKXezaImvJIR2mQMQ3RYhzgNUZGAOX7JCe3AY1BN+5VupdUIegLmLL7zOINEwCiDchHlucAj4Ed4H/ItBSydl10wodU0CmErokTwzFRLZDQ5aEln4ZQ+TnZETssUtJ0I5VXzZaEq1Oo6GTiq7wuc4JpozRWOyEVCdGzNv5OWSrJ8kVkeYD0tmI6jCkOdQ0sabZVqiroP+9odYeVeRTliEmV53A1ZNtLiVpFQrwIV3FoaTLAXL3mi0PtiNbYZBEJAcKH0qN1aVW2Ezsdmxm8oibzG9zH3IciPfEkF5IzguxugC0Faln4rhPFffddx/33Xff0zrOy1/+cj772c/+p7/PLZswXLlyhde//vVsbW3xqU99CmWF+G+KJpIkCUXx5PSgLMtuik6yjlst+paKspLLCic2v9BB8UV5yLUxmjHUm1BUTlo6gTDqKJX9vnhfXa8PHBdQ35gOiNaT6ze+xmgNtaHOQFNTRxW+7xHgs5pvsDoc4g9qgqQg9HIGTldBmA99d0tN4zokgZOo6uwlG7xWjRBoKwkhhW1zuF2yR+2Ww5yYEt+r8L0GPbDl/dLX1LvaFegbykJTlxpK0KnBnzV4884Poy2Vi2LmBLxNN3QjNz6Rxfa1FZjrwGUIT9nNr6fc85v2PVoKQWILXWArGdJ6EAVNqTAI7XPgvov4LuzaCkOUl9Qa8BtCpal74lU+NSHFMYqkXKRrPAd1XHHEhIKQgJINFq2sNODMpazaY0ROg2bJkBUJFQG5jkijCD0yxGpFZgYsi4n9nCF2MQ9Ajyr8IKNJfZqZ38EIEuBuN25HdAW0y7SWKm3lYUiHaZSTV3mdb4oweLQAb4w7gR26tOW8CgXoP0LpG47ThdYCT8/3uCUThul0yute9zpmsxmf//znOX36dPvczdBEzpw5Q13X7O3tHWtLFEXBwcEBZ8+e/Qbf5LM8sX/3f2Ch3ut4dkN0GESAJsNy0J4qJGE4ZW917S6eCkrP1su9yB5qjs1DfOzFulfJbdsQYhS0TSeQ59GJGYmUhOdQ5hWoQY0eFhgN5TSiPIypjxKS4QGjrUOGjvFgEwNFRM4GC2IyAge4s8/ZpV85CWnpuft4LY1SSuMi2iSsiSFL29aoS5KypDHitWhtoldnIrIgIMoqgqqhiqH2QVcGP23QBwZvz9jFS1QcRauhccMrxT/RpzjEtjK2gNvA5NDMoZnY15o51FNIx5Bvw+AyJA9D47QxVOnEMcXESvQyztBVHgTHKmvdxOIggrIizCrKDU12wsfzUpImpdQhuYpaOYJcRxReRKpiEp22ctwJaQsStS2LJQbNnBErBmwyIyVhj1086jaZ0NSEziEzCVMG4RK9WRObnOlqi2wWUQQx9Y4PpbHnR1Sj4wLzdQXX/S5ZuhM4ayBQ3eWopFvPPTpJ7RtJDStssiXMSWGgUEK4gjqAegPM1E2SICWdrSZwXBJV/ggEryD3pYonnNlnK74C/NMNjz0devZ/Lp5NpcdvZtxyCUOWZbzxjW/ka1/7Gn/+53/Oy172smPP3wxN5Nu//dsBKwjVd/X6+7//e5qmeRqUkv+JvSqt49YL2X6J5O03+mM9hd3innb/16A0jCKYhHDCaTVcxF5vpCUhlQOxhE6wu+TbsdfIr3PcjlhEdKSdq1RLG6yLkKbyKQ+GqH0wWwq2rb9E0y5CQwdQTFuzKZGDnjPiRrdKiQGrFu8g5dGCkBFzxj3evVVEHFKHGVUAOTE5IbHTfjhSY+ZqExUbfFOzqaZsVAuS/YrwqIGlsbvcq9h/hcXwcjrBJbmqHGI3rdfoNBi+DOUBFHuwqmDZwEDZ2z8DX1Pw7Wfh5TtQXoDqAMIJBEP3/oau/fA1rMz3i930/62bi2+xp4e65B6PwZ80bFwvMTKXYYmJVy0zwwwUzVCRxT5pHLR6Fdc5ySFbnOYqp7nKjE1SEk5xFYUhd1oYIgwl9NU7GXDAFlc4w8rRZvaqXR7Pz1Npn42Th2SLIcvDCc1cw3VFbUIaFWDmLjsVteaLwOPKJkyR+92X6FozCR2c50arlQR7CRNsoyR63gDGMWQLmF6DOsRuhA7di0TK88lC1Fgzejxl9yHX6RCZz0bcyxM3dJeBjz0L3+X5F7dUwlDXNffddx9/93d/xx/90R/x6le/+klf93RpIv/9v/93tre3+ehHP3osYfjoRz/KcDjk9a9//TP7g9bxDIZcqAQi/lSZuDgfSUUiw65gY2ATUg9qp7TnYzsawofvGWK2veMJdjcn+coOtsqw1R1S1HX1sMLbLPAHJUFSUJQJWT4g2kmJT66YJEeM4yl+ZDvnIhGduH83WLY7XFF+DKjwesmCMCO2OeA8F1oZ47CqiIuCqCiIsgK/qPCK2t06LYGIlNoUeGWNlzf4BzPGU7sjU6ohCguCusK/1KCuma4FEdMKAzZHUF6xTAh8J1PRwFEG15awSCHPwS8hrmBYwDCDaQP7xhZ5ag0bjc3DZg384wHspLBZwGEFxaEzBtXgD8CTTqM4MwtAMHbTO6XDOyRYUOe+QfXnMjEtkaaINGWiCdKK8KiiGkU0Q49NpgxYtfRLhSEhbRMFiyOp2naQ0C5rNCe4zkmuO6GtAee8DV4cfZ1UJWRejBl64Gt7Pp2Cmdm0PhhVSFk7ametmD8+YXlx067DQp/cpGsDCTtHRMOgY1SIVoOIQM3c729ymC2grKEREYg9OjloUYLsh3CMBasgzwueSDS9X3jxbCo9fjPjlkoY3vve9/KZz3yGN77xjezt7fH7v//7x55/y1veAjx9mkgcx7z//e/n/vvv581vfjM/8AM/wOc//3keeOABPvjBD65Fm54X0b9KQpcgSLN7iL0iOxMDHVgLYN+zxlTobsMk0WdBGGyuMaZzq5RdtCxQ4ishj7cO1wZqg6oaVGVQhYFMoXdq/J2MgIyQlMAriCict8S8FVMKKXqiQzVixdygnaZC53C5wz4TjhiwdIlASUKJ31R4pqFpFE3t4dcVYVW4HbEtnUcUeFmDtzREFyu4uOoYeCs6e+ZDOn6/qG+Ls+Ke+zeHbAkHc5h5kA2hHECxASYDb27VuXUIXm3plE0IdQRbOZwtYeZDFjjyg8MtNA6fZzQdrlXRGnsRucfFowL3WF9hvKATiXJzWweKeqitysQcS3lsfErT4UQ8Jz+tMG11R/AOHnUrx71iwIKN1vhKIiNmzoiCEE/VBKqidD0rpYBEQQKBlxP4KU0NdaVhCWahUZGtQsn4ttX/tPebJLGVkHHJ6eQVZFw8LN0yb2yybJxmSatf0kfOi+pjzXFnS8m8xPVS2oLPZjvi2Yv/CuGmpzrurRS3VMLw5S9/GaUUn/nMZ/jMZz5z7DmlVJsw3AxN5B3veAdBEPDhD3+YT3/609x+++38xm/8Bu9617u+ab9rHd+skP7rSWw5VZIFkWhU4G9DuG1r4G1rVnyCeeK/Ykx1io49IWZIcp2d0xUvFHahGkBzLaBJA8pweIz1WYYBy8SaF11SZ9iKj5iER8wZtbh88UOQFkTQqzXLLnebfbY4pHEYhCXDFuwY+xmRnxMPLG2wwic3MSM1Z8xRWzqPTUZsMpK0Ri8qO2wTx8CrsC3hS3QqggnH2XiAHkD0rdj1Zg/Si5A9CuME7r0N4tuA27BtjK+68UqhWEG+stMRbUN+AMUUTt0BwW32WCwg3rXjqfctVs+ct/gELrpZOmW/c7vj3qGjHorFiFx3BSzp5rA8pUnP+8RXazYeKrlyfszV8ztEyo7jlAkzB5ToqjqdgyhATtS2MB7lRaQkjJizYINHeRGHbFnyZh2T5TG+V5FEGWk6ZH64jWmUBbkmBV6SU65iqkUCjylLV5WQU3rpxlB+08zd+viFFbY7cOjmSYgQUoFpEitYVpZQ5GDEPEVCDtYXdBIEpmRcTvO8Lc9tsKZVPr/jlkoYPve5zz3t194MTeRtb3sbb3vb2/7/fq11PCdCVoaGDrzl9W5ua1mOoB5BnsBMMgaxU5RbDAQWCLnhDi1COLJL68sZg70492mWh3SbtQW2dP4i4JxpE5UwLIjCnKFatEJMEXkr5yyeEn3ZZutKOWPClC0OGbByu+DaiTpNGTND0VD1dorjYsEw38cEDXVkCJTFRySzkmRaomsrJpXf4VHe4RFdqAivNvDtWJxv7n57gN1oCi4hp8O8uedHY3jJS0AfQHgNK3P8MJ1xldshS5FHOZBkMLLiTfNrsHzcfk5Zw6XHYRnBf9uAMwFMv2RNSMdnIZ6477JPB7Is6Zwxa+xiKhUhOR3OAichPGzwL5eoscHcBlvRlOFixTKKWYUWAxKTUhC1CpC2wlO1FQV7G+JT8t/4F6ZM+Dov4YgJGywIKciIqXyfRmsCVRLrlOlwi2thwQZzdthnpjc50BOWc4/qaGB7MOJIKVFhZc6PsLoU4qAqctBirDl3YzGiSzBm7lysgbqyyUJTg3ky+WcBPTxZxUBkpad0yEspZ7wwKwzPhnDTsxG3VMKwjnXcfAiPUWwnpUXRF1CQfmxgRZpqS3WkrHrvc2pCKNA1xH5HrazoZPUl9xCb55Ku+yH325aECyly7JhjCpFaN1ZF0XH8M2JqvBazIP8HXJvBKgSEFPiUVI5KKWXx2mlHWsxlyIKNtlKhtQJfU3uKUunWb7HyG6qkxi8bvNqgfYPvNdSnNdmGIpjZVkW7LkgbQBgJfZppbX9nsIJg3o1bcwjVHmjfYu2uV3C5gI0StkqYVnC4cjwWQ7tpXdJpECoPHkzhQgAbCzuk1JAeAIXzGNsAX0zGpBLUryzIc+KKPgWdGHRkWtxKlJSESYk3qAmTsj1lKgJK5VMFmsqzxlUrBi07YsIhNV4rluVRs2CjNbiaM2KlBuRe1DJWYi8n9HLyPGaWbpFHAV5Yo/3GnlOpugESYKBW3SZezjvoIAWG48JWm3Z85PTvWmYKtGfbc/1oO3zCghBaUECXjQkFSBINSc5Fmn0dz9dYJwzreI5HX8lHpG3F1k9k76QfsEG3mojOsxgXiF4DoBVEqitrS1Wh3+3K6HQXRAxPtBo8+5iKjXXDPG3gHHh+jadqfD/H8worKezUGa2Ms130I/LWP0K3e5eqpUQOWfYsnE1rYW2ZE452aTyaxsO4cchVSB6GLXsjrAuiCipTUxiNyg3eEsJlg8kN2dCj9Dz0nsG7ZloQXeP0eZRbWEwBRe2weMatWQtormEXLs8xAFPrthxW8PUM/mUJu8Zu9C9h5YKERSjmlgL63wUGNTw8tffvxgL/00d7sxxYR8t4YCvtyndroWsPiZ8HI+wiuo9d785gkZaS4GyB2oLBIGeQ5DAA4yogxlNkQ48i8Sm1R6IGTv7BglNLQq5wmpCKs1yiIHT1nk1mbDJnxMKmOvhUTDhilz0eL+7gkeVJPFWwES8p44TlpEKnBnVkaGqPpnagElF1rOjW5xtdK/ftHLXuqpIsDNzjGpsshL3dq6z/xuJu7B1N5yshPOM+lzPiuJy04omYohdGrEGP61jHcyJKnlyJzrlVtn4SYhAxpcM6yI6p7wt8DqpdmKpOA0oEmoQJIflGjO2Xe3R4rx5lXZ8u8e9JLRrfh81wxjiaoVSD0oYxR659YJUVIvJWN8HaXGctXVLcJQesWsXGMVMrNY1hwJIJR0QUKBqCsiHMGrymOe5EeQhqH9ShQR1a1oNKDerAoByoUS0M8eUKc6hQI+fXcAjN1GIOjA/hGctKLS7BYzP4+xoOGmf50MCqgcrpApjeGqJW4BsIjTVTfIgOVid5l+AsX4Zdz//N3bbcbYl977A3i7qCvRmESxj6EEdWalon7uCee7HQO88BL3Hf6yE6UKuYOcnpMnc2DIDyDfFujdrSHMVbHIWbrpCkeJzbKQgZsiTikIKwJ/yUcp4LTlOyICVp6ZkpCS8dPsRy8P8wVWOmasxX47tZjBPinYzwFSXLozGrmcMX3Lixr4GFsqf1gfsdd2JLM5fcb3mxe+4iTx3ye40IPPTbFMaeAOzTGoG09R/RZJCyx9rd8vkc64RhHc/x6F9B+yH9VOkNLLCnu9hQijC/DyoBb8tuTasNC+MXkynxUZAevQgVSTyMXbEmdAp7DljXrDyqryag7fdb7HqUpwbouMQLC5aupA1SmKgIVcEJf4+xP2XJgLkDohkUKwbEZK5jvqQkaAGRKwbM2XSCxCWxVxDHBY3R7esGrBjUGUNyVODaI7JaCzAwxZo7nbbmU6ywydA5K6AUOGS+BlQKwRac3ILv2LK2Bf6BZUMcsyCSHb7Dcvi1G84Syr4Qq6g7hrZCEGXgF/CtCdztw2EGRQknnWTGqrHjNtDWi8IPXY2oAuOBmdCxV+T3Rdgkb9N9lhSfRr3XScGp39EaQT3SrDYj0jgi8C32YMaYBt0KONkqwpCMhAVDZoyPtZZEb+OICUfpNkfLHSpfYcKGJtA0WlMEISf0dRpPUWsPlTVda8K4+UhdQnuE1bo4oEtaxXttQcekWPTm+WTvt4m1SkoHGq0aJ2TmQ+0ms/1jEDCLiJQIxVJ8yaXU8cIKMY57Jo57K8U6YVjH8zRkJbwx5EopGYFTWVJbtFdaVYGnIdAdQ1Pkj6V3L4p5orBbYa+VsthMsOLO8+4PPiUiTTbwmwy/5buD59V4um6rDKLqKJbSS4ZoGkIKpyLYtGqQJYHDNFTOIKl0CUNG7NlVpsajaSCqM0ygMAkoqSSLVLBy332FXURG7ndLayG0FQVfBAAvA4d2kd4KYGtCV7l2CtxVblkPZQBVZLUTPM+2JnwF8wam2hW7DZTKQSKU1WaYAZmCkesQyZDn2K6RpIMV1inauJuH/Xy2rcRAU4AKLIZCCXFGwJsCXO3jUQSX4abO+FAPNdWOpjKaplSgDZ6uXbvIenYoDAUhKQMWDJky5oAdarxWflooljM2WZhNls0maROSmRDfzZ1RGq0ayjImr2KaRuNFJRiDaRSm8DC1Z89HaUdkbl5E/bxvgy3SCdKBE1VIec0x3I30LxpQfWnogM63XUbd6933eoP4wksYXiixThjW8QILEZgRJOIMzAUoh9gywUtAn6PFQYhqo1AnhTUx6N1ybLV24e5LS1fEJQVoJoyKGwoiQVAShxmJsmbKHg0lATuONimCQFscssmspV5KciEtjb5bpUFhUK2mw7hcsJUu8JYGtTRPdKYcYAEFcl/cEEV3ov+dY+zOVlo2BTaBkPc6JcZsCfuPwXxhBSJDA0MDQw0DDx5q4J9qJ2Vhuu739bKDGBhALe3HnMAuWQ/Xdr270w3vVfdGlVlhp9MBBG59q1dQPAT6JEQ7rr2wotsQy+7bUUSPGVg5DIuJFbnyKH3N4DAjyUqu7OwyHw1JVIqi4RHuZMYmiVPo9KkwTuvCo2aLQwyQOmmulAHBoCQepFzkHI9wZ6vtcLU8xV62Q7kaUGUx/iAl3LTqiabRlF5C7WkrFT3GGn9NgQfdvztuHh6k02ESXLCcf9KFK+nEykqcHrcP1aGV2Tw28aLJHbs3NXROXws6H/gXXtToZ0iHYY1hWMc6nsWYYDvhstWSnoPUq7exF0En6CQleejaErKJktsYq0Y7oltsRNRJuce3LG2RI9X20+ssoi4CTOlTJx516GEC5frgOSsGNGg2WOBTkRKjaajwSK0sEzEZOxwQUrBi6DwMLAhSTKciMlZBROn5DL2MkZ+ifdMB22XzKKJHOZ1vQ0NnlSyLj4ddpE6494hvUdYboxSS2+HUi2F3BfUM6ovQPAbzAh6rYCuG/5lY0cE6h39q4N9qmwScw7bcD9yMDLBL0pwOu2Dc/T4hdhzDcMtWMIoHbUUhvMd2m7R4KvTdmsUTRErzkTstKjAryF/qUZ7zCC42+P9es7wjoTgVMG7mjFdzVlGE8hvu5N+dRueQnIgRc05wnSXD1pzqGid5nPOUBPjUHDLhKqesQBUVSwYsGBEEBS/2HmbPO8FhuEMUp4SR1TZoSo/VQUA9jTstJXGtFAToBTqBLfHaEJVSef1Bbz6hw+cclnCtdEjPs3QiD5IYwHGBp/SGA63j+RzrhGEdL5CQLdbwhpsoQ8r9yKL0KmVr4/0QmmWAXWAEcTehs8Ue0/1VieeBwio8xhV6swINVRqDUWi/xvMrfK9ipBeMOWLgaHniTeBREbudKxhyYjwqInJ8alISDDbREIEnUYYUMypP11YlMTCdroRUjn3synudThFxRteuSLEiSlO68rVw/y/bjWh63bYgCKCqoFxB4dnCTePaNnpp2RXTxgIktytISjioYc/AwnR5S+SG0sEHEDdn8STFfRUxzdwO4MQQ/AAerSCqbT4zMK7AI5UESeAkRDZZkif5jdugJuCtDOaRBn/eoGtDlFWYXFP6PqXvk6qIwlll+04LI6CkwmeDBae4ypIh19llwIrzPM4h21zlJNrNj3G7yCFDBqQobVDa4MUNm/4U5du2SEZMrmLi8RKta6rKp176NKmPCT2LTRhjqz83KjT3sYrSiUvd/8/2BrPS1va9BarKaEt2IgcTMzcpVfStS194Ic2pZ+K4t1KsE4Z1vEBC+gmirCSkdQFxiXTjEswMqrD3OllNPCi0veXKNt0FxzDtfZRsxmS37iT21akafa6gTkPqZYyX5Phxjl9XBGXFQK0YqQUoyFTsmgp9M6mIioAaTeRsoxo0h2xhdSOtLLQoPzZYwGNOjO+MrHIVoj2F0nStEx+YWfZEGBYEurIJgvgITbE9AmEPhFgPL4BHbcKwKqxfBKkdnlUMKw9W1zvUSNTAYAy5bxW60wVcn8HFAC4Mbc51N7QSGqexjFRqS91cNZaBIbMGFlehQytcGI7s8xdnkISwuwtBQtdikakUvF6H0jq4AAAgAElEQVRBhwMQuqVIKTsr7uCRhuBaY5Uqz8DQZPizhks7JziMR2gMDZqckIbjNuJgHKYkIKRkwhFnucQFzlPybQxYcRsXmDNiyrh1GBX2xK6/R+CXzOtNZsUmOQk1Pv6oRA9q8lUIKsIkGpN7navqDl0yKOu9sEOkKibyCkI1vYJV4qycopYpoJbsMKQrq4k+g1QWVhzXnV7H8znWCcM6XmARY0sB/eqBWP0VwKPYK6ctFNsSwSZwAsotOBrAIoEohIse/KM7pLNqRmN3erfRtSTEt8ALafwAc6eCuxS1imhUSKk2WPoNi9vHXDizIo4yosAmBAkpR0zwqSgICSg5wXUCShZstPdDCr7MtzJizimutnRLwTdsMGfMjCRMiYMMJfbQ8vNfBl5dc3t+iTP5tRbkqS5gFxNhnUpZ+6vYpGIXok3YPW1tqvkXMAMwLwezeVzGR+2BugxmC8wZUH8N6v+Gl52C6pyDzclUSJXbiQrmR/DYAg6yDle6AqIBnLvNrnHXL9oKxiu3YWMTQqkAiR5B4qZ+l45dsAW8FIecdFO95T73ivugc2Buw2YwyjIwCHC4kbwFPYq7ZYXPJjMO2OYr3MsB223SVzloY07EAdtc5wQlAQ2azElM77LPLnsoDI3RXJ2d5bHZnW0iCGBKBdcVZl9Zd8u+eaTkxP0w9hSmdL9rZH8XU2wikQB3YUGul4FGMkn5exFkpXiZi6GF9HHOuZP/UWx2+cKKNUtiHet4XoXQv0pswzdyN+iSA/lzED1eMa9yBgrmAGqHHNdjK34jldoVnUiQlPQFHiG5yXmFuV3ZxfBfgVBhYoW+o0CfLSmqiOJSRDFJiTdTCGYoz7AiwaNmgyU+Fdc4SUjRAiAz4pY9UTv1x8YxKAasGDPDo7EqACrkUG2xqWctFTAlcU6ZJUfJiKq2FylVG4KtAl4C02pChcduvMdmPic8U6NWUEw8q+4Y1niZgVfC9WiXr5+5g41syUuuPMLgX1P4shuTAq7VcLmBwQXYnMG8gtm0g01cKOGy27AqY+mVYW61FgwW21BicxeTw9dcfqeWgIG9A9j24I4dSGqoHrYVDX+MtbwWiW+RG9imw/ZJYtTXHwosoLLJFeVIU8YwqadspEua0JB7AVPGVASc5CopA65yin12WnDqdU7QoFwiZxeXbQ44zRUO2eI6J3gpVznPBfbYYY8TXF2e5dr8NNN/26J4PMK7u0DdXtEUAQaN3i7RQUOzCGgyv0sU+iKnT+Zaudl77IgO/yD6ZwYnniHqUL77G5AkQqpvwpJI3fM1nZXr04mS7u9yHc+FWCcM63iBhNRiZXWXTnk/pGOecNzb2l0cVWCTBE935XxhTQg8QhrvokS0jd2Yzd1HCEVTFPc8IHb0gczyCU2jaYQjCK7HXbemVDM2qfAZMT8mHW0ZEWKKZGsLPhUbLBxY0m93umOmbLCgwmfJENw7jFJUyu1qAkOz69MYRZYGNIVH7XmYBMyLFBQGI5TE2v2roNz0WZwf0KSGpYrxHqsIs8q6dfpQ70N+EYIY6ruhnDr6pTtMFsEqxIlKWa2GpHR+o7orgAfG0jFXFXghTDbBNHBUu1zA0QuN6q1/gtEQ6xHt5mKELeWLqviAttJglCILI7IgoA7BhIakKIibjNRYUqu1uS6JySgJWTEgJ2LAioKQGZsYYMScIyYs2HBtpazDmzQlUZ2jPUOpQzITs6qHGAyxtySMU/yNAnKPJvApGp+yCVs79WN0X0l4ZE2WNVxee0SHdRCRVOnzJDh/Cae8BVB6UOrufjtQIt4hQk6h+yM4psTxFCF9oed+rJUe17GO52WIH/WTRYW1Byw57nS5C9xluXlh5GyxnXT0SeA8Vl5Y3CxFoveEe/wCluJWYUv5t7nHXZ+8VhH1pRD/ZEpwOiWOM5KwW0jEmCqkaOmTFT7XOElKwmmuMGTBgBUxlubn0TBgReQuyAUhOREhhasm2NcOCFtTpdzEnMmvcz67AkCjFVnsUzWacxf2CA/rbpFNgTkk+1WnZ7Cwv/XE7dd49fcdkJ6IWN4Tw8aArdsW+NMaVnDmETjzkBuHu+HsV4AvuSlQcO4kdhf8mGW85vtQzGgrNfcYqy20rEENYPsOGIhTvYy94EhG4N1Ot5CKbEAfkAq23fBSOg+l213bZAV1qdnbnXAw2XSU1pQmUng0pCqmQbPDPgURVzjNERMGrKjxOGKCwrDL9Vb+e8WAS5zliDEZMaFLNL5WvoSjfMLJ6BqnomtMNvYZbxxweM46ZsYuwYiHOU2uufDYneztTezv2MG2iHL3u6CjSkLXipljuwYyDkIW2se2I6SlJiwI4bYeGmf0ISGgEHGuvISlXkgPaMl/nAw8vxQh1/bW61jHCy5EvUhcpiRqrIuSD7kGFQOboELbAxaBOynpClXxCjZBkB1fxPFOiFAVJ8BYUWcRZuZhKp8q8lkywNcVcZCR+FnrF9GgCSjZYEFASUHIghG1MzqSZGGTGTNGHDF2jAnLrKjxeZgh/86d7LDHLvuMmLOhFsRRxsIPiPKaIG2IL1aYfYWemk4cU9wpocPCLbEthyMIHjXoP6kwy5p0ldPsNHBb0yHzV248DrCazxexC5ngT0UjOgY1tpYH/sSNkw9cs3iJgcX84Td0FRyR7BYwn7Gvx3lgtEmcAASFJFNg1zxZQA9BTcGcAL3VcMIcsLmYU0UKowzJtEQVUE5CpsmQy5xtEzJLg61bIa1TXGXAipKABRuc4hp38XUKQhp0C5TUQYPyGq7oM1zmTCvIdcSEA7PFtfIUl/KzmNynyQIynVhDswmosw2eztG6Bg+azKPejzCpO4+lBTGlU0c/ZX8nV904TejWcaEFXwauGpexLeiUzETE6RI22xDG0ZTOEvOpQigZfeeydTwXYp0wrOMFFgXHAY9PFVIgj93/9y0x34hqU2Vr54fYa991XOXB2WIHnpUjhA43KczNJXYRE5XqQ/u42fCohxoGiiq2K7JWhtQvWQQVgV+iowaGhiAqGAZzYp3hUxLpnNjPnKFV7qSBVk53Kmi1GUQJUqoXU8YsGLUCT75XEeiKmIKEgmiUEZuceJThlRV14WOMwgsqPN9e7FXd4JcV/rLBnzboKwb/EYNX+KjIIzsVs3ebz8DkbKxSdNl0HkbO8IlvpdPUkq7QENgBnYIWpULRS8CJM0leJztpKSD9f+y9ecwtWVn/+1m1atzjO5yxJ5pZwebaXHEg/iMRlShBg4BAA20UE8FgBKIJRBOEgBD+MGJMTC4KaofEwB+KhjiQJviHoj9/yEV+cLWBhu4+0zvvqeZa94+1nqp63z5Nw6Hb7tO9n2TnnL137dq1V9Vb61nP8x2EIilMFXFzlNK9iBdBV5Lvl/KdcKFqgNIQlQWhKqgCTaUVqoEq0hhPOe+PCo/AuUWEZMRtQlASsGLAEVN2OdVKRTeujJ0TUeGTm5C8jjhStqJQ7kWUBxYIuWTIodpi0UxpVgFNrjsphNAeq6k0jZiCVRq06qQT5HeJjYq0zYQ81PNAabcXMUcfCHyoJdv1oU6hkd6NqJuJl4TYmJ5MGqRHIsqQzVW2uT5jbW+9jnU8IUOa2N8qpNwqS0/xURTLP/e8DJz3lWwvUHyn3qSU3dWUzoJxRCewcyOWAy/PEyBR1IMQ4rA9lEJWwiNgw6BONXjTCp1k6KBCKUPol8Q6ZaSso2VI0UpLA50eg0sUxMyqIGSXbfdVhgEpiUoJw4IwLBiOl4yw+hAJGYVzJYxJnQ5E1T7iqmCUZTDzKA7su35svREO/RhTzRkUGZ5UGYS6eROYW0BdAS6D2QWzZ6sLxFatsVlie+5L8BrnROlcllXgFBylba5cbufkIj1RbxS2hFQ6lnSTp8xbAnGZ0AJY1X53VYRejY4U6RmffKJRVUNSZgS6YuEN2eU0C0ZtwlDhc8gGlzjHDqfZY5uAkgmzNlnIXKNjt95mpzzNSC0Z6iUHR6c5/OYpGhRGeZDAIMipspq61HiJwWioPJ+60dR7IczVcfiAzMkZHfMkplO49MCdfhuSWOxhE64aq8tthu5RQVN1/9rymDuZlRu4hE5GEjqDKskSJWsT34knRtLwZIh1wrCOdTwo+hK4zrKwtagUu1/nkgR0iEdxTzJAZMGRMtGLuKQoQSbYBEL66X02xclDkW1PG9RWjT/M8CKbKGhdE4W2upAo27LIiFvsg+/48UKxFIvsLfbZZq993X5V04IhZZV8Hzc58F7qkoSiBetNOOIMV/CpmDMh1AVnB5eZxxPuOfUMNv55znM+/hXOpEeYLYU3aNCRmxyk7X0f1tjqFvf/+6C4H8rLEEXW/+FwDnPHXtANDFXX1fGGEJ23oMeWCeDILMXSuTgnLmkQ50rZdheLo7jBfe4AJ/6ATWQucFzgUM6Fo176Zc1of0FZhVze2qYe+MeYK4dscMXRLBNW3MI3eDpfPZbIGRQ5ESkxVeDT+JpcReRENDdr6vM2qSgIQSlK5bNjTnNkpkQqxxjF5fIMR9kmVRjTHPmwo+zvAzsn79MxJA7pdLddZeJYuPFrqz3CojRYRGl5BOWCrpSzS6fy9VCulQIsKU+8/sSJNehxHet40oZAxkWMJqKD1cv/BTXWfy6KkQ4ZKMmCppPqlYeUx8He1IWt5tgB7WdHdBXcAJRv7MPrer+e1+CpxiH1KwJKPOdHIWXSiIykVxUwWCCkDcOAFUNW7fuVo2nG5ISUjJkxdAqUgQPpCfgyLCrOLvZQqmE1GlIHHjfwANkzEj7/s89jVC7ZjveIg5TALxmkOYN5TvV9ihJNUNYERWN/61nwbwZvB3Ru6YzDJQTORElV1kJBu/HxBhZKgqGVdAZbhQgqUL4FRuK507qNBf9pLJZBCDEi3CRDMnGnU1oVfUFQp/+lFHihQXsNeNZtdOyoqkdMW7try1ep8TCOFWHP3ZwRh2ywIiEjsaQYJTlkBgE0gceCIftsuTMFyulqGBSFCQmDnLhJSS/7NLuBnb8Fc2jocl1Z6IuomLCMi95DoAWy8BdGZVpZq9A6ckIUYnEplTj5exHaTN57SIIt16y0LwrWGIbrK9YJwzrW8aCw02mnzyAzksz0/axfuJGCEhs4+qXXGfiJMJ5sKrsQlUjpE0uLWHKTgG7SqoGVwvgeTe1DYlBR3f4FV3XAqvCpvJxBsLTy0/htC8KjbkvgnlvlidmRwjiuREntfCoMipCChLTVfBDp6YCybXkAJHXG6ewQUyj251OisuB8epk9c4qvnZ+SRjFl4uN7ln7YHFg/DTNWNFtg7lfwTexkrECPQN9IqzicFPbRTm4ylqfcGImcs5ymwrUt6I2r6BFIBb2f44nw1ibdRClVIHFJd3gTI/vTUGnNLInJTEzqJ65dAxkJpTOdSpzSRUxG1BRs1EfkXsShNyFVsUvyaiJyx79IBAIJWDvsGROOmLQsC6lQ1GhKFaD9hjDIKf2Y2g+s8qPxOoaDzOGZ+1cULZd0BTOZ2/ty4VHvT6FxXigtaELkIgX9KuUxqSBIUtC3uxaVSGnzPXFaEWvhpnWs40kfGrs8G9EBuWRmcrMbJfYOvOFez23dPIzAePaGHKsOG9ant4t4npTLZXV3yj2Xboj0nK+A2ddUgwRvI8A/m0JQYhpFmg9YHGziByWjzUMG4bKtAsRkrJQlU0o7QSYxsK2IASlb7Dv5puCYM6ZsO2LBBockbb3bhu83eKMa/2LDDV/ZgXuB+wyT+gGeOngAzoK6yXSrd1cWD6KGMKnhgqVPKmEybGEn76H7vz1IO04NtnWx6L0n4EURYNqj0xTou5xLdce4Y9jHalE/3Y2vOFmNsMnKgftcCOaUPR4lK/cI8iTkUnKeI6Zta+EQy+/c4Ighy3aMNjngTLXHren9POCf5/PJbYzVnHFL74BLnOMCN3CJc1xsZTgFW2IlozNiFgypnPGYb2qbykYG/5aM+jRUqwSzULBQ9jfs0glS9dtecvkKqcH03hdTj/ux7ZkwgMiHIrduYa14k7hWShlDtMSlNdGPii5DWcf1GNeUMFy8eJHz588//IbrWMd1HeLWk9HJ4cFxUwJpU9xDmzjUkaVfyvI1n8BiAnvquD0FHF/lbtqPc4CdtCK3+wM6a2ynVm1GmmqSUEcRuddgtEetfZrE52jksxhs4iUN2rP0vni4IhnP3fO6ZVKEDtefknA/N+FTtW0HaT1MmHET97PFPt/gKe0askFbJoCv8QYG/6aKaKsgvL0gzAoKE7LQIwbhiq14H1+5mdvpZ214B2x5+5R5QJGHhHVOWJdEq5owq1shqLaqLatkmbSl9L7nnsuid8aD56QpVl8gwE6AW1hmRoKlv4o6Yt+IKnCfG4HKsZgGKdEDIQVnucSIOftsozCc5yIFIXPGbfKQETFnxL/6I1bDEXNlH7IiLR27QizJL3GWy5ylKELK0mc73Gfsz5mXYw6qDfywQinYn08pVlYHoqk86qXGLD3MSlsgriROfeyFJAT77j2xUOlXGERsTLoOUtkpFQShZQO1iZhkszLoQ+zfjey0HyLuhDsg+SOQSsT1254Qxsujsd/HU1xTwnDzzTfzYz/2Y7zuda/j5S9/OcPhQwnhrGMd13MIXeyksY5YVta9h8xsSysrWIuMLseN/uTjch8QC2K5v4pMf1/iV8rCMonVYGqFwYfIFXZd/mIMNKGm1HZa9+OCMM5RfgAqbhkNYpAkuAdB9geULVBSRKKGLJkzZuz65rSHbuz+VIXv14R+Tjy0wEvb5ihIWDGqZwyrJX5Wd9i3CmJT2H5+VkNmBa110+AdGjuhCTNPqjLCYLiArRD4HK8e5L3/n7yz1XQuzTFddVzaGRJ9JeQBHftPTvnUYiaCrMGYChKPOswZsSAkJ6AiJ0RhWDFwbQRrUFV5AQtvxIg52+ySE7Fk6FKzoD03YKmwxiWWEzVjwIpEpWzqA2qlqZRP7icUUUyNT+kHZMrWj8rMvkZsr4nW9sGja+k4XG7bVZD219yNk7x+cr4yHjSu3dFeoJJZpHS4BMm6TtIr5Uul1ybYhicmGPKJFteUMPzu7/4ud911F3feeSdvetObeNnLXsYdd9zBT/7kT+J5j6+MaB3reORCMAyixSB31AS7LBMEnZhKCEIupBMCUPYfuYn72FXslnsuhQsxspIQ1mYfsR/SVR6kahFgpaZjg9KGIMlJpkd4uqExnu2rq67NEJG3CpJgcQ2yqhEkf4NHStJqOYQUKAfgm3LUtjlCCgJKGjwKQqYccSv3slnPGBY5waxC79foZYNOm26OOCi7BKHEJgNHdH5gUpXp+x+JEZZMdtDtT8ZIXhN7y7kb5/O0IlMt+LQvXJj0PicVh237MA54Gi1qvAXk2x7KM0yro/b7a+Uz9pdkXkymIirlW4AiISkJPhUJKXPG7LPVmk8JY+U0O5zlMqFfEPil+xmKMgjIiJ12xohq5FOOrJHV0gzZr7eYLTaojU8d+FakSkJ+/8L9bsGB9OipKDpWhSRdS/eQ8RBAZItF2Hc7XNBRKvq+LVKikBBeq9AsRcXr+o7qUVJ6fDT2+d3ENSUM73jHO3jHO97B5z//ee666y4+9rGP8bGPfYwzZ87wC7/wC9xxxx38wA/8wCN9rOtYx2Mcmk7Rru9mGWJvljL7n5ztBUnnlnHydIJF5zfYe+4pbH7hhCXb/ONqIeqKci8W3wofWCi0LtHbGcavSdOkLUXrsGEQpe1uCgd3lORBEggJhbVoPscltthvMREReSsAJa2LGu08E6zJ0iEb/DMvZBLMOOtfYSM5ZHLmiGm2ZLpaooydYdUSO5ldxLYHRthJXZyTRTxQgKCirHnIw1sWCL5OnCpF+VGqORO6XO+keNHJaBzOwi2Ifd2wkS2YZgr2rYw0gEkUzZaiHGvSOCDTYU9vIWGH0/wXz2qprzWaOWNCR1l9Ol/lNr7YikDZ0+3zTW7hAW4kIqfEGl4JfiIrBxwdnWK+nGJCDzZPrNjnwEp1/idLN9bCgDzCzv2ee/8iFr8gFRxJ5gB7gR65EyBZyIKudFHTZXtXqxyUdI4g128b4skY3xXo8fbbb+f222/nAx/4AHfffTd33XUXH/nIR/jQhz7Es571LO644w5e97rXccsttzxSx7uOdTwGISwIqe9K/TY48VzoZqK0JIgyKRk4xJ3nWzXIBnuPHXNc/9/R9h6kTi3AcuHPy0pbMJliqjSExtdQRGg/I4yzlobpazsDlAQYVEvDFFXChNSV14u2YhCTscEhGxyyxT4TZiSk+G4GieqScblEp4Z8HsFSoRcV1dKnWEYE44LB2ZVNNIqcqCpRFah+ReGQ4/S+yg2XKBCLJIa0yavec9EDgm7hKoqQcmrkdMFxgL/gRAYn/pWqg6hzik5GaPWLwLILq0SR65BUD8iryFJZfSuypEKD8mpKNJkDLKYkFITOpCpgxsRqLuCxyykWjFpLcnEe3S232au2mTFlbsYURUzZRNRDqHxNvhyQ5wm5CfDCkpoAnONoW1k4Usd1FSR/TelwixNs5eUKXf4rM4R03lZYk7RWHloGPqITt3ioZKBPp3xiJQtrpcfvIDzP40d/9Ec5ODjgwoUL/P3f/z3//d//zbve9S5+53d+h5/7uZ/jQx/60BoouY7rOKR3L+IzQuyXWUlKtKK333e8FFSjc8hUgX0UGkoffA9Cr5vsJKQ9IdVdea1Pv5QEQtNNhNpgjEddhNRVjTEaFVjAZVHFLPDwvBqlG2KdEXuZu91Vjipp2OSAhFXbqhBQV4VPSeDkkH03IiF1ExJVBWFREmYFwaqkWlR4c4NWNf60ocZnVfiUTUVGSVwWRGlOUykaT8EUTKBoCo+m8Aj9Cl/X3dAeYSc9SRSEKinsirr3usg/+3S2z4JHaOhUN6VzJMmC5HaiiSGS3nIuZD++pViaQFEFmkUSs/CGlEg1IcJgW0AlAUuGrQBT4YipKwYsGLUtoBkTDtjscVvsY8ecZt84HYZGUdchTa1RZUXTKFbLKWUaof0CaoNagZHxyFyiIJ0BsUkJ6JIwwXM02Dm/cr95Qpco9HGJpYImtniGtj23onMlE13pDh7bnURpzdHbuZTc1jiGx3t8VwmDMaatLHziE59gNpvxvOc9jw9+8IO89rWvRWvNRz7yEd773vdyxx138OlPf/qROu51rON/MIQt8XAR0yk1QWeIIHD/08BZqKyNdYtxSCO4HHby0XtuFyLcNODBTtwDbAtDVtIifzwHLtnJl1NQHA0oGDh2qGnv1f4gwx84Z8woa1Ucr3CGGo+n8XXOcYmMGIXhBh5gi33u46YH/epKB6wGA8JBwdbpfTY5YJMDlgy5aM4xYsl5LhK4rGfCjA0OOZftcj7doYg1adLdijKTUJqAjXTBZLWyLYtDbIlcTBEz7BwlWDtpX0jCJSJLslsBMoomxhkspbLvbinVDAGibvSeS75XgprbKoPnGeK0QpU5ZbKyxk/Awv3iIzZISZgz5oCN1hV0zoR9tlodDImIvFXn1NTMmLDLKZIw5Wnh17oNR1A0IRfz8+yl29S1hlJRH8Q2X13Jo+dlIrmr4NOFEbmJlSi/jHWxDLHql3I9ybhepsuNYwVFCKWoi4nOdom1bhVFVDkZkkifpK4IIEVaGtdvwrBWevwW8R//8R8tduHChQucP3+eN77xjbz+9a/ntttuO7bt29/+duI45m1ve9sjcsDrWMfjL6Q8ezVYOXSNcmlLiKygBqVh5HWTmwDvnMfPgyLEJgNCh5NJQFhqPafHtriRmHbfOizQcYHStdtdwZAF2joWMGaOT0mFzz5bbHDIiIXrvZ9h7LQZ7K8uGTvHzBpNQMmQZetZceveN/nhb/5vhs2SKMrxKgMZ6KAiiCoiU6BqQ1jV+GVjy+EzGO7UNAeKwK9tXWcfW10Qu2bthlHUiOveOIhao2AdUvdZWdgO3PBX2B6+4O7E7lpwJVfoDMJG2PlMxjO0SQMRmA3Q45qYDENDQUhCyo08wJgFO5xmzJyn8jVKQhaM8Gica6hu6ZRgcQo1mge4kfu5iXNcYsQCz50bsShfMCL3IobhksjPCQYVerOBGppaU9QhRR2S1yF5npCuRlRpAKnFWnDFjcfTsAnmrvttN9DN2QLAlVx5gE0upANngKqGxQzSFV0fTTSlpXIgPix9plHJ8WTh+peMfiyEm/7t3/6Nj370o9x999184xvfYHt7mx/+4R/mPe95D8985jPb7e68807+7M/+7EGff/azn82Xv/zl7+h4rilheP7zn0+SJPzsz/4sr3/963nxi1/8LdkRz3nOc3jhC194LV+1jnVcByFAL6EqCP9RlmmCbwh7z33Q2j68q+xKdifIdOnnSyLRh1UIwl3K6DKhwvFycwHGeDS+JgwqgjC37Al0a30t+gsejaUCOrKfgCEjChIyNJX7f9omCA2KgqhlUHh+Qzn0SZuYIgyI6pwoyME3mMBQKkvMrEtNVWjiZUmUl1A0nfZCha247PfGSPfG5STAXtriYra0ws5Jmg6gL9sJJqTEVhNiuv6+7EeonJKcyRiLqCegc0MSFPi6oaSiinKq8ZKhv2JazWmURvkVpQpYuTKGR9OO7YoBS4Zu/EJGLLiJ+1uhpkM2WDBiyLKtPKwYuHzTEAQlBsWSIUuGlkHRgKo9TNp0FN0VXeWqZ0fe2l1LhUXGT6oVMufLNRkYyFMoc2gE2Cj4hL4ctJS/5CTJYEtFok/DXMd3Gu9///v553/+Z17xilfwvOc9j4sXL/KHf/iHPP/5z+df/uVfeO5zn9tuG0URH/7wh499fjqdfsffeU0Jw4c//GFe8YpXMBqNvq3tX/SiF/GiF73oWr5qHeu4DkIQidI0D+jsDoV+KUtjsasswAvB1x3jwVlQHLNjlntxHwrRl/wVlV4xVRL6oaj6KaBR3USgPAh89CAlSVK0m7gGrEhIWynpAau2klAQssEB222HN+UAACAASURBVOwxZMWg5zkhegtDlhRNyG49gAamZo4JFffefDPKrahHLFr1yKjHyiiqkLyMUN6KSJd2JSvtBpnsherYd16UhEKSAFEulJL8Ed18JO0dEWeSuUuSL8nvBCvh4CZtVUOey3fGYLSizhRNbvmJNk8zqHGDiWHsLTldHFJ6AUfegEr7jJmzYMQBmy2F9YgpVzjjJvwBI5ZOUTNtcQwLRiSkDFkwYNXiSJTTeOi7Y2ZEZCYmLyOqIqCpVCf/rLGtrCXwNTrogeS0UrGZYxM1aUnImFYGisq6gS1XdGjSI2wG0tdmuJqV9ar36PtLXN9R4z0qFMhv1ZJ429vexgte8AJ8v5vGX/WqV3Hbbbfxe7/3e/z5n/95+3oQBLzmNa/5ro/nmhKGX/zFX/yuv3gd63jihMw20pKI6GrjIt5/2r0vCPPQ4hgErR7Qye6fwrYVRCNgC1smF4zZFh0QT3rzgngXHwSZ2KZYip37S4+mS4abMzzfloiHLJgya8veY+ZMmLHJAWNmQCfQJLbYE2btaxE5U444w2XG2Qp/H4JFjZ+VtkTuygGeQ/7L9yj3r4ehuVRiLq3QvunaCJIwiArxebqV7oRO60eq29CB+IRu+VS6qnhA17oRBoqcmk26BGJM1zkS7Qt5HtCqcZoRlEPNLB6wCAatCNOMCZXnE/o5kcpJBhmKhsbz2taDxTgM2wrCioGTlg6ZM+EiN7BghEGhMGyxz818k6/zVP5fbiN0Xh5S2RHKpuyzRuN5DXGc4eWKehnTZMr+lgOsb8cenVyItLMUHVuyL9Io1YfAwMUCdipoJAMTzmvgBk80qEd0mZzQKCVkW+G2Xv86DI9F/MiP/MiDXnvGM57Bc57zHL7yla8ce90YQ9M0LBYLJpPJNX/nNYMejTF89rOf5etf/zoHBwcY8+Ae1Fvf+tZrPrB1rOP6CVn2Nti7cIptDAudoY+c60HtTQQm7FoGfmC5ent0ugMDt2sB3cl9WCwtArqSuTyXz45w8smqxVdWVcRqMcEbl+hxQRTklL7wDaFGs2TIAZvEZG3lQbwMDtnEo2kVIUWHIaRgEKSMJ0umyYxpeYRnGkp8NI3jVlRty8OnZMKcCUdUE5/yxoDAL/F1RVkFNIVmcCUnPii6u5QsYvvVbEH9i4aQiDuJWJNUaKT6oNxzYUfk2J6+iBgJ60KSOElIhnQihRWoArRuGJS5xTKMFHWkCMmonNRzjW4loEXNscJ3bIgNN8EPWjdLq1+RkhK3YlkZMTWaETEhJee51JpTFQ4TMWPMshmRVTFZGVMUCXXqw0xRF5pa+V1CaujwMtt05ARp3UjlSjAhUmG4COwqSw0Ze7DSLqkYc7zMNaFrRchDlFBFx0Teh+POltcvjqF+lGiV3ykuwhjD5cuXH4QlXK1WTCYTVqsVm5ubvPrVr+b973//d6zSfM2gx1e+8pXcc88933K7dcKwjidHiHKdlFflBijtByHDV3TLOeH9YelpRkPtnks7WFbRfXXqkK4XLbiyvlK1fEZ0GaRk7+7ZVR1Qlx5BCGpYUxmrFCjR/7+mZsKMMXOnu1C1mgGi+jhwltc1mijI2Qxs6+IMV4irHK8AvyoJm5JQFYRa5Ig0sU6pfY8q8agSjVfXeLWizjyqhaYplf09CzpWhJTHpRojjLwMm1CctC+QSrhsJxiEPpah5LhLuYyzVNvF7VH0uRz+QccNgyQnqCu0ahjWS0JdUnvaVhHUgKUeUCgRX9Kt/kVIQY1Pg3ZTTcmAlBqvTS0WjFgxQFNTETBgxYQZBWGbSMhEVaExRqFq8CpjaZWpwRhgaKzbJMr+hm266tUCW3WQ6yim06uAzqdDiD6RhlB3OXA5gDpxGwnfVTITuQAF1Nh3e1VXeX79JgyPl7jrrru4cOEC73nPe9rXbrjhBn7rt36L5z//+TRNw6c+9Sn+6I/+iC984Qt85jOfQetvPym5poThl3/5l9nZ2eGP//iP+cEf/MFrAk+sYx1PnBD5vJN/TkJpEKi9zEZSKsC+FoTgh53g0AZ24Sa7PoW9yQ96D5E/lkWbfHWC7X7IqnHknrv39aTAP5MSJ5ZS2Vd1PBniwFijucQ5ADY4ZMCKjJiYjPNc5DQ7xGSMmbesipiMU6sDbtjdIZoX9lgE8+mGwYgR6H/lqK8su1Xu5wv4sjvuMdYsSpwk5TNSShfqf8nV1RpzOoafjO0GHUtCJDMkx+uH58ZdsAxiVS5DNgDzFPDDms3DBRgn6hTCFgvSIOIgGdH4EJOREXPAJh6GmJSSoBV0ElEnATkesteeA9u2SNhnmyOm7tCaY2yViTezIqKRT0ZMddqe8OVqws7+edJ60FVJ5DoZGVtdmakO4yHtIJm7H8BSWUfAc7Db79FBdA6xrphtJU2QlCIV3adXilulZL9Ct7z+WxKPB3vrr3zlK7z5zW/mhS98IW94wxva19/73vce2+6Vr3wlz3rWs3jnO9/Jxz/+cV71qld9299xTQnDl770Jd71rnfxxje+8Vo+vo51PMGixN4oZfaRxrg0y8UGMHbvy/J4bB9lDXUKXgCh37V8N+mgEBKCG5NVsVDehU4J9qYe0yUSS7ruyInICanQbtVvefICZPSpqLA+CKLwOGTJmDmn2GXEojVLAlq0v6hDfmNQ8/lz2Im/hok3Z8vbJ1YZAQWb9Yyt6ojl0yKWNyeM9lNGB5mdmGbYisERtrvTl4EWDN0hXSfooUQFhUkh49NgEwXBpgr6XyoKEgIwPelLIXiJU/bUqR1sud4NgxKA5gSiuGTbm1ENPMqpwg8qBqyYMWGH064QVHOFM1zgvBMbt7iQs1x2X6mYMUFTozAkpC1mQdpBA1asGHCJsxwxteyW2qcoQ/J8QNkEeEmFHhet4mezG1D/d4jZ8+w49okMBqt5cR8drmNGp8UgVR+5Dqno6JTyr5SFTtIr+0yJJw7o8bGOS5cu8dM//dNsbm7y8Y9/HKXUt9z+N37jN/jt3/5tPv3pTz/6CcPTn/70hz2gdazjyRPSnxW+nZhOyZ+XIO5k1haSu+O7KcDzQKtO0mGETRamHGdmStm470QodD8JmQz79L9BjTesCMY5QZSj/dodWdmjUwrRr2r9Iho8fCq22WOTgxb0OGRJSNGW10V/waMmbEqSqqBWHlkSUbt7RcgKjwI/L4nTAr+oUSXopSFY1OjcdHg5qSRM3EMWqgLmzN04pRxXGRYaqWA8xPJDEgDZJuB4C0IwCyJwJJUcqVpIa8LleAzBRIoqUVRGOzSHHT0TWF0NL2jQVFS+R6qswqOMsBhRFYTMmFASkhFRELWYEKuq6bPLaVeZsOfMfi7CozlGp2zwCKhse0IpfK+i9kt0lEPtYzwFhQcrhdlTcFHZhEzaLNAZT4pUt1B8pbUjHYcS20IzIrcpPSN5s8+OkNZD/7nu7fz6j0fC3vpLH/tP/s/H/vPYa9nRybLXg+Po6IiXvOQlzGYz/umf/olz58497GfiOGZra4v9/f2H3bYf15QwvOtd7+Ktb30rr371q7nppgcrv61jHU+uEISYzDKa431baU2IQYHMTE4VR7uWhMxeQ2x1QRT65D5U0SUUIiApkyF0ioWC9DfuqzcN3qhCD3L8qCBUJcoYmsYjVAUTNQdsZUGojn03S4tVWDo2xBVGLFpQnwAihV2xwSGjesUgL6i1ItUBRnXOlwBxVpEclviNndyC+yuCeyt0ZLrER4ZJRKg26aSzl3QrXVFqlMWqTGh9zIfYWed0rJKT6x1po0syJsmCsCTc58x5YANU5g5nS5OPAnIdkSs7eqUKqJWHpxonauWRE7c0ypSk9ZHYYxufymETTnHE9Bh7YsXAuVMOSUgJKVv8woAVCsMVTpOSMGqWDFiRE6GbhtBU+LqhTjyyfEC5SjD7GnaUrdoIcLQvtS0dhRAr5CQaGEJsEFiCYDryChpRcJTMVTIyn66/IaAa59DVojBFi3pdaXjuq7+P5776+469dul/X+RP/u//5yE/k2UZL33pS7nnnnv4x3/8R77ne77n2/qu+XzO7u4up0+ffviNe3FNCcPLX/5yZrMZz3rWs/jxH/9xbr755qsCJ/7gD/7gWna/jnVcp1HSlWOFkyd6zQ9wnPaQYhvkW1abv9LAJqixvXfO3aZieiRFiiH2Zi8UQeiohyI+tIu9uW/ipJAV9TCkGQRU3oCVMvhbOfpURhN5ZKEsvWmNjxKXLIhbpVQW7uOW1sraei9av4mCCE3FkCUTf87G8MhW8D1bOrfVBzspRKOCeJBxyuxymh3UWQO3G6bFinHhJpQG1AGdNYfCgvMO6Nh4wogQCqYkCv25R7B1UlHosyb6IYlCXzBKTmeApXVGrgVxxe5Lh4bkqCJOapqtHDOwhNHMDzmKh8z0iEM2WjXHFQNmTLjCGS5xDoMioCAmp8InpOA8F12qFnPAFgElt/N5znOR+7mJy5wjdRTKfbaYMbHr9aZhJz9FXsU0KJrUp96NaZTC2y6OV1JGvd8sINJDbGKw7X6rYDpEefTAbbPprr094FCwC5LVSmYnLKGaLssQN6sRXeIgjCE5iddv0vBYSEPXdc2rXvUqPve5z/FXf/VX/NAP/dCDtsnznKIoGI/Hx15/97vfDcBP/dRPfUfHc00Jw2c+8xl+/dd/nSzL+Ju/+ZuH3G6dMKzjiR196T9RWeoLK3h0usXClkixN1YBJzQWKWcGtsog7YS+1o24WfbL5pJUyH1aqgweXUdEWhkBgMIUCjWqUeMSEzc0jUdlLOGxLAOq2ifwSwLfSkPnhEyZta2JFQMKwhajMHbvxU4FMqS0q2CV0yjFiDk38gAhOT41QVoTLBr8okIXNQOzYsgSSoMqaF0sW/dK0WGQSsKMjrYvrIe+eFNf4VJCtpGxPPm+nMK+ICF085kkYUd0Ike9yo43MFAbdE3LtNBxg65qgrhCB4bcs7QDhWHFgPNc4Kl8HeVaCk7uiZSk9Z6YM2bEsm1ffJWns8d2K/gkbAvxnzBKkQQpqU4o8am9gEYtKcqIVTWgWkZw4MFcdcJV0hU4cmM04DgepK+oKbLjc467g7btBUk6992JkkEXd0o5AYI+lcyt/3wd30m87W1v45Of/CQvfelL2d3d5S/+4i+OvX/HHXdw8eJFbr/9dl7zmtfw7Gc/G4C/+7u/41Of+hQveclLeNnLXvYdfec1JQxvectbmE6nfOITn1izJNbxJAzpx4Z0NMn+69Iwl5lfZnmZ5UQJUpB2Q1CT45tK4iCQiL5QE3R9ZXFjFObEADvJjTguJe1W2Gpg8MY1yrc36LoMyGsro9SgMMZOXn1jJJ8Kg6JsJYMqarSrPlQ0aGJSR4xTrk8fEFK0Ko8xGUldkpQFOmvw8rrt6JODysETSehdOilomdBmdCV01XuvP8/I3AXd/CQ0U3l+8hT22z19uwPBiEirQhbAfSdLqVyIAZbDPviDmmFV45c1QViRhhF5GKI8UDTE5Ew4cj/BZ8GIOWMCl3DFZIxYYFDUaO7jZnY4TYNHRN6+PnBNixBbQYj9rE06yjCgHnosVxNW+2OaVWDZDEvsgl4qCIfY6oHojfXneCkE9O0glnS+Hu34i+iFsCKWvTclAxF50v5JqenUxq7vsE6uj0aF4aH3+YUvfAGlFJ/85Cf55Cc/eew9pRR33HEHm5ubvPSlL+Uf/uEf+OhHP0pd1zzzmc/kfe97H29/+9u/4+O5poThnnvu4X3vex8vfvGLr+Xj61jHdR5SWg1OvB7SzezyfEQnEtCnMhwA5+ia9C4MHZBPqG5yo5YV4KE7hFN0lD+fjurWr2LKjT8ApoZaB9TzoHWrTOcj6jRitHXAcOPo2K9p8JgxaQ2VxG3S6gL4rBjyDZ4CQELaUgBHLDjHpZb2d4pdbuQBRqMF8agzbt7gkA0OLL7BwNbunI29pXVL7B///VgmQkonWXxEB7S/Wsj8JWrcJ++7Is55Ney2wrpZnqfTfpBuEm6sBWOyAieI2eWEFagK4mVJTMnheMz81Jg4yjjHRfbY5h6sOVBMxi6nuMQ5ttnjPBfZZo+InEuc4wpn+B6+wm18sVVzfIAbucKZVg7aDlN4TEOjRpMSkxLbsnYfdwtwD/AFOhErEa2SmGHprGCvyQP3XFSdBfvQDvb9bqMRtiS2oEOwCn3SuIGXk7OO7ybuvvvuh91mOp1e1XjqWuOaEobv/d7vZTabPfyG61jHEypkOS8OTxJSCpA2RB9pJ32DKV3NfAM747i+QeB1XQvt3u4rS0uVoOi9n/S+RpgE426X7aGMgA3Tig/psMSLc0ylKWYj4iQlmh7iB9ZwSqypR87GaEBKwoqEjCFLtthvTZDkIXgGj7pdBY+Zk6wKwllNXNn37RqswVvUeLOGwFj9Qy4AFyCIqqtSP1tgvZwCkcqW1kTfDFGeS4tCqi9yuoLeeHp0HgtSqRAWhFQWpr1tQzfOTmuhiRT5SJNFVnGxUFFXtdCQq4gFQ0o/oAlojaMKwpYFIW0eg+K/eBaf44dYuVG3AtqeW736pCStcBbY/RmUE3iyQMqiCcmKmLxIKPOYchlRzULIVAdqvIKd20/RMX7l9wvhwbjf6hxEjyUTUlnRNZRzqEXwY0DXIxrR0ScbOixDepUTfP1H8ygpPX4nOgz/E3FNv/ADH/gAr3vd6/iJn/iJqwIt1rGOJ1Z4dKg54TdCt0wVEIEs7UW1Tvh/Mnt7vf1J8tCAadyk6FvVx77So5gpQcd6kNaFWFOIL4KI60En+SD7cHbQJlOYuabRPo0fUPsldRXgObvrTn+vwW91BOu2rSCS0D4l2pWWZVqz2g01MakVb/JKa1GtoERTyrg5D4OBsS4K9VRT5xo9rNHDyiogGo9hlZJUeYdDkMleEgUxRBSsg6D3+3mbDLO0c4SKKowLWSlLF0kSMZlABa/QZ0/E3WveyKBGxjpwOo2DnIgZExaMWm8HMYmq0ce8H5YMMVj3yjljdjhN0RIrbdWgj1mw/hINnntW4Vs3SzOkqn2qIiBPE8pVRLMMoFJ4QYWpFSb1MbXq2CdDOqEmSbok4RGjLSkOSLtLcuM51i5ba1ARNCE0ojkiaFS53oU11M/85PW+SuT1i2N4LECPj0VcU8Lw+7//+0wmE174whfynOc8h1tuueWqLIm//uu//q4PcB3reOxDlvnC+RPwQIKFlccnts84Tm4XztrQbStcQBe1gsYD5f6du4/LxCQrZqFTyj1XqJPiQigJg6ajVqbY1aWTgGiqgKYIYMvAKSizmLoMGG00qCglJaHBax0VhTYpLpN2cqtbZ8uKwLEqUifqNHMeETOiOMePq2OUSgBlQBnDptpnW+1SmYDKBNZC2+TkVUxd+qjUEC9z1D62DRPRMSL6uj/SHpfESHI3w3F8gTBOZMwyOomADexkOHcPSRBEKkCqEgKOHIDnG8JVjSYjiCuKUNNoxZGass8WJUFbBSiczkLmQI1HTNvHlCNOscOIOTc5meiAksucY4eO9qZdrz935NeMmJLATlWmoSg9yjykyiJM5ludC12jRiWN0lT7HgTa0iUrOpfpRW+cBHIg74kehkB2+hpMSw/8cSeAZbAXqcmxJQz5OxENajguCd2v1PXLHOt4vMY1JQxf/OIXUUpx8803M5/P+dKXvvSgbdbCTut44oTTS2gn+YQOQCCosH6clB4UW8DRiYf7GwkCy5BQqmNjSqu3wpbgZXP56ql7TQCRQe/9LbpCiLQtRJV3ApwzLfwiHi8YTmck2ppMKUwrCd04X4MKn8ucpSRoV7weDSMW3MgDTJixZMiQJee5xAaHaGqmHHGaHYIT8tNhURPnFWkYsoojtoojxpn9btUYzAUFlxT+qkYJuE4qLTKxSRtBEgaBi0BXVu/TLCVnk/cEkyetHEU3p0Fnqy3tIVE7lipED6ugM0OiKrxEcbQ5QCc1t/L1tpogypnWejpGnD5zItfwGbBi6OCOCbtss8c22+wy5RCACp8HuInLnG0rFZnTdhixZOQtyaKYpR5ypDcwQ8UkmNHMfPbvPU21SjC+W60K00SqLAu6Bb4kUnK5i26QXE/3YxUfRbJcAJIhoBsoC6hFp1uf2ImEiDqInKeU067feDxIQ/9PxDUlDPfee+8jfBjrWMfjOQTRLbVasTF8OOCWtDGkri1LN0ExOo+JugZT2m2UB3Vlb77GB0/bFa/gx4SpKb13sDdssafoq1QHdIUO6aakwL6CsYExNI1HUftoFbSywRIeDTkhIWVbJRD2hE9FSsKOEw1KSCkI2WerrUrYaXBJSE5A5V5fEvjWdsnTNR4VkS6Io9IaXZkZg2lOXBeofi5WYkF3NV3BR1a1J1mt0rIRnYFDOoCetDOkvSBVAxlLaVlIIiYaELJPEeuMu8+qGtTIMkbGRxnDVY4OK9CKCk2mI5ZBTOGFreCVrTpEpMQcscEBG8SOIjlwIwXQoNjlFAds4VNxit2WCSH21wZF0YQsqwHLakCtFE2tWcyn1LOAqg4xjddVRyrsNbVP5/wpi3upLAi4URKJA7e9VGskJ64BY+w1W1VO+VGAJCk2G5GHZGpixrako1yu43qIRx6lsY51POGiL/2cYP9sRB7vaiEYBQEayCpBPiNqeA6pVxubNIDdtqrBMxCYLjcp6fIOKb8LOK3pvS7djoLur1uSDSkpF6BMgwpqjGdoao9SB07psWgrAgrTAhsT0p6EdNl6TWhqYjImzGjwOGCTBSM2OCQlYY/tnsiTneoCXeLr0kH7UoqqoMwKAnJifJq4tP4TfQVCabskdG0CaUl4tA6SLXO1T6Ps+xtJQiD21kIdFIiJyD+LX5hgH4QlIQqQEdRak1YxhQ4wjjSjMjCFovY8VwnwWKgh+0xZOSnolIQlQ3IiJw1tWxS6qQmaitRLWHldwnCZc+yxTdxk6KbuEgZtMQ2rZsCiHrIoRuRFTFNomqVPehhhUt39dnGVLrEJwCW6OVxCkifRWhCRLPH1kESsP9YVUDVQ9zmsIsbUl4uWRCKjSyCeGMmCTX3XGAYAvvnNbwJwyy23HHv+cCHbr2Md13cIN+9qEP6rhdArpfVw8o9+AzjLcS9l14NQKQSRdbDEvXwa22boq08LnCLGloZF9VFUqiVxGLmvkr/0CXDWoIcF/jAljjLi2C7jFYZtdjnNrttVxQaHTDkiYcWYBRscMHStGUkkhCYptMqInA0OWydMmzCkLBlyyCZDFmxw6PwTYs7t7HL+vt32GOQ3Gvd7lTD0bsDSHYW6P3D/7tF1iwSYL3NV4j4j89IECzuR7VI6NoVAUrboWu39REGOyY11qiO+ntzEbrB5DMtnWw/dyEip2riNLD5ko63WWBnoIV8sb+Pz2fcTxgVRlLfjEZMRmIrDfINZYRX7fK9iMz4k1AUH+QbLcghAU2iqywnNIpCDsQt5YZGkdO6fMn9fTWRRKil7wDex19EtdKZT4q9xCOR9xcereR/4dParYlIh9OMnhlvlkyW+rYTh1ltvRSlFmqaEYcitt976sJ9RSlHX6wthHU+EEAzDw1HCxOgB7AxSYJdlMptMscmCcq8Lsk6U8UIwAZQlNDX4AdS+/WqxVs7d5mKKqbE0ub4bpbAF5P5d0Lls58Choq5CmlpTewmZbojHC5Lhgj22WTJyGP3iWIVAzKZ8KkYsiMjR1HiugxtSOL+DojW1siqPthKh3JQpVYoxc6YcceUUpMMOOFrokFprNv19puqISNf4QYPK3dANbBVcSen85Ap5l06boq9hccq9JjLSKR31VCoHym0vLRxJHACMzeeqxmMxSVgNI6bePgM1o3Sun/YQImZMXX2haROGBUMO2GLGmIVFHrBgROX4KONgzgv0/yL2LDxy6VgWp9lhqo64HJ5lxz/VJlqLesRudpp0NaRcRPaSmnuYQndYBLE/F+dpUXTsM0ekxdCPubtEE2yiINUEaeMc0nXkInn/JG5NEu0Vnaa3DLLomctFeX1XGyyX6JEv2D8a+/xu4ts6mj/5kz+xG/v+sefrWMeTI6QH8FDRbz+IToPjMVJz3M1IwAWiyyvLO4H6Kwd+9CBUHQBSdlf1Pi6If1Hnk7aFHE7SOxwBP7o8RtUG5TV4usHzK5rGIy8iKu2T68jRJ4Vq2RC4SV8SBjGbEudK8YqQZAFsZWGbPRSGnIiYnBGL1lPC7uMIL2nIkqAdam9hiBY5gV/jeQYlfXSxLKgt2LDFMIjOwoLOJ0KKNlLckdMjlReRiZZOk2O4tivx/p3RtXpMAdXIo4o1eIawKYm8zF0dtlGTE2HwWmMoTd36gCZ4NBwxYEmDZsaEQzbc11Uoz+B5TUurtHyTMR4NK5OwLEesiokFnzYxi2rCshpA46F0055z43sQKPu7+g6TfbtvGRNpU8lcLWBRuZb6TB3BJl7NgPLY2lBAJobu+u7306DLwvpUl3U83uPbShjuvPPOb/l8Het4coYsPyO6ZWpfJUjuqiNsX0Ga7eK5LHD/3ucUoH0Iw65MLnmG3HOdvXJ7z5VJMXXbbnJcxEnK6HF3aCqo8JOMKMyJ/Jy8jkizhCjMUcpQGR+MQlkOJEoZfFWRkDkwZESFZsCS01xhg0OGrIjJ8KnwaNhin/NcaCfHAUs2OWirDWIIXbh6hk9FYEqmR0sml9P2dyuw1syJsfnUHh2jQRKqvuLiJh0AUpgQfXkAUb7su1b2V9Ay1l73vqkVtVEUI029qYjzHJ0bcqOpAg+tGpSCFGv+FLlx0tQtVXXEki0O2opLSsKCUcuaEL2Fy5zhMmfb5Ooi57lgbuDK6gb25mdaPQaDwtMGFZeoQYUZezSlxuQKUzqhpgPgG8pWDCTv7VNS5XfLWAkYsq891k+uRLQUOinpVh5asg6hTPYzlaC3M3lf6BjfKhm/PuKRsLd+qP0+nuLxVe9Yxzquq3DmAS1eQUoBEkKn1BxvTQjaTFoSR7Raz2bzuFlSTte9aYHlfAAAIABJREFU2Ha72qUrF/eru6I6fZEO4S4eCUs65cItaPKQYuZTpUNWmcHbKvA2CvIioigi6jSiKX10kuPHOVGU4/t9swU4ZJOLnEdhGDjpaNFkGDOnIHAraFuVmDBjk4PWNvs0O5zhsoP+FdzI/dzgXWB+dsByOwYFnucst5uCqKgIygZzCpsUYH+nuuTGQ/QnoEP7i3eCACEF8NhLBtpTJQmaFIMSu53RiuwGTbntEdY1YWooQk3pKcKiJigb8lgT+AWbHFjFRaeTID4cQqGcM+YKZ7ifm9CuUnPIBrucJnGCV2JrLZPF0/gat3n/ydenT+X+0U2tDkNORGoS5uWI1WpItRfT5AFGaLR7qoMMiO6CVBNW2MRAqjfSopHEQaShJfHawyYfcj2VvX2ZxpZfWi0F6Qsteh/oe5Cv6PpGx6+pdTy+Y50wrGMd1xzSC5DarKyUnJVhe5c+qdMglQe5Gw9oSwIqshWG/mJFJBz6yo5iiCRMT6G9i33xmI7QISyKETZhGBuMUXDoY6ISb1pBZFeHvl/h6wrtpfh1QxRkxP6KkTdvBZkSVraEjqG2Ys8toA8goGqlhZ7BV1vqn9AsRS1yypGVoi6P2CoPGQQLkiClDALKwN6aPNMQlxDUNZ4xbXtAiUqjQEukFSFdHjGqkslN2hTS1jjZbhdBIqkuSCXiDHDK4KsGb2HQXoMKwCjI/JB9RqRmQOpFPQxDyJxxq63Q4OHRMGPKJc4xY8KSITWaHc60rYlVOiJdDR2mwccLSrRfklzKifYr5ucGLE4lVtqpCSjKmKKMyaqYqghplFNyvKQ6amRfT6HfehBFTFnk9+d7etfSAlt1ECxjv5DQtsSkYibJQEpXRevzYOV9obxIW+76b0eslR7XsY51PEz0a7l95TpJBk5aIPbBBAIzD8HbBLVh/6/8zrHypPuk3GPF20C6IVM6SeMhnaiTSPnKyvsMMDCQgCobVNGgBwV6I8fzGrSuiYOMOMiIImkUWCDjJgcOr7BwvhENPhURRStRLCtiK13csMERZ7jSqkIKEFLmapEsOt3sc6o+IPc1BdphAcL2O5pGYxpN7SmMbtClQS1NR/nr0wAzHux9AN2c9VD3X6noCK5VTpObOIOsgRWYEdSBk3hWIUfBmCMmZM7LwZo+JRyyyYqECr+lnh4xZY9tajQJKRkxc0bUaAJK6tpnXkxRxuBRk6qQQmm8pYZDj2LqUdaOrtlo6jKizkPq0qcpdSdMdeDGoO9oqnq/sa9FIeqOcHw2kOtsRScZnfTGr690XmMVSo203EQc42RIqUfRAWpk8L+dkINfx2MV64RhHet4xEIUfx7qz2pKR6dUWP7elksOTix5xY1SSBWarpS+6r0vbYm++GR/V/2kY9O0lQs9LfAHmdVpBsKwIIky529ol5MnFR9rNBc5T4PX+krYQ61a7IIdhZKYjH22+TLfy1ku8zS+5pKFbjU5YWadKCLNUTRqv1NcGGMyYpVRxT6rOLLHVhQkq4pQ15175w629C6T27UsWKV7tA/cB/xfwDOAFNQDdG0KrDZC7o5xxJyYlIwE8eH0aCgJ3Fhm5EQcsoHCcIqd1ltiguIMl8lc6vS9o/9DMkzbPX1VPZ1vcgsb33fI4Lkp96pbuV/dSCoulaMFVRmw2N8gWw7hisMqeNjLcOXO/6nedSMPQ8cGGfPgObtPS92kc60WEK3IK4RAY6DIoSrpsizhwoqM5iEdXkf3vvxqicXVQg748elyuVZ6XMc61vFthkjdSl/gZEywM7azjGRkX/ND8NXxVa8YW07o5HjlX0Gun3SrlO3lEPpEjCmwbY6vDoG6sLTKZLQgHi1JtBVmqtGsSBixJHHLbWk9ZMRoasbMOc0OG87VUtGQkjDliKfzVYYsqfFJqpxpviDWKX6UUSvPWmWXBaM8JXTcgjQMSEPr4BiYEn8H/H2Djho8v0bnNV7ZdBROZexYzLHzkkdXVRHDpL768H08/MJUEoVT2ETBx0JNfLcvY4GXeexRDHy0LkgwlE65UcylRPr5LJcpCThiSo0mJmPEgoSUBjsOKwYsGDJlhqYmIyJVCTuccuqPmyyaEQ/sPIWjg01WyYA0its2EIDnNYRJRnTTimIaUq5C6jxEq4YkWWBqRXY0osqCTjo7o8trp3STv1B2ZxwvjElCGtB12HpKlxTKgnR14ECUUpJYYMsdGV1mm9H1zUTE6eEqDH2d73U8lvG4SxiWyyUf+MAH+NznPse//uu/cnh4yJ/+6Z/yhje84dh2d95551V9vp/97Gfz5S9/+UGvf/jDH+aDH/wg9957LzfffDNvectb+LVf+7VH7Xes48kU0tg9qf4oJPcCO7sJ7VKg/caCxWosh11rS6eUXUlXQ/QCJHmQhZkwMaXE3JfrFRqhSEnLitEdkgk8TOBRZRGVrsl9ML7CaM9aM9OZRnmOzSAtAvt/a2W9YoByN/wazRFTStdOqPHB89hXG6SEaCpCCqYs8DxFYzKLAlERGRHDNGUrmxPMaoJFYyeu/m+SirZQAffoFpx9xL+cDmFT7NO1LkTeWZKnvgmVAEhTOpaJgCAn9pyoEvS8QfsNka4oqYi9koFfMGxyRlkKGgajlCIO2PM3KFTYjk+FpnJKFFKRkQQgoKIhZ0BK45KMU2qXpX+FZThm5Q/IdEzl2jZLhmQ6oo6h9DVNYDAjQ1CXeLUBY2iWGqNUN359D45WpZGuvSPYjT5gVi7xPtzA9B4oMNqKYxhpGwjqVDiaV/vwwyUKAkDpa3c/PmOt9PgYxc7ODu9+97t5ylOewvd///fzmc985iGNrKIo4sMf/vCx16bT6YO2++M//mN+9Vd/lZ//+Z/n7W9/O5/97Gd5y1vewmq14jd/8zcfld+xjidTyHK3Dy6QPq0wJC5hZ6ubaVF1ckNWTntBRbYXfERndCQq1BrLzDzpD9HQUeZEqEhhJziR8M1Ue1hq0OANG9ejNtRFYLn9g4AiDoijlEjnTiDaOlFa5cYjRixQGKsLwJCFkzQOKDnLZRaM+f/4npYRoVVNo7S1aW4iNtQB57jMhnfINDhiyowJR6jaQAHbR3MGM5dwDbATvvhiiPCQVLrFEFHGQiY/09v2Ih2m9BB4gJZNYrBzm1o6JckzwDn3mRy4CbgRmziMgU1QY0OkayJdQwwmVNRBjQlzdFRDragONU3owY1QeYrT3mVqJ+F8hbPcy62OZmlfCyhZkXDIBmIpvsU+N3CB2miMUUy3jhhuL7lszrJrTpERsWTEZc5yibNcMWc5qLZoam3PV5xilprlhQ3KedTJIezRYTtSLFFhRgeM7HtKCLFB5va+IqQ8z3vvl1h56LZMseNOlmQawuXsP++jMPvRl5d+fLYgToYYwT8a+308xeMuYbjhhhu4dOkSZ86c4d///d95wQte8JDbBkHAa17zmm+5vzRNeec738nP/MzP8Jd/+ZcA/NIv/RJN0/Dud7+bX/mVX2FjY+MR/Q3reLJF3/GpbzggCcQEOyMl2GxAtnHhB1YK+mpulQYribxFh2PYpFPWHbrn0g0RxoQwPeV+EwMDQ7yxZLA5Q2kDHi1BL/YyYi8lVpbyCLj2w4wRS6uRQEnslByl+iDl9i0OGLrtxH/i7N4OT/vafcSDjOYGDx1Y62Y/q9FpjTZ2bc1l4BKEp0tLHYUu36rc0K7otBMOsfONVAvE4+AZbrsvYKsKUtAROukAO9EVUO1CcRGCcxA+233uPjqFx5k7dTKWAvDf6PZTeAE7t2xSbvmcLg4wjeL+W84xC0YQgNY1gSpo8MiJ2GWbi5xnwYi8Z0K1YsCSEZc4xwVuaM/JTnGK/XKLJEyJgpxlMSQtremUrVb4lHVElg4pi5jGKMgU+b6BpbJASKmYSMXgapfunG4ulxDhK2lBSAtMEgt5LlRMdzV02tGS5UmWMabLOqRflJ/cgQspd6wBjo+3eNwlDGEYcubMGQCM+dboJWMMTdOwWCyYTCZX3ebuu+9mf3+fN73pTcdef/Ob38xdd93F3/7t3/La1772kTn4dTxJQ1ZNsloSAQVBi8tMLuwI15Zouf4atHd8dwUd8UIQ6SL5IElAH6kuHhKCXxBlaiFiDCq8rQKSisrz8P2KwBdtwqwF6MlDnBB0K3pb4lM6PKXCp0S7G3riYHtDlu1nALxBw/x8ggkbkiQF3VChyL2YKgja4rzvVQSjknyiYRiTlDlRVZJOIvImgAy8RUNyuSDaLbuFqVAlx9jXLtFRAIVR0q/otiV08MY2R/MGdFUJ8ZQY0lUWvN44Cg0RYBP0/8/em8XcspV1v79Rfc3+7da7ut3Q7EYUsuEDu+/EL+qngWyIF0Y4eIzGBC8MCQYx0Wi8goBEMV4QSc6VRMmXKGqECzBK0EOMyvY7iOjZwAZ2t7p3vd3sq69xLsZ4qupde20QZa+9wPkkM+uds+asWXNUrRrPeJ5/M6oY5GvymU868MkHHngVgZPaq8BlbS22FgzJCRgzw6dggWFXzBlTowjI2OK0sQ0fsGTLPeGEbbbdU/pqxVPu/VzlooyaaWR4GhyFE1SUpUflWt+K0IWlVQsd04BbSVXrIyHU01vd2KHFgsS0nbacM0kXBSYpTbHtH8lgRVKzoE0UhMoy7OxAdlLf8qXCMnq+loV89u6JDejx2yDW6zWj0Yj1es3W1hZvfetbef/730+/32/e87nPfQ6A1772tWc++5rXvAbHcfjnf/7nTcKwif9kCMCgy18T4JdsFx1eD5wAVNCaX94acp+U/KKbcwgAUnbpdt4vIEjpilSgwhrVs/TJOEM7miwPcVWF55WNTLPgExxbLA/JiEnsWjewc6rG66g0htZv0SQL68a+OtQ5XlXheJrVVo9aK+rSVCwIoKgCch3gVQVhlRMMM+o9jAjTShOsC8igDB2ywAcX3KgmVEXbxhGchuARFhhPDVn9Cu1f8rgcUzXQZszcPrhbtE7L0mLvanH1aRfNcvpkfts2FYRJsqAoPBZBjAo0E+bULtSOIlExc8as6FPgEyDCTh5Lhhyx24ynT845blLiN14b294JC29IUOQ4BWReRB06DYvEXCqKPArJ64AsDygqn3LLo0p99KlPXXkUsUflGZxENfWorvtGN0OsHrY715AkD6Lh0MWPiD22PJfH0o574UI9gNqCcHRlkzSpLMgOxPq66OxQgDaaVlb61gVjF3SxiRcjvm0ThosXL/Krv/qrvOY1r6Guaz7xiU/w+7//+3z+85/nb/7mb3Btpn39+nVc12V3d/fM54MgYGdnh2vXrr0Yh7+J76gQOqX1Q2gEESRhCDljOuU5EPTM/W9+y648DFZhq7OrPVp2mtDk+p3tXUVqr/M34GwVeOcSlF+foVv6FJbhYESGA3L6tl8sdMquGJOg/w07YgpAnxXnudFgG2ISg+0v5kzSJcFpCYcGKAjaDMEuZmJ/xooLJRp1CYMX+DLwVVCJhhRGrBmJMEIA6qJusQ2C9BeGXmXHTMZN5iR5FLQmXjFt5VzmJHmfZ0+VJBJdC+c1bWvoKQM50Q+Ct12ytVzCwrhZlj2HJPYIPKNjEZJaZYmCLU7xKDrqjz1SIjJCZoyZMuEaF3iK+0mJWOk+/zp/hK8tHuDS9jPsjm6QEqFRnOdGcy6klSUUzqLnE22lFPhM1YSVFZFa+yOWyRZl6BvMhrhQ151xWN/mMaLFQiSYqkVmxoElrYPqGsit6IXkBpUoZiW02ZdHi1GQ11e0IJXbhRzc3Sf0tBFuusvjve9975nnb37zm3nwwQf5jd/4DT760Y/ylre8BaBx2LxdhGFIknwjB8JNbOIbRYG50UnbQRrDXWGaHDMTTQxfvV7fsg8Fvr2RnmDupWPaMrhUGApa9L/gFW6VkBb8wgjqlU9x3UFF+oxWziJWFLVP4OcEft6oL0ok1ioJTHKxxSkKzRUuc8Qu57mBQ83T3NfgGSTp6Htr+vEaPEUx8qlrc9PzgoIgKNgNj9jfOaCqXPIqYKuYs1XMzXHvYTAKGtQCWEkPgZZCObPD+6+0lW7MeFQpFMd2eEtYlzDNjTJklNtKvAtBDVFlcH83gGEFE4vFU8/AJIBxAP4EvB2MY+MO8G/2+x40x6r+0nwPsQYPVAVeUNOfFET9imGYs91bcXF4jBPX+GHBchBzOhpTugZUWuOQENO3Xhsv5yuWhWLK3Ek9ZF2OuF7vccyYmASF5ibnOGXLSlEbma0Sz2BQFCzVgEU15DSfsF4NKKYxxTKkKtwWl+t0xlbolLLYFzCkFAekQFDYcyTYEsE6inhWw1ipoRY/ifUtO5BkoZso3AqkkOjSKu++ZOG/UnzbJgy3i3e+85385m/+Jp/61KeahCGOY/L89v2uNE2J49vVhDexiW8m5M7bZfN0nwtP0oK8atfK6doEQeiUsiupFHRFIaU10ZU3FjMqEdOhs03aHZFGeRrXL3GjkkDlhOS4fo6jCgZqRZ8lIXnDiBBBJnkekzBmRkRKTtCU1ocsKCw9VIB6MSlxlRKlOVQKT1XUKCjBS0v8qiBwClyvAhc8t8QpdJsUDewYCMBR5hPxOihprZdPMfMQNC1vtQZlX69KM2fpyuQdx3bh6tpd1HY3U9p5Ms6gN4PKgdIHt7bje92+cWmP79B+r2AaRHXTAQeNU2q8soagpHJyKt8h931yN6RwzJjVOM1kL/+u6NspUbGiR0qMH1U4GvLAa/QwRFFyTY8ZYxZ6SFpF5HmATjzy0ug8rKse66JHvg6pFwG6uGXFKsmmLPK7MBwRbhSogczz8l4RypLfLxRe+Xwl2B55Uf5PyE7FMUySiVuTgW8nWqX7AtEqNxiGFyyiKGJ7e5uTk5PmtQsXLlBVFUdHR2faEnmec3JywsWLF7/OHj+JuVt043uAV34rD3sT3/YhurnCivA4K+Iks75wIKVRPASs4I0ftB/dwpS+BXy3TduSuLX70bPbpQ0hVYcBMNA4/QKvlxJFCb1ozUgtGDNrsApiUS0WzBOmDFngUONTWBfKVeOo2GPNgCUj5o1EdETK2No2R6REeUF0WuHlRmhJ2gMqAbXWTTKjFWilULlucXFKjt3+vgK4hpmgZXIThcE+7aSW2KFdGwxpbSmnsYbYgxsVXNcQalMoWNhdBhjtTcm/Bg5su4a4oiKoh1D2wTkBp8AwVnbt950CL7U7kFZHl8ESgI5Ni2Ld91k5fRZ6BAo8lQMha3oNHuGYHW6yxwH73GTf/Kv2cIcV3rBkiyk91iwZkBPYFpLmgH1O6m3SIiJfRpRHMdXKbzw+pLXkqBodWB+RWqFrddaUqgsSFQBoYca0uTalkhDa58J67FqHi5t1DU0fTQf2w5L9wVlxje5UJMAJSSrkgOAWAYjbxBcwpadu3Orlson/aHxHJQyLxYKjoyP29vaa11796lcD8Nhjj/GGN7yhef2f/umfqOuaRx555Ovs8fXAhRfoaDfxnRNCq5TEQJx8uv7TDmY22el8zlonFqrNH4a0yQC0k2FXba8bKQYTMMFcqj3OFjpsBBT0WVsNhZ7trWdG/IeoQeaL46QkCiUea2JCciJSJkwZsKTEIydgyIKAvCmHLxjhxBp1XtPTa4bM8XQFNQRpRZgYkB8O5KFLFroE/1IRfbFq1Sq74dLCP25g5pwhrYzzqX1U5n0rD65dA1aw7Zh5zfx+s/s1cIWWaJHZs7dtz85pDc8AL3sJ3HsPzJ+F9WMweRgGL+csvTKglWIe0zqJ5qAvYqy5E6i0RxL18OqKy+kNVl6P43DCyjGjLvTKPQ7Z5waP8HkcapYMSInY5ZABS/6V7+Ep7mebE0JyrnGRQ/aYMGPtGhXIwg9g5JDVIXNGrInJCJuValF65FlIMYuojkN04LQKoV3ogHQBJBE6xZRjxNdEnNlHtEmHUF2lylV0sAwFUMtGAUMIrefWyX+K6bt1S2cS8tnnSwJeyXMXdNeB//t53v+tifoF0mHYsCS+BZFlGXmeMxyevbu8+93vBuD1r39989qP/MiPsL29zYc+9KEzCcOHPvQh+v0+jz766J056E18B0e3B9AVP5AQiL3I5coSujL186gHUdzKPwtwUUgX8vHb4Z9U53UpG9+aVEDDbCjxSIiIrFmUIPI1CsdKPOcERpuBxIoxyxcMrPPxkICCEXMCclwqFgxRaIbMiYuU3iojWmdEqxJnbYyb3LpGKY2y6onuQhNMa7wD3fbMr9L2zuGsh5eIBin7XDqN2zQwEh/YCgyLsO+YtsQ6BUfDeRdmNZzUzxXNFPzeVh/uG4DO4OqzECYwCMAXoSNpPQjlUnQiZpj574LZrp7Fal+A71YMkgTXqYnKAreuceua4XTN/uEJumeoj8kgZN0P7RkxjxO2uc55ckLWxNS4pBjHSo1q6KwKjXI0uRMw94wb5oQT1vRYMKSySpJl7ZFGEVkYkfVj0iQmSWLq2mSZdeFRFz5MlUkSpJsW298ql7FgFaVjIL4VXmd8CgWZ6rQ7hC9c00pFy3PsToRyIYZu3YteylAbfYYXK+7KhOGDH/wg0+m0YTB87GMf45lnngHgHe94BycnJ7z61a/mp3/6p3nooYcA+Mu//Es+8YlP8IY3vIGf+ImfaPYVRRHvfve7efvb386b3/xmfvzHf5zPfOYzfOQjH+G9733vRrRpE9+CECtfl9uXS6VFIbOf5fA5hXmEQct3l811Zze681HsNkkiBGcp3jzScxfaZahAQ6ld0srICmeElI5H5RjRJXGblD66sWOuyK18sdAtjUJDSM9iGkIyFgzxKFnTw6cw9tV1ySBbw1KxnI+oZw7MQTkaJ6oJSYn9FPdqTfR42bZRlvZxHSPm1B0PRbsShgZ4VxWQ9qC0jBN3CRMH8gBONSxLWNYmgQictpgjnXUZpkaL04f9CE5PYbGAi1sQjyA9huwU3BDcIfjiQC70zVM77sIoEHrnHvjjEq9fNmX82MmIyVAn5neWey6l7zJnwKwcsHSHLJwBTgZVFnDon+fE2+Y8N7iobpgKv6fYdk4YOXPbctA4aDLCRo0zxviDuFQUVrWzclyCICcLUvJRgpMPKFNNmfvowkGnTgsZWNixlnaFuJ92TSnFUlw8N6QTJ92GGkgl6xC56JK2siAnOacFptyaEHTlpu/O9sKGJfEixgc+8AGefvppAJRS/Pmf/zl/9md/hlKKn/3Zn2Vra4s3velN/NVf/RUf/vCHqaqKBx54gPe97338yq/8ynP294u/+Iv4vs8HPvABPvaxj3Hvvffye7/3e7zjHe+40z9tE9+RITe9LjOiGz1MRWEXo/iYAk+Bdw8El81Hl7SaTiI+dA7zP/SYViBSXAd9THdjzFl6uqzMx0CpzOcHkBQ9krIF+JaBRxW1uozCkpCe9xXu4RnuazANESkj5uxwwg7HVqMh4HM8gkfJfTxNSMaUCXnsk0U+0/0JT+n7SbX53oCMiJTL6gr3q6fokxHPy5YaKhVoS69sWg5ScbjKWb0eDUkCzxzC1KoQDxTsuybf+PsSFhr61qtqVbcQkIxWuFi6PT3gX6bw2RnsadjVUJzA4an5OpElGoawcwCDMW21Q8b9H+zY79C2hwJMVWWCqYZIBWgXuATrSyGnlwcMbya89PErtrOlqK8o9A0Hva3QE2XOjKdhF7JtnxvxLtcCIzd9jYucssWKfkOTXVvKpnh+nDKhwD9DlxX6bL3wKW/G6MpOUDPayonoV2h7PoTdKAQzYVUIblGu54V9f5qBFi7s7bAHS9oM8flYEqvn2baJOxl3ZcLw5JNPfsP33M546uvF2972Nt72trf9Rw9pE5v4BtEtB3QVlKBtVeSYDvq9wD1Qjg1bQjyqMsyKbIyZmaQ3LM6Vud29zHqCeRAOvMhKj+w+RhpvWNDrrXE8s2rrkdBnieeYNZFYKQszouuoqNBsc8IWp1aTcMYuRwxZNPLPD/AEvSplki+IKispXVQEWcHucs6FxTFV4MIQnKs17hcrYj+hP8zwelVjIc0aM2fM7DjsAg/b3/05TBIhHcjAjsVVcFbQy43p57BnuzoZpKWhTopHl7TiI3smZP4TkokwUUttkowKo9S9F8Ol0OyzKix5xQd/CwN2lFNe2Z1cxiQFIrIV2B2LpHSntVRsO+T7LunAo/YUT29f5jTaxndLfLfgi1sP86WXPcTRbJ9pskO0u6S/PWc/vMkkOKVyHRYM+BIPcf0WrFWOz4o+q3zIajFCuZposCTwCnJ8RszZ5yaHp/s8+czLKVeRqS7IZSyYmhmtlpJgNA6B+zAJrSQVp/ZvAa7uAXEN6wKSktYuVdwqh5iazoG90PdpaRhyEIJwhbs9WdgoPW5iE5v4D4ZgGuQ/uwC9pNZbmO31AdRfAy6CPgdFZQx8JlYqWjh/NWbCEUFJa3TJKebmPLGvT8ENSvyLGXXfoShD9MqjLEOCUYY3yfAcI+lsVBnXlHikRESkDFkwYk6fJWCAkue5wZhZs1qVCsScEf16zcurK4yqBamOcXXNsFxSVS5L1eeZ8l6emD3MReca/y373/SXq5ZVl2ISm21MNeGrtIlTt/Lcw0zC0CYUMtv74IUw8MzQhUCm4biCQsO9phPCcd1OQyI4LFJa1S3PhWGIPQStbeKhQHnghOBaeeRyBk5kXmNAa7EtSpNCh51gKg6C37P5YzoOmG31cVLN4DAlD1NWg4LahdTzcVTJoF6R13N04BKNVsSDJaG7xnELCkuvDMkYsKTAMCM8SkIyPAunxHFxVM2QKRpIiOhb+/LISwnjjLLwKXPHMCigNVXdsYMk3TYZtB177hRtRhbSVh4k+XWU6d/gGepKHYCWjLdLhxnR0mAEjLOi7Xl02xTPZ4yxiRc6NgnDJjbxLQt1y0NCtO8FobjGrK6+DDwF5f8B5bZ5T11BFkLqw03VUt4KzKpbugorTDKxwgDtKuA6eL2c3vaM0guorvvky4g8jYjvmTMYlKy8Pis9YF+DdLXeAAAgAElEQVQZ18gZYw7ZbeiSEUnDlBBWRJ8VT/ISTthu/BAO2WWrnnEpOyCi5NnoEp4ueUn6LGkQ8lR0mU+5/5OPJm/lfyz/Hx6YPUF/e4V+0IgoNeqMS0zR5d+A7wLuBf2vwNcwKpFjjCLhg5hk4SlaX6IY3G3oJ5DksFob3MKRC6FrGI+nNaw1JLrtZghjcN/u8tieFSnaiCREpuE0gYMURh4MQvCHBseQXYfiWYh2welOngtMkrNrdxbR4vhklb4Legh5HbJYjZkcL9g5mLI3mvHg9pNMBwNOvQHfM32c4Y2UasehvmAqPiSadRwwdwc8zb3cZJ+H+BJLa161pkdkaTVLBiyCIdNgQmb5IsJuqXBZ0afaUQx3juFmzfLKFrVtSehaoWtjdc6YNhEIMYarmtY3KsBQTIb2vB4DTwNTx0igCwOjAvKhxT9ou+MHaUtnEgJ+FJ6nZMnY94kE590TG3vrTWxiE99kWJpko8dwa8iy8wbwFcys8ipaZaAIyhBu5HBSQhTAlmfujce0Zj8R5l4rJfsn7WsXIA8iqsf20ANF3XctdEJTjnxWaY+68KmSEHeocMaaAo8+a47ZYUXfGk+tGbFgyIIpW/RZEZCzzwGH7HGTcwaZ79Yc9HZICEhVSEzNuueTK49aKf6H/7e8fvDX7B0cs/ulI2Ov3ael18/s4yHg/wL+CvgoFKdQrCF4GfiX7FB1KfnQ0Bud0mBG3T54KQwiuPc8HObw/12DRQKeNsrTYjYu1hOnmDnuMmYum2KSBYWRW5A8bK5gGEEYQ7mEfAq6NNLQDdX11O5AtAvEd8KhZQnuYrKUa6C+AmO9pO+kuOcqnItGr0JdsV/chxvnzvHVrQGH3i4zb2KxJAkzZ8yMoWU/eOxwzDYnDFlYwGrNmpibnMOlwqewAlvgURCSkxAxZ4xHSYGP2gJ/kDUVhvV8yHo+gspKeAu+ZEgLaBQpbWE5yhxucTPkt2yX1kZVQpFb1oSkaF196lujS68U0ae7y/Z6Y2+9iU1s4puMLrP/diH8MykZyA2ypKnh6tJA/MsA6twY+UQeKKft74uPhOQnUrhIQLsOZe20FVwFuIrqxEcfDwwCcFizrGPq9By+V+J5hTVLFk6E6X9XllGxpscOx4yZNW6LAK6qmKoxlS2DF3hccS/hUzBiRi9OGeytyZ2Qr47uw9MlkZcwzFYMkxXOsTb98B7mtw2Bhy1zQYEjEsMntBbW0PpEYHAF6xVkqf1bwewEDis4LMycJJgFGXk5Q/L3irPixCUmfVvL8GkjLz3RsFfBqIJcm0WyPjbMjDwzr+UelFaOIwxg6Jh/vR6ofQx8xUp0e16F51ft4tomRfGTOcpb0OuVDOKUPimJOibyU/ywIB2fkA1CSlxyN2DdjyhDhy1OG+rsmh4eJTEJJ2w3FYbKsmEK/GYycqnw/BLPzylKn7L0IK7wVEKd+tSp317ea1rlx27JRlpNMriRPZ8yoJ1rlMoBLZxh4amqzlk4tSdddtYdoG7TaBN3OjYJwyY28S2Lb+SkJ+vbmFamUBIHQc5ZIYZaGzpaVYFyW9ldoRjKwmOMKYd7tDdyUeDtqPhp5VArDzfMcaOMylOsqj6RkxKj8Snps7LelDkeZeMRAdj+uGoAkIKuFxqfyEQvGTBkwT08SxCkJIHPctTn9L4xAQVDFEFSMEjWMNVm9X3NPi4BDxlcAg6mLfEUbfLj0/EpML+tziCvIKmhqGG6gmur1mpD1DHWmGlIWPwiFyC2CLJgFkzDTUwuMwYGGtYZLHKIXRg5RvW4KiA/BseDzIPUNfi+0mYilQeRD37fzI9KshNZgUe0bpsrqGtFrRT5aUA671GPHLxRzYglA7WmCD3qnstQrZi4MwASL+ap6DIrho2WhpiUl9bnvNsgK/CZY3QaxHrbJA+mIlZVLlkeUmsHxyvRPbdl4hS0rImU5zpMd5WgRRlSbMGhnftrxyo/lp0PykMEObqsiK5lZsLd1o6ADehxE5vYxLc8QkyiILdvQcTJCqtHI2ijHCMX7ftG/KaiRdlDy4vv00L9dzHYsT6tg2UJrMDZL/Dus66VDvh+QRSmxColJjGmUR33yiFGQjpmTWQRiDkBNzhPQsw+B439dU7ACdtA63R5zA4TpsZ/sVzw8uRZnNpCJtcaZ6VbJP6u/fk7mF64rFYjDBL/pVh6Hi2j4gj4MgQV7IwNU+LkJgzX8MAtoy4ikQvMPH2OFlLX5bbAWc0oB9NB2KOF6A0xOL6e2+7biWFwCdxd41YpYE41NHRKtYfpe0xoLbOTzpdacGS+45LsuHyZl/Il/RCZikhVREJEQmxok6rPljplqMTmVFErhwIPcybjJmEwqSCNFoOcH41qqhDGSaRPectUUGUB5ToyP3aiYaVMgpDagbwdacGx15wMbsZZRfSMdsbJgbqRhKTR9UbzXMlPSSDGtOnc8W0O4L9WPPbYY3z4wx/m05/+NE8//TQ7Ozt8//d/P+95z3t44IGz/wsef/xx3vnOd/J3f/d3BEHAo48+yu/+7u8+x8X5G8UmYdjEJu5YiJ2fJAyi6yyoOXGcmoAeQdmDyjbJXdfcK0U0IMfcMzPMJCuruyltAiEJRQ/qU58iV6bvHkLVj8l6FeVgTj1Q5PhMGTNgRY81OQFTJsQkBHYpGZNwkWuEZBywb1D2pI1hlSQe4nqp7eq29iDrOfh5RZDXlH2HYujhFTVBXqEEYZ9jmBCCEZWKtNBMp7S0vrn9XSUUU6iXhsUXR+CHpl1QZHBawo3aUCXFbkO65YqWFbiHSQ5EiVscDl7mwr3KkFfqGtLKVDLAzKWRC0EG+XXwEvD3wRFdgoE9N+LHIEDBof0yMRSzi2u/qnBmmpcvn+HcckptxYDkkRNQBB68pEJfMAch4MUlQysF3aPGIScgJWpaDyKqNWPMFS43zBjBPhi/0oCFP2TuDKmCDD1wKQqfvAjM+I8VVRVQK1syEHv2Fa1gkxCCRMAxo0WYdgsEwpRZKWt/PbQXwRYtnbKrT606rz9fy+/FCzlXL8R+ny/e//738/d///f81E/9FK961au4fv06H/zgB3nNa17DP/zDP/Dd3/3dAFy5coUf+qEfYmtri/e9730sFgt+53d+hy984Qt89rOfxfdvIw37PLFJGDaxiTsa3QKxALhkdhRMg70xNm5/wtPrSOWKQE5Ka/sMbaNe9BgUMMao+B0HTSJRq5I6cEjLCJ3XhG5O6XqWJaca1UfphfdYo9DMGDer0ZiEPQ4bIyrzixx8cnxK4iLFT2v8dQ0rY1etFqbDgpVvPqMSeB14lraKsLJDs2V//gEmv7ppf7OV0FYr49/V88FV4LkGU1fmbSXd4eyaVRbI0lqX7g20LuA1EGmDpxDTTE9DoCF2zCGvalhl4GcQFdBXEJSYCTbHLJyPaFmDS7vNYlwlP2QLXMfId+/oKTt6Sjl2KEcOS4aN4VThuXhOjmsnzQqXmIQBS/Y4pEbhUVHhkBKzos+McQN6rFFsc4JPQdIkFKbasGRA6GTEzprcN82povApSp+8CsjzkHrbhcw/K83tcnZe7yYMLi3LWLQpJHFoWhrdN4h8uoQkCvL37YTR/mvGu971Ll73utfhee00/pa3vIVXvvKV/NZv/RZ/+Id/CMB73/tekiThc5/7HJcvG47y937v9/JjP/Zj/MEf/AG/8Au/8O/+zk3CsIlN3LGQloTcWaUEMMQsR/u0vElZ69rGr1JWQtppoRARZtaSUnGP9mbt06gM0qNd0bnW7joq8IYJWtXkWUAvTBi4BnmeEzSVA1EGHLBkyIIFQ07ZQlEzsEj1Eq9JGDJCBizZ54BhtsY9Ubg3HOprDtUxpCcaVWmjGBCBjhSc1wYQeIDRY1hjJtormCrKeTs8VzGtCQEg9Ax+IJBSv7Y40QSSCuYlZFU7DXU75dKClybQGmM61es8HIw99o3azPlzexhjBZFjKgxHFj/Rx7RD1JFN+3r2S66C4xvqpbLtJHUKzldo88PzGKqiJH0XgItQ7DusXxZwhX2ucJmZHpMScc45YGs5bS8jBYHK2XYOGThLQidDK0iIOWGbG1zgBuc54BwlPrscMWFKagGta/qNyqdQaeeMmDGm9hxq12Gajsl0ZBSyRAJb5LpDe57yzvUmwmPd3FhMXUUlOsEyJbrpm2QcZecid+0ORGK9m1DcHVG9QBiGr7fPH/iBH3jOay9/+ct5xStewRe/+MXmtT/90z/ljW98Y5MsAPzoj/4oDz74IH/8x3+8SRg2sYm7O8TVsqsEOek874ZFkdeRoQDITbfCLHl79qM+5gY8wYAHhd0pCzYrcejuFLi7KSows1XfXzEKFvQcM3UEtjgtJXBRehRKnwgDjVjQY41PYY2qntuaSOKQL51/KbOdEUcP7KJKTVjlDPSCEQt2nGN21SHRuiRcVfAIhpYvktALTL/gOmZCehlmEn4G054oOQuEzIwWw/UFLHPjLyG+RyIjIMULyTmEtyKG4/NbzsKztAaMof2aXMNpZfF82mxbAosSnlpAlWCSH8wHBgp2PONlAdDvw2QCrtx9a85Wib4CBBBuVfjjlJinuV9fp0xcdOXgXyhwz5WmVWMnbNXXeJMKPanJJw7rOCKhx8r6kGoUWzYROGKXjJAahyWDpgJR47Ciz4JhU0VaFn0W2ZA86ZEve3DTMYlbF4eY0RJ/pva3CDBVQCGS/57QwhWqErSUIrQ90Vc7O5RShAg5ydm7+0CPd0torTk4OOCVrzSOnVevXuXw8JDXvva1z3nv6173Oj7xiU98U/vfJAyb2MQdDyH0idKdopU2lLJA11kqAO209XWZ8cQYU1ZyUqiQPrK8VxZusWlN1PMAZ1CgBjVF7bMqejhejeeVZIQ41Jblv2g0GETsR0CQfZb0bBFfuuw+BREpHiUVLo5b47kFw3CGT4aDboibrp1RShyyxKVeKfyjGi+rDSpxF5MkiMvxBDMZTe028S6yY1KUsF5AsjYtgZ4VanJohbpFCkh1XhdJIPHyCuzfwuSU+s+2YwQ4Yx88B5IMisK8NwqhNzEKkIJSAZrVdtiHQc/AUADCsQVCQktBFDOLPpz2xpz2xyR+ROpH+JQ4VKR5TK4DvFGBO6xarWs5+BjKnkvqBcwYcZNzjb+EwTGUlEb7kQqHxDpZzhk1K1kBsRbWXSSrQ/IqoFz76KnXDqpkX13Rxa5zqoiaShVEWmfiOVFh2BJ4tDoMYocpFYeAtiUh7ThJz+6upOFuEW76yEc+wrVr13jPe94DwPXr1wG4cOHCc9574cIFTk5OKIri341j2CQMm9jEHQuZ8cW9L7SvS0NXUGOCAuyiyCpzg81V6yIoUsoChJRm/DHtTCi2xJjtdepRzz3ciYJKs/YHJP6A0g3JvYg6NPLSEWlD0RMnS5+Cc9wkJmHFgNK6WQZkDbsCsHQ+GLBgYI2rxNxKnC2XDKzor4uOFWVUw2GJd1obzMKIFvMmmDjBBFykRd5b+ey6MvRKFGz5hsaY2NeKyig9igVYQtsNkBa7zNnWUJJrGKjEvjKP3QAmHijfQhFKA4AECH1TMYhGoG3lXNnJvA5AbYE6DyqyXypMCnF+lNzQYhnSScjpZMQyGbBMDa7AdSqW1YCEmCDI8YPijAhi7Tnkvk/qmjbDnBHH7DQgSLE3NyobudXacOwZ8ZqEQUB2Ihle1AF14aFzx1yyMljrzqUs0f0t4pwqGZqcS0GbOpg/aplkRe1ql7MgB+tb3pwtMV3ZxK3xxS9+kbe//e384A/+ID/3cz8HQJKYpD4Mw+e8P4qi5j2bhGETm7jrQpZdffs4pV3HyqNbpxaVnBLqHuShIfdLKfgIM7neZz/WFb8bYcgXQxpqJdft+3ehWoRUVwJDD9jT5MsB06xi954b9M8fMWXCnBHAGZnoETMSYq5zkRqHLYuyFzJfTNJQ+DICfApWDEitf8GYKadsc4Pz7HDMeXRDAjwzTCJXcR6z3F/TVhrE8fEEU8E+gbCGnT0M3MOqDo6AZQInnQWpaDNISLtdijAre0Zi4H7gvAt7HkQWL3G6hHVqaJVb9h7ravBPoZ5BZs3CwgdBJ5D9Czg9CB8yNEvofNlljPkWmHnR0i33rx+xp47RX1HwpIJYmwNPFLpWcEmjztF6mm1DshNyY3eXw/42UybscsR38Tg1DhkhC4bMGFtPkLABsha2miAJQ4XLml4zPlUWkC8GBk060SZhVfZcyCV66/wtlF+Z60XKu7vdwQhWFLlNOsQTfEWLjjyk7XGseK4+w90TL7bS440bN3j00UfZ2triox/9KEoZ8EgcG0xUlj03yUrT9Mx7/j2xSRg2sYk7FlIcl/92YoUkDwGACfdMVgUrUBX4jnFAkpXeNqaPXWCa79A224UT72Dutz4tQn2OSSRGytzMn4Xw3Ir40gp6FQmx5UgUBOQE5ESk1Dh8jZfZI1eMmbHNCR4VKSEpIQsGbHPCJa4yYEWBz/jGgktP3SR2UoJhzlay4uLyiPAkIzpOcfdL3PM17pdq07+Xoso1TFIkvQKpmsztb76GqaaEZiwcsfl2oEqhWIAqYOCA44JbW+IJEDrm4Vuz0HkF09p8XNP6RsX2NC3WZl6cFZDVtpNgJ0HP7td3jM6WmwCHBqMQ9o2EtFrxXDag9Ph37MOW951BjTOE5WsjVv89ZuX2mTsjvla9lAO9z37vgK3otB2PAFRY44SV9Uo19IOEmJzgDM3SIFViCgI0Cp+cPiurwtFv2BQSTlDg99dU84B6EZjraUBrLCmVLcEldES1msu4aw3apwNFcEELMqSgTZpFAC3G9Keu2oe0JQQ48Z0Vs//1Seb/65NnXqtm3xjgOZvNeMMb3sB8Puczn/kM58+fb7ZJK0JaE924fv06Ozs7G1rlJjZx94aACwrOyuBBC+zyMXddWUkJlbIy5gmBC6Fql8or2mptn7bULbS127HRbJVC9SqcuMQd5KhRQeUaTQaNwC0zKyRctL4DaCsRtKbGsROTaVkMbQuiZysNCTG18iiciJ6bEDsJoZsRuSmBm+N7OS4ar7RUg/O0Lp3ieijCmML1Fw8K0TYQemJuVvVVZtgS6LYY02X5CRYUjD5DSitg2P5uy+y0FtlObdoNos9QG+sHYsdsS/IWheKuIb9hdLc8x6h9l7kxrQpj43DZJG+nnctC6BkH5mDVJVAXDKvFUTXeqsTPS7xRhTuqWsKNBqXNrKzQ+BSNp0SJx5IBS/o2qYtsO8nc+jNCMkISS8FMiMksXqUN1f5T0GIQ5Ljp/C3bs86gS4dN6ClNSAIgF2dX+lnUMBJ7ZjqMobuwwvCtUHocvPVRBm999Mxr6f/7OM/8t//zeT+TpilvetOb+MpXvsJf//Vf8/DDD5/ZfunSJfb29njsscee89nPfvazPPLII9/UMW4Shk1s4o6F3EllBh/QqjyK/bWQ+IXcrs37dGgUg1QOo9AoBslCS8r3Q8zsJl4TJ7StCUkiBNdgAZHuToH7QEKlFEkSEwY5ygftKLRShKRoe0MPyDnHTUbM6bHCQTfywi41McfsccSQJWt6DdNiuj/hcH+PIQvOcdO2NuaWgXFClJaEaYX7YIVb1qgnMJLQ30PLrJNV7BGtSqLQKSWxUFYqemGGKQhgpeEoMyrbSkGkDLNhXZtk4CZGBbKbmnVVuJ0a/NroDm11zmShjAP5xDFJw1ob9kQzac4MK6LvGujJShmMg+eBM6aVj5Rq0Jxmvqyecqi+5uLv1oy310ziNbg3eejqk+ipMlKWL8W4ou8D24pkO+TGzg6nXqu5EJKxpseUCQsGKHSDTchthWHJgCmTxr48tXyXWjtolFV8jNEOxofkSJlBE8CpXIMiFtZtQchl7tMyI6U6JDNPgcExaHHpumIGjwWmJSFf1o3nM6n6rxVVVfGWt7yFf/zHf+Qv/uIv+L7v+77bvu8nf/In+fCHP8yVK1caauWnPvUpnnjiCd71rnd9U9+5SRg2sYk7HgKxE+2Fri6DzI7SUxArROtjXQ0MvVKqBz1Ma2IbkxgIndKnpbAvMYnDCHODvooxQXpYU8U+9dzFjTPcoGA9H5KUiq3xEXF/QW172rJqTYms42GKY4EBA5Zc5goxCSkRB+xzzA4ONQMWjJlziasNiPIaF3mGe7nEVe4hoAo8Ss/n0vSAC/Obps3yXWaIdGA9GE4xHMermMRIVuIrO4RL4ClwEoguGmpjdgheBpcccELwI7Par1JYl+BXrYSFTEnbmMTgit29DK0oboMRhpr0oeeBzsxY75yzQMfUjv+WqSQ4CtiHwQMGy+BVtEkctNoFgnF14OB/7nF1sk/uBeR+QKgyPFWyyIesqn7rQRFA7TtkXkDuBRS+ATBqFCv63OQcC4aN30dIZgsyKRVus013yk++9RFZFEMW+YDa0fjDJdVJSDW1LbIBLbTg683drh20NSZ5TWlVsWTQu1gGHdkL8xiTKEgm3I3a7mz6db74zseLofT4rne9i49//OO86U1v4ujoiD/6oz86s/1nfuZnAPj1X/91/uRP/oQf/uEf5pd+6ZdYLBb89m//Nq961av4+Z//+W/qeDYJwyY2ccdDlvpCcwg4S+rrPu9uV0YLOfUMCE0Q63KfEvh/V8xJyvkL+3xEK/mwVujSRacOnIJWxg7bGVSkZYjKBnheie8W5PisidmyNXRDzFhbyl/NnBEpEQrdkSPOGbJgh2P2OGxcLkvrjRmSM2fEKF0xWZ/ik5FM7C1JgU+NW9dmbnAw7Wwpi4sso6D1PeClJrlQGlgZpoRrXcJdZcCJ9QqqmaFe9rUxljpHp8KQg5OZ6sG+A2MfRp5tHtkczulDbwC+C+XMfL8bgiMTv1RxxNNjB7w+reU1tCSZIWfFjWroJQl7nFAWHmXpGhqqqhl6a7IoaC8HoKodUi8gCSPbbuizZIBH2ehnmK/zSYjPgBvnjOyEpPAwzJiUiISw8RvJS5c699HKMdeaWFpLN03AmnK9id5SB35zxu0L2r5Pw5xQ9g1d2oWAbkQHXewxpddxd0WJg/sCJAzl10kYPv/5z6OU4uMf/zgf//jHz2xTSjUJw+XLl/nbv/1bfvmXf5lf+7VfIwxD3vjGN/KBD3zgm8IvwCZh2MQm7mAID05uLCKJK4R66bDLc6+zXQALlSnhurRgcjG9XGLupcLN76o9igr1FmZ1W2NYEz0gVtSVT608/CjBiXJSArLMJ9QGw5A4MamKm5VUgc8Iz7ImIqZM8CnY4pSQzLYuZpzjkG1OmDC1vhQZXl3h1RWrtM8iGbEzn3JxcUC27ZJtGWdOBbgnGleIJGtMshNhFpiSc/UwyVBAa0MwMxWFQIo3otA0N1oIrobAzk3SZigr81jWsCpMVSL2DOYgiDunYohJBKxcgC8YVRlfKRCJ7aWM/SEtY3ZNW/UZ03akbGFplC4YVEvq1KHODUYkUTEqVPj9Ck+VuJ4BEdQ+FKFDZi3JZ4wb18oJ04bqOmfEDfYNpgSH2mYphm5Znin6l3gorybyUqokNLRK5RhNbF+11SvxhJA5XPAJXXdKgeLIuHRD8oPa6XQdclqeMHbQOv2aRi98E5/+9Kf/3e99xStewSc/+clv/MZvEJuEYRObuGMRcXu3SmhvkkJpczHTWXcJav8MlHn5XGd3sqoVXYYehtIu1Ld7MCqKkmAIUl2Ed/Y07Ngltw2tFVkeUpUuk3CK55ccscuSAffzNBEJJ7a2bhD3BTkB+xxwP0+xxWmj+jhgiWsts3tFyiRZob56Ck84OH4NEQSnFUGvtuBGDf8C6quc5UJ6dsh2MCyJwj5PMAnQwv7m2A6f9NmXtHo/PVpsnf25i8RQJmMHJrcuuhz7HQO7LymvW2BlUzlXtFaX0sPoLjrlHEkHSlQQ9zHaEhbYme+4JNseqTaaCl/mYZ7gAa5zgVO1xcQ9Yegs7FVSEyqBLhpXSo0iJWLKmMwu61f0OWGHJQNSQio86/thwKwLhqzpoVH0WTUASTfK8fWK8jCimkatQNjt5myhUzq0bpVyva3t7x9h5n/JAQJAlZBnoK3xCSPOghtvYhpES1psw93lVlnjvSC0yvoum6LvrqPZxCa+o0Ng46LgmGFugFIqyDA3SdH87RrvWJU7122hDqK6J6u9BLNN+P7HmEnzfsyEJ6X8jLbQMQL2Lb/+OlTbIXVlbgvKqYkHK8Iwo3JdSly2mBKRMGdEQtwoQULLqFjR4wbnmTLGpW4omvLv2Juz1ZvRv39Ff3tF4foUro/vFoROzuRgzvhgaX7HQ7SIfPldT9vfJsJ/QumTRAnaFf+Udq7JoMwMY0Fb+t+8hpmlY6oaphqOxY3Sgf3a5FHkoJfGGbNams8qDBNCuVCkhjkBFi8xA2dAWxzqqkNJEiEtjGftcY/NefKXFc5SE+3WDLZzIh7nfq4aBoMbEA5yvEFhxmMM1Q5kQ7+Z9HMCQjJiEmaMOWCflNiKNhVNlQFo2hgiFV1Y67CmdZH7lKuY2nFhqGGm2kRTogtMndvrS5IjuaQFyzCjrcTUmMSixNiLsuQsPUboMNK7kOz2DNViE3cwNgnDJjZxx0L45brzXELKBBEtT1De44By2jK8QB26JV6n83qXQilcfTFVkK+aYJKFHmbSqoBCUVcuKnfwwhw/yHGcGoUmImPIgjEzfArmjMjtMVa4BOSNy+WMCUsGjZCTYf4nVtOhIHFjTt0J/Z0VvZ01mV3N7nLEbn1EXSiSMoZ9UJ4myDOCoiCsC7xVZY5bci8pa4s+gHgcrDBzjyQbVttBCXLf2lEq10hbBB6EnuWxKDNujmcnfanidGmCNlEQoKKSbpO03aWrJNIbgmfoQR0pytDBcTSurlECZbEVhmU+YOaOqByX2nFwqeizMloYjoPrVCinplLW9lqZ2o1CNwDGEq/xhVgwJLElfpeKHmvT5rA6DYI50U2bAqrKJa8DqqpTIhFPqKzzENZS2goAACAASURBVOVGnzNtFW6t0nTBkdKCEQ+QrqhTM9CzzkN0GmT73RcvBujxxYhNwrCJTdzxEBDXLq0hhE+LyZewGroqtLaMtKtVKf8GtCu8bVpdmz6mbO9hVthSgt/GUPoGmJaGA0yVef9EgwKlNFFvRdRfkRYhReoziubsOYeAuYmFZFS4th9ungfk1DgN+n7IgvPcoMea89wgoEChmwQhJeKYHQp8SjxGzAmdlJNLY5aXTDnBo2RST5msZ0y+ssLLK9NaeRnG2VK8JW71zziixT2IzPTagCBdofdNDU7hXNQZHwmZ8GOahEtl1hlTDMBc+xkHgj073kKpEByJwlR5LtrXYyi3HJIdD39dEU8NEEL3aObCg2iXL0cvJSMi0wERGT45GRGF8oncFM8pLT3SB0cUGFRzPqZMeJKXNJgGeXiUjJkBBtcgDpXCYJEWRV76JFmMdjReL6FcRVRzr23DiGtXN2RcBNchCYTGVIFEi6nAsF669JSGZiwZrLIDdpm2FWGuPtOS6JY47k5thu/E2CQMm9jEHQ+ZicDMXJcxnsbdZZlFKyq/lSPsLq5KzD1UaJUOZpIMMaVtkYoWRT7BVwa0K/QrtAWNJca6eFtTjxzW10ekuo8zyfFHKQecY257Aj4lfZZMmLHDscUnmJXriHlDwTQTmOmnH7FHRNq0LzSqUZE8zw3Oc4MCjzV9IjL6rAjJCauM/jQjXua4WzU6BmXloAkx/X+H1r5b5pEIkxCtaROoiR2zqR2zfVosnYhtSgjOQMCkXQEi6ctDixvxMCtl0SAQXMMYMzme0OR/vlfjegVqrFHb2lQ+RJMIuMe5zp5zYkCPmWMhiqZ6oAOFuk+jzmk0DqXvkG25rHsi/zzhiF0KfB7gicZMasmgYbKkRFS4RKRNAudT4FJZkacBOlAMvCXr+YhkPkBnnZWuwG26qljd6GIZTu34StvsxI7nmaqCoEGXmP8PtR04AdqItBa0WYhgf8QG88VVfqxwcO4C86kXOjYJwyY2ccfiVk/EPmb5KT0BUbuR5rat70qZXcRvpIUrRpfLzm6hZaB1JiG2MJNkZF+3JfmGEic2FpmCtUb7Gh3V4Gmq2mO+3GKhTfXD9zLG8ZTad5uEICIlJyAjZItTznETj5ISjy1OucC1hlZZWcMjscU+Xx5wvrhJlsekRWjqDbrAW5d4aYWva1xdt+3rmlYVUYCGwhCRyV8SJBl2MUSUSoy0w4WYIqdGxk38sKEtuUsVQxITmThlQSySyAK6lJW4HIPdh3K08Yys7HYrtpX2ApJe0LhylIVHXvqcWomrKVssvQHRTkIwsLO0C7jG9TMlboy9xDRMStor+ta5ckBC1LwuZfScgMQmEyUuZeVRlh7VyqOeei1AU64p6RKIormMm1zGa1rgowibdt8j874PVLXR1NYuBpghF21uHz1aemVO6y0hz89kH5t4AWOTMGxiE3cspMktq4YQM+vnmDKrzF6SCWD0iEvHWgFjXq+UacbLPXRJ46fQ3ENL2nvtmHaFLXgGzdmKhfSiraGmM6xwhiW6UlRrn7z0QTsEKscLK/IgZOEPKW1VQCShxdp6wNIqQhr3gsAKAhk2RdnIS/dY41U1aTGAFKK0MO2NOjeVgDWovjbDckoL7BzTTvArWhVhSSi60sViEprSAiMFKNm1rQxoWxNzO65CXJGEQhIJEeyU3n23t1/SmiqK3oJIdktFAlr2QAiMoNx2SXYCFuWARTkkDwNSP+IKl7nKJa5xkWO1w9BfEHtrSselVg6tS6jXYBEKPHJCKhwLnQ0bLENOcEZCusJlZa2uS+2ha0WehaRpTLUM2vGVib/s/C0JQ9V5TbC8Mr6S5HYxD5J8gQE8ViLgEHdOoNhaDmkrCXICugjeFz/q2qWqXwAMwwuwz/9MbBKGTWzijoUQ1sUj8ZS2hu5w1nxHboQWDVeHlhXhGs3jtWcmNWkxiK6CRNT5mouYSUkmQEGvZ7SOlgnmJn+vhjGUdYiahrAEVWvc3ZTeZMr58ICRP0MpjUdJTIJnE4CAnIlV4HuCBxgz4wLX+Te+my/wSiJS9jngXp7hfp5q9BnyICAPQiMapWvu42kucgXuAaeA6KQknFYtnkAm3YSWBTGlnbhFQEhjxugK7apfqgeik3DrqTm2p+XrKRgKZkTyOwFZSgI2w+R/ItMtVMwRht4qAkYCkLQqnf1+Sq+XsX86oz5VcEmhL0DN/6bCocal9hRqV1NOHGZxn5NgbJU1d5lasGlKRGFv7QtG3OA8Bb6lXuYW6uGTEbKiz5RxoxBZFD5ZFlKsYyMJnan2WH1M22tpf/uQNikQuqh0EKzOWONS3Q2p1qzs9tKz1YW5HXypIBxhKJVdY7a6s32DW7jTsUkYNrGJOxZy05MZTewVpQQgkHz5bykNc9dUFDzPgB9Lt5XdDWlXaxUt0WKMaUMMaFfDsoKuMJCJi7SMgT2s9KH96kRBqXF6BX4/ZTic0+8tcZyK0vGa1emCYccq2WfGGM+qP5oJqYdLxT4HdsqrmDHmq7yMK1wmIKdSBjx5WV3hAte5ykWucZ6AgtDNGG4v6Q8S4+SoNb6bE+Y5/YOUMCgMFmHM2ZL3EjP5jzGJkyQIMuQixCSlctFpqDl7VxSAqZTZhYoq2hCis7VN6+XR7Tp5UA4d8m2HeqSgB5XrGYMv12BTysAnD4PGFeso2+Mw323aLgEFrjX+qpSLHxWooGbl9VjQZ86YOUOWDJuWQkLMlAkzxswZW+XNoqFOGp0FYzRV4lGUPnkRUCQReRJRLz30ymlbEAs7PuK+taClREo1RyoPUt1KOetKLZ8VwKQ811KeUPailos0tBdmRluikBN8d/lJVJVj/l++EPu9i2KTMGxiEy9aiGPPANMvEJOArhmE0CAsf0/+DGhL5XBWJHJgH7LqkxWe7nz2VjNCmUDXmMmgUG2FOIC69ijzgCSAynEbmqRrEQmOxecHFskQWLR9QoxCs8MxHiUhWWOCJNS/EXO2OCWy4IyMkMp6XWqlqGKXVdwjICesMwZZRVhh5paaVtJCEgZxBhcZC6k85J3fKuO4sr9XEgM5JTJeUomBdjwEyC8PYZxMaDUyJGkIMYnbOdrKglU9riKHKlTUtQNVp0KfYrmd5iC0nCMXas9hFcaUvjH2KvFxqAnJqa19Z0KMg8anJCa1cBXTuhCraxlnc86KZruE41UQ1tTaRRedi0WuE2E/dENaNAFnGcMNVoGzcIPOZW1+qguVDKicYOvkxZS2hCMSk92Qk7zBM7yQsUkYNrGJOxZywxNi/jnM8ljoZHZWaGYd6eGGxs2vzAwvcBRAZP/rChbBozVl6tndSOm9R7swG9uvLYAnMC2JHSySX7Xt4T7ooaJaBVReQKqGeEHBYGtKz11QKwePkl2OiEnICYhJ2Lc0SqApeZviRt4kBp6dTWSCeoAneIAvc1Pvc8QuW0wZMSNUGQ4VUyakxPRYMyyXTKZL+klqjjECdUTLTBCqo+geLO1vE52KrmaWrHoXtPbS3f66iGEJbkIecmp8WmFCwTKINkRJo4roJTXel+qWaTEsYCch23ENvbKsCdMKtKE+Xjo+pD52mhGqcdDKoY4Ved/nYHeHE38MdluXpjpnxAH7hGRc5JrNeaLGvlpsradMOGC/sbR2vBrlapSjwa3QfccUrW7GlKlrrq2Bvaak0tKzf3eTMrlkVefvyG5fYlo1ol0meJHKNXLnZWHcvLTlwmpBTpb2X8mIJSvphrxHsrs7S7WsSte0Vl6I/d5FsUkYNrGJOxbStL7dTUAg99Iclx6CXQ57wDC0qzfVssykECEhNEARDCowE+IucN7simuYCWBkPyNiejmtOdUuRpfBN4qPbpThhgV55aHSmCDIWbk9vsZLiEjZtqZUz3Ivjl395fikxHwXj/Pf+TsmTPEoLZfC6DaE5Dh2VXhvfpUHs6dxKXFUzTKMWAURIRkeFX2WRH5CsQOr0icsKrykNhNXiVmECuhRYCDC/R/a4ZcJfUUrJd1th0u7fGrHRVoQVlSpESaSQtAcM+lt2fGb2/eN7GcPO98/Bi2FJM/SK1cFaz/k5sC0FBYMOY53ON7fsYlAyBF7HLLHsbvD1Bmj/LpJtgS8CFjsgo9CWyqmCBaXZITMGBu1SOtQqdAtO6KISPOYqhZba58qDdGZvValKyDjI4lWF9QomERhVAjIUXAlNhFtklm/8/cCMyhhpwRfRlCMzcBxiTZbOcSAQuSEyYHJSYK2h9TVa9jEfzY2CcMmNnHHosthFAejW7fLDc42zpUyVou+XbZJD17TGvoJ60zKxFIO1piJakgLEpTeukyc61u+0uu8X6q+CpRb43k5kZcSujm1cijx7Lo2a4R/pkxwqIlIGTPjPAf4FDzOdzVMiTEztjhpnkvETk7k58134pjZyCQZRhSqUJ6xvPYMc6KuFJ6ucbU+W1kRnELXhAvaqoKmpT66tAmDVBTk9MjYBp19dSmWkjDcsO8Z006iK7tP8Q/bArUGbUGbyte4viaIS3pxhgqc/5+9dw21LjvrPX9jjHld9315r3WL5UnFox47icaG80FaRTEkIrQSMYIXjDYxUhJNqxj8VJCgJmKDGPwQNEJoiDdII9GWYFRsMNGOHhviScSkKlX1XvZ9r8u8z9EfxnjmnHtXaW5Vb711sh5Y77vXnnPNtdaYc8/xjOf5XwijhnDTMN1sqEYB5ShkLzzmanCbJZOuSlARdVWF3FcQDE1Hl2zR1EQsiTwzIqT2Alk1QUebLOuYokqpq5C6CbClghLaMqCtAlgPpKDlehlSLIUVsRlsGzIqhnRK2XdIzRTV5wa3UWX+ICtoh5Qf+dsp6HS+L7hYDnsgcK9xDm1jXhQMQ9tsKwzb2MZXaQwTBg9m7ELuoIMZTLegG5csGN2XumWFO5Abfs5fspTHpXSscJObtC1qegOgih5vKSvnCjdZCF++xTMhaiJqGjTWamJbEDQ1WTWiIGYVjglMzVydsVCn7HLMEXt8nG/FohixYZdj9jgkoEH5/ntMgQ5bVGi7fvqcM+acseCEBWcdsj+mJNIFcVQQJRVRYAmTFjNr0KWFM7AVtMo5K+rIomSeyellpMWESioRNX1VQfAJl2ElwhYQr4qYvkoRAXuuot5q1Uk3NwTUpcEuB7iQBFRs0UmLtQrbapKmIqYiXZcUJyuwYEMoTEDh6ZINARvfXjhjzik7HLLvjaYSr/7okoQVEw9+nFN7loSyFtVaqjrkvJhSFomrJNQG22qfMKkebyCVJwE0rrmo1ixMB6G0DisLQmUdWmBvBseTykIjF2sG5QmugnBAT6EcTv6ybzN4s+cLy71MGJpGY1+UhGELetzGNr5KY+hWeUrPiYS+JeFtD5V2zkZB6A0QfIj8rpAqBNx3OUbgjSQd7XBGrwh5PnhN4rft++2GXkFSXAc9iLCKIg6XU0JVs7dzBxspniweITsfUx+koMFcyYmmOcfxDk+rB/gE3+LXtA0zzknJOOAKz3KTiLJTG3QfJSem6CY9cbqUBOM6d9jhBIVlzJqHeZJ5esbqkZigsizyFXFVwVWoAk2eBpjWkuQ1+tSipD1wjb6ELnPKiX+McQwSOSUCKxHJZxG8Errgjh/DG24M7RTqkXvvVTDizDMVTplTqbA/poJIlSQUVCokV4kza1Tw5OQRnrzxiq79ITiGgpgaw4IzUjZsGJN7ESZR1jQ0JOTOY8L7RCQU1DQoLJt6xGkxp2pDWqsxYY0Oa5o8pt4kkCiIrUsW1/QJ0rG/LiTZOqK3Hb/sBSUCViIjLYnFUFb6lF7HoesfyQ4iVypuY0KzwG+XRELKP88XgmnYWmG/kLFNGLaxjXsW4l4E/Yxd4mZ2ucHJzXLiQGDNJdS31lCGsNbuJi7lccGBTehBeAXufrpPX9CQKq+U4DXuvjtIDIgGH+UK2FjTLGN0rpgtzkgmGwhbam1Io4zxLMNELRO1Zic5Igk3GNUQUnX4A9OVjN2k5rwvG2+s5MSdZNKbeIFiYWI4jQA6hsWcU8asWTKjUAkTsyRWGYXRFK3r41sNOqjRmxpy50TZMVaFLSHfc0mv2CgVbjlFUlUYjp9MdMIYENnjNagITGSJ2xqrc4jOGLUbrlcH0HpjqEBThZpCxV4lM2PuMSAAV5tDvrn6bxdOu0WRRyGbMOacOSsmfvTcqlbokhtGnLADwCv4HCWRH133yE3CfnzoQZAjhyKxIW2c005W1FVIWYXumthRNOch7XnUU0YFqCjXkYBEoa9+iYhpNdhPAJNSnRAabIEr5dexvyalBLNLT6Wc0vcvxArzsruVlMyGicW9Y0w0taGtXvgKw4tRtfhKYpswbGMb9yyk7i0hM9ESd+cVz9/IU8yGrx3oE5fQ8QC1ukinFNqkVDLlviqceGkHy1vL4k748SP6ZELocSW0NqSpLdZo2sKQNynWQhRURHGBmZRoUzlAZm1QukErB84bsWbGOQpL43EPKZtBZcEV04WiueCUOWdElGjazn7Zba8wtFg0GSE1hhSneljoyCs9aKK2YlzXBHWLkraKQEZE0VImrJIePCoIf8FzCO60GexbD44jpfXBBKqtJWosuiwJotqfJuf90GCo0FQYNqQEjKm9W2TjP7tCYWzT/S4jJVcJGREbYk5ZsO4EM1wIXVIShpKwA5RGlFSefml0Q6RL//EtJZXHN7i0rqpDTBXSNIamcW2KtvRjEPvvKkUAwc3IdTJsl0nLYuhsKRLScikLdqT1pRStnM5IdwF6pGXn8yHgiVNccgAXwRDD8v3wTe5Ph8uXY2wThm1s456H1KUTeu38Nb2AQkNPg5D9xf9YhBV8o7017sYsIEXpJc9xmjciSCQleOmKSDVCRHkEMIj/XyrC0tO+aqnDkOPPX+0lmgE1sugrFcEjGaTQbBKiqGCyc0oYOTDkAzzDK/lX5pwyYkNMTkSF9tS3jIQTFl3LoUVzws4FJoChIabA0HbbFpwSUXm3SydGpLEkNiOoC9KsImqcQqRVDnDYaTCc49rk4msgE76EVA5koSqL22EI5kGUDgVo6YH7QdgS0FKNnTtlHsfkKqFVuqMgjtWGQ7XPHa6xUSNylXA7vM7t4AaZSshUyh17jQOuOFEma5irc8asGbpQilOljNflbYL/yH3jx7Uuis6dsvAgyjCoCExNUcTO2joB5hZWqgcrDqtT4sopcuPCepTE4rJbpbS6xGpdLm8FmABC4+d360/CFCrrz41ckHKhysmRgwnfdRgVF7FCL07Y1mCbF2E63UpDb2MbX+0hbpVyg9vFNcMvsyagBy0MzKi6Za/qV8JiwmQGh5KVb0a/QvS+BSjcZCdulYJUn+ISCmkbz/zvQvr2hQFiSzApGS2WhJMCFbcEuiac1CQ6Jw02XOEuD/AMZ8z5//gGQioWnHVCT+JWKZPdZ3gMoPu9mFPNOGfGeQeC3OWYCSsCalpvumT8SnpU58yLFWldErQNlKAEeNfiFqd36ScvqTJcpuwXg23P52YJfa++wiUghzgZamFX+Ltr0FrGbU2aNrTz3LEk5DzMYV8veZRnyGYh2SJEnRvUqSFbhOSzEFVoKA0WTaUMp8mUZTimJGLNmCP2OGGHc+aUhB7DoDlnztJTNS0KTcuY9QXFx9KPtqvVrFlXE5blhKbVPCeETjmUhpZEShIFYaCM6aW7C3qszWW8A/5YCc5PopALdvBo5c2FhiG9DtlnxXPBkcMPfZlBsY0vN7YJwza2cc9D6uGi7yx0h2FIk10ewaXX+7q5VT0bQiiUojcgPHlwN/QFrqowpAdKxdZPXl1yIAmC5DBHqp80PU3QjjVVELmFZFt3i09FiCLmnFlnRpWSUxFyyD7WrwJ7UqB7iDZA5fsootUwYsOEVWdi1WDISYQgSODxEAk5icrQQYO1FmsddtQGPmk4o1+gCj1VKgyCA5E56LLJ0jAuOyoPtbZm/Vg2I0UdaAobkVcJbWicNHRkqMMAnTYEo7rDtFZRQKUi1uGU9XiGDVsHvjAaG7rWRKFiUK7xElKRkjH3SdiYtQdGBmwYdXoMosg5DDGpAjpKbIUDXpqgxtaBU6CUGHo9DSmTbXfAXoBRCAybS/sLCWjIvhiaVzXKiTiJRjYRffVNAIxyUkR7WhKFIZji8sm6B0yD+sWRhqbesiS2sY2v8pA7J/RLL5mVRB5vKCJgL20Xn+vQzYaNcv1faTXIDTqnFxFa4CoPweAQQ/liEWsSzQLRHhA9gXNQlUXv1OhJ60GAlrqMaJoAFZeEgcYqhQot1ihqFXDOjAVn7HNITsIJOxe0Alo0ESVzznyCUJCSdxPhDFd+H7PuJr6CmIqQiLIDRko1ItQVbQC1VQSDOdKWoM7p2wfDSrYZDPXSfdcLktDdQeglppf+f8GOhH6cbwBXwe5BO1VUI02mIpa1wyqgodIhpY4wQU0clFjthDyrNqKqQ54NbvLs5KYbj7yiaCPyJuG8mpM3CVdGd1moM2qtaZXu2CUzzsgYccwuBTEpWVdVkOSg9K2HDtPgcSPOClsRBDXjYE2hEjKraJTPCKx1oE25NKU9IdfS5clfEi/85SwYh07/2v8/9EFRBoxxg0HgnFqtxaF7RR5yyAcenjjos5VhOUiAFdt4IWKbMGxjG/c8BEgArmYrsxD0rpXCadxceq1gGTy/TzfOvbI2TjxIWrjSgw9xVQOhSw7dKkucgN4D/udncISNBb1qnwj0JGCu1owfOyW+vunBgUDdGIoyZqqWPBI/RWwKChUxZcU17nDOjGd4gICaXY47JUipLOxwzC7HHVbhMT7No3y2wzCU3iBpSL+EXuVwh2OmLN3rm5YkG4Adz0E9Tc8AWeNoprIaFvCe6DSIydLzqQo3/nQJg2RK7y0hk+ahGzt1CMGoJUgto2LJ3snKDVjihJtsqlA7FvahGBnyxBCVTiL6GzefoV1rP7wWmylYKdpnFXal0P+lpXml4iwZs46czGdF5OmbC3Y5piQipqD0vxf/iIqQjIRz5pyw45s8c0ZsSMi7hCIIa0xYsypCKoVLNqcW7qpe/bKlryLM/f9Hfpyu0HcNBDsyZENKG2xFX+URASgbuBdUJZSFvyBndIJOnVuYtPWgz2yFwznM9u5BS+JFEm5iK9y0jW18tceQby6C/BU9yEDAB8MQhBm4G2IDTKANodL96m6o/jjmYl956Fa5i6soKHr5Yll5L+nFjKQtkUCrDdlnJ5SfTy5UJ/S4JlwU1DriqbNHSYKc0XjJRo25XV0n1Rt2whOuqAOuccdVAqi6hEGMq5xagUO/H3CFkApDzxaoCGnRHV1z2KIoiZwapFYUSUC7bIiOWvTG9gaIUr2W8Ym42AoXTYHLbXBxVBbdhqFDaEnPDBCtoNK9j9oAxqKmFvUQNBNNmWqMsoRtQzNSlCNDEQYUKuI8TClUylm44Gzs8AglESf1DufVnPJ6SFtqRnsboqSgDBzyAKBBe6Fth0lo0RivvWBRXh0y9FdSS4NB05KSYWioCL2LpWv1GNUQ0KAnEEQVVRVSlSGNjmhHgTPHEsZj4a8ZcAnnmL5jIDbXwkSRn+XyNvTVnAgvsa08piZ0DArBkjSB92soBgcYOoRJ5ibcTvwJv78m3ZdzbBOGbWzjnofUZ6XNMISeS/th2IIQft+wBuxbFjboF1BSoRUcg3Quhn480oqQ1oQo+glWYTX4HfRWzjG0oaIuQ5rKQAuGmtTkJGxIgxVatzREhNb11gsiTq0zSdrlpEsApBQuSYOAHq0H4p2y4JwZKRmOTFh1r40ouxbFmDVpk5EWJVHduGmyar2CoHJsDplXNv67DceqwlUMjujnHFkFezZFcw52BboEXdNLRA/CamgjZ4RlRqBEHVJol94Kwc6BVGGNddtG7lzp0BKalspAazRNpqkzQx0FDtfQhBRNRBWENKkhqGv0qkXHYCJYmREbnXYiTpIESOtBAI8BNRZFSUSDIaAmISeiJCe5gB+RsW5ijY0t5CmttrStcWyG3EKuekajdAJED2RDb/MgSadgbaQ6Je0Ned5ZkLdOf8Q2oGr/kB6bJNMyuPL7of50S19qu0eUykY5SvGLcdz7KLYJwza28ZKGAAYugx5lmwAjZQaKcOUBUbiTJGKwWeiS68Fzkedd4EyoSlwLQ9wqh74Iol4t8skeCKnSFvNwjplWoC2jYMP1+A4T48AAqcpY7J4SUYJVZCphJz2hJmDJlJyEu1ztWgszzll4qmXKhs/xim7iktXviDULTtnjmD2OuMrdTrhJYQmqlvS4Jtw0rgVximutFLiCTIZrgQ+Bi4L23+BdOumrCtIFSl0uVn0G6qch3gU9o8/rhApooS2corGtIB6BmfsxFf8OP38FUUuYttgdsPuKYNcS7lSMx5UXLTyDFNpbCvusdqd5ATZT2EI5dsVYwa5F7YHdh2wn4XPJA3w+eoA7XKMi5Dp3AMttrnPAFY7Z7dwrcxJucaOrQEgLQpI2EX+6QMu0TiJc/u8YwYGnWw4lRMTIa+hVUtJXGoRuKYUCMaEKcOyVZ4BKxEEE7Ch9ohV9i07eTPicDb2RxeXYWl6/ULFNGLaxjfsyRPFOSgVDeqXA0KErGQS6VzIUt0oxkRLmprhVrnDJwB4XV8sal1DEftuc/mauweaa+smUNgkxNwqy8YinVo+i/CooCEvieI2y0OQx16NbvHL2KaKg7GSKnWqj218EmaSSMGHNjDOmLBmz6fYNPX1SHBfXjIkpmLBkHK1prhQkm4r4tMaMLbyCvt0NTgpaEgIREwI310zo++lS1a6BZ0DdcvCQ8DGvJ6Tp2zs7fmxOQUcQv8JVEPTIj/VNOsXNekdTXDPo1hIvG/JxxPJKSlJVTNcbdGKx4uKo4c43XOFZddWNe6BYthOW7ZSlmZDrhGm4YhRsugKTNYqSsGOk3OWK8/VgwooxKyacsuCURSfwJMBT0WZYMuWMeVeRmTg2JAAAIABJREFUKImcNkOZkJUp1SahzhLnN9HgEq1z1fulSaIl/hBHfvyFHTGktl4OsSDP5HoMoRq2FsSCVZC4IjYiXE6hbwzdKiVEHONFDmGJvBjHvY9imzBsYxsvWcid1i9Vgb5MENLfhWS73JEK+v6Db54LVQ36VaCiZ0CIsJC0I6StKzd0KSWPcRPdiD5Z8HcJYxpG6YpoktOOLSROqVp5yWNjKsKwwtgWbQuuh7f4OvXfqTF+VauGfondKCz8dJZ6AeMdTplx3hEm3VfxNs5WMakyRnaDClpaa9AFmKJ18s+avu2S9J+9m9ikLSGrYmlNyGukyuLFq7QA9GRcpRAkEzyO+ahEz2LHj7dQVhNQ1mKOW3QLqgSjW+JNRVDXzpyx9TUij7NIgoJFKEqGEG9KxkXGIjynTCPSBzOCaxV5EFEZ9wVHfhwLrwTpAKQ1I9Zc4RCAgJqSiBUTlkxZMenaEwrrhbEaaj9oDZpAN4RBjU5zQtNQZglVHfdaE9Lyke87pN6O/NgKkUE6BsPum7R/RDUyxOEjMuMwC43QT0SwrBy8sbQhxoPnMsPK39a/l6Vs48uJbcKwjW28ZJFzkSUhTd0hub2iXzVJ71aawtJ7aF3/dKU8xZIelBf63USoSZIBYaeJFpQwKeTmLpLHXi5ZRS3BuGS0vySZbSjCEGUsSZoTqcq1B6g7XMJovOF69SzxpqAJRrSxJtZF14IQKWhNyx5H7HPYsSSmnDNh1RXFqw7FEGJsw6xeMmuXZCaExmIyi8oVrVHYEHRlUQE9kl966U0/XAR+XKSiLeJBp36cvB11FzIPScVGaIQi1Cksi7Eft4FYpyktZtN0XmPxtCK+VfVsv8SdkzZX2I1izKZjOJQ2Ynq6gZWiTELqRYCOW5odxbkesTapo2tCJ7lde0Aj0ClkRpSEVJwz5Y5vVUi1ISdhzJopSzJSMlJCKkdXDUqioMAmmqY1rMzcuWvGGrtQsAG7VhBqrFb9/Jxy0Z1SwKCX3S+F9JPSt4k2/postas0NHNohCUh4AipsglKVS5YQfVm9NzXe5AwbCsM29jGNl7ckFKq3D2FTil32dHgubQgQnqIP3SlBR1AFDtnyzVusnsMd6888IcWt0pBtEMPnxCaoAg+yVtnoGgJrmYwazg+vgIHYPYLwnlOEwc0JicmpyHmnCkxBQsC7hze5M//9XtZLE546JX/xiw582ZLedeGSMnYkPIMNz1rorggBR2Td/bXESWRLnkyfYARGfvcZWQ2nO2nKJs6LYaqJMkaTO0nicKzFU79EMrkJKwJqaLIqlcErIZYMxFqqnGtmsiP6Yq+dTMdjK3G0Qob4N/88+u4NsUD/rmoR8o5SKF8pSa/GXCmHNXx8zzM0zwILTSt4ba6zrHeJY4KwqCiVQ7M6MYtIyXv2hLS9hG8iBO7csZVR+x1bJOAmhGbjnkirYkRGyyqO3JBTK4TzKwhGm8oioSyimAOttDUdxOsjtx4CrTgMp1SkoOhVLRUajJ647OuWuHLW+fA2RCxK28gF+7aD6YIkcjfj/ht3INp7rJNzAt53PsotgnDNrbxkoUsd+Uhs1LstwsCEXpapfQSZIXl928VVLm7yQYhnCj4F/q2r3Q0hjoNojad0pfqhS4pWgN7YCNFs4ppbY2KW3TUYkNLXQdkNqHUIYYRka6Iw4JQu7vcZH7O+GvXxHGOCWsK4m4yC72JlFOADCiJyEhpMUQUXohoicZiaIkomLBi0Z4yqgrGtUs2grKiWdVQ4OyT2gZdWweABLfKv+u/n+DopLcuVQCRLy5xvfShjYecptqB9utnoGyhOIe2hPgO1BHc9czWKxbGEfBPYEIIGtApDlj5WfqKz3xwKn3CEN5tUXdrzHrJZF0ym+Q8Mn7Ge5FpNusReZZgohqTtDCGZqYo9gKKSUjpVR7d1WG6qoxgR1yDpyWkcs6VHcDUUhCTkXYsCXl94amaLdrRX3WDUQ2qbqG2tGVEW4RYa3qojbS8pHoldErBj4RcLJqJWdohDoib11DXYL35VMcwlsT532NH+P07Dqz02+6zZfrLOLYJwza28ZKFAATEtu/yY6jFMOSSSw3cG1ARO05fnXhlPGBloNCwUG4VLC3e4U09HBxWcheZaIcQiVDRNgGq1Ji0wMZg24A2D6iJu4pwHOYwPcfEDUq3LEZn7I3udKvegNqrBDjUoaDznadB6MvgzpRKKIC1f93Yy0NPWTKza1LrjqFbS1wU6OyS0lKJSw5OcUmALDqHZlzlpe2iPnxZAgO6krMtnGqkLaFtwdZOKqBpXTfICnaiATul1xySolDgf54PTqPHXZjCYo4bVFWhS4uOWkbRBqMblLXkVUJZRhjVoE0LDdStYW0T1r5uIxN+RdiRT0UZU7AKTvmRTsxJ9pfz4b6u6fANwpborj6rsLWhLSKaIqItg4v0vyEzeOjmLg9JmqCfz6VdIV2F2roBbjvdaHq1LdlZkKrS0pOWhSAs5cPcgxiypF/o495HsU0YtrGNlyxietVGWepfRnlLCG1MQJJy15Xl2xS45pThmgbCEMYxJKrHTAqecoxLIkS4SeMmNWlZBPSl9o7ZabGJom4SWFnnYQEoZTswoY3dpLQhJUlyUIqUjAkrEnLmnLLLMTUhGSlH7HHKAnBiQo/yb9zkWVIyYls4d0YCxmrNHkfE5F6Yya2TAUzSksY1QeWdLSXXOgZ16odu1w/TMb3K4Jl/HvvvOQSCDkOOtwFVQGQhamEiIlAeqL8neJGxH9eH6NmvQrO8DvZBeobsGahjLq64LWz2Y072J5yyw7mdkfhWw22uc6J2SVTeWVRL6NZ5aYSqxrEfVWdOdcKCFRMyRr6FkXVsCHlTwYtIyHPBPojjhzOSVDRFRLVOXQ6grLsclOoESLv5XBJRWfBD3waSSzqjV9/cBbIANgEUqQdJ+vKPlYPd9QeTkykloyENWU7evXGr/GqJbcKwjW28ZJHj7pI7XKxR/0chkPKIvgwgdMs1GAXhDMLIOS/JAswD6y64VZ7T998t7r57DTfhyc18FycJbECZFhMXKG1p8gitW9LZkjAsoYGxXrMfHhAFJZUKuMIhr+Cz1IQcs8uKKSF1R4lM2bDPAbucsMdR50b5YHGba8UBLZpGG1ZxQhk+/62qNZosDVGJm+yCZUt80GDObd9aOfBjkOJ65yIVPRzumOcIMnUoftF0ENXMkt762rjWRHnuxje8AWZGz7ZI6cWhPg7qKXq1yas47wkpya+AM5js5iR7FVeKJXURoGlRWF7BLWda9VCLum4vfGZlLDpuqceG80VKlsRc5xYFMRvGXVuhp1jOOaMi8DiGhPyCg2VK1oEiSyLX7hEGhbKYpCBWEEclGssmnlKGCQTKfZ85vVtl5p9LF0EeIjImoqfCllzTY39boAgh91LRdeUvSkGsClBC0LpCFRpaid6DEBzmi3Hc+yi2CcM2tvGShUz48Py2iMOQZrCh12yW5ZzgHlJnVNB4uWhhBkhOkQxePjTAHD6X+6y85dr/PAa0wm6Mc4JUGpU0hFFFkm4IqB2mwDtHOjVH14oQv4fII+/BUhOQULDgjJQMRdu1J57RN7kbXHWVBpWjVY2mIWNE7fUGOsMk5XrnSpQMkwZmisC2BLZFp9a1BaS0Kzfgc1zyMKb3zpBKtlS4paQurYOhJpCoEtagGid+aGPnn9QB9pU/vjAkh5OhyAec+H0koZtBMG8w84bcJpzZqf/YmlPmrIIJwX5NuOO8HkzQXrg8qjjgXI8pu9ZEQE5KQURNyJIpJ+yQdSqQTsBJagyiwinulkM/0JqAso4oG2eclQY1KGhrA6Ht8S9DhuOQsSM/K3plTSExDA3RhIopBY/Kaz/YcLCjHLQevGgy+ADytyTyktt4IWKbMGxjGy9ZBPRCTKsvsK94KEPfMB3R9xt8O6NN/CTXuub6iIsOlYKVlAKFSEd7+mTX65ciRoGTWd632Kmird0twybKYS1bh/aLVIlRLRtGpGTeDApO2WHKkjlnuETBUJJSEjPnjITcT2ILFl606SC8wmm4YMEpO5xwg1vsceTBks5hwY1CgLUKY1tCWzmWYgTVVQ0BmFXrJh7BcOR+LBbAs/55jJtnRDp6Sb9wlcTJJ1F2DY0HNwYlaD/RqQBCKfKIkqGMtxSRMlxi8CCuwjOm185Q/jM87B8+F1wuRtyZ70MJdW14Rj/AgbpCQkFkCsK0xERusm9kQveQUcEsuN+Fnm+Sdq0Fd0W5pEAqCwXxBQxDg7PUXjN2HAybUDQRttaYoEZpS5EnlHlCXQf9d4YeKyJVApnDJRnL6CEH8r9AFKrBzxYorUOadgMrSlCS5Ul2Ih/AXnp+D+SVXwJa5Xq95td+7df4u7/7Oz7+8Y9zenrK7/7u7/KjP/qjF/b7sR/7MX7/93//Oa9/1atexac+9akv6eNsE4ZtbOMli/9Ioe5ySI9A9pcaNrgZyOCa8hv63q7UunETobQY9nGr3hWO5rePu+8u6eSIu3sx/tCVQuUNwYM5atZS5wlNa8jyBK1qkrjAGGezfI07PMLnqAk44CoHXOGIPdaMOWPe+RQsOOWYXd+KOKUkYsOIBafsc9iV4u9wjSP2eJCnGbHhiH1O2GXGGbN2yTxbk9blxR78EtTS9iVx0WBY0ntMyOr/Fq40PsdhG864mDB434cmhZOnoTiBnTGMZZiFJnnkj/0A8M3+lN6mr5qvgf9Gj3OQuU1Oq2g//HdQ/wI3vvGAq48du+TmAP6n9F9oYgdctGMovtGQPxJQELNkyl2usWSKRXWqmJqWKUu0r+CI7sI5M86Ys2FEQey9PPqJVc6V6DLkJOQqJolyxsGG83LKKptSbVKaInKy0R7r4jQUVN/SEcajqECKlMLlEBbLEIBaAa0wIGSDUF2GjmBDXQbBLgiC8vnkol/+cXBwwBNPPMEjjzzCq1/9aj72sY+h1PMnR3Ec8/73v//C7+bz+Zf8ntuEYRvbeMlCVkJfSq9V42a5lD5ROMDNXAKiLP020TP2P4qrtphTCd3yLr1CorDShiFl9krTZBEqstjatQKq0YhVHFKZEadBjYlrDtoHeLL8T7TasI5GxIuc6e4pKrg4aeUk3ufA6S9c4w5XuUtA3QkPiSX2mDVLplSETm+BnJwUo1pUrCjDvKNqApi0JUpbVG3dUMlcI5O8VBQEOyptBMEWyNwzOE26gsk1SOcQSZtn4cf21O+3i9NgkMqOSHLvuHG3khsmzlNJ8j87xyUsMzga73D42C7VXki1CAj3K0zWcmbmLIMpJSE2gMnukoCKJVMyD0hs0WwYURJ1mIND9i/YWwuYtEV3ipviZikPYUhIIuHShg2Fiil00sl266gCa2nPI9rMTyfCdhiSFoYtCMH3Dttf0INGRQ2yq1gYaGP6coWUI+SizgfPY3rtb2GB3IOWxEugw3Dz5k1u377N1atX+Yd/+Ade97rX/bv7hmHIm9/85q/442wThm1s4yWPL4RfGIZQKUP6Orv0cge8dGtd/bxKoYxAq14GWYDjhr6aK3CKnL51IWA03xa2SmGbsL9rhGCtpixDSkYEcUVMzkaNuVtdZaQ3LPQpTWG4u7lOFOeMojWR6oWYQkoSCj8hZZ31dUhFg8HQsPDsijXjruWRkKFpabWm1O7DlxjCoiZc1s7WWtQbh2VdWdmKk7hYJ4seg9Aeg0vbK9eCSIUqKdslLxN8iCQLwgSU403osQqCF0lxCYXkeg1wCE2kqRYBZRlQ3w4hgjZsKFYRWR5TTiMYQ3qaEa1rAmMZhSXjaA2R5VxPKLVLGDakaFoyX8GqCLtEokV3SYG0KYRGKYJOYj3urLJb7+mREJkSFVmayNCEAWXZUlcxtjFYdK/DINebEHsErxDQO7oL1kHGWiZfKRjlxj0qz8jpgA/SmjODg0r/TdMLj9xfjo8vVERRxNWrVwGw1v6H+1praduW1WrFbDb7st/zvksYvti+DMCnPvUp3v72t/O3f/u3RFHEG97wBn7jN36D/f395+z7/ve/n/e85z187nOf46GHHuLxxx/nZ37mZ+7FV9rGNl6gEHOIq/QN84KeYSEAhMZRK5sSmh2o9pwug6LXghLM5EP+keFW3IL+F8CkTHbgbvi7uAoFeHVqB3ZTypKO1uzv3MLENRkJD9uneI39JE+tHuVvTv8XmpFiunPCQ+bzPMxT7HHEDsdc5zY3uO2/oWXhvSTEnllkpGWi2+GYhcdECMIfnJES54bJp0vidd1P3Bv6yXyF69wEfhjP6Ceuqd82dFwUnQbo/b8iemKKrADFP6LhopKm8u95G3gW1KfpKw9fi1PjjAZeEjlc5Yir6hi7cS6V7AJjaD+tsbcV5rEG/WiL3VVOnjnFMVmuQD3X5Kkh1zGbDrOQ0vhVdjWgtN7muqdbJhhf1ZE20IgNCtu1I6Q1EVOCWkEETWjctjhBhS1q3lBvEppVBKHqMTFD5csDXIFMXConfizFafTQn4ehM+gKkPbSmaJnFa24iCIV0ISIXkhf4x7EfS4NvdlsmM1mbDYbdnZ2+KEf+iF+9Vd/lfF4/IVfPIj7LmH4YvsyTz/9NN/2bd/Gzs4O7373u1kul7znPe/hn//5n/n4xz9OGPYcqd/5nd/hrW99Kz/wAz/AO97xDv76r/+axx9/nM1mwy/8wi/cy6+3jW18mSGzVYBrlof0icJd3Ew/46J84zVoRk7AaTjxn+ImOPFKOMfdpIVOKaV6ue+KyGSMm1wzYMd2sr5JnLGYHjFKV4RB0U1OTx59Dc88+TWMxhteefNTzNNT5vqUkApNy4qJd1WccpsbgEsYdjlmwSktGoVlxjmjgXvlHa5zxpyp13dYMcXQEFChFwWbbwypvKdGcNYS32rQ1va00pi+BW79dx+CQSd+jEQiWxIkGdaAvhc/92Mj/XYxF4W+CiHPXwk86vefDM6BbPeW1askZRmnHDd7nLS7EEJpQj69eYyD8gr/dfT/8FjyaY7CPVbBFAxo0xKFTjI6MCVrxjzDTZbMOivriJIVE+5ylRZNQk5FyIopG0ZkpJwz45wZ17jNDZ7lgKtkpCyZcswOYzakNuOo3OOsnrnag9W0rUbplmCUo8ua5iSmvRu4JEgouyV9sjDEMEhlQYplw+pPDqwquFM5CkqaQnUO9ZIe2Dh0SlsPDiQaJ/fdNHdP4+bNm/ziL/4ir33ta2nblo985CP89m//Nv/0T//Exz72MYz54ls2991IfrF9mXe9611kWcYnP/lJHnzwQQC+9Vu/le/6ru/i937v9/jJn/xJALIs453vfCdvfOMb+dCHPgTAT/zET9C2LU888QQ/9VM/xWKxuDdfbhvb+LJD8A5Ca5A/cgEiSEtjTA/BL5z0YBM4ENplIOMEdwMX3ILo+gulXQB9Ul4OGLArlFPj09AWmiJPaLWGENSoRc1q8uWY5Wd3mEQr9tch+rolulZyomJW9YQ9c8i14G4nSSytCFnRuo/ZUBB3iH7xmJjYNYtqSURBEUSoomVynJPYgmBWo20Ld8EcWtSB/+xiuHWHHjaydL9r7kBzG8wUjKf42zPIKjd06XWIp27/9hyyY2gqSHcgFPKKL7sXLZyvQe3D/BsgHLvjlUeQ3QZtnUxGcB3Mg6By/7keBvWfIMgb4rxiIsvzEPJRgv06xeprR9hnNMmdglm6IkwbSKFJNYWOKIIQ5ftJ2pMlBcyovSx0SkZN0IkzGWriropTd2JbbrydbsaYNQ2GlAxDw5me0SpNmcfUZa8YCWBr7UCQAS7Zqugnf7n2LD0NU9EbdkHfbTjH5canrZOLNoCxTl6zy9Dkf3H/kiqbLPeHvY0XMe7jCsO73vWuC8/f9KY38dhjj/HOd76TP/zDP+QHf/AHv+hj3XcJwxfbl/mjP/oj3vjGN3bJAsB3fud38thjj/GhD32oSxj+8i//kuPjY376p3/6wuvf9ra38cEPfpA//dM/5Yd/+IdfhG+yjW28kCFyeRINrn4uS2K4iAiXpvAucBWKGIrgou11TN/2FRrbFf8S/O99/54N7uYvhbscJwccWcomplwmnRhStMgY3TinOQ7gWLG0c9bFhDM9Y7k/JtcRq2qKBXYCJ8coegCK1msHJJ0DpgDxxOJ6xhlX20Pm9YqwaVg3CWppmT27IW4q7FVgDeqzXCSO1Lhk4TP0+hS3gc9DcwDVIXATzHXgxCUMxRGsc2fPESfQfh7qp2GTQd1CcOQTggSsdhYI6wyOziB8BEaPuOSAEqq7sP5nMBmkIahH3fzHMU5M6hZwCOlxSXpUsqeX7jSOIN+NWU/HxNdz9p89ZvzZksniLnrRYhYNxSLidrjPOnImXAE117jDhBUbT8eNKZhzxj6HnHkTqoqACSvfjNAdyDFj5KsTpmtPSNJQqogkzBnpDJtH1GVA0xiscE4zIFKu+iJg0hN/fUkSmtH7mQjIUfylRML7rj8/Vjkf9bZ1GtxAj9AVDqygLIU1IX8DAsbZxjDe/va38yu/8it89KMffXknDF9MPPPMMxwcHPAt3/Itz9n2ute9jo985CPd809+8pMAz9n3ta99LVpr/vEf/3GbMGzjf6AQWLqAFG7i7sD7wA4EbsXKBnjS/3oXd0MXCqIYY0rCsKSv/K5x6oR7uBv9M8od76Z1k8PTiupWwvmnI2icEmPyig3jbzplPjthHK3YIyc0Fedqxt/xrVzhkKvc9bRIZ2fthJ1yxqx5BZ/jJs+SkNNgWDPh3BRUacA43zA7yUiKEnXTYktQZ/57LOgZEuKUWPnPf+y/r69eh18Dwau9EuOncKyIFuYhTAPQt8DegXIJTQ6zGIIEtLRtFBQ5nB46+4MHdiCpIPi/cRPnDRjtQ/y/gpqASkFNQc3oe/fS/ngNDl8xcjpcaKeyOd1dskhOOHztgvybXkWic1KdMddnGF1TG91pJwBElN6lMvHYhREpG3Y4waI4YcfbUjWeuRL7/3sq5ZBq2aJZMmXJlBbD1CwJFjXhKGd9Oqc4G8Fa9QZTCa5CcFnnQ6zFxf7hnF7cakKXxHWKmtJu6PSYRMQs9IO/g7v4BNOwdieQsT/B//r8fyovZLwQFYa//j/dYxiby05oL0wkScLu7i7Hx8df0utelgnDrVu3ALhx48Zztt24cYPj42OqqiIMQ27duoUx5jlAyCiK2Nvb49lnn70nn3kb23hhQ9gS0iOQEOqZ3Exr3IzkOYDDsq9YDwudTeR7T3EJwQ69RK8A0YRB0dIXNwzeecntE+/nTHZPCWPXJtmdHXJ951lsADkpCTlTtWSXI74Gy5xz5px1LYkJq85oyklIZx3wMWkLrlTHzMoVpmgIq5qYCh22KDExEoVGYUlIa/sWLjHKBvv4xacqnXYDCU5c6ciNg/b2CPXa4UhVAiaBzQbsxtl1hC3UKycXMB6BNt7CYwXlBspDKG5B63EkVeSUjmcz2N0BI6SXFW7iHLnzUVlPtNiBYNpwc/k0o80JSdQQTxrMQw3BlYqYEqVbouiIenrm5L190alpAoryFk1oYAplEpCHEblO2OW4o1sWntx6ztRXckrGHb9WsSElY9ypeNaEFEQ02lCEEUGaO5+oFGylacoIuzQuWZCcQ64jobRKZWGME6zK6AWu9ukLBYVyD2mp1bFrs3VIVeFv6sHfxHrwuAfT3AuRMPzXH3KPYfzb/wv/+zd/hQd+biyXSw4PD7ly5cqX9LqXZcKQZa40G8fxc7YlSdLtE4YhWZYRRdFz9pPXy7G2sY2XRwioQGwRL/8JD8EGglQU5GLtsAet7qu0MvnLog3ctoSeCRBfej7U+Y9AhRYVNqiwhQmYcUmwU2EilzDoysKRdrPoFIx2ALxhQpD6hCCg5ibPco07HX1SxIkBDC2Bdd9DNxalWppE0aiAJtcoC1q1mLrFNC1q6HpcD76v0B3lOwkLT76veHz57XbjPJACATFal0BYbypqa2fjkUxc9ZwMmtq1KdoWmhW0GXAGtfLu2mJzLY+h2qTPA9sQaqNQRhGf5uycVYzikiSvMWOLDRW5iWlMwNjkhG3dJ3FA02qqOqBQEXmbUNtJR6Gcce7rC6YzmXI1nt6d0gl8G38OGt81aMhIMYwcOVZHqGRJFDo/iroOKTctTRBhW4NNdc+AFBCk0C1r3Jw/wiVzhf/52uB8rfy56KiqBqxxvuJN4q5pq+jpP40/+DkX8T5ffVEUBWVZMp1OL/z+iSeeAOB7vud7vqTjvSwThjR1JbeiKJ6zLc/zC/ukaUpZPj+1Js/zbr/njz/D18QG8Y3Af/kSP/E2tvFChbjxSRIsM78s46RcIH/aU9ySWQRtPAy9Vr3WzQzXLz4FXoFTKlS4SVMMNEXmIRwcyv9ZqUVDsJeh5xVYKKuIu+c3aRt3o376FNRduHrzWR76us9ioxUW5esIEy/hlFMQUxNwzC53uOZL4vA1fI4buKqi0hYSiJKSxfyEKcuOVim4h4ScpCxJM4s58aDHqX/cpVfSluETiWgpoUsZXQPXQF2DSFgVR27/xTV/Co7d82hGl3DZyrlmmhjMDiQpzFO6ygHmYlfdyj+PgnoQV93ZheBh0A9CPjJkScQpCzaMWXDCvF2S5DW2NdxNdylMyI38kFm1unA5FGHISTrhINjnNte7dkVIRUKOpu0SCICIignrLmHISKj9a8S9csy6o19WhM6lMsa3QxKyJkWZljJoqOMUu1ZOXvyUnsbqWy1d9WcoqR3Rt83k98PLfGR78a2NgnIGdepPzh8C/xd9tlFzkef6IsVLZG/9W7/1W5yennaV8g9/+MM89dRTADz++OMcHx/zmte8hje/+c286lWvAuDP//zP+chHPsLrX/96vu/7vu9L+jgvy4RBWhHSmhjGrVu32Nvb62iVN27coGkaDg8PL7QlyrLk+PiYmzdv/gfv9D3Ac9se29jGSxfSq9WXfi/SgwL9n9L3FISCJjN/61B8YQiUf31UAAAgAElEQVSnFSwrJ104Cd2hP+9fOsOt+ipcyfgK7sYt/WZfFba5ob6dwkECBahRg7maE4y9WuOsJnqoRKUVB8EeG1KO2fEd89IrBWQ8zFM8yNOdqJOA7UR4yKUXS6asSNl0lQdRhUzJMHVLWlaEmwazsQ7PcI6brKRXfnbp51N6YP2InuJ/CtwGewTVBmrfymgayE99vlQ58GOuXTVBCjEpjlUR7rq2Bod0oL5NAeebAX7Px2QBsx1QfsJctk5+YDJpmI8LRhzRqlOCuCKYNOgHLFy1XE8PaRea+GaJ8mq/dawo54ZVGrPSYyJKvpZ/pSTqaJQbRh2NUgCmIzbMOOeEBcfs0Xhqq2NR1B0mAhzrIqboXC2rNiQvE7I6dR4jl0OwC5qLas1yybZ+u4AdhfQg7pYt0FgozkEvoclc2aYV4M0SJyryv9GXIwrgX4D/47mf53+AeO9738uTTz4JgFKKP/mTP+GP//iPUUrxIz/yI+zs7PC93/u9/MVf/AUf+MAHaJqGV77ylbz73e/mHe94x5f8fi/LhOGBBx7gypUrfOITn3jOto9//OO8+tWv7p6/5jWvAeATn/gEr3/967vf//3f/z1t217YdxvbuP9DVB0lhAcpandDwx2BqEu9XYAHkRcmss7Yp9GQqr5cLxNZTa8zsMYt4IbhbYztWmGPA/c2FtRYozZgJy2MQM8rot0CgpaCmLKJOG0WBLYipCYJchKTURBzypymDmhaTWIKUpN1So83uMU+h2SMSMhIKAj8sk7TEtCQqIJAO+vnGjB5SxBaVOR1GIS+J+6JYnwoeAYpkyc4SWdPu1TWYRMEw6HWTiraBG6BvSmg9PgQE0CauNaEanHViyt0jBRdQ1T4eW4gzhlo53wpp9dEkEQQpZYwaQjrhlZpVjdSVtdDJpOMZFyQhkVXiW9bRZ1qqrGmijWNUShaQlpSMiJKjK8OlESdx0REyYQVNYE3oRIXSzodB4X1So+994SoRVoUSlm0bgkC9wWaxtKWMW2uLio8ihijhBhOXTadEiaxpofjWKAOoI24CEaBiwqPQ3vMe9CSkPzkxTjufxCf/exnv+Ahns946suNl2XCAPD93//9fOADH+Dpp5/uqJUf/ehH+cxnPsPP//zPd/t9x3d8B7u7u7zvfe+7kDC8733vYzwe84Y3vOGef/ZtbOMrD7k5RvTCNQL6SnE3SaGYSXl2IGbTtK7JLglEptzkJVpQonY484c98A9NT8AY4V4j+g1eAdFWhnppuiJHhaUelxhbgbZUVUhRxmgsWjUkKic1OUfsYXgVTWNQlWU/PmLPHLHHERtGKGwnFy2l8ZiiWx3HFEzVCmNqrHIKlGEDZtOgxKWywoEfjX/uMQjddzjCtRkE5AmoCsLAPcCBG1XrRjRK/Mi2kLVu0o8iiFP3s105kGN5Fdopjn7pRTfTJcRnjmapvGRGmzjbhDaG8DqE19xryhB0AbVWHPznEcsHUh5c1yR50Z+PjcNUVKGmthpVQFRVTFnTKANaowzEJidVztmyRaNpiShJyThml3P/xWece7Bj6vUcXJIwpLgCXfKgVcMkWpFQOGpsNaKsWje+kiyIpscwYZDLuPLXU4Rry4jlt1y+a1wLohhDmYIN3KB1WtJCrTynpyBX/uRu44WI+zJh+EJ9mdlsxi//8i/zB3/wB3z7t387P/uzP8tyueTXf/3X+aZv+iZ+/Md/vDtWkiQ88cQTvO1tb+NNb3oT3/3d383f/M3f8MEPfpB3vetdW9GmbbxMQ1B7onwj0owDwymgr7WLio4saVN6nuEgcRAviT3cCtvgyvbQi01KP35ET9bY9b9X/m32bUelK3VKfRBhkhKT5qDAWsUoWjGPz0mVY0E418ozrkZ3uR7e5hH1JNe5fcEoKSZnzjkhJUum1N6MKqRyIkR5y/i4ZlQ4gIVqQMW2Vxk8wiUMogkgLYmFf3jdA24DT+GShmv0yo/QYeraBgqvx9DUEAWwM4FR7NoQTQHlGRzcgc99ys11eMD/Btfl+c8KJjFEMVQllAVsGtiIJoEvFikNIw1aNxybY04Txf7Ntveu8PhvM7akD9aOTXsD7AQsuZNqTmEzjznbHROErv0wYUVIyRlzDrmCRTFmTYTz+3BnpO3wDismrBkTU2B8ohFTcM6MkpB9jryf2VXyYOSuBeMBCAUQeDaNUHehN2uVzoJgGqTadYJL4kTMcYMr35RzqAUtufIDISIP9xjMfh8LN72QcV8mDF+oLzObzXjwwQf5q7/6K37u536OX/qlXyKOY974xjfy3ve+94IsNMBb3/pWwjDkve99Lx/+8Id5+OGH+c3f/E0ef/zxl+LrbWMbX0EM6ZRCnZTH5WQBen1icDdVoQGIWYQ898vmaQB7gTvcZVdgob5bekNA6DshNb3z45ly/eYpqKBBxRWtVTSrMeN0yWR8xiRYM9GrDsMw54wFp4SqIlcJT/IIB1xhl2OmfrbOSKkJMdSsmaCw7HBC5BGYyrgEQSk3SSnBIhR+iATaIRNSiqPwyTwj/XIBg4qBknhIrEBlEMZuzqord0bmIwdyTMZgUjdOuoEwg4WFRwOo/Kq6qV1yMMMlAYF1zpVm7sCTOoA4wCUqV92xVAzBDNQYHrYN1xpYVNCGiuohTTtVRKsGU1t0aHtQZw1EliyNOVtM2YwTChN63OfGMyL0BTyCU39sOlXNsQdB1gSMWXOFu7QYKkJGfuYX9UiNazul5EyiFWoH8mRMvhnR1EHfQRvRdw0kh60H11hIb0QlyZO0kIQclBnIFTQL5z1OTe8VLviFFwOJ+NUb92XC8MX0ZQC+/uu/nj/7sz/7ovZ9y1vewlve8pav5GNtYxv3SUhfYPh8KA8NF5anaNzsKBr70C+J/HMduRq6oPYq+sqCrLqlgiCHH/ajC3oPIOPfTisInZiA2ljvOxDRVhHKOrpdM5KpKfD/m04q2r1Nw4oJc846v4iUuacAqs7qWqoPBC3FOMDEIWFToyvr2hEyhxhc0iDqlql/LiwI0QYQ+uRm8N3k+9UO06D86k8rCA0EEWjJv3yVQacwa2HsRQobC7V2qtpY7/lVQl1CNIZYkoMY6jk0uzjqagrRLsRziBpH49TeVMteUc6IqvRJmpxiyS3H0Mw02SKkCjRhU2NsjdEVhUpYM0YBY9Yde0JcLFO/UhdL7CnnRL4isWTatYQiSkoi1ow7bERsctoxWK0oi5hGphv5XIKZERakUHuFIenxIxeqDhvcRutN1jrerAhwSHVBZKMHdJ4XM7YVhm1sYxv3XwgxPcLVcrV/fjkSv72kb0FMcDfQgt5zInfPgz3XeC9x5V8J0SK46h/gbup7OLzDmP4mDz31cu4/2lLRnoe0h6Hb9wqszJwVc8YPnDF5+JhUuwpDQURhYxacsuCUPXXEmDXH7HLAvsf1Ow/GCasuiVgyxaK4zhptak5HY8oyYCdfEc0q7Ax4GtS/4JKg1n9uwWBscPOLuHUKyFO0GFp6OmDi1BfLDVTrfpiKzMs4pBCIkZUnrtRLKJ+B9RrWNaxsr0UIrv2wBvaP4dq/9kO5Uk44EVwu9/AErqdQ5VAbiF8F4cOWJK1dO+VBXBtJzo1vG9kJRKOSXXOMqVuSrKYONHkacKYUG0adaJZUDHJPdu3TuV4LoyL0bIsJMUVHzVwy5YSdTopa4nmFmZ/PcVqKZuf+XBzgpLwlYSiBsvU7HNMrXd2htxS9HKIzvY0XIrYJwza28bKKADejXVZ4vBx+BdYJDJS4WU9mxBw3A+4AC7fsbTde58ZjInTQ0+Cewt2jF7gV+Tlu0hW76xi3r6f1cXfwUSTpkApFAqSWvB1R3Q5ZJzlhnHEnv4EuNAEVoapIkjVpvGIWLZkEK1IyZpzzIE9jaDhml4ScR3gSQ8M5c3StuF7eJbIVZ8mYMKuZnmUEtnH9flndCgv1SRxeQdov0u0Z+e96ix4M6nMztXEtg3B2ccRV6JkUvhrelFDV7jjRayGcwjRypJQGsB6f15y5R9j65pGvUDQPQeMlNJSB5LZ77/B/huAx0Ct6vQiRYp7geh0zsGMoRyHLUYrSllGWo3RLngYUJiJTsa8ibJAZvCSk7FpYfUSUaFqO2OWUHSyKERsO2eOMRdeOSMixKEoiiiYir1LyPKFtBhUxoVdK0UtiRY8pEYzJDn3VKsP3gUR3QaoJ+7hyhHi340+CgCIuiha9KLGtMGxjG9u4/0K4ZsJD+0IhGAYBOUK/xJaJoYa29jw/v8wzpveSGLpVymJNiBiyko7pV+PQo94z3OSwT4cDCKcF4Twn0F5HIagxpmIndIXugJqQmijIiHXGTJ0zZt2VvmU17MSK14zYuCQDp5iU6YS20OhVQ1A5OmCXBIhqti+sOMlFPxwJfQFGyt+idTWYEJT38NJ+DrQe9NiU0CwdQDGwTsCp2bgKwiqFooAqhFJ7J+wMRhuYbxweIYwc/iGvIV/jEjRhn3hTplLD52/DSQFXTmCxdnjGMHCv1SLxfdWJQKmxJWwatGoJTQOxxY4UUdKg0hIbVf8/e28SK1t21X3+9j59dLd/TebLTDdgq0x9LleBrVINkAChGtgqlcSoBgViwBRkmCCmSCAB9og5MPokKEBiAhNKRiojMF3Vhwq7PkPamc58/e2iPe3eNVh7nRP3ZRrsdOZzk2dJ8e6L7kScHaFYa6/1byCBLrbSdTDCXtlXfFT8AhDYEmDpQp+hCVTNoROhI4udKbDWEcUtUV4H2meMt2EB9nEMMDhaapGaMfiXKBblGihTqBS/U0AXJDH7A6gJlT7mJqZtjHceY8EwxhjfV6E7p281FMNwyGCFXTMAJTVDKqLsQIboUbAnVn+GeXi4cuZnDL/Dimbfn3achPtrMHOHfbHDJwbXRSQHJfPjSyZ2w4Rd2Nt6fij/Kh/i1TB62PWFgQlNbaUBxrRM2fAS3+CAa1piIlombIhtyyqd4HeW04sLcltjDj3eye6cPJxmhSQgBWouwhK9gbTCNTkpG3VDP5KgYICDeKEyto1cAKIkmFKFRLi5hDcuxKV544ZaZI7UUS/Fwb1yLp2E3RKWyuj4NzmGjcG+CPYIXvs/4etP4aOdnMok5E1zEAqGDAFMfgzSeUu6buW2E/ALsNOO9KCDwwpmMmKp8pgyTnBBW0GokjZ8g3yPK0lpKCjDdderPlZkNCS9psOOgsQ2FOkWEzniqKGKCmo7o4vCrEWBpKrmDIMNxL4BpSqO2r3HlAa6RDpjdQlOv9MqHVkxVK/PIZTZ+V4c93soxoJhjDF+oEO3aREyR1BO5DU3Z7tTJHPOJYM0g5kUEwZnQeXHK01RlXe/zvCjr+OJF4APQna8Y3rnmqrN2V4f9K+Y0PSdA4BzTlmx4IBr5qyog2tlRtWrP85Yc5tHJDSsQqt5zoo4jCRm3ZZ75QOKeEf7AcPOxeRNi229vKcNMvLWwkaxcoYBZKeTmzmStPW6dlF0rBI6FdYLdiENzAQTg5kIkJEdnBg4y6HpoNwJ+HFfN6rIYVpIwbG9D/kZzF4BPoxIdSPiUDYAL//HGH7UQ3oFSQXmGKx+JorzU+piEj6HU7m0J5byJKbLzQ1NjcqmlEb4KiX527pVfrPQUURCQ0mODSJRDYkwLWyNzw0bP+d8m9ElMSx8oLmam47Ubxdl+GwiZASmQMmnwKUKaszDie+zJLY8F8Dj+yjGgmGMMX4gQ+cESl5P967vO/lopPSjDlPLNjmKBo38Awa7aC0I9r2vtPsAUmTcCo97BK1L2W7mdAcxfmaodzmr82PiiaOYljc6CR7DOSc85TRw/Gtc2FpmlNhgfHTNgii0v4F+PJHZHVUaU5sZuzgj9Q3zZE1eNaSVw1R+MEHSQmGGdBR0dn7KYFgVMRRMYbfnO3GvbLZQBT2GzIrEACBJuArqGAmsOnhcwq4NhlTPoAC7EtoWog7iFqILiAJWz782EAUW4a1mMUwSOEqk2OCSHrfQu47q5zJDkuyBfHaR8WTbDqe02NC1N6nBJwZvZAQR0fZJvwkm2CV5PwoahhVgcGyZsmXCKshOt8TE4Rh4uG4W7FyOKRqizuJWKb60g7LjfmwRDIxKQ2u3RwuiroWmgV0l3YVuyYBh0NaEYnR0RPEex4hhGGOMMb5/Q0cR9pnrOjvQUPm9DvlxXSLE/1nYJluo9n7YtYDoeGvNoXWJijp5YAXt/YR2mUib/AVPW6S0aUpUdJgJpKaSWXhUk0QN62jGLs6ZsWZqNkySDZN4TUfUSxfreKIhZcIWhyWjorQ5ZZqHaYFlajYkaYXNRaY6njts5TFqbuQYTA7V+Egpl+ramXKToddK0dC1kuid69mjOKVaRjJe8JEs3y6DUj3D9CMJzL/KB4HDRgw9WwUDPgF3Casc1imYa7Al+DsQnYiytzBRwntXz7F8733PkWIhTJ9s7UnpbtSQPgebeaK0I84cUTp8qBumbJjdwI0Avc01QX1zx2RPJtqS0vSjCjx0PsJbT1rsMM7RlOCSBJeGSsuE9VChRrUh1zXXhFwDGw9bJ4vtFcx7jlQYiqJU4Mm+1vl7GGPBMMYYY3z/xrN0Sr3+bCi9UlWLmqBnfEeKhTQTFN83kF3fbSQJPeEmNU4T1inyu30//P9letli3gQujYw1zmBVHrHaHt18KxNg5mEqs3Mbd8yPLpkurikoOeSKD/B1znjCU0454JoP8SopNfcZjOQmbDnkimnYXTYHlubAkp12FE9bzNrLe3o1nNdM3hNvhPcZ4Bw3opHzNg2kRyKyNN8D5neNjBxARhRVB+criA/gYx+A7E5YEzUavQrrqEnhEsw5+GOkKzCTus3fC9f/CWFt/C/AjwLfAP80PDemn/Mb/Sinexdt4VfsqXGGj66GFEeKIz72RKct3uopp2zxvaiTghvVW6IixYSRhMOS0PQaGhumPOQOLrIcRtdM2bIjp0wbyqKlnhR4O8E3Rr4zm/C9WTFoYaid+j5e5nEiIMd6Cs1BeNCcAeyoH5aiJZ8DS+J9EmPBMMYYP5CRMgza324OrdvQfWqmZxgoHwv8v6mEQREn0MSSXFWnIUNobyfICEItng/DdWVZWIbdr8pGhPk+2723OgeOPNG0IpvseDl9nReS+xTJhilrDrlmwrZvd59wziFXvWQx0KP0IzombMMYY2+HmYX3uUTolB2iXbBBEpXWVLq71WXREcUq/F/xDMhGt6mgrGHbiMvkdQ2Nh6iFzRKefhXq13jbDkMWVLTvtHC3FjPG8j7kEaQRvJrD/QTuLgWn+OApXP8fcFjCTIGWBooIFjHcymA2D5+LglMVrKkAzxyhyh4hhUPIBKnriEpHfRBRHUTBs2PddxSedavMqXCsife0GvZFtxoSYlrWzPrbv2lMEbyFgh6VyaLqoY/ChXAOO6CMoDmCTmczelkztMD2dajfo/gu2Vs/7xgLhjHG+IEMbcm+rWwOQ38XBi8K1ey1yK/2BbiJgCDV3bJSjiWSgBQi4cJTDrjJpAheV33rHwZGaMQwd596ilsbJrc2FOmaabLiIL4ijuug5igCQQbPhC05JRO2RHTBJFu27WrPLK1zj+kgbZ3oMADxlcM88rLb1g3oPtixQpKpmiUp1V8NkHSy80x3xViIrCTu1MI0kofHCI7B51C10D2RccZ+5DFMguRFGgd5awNZJHXaYgH1ISw+JMc9vIL4CuYrKCr5HKIY0i3kXqAnXCN5UxU6j5HCqGDo0OskajN8jrb1WO9pQ4tB6JNNKNKSm8UX9EZgIifdEtH1jImOiJySiow2EF8rMnwX0VUZbpeI6VmNfL8ShsKyYIAj6GegjQQYQJ1rA1cprFVe1O09Qa9/j2Xd7+MYC4YxxviBjG9Vp0EjMCR6J6MdklVPwScyWG87+iG/MaJ9rOJBb4RDvIgkXIckqI+Ew7UMztv6EtqdmMtzp8crTo4eskB0F7ZMxcQoqA4KnXLNy3yDGSs6bJA19jda5TNE5MkR4VxMUjuyusXWXhQfX2V4308YxJlqBlJJsNfoxYX2LZmtJH0fqHTei3GiATIXugWyCactEXnk29DsxFyqq29O1ZNUXC9NOH6UQ64NoAm8/CF4+ZWwXhkcfw3Ma5A8gGQlLImoAFvLpX/fWvQcIuOW20iBpnLYR3vn5+lxDX7GINEBEOCN6kr5bKg+hthhG1zQbVCwqnpSyPhigmsj2nVOt0yEXqmsDg0VolItDy3onmVJJJ0gRbd6gxYGquOtwMdvVjS/i/Fdsrd+3jEWDGOMMUYIHUkskQwzR36ElwxZtALbivtSGsmPuyLx9Qe+AD6E/F5/Cdk13kVa3wfh8RMIne0+atIeZR/Tcso5ZzzB4khouMUjjrgkpulHEBEdeTBIOuMJc1YBDBmLoFNcs5tGsIzIX+2wq9D/fxP4cniPatapgkGegTWhTRptj3sZQdSXwpTAQengaSd/Jz4MgSpILEwMPFnCV5YyqvAtlH7AW4K8vtkOBUNP3dRuxr8wjDICODBr4OUW7jjpOsxyODyGyR7+gRwpEPbXXV3QdeSi5Jn/4FtRk7Fh+u+OFRT8uGLRUzO7t1MjVZzixrx9QlwjYy8dTajPx7579cqBvwJ3De0WqSpUQEPFQxQEWb3lJcZ4ZzEWDGOM8b6OfedLpQmobbbqKCvyLGxZfQydGdgFqgWl0tBb4P/jBs+fkFxZMVA0r8Nhgyvjrp7SXiWQeWzq2XDIU7thNrlmlq/YMuExt5iwowjiTnNWTNn2bXEQfYeURjgVVUe26Ug6hzkNO81HSFHzMgMKX820NElNGJD7WgSFHGQ6iKcyhmg3wlyYAYUR1cU4jBK8F1xDksAPHUMXlrULQode6Z0hEW5L2JRijz1VKxCVxygYxvIfhOgWHFzCbBM241bUHlX8sL8cIAWEmj3p+aoVScTNDXgNZgWJcxjf0CUpu4Rg7lXhMX1HYcK2L/BAireSnDoIOPnQkXBYalKiAI7sJivMC1BmE6rHE1wXDX5RilvU8ZXiW5TwoJ2q1siCdoaBLqx0CpUnjcNt38xn4l2MkSUxxhhj/OCG/qDugx8VkKAul/vUzL3r3soP9n6i0d9tGFyG50gxkDB0hvd/VPWlLGAMTZLTZDnMPHbmiBJPltYYBxGOHZMg4bQNokCOCRs6LDVJEHIyvUlSREfsO6LGYbwf6h7HkIRVoU9trVV1W5dI6ZQKSGjEijqOwSZyuwljCB+6D86Ci4NQk5elSlJIirDU+1LINrx+BVMrDYHUBntrTf7akdH1O5F19RGsAuPCdBBXEO9EcjqLIcshXourpVGqqF4UFLlPF1WtiRrizhFFUJmWJGnIA/tAZLtlgToiooBxaAK+YcoGh+m9Rz2ehqTXbbB44qwliXe0XUy9KQbgq3pGqM6H4kZU2Vw/uylQGSgLqHNwMzHO6LE41wxAR62Oxng3YiwYxhjjfRnqVKShCpBqzahABDVU0H59RfBlpP/5mAIfZACqFcBHGawr1BEyuFX2XWptZlQIbfOMvuiITcsPL77CRw7+hZySLLSVFaVvcMxYkVKzYs6OgoKSLRMqUiJaTnlCVNTsigj3BhRfbTFbP4xC1OeiQHAMTxkoh0rrh2FSswvvPbBQbQPZZFiGroVqC6sKHpUC+5gBD5bwX5aDW/g3i1Okvnqyg8e78B7eLv5+7/+haFM/p8NwjDsWbsdwkMl7jLTjsA+CPA3n+uztJ+E8jyChZvYMRkQLMpXpzim54pCaNJhPQRxmOeXe3KkN1uXrdkpZ5dR1hnNWcvw8vOY+2FFHEHqeHTJx0FFLhOBo6lSUSVkgBcI+K6LlubAkxg7DGGOM8YMbOrzXDgMMmsfaA75mkHZUf2G1nNTCIYFlJHN2fapS4vb9rXSsXCGFwS0kOR2HQxae4mRNcbTiOL7kML6mSSL+Hz7eYxhmbJizCvoKZd/mnrMiZ0eEI6XmmAvmrAWpv0yZPd6RLltM6m8qU8ZIAnojvD91/64ZqIdqtVzsPV69qS2DZDQC7cgiOJnCLBWpZ7eEgxw+cCoCTyxhu4XlVrB6JUPXXYc//00RChEVXTpDkjhIDbcUQafmG9AGHQaVmTahy5EhBljqdyFIzHCcw3B+yjgIHyPzvcsNXIMP3w5PSkVES0NKSU5F1gNSZ4F+aUJXQTANgmVQAKQHXBvTrKe02xTv9kCUOgXTRpdeHodLzYDFaML3bBXuu1TurraLnrXCHOPdiLFgGGOM92VoitkXBdgxQNT3GRbKjlCYXvAe9on02y0DKE+1Fx4zMDV1B6vq03pRZmcvRulh6ugw7EhYugWbekoelRTRDhtm5zuKHpFv8f18/ZinnHBOHrQXLE5a5kbGG32bW193XwRQpzCEv7qjVVDgjoFWGXFTviKIZBojO/kIkYp2kWhfbYzYXJcNbFvYdIPjgSpUNwwNjYmDqb5HTYq6RoG+ajaiDJmJxhWFhTwVT4kqFGqZFTyFSZFEe0rvZk4Yp/QFhSZq9SR7BqsY0+IxvdpmS9xf74jCuMHRELNhypIFa+ZU5EH9cSgMvLO4JsZ3Efi9gkG/gtpduEYKtqfhogtVArUXWeiyFJloqvBgvawY0JLPgVY5dhjGGGOMH9yIIYDVBgdMzaIeya7x3m1aYGjmzGRY74Pr4BHDhEMB6sqtV0EnTUw1slOvkKSQAImhsSmumLNx896dKYo60klLnDms9XQmYmnmNCbhkCsiOtbMmLLhiEuOueiTU0ZFmtdw6nA7gystZuOJNsH9Sd0nbyNJSEcOetqqFVEwaDHsN2S0qNDnOoadeSUFRDyFqoRvPIUHzZD3yMLz9/iVroOuA9sFL4kSGUvcl/fhK2FZmBzSBE5bOE2C7lIC0zlMjmCqapwKQdFiQKdIKjKkNNJ9Ua1nWJPWg3We1NTEpqUkD66UA8XSBHnokowrDnnCGZccsWJ+g1Uhb8cRm5YkriA2dHGMj83gIKrsiEA3DgsAACAASURBVF34v46GagZ65SVw7cFvwV8zUCceMhiCaAtCv79jvBsxFgxjjPG+DO29g/yUqwHEflgG/qPdu70BzgVNlx5Cm0tbX5sTKtR0gtApVQDpPsJQUP0FbXDcBV6A7mFGt00Gyl8BZgr12YzLo1ukxZZpvuQse8JpfE5CQ07JKU/JKXmDe1SkfIhXOeSqn7UD7CYp10dT8lXDwfmGyAak4r6M8j6dUl06r5BcpAZO+6EA/C6cTwAvgjzfbaF6CLMYPvFB+Hj2zCZU1SMDDuH6Ai7P4fA2HL7IwBx4Afwh1P8C7UNI/wdIPgDx/WBURQBhzsLa6wZbZ/1HDLIEOwYZ74NwnxZ7+yOkEGnVETed2F9nOlYwVKR9pyej4glnPOGMK47YMKV9m9QS04pted5ymF6yyg+5SM5oklT8JLSrolCaI2SMch4uT8N3pkEMOupDaGfhxHRu9JQBd3PEQL14j2NUehxjjDF+cOPZXZd2DpT6oFvg/R2aDbcHzp+ZCPxe2Q5aAKgwkNYfalalOEudeKiw0BZ4BP7CQBJBBmbiye5uSV/akRQNWVExjVdM4zVTu+ntrmNaXGiNS4s8ZsuUaxoqMtZRS5w7UtdQdBW5q7HWD+9LDZzU1FATuCo/qqCTtuvZWxal+il7IQgmuQbapcgDdDt4auB1D7tnJQm0jR3ezmQnNhpPVvDgTUhb0SZaVbAuoHsKroTo6xBdAatQsyGaDySi9Hi6gwMD0xkk2pXXkZGeR7J3zhFDsaMJqpBOhlBcofVxsLwuqMgDH6HpXSzXzFgzE7MpIKWmJe5vc2FIVJNQ+py2S9j5DBfZ4bujF9Ve2Ac/asdlyzAaMvqFcwwcUv0Oay9fuw1jvBsxFgxjjDEG8qPaMvSwSyTD74veREjGD5rCvhNv5siGuXeoAKyVDObNMEJWqWhVn54wzNPN3uFjT3zUEp/VpC9tye9tmNsVcyvqjRO2/d9J0F8QHwNxUtQRRUdEwY4kqkmilsNmybxekZhGrCW1yNHRgyYjvb5DdukwYDMCrfKGWycMo4VQPHgHrg5Gig7qDq7q4XDeQacXL0D/2MBpIHDsGtjsIO8gdXDpYVkyqDMuEY2iSE4hbyDt5PoEmDqYxqEO0dHLAsmnuvYpQ+Gz2TsHHV0EVohLDG1iqW0SJJ9jOiwdlnYPr6BFgZabKiGtctANCbtQbOxcQVnn1G2B83aQDddiRZsCqomhIFot0pLwXVJNpiaCTm/U8ZpyYfc1yd/DGJUexxhjjPdnqFD/s5K6OgDfAY/BpeIYWKcBsacE+gJMJjKHR8gP+xWDVsOngI8hapCvMEg8zDxm5ilOlkwWS8oqZ7crOMyuydKKLRNqUo645CRwDhOaHruQUxLT9smrI2LBkjkroqRml8QQe/KkxSYeUyBjEnV83KdT5ojM9X604f6dnGJv8qly14HBF8ViP91uoPoGvNTAD03E8hrEqKrawqaBdQe5lbHFroN1Cx++BcevMIxLlFVyD0n8/4i4bKq41P57UrdKz0CVnITnv4x8Hqo/seMmKFXFPPeiTiN285g6ABszKiI6rjjkMbe45KhX3Zyw7RkRKufdEVGRcsUR5TeTlNwXZfRIMaO0ScWTrBhonyB1wBpYGbhMYa0VnTJ49AHPKUbQ4xhjjPH+ChUcqJFtrOIW1IJyxkCvVFyD9rv1+lIe4y2URpKxm0A3h1uRJK27SDI4R+oS5RS+aPAz2K3mNFVGOtmSFzu2tsDgOeMJh1z1re5XeI3bPOq9J3JKDJ6alIyKE86ZsLmBZWgSi7MJyXVH9rCT5DploHzuGxxpuOG0ehCkdk1US+IaSWo76CphReBEPdsD9U6Kg8sOahcAjg5aD5GD89D4SIB/fgz3V5A7yDooH0AVljiK4XAJs60sd27hbgvHsYhDRcF/4i1t/hLpSmhhobRSxSTq7l1HSjn4AzHNGr4dpqdTtkHNEaAl7hUfa9JApyx6aeiYjoJdb4fdh0pEb83NCZl+/R4i+hjKllAwZNNAW4tRR7uTv2zCF2oZPsgdw1zjOXQY3icxFgxjjDHGXijvUH2pVZlINaDV2QiGHrYO8rVfHAqNLrSKk8D9UxfILZIMFLWvYkHncpg2TujSWHaZC0szyWjyhCyqyK2AHI+4ZMaamJYdBTUpCQ0ZlYwiaHrqZU2KI6IhYeJLJm6HUd2B/XG3sgr2PS4UKJhzE6ugLAMdoU/od5mmCiZQHqIUfCqqjLEVnYZIRzB6rNDI8WvwK3mpQ4QpEVcQ11AE5WNbwPwIJneAFaSlTH9MBuYQKQT2sQgxQ9dAMRmT8AJHDCDVZ104w4gl3jqytsPlNT4X8qQJrqEmUFpVlltHEkqxVCdLF/Al4jbqwIJLLT5J6GKP38fTwuBaecRgiqX0zwZxuNxY+X65fQqwajDod1TpIW9n7/4ux9hhGGOMMd6/odrEb0cP6JCsr8AE3cIqb1LBaMH6MclhbuR3+5KhkaHI/BgpDh4Cjw3Mwc8NzSbHbyKy0w3OWq7TBamt+DD/ygvcB+CCYyoyOiwWxxGXvMw3AMJOOJYE5sXiOm08+W5NnHTS6TDIRnTf3RuGZK4j8Smy89ZZuo7GL5AErcrEbfDm8sErwkOXQnMoLIaF7v4BUwpo0ftgs3EfrldwZwIfPRGDq7oTied0AvFhGHW8AO0cGadcyLG2YSxiG0gei0R0lINdIKMXGFgXMLiY67nqx63AzArMFaTWkVgHZ9Jt8BhaIuJgZa0F2oQNLRGeKU3wjNAOhA/CFgYv/1qDTyxdkWLmXvSz12bI61l4b3PgJcQo7A2koMwQoG0TQ12I5ToK8ND5yjKcrI7WxjT3bsW4kmOMMcYzETFIAr6N2+BbQjUdtLuQ3LyuSfbFcNEkHSOJdolIQ+ss/QDsYcfRvSec3H7CWfSIs+gJh0a6Cga4zwv9q3sMKXXQZXBccxASmzhWZlQctGvOygvyriLyDi7A3GcQBdwPj7TvHzPg5vR2BQeqVLRuau8jO/hW7KvrrUhFAzxcwVceCVtia0DFDTMvIk3qP6VCTNUV1GsZW/guFCAbeOkCTl6D1/8ZHqsHxf4O1ArL4uUmmIMmMLuC7FwKBxYMWhnL8KJKelG8wx2GEY1SMwsT3ConPVPC4pgET49nQxxESxyWKw4pyfvPY0fBugvS0FWGc0YKlTmDdIJqT+gIYsWgwbDim5hPqma3IlgVMamynO9xjLTKMcYY4/0V6laZMwy6v5VwDJqFDH/tVJSLkgTSwLO/jySlFxEw3xnYew3RvYa4aEiKlmTRkCwaivmaLN9xyFWv4BjhaIO1EdAXCgpunLAlp+wxDRO2zFmy8GuKriS+7jCXBHtl3losaI5R0cv9pOz27tNCIdAb+x0xYkYVN6LG2F7BrIUPx/BCcKlsWxnBl8ESexaWQpscSQzpM9bflkBysDCdwMt6f8cAKwmf2EEMi0hcL5Nc2K+cIfgRFdFStqEaXGnBoGMjxx4jwRMlbRgvyLChIgsjBhk/pNTkVFTkuMCgaImJabG4wJDIaIlx3uKcFYYERtbxMqylijSFbg1rBliNdny00FR1zg4BhPTSmOuwKPqEZ8G7Y7zTGAuGMcYYYy+UVvnv0dFUH1mHyvrrrj1lj4gpACYRpF6y97A58GEkiR2DecVjXumIFxXpoqSwJbkVwymVeVagI4gBle5wM0pmrIPN9YaCXV8oTNkwZS30y67GNh6zRRKTqjZqaIJUqmEc3ue+frNiA9TZUWl9ngHV38lYwDaBFdHAUQu3gt113ULlYWfh2kme1J6MkgImFiZ7v8w3GHsRHOZwNA2PbyFrIfVSaEQKZFRJay0GbiOF2pybMtNKo9SLUl+D1EY3N3QLS5Ml1KTUZNQkNOFSkfV0SaVP1gEYqZ0Fj6EkZ+cLapfQtgldk+C3EVwZODci6KVdD8WGKLZi35a7RnipOLENta1Qe1kjrYkn9OjT3nr0ORQMI61yjDHGeH+Fggs0M75t75cBlbbPktBMo/cvoJtCZweZ/48A/z1Cp3yZXtypu0rprlPcByPaOKLLIlxmOAidhYaEx9wGoAuUvYyKD/FvpNQsmVOT0BL1dMos2CLFiJxx3Himy0YS0O23OSUdMeh0RSmNhKW4CH+PwqlfID/mp2HZDDf1A84haiE6pB9hrNZwsYJlK6umuWAVnqJO1m/sRIdBPxFtavTxRFZ+JqvMHeAshpO5dB84YfAIS/c+qn365SFCtXx2oqDjgTPwZ1BNInZFwkPOeMTt3nBKGBMJ55zwkNtccsyaWe8voQXDjiKUbTPW3ZRdWdDscpptgX8QwdeNVE37mgva5dFibIc8ZgtsPdQ11BWDV8Q6fMEeIdWgIlnXDHoMY7wbMRYMY4wxxjOhYLFvVjAY5IdZt6JpuH6ATM/T8HzdO4ct7SPg78Lfhwwgu3ty6V7L8MuYtpiymxyyKY55Pa+Isoo4q0mThkW85IO8ygs8YMKmd6hMqMPut2bBMugx5MHlco2feJ7emZP4loQGE3ad6aojW3YDY0MvUyTPqFvlQThVVV+6ixQeSsdUmWfFfKoUszpB3oWihbM1HEXQ5QEUWYp4UwtEa4hXQixpldlgoD0Q9e3l61CuYf4/w/RHIP6KyENnWQBGapte8acZQ+tCcaj7BAKlhGYMHYmMYWe/Ry5IqPsxTxcYJ1smTNmwYIXFUbClCn2hKCTpmhQbaJUtcSg2GNgPqmWxXxFNkYJGd+23EDzJfeCBgVUKtc4jLMMMQ/muugDTcGKPec9jZEmMMcYY789Q/uB/9Au431HQ/bEWEKE/v2+1uE0l81kz2Fdoy3wBfhLhvCWNtuTpFmJoowgbWaxx5GbAJlgcJQU1GQlNmKGXwYK57ul+OTscES62NHFCTUxCRNo1ZG2DycAXYJQqSXjrujndV8VWJoGaamkCVmCejjVKbow4ygbWS3Gp3FaS61ovRlL9LhpRbJx0Mq7YNUEmwUBTBkHGFioH9Qp2TyFZCRsiaSEKI5PUw8FWljUqhFDQf0RqnFUw2Cvs9j4HdedkOC8TPuKUmilruoBN2FHQkFCwY8om4EFtD4KM6PpRUkcsyIfO4asYt0vkJFsz0D7z8HVze189pbRqt0Hvv8HD1A9jwqDzrdWRfn+fA63yuxCbzYbf+q3f4m//9m/50pe+xNXVFb/3e7/Hz/3cz73lsV/+8pf57Gc/yxe/+EXSNOXTn/40n//85zk9Pf22XnMsGMYYY4x3GGoGsWBA1Gmol/UFww/5Qm73Ue+QzQmYqcNGDn8IvOSZHVxxuDjHe4P1nqndMLNrFlwzYUtLwqMwV1AtgIItR1z21D2LY8GSloQ102DC7ALpwZK0jqwsiYzYXvudgBV7z4XLcNGCQTEM2oHYXwJlTWhnQQuJ4Ha5W8PDp/C4Fh0rHUE8i7c89nDm4dzC0wimnYg3bc+DD4UWLH8N0f8lQpoTI0yLFHmtuRXxzDgRsGOk5xPGDL19tfpnKOtDd/PBpNR78KXBTwwGTxKkt0uK3ljK4siomLClJMMhbIp9VkQVrjtvadqUZlvQrTKhUarDp46BlIWiLpVPGBZMwY/71ND+Q1FLVBg0QUC6XM8pvgsdhidPnvDrv/7rvPLKK3ziE5/gC1/4Asa8tTh64403+PEf/3GOjo74zd/8TVarFb/zO7/DP//zP/OlL32JJHkr0+WbxVgwjDHGGO8wcmSCfoz8eF+F21WveJ9xodtYK+38T9G7JBb/7ZrJf1pSmZTaZGxMRrm+Q1dmmMbwwuHrFNMt1xywYUpGRRKyRsGOE8455JIJW4644haPOeSKjIqSnBVzckoWXJNTk1Fh045NnJBVHXnVQgY+QRgUKg19l4EFoWqU+6GnXIVTPURy1W3gR8J9X4fFEopT+IAfSBdvZ7gclRCX0B1BewT2Auw1uGO5cAcZjTwUjQSTC7DSlmKlzSFEEaRXEHVBf2HOgFe4F95nw9Bx0M24Uivn4BeCXaiKGPfvZAiVip6wZblXLHaBRaHS0HWXsqwWbMsZrft3kpNSOfccPG+Ejo08UPe8kbDQCnK4YnAR+8G2tX7hhRd4+PAht27d4h/+4R/45Cc/+baP+43f+A12ux3/9E//xL179wD41Kc+xU//9E/z+7//+/zCL/zCt/yaY8EwxhhjfJux71qpwjnl3v3aZ9Zt11oe183FkviNKfz9FD5o4EPQvJqxW89po5jORnRpJuqIixiz8JyXp1SR/FRJR6GjMDuOkwusdVxyhAuw+oyaKw6pyHjKaU/9m7GmISajkia5aUiiljh1xNYRK+4/aYlnDcnGkWyc1Djqkqw0Q8OQkxYMzAntKihoPwVOIZqEkYHmL71/3w4cBj0sNYMKRQAduCdweQ2bCI4amCu9UEGKBYN/mE6EJgyCU+rJEIfryuFUT4xE7msWlubI0iaWLhZgY0vCKnBR2rBSK+asmbFkzhVH3OcFnnDGijkbpuJg6WZctkdsXYGLpOvDLog07ZtLXSN5XrXAKgaqqBJxNog5R7cL0BrPABzRA6gXebN3oH231fcwvgs6DGmacuvWLQC8/+ZMkD/+4z/mM5/5TF8sAPzUT/0UH/nIR/jDP/zDsWAYY4wx3svYp1WqoAEM82MtJhRNF5yO3E6U+R5b2E6glkzZPMxo8mx4ypRe5c8Yx9Idsq6ER2jjjjzZsUiWTPymt1PWtrn4FQiuwQbFwQnbnt6nCoWKe4jiDhu74H65oUjBzDriq+BoecCQmxqGHa5SAPV3WlkSawRj95RBATIL/xdP6KDSFJ6nmgh6UT8HHc0HSWhfQ7WSp86VqaGYVC0M9LmKVdDlz8Oxr8NjVEJaJ0p74EgfGTpnRVAJAuNBqJPbIIOpOgziVHnAJUdcccCKebC/zlgzY+VnlC6n8zHGOqxxGK0htchSy4dLbkIO1NPCIDWBBdoAAnEKdNhwkylRMnBjlSnx/tZhePPNN3ny5Ak/9mM/9pb7PvnJT/Lnf/7n39bxxoJhjDHG+DZDKWubZ25/ll55gPTndXAeMsACaY93wJcR+d+XGXQN9Hc+Ad9Y2kkO00zGF4stZ0dvcFI8weBpiTnlKQuW/c63DdbWBSUxHRO2RHTsKEipKdj1OgKq9aAsgKJqKHatJM/DcBqab5yIMVEiXYcckSyuGdr9cViafYFMxT3MEMqj/uqqR9KWQcnYh2UqEPXLB8APQ3QP7h7D3bPwWgfhtR+F55pwbHWw3A/DoBuhIwilX6rBYwZ+CknZkb7ZsTuO2Z0OXZ2Umllwf1SWRNOXXSkNCQkdO/Le7jqNGo6iS8o257I6pOxy3CxUQ5EZRKeejTmDIqVeT4HHC3BzoVQ2JX1bpHfR0uuKEJnRA0ne69Bi77047ncQDx48AODu3btvue/u3btcXFzQNM23jGMYC4YxxhjjHYZm0pib7Agl+nskowW3o2gKyR2YFnBg5GEZA15N5Yun9MwJ9Zqw1lEcrMlP1uySjCsOOOaSmJY1s5CwJIWtmTFnRcwTSjLOOQlprSaiY8WcCdswpkgEBBnur1LPMnr7HanBkU4b0rYhrTriykkyewrmKwhIT2WvL3hrMnwE/KMYLDYVVA2UNUwOYX4iOkRUotewXgeZCg9P3pSmxcMUVhl8oIB7qdhzFPvmWVqXBQuPfsS/r+aoJlmKv9DHx2Ci4XFp1RGtHC5LKFP6tWtIcVhSKqZsaEgoyQKGwfcmVCUZS7egrhMBO7oEE3fE0y2uSujKDL+zQydlzuBKqUnyTeB1hgJS2RJ9EtVWxN5MpS8e9MHvxZzgbeJbIRW90+N+B7HbCV81y95qLZ7nef+YsWAYY4wx3uPQIbxmHd3JabsYBv3hwJTwSWBJmCA7jCQxbaHv/+63QOWx047osMYUDTZuiawwHkBb5mIyldD0stDgaYjZhu16TsmMzV7iE5XCOIgYl+SsmUnS7Dek/gbA0mAxqcV0lti6flRhdCnUuVIdOKcMiaSlZ6sawHpxnoyS4PNwgCTFWhS1o8UghRC1kDoopuAyyK4hWoLZXztlPSit04TbdeOdISJTMwaWBPTFRZcY2tjSTSO6aYTPDFjYmCmroHktio1FL8qkUs9AKNZqtkyCJLQYUzlj6EyEIYYO/M7iKztgNPa/Rsou2YZzOGcYoyi+o9VxmI66dDyhM6GaAQTy/h1FaBSFOI9V1Vs1VcqyvPGYbyXGgmGMMcZ4hxExZCUYtrqquqcmBqFg6GrotoKEv8ghNlCYm5KHayTRhk2jmYI9rbEHNQ0xdptzVFwxTTd0RGyZkFH1NL6IloyShCYMJWQX5bEUQadBC4CSnJQai6PqTRQkDL7HOmSIYZX1DmesXKzBtwbz1MvpvoSwKi4YsA2q0aCY0LBckRPr6qxAuig6mgj5bZrANKbfcZ8WcDqDj95FCouvIS2HIN/MafgIzsMaqlr3PkHlFHiB3oPBG/DGQA5+AnURUeYxOyPqjOo0ec1hb+blgsqmFgslORumbJnQYTH4/r7Ui4BWGjdsvKPtUupdQvs4x++im5oW+7gG9esILpzcRjo3l0CjRYBWGBmDhLk6nKmVVwDaPi+mxLtBq7z/n+HBf755W3P9HR1SRxE6mtiPBw8ecHJyMtIqxxhjjOcRyoB4dmiuQjopwzxZPZQzuGxh3Ul/vUrhVQbbCoPUGLeAD4F/xdC9kdNtMjj2+OOMi6ShThNyqt7cKKYlo2JHwRVHFGxZsOqLg4oLEhpq0p41MWdFQ9IXHmbPkDkLmoUdEbXPOSsvmLYblllGmSZ0eUV3UJFVHUnkBuyn8iZLBhsD1RnQDvoMSfwrJPErW0A3xI+Reku7Azqi+SoD5sAihYoCKrVg8GHtZuExOo5IwnvKhTbZTC1VFuNi6SRIAQENKZsg8Qyi1Kh/tUioyHoJ7isOecRtHnOLiyAPvepmXNeHbKoZzS6na1I6H+FKi+9sj4HtRxDn4aKqmg4ZUVww0FpLgjSmGkwpnUQX8YqbghLqS66KT98H8cL/Jpf9uP5H+OsffceHfPHFFzk7O+Pv/u7v3nLfl770JT7xiU98W8cbC4YxxhjjHcb+AB2GbWPKkO10G6l0t2toX4F2AY+jwb9hn1yhSdfrq4QWtvO4ztG6hNLn7JqClIZ5vCS30lWIafFBQGgdCgOVit5RYPAU7PrdckdES0xOScGOmJYkJBi9ryTjcXzKtV2QRiWpr8jalqR22NYPY4icm11wHW0oZmDf+btgMPlUUSZlAwbXyx7TsR9BZrqneqrMcwTcATc1NHcsJJBcOSxe4CSWfqRvKohiTxJ3tJGlsRGNkeGMCl41YSXkCFpGGSI6Uuq3KD1GdDgsFkdiW/JY7K2t9dQVdNtCigXMTQKDFg9Kq1Qp7QkDUHSGFE3rCNYJdGnwDleeqDpVqVy0Y6iWYJi/vIfxPWxv/TM/8zP8wR/8AW+88UZPrfzLv/xLvvrVr/Irv/Ir39axxoJhjDHG+A5DB/hqj6gGBrq91aygFIPb8pgmGlyXtD1/giS4xd6htJCIJGl1LqJuU9pObK4n0Ta8C9FcaIh7xUexX5bqo8PShQRYhR6CuFBkHHDdUzHVWEkBkbVJ8YnoCJzxhOOuIq4dadVham4y91RCOuJm8aAaCS20a2jfBGvF+duoZIAeR/0djpBOxD4hRWmTcwZ/MJWlPhKmQ5cbMIYYJ92DAowdXp8dRHhs12FzLwwJE9FhyOKKOG3e4japXZz9dWlIWLDEYTnnpJfsNsbjE4OPDFhD52KMzW9iFbSIUn0LGAosdQyNwnnq41wElRUZyk4f6BDASMQwklD0hxYTz6Fg+C7F7/7u73J1dcX9+/cB+LM/+zNef/11AH7xF3+RxWLBr/3ar/FHf/RH/MRP/AS/9Eu/xGq14rd/+7f5+Mc/zs///M9/W683FgxjjDHGdxjqIqQqRNpH158X7Tio38QxYOXPPYYO8y3go9xUnFZKXQjnDFWV4awnz0ripO3dKo+4wmO45JAjLrnNIyI63uQeJ5zzIV7FYXnCab9r1oi4aUp1876OQ65YsGTJAo8l5SmFrSW5lwxdcRiAe7o71ENeAG/C7r/C+f8L07tw/DGINsic/hDBGmhtNQtL9oRhpq+kANVeMOH+4AkRLTyTs3YwyzoBcw8pPrQwA6ilUElXjgTHNLzp8jiiPI3fMmVSf44rDtkyweKYsSai68GjqnvhsFxwxKpesL48ot7leB9ArrEPmA0jTadlOKc7YR23CF306wyFhE4f3jaisGgLZI7TMphOKVPiOcR3iVb5uc99jtdeew0AYwx/+qd/yp/8yZ9gjOFnf/ZnWSwW3Lt3j7/6q7/il3/5l/nVX/1VsizjM5/5DJ/73Oe+LfwCjAXDGGOM8Y7jWTqlZhlVMNLryqJ4xgZxhSSHDwD/HcNvvLbis71DtMC5wV9FtCcFHFuyqIGk6UcQFxwxY8NLfIMZG7ZMeuCipaMkJ6LFY/uZ/IQtc1ZBvHF64+wsjiMuOeSKCdve+CpnR7TfK54APyxmUkaxC7qDrvYuJ8A9KD4BtyqIfNj76iheCwbtUGjbHiGW1KcR7cSSfrUjeejk8ZNwbBueH4djBZXI7rahejnCLQxE0EYxdZQSma7vqMgnJsTSMs0oTcYunKkLYxkFN6rmgrJMSnKWYYYyDVldgaTzZMP10RXL6YJ1M6Ouc7oqxRdWikOdGmhev+amv4WKTulX6watUpWd1BZU5wGOAQD5fYJd+A7ia1/72rf0uI997GP8xV/8xXf8emPBMMYYY7zD0F/2/4iErnOFLbKlDICF3QKaGcydXIwVMYAjBpngDQMsIgVyg9vFdEsoD6Z0heyQbNaRHFaY1JN2Nd5YyignMc2NxBiHDKygvZY4yEoPYfBM2TBl09+vLXmDpzMRuyQTEQQLduqJso7Id8Rbjy39TRyGnnbAOsRA7Bh2zqpgrRsxhAAAIABJREFUfIQUFSpcqHLTQT/BnIApPGbH4AWhbNYM6UhEDJ5LQSjKnAjbBKCzll2ckjQtSdngYkNXWBpjaTG02N5hsg1FREXGlgk7in7NtGhYMec6SHG78FwtKloiWhfjXIR3wiqR3C7FSw9zgUEu+yqs2SFDp2mra9GJ2mMvi7lmoNaskYpDgY5aMKi+9Hsco731GGOMMca/F0qO/3Yj2BR296DL4V87eLWFwxQOoqHdfoYkUd1MvoTswC+hi2O2xUEvOpSclMw+csUysjypz5jaDXfsI3IjXPNrDrjguMc5qMLjijmPuRWwDo4opMo7PGTCtmdPqFujw9LamDaLSLIC5jKyKNhRtDVF1BBHDr+T1ojNPGReJJEJ56EeSYTznCHJUfUYzoF/C/cHeqTJIH2jI9W1WDDYamdIstrKYzmkF9WMnCfftkPxklqqoiPddRRXLd3EUGaGKBI7al0DSxfwIFI0xLRMGaS41b76moMeD6JFxZoZVxyybA9Yrw6pdgXeWXxpbvpIaL4/R6YJl2FdEoQCqnTTJjzOtQhoxAXGxCNE3Wn1zPdQC9l96dAx3o0YC4YxxhjjOUfGoFR0H6YLmB/AwkgCLZHkoV0F9U5QFmcwvewtpW973C3LzuekDWRpRRI1VDajC0g6g2fJgjS4VcouOupb68qmOOGcM570dMwsSEnvKHjKKXNWTNhSBd9LkOTZha19SofLLcv5BLv1LB5tSa/agS6ou2b1SYCh9b6V5eAE+Mm95XoWt6ekAJ2bV0iO1Pa9MjLCtMhs6amJRVyTFh3t3LC7FeFSg7PmLYVBR8SOCTPWFOwCQDRlwhaH7YuEKEhvW1zQykx7LINPDdFxy243Y3O9oC33wCj7sUEksAPTo5ev1ryfIViW8xQuItEmaFfhxle4CSBROqVjwDI8hzT3Par0+G7HWDCMMcYYzymUZqliTwHi36Xi56yaBSrW9yJSV6iKoSoBXjDYSRvgDYPbxjT3ClyV0HYdLk/g0GAiR9smGOtI4hZfR3TbhIP0ktPJY7wRwSeDJ6ekIeWCY+7xBnd50CfHNbOeeaFdiCjsXOO2Y7KpmdWVKECWMLmsMWtPtA0aDU8ZxizKpNDl0M75PgtVJZNj4DG4jWX5P00pfyhj8ddrJv9SDo/XJZ0gyXZ/HBI6Dt2JobkVYfEk6w7bgnUWH0vVkuLp6HCFx08di3bNtKl5nJzyNJkzZ8ktHtGGAusxt1gzC7LaDWc8IablgmPOOeEJZ5Q+x3lD6wM1U3N5yzBKUVrpWXivB3K+fIMB8LcL59N20Lbg9MPfp1YWSFW2CweqkdaF8jTHeDdiLBjGGGOM5xSqYKSiA07+1p3sgJUFt2EwZVJ0vCoXakGhu2kLPAV/bemylG6bUi8N7SLBpx6fQVUWRFFLlpe4OqGpchwWkzqyqCSzVW+utGXCNQtu84iCHWtmbJjK4/GsmfW7aqURWucoqoq0bajzBNN5iosKu/V0E0MXGWzpMdcMAk0Rg4DTUs6BOdJN2dGzHkiBr4B7Yrj++JyLxQFua4he74hdh00d7q7BHxmiWkiQPeByS6/s2N02VGcReGg7L8qVlcM3JiT9joyGXZKyISdynqxtcFHEhmlgiay4ZkHDtMcxKP0ypeYw7PJV42Lnc7auEG2GtMLmDpdF+MQIlXNn8N5KZ+mEoaPwCPk+KAtXo3biVOlzBmMppVEqj9WHRauQytLxXAqG72EdhnczxoJhjDHGeM6hkPgZcAhtIT+MJYPCYQz838B/QToNZ+GpEZJIj5BEq3bTLbAykmifQreL2J1M8IWl3RaYuKXrIub5irOzx2zbgtd2L3GSnnMnf0RGxYLr0IovOOeEr/HBXoNAmRIVGU8445ArHFZohKlhexZTMeWKQ+KZ487iKSkVZRFjn3qKaUt07iWJ64b4BfAvAyswSp28RLrsx+ExKXAP/ANDdS9jdTAj/t8b3P/qOfyva4plxe6jMe7YUJy3JFf+pgPmFPwr9E6WZZRyPRe2SB5GMyV578OxNHOuzBFlllOlOc6YHrvwgLt8nQ/wkDssWFKw44oDlszZBfzCjgKH5RaPmURb8qjiMttSLgrKo5zydk7XyZiocyntG4VIVJ8ijJn/GtbnR5Auw9fZ8zhLIU8HMGhvirFl4Jt6BpCEGi7puGKM7zTGgmGMMcZ4TlEjWR6kaFAZv4fIlvMOdGfgS2gbsTKmgPsNXDhROZpEAohT++cLBgDgkn4z6aqYJp4Iq6KNMJmlnkQsfcauPcAfOLoTj09FefDKHPIqHwJkvyp6CyaAI3dBowF2Qcwopg16A8dEdEzNJjAr1uTxmmpqMV1E2nbE1mPPPByDj6HNLXUR4QO4M3GO9KrDnCKFkSbIf0MwfRlEecfdv3/M0T9ck2Y1Sd6Qzlps4sn/scO3EM285NAOKaxOEAzDCmLvmXQNWdrRUVPFCbssAwMZZRiveGJaCrYi12TkjXgMSxasmWFx3OVB310Q5ceGJ8x4ymkwp8qEQdEseLR6gXU9pyOiqyPcNsKvRIPBX1npNmwRf4wLBkbuMnwQLyEjiseIvWfVgFMwo36nXmfQYNg3plBzit138qX91uJ71N763Y6xYBhjjDGeU6iwgNLeVJlP28nhx955cbOklOubCKpI2vdqSqhmTX7vNmUMTMGnFn9h+02mjw1+Y6mihCqCYrZimlwTRy2NSVgzAwgN9pKalA3TQB+Me8aAUixXzHtLbZWj9tAbYRkk0TYmwWWO7LiF2NNOLM4YcOBSQ5dabBTWRo2idGlU4fIW2CPP/GLDfLkZ2t9BSDNeucGLQzF+ejGyhHbjsc4T5w6KlihyUBpc5rF5B6E4UOln+X8b0Bpxj9nIgn/HlgkALTEV2170qgsaF2tmrJhT2ZQuEgSjceDWMW4ZDbTROHzsl0iRsGPoIMyQYlANqSxgDNQWahO+O/s21u3e3/3s/azXyXsQI61yjDHGGOO9CO3Ja4Y8Y5g5lAxCT2owcEs8n+ceDkMxsYHeYHLDgGtQi2mdhyv4T3/Qjz3mjic/3nE8vSS2TT+Df9Y3QWfxalClj0tpeh2CuzzggOueeum9xbcRWdlhgOusIC4csdmA8VRJRLz0FE9bmqmlPDWYyg9yAZrbIkTQ6oOyRD6nz4VG86FKEnyAgTmRgM/kPnMVHpOHvxdgJiIClbmGfNNQzyJ2pzE+yDIb6zF2MN9S58ojLjngumdHzFiTUWFxtMQccUlNSkSHx4hvR+wo5muSAESobEG3jHFF1NNhb8gkqNS1RYrDGYLrOCGoXyZyucqhniFVRh0ekDOMItStCgZO7hjvRowFwxhjjPGcY8mAyJshfff9nyLNfim9PnJzAA9quOhgkcIkHtgSEwYmxSnwn5BEow7HpRyCI4jShmhe4XLHxhTMaVmwxuDZMA0t+R01KS0xKXXPhmiJueSIjpgznjAJ3H/dlS+6NbfKc+bthsS10HhOr1aYVkCGbCG6dphLsE89aeKIZzVmK2ODfqO8rzMUnClNC3wFac1/EvgwMslZMthchw6MeRzW40WGjsUEUVeMwWiBtYWkckRlDZFUKsnCEx91+FC4CCOk62mVuh5LFuwoegCo9CFaDrnCA485E2no5QFNJRbjrrVSLDQeLoI0tGot+fBR53vn/lXgiwwNKS0Aa3WtVLZEcEHlkEGnexUO0jKYVLyHMdIqxxhjjDHei9BfV/WBTp+5wOADPZf/mzq02WMo7TDCVo8rBc6rK6MWCwAN2KMO+1KDPWuwaUvrIzbVlDwuieKuT3jaVdAowvw7peq7C4aaA65ZsOxVJHN2JKZmm2S0kSWhxaYOEkiblqKR3bg1UE8TdndSoXomLcllh33q98B8z0QNvjXsflQcHYppTRY3cAf8i4bmwOJSSNaOaOMHsatIjKjauxZ3YGAOXRLREJNUHUVe0yYRuyLFWlGy3OY5K6b90gkmIQ+rE/fARpWF3lGwYkYZZj8OS4TjkGvaKCErGqpY1rNxCbu2oIlSHAm+sYM+hQqBKjNGL1pMWFkHaRxYSBPoDHTKP1UXsI5BuEJnO89B6fF9EmPBMMYYY3yXwjHYEsKgF6x+zxOkEqjAXEMyhTiH0sguFQ/WQ2wleczCIS+ReiRnmHoYh4lb6DxuFeGiiMZmbCcV69mOqd0Q27anCYKIPanAkygfQh7snGes+1m/SinvbM4ynQUlSTFrMlNH0VbMq5ioc1KQJCmbPCMxNRO2TB5CPHXiWqnOzOouGZwaPdB8MKY6TUj/tZVN9BH4Q+imBtcZ4jc8JF5AocG80c8N7QuW7kDMn1obUZPgW0PctFRJzDrLiExHCpQklGTUoaPggsNnHSSvrjnoOzEOyzULzjlhxZwdRS8lndAwi9Z0hSXOpbor25x6F9FWEaTxYG6q9uBluFwxACAXDF4c+nVJrJyfUwzMviy00kPqvb8K8HgPY6RVfm/HF77wBX7yJ3/ybe/7m7/5Gz71qU/117/85S/z2c9+li9+8YukacqnP/1pPv/5z3N6evq83u4YY4zxlggIxX5wr+j3KZIxz5HsMYPuAK5vI3zDjL6VkHqYBaTfawhY/hahSADuAh8Fdz/BPYyla30ih2QC5/9/e+8ebFdd3v+/1nVfz/0kISEohRrRH0UQAdMWO2IVRbRVo4wdUZjadqxFRm1V2mmdji06A9apddTSOsiof9RKbZl2sB0Z0E6xolbRov2KIveQ27nts6/r8vn98fk8a61zkhCQ5Jzk5HnN7En22muvvc5eyfk867m837MhS7SZbCwyWVtY0R7nuXHKjIBl2tQYcAY/Y4a5oiFykgUSIhaZICQtmgKBYlSxF2bMBWbFZEHNs2OcTfo2QzJGeYctC+kB7FpYB79pGO/2GB8BXVOUWvy9hvpcCgMrH10kR2JgAkJygsdymAMzC9Rsx+go9hk2QzLf0PD6hUtnkx5NeuxlC/NMMcEiYyyxh1OYZ5qEyBpL0SEiYZk2GQGLTDDPNAA9mjzBKczlUwwGddLMjq5ko4ik17DX4UHXhyKeGvuxrpv7WWnnPYWtWO2Rq2IgSSAdgBHziQewUpGm3Kf4UzSzlaPBCRswCNdeey0XXHDBim1nnnlm8fdHH32Ul7zkJUxNTfHhD3+YTqfDjTfeyA9+8APuueeep23vqSjK0UIK07LS1Si74aDsXHQP44Se6LkGvyYENTtBIbGGrBFjlEqRmTvkqS4LIXFKy2Dq1j1hYKyaY+TKDoCTavJWmFENqXGAGaaZI3LNj32aRIyKRsmQtJCgrjEgJWLOs4tpnQFjdBhjiTpDPPLSwFNMP8WdcROYTeDNAV3wxgxmAmhAnnmMmj5p6MMI/IEh7uZ4nmE0FUAIcS8jyExRqvEHQN8uplk7pFdrkHo2u9NMhrRGA+bCSfbWZmh1+vx/nfuZHxtnbmwGn5zN7MUnAzwWmGSBSWJGbGIfCTHg0aXFkJhxlsCHpdo4Q2PLTKO0QbZcJ089GwiIs6ZM24qiY4g9Z6lQiTP6AGuL3Q2hL42MubvY0rMgUzhyux+wsjHiGKFjlScGF198Ma9//esP+/r1119Pv9/nu9/9Ltu3bwfgwgsv5OUvfzmf/exn+Z3f+Z21OlVFUVYgvQwyH1n9rSumC9VVNMfeUbopCjOystL9CIzvJIdDOyYQBmVFwzUOFtUOyo/MU59sGDP0c/ChHUAclLllWfwjEgKyYtyyQZ+aa6TwMIXdtf2ovHiftXo21BhQGyRMdhdpj7q00i5+lkMK4SAvTZaqi44oWXaxN8k/YUVzoGfslCE1+6fXtyZX3iKlamZsnSpN5E7O2M/x3Nce9nOCbm4lrWMI8pz6MCHOUwIvw3heMT5qlS0zcjcGm+MXPR7zTLu5kYQGg2KstEuTPGtg0oB8FFllx8j9DH1KNWfcZZUSxIDS2yylLNPId1IgIxUpZUniWNQGFNgAAYMxhk6nQ6PRIAwP/nFuvfVWLr/88iJYAHjZy17Gjh07+OIXv6gBg6KsO1KkruLh0gCUwgsD7IrxbFu8T/ZTmgyJOUEL8inoBzbFDaXs8AF3mAau59Ijz2Ly1DbQ5UCtNsIEXuFeGTOyRkp4DKg7x0qPZacqOMliETjY/YeFE2bg6vwxQ07hCdrdPlOPLRMuZZi+Z22qB1ZsydQ9jCyU8g30DN4AW0apQfadgOx+n2B7hj9piOcy4mGGmfZg3MOrgZcZ6gupDZTOADY7CeZJMDNYr86eD76h7g2IlnPqj2aMpn162yMaaZfJfod9tWkenNgGUPz8tqQyxpAaKVGxXSZKxLzKmlEZ+nmDfBQwGDRIe3XyJCJvOFepJa8sRYjLZo2yB2XortdeyraEHKvRkaTgDcGIdnSbwnTjINdK+bdzjFEdhhODq6++muXlZYIg4OKLL+aGG27g/PPPB+Cxxx5j3759vOhFLzrofRdccAG33377Wp+uoihPCUN5251QGi00KW+5a9jb1WWKhoVkHDqB3X2aUjlQYg/RahAGQOIR1RNarWWMD8u0GKdDi24hSCS6BI3CxNk+wsoqkeMXEwRiViX1/ln2k04M6TYi8qzGMK8R5ik1M7SKiqIfUUlB1/rWhtobQZoGPHbWKczFE8zG+5n0Fqh1U7zUY6nVJPMDxhd71Ecjm01wkyNp3WfYDMhiD0JYZpz9k7PU/AHTwQGSScNSy4or5b5HL2rRDccYeVaSyl4Jjw5jLNMuyi45PikhS4yxxDgpYSGdLQ6hkZ8wVZ+nFg8xbZ9ev8380iyjpbq9Jqm7JuJEvUgprVBNxctl7mBLEkEE9QASH9IQG1kssbK5UdITI467zsETmBM2YKjVauzatYvLLruM2dlZ7rvvPm688UYuvvhi7r77bs4991x2794NwNatWw96/9atW5mbmyNJEu1jUJTjElEmAhtANFhZY5CyhUgCRqUFtmQXutg79B62875G2WQ4SRFIpEGNvpkgnuzjTfRI3B3zkBp9GoX9swQBGQFDN2zZp1GMZVpFRHvODXpMMV9YP3fCNv2wjnGLrZQu6m5AURoP7aRGg3bSpZ328Ea2dNIwfWaynNZin2hoswxeA+qjhDxNCaYyK8BUKb34xhAlOUHuQQy1aEgr7oBnGBHhRzlE1nc7JyD1AhICBm5s0n7zXhEmyXMxn0qdqFWfBkuMM6BB3/1Ey6bNctqin7bIRyHDhQb54yHs92xladldI3EgFb0laUHw3KVdrLyWGbvBLEEuSk9PUHZLSklCpiPW6BZdMwzHNzt37mTnzp3F88svv5xdu3ZxzjnncN1113H77bfT79tUVK1WO+j99br9z9Dv9zVgUJTjFkPZzSjyjuLKFLnn49jMQ+JuLJvg+eUY3gB7A1pzu4l5VR9brvAgiWJSP8REhqA9IvVCUj8sphpkcc8ICkXDmBE9BkV/g52SGBW9CxEjfOwopfVhaOJXbp3lbr3NMhMsEjqZ6TSPSfKYQZDiRRlhmBOOMsa7S0wuQ/9Ag05/kqg5ImymBP2cMEnxxw2mAblv/TH8xOAnhtooAx9MA8J6lzoDBkHMwK+Te77LFxgCNzqaERSql5JN8MmL0os0ggKFqNUeTqFfyb+MiOmbBt2sxfJonKRXJ1uIMfv9cm13yt8MKZMBVckEEQPNsAGD5/49ZD3IDlBKiy9SjlZWSxLS+HDCLnPHHRvqmzzzzDN57Wtfy5e//GWMMTQaNiIeDlfXR2EwsMVC2efQfIWDO2zPxkrJKYqy9mTYwCEDtmBXlEoPRNaE3ClBjmHjCufayCnY/jhn+lS0PgDB5Ijw2X0y39DtthirdWjWeuT4dGlRZ4CHKRQOJ1kgIKNPk6Ri2NTHOkFOsEjMiD1sccJOg+IhQcOImAF1FpjkADNMsMgEC0yOumzr72ex0aJTazG10KW1NGAwHbLwC03+37PP4gmzhS3+Hmb8A9SnBrTSHpODLjEJ/XoIKTSWUsIstyZXzlciTHKCNCeMDUEjo+ONMc80DXpMsMgTnMLP+AVmOMAW9hQCTUAhmy2IyNUCk4W3xgKTViIbj3owIKmH5KHPIEgZZQ3StIEJAxs0HMCu8b67Ngtum/McKz5uGmtCdT/wgEc5WrsXm5qYcW/aA9wBfHvVv5k16GE4VlWP46yasqECBoDTTjuN0WhEt9stShFSmqiye/duZmZmjpBdeCV2kFtRlPXByiXZh9QbRIdhH6V1sYzPTYOZKHsXoGxyFGtpU3k0sTIPXoTpedTbXeqNPqOgxn5miF3nv8ggy7hkjs+QGk161BnQpwn0mGaeMZaL1zOCQuRIAocaA2ISAle+8K2XI61kQGs4oNZLCLs5Yw/1qS8mxHGKHxjihzImOj3OOvBTnp0+RuM5A2rPGuKPZ4RRRtxL8AeGepaShR6jsYAk8onzDD+xDZTGB68OUZLh5zlxssT4cMCoGTAcq7El28/s6B4GUcwgjgop6C6twgY7JC28NHI3fGrLEyFtJ7P9GKeyJ9nCwvIsvW6LrBuQ90NI/dJAMnXXRtQ529ik0SI2FpA+16XKAyhNp0TpSrpFY+ClwC9TTkpk2PGSv36m/xAVNmDA8MADD9BoNGi327TbbTZt2sS3vvWtg/a75557OPfcc9fhDBVFefpIx7v8XTINgszqOUnpxINODfygVJvGvW2EvSEdo2iEDGoZUTwijkZEUeI+LSJ0KfeMwN41uxKET47BIyNw/QgZvrOHDsiKtH6V3CkmehgMHk3Swjo7ICNymgxp6GPqAXQNUZDge3nRtpEHPv2wQc80GTfLTKQdUmMbFrPII8lDRqOYPPAI4xFhmMEIEj+iH9cYBRFZLSDwM2KGJF5M32uQEJDgE2CIyEidWJUUWNJKl4YECAkRQ2I6jDNyX7JMS1jDrhDjge/n+FFO1jWk8761t14tj+BRSijINZqnnKLoArlIP4toheh3pBTOW4hWR+62r5HSo5zWsTjuccQJGzDs27ePTZs2rdh27733ctttt/HqV7+62PaGN7yBW265hUcffbQYrbzjjju4//77ee9737um56woytNFpiVG2IAgcM9Xp5kjyoUjhDSD7iYrI9yyNf1Cm0C0DE6jGOWrxX1aEwvgQZ74xCS2H8G3D8+zwYAVXepQY0hAViyaTbqEJAypFVMVMmEhOYRq4yBAm2UmWSiyDkGUEUQZtZadyGArYDzqB1LipYzBjpD5iTH+JzubPdkp/Mrov2nnHUaxzSSYMY8hMUtmgjDJ2TI8QH1gSzWdeo0nGrN0gjZDatScgFSnNcY+NlNnwDhLLPttFqLJIuDpuJ94gkXaLNOjyYi4aHxcYJJFFzAYPPYzyyITBCZjMpiHccOg3YcMRqMGy3sC8l5sqwoRdm0P3HWYx1YVxHkUbLCQGfv6KHFy0BG2Y7VqR2qwKSVplE0pbUzXYNU9Vh+hAcPR4YorrqDZbLJz5042b97MD3/4Q2666Sba7TYf+chHiv3++I//mH/8x3/kpS99Kddeey2dTocbbriBc845h6uvvnodfwJFUY4eUq5wt6d5AsMYFiZhGME+37o9nop1egRbzZgCJqG/2Gb0aL2ofvjk+H5ONN0nmuxTi0fUogGLjDt765CIlK3sZpIF5phhmbEVGYgGfSZYxCcnIaLGgHE6RaZhgUnmmCbHJyFiC3vYxuP0yPDIrZ+DF+NPGLw25JFtZDz7if/jouXvwuaMudYYY4t96lnG0mQTEwRsWZijnoyIamkxLREa68IZu/Pr0WKeSQJyTuPhYnIjIqFFlwPMcIAZGvTZzF4yArq0imZIsbmWaYk9bGYPp7BMm+W8zdJwjN6wzXDQJJuP4THI53zy3C05eyl1uxawys49ygbHOWyVYQzoJPZhZO70Z9jJCDFw6LprLypQst9xttpuAE7YgOF1r3sdX/jCF/jYxz7G0tISmzdvZteuXXzwgx/kjDPOKPbbvn07X/va13jPe97DBz7wAWq1Gpdffjkf/ehHdTpCUTYMUqZwi4QJwIQw9CH1bK18K9ZnQrLYoohYhzwIyPPA3rxKdaNtSHOPJPMwxiM3Hv20QZSnTIXzjAdLRRZBygwJEdY/0WYb5ioKiBFJoVFgGyTtKGKTLmMsu6zEqJhAqNOnTp80ikijoHjncqtJP6wz1likFmXsb05jMo8wHBH7Q/xGgolzRrGP8W1ZJAug7vUZuV6DoevGEOGl0C39I2KWaRMzYhu7XcElXVGSsKUaW3pZdr0NnvuZATIvIA99skFA2olJl2r2+w6xJaCA8uZfFB2lOVWkN3yKSVk6AXQMLKbQEe3oGqV0pWRtRLNjNU/W2H6UkETH0UaloY8O11xzDddcc81T2vf5z38+X/nKV47xGSmKcuwQk4jDyf56lBLSMo/nBAk8zy4+Z2ADASirFxVHyBWl7xZWgRGfPAlJ8xgv8MAzhH7GeLDEDAcKTQYZL5Tyg52gqDOkTp0B08wVZxq4JkdZfmfZzzZ2F0JRsjRbl4ucxLlhBnmGyQIeD7axHLeJ/T51v0+32SAzAbMMqDOEJgy9kNT3ybzAKUd4RWajT4OUkDoDkjwizWOM7+P5xo1ReozTYYLFoodBxiuH1IoeDeNMMDLCwngrIiHIM4LU4A/AW8IGBQ1KR0pnnEVGmRBoYIMImQpwfmLWtNS5kg4z6KTYYGEKm5KQpoe6O7BIiFf1O6pNLMoz4YQNGBRFOZkQW8PDjchF2FVeyIAB1COYcFMVP6X8jSeulWJXIcI7otPQB+Y90qRGNooZdceIsyGnbn+ITbNPkHkB+5ld0asgi6aHYUDNNv3hFQqI4r0gwcXp/Iwd/Ljoh+gwzhLjjLNEiy6LblyxxpC66TM+6NFe6rPt/v0k8yGdcxqMTgvYPniCxmjoigVO/CmM6DWadMOGCyPsUGfMkAkWGWfJHjdJqQ1S9kSbeLhxKuPeItt5hGWn4CiIiJVoMyy5cwVDgz76o/hRAAAgAElEQVRPcAoLTOJhiAcp/UfG6eyfxix7K3sOpSXlCXc5c2ygILIKLUpphQX3p3E1ilwOULWzrP67iChtP2WUfo3cKiUAPdoci6zFM0ADBkVRThCkue3JXpNmtwG2+TG2csIi2iT0Kcf0RpSSDtWRywzoeXhpzvjEAmMTC0TNIUOvVgQJ0vwn2gM5vssg5LRYos0y4ywyxQIRCUuM0cZ3ttkh+9hcBBCiJrlMm8fZiqxAKSGpF7Icj2MmAro7WiSjiObUMk2/i6n5DCJ7F53hW+dIzxAGIxr0CzNMKT/sZiuRU5gcRA063jiZH2A8wyLj7GELicssSDFFpkGWadOlVQQQS4yzyATLtBmkDQ4sb2ZxMMmgHRFEQ7JejOk5N9EupTtljXKbJA262ElZ6WXIUkgTMB42EBAZyAlshmFA6S8SUCpBQTlyMaxsU54pGjAoirIBqMo69rGrz6LtsO+F4Gr5hAEQwmIAB4JSOLKHrZtLhkEklqexmYgWZHlAZzBBNxmzpQM/xQtSgiAlIiH2EuJgRN0brhiXTAk5wIwzsuoTu16BeaYKzwnAlSCyQuJJXDLl4YWGPPTpNZpkBEwTkeEzCOv4xSin1aWUngKZ0kgISVwAkBLSxU5L9P0GvbhJjSEtuoWYVEpIRlCci/QvLNOmR4vMlTc6jLGYT3JgNMvccJa54TS9URMvycsBBbk0fWzAIL2JIp8gPYsL7jGgnIYs4sNqoCi1IwkYpPlBMg8SKEgAsQa36cdqrFIzDIqiKEebFPtbO8DejXaBhyFrQ9aCuRDmPWx0IBKPPsSeDRrkTlcssSWN/gLIJwIW9sywsG8G2qZQIfTCjLA5IIgTPM9QDwdM1hZoh8tOzEnKATEHmGUbj/MC7iUlZD+zxZlL06QYXEnfQBUxsWrRdXbaWTFpART9BaIVkRKyyAQZgXPOzPBc+WCMDgPqPMEpRCRMsFhkOSRLYsdFaxWbrUZlJNS4xsma3S+r8UTnVPYOthA0hgTBiPSRBvl8VAYL4hfWwwYF+yn7GRbtpWLASiMqE0IYghlYZ0pj3AK61x1AJiG6lFHHGqg6nsRowKAoygYgpNR8lvTAOIWUYNCAaBI8cWZyifopbAZh0m1axNbTn4sdv2y5bdjDkHr2BrdhML5P2mtA5tMc71Bv9Am8cpRviTGeYAvTzPF87mOcDkuM02FsxZlL5qHrvCZadGkXMpWWjIA9bKHjRjflEbvIxicvMhriKimNiWKOVWPIfmb4CWcCHrHzuqhqQ4gUdEhKm2VSAvrUqTEgKqIojxyPPk0WmGA+nCKcHDCeHmCUhiQmhi0GagbmvIN1k8QvLMMGDwGwzf193l1G0WRqAv0x6DWgM4LlEYWNeaEtLSmhIesaMBxn2YBjgQYMiqJsAEQJUpyMZD7SNceZFmRD8ORXngsuhhEsh/amdQRs4uDRyyV3qGpzZOpBBib2yP2QNA1J0ohBWCNJIxb6M1ZNsdGHwGNEjYFb0OvO01EmIqy3hCFzd+3VxkiwPQwGjy3s4XTzIGNJl0Y+II188gB3DENCSC7CCw6xoZZxSPBoO5GpRsXXwvYoREXz5oA6HcaKTIfv5iTkmF3Xu2DwqHt9+lGN0I9JjXMRzT2rtjmgzCwMKYUYpTQh/lHLlB5j0orSz2CUwmhkH2lGqbvQcX+XnVN3kZqopfWxQwMGRVE2ADKjJwutuFxGWE3lBuRL5XOmgQAWPdvjkLrN26HoN9zvDlXtbZCxS4PdyTcY45OMYgjsiKWfQT6IGfM7TNYWaQY9lt085xgdfHKXVbAZAknvp66xsE8T3zVUildDRMJWHmcbuwkzg59BGnjkgSEgL+74E0J8d6ub45ESFVmGRSaKEkRV3jp32YisCAt8OrQ5wOxBN80JMQNqHGCGRSbJ8YuSB4DJAkwSwMgr+xBGrBxdFX8ImZLoY5MFElBI+8EwhZFkDUSgQTSjFyjFD2QfkYfWYOFYoQGDoigbkISy/b46bhm7502scQN23TkFeDF2vVmilHKoIoubjP2vIssCBoM6E8Eiz5r6CTPBAZp+jwb9YlH1yVlinP3MrrCJNq5EYMsGm1Y0H57OQ2zjMf6Ps/iOdz4/rj+X/cyyw/t/bGYvC0ySEjHBAm2WaWCdeO37AzcdUaNL05pc0XXdFQMSrIW3+F4sMsk8U84ZY6UXhpRF5pguTKmkODKkjsl80l6dZNCAugeTxgYOOXalWaRMDMhESrWCII2ROavS+3VKd0qxt5xgpXCDzGcODr4wylFDAwZFUTYQIhEti12AvRstVICwBXRRBtoCZivsGYN726UxphumoEYZczQoa+uLOPMqDxP7pFmdcNKn/txl2ARzwRQDr1ak/TN8ApdZqPYXAMUUhPQliPjTOEvMso8c2MtmUtd78TzvRwRk7GEzP+MMQuyURodxlwHoEToDKZvNSKg5ISbJKOT4LNMuMgsjF64su5HJ6kjliLhwq2zTYQt7ilIHQGYCkiRkMKpBlBJ6A7LdMeZAWA4xiFRC6v4UUym5XB3KpkepMGSSDTpA2SVZHZdcLeL1ZGO3G4+77rqLSy655JCv/fd//zcXXnjhUf9MDRgURdlASFv+amJKNUCpd4tW8QgWM9upX1/1EFNECRTGsYGClNID3N20Z296gZGJmUunWWKCiBGBl0MAoZcSMSpKBjJGWWdQBBF9GrRZpk2HCWftNHQDlraMMSwspHG6CtVeiJSQDuPOintYNEWGTndBRiMH1EiIncBzUAQMiSthSLZDjKWspLWhSZ8p5ospipQQYzyyNCQdReQDH3oeLHk2qJJqgQwxLLFyrDJ1l0yGQlL3dw9rhZ34NnBII8hzyOX6yZ+i9Hjycu2113LBBRes2HbmmWceZu9nhgYMiqJsUESyCGxgsIlyNZKJii3ABJh6KRo4wDY+SklCFrUUGzSI8rDU58Oc4KwB3i8kDIkZduLy44Fm1GeytkAYpOC5pd4zTjypT80t7AkRPZpsYh+n8Si2EdIvJhymWCBmxP9xFo+zjUkW2MS+YsxSshO2GRGmSDF4TnUyKHQVFpgs5J5l4a9acfvkTsdxkYgt5ATMst86aDrENyLHt6UPE5KPAtL9bpxS2gkW3cPJYnDAfb8xds3PKftDxMa6hpWD7hsXXExCbwJGc5DPUXpISGpi9fU+ebIMABdffDGvf/3r1+SzNGBQFGUDIh3zMjUQYlejwzQgiN6PDFfkUEw2So/kFGVv3RI227ADzGk+6aiB99Cq40ZA09AJm/T9CYI4IagPadW6jMWdIqBYYIIuLRoMaLNcyCzLXb40SA6pM84Sm9jHLPsJSYkZFY2Uc0yREbKdR6gxKgSbRtQYETHAKlQ+i4dZps0+NhGSMMFC0Ush7GMzP+MXqK3IaFhEMXJAnYCMRj6Afkiy3IQ0KKURltxjzj1G2EAhx1aJpMlxRFmeyIG5BB5NIOtD1oO87x6p23k/NvKoliR8CgOQwg59LXkyn5NnetwnxxhDp9Oh0WgQhsd2SdeAQVGUDYiMWUIlx83KQrl4DHiQzUBWK3/vi9qwtDp4lE2PAfBs4DTsREXbw+QBZuj2dzGKF2T4UYofp/hhShQNqUUDfD8r7vAHxIRktOkWPQxg9RBEjlmmJJ5gC4/m2+n122RpyExjP624Q+oUHsUlc4lxIhJSwsKO2jY3NgoVyOoERq8SDHRpOjXHJn2aK84JqLhWRgxNxHI6xtJogsGoYacjBp4NBHxKHyiwWYOgOEg5zJC7fWPsGr8E9H0YBM7OekCp7CgpCPmAWuWN0uQq+548XH311SwvLxMEARdffDE33HAD559//jH5LA0YFEXZgEjnnBTHZbVKWNmaXzU4iK0PdO7DKLDW2IHTFRhg16gGtmdyFhssGOyUH+6j2qYQkfSyHH+U2nt8b0iNEbVsSGoCuvkYpgZeI2fKm2ecJQIvI/fL0oBd3MNCC2GOaebNNPOjWYbDOpuiJ2jGHZaYIMNnlv20C1MmS0hKgz4ZAT2axVRDgz6TLGDwil6EjIB5JpljmhZdxp0ipExRiJpkQmSzFyZiMR1neTBGOqiXzY2yrsuNvue+M8kkGMoeVDFt8ihNpzK/soOkeyQY6LqdZfJF6kMyi7leUxLy7+1YHPfQ1Go1du3axWWXXcbs7Cz33XcfN954IxdffDF3330355577lE/Gw0YFEXZwMid5+FYAh7BrlTLQNuKPHWa0G2BF4Mf22BgHKsAGQIPYqf8qoRAy7OVkAbkUYTxQlKaDDwbSHgNAymYnkdw2pDwrAG9uMFuTqFWG1KvlQveJIuM0eExTmWZFjEjWkGP5038gDGzzE/9M3iCU9jMPsZZKoSVDoU0VNYZsJ1HC8fJqhrkApMEZGxm30HHErMpoAgy+tStGFTiwT73yN1XeS+wBxs8hcVBymBCShAiDf0Ylf5F0VsIsBKcD7kv/OTKHByJnTt3snPnzuL55Zdfzq5duzjnnHO47rrruP3224/6Z2rAoCjKBuZIo3ayCImqkBNmCAIIXR7dwwYMUp6QfjupaIirckBZ6RjZ7abmQexhau61BfCnUoIdQ7zpFBN4eIEhihK8wHo0NOnRoleMYMo0RULEfD5FZ3kSRh7LcYMkDjCRx1Jg5aZDUlr0ClGoAXUWmcAnp0nP9TpMF+OSfRpOktoU2goBWTHqKaOVIvQkpZSIBGM8BoM2abdBPgztz33APXzsWh9S9ilIMkCaHaXPQXpFJNPghxB6paomW7GRh8huimvl8eRGuX49DFXOPPNMfuM3foN/+qd/whiD5x1dRywNGBRFOYkJK49D/HKV7HiMDQykzh5SajXk2PXMW/WeGqU/kjT81YExA5sNpuaTJT5+CpkHeQhJaPBTjyD1GIQt69Ad+qSBTxbYXoURPp7xiMyIBl1Ct6gEzk9CPCUygmIKosaw2G9AvdBaEDvuOgPaztjqUIjolD1mWO5nXKuB2IXLRERVdFEUu0XJOcEGCTJBIcFE8fUPwRNRJjmAX9khowwaSn8L+4Eybnkijlp+yT2qLB5qxydl+/btjEYjut0u7Xb7qJyZoAGDoignMTG2wL5qwiFNIRtAXIPQ9UFk2H6FBFtCF8FI0RaCct0SoSe5QRxi1SRPhbwVkfdCu82DpAd0PWgYGDMsdN3zlsFrG6Jmn7jZo1Yb0Yh7bGnvZbo1X34eEOQZdW9ASAK5DQoWvUkyz6ftLROQ0TcNIpPQNl36XkbiR4yzxASLRTABtuSwxHjhhGmMV2zPTEDm1CEHpk4e5YTBgGypTjYX2/MR988l96cEClD2Lj5B2c9QZCBcNiibg+xhVqYfoMwWSYNEFWkwEf+QtQ4YinTIM+A33aPKvcChxZkOxwMPPECj0TjqwQJowKAoinIwUQRhEypNiMVaZbAiTW3KXkooswqiK+Bhb4Q3AS8y1hVT/Cg6HsSmtNOODEFjRNAalk6OAfhBzmx9H5P1Bbp+k17a5KEnzuSBxdgpTzoRqCAjqg/wspxsvkaWRKTNgLA1oj2+iBfkDEcx2VKMmY8IWwnRKT2iKCFw45k1pwUxJHZNkCHDUcxoJI2jHsmgQTK0ug35yCd/zMfs8TG9wJ63DKHMYss0ovA4wK79C9gAoum+x4H7DutA0oV0Ccwih+5XkKbGQwUDkuaQE9j47Nu3j02bNq3Ydu+993Lbbbfx6le/+ph8pgYMiqKcxEjn/Tx2hXd16Dy3GgAZ1pkyj2EYAw1YrtlFTvoTZSxQ7Jilt6GODSxSbENgx91mJ86YqeXstX3nJxH55FFUBhyxwYSG3oFxGIaMNgUM2zH97jij5QZ+nBAEKWGY4nk5nWGLPPXB9wjrKfX6gLCWYHyPYdJgsTsJqUe9OYSmR+5ZBUnAhQdJUcZIM+vAmaYhaRaSJyF5EpGPQvJR6JQbPfu1rVZtTCnNpUaUjpVL2IChQ9ngaAAztJoLpmcfgE3RSFpi2V0jGZ+sYljpcrVe/Qxr38NwxRVX0Gw22blzJ5s3b+aHP/whN910E+12m4985CPH4Fw0YFAU5aRGmh19ygXKg2y54mUQwrCBXcR8m33IPVhw9YAIe3fdwgYJPvb3/Aw2IOgB92EbFaDsfRjHLriB3Z6HEXkY2WbBSVyTpWFuf4u5jiEcDvFnE9LFEK9n8NsZQT4i9oZ4uUe6XCPPI8LxPrVWl3a8ROwnmNxKLHtpQBgntFsd/DglC3wy45PmIWkeYfIGfpAThCkm88gTH5N7YCAfRaT9epl573llr4KUIKSloIst0SxRqjfLZIQoafaAxIAxkA4h6YBxaRValGUikdvsU3acSvdkRulSWqpQniy87nWv4wtf+AIf+9jHWFpaYvPmzezatYsPfvCDnHHGGcfkMzVgUBRFoY5dpQ32NngzdrWXZoSqzHSflfOCjhS7MDaxwcIsuBv4lbSwypExpUcW2BT+TGXbE1hPhlMNPBfS+RreD2qYfUDPI91TJ5uoMfKsr4TJPbx2BjGM6hFzg2lMEpD264RRSnOyQ5YE7JnfRqu2zPTkXoYmpjNsMOo3yHp1GuMdWpOLtKMutWjI4nCCxcG4PR8D9J0mRdvY7Mk+ly1pYtfsfZTGXHVK22rcd+EOZQOHDJKhm5wYq3wRMpIi6YlZ4FR38H2UkYfYXh8PUtBrn2G45ppruOaaa47BZx4eDRgURTmJkVqCpAbA3rWKdnE1dSDNj6HtQRiE5YBFF7sg1ijL7CNsNr3qdFmj7M2Tj5P3Zdj1ULLrkfvI3IORIZgc4c+mcBqYvkfeicn7oVV4bIwY275AfbaL387IY0iI8eOcRm0vBDCKQ0zo0/D7+EFG4ofEjNhW200SxAzrdUwMxjPOFruGF+U0/T69JCLzDfWZZaJ4aMsWeQBNDzPpkT8Uky+FpU6WOFEvULpUSmtBN4N+CklqSz8GVkZO0uwoaQlxrpIAoUtpbX1y9CscL2jAoCjKSYxkDmRUTyIAuVuUFv6KhaUJ7MsZ5ciljFJWxwbFunkC2/gYYAMDuUEWJ8yquvGwsr2FvSMXoyYPCA1Mgtf2Sqto7Od7scELwQwC8r5PnkZ4eYbHkByfEQ0IIA5GpCaml7ao+0NaYRcyHz8zjIKYUVBJi/SAnmfLGvKNeTleYpxshcFLvVLCIqAcp5TASbQXEretC4yMU3SsBgryBSxh/SIW3ENGKNPKgY+3EsTRmJI43HGPHzRgUBTlJEbEf2TFExfL2P0dypV/CNTBa0LoQ5hhexqwJYZiis0rDz2ilEKu1vPnKCWmA8qAwAN2AL9YOVQTqEG2r0a2WLPPQ+xintv3J6OYA49usWUMsc2QO/sqrm+zKJ/EFA7f9FjZNmCwIpi7KXwz+r0x+gNXPhDxpap6o/hwTGK/l37ldVn/+wHk1fESGZccYjsi+6ueS8QhB1PWCw0YFEVRiqBhidJ5yl+1TwRsgXwWkhakY8A0jFp2HSvWQOdaFbhgooGVSZZMRBMbLMTY9VDuvMex2YhHKHUd5HCBV56SnJYs+M8z9r0Pe/b0JTsxRhnzCBll42HXnUeTlSZbHqWokodtH4jcuT7ZROOiO/Y88Cg2OBCbh17lcZA/lIxQDFe9IGMm8+5xPJcfjg+lx2ONBgyKoihFx33V4fJQHYuuFpGLlSWQ5ZDIr9IMvBwCs7IlolY5hAgSSt+eVEEEKdWL1TaUmYYIm63vYoOESWC3V2buJUCRlovVP4IMF8hwyNB9lpyrrHsioDiJXbOlZFI9XoINInruTxmlHFD2akgQ0a0cU37WPIcsddMR1aZSZ8ZRRFbyXNIYhxqvXG+0JKEoinKSUL11l1nBxqp9ZH6yjV1FRStgSFlTyMD3XD+D63XIfYhDaHjlR40okxqT2GxAyMr1IaK02JbgwdgeAnwDLd96VTzhjnkqsMngjRm82Gk7GGz/Qe6VVtKVWKcIXqR8Ij0HUrqYBKaMHaPsU040ytdUdQ2vBgZ1t99S5WeSr9jIOGUCWVL5wNx9IdIYsvq7P8DBgZ2ylmjAoCiKQp3SMhkO6StRkGLHAPat2jcCmpDFblH1XLNfE5amoB+Xu3vYxXjGfbTYO8tdONi79gDYjp3wnAMvzQkuHuCflpL9b53s8dh6MznlSX8yJ9jUx2/bu908Ccl6dfJeYOWmQ7ufVZ00ZRliN/CIZ5szTzMw8MoeiXmvbDOoOkgbd6xTsJkEKaMsAQ/Y8y3KNNXJxzyD0dBNSIjwksg5G/e97ndfgExIyOv5qoMdL2hJQlEU5SRBuhNl9vFQSLG+4/aR/LyMP8QUWQYDhVZ0UoOOb7MI45S/dX3KPr4Uu/iOU8YfY9gYRj7WA+N55D+KMT8JyZeC8rSHwByYvT7Z3poVgBqBGfiYrg9DN1UhqpQx1hFyQBmYxDgdBa/MFkhCRTIiUkah8rk9bI/GPDbwmHM/j9O5KgZNKmag5AZMNc2ScXBdo4ONQo5kUa6sFRowKIqiFCN7T2aHHVB27ddZGTBUn0vev2GPlflW8Ch2QYXxy4mJBcqMwxC7Ttax8YfcnY+wi3EMBB75I85KWgY6RN06ABN4mCeicjGXgKQaC4nmg7zexU55bKUcBpG1XPwgqpMQMvoZUDpUzlFaWy9T9mVIBSEFUgNpBrkcfLDqIdrREiDI9TgRkPM9Fsc9ftCAQVEUpaCac1+NaD4fSgOgWsIQu0p3e56NQdaG7hSE07anQXr3RsAWbP/BEHgMO2q5CXu37zILBG77OKUD5IrFGBtApJSOmrg/+5QDBzVKEy0pGbTcn8uUwUutsu8SZeAhUw7idyHPD1BWaOqUSQLpiwDX3CgpiaqN5TI2RSHPZfZUXL66lefKeqIBg6IoylNCRhtWBxSSTZAMg+g2eJXt4zBoQuqvTEBMuJcX7S7MUjYLRu71SfdaROmPtbpcnmDL/pLyl7W1mvzoY4OJBffZs5SZAp8yUKg6fYtGklRg2u7YEWWppOdeH6eUUJCJjn7lXLMA0roNmAgpUxPSLCFZh9WZheoPcLhgbr3RHgZFURSlwLDyF7iPXchCVmo2yH6y8Lnu/yyzi3lVFbIq2CRCk9XpwnHKmEOmJsTUygk6rUhurL4JF/2pFLs+i8+FTCxK2UPWO+n9lNFOmYioylJICWTkto1RljdESkHKGgl27JQMjMhAi5qjKDlJc2NV6Uo+qNoFejzrMJwcaMCgKIryc+FTmkRU8ShnCGVUUBbDtNIE6IPnQWbAM9ZoquuVjYZQujdT+agGpQWG2GxLW4WIQ0kQUZWjNtgA5Azsb355nyAVAbGn3l/5UeRcZG2XUoNMn0oAJB4aXXd8gw0UiuhBxBv2YyOYJbfzasMJKGc1TwRUh0FRFEV52oSU8ok97C17C7tyS+MCEMQQx9aEad8QxiIYrwQfTwAPA8/CNiRKg+IcZcUDVrhyFx8tv9lFIyHGljcOYJUkPQ7u78wpsx0HsGu6iEDJ6/soSxri1yWBQk7Z5yDTGNJrAe4FcZuUIEIUHqvjlRI0aM/C8YYGDIqiKD8XskKuboL0sSu6uEhJCr5Z2VaHvAGjptUiyDLoNiDJIY0gD8uKxyPAg5QliKr3kmQQ/Mo26TPouO3i65C7bR3K0oSsAKLW6CZBmXCv55X3LVdekyBh9ftHWH2FNIVsBHl1bFK+q2XK8UkJoKrKUSdioKA9DIqiKMphkbvkwxFhb8MlLdCgnJ5oQd6GXPSWQxhlMErA88GE5Xil6Bb5qx4hpVKjmFt5lOtul7Jk0MQGF5L1l96J1a0XgphSSSvGMqUIY6tynICyDDGkXO9T43oXqn0IHmXAJE0ZNY48zqocL2jAoCiKckyQmn0N2xkIZR3BY+UMo1M5kgVfMgE+sBk7evkYNqMvwksSBPQoJxhEUTl072ljGySlrCDBAxw8QTpW+fsc1kBK+iaW3ANKA606ZZlkxc21TykkIVEK2IhC+j62YGsbeyhFGyQyOhEDB+1hUBRFUZ4xEaXMY3vVo4VdFBahNQbtcfD9cl2VBscQeDZwOmVTY3VyQZ6LfDOUo46iLinrtWQu6qzMMFSRngRRaV49rNB32yRQWTHtKJkX0VOQaGLZHWyJsq4ivtqSwtBJiOMZDRgURVGOKVI/8LErcd/9GWEbDGTsMrFlCbGylkU6dW/Psf0BhSKSuFJVIgev8nHSPiDiT/IW6b8UJetDIc2LotsgyRL5uKpnlMEOPMianxg3GSF1fRFj6FQeMkYhJQtpfDxR0R4GRVEU5Rkjd9zL7iFNCCl2xU2BnisReBDV7aPjxi7rOcQGlgK7znp9+zDNlWus50HNs8mMOodGShkiArU6YJD+hgVssLDanVJGN6VXUdQmR3kley5NDVKvWKZUeFrR6EBpKnWioyUJRVEU5Rkj6pCyOtexq/Yy8BBlw98MsBnSNqQyitmAgRM+MJPgjUM8Cf4EJM5QihH4CUQxBOHKKYrVSAZCAofVJYkBdm134pSFgBOU5Q4ReGpjMwuD3BlJyYLZx7p5ykP6EiQakb8rJxoaMCiKohxTqk2AUDYbyKosq/YmYEslne8aIrOcUvCgB+kQvBzyFgQ1aEZQMxD45Vjk6t/sIaWok5xCVVVSKgNjlC6afQ7OQFQ1qKTMYaD0gBA3KqllHGrsVJAGiBGlytOJipYkFEVRlGeMGDpI96Kswi3sCIMEEhNum9yBS8ejR+FT4RkIcghdWj/woO1GMENKf4qQlcZPEkhA2RMhOlI99zGi2BhQCjHK+/ORHQHNY8iiVaKMhrJXQYIFsb08VCZBAqKqLrYoSSnHMxowKIqirAlyRy0BQoOVt/AD7F35DNZvGkqRBRcw+MBYq5SOFsR1cgJrKuVRmlFB2bQ4wK7lbey45hxWFGoWO4GRUCYJZIzSGEiWIF2EeAbqE06gST5chBqWeHJdCiFx+x9fd8/PDM0wKIqiKEcNWY1XW0IKQ0q7yeqcYkWfWZwsu5J18PGQPjMAABCySURBVO1x/cyVGXyIIvCCVYs65fslw/AIZRAxwMpBV/0gRnLOLrtgDCSLkHbdcat2laLcmFUeh0NKMeJYpf0MJwoaMCiKohxTJACQu29pKOhw6NlG0WhoUupBuwyD8SCJIRGXzICV4w7SpHC4BVu8tWVKwT1Pcugd6j09StGFHPJqZkCmPyRtsdp6WkZAD2dJfXxNADwzRBLzWBz3+EEDBkVRlGPK4RYTUWCqqj/6lAGDlCZEF1rcocaw9QcoaxFVx0yXGvcrgUihvCzlEJFtlqBDnptVb+i5zxeGrHSQrPYjSBAiGQMZqzxRHCeVI6EBg6IoyrogKkqysItuswQMElBISaKLXZBbWMEnKN2nXAaCHBjazVEMgQsQpMSeZ/b1ohFTPkMCB6HiqnlYclb6WI+wjRMd97rUN04GtIdBURRFOWqI2ZKUDcSMSp5PYTMKouAIdjGfxGYVxPVRpi3kUVF69D07aukDWQCZu+vPASMyjTFlRkE41JxlZ9U+IsSwVHlezSiIvWZM2Y+hbCQ0YFAURVkTpOQgC7dYPcpduKT2h+4hAUHVGlKCg4xyQRfZ6crHyM19XnWBrJY/VosnySSGvC4SzoLcQXdZqeS0+vzhYDWokwFVelQURVGOGpLmF5lE8VSQhXs/1pKyqtPQxjpASc+C2FDGwA+wow6VHoaDHLdjd4yclaOMkt2oliEOh3Hv7VWey4eJiiWU45WHE2pSTnQ0YFAURVlTRti0ftWoAUrdZelZGFDe2VejgD3uzxB4FittK4UUmwlIsYGJZANid+zoEO85FH13HmJfOc/KkoRsrz4/GdEeBkVRFOWoc1AawFF1tYRyMW5R/qqWRdlgMwdjhzh2gl1o6hysnihlkSMhSk/LlFMS0uS4/BTer2xENGBQFEU5LpDygUwuSADRxjZEVqlONsj+UI5wiruU/IqXfRJKgaUqq7Md4q4pphISqByLu+iNwMnRw3BSdKcMh0Pe//73s23bNprNJi9+8Yv56le/ut6npSiKcghq2MmIrcCzsT0Lk6sedWA38H3gx8DP3GM3K0sRC+6xjF18ImxWYvXxmhwsIIX7nElW6jwoByMliaP9ePIgZK3XtpMiw3DVVVdx66238u53v5vnPOc53HzzzVx22WXceeed/Mqv/Mp6n56iKAqlWVMfu9hHlcfqX9WiophiswHSvFidwqhStZc+FD4r7x9lwao+BxtAHK5R0mlAHGd1943MWq9tGz5guOeee/iHf/gHbrzxRt7znvcAcOWVV3L22Wfzvve9j//6r/9a5zNUDs8PgF9a75NQ9DqsEVJO6K/aHgL/C7ygss2ndMA8VD9EzsqGREmZH863QfwtDpVlEGQs88kmK57s/RuZtS9JrMfatuFLEl/60pcIw5Df/d3fLbbVajV++7d/m2984xs89thj63h2ypPzv+t9Agqg12E9EaXHH7KyhDCODRYOh4w4SklihC07rC5FyGO1c+ahMNhgZuEwj9VCT8qxZD3Wtg2fYfjud7/Ljh07aLdX+sFecMEFAHzve9/j1FNPXY9TUxRFOQR1ytFKKQFElHLQUJYkDiffXMf2Ksh+hqemubAaGasUjq8mvOOHtR+rXI+1bcMHDLt372br1q0HbZdtjz/++FqfkqIoypMghk4JK6cflir7iHfD4RYpMZSSSYvD9S4cCXn/6p4GZb1Zj7VtwwcM/X6fWu3gDt96vV68riiKcvwgipBVaeYBdgLi6SKjmtGRdjwMNXeMLhowPBlrb2+9Hmvbhg8YGo0Gw+HBUqWDwaB4fTXyRX/+8y/hec973rE9QeWwvPvdd/Kxj712vU/jpEevw/qj1+Dn50c/+hFvectNx/jmcP+aH/fnWdueKRs+YNi6deshUzO7d9tofdu2bQe99uCDDwLwlre85Ziem3Jkzj///PU+BQW9DscDeg2eGQ8++OAxHKP/p2N03MPz86xtz5QNHzCcd9553HXXXXQ6HcbGShnVb37zmwCce+65B73n0ksv5fOf/zynn376MYnSFEVRlLWh3+/z4IMPcumllx71Y5911ll85zvfOerHPdTnrObnWdueKZ4x5ufthjkhuOeee3jxi1/MDTfcwHvf+17AqmOdffbZbNq0ibvvvnudz1BRFEVRnh7rsbZt+AzDhRdeyBvf+Eauu+469u7dy5lnnsktt9zCww8/zM0337zep6coiqIoT5v1WNs2fIYBbNT1p3/6p3z+859nfn6eF7zgBXzoQx/i5S9/+XqfmqIoiqL8XKz12nZSBAyKoiiKojwzNrw0tKIoiqIozxwNGCqoDfbacdddd+H7/iEf99xzz4p9f/SjH/HKV76SsbExZmZmeOtb38r+/cdq7nlj0u12+eAHP8grX/lKpqen8X2fW2655ZD7Pp3v+zOf+QzPe97zaDQa7Nixg0984hPH8sc44Xmq1+Gqq6465P+Nw+nC6HVQ1oIN3/T4dFAb7LXn2muvLbTPhTPPPLP4+6OPPspLXvISpqam+PCHP0yn0+HGG2/kBz/4Affccw9R9PMq2J1c7Nu3jw996EM8+9nP5txzz+Wuu+7C8w42G3o63/ff/u3f8o53vINdu3bxh3/4h3z961/nXe96F71ej/e9731r+eOdMDzV6wDWSOgzn/nMim0TExMH7afXQVkzjGKMMeab3/ym8TzPfPSjHy22DQYD84u/+Ivml3/5l9fxzDYmd955p/E8z9x6661Put873vEO02q1zCOPPFJs++pXv2o8zzM33XTTsT7NDcNwODR79uwxxhjz7W9/23ieZ2655ZaD9nuq33ev1zMzMzPmNa95zYr3v+UtbzHtdtvMz88fo5/kxOapXoe3ve1tZmxs7IjH0+ugrCVaknCoDfb6YIyh0+mQpofWTL/11lu5/PLL2b59e7HtZS97GTt27OCLX/ziWp3mCU8cx2zevBmw3/nheKrf95133snc3By///u/v+L973znO+l2u/zbv/3bUf4JNgZP9TrI63mes7S0dNh99Dooa4kGDI6nYhWqHH2uvvpqJiYmaDQaXHLJJStU0x577DH27dvHi170ooPed8EFF/Dd7353LU91w/N0vm/5++p9X/jCF+L7vv5/OQr0ej3Gx8eZnJxkZmaGP/iDP6Db7a7YR6+DspZoD4NDbbDXllqtxq5du7jsssuYnZ3lvvvu48Ybb+Tiiy/m7rvv5txzzy000Q93Xebm5kiSRPsYjhJP5/vevXs3QRAwOzu7Yr84jpmZmdH/L8+Qbdu28f73v58XvvCF5HnO7bffzic/+Unuvfde7rrrLoIgANDroKwpGjA41AZ7bdm5cyc7d+4snl9++eXs2rWLc845h+uuu47bb7+9+M6PdF00YDg6PJ3vu9/vE8fxIY9Tq9X0/8sz5Prrr1/x/E1vehM7duzgT/7kT/jSl77EFVdcAaDXQVlTtCThWA+rUGUlZ555Jq997Wu58847McYU37lel7Xh6XzfjUaD0Wh0yOMMBgO9LseAd7/73fi+zx133FFs0+ugrCUaMDjWwypUOZjTTjuN0WhEt9stUuNyDars3r2bmZkZzS4cRZ7O971161ayLDtIn2E0GjE3N6f/X44B9Xqd6elp5ubmim16HZS1RAMGx3nnncePf/xjOp3Oiu3H0ipUOZgHHniARqNBu93m1FNPZdOmTXzrW986aL977rlHr8lR5ul83+eddx7AQft++9vfJs9zvTbHgE6nw/79+9m0aVOxTa+DspZowODYtWsXWZZx0003FduGwyE333wzL37xizn11FPX8ew2Hvv27Tto27333sttt93GK17ximLbG97wBv71X/+VRx99tNh2xx13cP/99/PGN75xTc71ZOKpft+XXHIJ09PTfOpTn1rx/k996lO0Wi1e/epXr9k5bzSGw+FBNy4AH/rQhwB45StfWWzT66CsJWo+VeGKK67gy1/+Mu9+97sLq9Bvf/vb3HHHHfzqr/7qep/ehuKSSy6h2Wyyc+dONm/ezA9/+ENuuukmarUa3/jGN3juc58LWOXB8847j8nJSa699lo6nQ433HADz3rWs/jWt76lJYmnwSc+8QkWFhZ4/PHH+fSnP83rX//64g70Xe96F+Pj40/r+/7Upz7FO9/5Tnbt2sUrXvEK/vM//5PPfe5zXH/99XzgAx9Yrx/zuOdI12Fubo7zzjuP3/qt3yr+H/z7v/87t99+O6961asO0lbQ66CsGesoGnXcMRgMzB/90R+ZrVu3mnq9bi666CLzH//xH+t9WhuSj3/84+aiiy4yMzMzJooic+qpp5q3vvWt5qc//elB+953333m0ksvNa1Wy0xPT5srr7zS7N27dx3O+sTm9NNPN57nGc/zjO/7xvf94u8PPfRQsd/T+b7/7u/+zpx11lmmVquZ5zznOeav//qv1+rHOWE50nVYWFgwV155pXnOc55jWq2Wqdfr5pd+6ZfMRz7yEZOm6SGPqddBWQs0w6AoiqIoyhHRHgZFURRFUY6IBgyKoiiKohwRDRgURVEURTkiGjAoiqIoinJENGBQFEVRFOWIaMCgKIqiKMoR0YBBURRFUZQjogGDoiiKoihHRAMGRVEURVGOiAYMiqIoiqIcEQ0YFGUD8NnPfhbf93n44YfX+1QURdmgaMCgKBsEz/PW+xQURdnAqPmUomwA8jwnTVPiOF7vU1EUZYOiAYOiKIqiKEdESxKKsgE4VA/D6aefzmte8xruuOMOzj//fJrNJueccw533XUXALfeeitnn302jUaDF73oRXzve98r3rt37142bdrES1/60hWf85Of/IRWq8Wb3/zmNfm5FEU5ftCAQVE2KJ7ncf/993PllVfym7/5m3z4wx9mfn6e1772tdx888285z3v4W1vext//ud/zk9/+lPe9KY3IQnHzZs38+lPf5qvfe1r/M3f/A1gyx5XXXUVExMTfPKTn1zPH01RlHUgXO8TUBTl2GCM4f777+fuu+/moosuAuD5z38+l156Ke985zv58Y9/zPbt2wGYmpri937v9/j617/Or/3arwHwhje8gTe/+c1cd911vOpVr+Kf//mfufvuu/mXf/kXpqam1u3nUhRlfdAMg6JsYJ7//OcXwQLAhRdeCMCv//qvF8FCdfvPfvazFe//xCc+wcTEBLt27eLP/uzPeOtb38prXvOaNThzRVGONzRgUJQNzLOe9awVzycmJgA47bTTDrl9fn5+xfapqSk+/vGP8/3vf5/JyUk+/vGPH8OzVRTleEYDBkXZwARB8LS2H2po6itf+QoAc3NzPPLII0fv5BRFOaHQgEFRlMPyla98hc985jO8//3vZ9OmTbztbW8jy7L1Pi1FUdYBDRgURTkkCwsLvP3tb+eiiy7i+uuv5+///u/5n//5H66//vr1PjVFUdYBDRgURTkk1157LfPz89xyyy14nsell17K29/+dv7iL/6C73//++t9eoqirDEaMCjKBmG1l8Qz8Za47bbb+NznPsdf/uVfsmPHjmL7X/3VX7Ft2zauuuoq0jT9uY+vKMqJh0pDK4qiKIpyRDTDoCiKoijKEdGAQVEURVGUI6IBg6IoiqIoR0QDBkVRFEVRjogGDIqiKIqiHBENGBRFURRFOSIaMCiKoiiKckQ0YFAURVEU5YhowKAoiqIoyhHRgEFRFEVRlCOiAYOiKIqiKEdEAwZFURRFUY7I/w/UqBwE25GZIAAAAABJRU5ErkJggg==", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x11e3b2f50>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject <matplotlib.image.AxesImage object at 0x115c6b850>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h = SeisReadHeaders(\"npr3_gathers.seis\")\n", "im1 = SeisPlotCoordinates(h,style=\"sxsygxgy\")\n", "im2 = SeisPlotCoordinates(h,style=\"fold\",cmap=\"jet\",vmin=0,vmax=50,aspect=\"auto\",xlabel=\"imx\",ylabel=\"imy\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.3.3", "language": "julia", "name": "julia-0.3" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.3.3" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
ZoranPandovski/al-go-rithms	machine_learning/SVR Regression/SVR_regression.ipynb	1	30804	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Importing the libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.svm import SVR" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Importing the dataset\n", "dataset = pd.read_csv('Position_Salaries.csv')\n", "X = dataset.iloc[:, 1:2].values\n", "y = dataset.iloc[:, 2].values" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\satyam\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\sklearn\\utils\\validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", " warnings.warn(msg, _DataConversionWarning)\n", "c:\\users\\satyam\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\sklearn\\preprocessing\\data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n", "c:\\users\\satyam\\appdata\\local\\programs\\python\\python35-32\\lib\\site-packages\\sklearn\\preprocessing\\data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n" ] } ], "source": [ "# Feature Scaling\n", "sc_X = StandardScaler()\n", "sc_y = StandardScaler()\n", "X = sc_X.fit_transform(X)\n", "y = sc_y.fit_transform(y)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n", " kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fitting SVR to the dataset\n", "regressor = SVR(kernel = 'rbf')\n", "regressor.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Predicting a new result\n", "y_pred = regressor.predict(6.5)\n", "y_pred = sc_y.inverse_transform(y_pred)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xa573150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the SVR results\n", "plt.scatter(X, y, color = 'red')\n", "plt.plot(X, regressor.predict(X), color = 'blue')\n", "plt.title('Truth or Bluff (SVR)')\n", "plt.xlabel('Position level')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xbf079f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualising the SVR results (for higher resolution and smoother curve)\n", "X_grid = np.arange(min(X), max(X), 0.01) # choice of 0.01 instead of 0.1 step because the data is feature scaled\n", "X_grid = X_grid.reshape((len(X_grid), 1))\n", "plt.scatter(X, y, color = 'red')\n", "plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n", "plt.title('Truth or Bluff (SVR)')\n", "plt.xlabel('Position level')\n", "plt.ylabel('Salary')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 2 }	cc0-1.0
gonmolina/CCE_ProblemasResueltos	ProbsVVEE/Paquete de control con Bokeh.ipynb	1	42136	{ "cells": [ { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import control as ctrl\n", "import numpy as np\n", "from bokeh.plotting import figure, show, ColumnDataSource, output_notebook\n", "from bokeh.palettes import viridis\n", "from bokeh.models import HoverTool\n", "%matplotlib inline\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x9b96834f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G=ctrl.tf([1,1],[1,2,5])\n", "r,k=ctrl.rlocus(G, Plot=True)\n", "zeros=G.zero()\n", "poles=G.pole()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"f172acc4-1693-48a7-bb6d-967f49aebdba\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" \|\| force === true) {\n", " window._bokeh_onload_callbacks = [];\n", " window._bokeh_is_loading = undefined;\n", " }\n", "\n", "\n", " \n", " if (typeof (window._bokeh_timeout) === \"undefined\" \|\| force === true) {\n", " window._bokeh_timeout = Date.now() + 5000;\n", " window._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", "\n", " function display_loaded() {\n", " if (window.Bokeh !== undefined) {\n", " document.getElementById(\"f172acc4-1693-48a7-bb6d-967f49aebdba\").textContent = \"BokehJS successfully loaded.\";\n", " } else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null \|\| js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; 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true) {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"dbfbf0ec-f6a3-4a4c-959b-918963462e51\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " \n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "output_notebook()\n", "numlines=len(r[0,:])\n", "mypalette=viridis(numlines+1)\n", "gain=np.ndarray((numlines,len(k)))\n", "gain[0,:]=k\n", "hover = HoverTool(tooltips=[\n", " (\"gain\", \"@k\"),\n", " (\"Pole locaction\", \"@xs @ys j)\"),\n", "])\n", "\n", "p = figure(plot_width=400, plot_height=400, x_range=(-5.2,0.2), \n", " y_range=(-2.5,2.5), tools=[\"pan\",\"wheel_zoom\",\"box_zoom\",\"save\",\n", " \"reset\",hover],title=\"Root Locus\")\n", "\n", "for l in range(0,numlines):\n", " gain[l,:] = k\n", " source = ColumnDataSource (data = dict(xs=np.real(r[:,l].T).tolist(),\n", " ys= np.imag(r[:,l].T).tolist(), k=k.T))\n", " p.line('xs', 'ys', source=source, line_color= mypalette[l+1],line_width=3)\n", "\n", "p.circle(np.real(zeros),np.imag(zeros), line_color= mypalette[0], \n", " fill_color= mypalette[0], size=10, alpha=0.7,line_width=3)\n", "p.x(np.real(poles),np.imag(poles), line_color= mypalette[0],\n", " size=10, alpha=0.7,line_width=3)\n", "p.toolbar.logo=None\n", "show(p)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def in_ipynb():\n", " try:\n", " cfg = get_ipython().config \n", " if cfg['IPKernelApp']['parent_appname'] == 'ipython-notebook':\n", " return True\n", " else:\n", " return False\n", " except NameError:\n", " return False\n", "in_ipynb()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1128'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os as os\n", "os.environ['JPY_PARENT_PID']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
terramundi/geo	notebooks/0.2-iraq-pol_risk.ipynb	1	27531	{ "cells": [ { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "%reload_ext autoreload" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "import json\n", "import os\n", "import pandas as pd\n", "from shapely.geometry import shape, Point" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from geo import geo" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "POINT (42.451171875 35.82672127366604)\n" ] }, { "data": { "text/plain": [ "{'ISO3166-2': 'IQ-NI',\n", " 'admin_level': '4',\n", " 'alt_name': 'Nīnawā',\n", " 'alt_name:syc': 'Nīnwē',\n", " 'boundary': 'administrative',\n", " 'name': 'نینوى',\n", " 'name:ar': 'نینوى',\n", " 'name:de': 'Ninawa',\n", " 'name:en': 'Nineveh',\n", " 'name:ja': 'ニーナワー',\n", " 'name:ku': 'Neynewa',\n", " 'name:pl': 'Niniwa',\n", " 'name:ru': 'Найнава',\n", " 'name:syc': 'ܢܝܢܘܐ',\n", " 'name:uk': 'Найнава',\n", " 'source': 'Wikimedia Commons',\n", " 'wikidata': 'Q189352',\n", " 'wikipedia': 'en:Nineveh Province'}" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = Point((42.451171875, 35.82672127366604))\n", "print(p)\n", "geo.point_to_iraq(p)" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv('../data/iraq_pol_risk.csv')" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['Region'] = ''" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ActionGeo_Lat</th>\n", " <th>ActionGeo_Long</th>\n", " <th>polriskevents</th>\n", " <th>Region</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>29.7837</td>\n", " <td>48.8070</td>\n", " <td>0</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>29.8775</td>\n", " <td>48.3833</td>\n", " <td>21</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>29.9222</td>\n", " <td>48.4867</td>\n", " <td>1</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>29.9453</td>\n", " <td>48.4711</td>\n", " <td>0</td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>30.0362</td>\n", " <td>47.9195</td>\n", " <td>9</td>\n", " <td></td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ActionGeo_Lat ActionGeo_Long polriskevents Region\n", "0 29.7837 48.8070 0 \n", "1 29.8775 48.3833 21 \n", "2 29.9222 48.4867 1 \n", "3 29.9453 48.4711 0 \n", "4 30.0362 47.9195 9 " ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for x in df.itertuples():\n", " point = Point((x.ActionGeo_Long, x.ActionGeo_Lat))\n", " output = geo.point_to_iraq(point)\n", " if output:\n", " df.loc[x.Index, 'Region'] = output['name:en']\n", " else:\n", " df.loc[x.Index, 'Region'] = 'Not found'" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ActionGeo_Lat</th>\n", " <th>ActionGeo_Long</th>\n", " <th>polriskevents</th>\n", " <th>Region</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>435</th>\n", " <td>33.9799</td>\n", " <td>42.5556</td>\n", " <td>3</td>\n", " <td>Al Anbar</td>\n", " </tr>\n", " <tr>\n", " <th>352</th>\n", " <td>33.3558</td>\n", " <td>43.7861</td>\n", " <td>356</td>\n", " <td>Al Anbar</td>\n", " </tr>\n", " <tr>\n", " <th>155</th>\n", " <td>31.8172</td>\n", " <td>42.4570</td>\n", " <td>0</td>\n", " <td>Al Anbar</td>\n", " </tr>\n", " <tr>\n", " <th>152</th>\n", " <td>31.7587</td>\n", " <td>43.3183</td>\n", " <td>0</td>\n", " <td>Al Anbar</td>\n", " </tr>\n", " <tr>\n", " <th>280</th>\n", " <td>32.7683</td>\n", " <td>43.6065</td>\n", " <td>2</td>\n", " <td>Al Anbar</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ActionGeo_Lat ActionGeo_Long polriskevents Region\n", "435 33.9799 42.5556 3 Al Anbar\n", "352 33.3558 43.7861 356 Al Anbar\n", "155 31.8172 42.4570 0 Al Anbar\n", "152 31.7587 43.3183 0 Al Anbar\n", "280 32.7683 43.6065 2 Al Anbar" ] }, "execution_count": 199, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values(by='Region').head()" ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [], "source": [ "results = df[['Region', 'polriskevents']].groupby(by='Region').aggregate(\n", " ['count', 'sum', 'mean', 'std', 'min', 'median', 'max'])" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"7\" halign=\"left\">polriskevents</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>count</th>\n", " <th>sum</th>\n", " <th>mean</th>\n", " <th>std</th>\n", " <th>min</th>\n", " <th>median</th>\n", " <th>max</th>\n", " </tr>\n", " <tr>\n", " <th>Region</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Al Anbar</th>\n", " <td>43</td>\n", " <td>22872</td>\n", " <td>531.906977</td>\n", " <td>3157.280549</td>\n", " <td>0</td>\n", " <td>3.0</td>\n", " <td>20729</td>\n", " </tr>\n", " <tr>\n", " <th>Al-Qadisiyah</th>\n", " <td>32</td>\n", " <td>100</td>\n", " <td>3.125000</td>\n", " <td>5.540176</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>23</td>\n", " </tr>\n", " <tr>\n", " <th>Babil</th>\n", " <td>58</td>\n", " <td>1023</td>\n", " <td>17.637931</td>\n", " <td>112.556683</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>859</td>\n", " </tr>\n", " <tr>\n", " <th>Baghdad</th>\n", " <td>66</td>\n", " <td>8374</td>\n", " <td>126.878788</td>\n", " <td>954.423588</td>\n", " <td>0</td>\n", " <td>2.0</td>\n", " <td>7762</td>\n", " </tr>\n", " <tr>\n", " <th>Diyala</th>\n", " <td>78</td>\n", " <td>970</td>\n", " <td>12.435897</td>\n", " <td>66.620965</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>583</td>\n", " </tr>\n", " <tr>\n", " <th>Dohuk</th>\n", " <td>47</td>\n", " <td>208</td>\n", " <td>4.425532</td>\n", " <td>15.549003</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>106</td>\n", " </tr>\n", " <tr>\n", " <th>Erbil</th>\n", " <td>59</td>\n", " <td>1377</td>\n", " <td>23.338983</td>\n", " <td>125.058691</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>933</td>\n", " </tr>\n", " <tr>\n", " <th>Halabja</th>\n", " <td>19</td>\n", " <td>139</td>\n", " <td>7.315789</td>\n", " <td>19.485187</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>85</td>\n", " </tr>\n", " <tr>\n", " <th>Karbala</th>\n", " <td>7</td>\n", " <td>222</td>\n", " <td>31.714286</td>\n", " <td>74.738911</td>\n", " <td>0</td>\n", " <td>3.0</td>\n", " <td>201</td>\n", " </tr>\n", " <tr>\n", " <th>Kirkuk</th>\n", " <td>30</td>\n", " <td>1446</td>\n", " <td>48.200000</td>\n", " <td>218.717327</td>\n", " <td>0</td>\n", " <td>2.0</td>\n", " <td>1201</td>\n", " </tr>\n", " <tr>\n", " <th>Maysan</th>\n", " <td>29</td>\n", " <td>237</td>\n", " <td>8.172414</td>\n", " <td>28.180628</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " <td>142</td>\n", " </tr>\n", " <tr>\n", " <th>Muthanna</th>\n", " <td>20</td>\n", " <td>254</td>\n", " <td>12.700000</td>\n", " <td>26.399960</td>\n", " <td>0</td>\n", " <td>2.0</td>\n", " <td>104</td>\n", " </tr>\n", " <tr>\n", " <th>Najaf</th>\n", " <td>15</td>\n", " <td>173</td>\n", " <td>11.533333</td>\n", " <td>30.856504</td>\n", " <td>0</td>\n", " <td>3.0</td>\n", " <td>122</td>\n", " </tr>\n", " <tr>\n", " <th>Nineveh</th>\n", " <td>103</td>\n", " <td>4319</td>\n", " <td>41.932039</td>\n", " <td>117.752004</td>\n", " <td>0</td>\n", " <td>2.0</td>\n", " <td>680</td>\n", " </tr>\n", " <tr>\n", " <th>Not found</th>\n", " <td>103</td>\n", " <td>1613</td>\n", " <td>15.660194</td>\n", " <td>103.510116</td>\n", " <td>0</td>\n", " <td>2.0</td>\n", " <td>1052</td>\n", " </tr>\n", " <tr>\n", " <th>Saladin</th>\n", " <td>68</td>\n", " <td>1647</td>\n", " <td>24.220588</td>\n", " <td>106.128737</td>\n", " <td>0</td>\n", " <td>1.5</td>\n", " <td>825</td>\n", " </tr>\n", " <tr>\n", " <th>Sulaymaniyah</th>\n", " <td>51</td>\n", " <td>249</td>\n", " <td>4.882353</td>\n", " <td>20.447638</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>146</td>\n", " </tr>\n", " <tr>\n", " <th>Wasit</th>\n", " <td>28</td>\n", " <td>194</td>\n", " <td>6.928571</td>\n", " <td>23.063405</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " <td>120</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " polriskevents \n", " count sum mean std min median max\n", "Region \n", "Al Anbar 43 22872 531.906977 3157.280549 0 3.0 20729\n", "Al-Qadisiyah 32 100 3.125000 5.540176 0 1.0 23\n", "Babil 58 1023 17.637931 112.556683 0 1.0 859\n", "Baghdad 66 8374 126.878788 954.423588 0 2.0 7762\n", "Diyala 78 970 12.435897 66.620965 0 1.0 583\n", "Dohuk 47 208 4.425532 15.549003 0 1.0 106\n", "Erbil 59 1377 23.338983 125.058691 0 1.0 933\n", "Halabja 19 139 7.315789 19.485187 0 1.0 85\n", "Karbala 7 222 31.714286 74.738911 0 3.0 201\n", "Kirkuk 30 1446 48.200000 218.717327 0 2.0 1201\n", "Maysan 29 237 8.172414 28.180628 0 0.0 142\n", "Muthanna 20 254 12.700000 26.399960 0 2.0 104\n", "Najaf 15 173 11.533333 30.856504 0 3.0 122\n", "Nineveh 103 4319 41.932039 117.752004 0 2.0 680\n", "Not found 103 1613 15.660194 103.510116 0 2.0 1052\n", "Saladin 68 1647 24.220588 106.128737 0 1.5 825\n", "Sulaymaniyah 51 249 4.882353 20.447638 0 1.0 146\n", "Wasit 28 194 6.928571 23.063405 0 0.0 120" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [], "source": [ "s = results.to_dict()" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [], "source": [ "s = {\n", " \"polriskevents-count\": {\n", " \"Al Anbar\": 43,\n", " \"Al-Qadisiyah\": 32,\n", " \"Babil\": 58,\n", " \"Baghdad\": 66,\n", " \"Diyala\": 78,\n", " \"Dohuk\": 47,\n", " \"Erbil\": 59,\n", " \"Halabja\": 19,\n", " \"Karbala\": 7,\n", " \"Kirkuk\": 30,\n", " \"Maysan\": 29,\n", " \"Muthanna\": 20,\n", " \"Najaf\": 15,\n", " \"Nineveh\": 103,\n", " \"Not found\": 103,\n", " \"Saladin\": 68,\n", " \"Sulaymaniyah\": 51,\n", " \"Wasit\": 28\n", " },\n", " \"polriskevents-max\": {\n", " \"Al Anbar\": 20729,\n", " \"Al-Qadisiyah\": 23,\n", " \"Babil\": 859,\n", " \"Baghdad\": 7762,\n", " \"Diyala\": 583,\n", " \"Dohuk\": 106,\n", " \"Erbil\": 933,\n", " \"Halabja\": 85,\n", " \"Karbala\": 201,\n", " \"Kirkuk\": 1201,\n", " \"Maysan\": 142,\n", " \"Muthanna\": 104,\n", " \"Najaf\": 122,\n", " \"Nineveh\": 680,\n", " \"Not found\": 1052,\n", " \"Saladin\": 825,\n", " \"Sulaymaniyah\": 146,\n", " \"Wasit\": 120\n", " },\n", " \"polriskevents-mean\": {\n", " \"Al Anbar\": 531.90697674418607,\n", " \"Al-Qadisiyah\": 3.125,\n", " \"Babil\": 17.637931034482758,\n", " \"Baghdad\": 126.87878787878788,\n", " \"Diyala\": 12.435897435897436,\n", " \"Dohuk\": 4.4255319148936172,\n", " \"Erbil\": 23.338983050847457,\n", " \"Halabja\": 7.3157894736842106,\n", " \"Karbala\": 31.714285714285715,\n", " \"Kirkuk\": 48.200000000000003,\n", " \"Maysan\": 8.1724137931034484,\n", " \"Muthanna\": 12.699999999999999,\n", " \"Najaf\": 11.533333333333333,\n", " \"Nineveh\": 41.932038834951456,\n", " \"Not found\": 15.660194174757281,\n", " \"Saladin\": 24.220588235294116,\n", " \"Sulaymaniyah\": 4.882352941176471,\n", " \"Wasit\": 6.9285714285714288\n", " },\n", " \"polriskevents-median\": {\n", " \"Al Anbar\": 3.0,\n", " \"Al-Qadisiyah\": 1.0,\n", " \"Babil\": 1.0,\n", " \"Baghdad\": 2.0,\n", " \"Diyala\": 1.0,\n", " \"Dohuk\": 1.0,\n", " \"Erbil\": 1.0,\n", " \"Halabja\": 1.0,\n", " \"Karbala\": 3.0,\n", " \"Kirkuk\": 2.0,\n", " \"Maysan\": 0.0,\n", " \"Muthanna\": 2.0,\n", " \"Najaf\": 3.0,\n", " \"Nineveh\": 2.0,\n", " \"Not found\": 2.0,\n", " \"Saladin\": 1.5,\n", " \"Sulaymaniyah\": 1.0,\n", " \"Wasit\": 0.0\n", " },\n", " \"polriskevents-min\": {\n", " \"Al Anbar\": 0,\n", " \"Al-Qadisiyah\": 0,\n", " \"Babil\": 0,\n", " \"Baghdad\": 0,\n", " \"Diyala\": 0,\n", " \"Dohuk\": 0,\n", " \"Erbil\": 0,\n", " \"Halabja\": 0,\n", " \"Karbala\": 0,\n", " \"Kirkuk\": 0,\n", " \"Maysan\": 0,\n", " \"Muthanna\": 0,\n", " \"Najaf\": 0,\n", " \"Nineveh\": 0,\n", " \"Not found\": 0,\n", " \"Saladin\": 0,\n", " \"Sulaymaniyah\": 0,\n", " \"Wasit\": 0\n", " },\n", " \"polriskevents-std\": {\n", " \"Al Anbar\": 3157.2805493543206,\n", " \"Al-Qadisiyah\": 5.5401758444201734,\n", " \"Babil\": 112.55668330689114,\n", " \"Baghdad\": 954.42358786944544,\n", " \"Diyala\": 66.620965021177682,\n", " \"Dohuk\": 15.549003436332816,\n", " \"Erbil\": 125.05869107239644,\n", " \"Halabja\": 19.48518705632263,\n", " \"Karbala\": 74.738910628298299,\n", " \"Kirkuk\": 218.71732662392628,\n", " \"Maysan\": 28.180627800871147,\n", " \"Muthanna\": 26.399960127561599,\n", " \"Najaf\": 30.856503520713577,\n", " \"Nineveh\": 117.75200444869188,\n", " \"Not found\": 103.51011633785198,\n", " \"Saladin\": 106.12873702974802,\n", " \"Sulaymaniyah\": 20.447637573884691,\n", " \"Wasit\": 23.063404668882583\n", " },\n", " \"polriskevents-sum\": {\n", " \"Al Anbar\": 22872,\n", " \"Al-Qadisiyah\": 100,\n", " \"Babil\": 1023,\n", " \"Baghdad\": 8374,\n", " \"Diyala\": 970,\n", " \"Dohuk\": 208,\n", " \"Erbil\": 1377,\n", " \"Halabja\": 139,\n", " \"Karbala\": 222,\n", " \"Kirkuk\": 1446,\n", " \"Maysan\": 237,\n", " \"Muthanna\": 254,\n", " \"Najaf\": 173,\n", " \"Nineveh\": 4319,\n", " \"Not found\": 1613,\n", " \"Saladin\": 1647,\n", " \"Sulaymaniyah\": 249,\n", " \"Wasit\": 194\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'polriskevents-count': {'Al Anbar': 43,\n", " 'Al-Qadisiyah': 32,\n", " 'Babil': 58,\n", " 'Baghdad': 66,\n", " 'Diyala': 78,\n", " 'Dohuk': 47,\n", " 'Erbil': 59,\n", " 'Halabja': 19,\n", " 'Karbala': 7,\n", " 'Kirkuk': 30,\n", " 'Maysan': 29,\n", " 'Muthanna': 20,\n", " 'Najaf': 15,\n", " 'Nineveh': 103,\n", " 'Not found': 103,\n", " 'Saladin': 68,\n", " 'Sulaymaniyah': 51,\n", " 'Wasit': 28},\n", " 'polriskevents-max': {'Al Anbar': 20729,\n", " 'Al-Qadisiyah': 23,\n", " 'Babil': 859,\n", " 'Baghdad': 7762,\n", " 'Diyala': 583,\n", " 'Dohuk': 106,\n", " 'Erbil': 933,\n", " 'Halabja': 85,\n", " 'Karbala': 201,\n", " 'Kirkuk': 1201,\n", " 'Maysan': 142,\n", " 'Muthanna': 104,\n", " 'Najaf': 122,\n", " 'Nineveh': 680,\n", " 'Not found': 1052,\n", " 'Saladin': 825,\n", " 'Sulaymaniyah': 146,\n", " 'Wasit': 120},\n", " 'polriskevents-mean': {'Al Anbar': 531.9069767441861,\n", " 'Al-Qadisiyah': 3.125,\n", " 'Babil': 17.637931034482758,\n", " 'Baghdad': 126.87878787878788,\n", " 'Diyala': 12.435897435897436,\n", " 'Dohuk': 4.425531914893617,\n", " 'Erbil': 23.338983050847457,\n", " 'Halabja': 7.315789473684211,\n", " 'Karbala': 31.714285714285715,\n", " 'Kirkuk': 48.2,\n", " 'Maysan': 8.172413793103448,\n", " 'Muthanna': 12.7,\n", " 'Najaf': 11.533333333333333,\n", " 'Nineveh': 41.932038834951456,\n", " 'Not found': 15.660194174757281,\n", " 'Saladin': 24.220588235294116,\n", " 'Sulaymaniyah': 4.882352941176471,\n", " 'Wasit': 6.928571428571429},\n", " 'polriskevents-median': {'Al Anbar': 3.0,\n", " 'Al-Qadisiyah': 1.0,\n", " 'Babil': 1.0,\n", " 'Baghdad': 2.0,\n", " 'Diyala': 1.0,\n", " 'Dohuk': 1.0,\n", " 'Erbil': 1.0,\n", " 'Halabja': 1.0,\n", " 'Karbala': 3.0,\n", " 'Kirkuk': 2.0,\n", " 'Maysan': 0.0,\n", " 'Muthanna': 2.0,\n", " 'Najaf': 3.0,\n", " 'Nineveh': 2.0,\n", " 'Not found': 2.0,\n", " 'Saladin': 1.5,\n", " 'Sulaymaniyah': 1.0,\n", " 'Wasit': 0.0},\n", " 'polriskevents-min': {'Al Anbar': 0,\n", " 'Al-Qadisiyah': 0,\n", " 'Babil': 0,\n", " 'Baghdad': 0,\n", " 'Diyala': 0,\n", " 'Dohuk': 0,\n", " 'Erbil': 0,\n", " 'Halabja': 0,\n", " 'Karbala': 0,\n", " 'Kirkuk': 0,\n", " 'Maysan': 0,\n", " 'Muthanna': 0,\n", " 'Najaf': 0,\n", " 'Nineveh': 0,\n", " 'Not found': 0,\n", " 'Saladin': 0,\n", " 'Sulaymaniyah': 0,\n", " 'Wasit': 0},\n", " 'polriskevents-std': {'Al Anbar': 3157.2805493543206,\n", " 'Al-Qadisiyah': 5.540175844420173,\n", " 'Babil': 112.55668330689114,\n", " 'Baghdad': 954.4235878694454,\n", " 'Diyala': 66.62096502117768,\n", " 'Dohuk': 15.549003436332816,\n", " 'Erbil': 125.05869107239644,\n", " 'Halabja': 19.48518705632263,\n", " 'Karbala': 74.7389106282983,\n", " 'Kirkuk': 218.71732662392628,\n", " 'Maysan': 28.180627800871147,\n", " 'Muthanna': 26.3999601275616,\n", " 'Najaf': 30.856503520713577,\n", " 'Nineveh': 117.75200444869188,\n", " 'Not found': 103.51011633785198,\n", " 'Saladin': 106.12873702974802,\n", " 'Sulaymaniyah': 20.44763757388469,\n", " 'Wasit': 23.063404668882583},\n", " 'polriskevents-sum': {'Al Anbar': 22872,\n", " 'Al-Qadisiyah': 100,\n", " 'Babil': 1023,\n", " 'Baghdad': 8374,\n", " 'Diyala': 970,\n", " 'Dohuk': 208,\n", " 'Erbil': 1377,\n", " 'Halabja': 139,\n", " 'Karbala': 222,\n", " 'Kirkuk': 1446,\n", " 'Maysan': 237,\n", " 'Muthanna': 254,\n", " 'Najaf': 173,\n", " 'Nineveh': 4319,\n", " 'Not found': 1613,\n", " 'Saladin': 1647,\n", " 'Sulaymaniyah': 249,\n", " 'Wasit': 194}}" ] }, "execution_count": 193, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }	isc
ESSS/notebooks	Tracer test global mass fraction.ipynb	1	168030	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Test to evaluate the use of global mass fraction in tracer solution\n", "\n", "The discretized mass conservation equation of a component X can be written as \n", "\\begin{equation}\n", "\\frac{m_T\\phi-m_T^o\\phi^o}{\\Delta t}=\\sum_{faces} \\dot{m}_{face}\\phi^{up}_{face}+\\dot{m}_{comp}\\phi_{comp}\n", "\\end{equation}\n", "\n", "where\n", "\\begin{equation}\n", "m_T = V_w\\rho_w + V_o\\rho_o+V_g\\rho_g\n", "\\end{equation}\n", "\\begin{equation}\n", "\\dot{m} = F_w\\rho_w^{up} + F_o\\rho_o^{up} + F_g\\rho_g^{up}\n", "\\end{equation}\n", "\n", "Phase densities and volumes are inputs from a reservoir simulation as well as the phase flow rates between grid blocks and the completion rates." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "# Main inputs\n", "# 1D case in steady state and costant properties\n", "nx = 20\n", "number_of_time_steps = 100\n", "saturation = np.array([1.0, 0.0, 0.0]) # [water, oil, gas]\n", "time_step = 1.0 # d\n", "injector_concentration = [0.1, 0.0, 0.0] # kg of X / kg of phase [water, oil, gas]\n", "flow_rate = np.array([20.0, 0.0, 0.0]) # [water, oil, gas] in m3/d\n", "\n", "# other inputs that change less frequently\n", "L = 3000.0 # m\n", "A = 1.0 # m2\n", "total_volume = L * A # m3\n", "bulk_volume = total_volume / nx # m3\n", "porosity = 0.3 # m3 of pore space/ m3 of total volume\n", "pore_volume = porosity * bulk_volume # m3\n", "density = np.array([1000.0, 800.0, 200.0]) # [water, oil, gas] in kg/m3\n", "injector_cell = 0\n", "producer_cell = nx - 1\n", "injector_rate = - flow_rate\n", "producer_rate = flow_rate\n", "\n", "A = np.zeros((nx, nx))\n", "B = np.zeros(nx)\n", "concentration = np.zeros(nx)\n", "concentration_old = np.zeros(nx)\n", "\n", "# steady state for now\n", "density_old = density\n", "saturation_old = saturation\n", "pore_volume_old = pore_volume" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def accumulation_term():\n", " global A, B\n", " for i in range(nx):\n", " total_mass = np.sum(pore_volume * saturation * density)\n", " total_mass_old = np.sum(pore_volume_old * saturation_old * density_old)\n", " A[i,i] += total_mass\n", " B[i] += total_mass_old * concentration_old[i]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def completions_term():\n", " global A, B\n", " # injection\n", " B[injector_cell] += -np.sum(injector_rate * density * injector_concentration) * time_step\n", " # production\n", " A[producer_cell, producer_cell] += np.sum(producer_rate * density) * time_step" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def flow_term():\n", " global A\n", " for i in range(nx-1):\n", " cell_from = i\n", " cell_to = i + 1\n", " total_mass = np.sum(flow_rate * density) * time_step\n", " \n", " A[cell_from, cell_from] += total_mass\n", " A[cell_to, cell_from] += -total_mass" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def assemble_linear_system():\n", " global A, B\n", " A = np.zeros((nx, nx))\n", " B = np.zeros(nx)\n", " accumulation_term()\n", " completions_term()\n", " flow_term()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def print_balance_error(cell):\n", " injected_mass = 0.0\n", " if cell == injector_cell:\n", " injected_mass = abs(np.sum(injector_rate * density * injector_concentration) * time_step)\n", " \n", " produced_mass = 0.0\n", " if cell == producer_cell:\n", " produced_mass = np.sum(producer_rate * density * injector_concentration) * time_step\n", " \n", " flow_plus = 0.0\n", " if cell < nx-1:\n", " flow_plus = np.sum(flow_rate * density) * time_step * concentration[cell]\n", " \n", " flow_minus = 0.0\n", " if cell > 0:\n", " flow_plus = np.sum(flow_rate * density) * time_step * concentration[cell-1]\n", " \n", " total_mass = np.sum(pore_volume * saturation * density) * concentration[cell]\n", " total_mass_old = np.sum(pore_volume_old * saturation_old * density_old) * concentration_old[cell]\n", " change_in_time = total_mass - total_mass_old\n", " balance_error = change_in_time - flow_minus - injected_mass + produced_mass + flow_plus\n", " phase_mass = pore_volume * saturation * density\n", " print(\n", " \"\"\"\n", " Phase mass: %s kg\n", " Component X concentration: %f kg/kg\n", " \\tInjected mass: %f kg\n", " \\tProduced mass: %f kg\n", " \\tMass on I+ face: %f kg\n", " \\tMass on I- face: %f kg\n", " \\tChange of mass in time: %f kg (%f - %f)\n", " \\tBalance error: %f kg\n", " \"\"\"\n", " % (phase_mass, concentration[cell], injected_mass, produced_mass, flow_plus, flow_minus, change_in_time, total_mass, total_mass_old, balance_error))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio \|\|\n", "\tthis.context.webkitBackingStorePixelRatio \|\|\n", "\tthis.context.mozBackingStorePixelRatio \|\|\n", "\tthis.context.msBackingStorePixelRatio \|\|\n", "\tthis.context.oBackingStorePixelRatio \|\|\n", "\tthis.context.backingStorePixelRatio \|\| 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# assemble the matrix just for visualization purposes, so we can see the coefficients in their places\n", "assemble_linear_system()\n", "\n", "plt.imshow(A)\n", "plt.colorbar()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "solutions = []\n", "solutions.append(concentration)\n", "def solve():\n", " global concentration, concentration_old\n", " for time in range(number_of_time_steps):\n", " assemble_linear_system()\n", " concentration = np.linalg.solve(A, B)\n", " print_balance_error(0)\n", " solutions.append(concentration)\n", " concentration_old = np.copy(concentration)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.030769 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 615.384615 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 1384.615385 kg (1384.615385 - 0.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.052071 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1041.420118 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 958.579882 kg (2343.195266 - 1384.615385)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.066818 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1336.367774 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 663.632226 kg (3006.827492 - 2343.195266)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.077028 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1540.562305 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 459.437695 kg (3466.265187 - 3006.827492)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.084096 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1681.927750 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 318.072250 kg (3784.337437 - 3466.265187)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.088990 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1779.796134 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 220.203866 kg (4004.541303 - 3784.337437)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.092378 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1847.551170 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 152.448830 kg (4156.990133 - 4004.541303)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.094723 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1894.458502 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 105.541498 kg (4262.531630 - 4156.990133)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.096347 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1926.932809 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 73.067191 kg (4335.598821 - 4262.531630)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.097471 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1949.415022 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 50.584978 kg (4386.183799 - 4335.598821)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.098249 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1964.979630 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 35.020370 kg (4421.204169 - 4386.183799)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.098788 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1975.755129 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 24.244871 kg (4445.449040 - 4421.204169)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099161 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1983.215089 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 16.784911 kg (4462.233951 - 4445.449040)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099419 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1988.379677 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 11.620323 kg (4473.854274 - 4462.233951)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099598 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1991.955161 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 8.044839 kg (4481.899112 - 4473.854274)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099722 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1994.430496 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 5.569504 kg (4487.468616 - 4481.899112)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099807 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1996.144190 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 3.855810 kg (4491.324427 - 4487.468616)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099867 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1997.330593 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 2.669407 kg (4493.993834 - 4491.324427)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099908 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1998.151949 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 1.848051 kg (4495.841885 - 4493.993834)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099936 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1998.720580 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 1.279420 kg (4497.121305 - 4495.841885)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099956 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.114248 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.885752 kg (4498.007057 - 4497.121305)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099969 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.386787 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.613213 kg (4498.620270 - 4498.007057)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099979 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.575468 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.424532 kg (4499.044803 - 4498.620270)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099985 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.706093 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.293907 kg (4499.338709 - 4499.044803)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099990 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.796526 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.203474 kg (4499.542183 - 4499.338709)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099993 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.859133 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.140867 kg (4499.683050 - 4499.542183)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099995 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.902477 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.097523 kg (4499.780573 - 4499.683050)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099997 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.932484 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.067516 kg (4499.848089 - 4499.780573)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099998 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.953258 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.046742 kg (4499.894831 - 4499.848089)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099998 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.967640 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.032360 kg (4499.927191 - 4499.894831)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099999 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.977597 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.022403 kg (4499.949594 - 4499.927191)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099999 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.984490 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.015510 kg (4499.965103 - 4499.949594)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.099999 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.989263 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.010737 kg (4499.975841 - 4499.965103)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.992566 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.007434 kg (4499.983274 - 4499.975841)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.994854 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.005146 kg (4499.988421 - 4499.983274)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.996437 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.003563 kg (4499.991984 - 4499.988421)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.997533 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.002467 kg (4499.994450 - 4499.991984)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.998292 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.001708 kg (4499.996158 - 4499.994450)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.998818 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.001182 kg (4499.997340 - 4499.996158)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999182 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000818 kg (4499.998158 - 4499.997340)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999433 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000567 kg (4499.998725 - 4499.998158)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999608 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000392 kg (4499.999117 - 4499.998725)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999728 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000272 kg (4499.999389 - 4499.999117)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999812 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000188 kg (4499.999577 - 4499.999389)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999870 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000130 kg (4499.999707 - 4499.999577)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999910 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000090 kg (4499.999797 - 4499.999707)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999938 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000062 kg (4499.999860 - 4499.999797)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999957 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000043 kg (4499.999903 - 4499.999860)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999970 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000030 kg (4499.999933 - 4499.999903)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999979 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000021 kg (4499.999953 - 4499.999933)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999986 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000014 kg (4499.999968 - 4499.999953)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999990 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000010 kg (4499.999978 - 4499.999968)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999993 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000007 kg (4499.999985 - 4499.999978)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999995 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000005 kg (4499.999989 - 4499.999985)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999997 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000003 kg (4499.999993 - 4499.999989)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999998 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000002 kg (4499.999995 - 4499.999993)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999998 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000002 kg (4499.999996 - 4499.999995)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999999 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000001 kg (4499.999998 - 4499.999996)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999999 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000001 kg (4499.999998 - 4499.999998)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 1999.999999 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000001 kg (4499.999999 - 4499.999998)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4499.999999 - 4499.999999)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4499.999999 - 4499.999999)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4499.999999)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: -0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n", "\n", " Phase mass: [45000. 0. 0.] kg\n", " Component X concentration: 0.100000 kg/kg\n", " \tInjected mass: 2000.000000 kg\n", " \tProduced mass: 0.000000 kg\n", " \tMass on I+ face: 2000.000000 kg\n", " \tMass on I- face: 0.000000 kg\n", " \tChange of mass in time: 0.000000 kg (4500.000000 - 4500.000000)\n", " \tBalance error: 0.000000 kg\n", " \n" ] } ], "source": [ "solve()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace /\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio \|\|\n", "\tthis.context.webkitBackingStorePixelRatio \|\|\n", "\tthis.context.mozBackingStorePixelRatio \|\|\n", "\tthis.context.msBackingStorePixelRatio \|\|\n", "\tthis.context.oBackingStorePixelRatio \|\|\n", "\tthis.context.backingStorePixelRatio \|\| 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio \|\| 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width \|\| size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream /\n", " evt.data.type = \"image/png\";\n", "\n", " / Free the memory for the previous frames /\n", " if (fig.imageObj.src) {\n", " (window.URL \|\| window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL \|\| window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " /\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib /\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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+9hjTU1N0dLSUnb9Aw88EHV1dVEsFqO+vj62bt067nNu3LgxPvrRj8b06dPjwgsvjG9/+9vv6TkJEADkR39nNABPRgIEAPnR3wZgEgECgPzobwMwiQABQH70twGYRIAAID/62wBMIkAAkB/9bQAmESAAyI/+NgCTCBAA5Ed/G4BJBAgA8qO/DcAkAgQA+dHfBmASAQKA/OhvAzCJAAFAfvS3AZhEgAAgP/rbAEwiQACQH/1tACYRIADIj/42AJMIEADkR38bgEkECADyo78NwCQCBAD50d8GYBIBAoD86G8DMIkAAUB+9LcBmESAACA/+tsATCJAAJAf/W0AJhEgAMiP/jYAkwgQAORHfxuASQQIAPKjvw3AJAIEAPnR3wZgEgECgPzobwMwiQABQH70twGYRIAAID/62wBMIkAAkB/9bQAmESAAyI/+NgCTCBAA5Ed/G4BJBAgA8qO/DcAkAgQA+dHfBmASAQKA/OhvAzCJAAFAfvS3AZhEgAAgP/rbAEwiQACQH/1tACYRIADIj/42AJMIEADkR38bgEkECADyo78NwCQCBAD50d8GYBIBAoD86G8DMIkAAUB+9LcBmESAACA/+tsATCJAAJAf/W0AJhEgAMiP/jYAkwgQAORHfxuASQQIAPKjvw3AJAIEAPnR3wZgEgECgPzobwMwiQABQH70twGYRIAAID/62wBMIkAAkB/9bQAmESAAyI/+NgCTCBAA5Ed/G4BJBAgA8qO/DcAkAgQA+dHfmQ3A9evXx8KFC6O6ujoaGxtjx44dR71+y5Yt0dDQEKVSKRoaGqK7u/uI1956661RKBTi7rvvnvTzESAAyI/+zmgAbt68OYrFYmzYsCH6+/tj1apVUVNTE/v375/w+t7e3qiqqor29vbYs2dPtLe3x7Rp02Lnzp3jrn3wwQfjwgsvjHnz5hmAAPABp78zGoCLFy+O1tbWsnP19fWxZs2aCa+/7rrrorm5uezc0qVLY9myZWXnXnzxxTj77LPjqaeeitraWgMQAD7g9HcmA3B0dDSqqqrG/Qh35cqVsWTJkgk/Zv78+dHZ2Vl2rrOzMxYsWDD267fffjsuueSS+Ou//uuICAMQACqA/s5kAA4ODkahUIienp6y8+vWrYtFixZN+DHFYjG6urrKznV1dUWpVBr7dXt7e1x++eVx8ODBiHj3ATgyMhLDw8Njx8DAQMUHCAByYwBmNgB7e3vLzq9duzbq6uom/JhisRibNm0qO3ffffdFdXV1RET88Ic/jNmzZ8fg4ODY4+82ANva2qJQKIw7KjlAAJAbAzCTAXgsfgR89913xymnnBJVVVVjR6FQiFNPPTVqa2sn/Jy+AwgA+TMAMxmAEYdeBLJixYqycw0NDUd9EciVV15Zdq65uXnsRSCvvPJK9PX1lR3z5s2LP/zDP4xnnnlmUs9JgAAgP/o7owF4+G1gNm7cGP39/bF69eqoqamJffv2RUTE8uXLy8ZgT09PVFVVRUdHR+zZsyc6OjqO+DYwh3kRCAB88OnvjAZgxKE3gq6trY1SqRSNjY2xffv2sceampqipaWl7PoHHngg6urqolgsRn19fWzduvWon98ABIAPPv2d2QA82QgQAORHfxuASQQIAPKjvw3AJAIEAPnR3wZgEgECgPzobwMwiQABQH70twGYRIAAID/62wBMIkAAkB/9bQAmESAAyI/+NgCTCBAA5Ed/G4BJBAgA8qO/DcAkAgQA+dHfBmASAQKA/OhvAzCJAAFAfvS3AZhEgAAgP/rbAEwiQACQH/1tACYRIADIj/42AJMIEADkR38bgEkECADyo78NwCQCBAD50d8GYBIBAoD86G8DMIkAAUB+9LcBmESAACA/+tsATCJAAJAf/W0AJhEgAMiP/jYAkwgQAORHfxuASQQIAPKjvw3AJAIEAPnR3wZgEgECgPzobwMwiQABQH70twGYRIAAID/62wBMIkAAkB/9bQAmESAAyI/+NgCTCBAA5Ed/G4BJBAgA8qO/DcAkAgQA+dHfBmASAQKA/OhvAzCJAAFAfvS3AZhEgAAgP/rbAEwiQACQH/1tACYRIADIj/42AJMIEADkR38bgEkECADyo78NwCQCBAD50d8GYBIBAoD86G8DMIkAAUB+9LcBmESAACA/+tsATCJAAJAf/W0AJhEgAMiP/jYAkwgQAORHfxuASQQIAPKjvw3AJAIEAPnR3wZgEgECgPzobwMwiQABQH70twGYRIAAID/62wBMIkAAkB/9bQAmESAAyI/+NgCTCBAA5Ed/G4BJBAgA8qO/MxuA69evj4ULF0Z1dXU0NjbGjh07jnr9li1boqGhIUqlUjQ0NER3d/fYY2+++Wbceeedcf7558eMGTNi7ty5sXz58hgcHJz08xEgAMiP/s5oAG7evDmKxWJs2LAh+vv7Y9WqVVFTUxP79++f8Pre3t6oqqqK9vb22LNnT7S3t8e0adNi586dERExNDQUl112WXzrW9+KZ555Jp544om4+OKL4+Mf//ikn5MAAUB+9HdGA3Dx4sXR2tpadq6+vj7WrFkz4fXXXXddNDc3l51bunRpLFu27Ij/je9///tRKBSOOCrfSYAAID/6O5MBODo6GlVVVWU/wo2IWLlyZSxZsmTCj5k/f350dnaWnevs7IwFCxYc8b+zbdu2OOWUU44YiJGRkRgeHh47BgYGKj5AAJAbAzCTATg4OBiFQiF6enrKzq9bty4WLVo04ccUi8Xo6uoqO9fV1RWlUmnC63/xi1/Exz/+8bjxxhuP+Dza2tqiUCiMOyo5QACQGwMwswHY29tbdn7t2rVRV1c34ccUi8XYtGlT2bn77rsvqqurx1375ptvxrXXXhsXXXTRUcPgO4AAkD8DMJMBeCx/BPzmm2/G5z73ubjgggvilVdeeU/PS4AAID/6O5MBGHHoRSArVqwoO9fQ0HDUF4FceeWVZeeam5vLXgRyePydd9558fLLL7/n5yRAAJAf/Z3RADz8NjAbN26M/v7+WL16ddTU1MS+ffsiImL58uVlY7Cnpyeqqqqio6Mj9uzZEx0dHWVvA/O///u/cc0118Q555wTP/rRj+LAgQNjx+jo6KSekwABQH70d0YDMOLQG0HX1tZGqVSKxsbG2L59+9hjTU1N0dLSUnb9Aw88EHV1dVEsFqO+vj62bt069tjevXsnfEFHoVCIxx57bFLPR4AAID/6O7MBeLIRIADIj/42AJMIEADkR38bgEkECADyo78NwCQCBAD50d8GYBIBAoD86G8DMIkAAUB+9LcBmESAACA/+tsATCJAAJAf/W0AJhEgAMiP/jYAkwgQAORHfxuASQQIAPKjvw3AJAIEAPnR3wZgEgECgPzobwMwiQABQH70twGYRIAAID/62wBMIkAAkB/9bQAmESAAyI/+NgCTCBAA5Ed/G4BJBAgA8qO/DcAkAgQA+dHfBmASAQKA/OhvAzCJAAFAfvS3AZhEgAAgP/rbAEwiQACQH/1tACYRIADIj/42AJMIEADkR38bgEkECADyo78NwCQCBAD50d8GYBIBAoD86G8DMIkAAUB+9LcBmESAACA/+tsATCJAAJAf/W0AJhEgAMiP/jYAkwgQAORHfxuASQQIAPKjvw3AJAIEAPnR3wZgEgECgPzobwMwiQABQH70twGYRIAAID/62wBMIkAAkB/9bQAmESAAyI/+NgCTCBAA5Ed/G4BJBAgA8qO/DcAkAgQA+dHfBmASAQKA/OhvAzCJAAFAfvS3AZhEgAAgP/rbAEwiQACQH/1tACYRIADIj/42AJMIEADkR38bgEkECADyo78NwCQCBAD50d8GYBIBAoD86G8DMIkAAUB+9LcBmESAACA/+tsATCJAAJAf/W0AJhEgAMiP/s5sAK5fvz4WLlwY1dXV0djYGDt27Djq9Vu2bImGhoYolUrR0NAQ3d3dZY8fPHgw2traYu7cuTF9+vRoamqKp556atLPR4AAID/6O6MBuHnz5igWi7Fhw4bo7++PVatWRU1NTezfv3/C63t7e6Oqqira29tjz5490d7eHtOmTYudO3eOXdPR0REzZ86MrVu3Rl9fX1x//fUxd+7c+J//+Z9JPScBAoD86O+MBuDixYujtbW17Fx9fX2sWbNmwuuvu+66aG5uLju3dOnSWLZsWUQc+u7fnDlzoqOjY+zxkZGRmDVrVvzt3/7tpJ7TsQrQwYMH4/+O/q/D4XA4HBV/HDx4cEo7NsIAjMhkAI6OjkZVVdW4H+GuXLkylixZMuHHzJ8/Pzo7O8vOdXZ2xoIFCyIi4rnnnotCoRC7du0qu+aaa66JL3zhCxN+zpGRkRgeHh47XnjhhSgUCjEwMFB2PvU48N+vxvzV/8fhcDgcjoo/Dvz3q1PascPDwzEwMBCFQiGGhobe7zTJXhYDcHBwMAqFQvT09JSdX7duXSxatGjCjykWi9HV1VV2rqurK0qlUkRE9PT0RKFQiMHBwbJrbrnllrjiiism/JxtbW1RKBQcDofD4XB8AI6BgYH3O02yl9UA7O3tLTu/du3aqKurm/BjisVibNq0qezcfffdF9XV1RHx/wfgSy+9VHbNzTffHEuXLp3wc77zO4A/+9nP4rnnnouhoaFj9qeTqf7u4gfxcK/cK/fKvcrlcK9Ojns1NDQUAwMD8fbbb7/faZK9LAbgyfIj4ONpeNjfT5gs92ry3KvJc68mz72aPPdq8tyrYyuLARhx6EUgK1asKDvX0NBw1BeBXHnllWXnmpubx70I5Gtf+9rY46Ojo+/pRSDHkuBPnns1ee7V5LlXk+deTZ57NXnu1bGVzQA8/DYwGzdujP7+/li9enXU1NTEvn37IiJi+fLlZWOwp6cnqqqqoqOjI/bs2RMdHR0Tvg3MrFmzoru7O/r6+uKGG254T28DcywJ/uS5V5PnXk2eezV57tXkuVeT514dW9kMwIhDbwRdW1sbpVIpGhsbY/v27WOPNTU1RUtLS9n1DzzwQNTV1UWxWIz6+vrYunVr2eOH3wh6zpw5UV1dHUuWLIm+vr7j8aW8q5GRkWhra4uRkZET/VROeu7V5LlXk+deTZ57NXnu1eS5V8dWVgMQAIB0BiAAQIUxAAEAKowBCABQYQxAAIAKYwCehNavXx8LFy6M6urqaGxsjB07dpzop3TcTfTP7s2ePXvs8cOv4J47d25Mnz49mpqa4qmnnir7HK+99lrcdNNNcfrpp8fpp58eN910U/zsZz873l/KlNu+fXtcddVVMXfu3CgUCvHggw+WPT5V9+bJJ5+MJUuWxPTp02PevHnxla985Zj8o+zH0rvdq5aWlnE5u/jii8uuGRkZidtvvz3OPPPMmDFjRlx99dXj/vmo/fv3x1VXXRUzZsyIM888M37v934vRkdHj/nXN5Xa29vjE5/4RJx22mlx1llnxbXXXhvPPPNM2TVTdS8ef/zxaGxsjOrq6vi1X/u1+Ju/+Ztj/vVNpcncq6ampnHZuv7668uuqYTfh/fcc0987GMfi5kzZ8bMmTPjk5/8ZDz66KNjj8vUiWMAnmQOv9/hhg0bor+/P1atWhU1NTWxf//+E/3Ujqu2trY477zz4sCBA2PHyy+/PPZ4R0dHzJw5M7Zu3Rp9fX1x/fXXj3sPx+bm5jj//POjt7c3ent74/zzz4+rrrrqRHw5U+rRRx+NP/7jP46tW7dOOGqm4t4MDw/H7NmzY9myZdHX1xdbt26NmTNnxl/91V8dt69zKrzbvWppaYnm5uaynL366qtl17S2tsbZZ58d27Zti127dsUll1wSF154Ybz11lsREfHWW2/F+eefH5dcckns2rUrtm3bFvPmzYvbb7/9uH2dU2Hp0qVx7733xlNPPRU/+tGP4rOf/WwsWLAgfv7zn49dMxX34vnnn48ZM2bEqlWror+/PzZs2BDFYjG2bNly3L/m92sy96qpqSluueWWsmwNDQ2VfZ5K+H340EMPxSOPPBI//vGP48c//nF8+ctfjmKxOPaHUi4QAokAAAg5SURBVJk6cQzAk8zixYujtbW17Fx9ff0R/8WTD6q2tra48MILJ3zs8L/i0tHRMXZuZGSk7F9x6e/vj0KhUPbG30888UQUCoVxf1LP2TtHzVTdm3vuuSdmzZpV9v5bd911V8ybNy+r7z78siMNwGuvvfaIHzM0NBTFYjE2b948dm5wcDBOPfXU+Od//ueIODQyTz311BgcHBy75v7774/q6uqs38D25ZdfjkKhMPZ+q1N1L+68886or68v+2/ddttt8clPfvJYf0nHzDvvVcShAbhq1aojfkyl/j6MiPjVX/3V+Pu//3uZOsEMwJPI+/k3jz+o2traYsaMGTF37txYuHBhXH/99fHcc89FxOT+HeeNGzfGrFmzxn3eWbNmxT/8wz8c+y/gOHnnqJmqe7N8+fK45ppryh7ftWtXFAqFeP7556f6yzgujjQAZ82aFWeddVace+65cfPNN8dPf/rTscf/9V//NQqFQrz22mtlH3fBBRfEn/3Zn0VExJ/+6Z/GBRdcUPb4a6+9FoVCIb773e8eo6/m2Hv22WejUCiMvTn+VN2LT3/607Fy5cqya7q7u2PatGnx5ptvHqsv55h6572KODQAP/zhD8eZZ54Zv/7rvx533HFH2XfhK/H34VtvvRX3339/lEqlePrpp2XqBDMATyKDg4NRKBSip6en7Py6deti0aJFJ+hZnRiPPvpobNmyJZ588snYtm1bNDU1xezZs+OVV16Jnp6eKBQKZX8ijIi45ZZb4oorroiIQ/fs3HPPHfd5zz333Ghvbz8uX8Px8M5RM1X35vLLL49bbrml7PHD+ezt7Z3qL+O4mGgAbt68OR5++OHo6+uLhx56KC688MI477zzxr7j0tXVFaVSadznuvzyy+PWW2+NiEP39vLLLx93TalUik2bNh2Dr+TYO3jwYFx99dXxm7/5m2PnpupenHvuubFu3bqyxw/n9qWXXprKL+O4mOheRUR885vfjG3btkVfX1/cf//9sXDhwrjsssvGHq+k34dPPvlk1NTURFVVVcyaNSseeeSRiJCpE80APIkc6Tf22rVro66u7gQ9q5PDz3/+85g9e3Z8/etfP+Jv7JtvvjmWLl0aEUcezR/96EfjrrvuOi7P+Xg40gBMvTe//H/Ah7344otRKBTiiSeemOov47iYaAC+00svvRTFYnHsn408UkFddtllcdttt0VE+bj+ZcViMe6///4peObH3+/+7u9GbW1t2V/Gn6p7MdEfwv7t3/4tCoVCHDhwYCq/jONions1kR/+8IdRKBTiP/7jPyKisn4fjo6OxrPPPhs/+MEPYs2aNfHhD384nn76aZk6wQzAk4gfAR/dZZddFq2trX4E/Ev8CHjyJjMAIw4V8OG/Q1mJPwK+/fbb45xzzhn3v7Mf1413pHs1kYMHD5b9fbdK/X0YEXHppZfGrbfeKlMnmAF4klm8eHGsWLGi7FxDQ0PFvQjknUZGRuLss88eewuEOXPmxNe+9rWxx0dHRyd8ocO///u/j12zc+fOinkRSOq9ueeee+JXfuVXyt5qoaOjI+u/fD6ZAfjKK69EdXV1/OM//mNE/P8XPnzrW98au+all16a8C+p//J3XTdv3pzdi0AOHjwYX/ziF2PevHnxX//1X+Men6p7ceedd0ZDQ0PZ525tbc3qL+y/272aSF9fX9kLRSr192FExGc+85loaWmRqRPMADzJHH4bmI0bN0Z/f3+sXr06ampqYt++fSf6qR1Xd9xxRzz++OPx/PPPx86dO+Oqq66KmTNnjt2Hjo6OmDVrVnR3d0dfX1/ccMMNE77VyQUXXBBPPPFEPPHEE/Gxj33sA/E2MK+//nrs3r07du/eHYVCITo7O2P37t1jbxU0FfdmaGgoZs+eHTfccEP09fVFd3d3nH766Vm9/UTE0e/V66+/HnfccUf09vbG3r1747HHHotPfepTcfbZZ5fdq9bW1jjnnHPiO9/5TuzatSs+85nPTPg2FZdeemns2rUrvvOd78Q555yT3dvArFixImbNmhWPP/542VuXvPHGG2PXTMW9OPyWHb//+78f/f39sXHjxuzesuPd7tVPfvKT+MpXvhI/+MEPYu/evfHII49EfX19XHTRRWP3KqIyfh/+0R/9UezYsSP27t0bTz75ZHz5y1+OU089Nf7lX/4lImTqRDIAT0Lr16+P2traKJVK0djYWPbWApXi8HvXFYvFmDdvXnz+85+Pp59+euzxw292PGfOnKiuro4lS5aUvQIvIuLVV1+NG2+8cewNSG+88cYPxBtBP/bYY+PeYLZQKERLS0tETN29efLJJ+PTn/50VFdXx5w5c+LP//zPs/uuw9Hu1RtvvBFXXHFFnHXWWVEsFmPBggXR0tISL7zwQtnn+MUvfhG33357nHHGGfGhD30orrrqqnHX7N+/Pz772c/Ghz70oTjjjDPi9ttvL3vrjhxMdJ8KhULce++9Y9dM1b14/PHH46KLLopSqRQLFy7M7k173+1evfDCC7FkyZI444wzolQqxUc+8pFYuXLluPeYrITfh7/927891mdnnXVWXHrppWPjL0KmTiQDEACgwhiAAAAVxgAEAKgwBiAAQIUxAAEAKowBCABQYQxAAIAKYwACAFQYAxAAoMIYgAAAFcYABACoMAYgAECFMQABACqMAQgAUGEMQACACmMAAgBUGAMQAKDCGIAAABXGAAQAqDAGIABAhTEAAQAqjAEIAFBhDEAAgApjAAIAVBgDEACgwhiAAAAVxgAEAKgwBiAAQIUxAAEAKowBCABQYQxAAIAKYwACAFSY/wf1Ngm+5Q3NFQAAAABJRU5ErkJggg==\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7f4204e8f9c5464b9e3b33cd6451b18f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(IntSlider(value=0, description='x'), Output()), _dom_classes=('widget-interact',))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<function __main__.update(x=0)>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "from ipywidgets import \n", "\n", "x = np.linspace(0, L, nx)\n", "fig = plt.figure()\n", "ax = fig.add_subplot(1, 1, 1)\n", "ax.set(ylim=(0, 1.1np.max(injector_concentration)))\n", "line, = ax.plot(x, solutions[0])\n", "\n", "def update(x = 0):\n", " line.set_ydata(solutions[x])\n", " fig.canvas.draw_idle()\n", "\n", "interact(update, x=widgets.IntSlider(min=0, max=len(solutions)-1, step=1, value=0))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ekostat/ekostat_calculator	notebooks/lv_notebook_kustzon.ipynb	2	26884	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "D:\\github\\w_vattenstatus\\ekostat_calculator\n", "D:/github/w_vattenstatus/ekostat_calculator\\core\\__init__.py\n" ] }, { "data": { "text/plain": [ "'0.19.2'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# coding: utf-8\n", "\n", "# In[1]:\n", "\n", "\n", "import os \n", "import sys\n", "path = \"../\"\n", "path = \"D:/github/w_vattenstatus/ekostat_calculator\"\n", "sys.path.append(path)\n", "#os.path.abspath(\"../\")\n", "print(os.path.abspath(path))\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import json\n", "import timeit\n", "import time\n", "import core\n", "import importlib\n", "importlib.reload(core)\n", "import logging\n", "importlib.reload(core) \n", "try:\n", " logging.shutdown()\n", " importlib.reload(logging)\n", "except:\n", " pass\n", "from event_handler import EventHandler\n", "print(core.__file__)\n", "pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###############################################################################################################################\n", "### Load directories" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-10-12 15:38:05,187\tevent_handler.py\t117\t__init__\tDEBUG\tStart EventHandler: event_handler\n", "2018-10-12 15:38:05,189\tevent_handler.py\t152\t_load_mapping_objects\tDEBUG\tLoading mapping files from pickle file.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "D:/github/w_vattenstatus/ekostat_calculator\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2018-10-12 15:38:05,871\tevent_handler.py\t128\t__init__\tDEBUG\tTime for mapping: 0.6818232536315918\n", "2018-10-12 15:38:05,871\tevent_handler.py\t133\t__init__\tDEBUG\tTime for initiating EventHandler: 0.6848235130310059\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------\n", "Time for request: 0.6848235130310059\n" ] } ], "source": [ "root_directory = 'D:/github/w_vattenstatus/ekostat_calculator'#\"../\" #os.getcwd()\n", "workspace_directory = root_directory + '/workspaces' \n", "resource_directory = root_directory + '/resources'\n", "#alias = 'lena'\n", "user_id = 'test_user' #kanske ska vara off_line user?\n", "# workspace_alias = 'lena_indicator' # kustzonsmodellen_3daydata\n", "workspace_alias = 'kustzonsmodellen_3daydata'\n", "\n", "# ## Initiate EventHandler\n", "print(root_directory)\n", "paths = {'user_id': user_id, \n", " 'workspace_directory': root_directory + '/workspaces', \n", " 'resource_directory': root_directory + '/resources', \n", " 'log_directory': 'D:/github' + '/log', \n", " 'test_data_directory': 'D:/github' + '/test_data',\n", " 'cache_directory': 'D:/github/w_vattenstatus/cache'}\n", "\n", "t0 = time.time()\n", "ekos = EventHandler(*paths)\n", "#request = ekos.test_requests['request_workspace_list']\n", "#response = ekos.request_workspace_list(request) \n", "#ekos.write_test_response('request_workspace_list', response)\n", "print('-'50)\n", "print('Time for request: {}'.format(time.time()-t0))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "###############################################################################################################################\n", "# ### Make a new workspace" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ekos.copy_workspace(source_uuid='default_workspace', target_alias='kustzonsmodellen_3daydata')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "====================================================================================================\n", "Current workspaces for user are:\n", "\n", "uuid alias status \n", "----------------------------------------------------------------------------------------------------\n", "default_workspace default_workspace readable \n", "e86ae1c5-d241-46a4-9236-59524b44e500 lena_indicator editable \n", "2c27da69-6035-418b-8f5e-bc8ef8e6320b kuszonsmodellen editable \n", "78bd7584-5de1-45ca-9176-09a998a7e734 kustzonsmodellen_3daydata editable \n", "====================================================================================================\n", "78bd7584-5de1-45ca-9176-09a998a7e734\n" ] } ], "source": [ "# ### See existing workspaces and choose workspace name to load\n", "ekos.print_workspaces()\n", "workspace_uuid = ekos.get_unique_id_for_alias(workspace_alias = workspace_alias) #'kuszonsmodellen' lena_indicator \n", "print(workspace_uuid)\n", "\n", "workspace_alias = ekos.get_alias_for_unique_id(workspace_uuid = workspace_uuid)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-10-12 15:38:20,849\tevent_handler.py\t3070\tload_workspace\tDEBUG\tTrying to load new workspace \"78bd7584-5de1-45ca-9176-09a998a7e734\" with alias \"kustzonsmodellen_3daydata\"\n", "2018-10-12 15:38:21,167\tevent_handler.py\t3088\tload_workspace\tINFO\tWorkspace \"78bd7584-5de1-45ca-9176-09a998a7e734\" with alias \"kustzonsmodellen_3daydata loaded.\"\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "###############################################################################################################################\n", "# ### Load existing workspace\n", "ekos.load_workspace(unique_id = workspace_uuid)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "###############################################################################################################################\n", "# ### import data\n", "# ekos.import_default_data(workspace_alias = workspace_alias)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-10-12 15:38:26,679\tworkspaces.py\t1522\tdelete_all_export_data\tDEBUG\tAll files in export directory are deleted and all \"loaded\" in datatype_settings is 0.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤\n", "D:/github/w_vattenstatus/ekostat_calculator/workspaces/78bd7584-5de1-45ca-9176-09a998a7e734/input_data/exports\\all_data.pkl\n", "D:/github/w_vattenstatus/ekostat_calculator/workspaces/78bd7584-5de1-45ca-9176-09a998a7e734/input_data/exports\\all_data.txt\n" ] } ], "source": [ "###############################################################################################################################\n", "# ### Load all data in workspace\n", "# #### if there is old data that you want to remove\n", "ekos.get_workspace(workspace_uuid = workspace_uuid).delete_alldata_export()\n", "ekos.get_workspace(workspace_uuid = workspace_uuid).delete_all_export_data()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-10-12 15:38:53,771\tworkspaces.py\t1834\tload_all_data\tDEBUG\tAll selected data in (status 1 in datatype_settings.txt) is not loaded.\n", "2018-10-12 15:38:53,806\tworkspaces.py\t1917\tload_datatype_data\tDEBUG\tNew data files has been loaded for datatype: physicalchemical\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤\n", "D:/github/w_vattenstatus/ekostat_calculator/workspaces/78bd7584-5de1-45ca-9176-09a998a7e734/input_data/exports\\all_data.pkl\n", "D:/github/w_vattenstatus/ekostat_calculator/workspaces/78bd7584-5de1-45ca-9176-09a998a7e734/input_data/exports\\all_data.txt\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2018-10-12 15:38:55,463\tworkspaces.py\t1917\tload_datatype_data\tDEBUG\tNew data files has been loaded for datatype: physicalchemicalmodel\n", "2018-10-12 15:38:55,464\tworkspaces.py\t1917\tload_datatype_data\tDEBUG\tNew data files has been loaded for datatype: chlorophyll\n", "2018-10-12 15:38:55,501\tworkspaces.py\t1917\tload_datatype_data\tDEBUG\tNew data files has been loaded for datatype: phytoplankton\n", "2018-10-12 15:38:55,565\tworkspaces.py\t1917\tload_datatype_data\tDEBUG\tNew data files has been loaded for datatype: zoobenthos\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "self.all_data 0\n", "MMMMMMMMM\n", "--------------------------------------------------\n", "Total time: 0.9752404689788818\n", "time_preparations 0.0\n", "time_list_group_data: 0.04780101776123047\n", "time_list_calc_integ: 0.04320263862609863\n", "time_list_add_row: 0.4178156852722168\n", "time_all_calculations: 0.880638837814331\n", "time_iterator: 0.0\n", "time_add_data: 0.04680061340332031\n", "Done adding integrated_calc \"CPHL_INTEG_CALC\" using parameter \"CPHL_BTL\"\n", "time for integrated_calc \"CPHL_INTEG_CALC\" using parameter \"CPHL_BTL is: 0.9752404689788818\n", "Saving data to: D:/github/w_vattenstatus/ekostat_calculator/workspaces/78bd7584-5de1-45ca-9176-09a998a7e734/input_data/exports/all_data.txt\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "###############################################################################################################################\n", "# #### to just load existing data in workspace\n", "ekos.load_data(workspace_uuid = workspace_uuid)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "62780" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "############################################################################################################################### \n", "# ### check workspace data length\n", "w = ekos.get_workspace(workspace_uuid = workspace_uuid)\n", "len(w.data_handler.get_all_column_data_df())" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uuid default_subset alias default_subset\n", "uuid e2ede510-cd06-44f2-a775-8f0c6840057e alias test_kustzon\n" ] } ], "source": [ "############################################################################################################################### \n", "# ### see subsets in data \n", "for subset_uuid in w.get_subset_list():\n", " print('uuid {} alias {}'.format(subset_uuid, w.uuid_mapping.get_alias(unique_id=subset_uuid)))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['LONGI_DD', 'DEPH', 'RLABO', 'WATER_DISTRICT', 'STIME', 'WADEP',\n", " 'STATN', 'MYEAR', 'WATER_TYPE_AREA', 'WATER_BODY_NAME', 'VISS_EU_CD',\n", " 'SDATE', 'LATIT_DD', 'AMON', 'NTRI', 'CPHL_BTL', 'NTRZ', 'PHOS', 'NTOT',\n", " 'SECCHI', 'PTOT', 'NTRA', 'origin_dtype', 'origin_file_path', 'DIN',\n", " 'MONTH', 'YEAR', 'POSITION', 'MNDEP', 'MXDEP', 'visit_id_str', 'date',\n", " 'CPHL_INTEG_CALC', 'CPHL_INTEG_CALC_depths', 'CPHL_INTEG_CALC_values',\n", " 'index_column'],\n", " dtype='object')\n" ] } ], "source": [ "############################################################################################################################### \n", "# # Step 0 \n", "print(w.data_handler.all_data.columns)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "############################################################################################################################### \n", "# ### Apply first data filter \n", "w.apply_data_filter(step = 0) # This sets the first level of data filter in the IndexHandler " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "############################################################################################################################### \n", "# # Step 1 \n", "# ### make new subset\n", "# w.copy_subset(source_uuid='default_subset', target_alias='test_kustzon') " ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "subset_alias test_kustzon subset_uuid e2ede510-cd06-44f2-a775-8f0c6840057e\n" ] } ], "source": [ "###############################################################################################################################\n", "# ### Choose subset name to load\n", "subset_alias = 'test_kustzon'\n", "# subset_alias = 'period_2007-2012_refvalues_2013'\n", "# subset_alias = 'test_subset'\n", "subset_uuid = ekos.get_unique_id_for_alias(workspace_alias = workspace_alias, subset_alias = subset_alias)\n", "print('subset_alias', subset_alias, 'subset_uuid', subset_uuid)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###############################################################################################################################\n", "### Set subset filters" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# #### year filter\n", "w.set_data_filter(subset = subset_uuid, step=1, \n", " filter_type='include_list', \n", " filter_name='MYEAR', \n", " data=[2007,2008,2009,2010,2011,2012])#['2011', '2012', '2013']) #, 2014, 2015, 2016" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'MYEAR': ['2007', '2008', '2009', '2010', '2011', '2012'], 'STATN': [], 'VISS_EU_CD': []}\n", "subset_alias: test_kustzon \n", "subset uuid: e2ede510-cd06-44f2-a775-8f0c6840057e\n", "{'MYEAR': ['2007', '2008', '2009', '2010', '2011', '2012'], 'STATN': [], 'VISS_EU_CD': []}\n" ] } ], "source": [ "###############################################################################################################################\n", "# #### waterbody filter\n", "w.set_data_filter(subset = subset_uuid, step=1, \n", " filter_type='include_list', \n", " filter_name='viss_eu_cd', data = []) #'SE584340-174401', 'SE581700-113000', 'SE654470-222700', 'SE633000-195000', 'SE625180-181655'\n", "# data=['SE584340-174401', 'SE581700-113000', 'SE654470-222700', 'SE633000-195000', 'SE625180-181655']) \n", "# wb with no data for din 'SE591400-182320'\n", " \n", "f1 = w.get_data_filter_object(subset = subset_uuid, step=1) \n", "print(f1.include_list_filter)\n", "\n", "print('subset_alias:', subset_alias, '\\nsubset uuid:', subset_uuid)\n", "\n", "f1 = w.get_data_filter_object(subset = subset_uuid, step=1) \n", "print(f1.include_list_filter)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['SE641250-211751' 'SE648760-213140' 'SE652400-223501' 'SE653840-247900'\n", " 'SE653870-235570']\n" ] } ], "source": [ "############################################################################################################################### \n", "# ## Apply step 1 datafilter to subset\n", "w.apply_data_filter(subset = subset_uuid, step = 1)\n", "filtered_data = w.get_filtered_data(step = 1, subset = subset_uuid)\n", "print(filtered_data['VISS_EU_CD'].unique())" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>AMON</th>\n", " <th>NTRA</th>\n", " <th>DIN</th>\n", " <th>CPHL_INTEG_CALC</th>\n", " <th>DEPH</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.180</td>\n", " <td>6.238</td>\n", " <td>6.42</td>\n", " <td>NaN</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.180</td>\n", " <td>6.238</td>\n", " <td>6.42</td>\n", " <td>NaN</td>\n", " <td>0.25</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.179</td>\n", " <td>6.237</td>\n", " <td>6.42</td>\n", " <td>NaN</td>\n", " <td>0.75</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.175</td>\n", " <td>6.245</td>\n", " <td>6.42</td>\n", " <td>NaN</td>\n", " <td>1.25</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.165</td>\n", " <td>6.266</td>\n", " <td>6.43</td>\n", " <td>NaN</td>\n", " <td>1.75</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AMON NTRA DIN CPHL_INTEG_CALC DEPH\n", "0 0.180 6.238 6.42 NaN 0.00\n", "1 0.180 6.238 6.42 NaN 0.25\n", "2 0.179 6.237 6.42 NaN 0.75\n", "3 0.175 6.245 6.42 NaN 1.25\n", "4 0.165 6.266 6.43 NaN 1.75" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filtered_data[['AMON','NTRA','DIN','CPHL_INTEG_CALC','DEPH']].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "############################################################################################################################### \n", "# Step 2" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Load indicator settings filter \n", "w.get_step_object(step = 2, subset = subset_uuid).load_indicator_settings_filters()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "############################################################################################################################### \n", "### set available indicators \n", "w.get_available_indicators(subset= subset_uuid, step=2)\n", " " ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "###############################################################################################################################\n", "# ### choose indicators\n", "#list(zip(typeA_list, df_step1.WATER_TYPE_AREA.unique()))\n", "# indicator_list = ['oxygen','din_winter','ntot_summer', 'ntot_winter', 'dip_winter', 'ptot_summer', 'ptot_winter','bqi', 'biov', 'chl', 'secchi']\n", "# indicator_list = ['din_winter','ntot_summer', 'ntot_winter', 'dip_winter', 'ptot_summer', 'ptot_winter']\n", "#indicator_list = ['biov', 'chl']\n", "# indicator_list = ['bqi', 'biov', 'chl', 'secchi']\n", "#indicator_list = ['bqi', 'secchi'] + ['biov', 'chl'] + ['din_winter']\n", "# indicator_list = ['din_winter','ntot_summer']\n", "# indicator_list = ['indicator_' + indicator for indicator in indicator_list]\n", "indicator_list = w.available_indicators" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "apply indicator data filter to []\n" ] } ], "source": [ "############################################################################################################################### \n", "# ### Apply indicator data filter\n", "print('apply indicator data filter to {}'.format(indicator_list))\n", "for indicator in indicator_list:\n", " w.apply_indicator_data_filter(step = 2, \n", " subset = subset_uuid, \n", " indicator = indicator)#,\n", "# water_body_list = test_wb)\n", " #print(w.mapping_objects['water_body'][wb])\n", " #print('*************************************')\n", "\n", "#df = w.get_filtered_data(subset = subset_uuid, step = 'step_2', water_body = 'SE625180-181655', indicator = 'indicator_din_winter').dropna(subset = ['DIN'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "############################################################################################################################### \n", "# Step 3 " ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "indicator set up to []\n" ] } ], "source": [ "# ### Set up indicator objects\n", "print('indicator set up to {}'.format(indicator_list))\n", "w.get_step_object(step = 3, subset = subset_uuid).indicator_setup(indicator_list = indicator_list) " ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CALCULATE STATUS to []\n" ] } ], "source": [ "###############################################################################################################################\n", "# ### CALCULATE STATUS\n", "print('CALCULATE STATUS to {}'.format(indicator_list))\n", "w.get_step_object(step = 3, subset = subset_uuid).calculate_status(indicator_list = indicator_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "############################################################################################################################### \n", "# ### CALCULATE QUALITY ELEMENTS\n", "w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(quality_element = 'nutrients')\n", "# w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(quality_element = 'phytoplankton')\n", "# w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(quality_element = 'bottomfauna')\n", "# w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(quality_element = 'oxygen')\n", "# w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(quality_element = 'secchi')\n", " \n", "# w.get_step_object(step = 3, subset = subset_uuid).calculate_quality_element(subset_unique_id = subset_uuid, quality_element = 'Phytoplankton')\n", " \n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
ioam/holoviews	examples/reference/elements/plotly/HSpan.ipynb	1	1978	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#### Title: HSpan Element\n", "\n", "Dependencies: Plotly\n", "\n", "Backends: [Bokeh](../bokeh/HSpan.ipynb), [Matplotlib](../matplotlib/HSpan.ipynb), [Plotly](./HSpan.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import holoviews as hv\n", "from holoviews import opts\n", "hv.extension('plotly')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``HSpan`` element is a type of annotation that marks a range along the y-axis. Here is an ``HSpan`` element that marks the standard deviation in a collection of points:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xs = np.random.normal(size=500)\n", "ys = np.random.normal(size=500) * xs\n", "ymean, ystd = ys.mean(), ys.std()\n", "\n", "points = hv.Points((xs,ys))\n", "hspan = hv.HSpan(ymean-ystd, ymean+ystd)\n", "\n", "hspan.opts(fillcolor='blue') * points.opts(color='#D3D3D3')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Like all annotation-like elements `HSpan` is not included in the calculation of axis ranges by default, but can be included by setting `apply_ranges=True`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(hv.HSpan(1, 3) * hv.HSpan(5, 8)).opts(\n", " opts.HSpan(apply_ranges=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For full documentation and the available style and plot options, use ``hv.help(hv.HSpan).``" ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 4 }	bsd-3-clause
cuttlefishh/emp	methods/figure-data/fig-1/Fig1_data_files.ipynb	2	17216	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Figure 1 csv data generation\n", "\n", "Figure data consolidation for Figure 1, which maps samples and shows distribution across EMPO categories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Figure 1a and 1b\n", "\n", "for these figure, we just need the samples, EMPO level categories, and lat/lon coordinates" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>#SampleID</th>\n", " <th>BarcodeSequence</th>\n", " <th>LinkerPrimerSequence</th>\n", " <th>Description</th>\n", " <th>host_subject_id</th>\n", " <th>study_id</th>\n", " <th>title</th>\n", " <th>principal_investigator</th>\n", " <th>doi</th>\n", " <th>ebi_accession</th>\n", " <th>...</th>\n", " <th>adiv_shannon</th>\n", " <th>adiv_faith_pd</th>\n", " <th>temperature_deg_c</th>\n", " <th>ph</th>\n", " <th>salinity_psu</th>\n", " <th>oxygen_mg_per_l</th>\n", " <th>phosphate_umol_per_l</th>\n", " <th>ammonium_umol_per_l</th>\n", " <th>nitrate_umol_per_l</th>\n", " <th>sulfate_umol_per_l</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>550.L1S1.s.1.sequence</td>\n", " <td>AACGCACGCTAG</td>\n", " <td>GTGCCAGCMGCCGCGGTAA</td>\n", " <td>sample_1 stool</td>\n", " <td>F4</td>\n", " <td>550</td>\n", " <td>Moving pictures of the human microbiome</td>\n", " <td>Rob Knight</td>\n", " <td>10.1186/gb-2011-12-5-r50</td>\n", " <td>ERP021896</td>\n", " <td>...</td>\n", " <td>4.244831</td>\n", " <td>13.631804</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>550.L1S10.s.1.sequence</td>\n", " <td>ACAGACCACTCA</td>\n", " <td>GTGCCAGCMGCCGCGGTAA</td>\n", " <td>sample_2 stool</td>\n", " <td>F4</td>\n", " <td>550</td>\n", " <td>Moving pictures of the human microbiome</td>\n", " <td>Rob Knight</td>\n", " <td>10.1186/gb-2011-12-5-r50</td>\n", " <td>ERP021896</td>\n", " <td>...</td>\n", " <td>3.027416</td>\n", " <td>9.425835</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>550.L1S100.s.1.sequence</td>\n", " <td>ATGCACTGGCGA</td>\n", " <td>GTGCCAGCMGCCGCGGTAA</td>\n", " <td>sample_3 stool</td>\n", " <td>F4</td>\n", " <td>550</td>\n", " <td>Moving pictures of the human microbiome</td>\n", " <td>Rob Knight</td>\n", " <td>10.1186/gb-2011-12-5-r50</td>\n", " <td>ERP021896</td>\n", " <td>...</td>\n", " <td>3.196420</td>\n", " <td>10.491161</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>550.L1S101.s.1.sequence</td>\n", " <td>ATTATCGTGCAC</td>\n", " <td>GTGCCAGCMGCCGCGGTAA</td>\n", " <td>sample_4 stool</td>\n", " <td>F4</td>\n", " <td>550</td>\n", " <td>Moving pictures of the human microbiome</td>\n", " <td>Rob Knight</td>\n", " <td>10.1186/gb-2011-12-5-r50</td>\n", " <td>ERP021896</td>\n", " <td>...</td>\n", " <td>3.714719</td>\n", " <td>11.384689</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>550.L1S102.s.1.sequence</td>\n", " <td>CACGACAGGCTA</td>\n", " <td>GTGCCAGCMGCCGCGGTAA</td>\n", " <td>sample_5 stool</td>\n", " <td>F4</td>\n", " <td>550</td>\n", " <td>Moving pictures of the human microbiome</td>\n", " <td>Rob Knight</td>\n", " <td>10.1186/gb-2011-12-5-r50</td>\n", " <td>ERP021896</td>\n", " <td>...</td>\n", " <td>3.969038</td>\n", " <td>15.162691</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 76 columns</p>\n", "</div>" ], "text/plain": [ " #SampleID BarcodeSequence LinkerPrimerSequence \\\n", "0 550.L1S1.s.1.sequence AACGCACGCTAG GTGCCAGCMGCCGCGGTAA \n", "1 550.L1S10.s.1.sequence ACAGACCACTCA GTGCCAGCMGCCGCGGTAA \n", "2 550.L1S100.s.1.sequence ATGCACTGGCGA GTGCCAGCMGCCGCGGTAA \n", "3 550.L1S101.s.1.sequence ATTATCGTGCAC GTGCCAGCMGCCGCGGTAA \n", "4 550.L1S102.s.1.sequence CACGACAGGCTA GTGCCAGCMGCCGCGGTAA \n", "\n", " Description host_subject_id study_id \\\n", "0 sample_1 stool F4 550 \n", "1 sample_2 stool F4 550 \n", "2 sample_3 stool F4 550 \n", "3 sample_4 stool F4 550 \n", "4 sample_5 stool F4 550 \n", "\n", " title principal_investigator \\\n", "0 Moving pictures of the human microbiome Rob Knight \n", "1 Moving pictures of the human microbiome Rob Knight \n", "2 Moving pictures of the human microbiome Rob Knight \n", "3 Moving pictures of the human microbiome Rob Knight \n", "4 Moving pictures of the human microbiome Rob Knight \n", "\n", " doi ebi_accession ... adiv_shannon \\\n", "0 10.1186/gb-2011-12-5-r50 ERP021896 ... 4.244831 \n", "1 10.1186/gb-2011-12-5-r50 ERP021896 ... 3.027416 \n", "2 10.1186/gb-2011-12-5-r50 ERP021896 ... 3.196420 \n", "3 10.1186/gb-2011-12-5-r50 ERP021896 ... 3.714719 \n", "4 10.1186/gb-2011-12-5-r50 ERP021896 ... 3.969038 \n", "\n", " adiv_faith_pd temperature_deg_c ph salinity_psu oxygen_mg_per_l \\\n", "0 13.631804 NaN NaN NaN NaN \n", "1 9.425835 NaN NaN NaN NaN \n", "2 10.491161 NaN NaN NaN NaN \n", "3 11.384689 NaN NaN NaN NaN \n", "4 15.162691 NaN NaN NaN NaN \n", "\n", " phosphate_umol_per_l ammonium_umol_per_l nitrate_umol_per_l \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " sulfate_umol_per_l \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "\n", "[5 rows x 76 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load up metadata map\n", "\n", "metadata_fp = '../../../data/mapping-files/emp_qiime_mapping_qc_filtered.tsv'\n", "\n", "metadata = pd.read_csv(metadata_fp, header=0, sep='\\t')\n", "\n", "metadata.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['#SampleID', 'BarcodeSequence', 'LinkerPrimerSequence', 'Description',\n", " 'host_subject_id', 'study_id', 'title', 'principal_investigator', 'doi',\n", " 'ebi_accession', 'target_gene', 'target_subfragment', 'pcr_primers',\n", " 'illumina_technology', 'extraction_center', 'run_center', 'run_date',\n", " 'read_length_bp', 'sequences_split_libraries',\n", " 'observations_closed_ref_greengenes', 'observations_closed_ref_silva',\n", " 'observations_open_ref_greengenes', 'observations_deblur_90bp',\n", " 'observations_deblur_100bp', 'observations_deblur_150bp',\n", " 'emp_release1', 'qc_filtered', 'subset_10k', 'subset_5k', 'subset_2k',\n", " 'sample_taxid', 'sample_scientific_name', 'host_taxid',\n", " 'host_common_name_provided', 'host_common_name', 'host_scientific_name',\n", " 'host_superkingdom', 'host_kingdom', 'host_phylum', 'host_class',\n", " 'host_order', 'host_family', 'host_genus', 'host_species',\n", " 'collection_timestamp', 'country', 'latitude_deg', 'longitude_deg',\n", " 'depth_m', 'altitude_m', 'elevation_m', 'env_biome', 'env_feature',\n", " 'env_material', 'envo_biome_0', 'envo_biome_1', 'envo_biome_2',\n", " 'envo_biome_3', 'envo_biome_4', 'envo_biome_5', 'empo_0', 'empo_1',\n", " 'empo_2', 'empo_3', 'adiv_observed_otus', 'adiv_chao1', 'adiv_shannon',\n", " 'adiv_faith_pd', 'temperature_deg_c', 'ph', 'salinity_psu',\n", " 'oxygen_mg_per_l', 'phosphate_umol_per_l', 'ammonium_umol_per_l',\n", " 'nitrate_umol_per_l', 'sulfate_umol_per_l'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "metadata.columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>#SampleID</th>\n", " <th>empo_0</th>\n", " <th>empo_1</th>\n", " <th>empo_2</th>\n", " <th>empo_3</th>\n", " <th>latitude_deg</th>\n", " <th>longitude_deg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>550.L1S1.s.1.sequence</td>\n", " <td>EMP sample</td>\n", " <td>Host-associated</td>\n", " <td>Animal</td>\n", " <td>Animal distal gut</td>\n", " <td>40.015</td>\n", " <td>-105.271</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>550.L1S10.s.1.sequence</td>\n", " <td>EMP sample</td>\n", " <td>Host-associated</td>\n", " <td>Animal</td>\n", " <td>Animal distal gut</td>\n", " <td>40.015</td>\n", " <td>-105.271</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>550.L1S100.s.1.sequence</td>\n", " <td>EMP sample</td>\n", " <td>Host-associated</td>\n", " <td>Animal</td>\n", " <td>Animal distal gut</td>\n", " <td>40.015</td>\n", " <td>-105.271</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>550.L1S101.s.1.sequence</td>\n", " <td>EMP sample</td>\n", " <td>Host-associated</td>\n", " <td>Animal</td>\n", " <td>Animal distal gut</td>\n", " <td>40.015</td>\n", " <td>-105.271</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>550.L1S102.s.1.sequence</td>\n", " <td>EMP sample</td>\n", " <td>Host-associated</td>\n", " <td>Animal</td>\n", " <td>Animal distal gut</td>\n", " <td>40.015</td>\n", " <td>-105.271</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " #SampleID empo_0 empo_1 empo_2 \\\n", "0 550.L1S1.s.1.sequence EMP sample Host-associated Animal \n", "1 550.L1S10.s.1.sequence EMP sample Host-associated Animal \n", "2 550.L1S100.s.1.sequence EMP sample Host-associated Animal \n", "3 550.L1S101.s.1.sequence EMP sample Host-associated Animal \n", "4 550.L1S102.s.1.sequence EMP sample Host-associated Animal \n", "\n", " empo_3 latitude_deg longitude_deg \n", "0 Animal distal gut 40.015 -105.271 \n", "1 Animal distal gut 40.015 -105.271 \n", "2 Animal distal gut 40.015 -105.271 \n", "3 Animal distal gut 40.015 -105.271 \n", "4 Animal distal gut 40.015 -105.271 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# take just the columns we need for this figure panel\n", "\n", "fig1ab = metadata.loc[:,['#SampleID','empo_0','empo_1','empo_2','empo_3','latitude_deg','longitude_deg']]\n", "fig1ab.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Write to Excel notebook" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fig1 = pd.ExcelWriter('Figure1_data.xlsx')\n", "\n", "fig1ab.to_excel(fig1,'Fig-1ab')\n", "\n", "fig1.save()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
tiegz/ThreatExchange	ipynb/ThreatExchange Data Dashboard.ipynb	7	16310	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ThreatExchange Data Dashboard\n", "\n", "Purpose\n", " \n", "The ThreatExchange APIs are designed to make consuming threat intelligence from multiple sources easy. This notebook will walk you through:\n", "\n", " - building an initial dashboard for assessing the data visible to your appID;\n", " - filtering down to a subset you consider high value; and\n", " - exporting the high value data to a file.\n", "\n", "What you need\n", "\n", "Before getting started, you'll need a few Python packages installed:\n", "\n", " - [Pandas](http://pandas.pydata.org/) for data manipulation and analysis\n", " - [Pytx](https://pytx.readthedocs.org/en/latest/installation.html) for ThreatExchange access\n", " - [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/) for making charts pretty\n", "\n", "All of the python packages mentioned can be installed via \n", "\n", "```\n", "pip install <package_name>\n", "```\n", "\n", "### Setup a ThreatExchange `access_token`\n", "\n", "If you don't already have an `access_token` for your app, use the [Facebook Access Token Tool]( https://developers.facebook.com/tools/accesstoken/) to get one." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pytx.access_token import access_token\n", "from pytx.logger import setup_logger\n", "from pytx.vocabulary import PrivacyType as pt\n", "\n", "# Specify the location of your token via one of several ways:\n", "# https://pytx.readthedocs.org/en/latest/pytx.access_token.html\n", "access_token()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optionally, enable debug level logging" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Uncomment this if you want debug logging enabled\n", "#setup_logger(log_file=\"pytx.log\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Search for data in ThreatExchange\n", "\n", "Start by running a query against the ThreatExchange APIs to pull down any/all data relevant to you over a specified period of days." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Our basic search parameters, we default to querying over the past 14 days\n", "days_back = 14\n", "search_terms = ['abuse', 'phishing', 'malware', 'exploit', 'apt', 'ddos', 'brute', 'scan', 'cve']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we execute the query using our search parameters and put the results in a Pandas `DataFrame`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from datetime import datetime, timedelta\n", "from time import strftime\n", "import pandas as pd\n", "import re\n", "\n", "from pytx import ThreatDescriptor\n", "from pytx.vocabulary import ThreatExchange as te\n", "\n", "# Define your search string and other params, see \n", "# https://pytx.readthedocs.org/en/latest/pytx.common.html#pytx.common.Common.objects\n", "# for the full list of options\n", "search_params = {\n", " te.FIELDS: ThreatDescriptor._default_fields,\n", " te.LIMIT: 1000,\n", " te.SINCE: strftime('%Y-%m-%d %H:%m:%S +0000', (datetime.utcnow() + timedelta(days=(-1days_back))).timetuple()),\n", " te.TEXT: search_terms,\n", " te.UNTIL: strftime('%Y-%m-%d %H:%m:%S +0000', datetime.utcnow().timetuple()),\n", " te.STRICT_TEXT: False\n", "}\n", "\n", "data_frame = None\n", "for search_term in search_terms:\n", " print \"Searching for '%s' over -%d days\" % (search_term, days_back)\n", " results = ThreatDescriptor.objects(\n", " fields=search_params[te.FIELDS],\n", " limit=search_params[te.LIMIT],\n", " text=search_term, \n", " since=search_params[te.SINCE], \n", " until=search_params[te.UNTIL],\n", " strict_text=search_params[te.STRICT_TEXT]\n", " )\n", " tmp = pd.DataFrame([result.to_dict() for result in results])\n", " tmp['search_term'] = search_term\n", " print \"\\t... found %d descriptors\" % tmp.size\n", " if data_frame is None:\n", " data_frame = tmp\n", " else:\n", " data_frame = data_frame.append(tmp)\n", " \n", "print \"\\nFound %d descriptors in total.\" % data_frame.size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do some data munging for easier analysis and then preview as a sanity check" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from time import mktime\n", "\n", "# Extract a datetime and timestamp, for easier analysis\n", "data_frame['ds'] = pd.to_datetime(data_frame.added_on.str[0:10], format='%Y-%m-%d')\n", "data_frame['ts'] = pd.to_datetime(data_frame.added_on)\n", "\n", "# Extract the owner data\n", "owner = data_frame.pop('owner')\n", "owner = owner.apply(pd.Series)\n", "data_frame = pd.concat([data_frame, owner.email, owner.name], axis=1)\n", "\n", "# Extract freeform 'tags' in the description\n", "def extract_tags(text):\n", " return re.findall(r'\\[([a-zA-Z0-9\\:\\-\\_]+)\\]', text)\n", "data_frame['tags'] = data_frame.description.map(lambda x: [] if x is None else extract_tags(x))\n", "\n", "data_frame.head(n=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a Dashboard to Get a High-level View\n", "\n", "The raw data is great, but it would be much better if we could take a higher level view of the data. This dashboard will provide more insight into:\n", "\n", " - what data is available\n", " - who's sharing it\n", " - how is labeled\n", " - how much of it is likely to be directly applicable for alerting" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import math\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from pytx.vocabulary import ThreatDescriptor as td\n", "\n", "%matplotlib inline\n", "\n", "# Setup subplots for our dashboard\n", "fig, axes = plt.subplots(nrows=4, ncols=2, figsize=(16,32))\n", "axes[0,0].set_color_cycle(sns.color_palette(\"coolwarm_r\", 15))\n", "\n", "# Plot by Type over time\n", "type_over_time = data_frame.groupby(\n", " [pd.Grouper(freq='d', key='ds'), te.TYPE]\n", " ).count().unstack(te.TYPE)\n", "type_over_time.added_on.plot(\n", " kind='line', \n", " stacked=True, \n", " title=\"Indicator Types Per Day (-\" + str(days_back) + \"d)\",\n", " ax=axes[0,0]\n", ")\n", "\n", "# Plot by threat_type over time\n", "tt_over_time = data_frame.groupby(\n", " [pd.Grouper(freq='w', key='ds'), 'threat_type']\n", " ).count().unstack('threat_type')\n", "tt_over_time.added_on.plot(\n", " kind='bar', \n", " stacked=True, \n", " title=\"Threat Types Per Week (-\" + str(days_back) + \"d)\",\n", " ax=axes[0,1]\n", ")\n", "\n", "# Plot the top 10 tags\n", "tags = pd.DataFrame([item for sublist in data_frame.tags for item in sublist])\n", "tags[0].value_counts().head(10).plot(\n", " kind='bar', \n", " stacked=True,\n", " title=\"Top 10 Tags (-\" + str(days_back) + \"d)\",\n", " ax=axes[1,0]\n", ")\n", "\n", "# Plot by who is sharing\n", "owner_over_time = data_frame.groupby(\n", " [pd.Grouper(freq='w', key='ds'), 'name']\n", " ).count().unstack('name')\n", "owner_over_time.added_on.plot(\n", " kind='bar', \n", " stacked=True, \n", " title=\"Who's Sharing Each Week? (-\" + str(days_back) + \"d)\",\n", " ax=axes[1,1]\n", ")\n", "\n", "# Plot the data as a timeseries of when it was published\n", "data_over_time = data_frame.groupby(pd.Grouper(freq='6H', key='ts')).count()\n", "data_over_time.added_on.plot(\n", " kind='line',\n", " title=\"Data shared over time (-\" + str(days_back) + \"d)\",\n", " ax=axes[2,0]\n", ")\n", "\n", "# Plot by status label\n", "data_frame.status.value_counts().plot(\n", " kind='pie', \n", " title=\"Threat Statuses (-\" + str(days_back) + \"d)\",\n", " ax=axes[2,1]\n", ")\n", "\n", "# Heatmap by type / source\n", "owner_and_type = pd.DataFrame(data_frame[['name', 'type']])\n", "owner_and_type['n'] = 1\n", "grouped = owner_and_type.groupby(['name', 'type']).count().unstack('type').fillna(0)\n", "ax = sns.heatmap(\n", " data=grouped['n'], \n", " robust=True,\n", " cmap=\"YlGnBu\",\n", " ax=axes[3,0]\n", ")\n", "\n", "# These require a little data munging\n", "# translate a severity enum to a value\n", "# TODO Add this translation to Pytx\n", "def severity_value(severity):\n", " if severity == 'UNKNOWN': return 0\n", " elif severity == 'INFO': return 1\n", " elif severity == 'WARNING': return 3\n", " elif severity == 'SUSPICIOUS': return 5\n", " elif severity == 'SEVERE': return 7\n", " elif severity == 'APOCALYPSE': return 10\n", " return 0\n", "# translate a severity \n", "def value_severity(severity):\n", " if severity >= 9: return 'APOCALYPSE'\n", " elif severity >= 6: return 'SEVERE'\n", " elif severity >= 4: return 'SUSPICIOUS'\n", " elif severity >= 2: return 'WARNING'\n", " elif severity >= 1: return 'INFO'\n", " elif severity >= 0: return 'UNKNOWN'\n", "\n", "# Plot by how actionable the data is \n", "# Build a special dataframe and chart it\n", "data_frame['severity_value'] = data_frame.severity.apply(severity_value)\n", "df2 = pd.DataFrame({'count' : data_frame.groupby(['name', 'confidence', 'severity_value']).size()}).reset_index()\n", "ax = df2.plot(\n", " kind='scatter', \n", " x='severity_value', y='confidence', \n", " xlim=(-1,11), ylim=(-10,110), \n", " title='Data by Conf / Sev With Threshold Line',\n", " ax=axes[3,1],\n", " s=df2['count'].apply(lambda x: 1000 math.log10(x)),\n", " use_index=td.SEVERITY\n", ")\n", "# Draw a threshhold for data we consider likely using for alerts (aka 'high value')\n", "ax.plot([2,10], [100,0], c='red')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dive A Little Deeper\n", "\n", "Take a subset of the data and understand it a little more. \n", "\n", "In this example, we presume that we'd like to take phishing related data and study it, to see if we can use it to better defend a corporate network or abuse in a product. \n", "\n", "As a simple example, we'll filter down to data labeled `MALICIOUS` and the word `phish` in the description, to see if we can make a more detailed conclusion on how to apply the data to our existing internal workflows." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pytx.vocabulary import Status as s\n", "\n", "\n", "phish_data = data_frame[(data_frame.status == s.MALICIOUS) \n", " & data_frame.description.apply(lambda x: x.find('phish') if x != None else False)]\n", "# TODO: also filter for attack_type == PHISHING, when Pytx supports it\n", "\n", "%matplotlib inline\n", "\n", "# Setup subplots for our deeper dive plots\n", "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16,8))\n", "\n", "# Heatmap of type / source\n", "owner_and_type = pd.DataFrame(phish_data[['name', 'type']])\n", "owner_and_type['n'] = 1\n", "grouped = owner_and_type.groupby(['name', 'type']).count().unstack('type').fillna(0)\n", "ax = sns.heatmap(\n", " data=grouped['n'], \n", " robust=True,\n", " cmap=\"YlGnBu\",\n", " ax=axes[0]\n", ")\n", "\n", "# Tag breakdown of the top 10 tags\n", "tags = pd.DataFrame([item for sublist in phish_data.tags for item in sublist])\n", "tags[0].value_counts().head(10).plot(\n", " kind='pie',\n", " title=\"Top 10 Tags (-\" + str(days_back) + \"d)\",\n", " ax=axes[1]\n", ")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extract The High Confidence / Severity Data For Use\n", "\n", "With a better understanding of the data, let's filter the `MALICIOUS`, `REVIEWED_MANUALLY` labeled data down to a pre-determined threshold for confidence + severity. \n", "\n", "You can add more filters, or change the threshold, as you see fit." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pytx.vocabulary import ReviewStatus as rs\n", "\n", "# define our threshold line, which is the same as the red, threshold line in the chart above\n", "sev_min = 2\n", "sev_max = 10\n", "conf_min= 0\n", "conf_max = 100\n", "\n", "# build a new series, to indicate if a row passes our confidence + severity threshold\n", "def is_high_value(conf, sev):\n", " return (((sev_max - sev_min) * (conf - conf_max)) - ((conf_min - conf_max) * (sev - sev_min))) > 0\n", "data_frame['is_high_value']= data_frame.apply(lambda x: is_high_value(x.confidence, x.severity_value), axis=1)\n", "\n", "# filter down to just the data passing our criteria, you can add more here to filter by type, source, etc.\n", "high_value_data = data_frame[data_frame.is_high_value \n", " & (data_frame.status == s.MALICIOUS)\n", " & (data_frame.review_status == rs.REVIEWED_MANUALLY)].reset_index(drop=True)\n", "\n", "# get a count of how much we kept\n", "print \"Kept %d of %d data as high value\" % (high_value_data.size, data_frame.size)\n", "\n", "# ... and preview it\n", "high_value_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, output all of the high value data to a file as CSV or JSON, for consumption in our other systems and workflows." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "use_csv = False\n", "\n", "if use_csv:\n", " file_name = 'threat_exchange_high_value.csv'\n", " high_value_data.to_csv(path_or_buf=file_name)\n", " print \"CSV data written to %s\" % file_name\n", "else:\n", " file_name = 'threat_exchange_high_value.json'\n", " high_value_data.to_json(path_or_buf=file_name, orient='index')\n", " print \"JSON data written to %s\" % file_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-3-clause
nkmk/python-snippets	notebook/regex_method_basic.ipynb	1	7713	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# https://docs.python.jp/3/howto/regex.html\n", "# https://docs.python.jp/3/library/re.html" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import re" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = 'one two one two'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_sre.SRE_Match object; span=(0, 3), match='one'>\n" ] } ], "source": [ "m = re.match('one', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "one\n", "0\n", "3\n", "(0, 3)\n" ] } ], "source": [ "print(m.group())\n", "print(m.start())\n", "print(m.end())\n", "print(m.span())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_sre.SRE_Match object; span=(0, 7), match='one two'>\n", "one two\n" ] } ], "source": [ "m = re.match('one two', s)\n", "print(m)\n", "print(m.group())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_sre.SRE_Match object; span=(0, 7), match='one two'>\n", "one two\n", "('one', 'two')\n" ] } ], "source": [ "m = re.match('(one) (two)', s)\n", "print(m)\n", "print(m.group())\n", "print(m.groups())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "m = re.match('two', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_sre.SRE_Match object; span=(0, 3), match='one'>\n" ] } ], "source": [ "m = re.search('one', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_sre.SRE_Match object; span=(4, 7), match='two'>\n" ] } ], "source": [ "m = re.search('two', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['one', 'one']\n" ] } ], "source": [ "m = re.findall('one', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['one two', 'one two']\n" ] } ], "source": [ "m = re.findall('one two', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('one', 'two'), ('one', 'two')]\n" ] } ], "source": [ "m = re.findall('(one) (two)', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<callable_iterator object at 0x10e786470>\n" ] } ], "source": [ "m = re.finditer('one', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_sre.SRE_Match object; span=(0, 3), match='one'>\n", "<_sre.SRE_Match object; span=(8, 11), match='one'>\n" ] } ], "source": [ "for match in m:\n", " print(match)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ONE two ONE two\n" ] } ], "source": [ "m = re.sub('one', 'ONE', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "xxx xxx\n" ] } ], "source": [ "m = re.sub('one two', 'xxx', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "oneXtwo oneXtwo\n" ] } ], "source": [ "m = re.sub('(one) (two)', '\\\\1X\\\\2', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "oneXtwo oneXtwo\n" ] } ], "source": [ "m = re.sub('(one) (two)', r'\\1X\\2', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('ONE two ONE two', 2)\n" ] } ], "source": [ "m = re.subn('one', 'ONE', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['one', 'two', 'one', 'two']\n" ] } ], "source": [ "m = re.split(' ', s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p = re.compile('one')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_sre.SRE_Match object; span=(0, 3), match='one'>\n" ] } ], "source": [ "m = p.match(s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['one', 'one']\n" ] } ], "source": [ "m = p.findall(s)\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ONE two ONE two\n" ] } ], "source": [ "m = p.sub('ONE', s)\n", "print(m)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
paulrozdeba/varanneal	examples/nnet_barimages/data/BarDataGeneration.ipynb	1	71802	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import scipy as sp\n", "from matplotlib.pyplot import imshow\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from PIL import Image" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#These arrays define the 'basis' of the angled bar set. All angled bars here are either h_i (at zero or 180 degrees)\n", "#d1_i (pi/4 or 5pi/4), v_i, (+- pi/2), or d2_i (3pi/4 or -pi/4) where the angle is measured from the left horizontal\n", "\n", "#All data will be generated by adding noise to the image such that the values of the pixels are no longer either zero or one\n", "\n", "v1 = np.array([[0,0,1,0,0],[0,0,1,0,0],[0,0,1,0,0],[0,0,1,0,0],[0,0,1,0,0]])\n", "v2 = np.array([[0,0,0,1,0],[0,0,0,1,0],[0,0,0,1,0],[0,0,0,1,0],[0,0,0,1,0]])\n", "v3 = np.array([[0,0,0,0,1],[0,0,0,0,1],[0,0,0,0,1],[0,0,0,0,1],[0,0,0,0,1]])\n", "v4 = np.array([[0,1,0,0,0],[0,1,0,0,0],[0,1,0,0,0],[0,1,0,0,0],[0,1,0,0,0]])\n", "v5 = np.array([[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0],[1,0,0,0,0]])\n", "h1 = np.array([[0,0,0,0,0],[1,1,1,1,1],[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]])\n", "h2 = np.array([[1,1,1,1,1],[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]])\n", "h3 = np.array([[0,0,0,0,0],[0,0,0,0,0],[1,1,1,1,1],[0,0,0,0,0],[0,0,0,0,0]])\n", "h4 = np.array([[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0],[1,1,1,1,1],[0,0,0,0,0]])\n", "h5 = np.array([[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0],[1,1,1,1,1]])\n", "d11 = np.array([[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0],[1,0,0,0,0],[0,1,0,0,0]])\n", "d12 = np.array([[0,0,0,0,0],[0,0,0,0,0],[1,0,0,0,0],[0,1,0,0,0],[0,0,1,0,0]])\n", "d13 = np.array([[0,0,0,0,0],[1,0,0,0,0],[0,1,0,0,0],[0,0,1,0,0],[0,0,0,1,0]])\n", "d14 = np.array([[1,0,0,0,0],[0,1,0,0,0],[0,0,1,0,0],[0,0,0,1,0],[0,0,0,0,1]])\n", "d15 = np.array([[0,1,0,0,0],[0,0,1,0,0],[0,0,0,1,0],[0,0,0,0,1],[0,0,0,0,0]])\n", "d16 = np.array([[0,0,1,0,0],[0,0,0,1,0],[0,0,0,0,1],[0,0,0,0,0],[0,0,0,0,0]])\n", "d17 = np.array([[0,0,0,1,0],[0,0,0,0,1],[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]])\n", "d21 = np.fliplr(d11)\n", "d22 = np.fliplr(d12)\n", "d23 = np.fliplr(d13)\n", "d24 = np.fliplr(d14)\n", "d25 = np.fliplr(d15)\n", "d26 = np.fliplr(d16)\n", "d27 = np.fliplr(d17)\n", "\n", "size = (5,5)" ] }, { "cell_type": "code", "execution_count": 213, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#generates centered data\n", "for i in range(2000):\n", " datah3 = h3 + 0.35(0.5-np.random.random_sample(size))\n", " datav1 = v1 + 0.35(0.5-np.random.random_sample(size))\n", " datad14 = d14 + 0.35(0.5-np.random.random_sample(size))\n", " datad24 = d24 + 0.35(0.5-np.random.random_sample(size))\n", " with open(\"datacentered.txt\", \"ab\") as mydata:\n", " np.savetxt(mydata,[datah3.flatten()] )\n", " np.savetxt(mydata,[datav1.flatten()] )\n", " np.savetxt(mydata,[datad14.flatten()] )\n", " np.savetxt(mydata,[datad24.flatten()])\n", " with open(\"labelcentered.txt\", \"ab\") as mylabel:\n", " np.savetxt(mylabel,[np.array([1,0,0,0])])\n", " np.savetxt(mylabel,[np.array([0,0,1,0])])\n", " np.savetxt(mylabel,[np.array([0,1,0,0])])\n", " np.savetxt(mylabel,[np.array([0,0,0,1])])\n", " \n" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7efe3bc57a90>" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPcAAAD7CAYAAAC2TgIoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKOIGQhE3EIq4gVDEDYT6D/znNvjjXTjvAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7efe3bdcb438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "imshow(d27,interpolation = 'none',cmap='gray')" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPcAAAD7CAYAAAC2TgIoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDSRF3EBSxA0kRdxAUsQNJPV/Jb0yeE3x+vMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7efe3b55b048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = imshow(d27+ 0.35(0.5-np.random.random_sample(size)), interpolation = 'none',cmap = 'gray')" ] }, { "cell_type": "code", "execution_count": 161, "metadata": {}, "outputs": [], "source": [ "#generates all types of data\n", "for i in range(2000):\n", " datah1 = h1 + 0.35(0.5-np.random.random_sample(size))\n", " datah2 = h2 + 0.35(0.5-np.random.random_sample(size))\n", " datah3 = h3 + 0.35(0.5-np.random.random_sample(size))\n", " datah4 = h4 + 0.35(0.5-np.random.random_sample(size))\n", " datah5 = h5 + 0.35(0.5-np.random.random_sample(size))\n", " datav1 = v1 + 0.35(0.5-np.random.random_sample(size))\n", " datav2 = v2 + 0.35(0.5-np.random.random_sample(size))\n", " datav3 = v3 + 0.35(0.5-np.random.random_sample(size))\n", " datav4 = v4 + 0.35(0.5-np.random.random_sample(size))\n", " datav5 = v5 + 0.35(0.5-np.random.random_sample(size))\n", " datad11 = d11 + 0.35(0.5-np.random.random_sample(size))\n", " datad12 = d12 + 0.35(0.5-np.random.random_sample(size))\n", " datad13 = d13 + 0.35(0.5-np.random.random_sample(size))\n", " datad14 = d14 + 0.35(0.5-np.random.random_sample(size))\n", " datad15 = d15 + 0.35(0.5-np.random.random_sample(size))\n", " datad16 = d16 + 0.35(0.5-np.random.random_sample(size))\n", " datad17 = d17 + 0.35(0.5-np.random.random_sample(size))\n", " datad21 = d21 + 0.35(0.5-np.random.random_sample(size))\n", " datad22 = d22 + 0.35(0.5-np.random.random_sample(size))\n", " datad23 = d23 + 0.35(0.5-np.random.random_sample(size))\n", " datad24 = d24 + 0.35(0.5-np.random.random_sample(size))\n", " datad25 = d25 + 0.35(0.5-np.random.random_sample(size))\n", " datad26 = d26 + 0.35(0.5-np.random.random_sample(size))\n", " datad27 = d27 + 0.35*(0.5-np.random.random_sample(size))\n", " with open(\"data.txt\", \"ab\") as mydata:\n", " np.savetxt(mydata,[datah1.flatten()] )\n", " np.savetxt(mydata,[datah2.flatten()] )\n", " np.savetxt(mydata,[datah3.flatten()] )\n", " np.savetxt(mydata,[datah4.flatten()] )\n", " np.savetxt(mydata,[datah5.flatten()] )\n", " np.savetxt(mydata,[datav1.flatten()] )\n", " np.savetxt(mydata,[datav2.flatten()] )\n", " np.savetxt(mydata,[datav3.flatten()] )\n", " np.savetxt(mydata,[datav4.flatten()] )\n", " np.savetxt(mydata,[datav5.flatten()] )\n", " np.savetxt(mydata,[datad11.flatten()] )\n", " np.savetxt(mydata,[datad12.flatten()] )\n", " np.savetxt(mydata,[datad13.flatten()] )\n", " np.savetxt(mydata,[datad14.flatten()] )\n", " np.savetxt(mydata,[datad15.flatten()] )\n", " np.savetxt(mydata,[datad16.flatten()] )\n", " np.savetxt(mydata,[datad17.flatten()] )\n", " np.savetxt(mydata,[datad21.flatten()] )\n", " np.savetxt(mydata,[datad22.flatten()])\n", " np.savetxt(mydata,[datad23.flatten()])\n", " np.savetxt(mydata,[datad24.flatten()])\n", " np.savetxt(mydata,[datad25.flatten()])\n", " np.savetxt(mydata,[datad26.flatten()])\n", " np.savetxt(mydata,[datad27.flatten()])\n", " with open(\"label.txt\", \"ab\") as mylabel:\n", " np.savetxt(mylabel,[np.array([1,0,0,0])])\n", " np.savetxt(mylabel,[np.array([1,0,0,0])])\n", " np.savetxt(mylabel,[np.array([1,0,0,0])])\n", " np.savetxt(mylabel,[np.array([1,0,0,0])])\n", " np.savetxt(mylabel,[np.array([1,0,0,0])])\n", " np.savetxt(mylabel,[np.array([0,0,1,0])])\n", " np.savetxt(mylabel,[np.array([0,0,1,0])])\n", " np.savetxt(mylabel,[np.array([0,0,1,0])])\n", " np.savetxt(mylabel,[np.array([0,0,1,0])])\n", " np.savetxt(mylabel,[np.array([0,0,1,0])])\n", " np.savetxt(mylabel,[np.array([0,1,0,0])])\n", " np.savetxt(mylabel,[np.array([0,1,0,0])])\n", " np.savetxt(mylabel,[np.array([0,1,0,0])])\n", " np.savetxt(mylabel,[np.array([0,1,0,0])])\n", " np.savetxt(mylabel,[np.array([0,1,0,0])])\n", " np.savetxt(mylabel,[np.array([0,1,0,0])])\n", " np.savetxt(mylabel,[np.array([0,1,0,0])])\n", " np.savetxt(mylabel,[np.array([0,0,0,1])])\n", " np.savetxt(mylabel,[np.array([0,0,0,1])])\n", " np.savetxt(mylabel,[np.array([0,0,0,1])])\n", " np.savetxt(mylabel,[np.array([0,0,0,1])])\n", " np.savetxt(mylabel,[np.array([0,0,0,1])])\n", " np.savetxt(mylabel,[np.array([0,0,0,1])])\n", " np.savetxt(mylabel,[np.array([0,0,0,1])])\n", " \n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#loads centered data for testing\n", "datacentered = np.loadtxt('datacentered.txt')\n", "datacentered = np.reshape(datacentered, (datacentered.shape[0],5,5))\n", "label = np.loadtxt('labelcentered.txt')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7564th saved image\n", "horizontal\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe6515cfb00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Tests that labels are correctly assigned in data files\n", "i = np.random.randint(datacentered.shape[0])\n", "imshow(datacentered[i],interpolation='none',cmap = 'gray')\n", "print('{0}th saved image'.format(i))\n", "if (label[i] == [np.array([1,0,0,0])]).all():\n", " print('horizontal')\n", "elif (label[i] == [np.array([0,1,0,0])]).all():\n", " print('first diagonal')\n", "elif (label[i] == [np.array([0,0,1,0])]).all():\n", " print('vertical')\n", "elif (label[i] == [np.array([0,0,0,1])]).all():\n", " print('second diagonal')\n", "else:\n", " print('screwed up somewhere')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1392]\n", "[1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe64ec24780>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[597]\n", "[597, 598, 599, 600, 601, 602, 603, 604, 605, 606]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe64ec58cc0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[6606]\n", "[6606, 6607, 6608, 6609, 6610, 6611, 6612, 6613, 6614, 6615]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe64eba1cf8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[6297]\n", "[6297, 6298, 6299, 6300, 6301, 6302, 6303, 6304, 6305, 6306]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe64e962048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generates random assortment of images from centered data\n", "for w in range(4):\n", " j = np.random.randint(datacentered.shape[0], size = 1)\n", " print(j)\n", " i = [j[0],j[0]+1,j[0]+2,j[0]+3,j[0]+4,j[0]+5,j[0]+6,j[0]+7,j[0]+8,j[0]+9]\n", " print(i)\n", " plt.figure(1)\n", " plt.subplot(2,5,1)\n", " plt.imshow(datacentered[i[0]], interpolation = 'none', cmap = 'gray')\n", "\n", " plt.subplot(2,5,2)\n", " plt.imshow(datacentered[i[1]], interpolation = 'none', cmap = 'gray')\n", "\n", " plt.subplot(2,5,3)\n", " plt.imshow(datacentered[i[2]], interpolation = 'none', cmap = 'gray')\n", "\n", " plt.subplot(2,5,4)\n", " plt.imshow(datacentered[i[3]], interpolation = 'none', cmap = 'gray')\n", "\n", " plt.subplot(2,5,5)\n", " plt.imshow(datacentered[i[4]], interpolation = 'none', cmap = 'gray')\n", "\n", " plt.subplot(2,5,6)\n", " plt.imshow(datacentered[i[5]], interpolation = 'none', cmap = 'gray')\n", "\n", " plt.subplot(2,5,7)\n", " plt.imshow(datacentered[i[6]], interpolation = 'none', cmap = 'gray')\n", "\n", " plt.subplot(2,5,8)\n", " plt.imshow(datacentered[i[7]], interpolation = 'none', cmap = 'gray')\n", "\n", " plt.subplot(2,5,9)\n", " plt.imshow(datacentered[i[8]], interpolation = 'none', cmap = 'gray')\n", "\n", " plt.subplot(2,5,10)\n", " plt.imshow(datacentered[i[9]], interpolation = 'none', cmap = 'gray')\n", " plt.savefig('figure_examples_centered{}.jpg'.format(w))\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
kootsoop/DSP.SE	Python/Q59010.ipynb	1	6750	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "from PIL import Image\n", " \n", "img = Image.new('RGB', (1278, 1024), color = 'black')\n", "imgplot = plt.imshow(img)\n", "x = 500\n", "y = 600\n", "r1 = 100\n", "r2 = 200\n", "circ = Circle((x,y),r2)\n", "ax.add_patch(circ)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
Ykharo/notebooks	C elemental, querido Cython..ipynb	2	252324	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cython, que no CPython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No, no nos hemos equivocado en el título, hoy vamos a hablar de Cython.\n", "\n", "¿Qué es Cython?\n", "\n", "Cython son dos cosas:\n", "\n", "* Por una parte, Cython es un lenguaje de programación (un superconjunto de Python) que une Python con el sistema de tipado estático de C y C++.\n", "* Por otra parte, `cython` es un compilador que traduce codigo fuente escrito en Cython en eficiente código C o C++. El código resultante se podría usar como una extensión Python o como un ejecutable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "¡Guau! ¿Cómo os habéis quedado?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lo que se pretende es, básicamente, aprovechar las fortalezas de Python y C, combinar una sintaxis sencilla con el poder y la velocidad." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Salvando algunas [excepciones](http://docs.cython.org/src/userguide/limitations.html#cython-limitations), el código Python (tanto Python 2 como Python 3) es código Cython válido. Además, Cython añade una serie de palabras clave para poder usar el sistema de tipado de C con Python y que el compilador `cython` pueda generar código C eficiente." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pero, ¿quién usa Cython?\n", "\n", "Pues mira, igual no lo sabes pero seguramente estés usando Cython todos los días. Sage tiene casi medio millón de líneas de Cython (que se dice pronto), Scipy y Pandas más de 20000, scikit-learn unas 15000,..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ¿Nos empezamos a meter en harina?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La idea principal de este primer acercamiento a Cython será empezar con un código Python que sea nuestro cuello de botella e iremos creando versiones que sean cada vez más rápidas, o eso intentaremos." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Por ejemplo, imaginemos que tenemos que detectar valores mínimos locales dentro de una malla. Los valores mínimos deberán ser simplemente valores más bajos que los que haya en los 8 nodos de su entorno inmediato. En el siguiente gráfico, el nodo en verde será un nodo con un mínimo y en su entorno son todo valores superiores:\n", "\n", "<table>\n", " <tr>\n", " <td style=\"background:red\">(2, 0)</td>\n", " <td style=\"background:red\">(2, 1)</td>\n", " <td style=\"background:red\">(2, 2)</td>\n", " </tr>\n", " <tr>\n", " <td style=\"background:red\">(1, 0)</td>\n", " <td style=\"background:green\">(1. 1)</td>\n", " <td style=\"background:red\">(1, 2)</td>\n", " </tr>\n", " <tr>\n", " <td style=\"background:red\">(0, 0)</td>\n", " <td style=\"background:red\">(0, 1)</td>\n", " <td style=\"background:red\">(0, 2)</td>\n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[INCISO] Los números y porcentajes que veáis a continuación pueden variar levemente dependiendo de la máquina donde se ejecute. Tomad los valores como aproximativos." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como siempre, importamos algunas librerías antes de empezar a picar código:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creamos una matriz cuadrada relativamente grande (4 millones de elementos)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(0)\n", "data = np.random.randn(2000, 2000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ya tenemos los datos listos para empezar a trabajar. \n", "\n", "Vamos a crear una función en Python que busque los mínimos tal como los hemos definido." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def busca_min(malla):\n", " minimosx = []\n", " minimosy = []\n", " for i in range(1, malla.shape[1]-1):\n", " for j in range(1, malla.shape[0]-1):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Veamos cuanto tarda esta función en mi máquina:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 3.63 s per loop\n" ] } ], "source": [ "%timeit busca_min(data)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Buff, tres segundos y pico en un i7... Si tengo que buscar los mínimos en 500 de estos casos me va a tardar casi media hora." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Por casualidad, vamos a probar numba a ver si es capaz de resolver el problema sin mucho esfuerzo, es código Python muy sencillo en el cual no usamos cosas muy 'extrañas' del lenguaje." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numba import jit\n", "\n", "@jit\n", "def busca_min_numba(malla):\n", " minimosx = []\n", " minimosy = []\n", " for i in range(1, malla.shape[1]-1):\n", " for j in range(1, malla.shape[0]-1):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 4.97 s per loop\n" ] } ], "source": [ "%timeit busca_min_numba(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ooooops! Parece que la magia de numba no funciona aquí." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a especificar los tipos de entrada y de salida (y a modificar el output) a ver si mejora algo:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numba import jit\n", "from numba import int32, float64\n", "\n", "@jit(int32[:,:](float64[:,:]))\n", "def busca_min_numba(malla):\n", " minimosx = []\n", " minimosy = []\n", " for i in range(1, malla.shape[1]-1):\n", " for j in range(1, malla.shape[0]-1):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array([minimosx, minimosy], dtype = np.int32)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 5.25 s per loop\n" ] } ], "source": [ "%timeit busca_min_numba(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pues parece que no, el resultado es del mismo pelo. Usando la opción `nopython` me casca un error un poco feo,... \n", "\n", "Habrá que seguir esperando a que numba esté un poco más maduro. En mis pocas experiencias no he conseguido aun el efecto que buscaba y en la mayoría de los casos obtengo errores muy crípticos. No es que no tenga confianza en la gente que está detrás, solo estoy diciendo que aun no está listo para 'producción'. Esto no pretende ser una guerra Cython/numba, solo he usado numba para ver si a pelo era capaz de mejorar algo el tema. Como no ha sido así, nos olvidamos de numba de momento." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cythonizando, que es gerundio (toma 1)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lo más sencillo y evidente es usar directamente el compilador `cython` y ver si usando el código python tal cual es un poco más rápido. Para ello, vamos a usar las funciones mágicas que Cython pone a nuestra disposición en el notebook. Solo vamos a hablar de la función mágica `%%cython`, de momento, aunque hay otras." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# antes cythonmagic\n", "%load_ext Cython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EL comando `%%cython` nos permite escribir código Cython en una celda. Una vez que ejecutamos la celda, IPython se encarga de coger el código, crear un fichero de código Cython con extensión .pyx, compilarlo a C y, si todo está correcto, importar ese fichero para que todo esté disponible dentro del notebook.\n", "\n", "[INCISO] a la función mágica `%%cython` le podemos pasar una serie de argumentos. Veremos alguno en este análisis pero ahora vamos a definir uno que sirve para que podamos nombrar a la funcíon que se crea y compila al vuelo, `-n` o `--name`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython --name probandocython1\n", "import numpy as np\n", "\n", "def busca_min_cython1(malla):\n", " minimosx = []\n", " minimosy = []\n", " for i in range(1, malla.shape[1]-1):\n", " for j in range(1, malla.shape[0]-1):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El fichero se creará dentro de la carpeta cython disponible dentro del directorio resultado de la función `get_ipython_cache_dir`. Veamos la localización del fichero en mi equipo:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.utils.path import get_ipython_cache_dir" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/kiko/.cache/ipython/cython/probandocython1.c\n" ] } ], "source": [ "print(get_ipython_cache_dir() + '/cython/probandocython1.c')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No lo muestro por aquí porque el resultado son más de ¡¡2400!! líneas de código C.\n", "\n", "Veamos ahora lo que tarda." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 3.34 s per loop\n" ] } ], "source": [ "%timeit busca_min_cython1(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bueno, parece que sin hacer mucho esfuerzo hemos conseguido ganar en torno a un 5% - 25% de rendimiento (dependerá del caso). No es gran cosa pero Cython es capaz de mucho más..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cythonizando, que es gerundio (toma 2)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En esta parte vamos a introducir una de las palabras clave que Cython introduce para extender Python, `cdef`. La palabra clave `cdef` sirve para 'tipar' estáticamente variables en Cython (luego veremos que se usa también para definir funciones). Por ejemplo:\n", "\n", "```Python\n", "cdef int var1, var2\n", "cdef float var3\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el bloque de código de más arriba he creado dos variables de tipo entero, `var1` y `var2`, y una variable de tipo float, `var3`. Los [tipos anteriores son la nomenclatura C](http://docs.cython.org/src/userguide/language_basics.html#automatic-type-conversions)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a intentar usar `cdef` con algunos tipos de datos que tenemos dentro de nuestra función. Para empezar, veo evidente que tengo varias listas (`minimosx` y `minimosy`), tenemos los índices de los bucles (`i` y `j`) y voy a convertir los parámetros de los `range` en tipos estáticos (`ii` y `jj`):" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython --name probandocython2\n", "import numpy as np\n", "\n", "def busca_min_cython2(malla):\n", " cdef list minimosx, minimosy\n", " cdef unsigned int i, j\n", " cdef unsigned int ii = malla.shape[1]-1\n", " cdef unsigned int jj = malla.shape[0]-1\n", " minimosx = []\n", " minimosy = []\n", " for i in range(1, ii):\n", " for j in range(1, jj):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 3.55 s per loop\n" ] } ], "source": [ "%timeit busca_min_cython2(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vaya decepción... No hemos conseguido gran cosa, tenemos un código un poco más largo y estamos peor que en la toma 1.\n", "\n", "En realidad, estamos usando objetos Python como listas (no es un tipo C/C++ puro pero Cython lo declara como puntero a algún tipo `struct` de Python) o numpy arrays y no hemos definido las variables de entrada y de salida.\n", "\n", "[INCISO] Cuando existe un tipo Python y C que tienen el mismo nombre (por ejemplo, `int`) predomina el de C (porque es lo deseable, ¿no?)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cythonizando, que es gerundio (toma 3)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En Cython existen tres tipos de funciones, las definidas en el espacio Python con `def`, las definidas en el espacio C con `cdef` (sí, lo mismo que usamos para declarar los tipos) y las definidas en ambos espacios con `cpdef`.\n", "\n", "* `def`: ya lo hemos visto y funciona como se espera. Accesible desde Python\n", "* `cdef`: No es accesible desde Python y la tendremos que envolver con una función Python para poder acceder a la misma.\n", "* `cpdef`: Es accesible tanto desde Python como desde C y Cython se encargará de hacer el 'envoltorio' para nosotros. Esto meterá un poco más de código y empeorará levemente el rendimiento.\n", "\n", "Si definimos una función con `cdef` debería ser una función que se usa internamente dentro del módulo Cython que vayamos a crear y que no sea necesario llamar desde Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Veamos un ejemplo de lo dicho anteriormente definiendo la salida de la función como tupla:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython --name probandocython3\n", "import numpy as np\n", "\n", "cdef tuple cbusca_min_cython3(malla):\n", " cdef list minimosx, minimosy\n", " cdef unsigned int i, j\n", " cdef unsigned int ii = malla.shape[1]-1\n", " cdef unsigned int jj = malla.shape[0]-1\n", " cdef unsigned int start = 1\n", " minimosx = []\n", " minimosy = []\n", " for i in range(start, ii):\n", " for j in range(start, jj):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)\n", "\n", "def busca_min_cython3(malla):\n", " return cbusca_min_cython3(malla)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 3.62 s per loop\n" ] } ], "source": [ "%timeit busca_min_cython3(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vaya, seguimos sin estar muy a gusto con estos resultados.\n", "\n", "Seguimos sin definir el tipo del valor de entrada.\n", "\n", "La función mágica `%%cython` dispone de una serie de funcionalidades entre la que se encuentra `-a` o `--annotate` (además del `-n` o `--name` que ya hemos visto). Si le pasamos este parámetro podremos ver una representación del código con colores marcando las partes más lentas (amarillo más oscuro) y más optmizadas (más claro) o a la velocidad de C (blanco). Vamos a usarlo para saber donde tenemos cuellos de botella (aplicado a nuestra última versión del código):" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<!DOCTYPE html>\n", "<!-- Generated by Cython 0.22 -->\n", "<html>\n", "<head>\n", " <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n", " <style type=\"text/css\">\n", " \n", "body.cython { font-family: courier; font-size: 12; }\n", "\n", ".cython.tag { }\n", ".cython.line { margin: 0em }\n", ".cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 20px; }\n", "\n", ".cython.code .py_c_api { color: red; }\n", ".cython.code .py_macro_api { color: #FF7000; }\n", ".cython.code .pyx_c_api { color: #FF3000; }\n", ".cython.code .pyx_macro_api { color: #FF7000; }\n", ".cython.code .refnanny { color: #FFA000; }\n", ".cython.code .error_goto { color: #FFA000; }\n", "\n", ".cython.code .coerce { color: #008000; 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Name.Variable /\n", ".cython .ow { color: #AA22FF; font-weight: bold } / Operator.Word /\n", ".cython .w { color: #bbbbbb } / Text.Whitespace /\n", ".cython .mb { color: #666666 } / Literal.Number.Bin /\n", ".cython .mf { color: #666666 } / Literal.Number.Float /\n", ".cython .mh { color: #666666 } / Literal.Number.Hex /\n", ".cython .mi { color: #666666 } / Literal.Number.Integer /\n", ".cython .mo { color: #666666 } / Literal.Number.Oct /\n", ".cython .sb { color: #BA2121 } / Literal.String.Backtick /\n", ".cython .sc { color: #BA2121 } / Literal.String.Char /\n", ".cython .sd { color: #BA2121; font-style: italic } / Literal.String.Doc /\n", ".cython .s2 { color: #BA2121 } / Literal.String.Double /\n", ".cython .se { color: #BB6622; font-weight: bold } / Literal.String.Escape /\n", ".cython .sh { color: #BA2121 } / Literal.String.Heredoc /\n", ".cython .si { color: #BB6688; font-weight: bold } / Literal.String.Interpol /\n", ".cython .sx { color: #008000 } / Literal.String.Other 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<span class=\"nn\">np</span></pre>\n", "<pre class='cython code score-8'> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, -1);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre><pre class='cython line score-0'> 02: </pre>\n", "<pre class='cython line score-9' onclick='toggleDiv(this)'>+03: <span class=\"k\">cdef</span> <span class=\"kt\">tuple</span> <span class=\"nf\">cbusca_min_cython3</span><span class=\"p\">(</span><span class=\"n\">malla</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-9'>static PyObject __pyx_f_46_cython_magic_b76d9f95ffc9db5b7e97e92e04623490_cbusca_min_cython3(PyObject __pyx_v_malla) {\n", " PyObject __pyx_v_minimosx = 0;\n", " PyObject __pyx_v_minimosy = 0;\n", " unsigned int __pyx_v_i;\n", " unsigned int __pyx_v_j;\n", " unsigned int __pyx_v_ii;\n", " unsigned int __pyx_v_jj;\n", " unsigned int __pyx_v_start;\n", " PyObject __pyx_r = NULL;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"cbusca_min_cython3\", 0);\n", "/* … /\n", " / function exit code /\n", " __pyx_L1_error:;\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_2);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_8);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_9);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_12);\n", " <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_b76d9f95ffc9db5b7e97e92e04623490.cbusca_min_cython3\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", " __pyx_r = 0;\n", " __pyx_L0:;\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_v_minimosx);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_v_minimosy);\n", " <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "</pre><pre class='cython line score-0'> 04: <span class=\"k\">cdef</span> <span class=\"kt\">list</span> <span class=\"nf\">minimosx</span><span class=\"p\">,</span> <span class=\"nf\">minimosy</span></pre>\n", "<pre class='cython line score-0'> 05: <span class=\"k\">cdef</span> <span class=\"kt\">unsigned</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span><span class=\"p\">,</span> <span class=\"nf\">j</span></pre>\n", "<pre class='cython line score-14' onclick='toggleDiv(this)'>+06: <span class=\"k\">cdef</span> <span class=\"kt\">unsigned</span> <span class=\"kt\">int</span> <span class=\"nf\">ii</span> <span class=\"o\">=</span> <span class=\"n\">malla</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-14'> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_v_malla, __pyx_n_s_shape);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_t_2 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_t_1, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 1);<span class='error_goto'> if (unlikely(__pyx_t_2 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>;\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", " __pyx_t_1 = <span class='py_c_api'>PyNumber_Subtract</span>(__pyx_t_2, __pyx_int_1);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", " __pyx_t_3 = <span class='pyx_c_api'>__Pyx_PyInt_As_unsigned_int</span>(__pyx_t_1);<span class='error_goto'> if (unlikely((__pyx_t_3 == (unsigned int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", " __pyx_v_ii = __pyx_t_3;\n", "</pre><pre class='cython line score-14' onclick='toggleDiv(this)'>+07: <span class=\"k\">cdef</span> <span class=\"kt\">unsigned</span> <span class=\"kt\">int</span> <span class=\"nf\">jj</span> <span class=\"o\">=</span> <span class=\"n\">malla</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-14'> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_v_malla, __pyx_n_s_shape);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_t_2 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_t_1, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 1);<span class='error_goto'> if (unlikely(__pyx_t_2 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>;\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", " __pyx_t_1 = <span class='py_c_api'>PyNumber_Subtract</span>(__pyx_t_2, __pyx_int_1);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", " __pyx_t_3 = <span class='pyx_c_api'>__Pyx_PyInt_As_unsigned_int</span>(__pyx_t_1);<span class='error_goto'> if (unlikely((__pyx_t_3 == (unsigned int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", " __pyx_v_jj = __pyx_t_3;\n", "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+08: <span class=\"k\">cdef</span> <span class=\"kt\">unsigned</span> <span class=\"kt\">int</span> <span class=\"nf\">start</span> <span class=\"o\">=</span> <span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0'> __pyx_v_start = 1;\n", "</pre><pre class='cython line score-5' onclick='toggleDiv(this)'>+09: <span class=\"n\">minimosx</span> <span class=\"o\">=</span> <span class=\"p\">[]</span></pre>\n", "<pre class='cython code score-5'> __pyx_t_1 = <span class='py_c_api'>PyList_New</span>(0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_v_minimosx = ((PyObject)__pyx_t_1);\n", " __pyx_t_1 = 0;\n", "</pre><pre class='cython line score-5' onclick='toggleDiv(this)'>+10: <span class=\"n\">minimosy</span> <span class=\"o\">=</span> <span class=\"p\">[]</span></pre>\n", "<pre class='cython code score-5'> __pyx_t_1 = <span class='py_c_api'>PyList_New</span>(0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_v_minimosy = ((PyObject)__pyx_t_1);\n", " __pyx_t_1 = 0;\n", "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+11: <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">start</span><span class=\"p\">,</span> <span class=\"n\">ii</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-0'> __pyx_t_3 = __pyx_v_ii;\n", " for (__pyx_t_4 = __pyx_v_start; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {\n", " __pyx_v_i = __pyx_t_4;\n", "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+12: <span class=\"k\">for</span> <span class=\"n\">j</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">start</span><span class=\"p\">,</span> <span class=\"n\">jj</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-0'> __pyx_t_5 = __pyx_v_jj;\n", " for (__pyx_t_6 = __pyx_v_start; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {\n", " __pyx_v_j = __pyx_t_6;\n", "</pre><pre class='cython line score-44' onclick='toggleDiv(this)'>+13: <span class=\"k\">if</span> <span class=\"p\">(</span><span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"ow\">and</span></pre>\n", "<pre class='cython code score-44'> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyInt_From_unsigned_int</span>(__pyx_v_j);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 13; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyInt_From_unsigned_int</span>(__pyx_v_i);<span class='error_goto'> if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 13; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", " __pyx_t_8 = <span class='py_c_api'>PyTuple_New</span>(2);<span class='error_goto'> if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 13; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span 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= <span class='pyx_c_api'>__Pyx_PyInt_From_unsigned_int</span>(__pyx_v_i);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_t_11 = <span class='pyx_c_api'>__Pyx_PyList_Append</span>(__pyx_v_minimosx, __pyx_t_1);<span class='error_goto'> if (unlikely(__pyx_t_11 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre><pre class='cython line score-5' onclick='toggleDiv(this)'>+22: <span class=\"n\">minimosy</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">j</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5'> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyInt_From_unsigned_int</span>(__pyx_v_j);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 22; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_t_11 = <span class='pyx_c_api'>__Pyx_PyList_Append</span>(__pyx_v_minimosy, __pyx_t_1);<span class='error_goto'> if (unlikely(__pyx_t_11 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 22; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", " goto __pyx_L7;\n", " }\n", " __pyx_L7:;\n", " }\n", " }\n", "</pre><pre class='cython line score-0'> 23: </pre>\n", "<pre class='cython line score-66' onclick='toggleDiv(this)'>+24: <span class=\"k\">return</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">(</span><span class=\"n\">minimosx</span><span 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onclick='toggleDiv(this)'>+27: <span class=\"k\">return</span> <span class=\"n\">cbusca_min_cython3</span><span class=\"p\">(</span><span class=\"n\">malla</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-1'> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n", " __pyx_t_1 = __pyx_f_46_cython_magic_b76d9f95ffc9db5b7e97e92e04623490_cbusca_min_cython3(__pyx_v_malla);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_r = __pyx_t_1;\n", " __pyx_t_1 = 0;\n", " goto __pyx_L0;\n", "</pre></div></body></html>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%cython --annotate\n", "import numpy as np\n", "\n", "cdef tuple cbusca_min_cython3(malla):\n", " cdef list minimosx, minimosy\n", " cdef unsigned int i, j\n", " cdef unsigned int ii = malla.shape[1]-1\n", " cdef unsigned int jj = malla.shape[0]-1\n", " cdef unsigned int start = 1\n", " minimosx = []\n", " minimosy = []\n", " for i in range(start, ii):\n", " for j in range(start, jj):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)\n", "\n", "def busca_min_cython3(malla):\n", " return cbusca_min_cython3(malla)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El `if` parece la parte más lenta. Estamos usando el valor de entrada que no tiene un tipo Cython definido.\n", "\n", "Los bucles parece que están optimizados (las variables envueltas en el bucle las hemos declarado como `unsigned int`).\n", "\n", "Pero todas las partes por las que pasa el numpy array parece que no están muy optimizadas..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cythonizando, que es gerundio (toma 4)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora mismo, haciendo `import numpy as np` tenemos acceso a la funcionalidad Python de numpy. Para poder acceder a la funcionalidad C de numpy hemos de hacer un `cimport` de numpy.\n", "\n", "El `cimport` se usa para importar información especial del módulo numpy en el momento de compilación. Esta información se encuentra en el fichero numpy.pxd que es parte de la distribución Cython. El `cimport` también se usa para poder importar desde la stdlib* de C.\n", "\n", "Vamos a usar esto para declarar el tipo del array de numpy." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython --name probandocython4\n", "import numpy as np\n", "cimport numpy as np\n", "\n", "cpdef tuple busca_min_cython4(np.ndarray[double, ndim = 2] malla):\n", " cdef list minimosx, minimosy\n", " cdef unsigned int i, j\n", " cdef unsigned int ii = malla.shape[1]-1\n", " cdef unsigned int jj = malla.shape[0]-1\n", " cdef unsigned int start = 1\n", " minimosx = []\n", " minimosy = []\n", " for i in range(start, ii):\n", " for j in range(start, jj):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 147 ms per loop\n" ] } ], "source": [ "%timeit busca_min_cython4(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Guauuuu!!! Acabamos de obtener un incremento de entre 25x a 30x veces más rápido." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a comprobar que el resultado sea el mismo que la función original:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1 1 1 ..., 1998 1998 1998]\n", "[ 1 3 11 ..., 1968 1977 1985]\n" ] } ], "source": [ "a, b = busca_min(data)\n", "print(a)\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1 1 1 ..., 1998 1998 1998]\n", "[ 1 3 11 ..., 1968 1977 1985]\n" ] } ], "source": [ "aa, bb = busca_min_cython4(data)\n", "print(aa)\n", "print(bb)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(np.array_equal(a, aa))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(np.array_equal(b, bb))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pues parece que sí :-)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a ver si hemos dejado la mayoría del código anterior en blanco o más clarito usando `--annotate`." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<!DOCTYPE html>\n", "<!-- Generated by Cython 0.22 -->\n", "<html>\n", "<head>\n", " <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n", " <style type=\"text/css\">\n", " \n", "body.cython { font-family: courier; font-size: 12; }\n", "\n", ".cython.tag { }\n", ".cython.line { margin: 0em }\n", ".cython.code { font-size: 9; color: #444444; display: 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<span class='pyx_c_api'>__Pyx_ErrRestore</span>(__pyx_type, __pyx_value, __pyx_tb);}\n", " <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_db10c794e43f00f7b90f23a8e05093c1.busca_min_cython4\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", " __pyx_r = 0;\n", " goto __pyx_L2;\n", " __pyx_L0:;\n", " <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&__pyx_pybuffernd_malla.rcbuffer->pybuffer);\n", " __pyx_L2:;\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_v_minimosx);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_v_minimosy);\n", " <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "\n", "/* Python wrapper /\n", "static PyObject __pyx_pw_46_cython_magic_db10c794e43f00f7b90f23a8e05093c1_1busca_min_cython4(PyObject __pyx_self, PyObject __pyx_v_malla); /proto/\n", "static PyObject __pyx_pw_46_cython_magic_db10c794e43f00f7b90f23a8e05093c1_1busca_min_cython4(PyObject __pyx_self, PyObject __pyx_v_malla) {\n", " PyObject __pyx_r = 0;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"busca_min_cython4 (wrapper)\", 0);\n", " if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject )__pyx_v_malla), __pyx_ptype_5numpy_ndarray, 1, \"malla\", 0))) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " __pyx_r = __pyx_pf_46_cython_magic_db10c794e43f00f7b90f23a8e05093c1_busca_min_cython4(__pyx_self, ((PyArrayObject )__pyx_v_malla));\n", " CYTHON_UNUSED int __pyx_lineno = 0;\n", " CYTHON_UNUSED const char __pyx_filename = NULL;\n", " CYTHON_UNUSED int __pyx_clineno = 0;\n", "\n", " / function exit code /\n", " goto __pyx_L0;\n", " __pyx_L1_error:;\n", " __pyx_r = NULL;\n", " __pyx_L0:;\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "\n", "static PyObject __pyx_pf_46_cython_magic_db10c794e43f00f7b90f23a8e05093c1_busca_min_cython4(CYTHON_UNUSED PyObject __pyx_self, PyArrayObject __pyx_v_malla) {\n", " __Pyx_LocalBuf_ND __pyx_pybuffernd_malla;\n", " __Pyx_Buffer __pyx_pybuffer_malla;\n", " PyObject __pyx_r = NULL;\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"busca_min_cython4\", 0);\n", " __pyx_pybuffer_malla.pybuffer.buf = NULL;\n", " __pyx_pybuffer_malla.refcount = 0;\n", " __pyx_pybuffernd_malla.data = NULL;\n", " __pyx_pybuffernd_malla.rcbuffer = &__pyx_pybuffer_malla;\n", " {\n", " __Pyx_BufFmt_StackElem __pyx_stack[1];\n", " if (unlikely(<span class='pyx_c_api'>__Pyx_GetBufferAndValidate</span>(&__pyx_pybuffernd_malla.rcbuffer->pybuffer, (PyObject)__pyx_v_malla, &__Pyx_TypeInfo_double, PyBUF_FORMAT\| PyBUF_STRIDES, 2, 0, __pyx_stack) == -1)) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_pybuffernd_malla.diminfo[0].strides = __pyx_pybuffernd_malla.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_malla.diminfo[0].shape = __pyx_pybuffernd_malla.rcbuffer->pybuffer.shape[0]; __pyx_pybuffernd_malla.diminfo[1].strides = __pyx_pybuffernd_malla.rcbuffer->pybuffer.strides[1]; __pyx_pybuffernd_malla.diminfo[1].shape = __pyx_pybuffernd_malla.rcbuffer->pybuffer.shape[1];\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n", " __pyx_t_1 = __pyx_f_46_cython_magic_db10c794e43f00f7b90f23a8e05093c1_busca_min_cython4(__pyx_v_malla, 0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_r = __pyx_t_1;\n", " __pyx_t_1 = 0;\n", " goto __pyx_L0;\n", "\n", " /* function exit code /\n", " __pyx_L1_error:;\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n", " { PyObject __pyx_type, __pyx_value, __pyx_tb;\n", " <span class='pyx_c_api'>__Pyx_ErrFetch</span>(&__pyx_type, &__pyx_value, &__pyx_tb);\n", " <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&__pyx_pybuffernd_malla.rcbuffer->pybuffer);\n", " <span class='pyx_c_api'>__Pyx_ErrRestore</span>(__pyx_type, __pyx_value, __pyx_tb);}\n", " <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_db10c794e43f00f7b90f23a8e05093c1.busca_min_cython4\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", " __pyx_r = NULL;\n", " goto __pyx_L2;\n", " __pyx_L0:;\n", " <span class='pyx_c_api'>__Pyx_SafeReleaseBuffer</span>(&__pyx_pybuffernd_malla.rcbuffer->pybuffer);\n", " __pyx_L2:;\n", " <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "</pre><pre class='cython line score-0'> 05: <span class=\"k\">cdef</span> <span class=\"kt\">list</span> <span class=\"nf\">minimosx</span><span class=\"p\">,</span> <span class=\"nf\">minimosy</span></pre>\n", "<pre class='cython line score-0'> 06: <span class=\"k\">cdef</span> <span class=\"kt\">unsigned</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span><span class=\"p\">,</span> <span class=\"nf\">j</span></pre>\n", "<pre class='cython line score-0' onclick='toggleDiv(this)'>+07: <span class=\"k\">cdef</span> <span class=\"kt\">unsigned</span> <span class=\"kt\">int</span> <span class=\"nf\">ii</span> <span class=\"o\">=</span> <span class=\"n\">malla</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0'> __pyx_v_ii = ((__pyx_v_malla->dimensions[1]) - 1);\n", "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+08: <span class=\"k\">cdef</span> <span class=\"kt\">unsigned</span> <span class=\"kt\">int</span> <span class=\"nf\">jj</span> <span class=\"o\">=</span> <span class=\"n\">malla</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0'> __pyx_v_jj = ((__pyx_v_malla->dimensions[0]) - 1);\n", "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+09: <span class=\"k\">cdef</span> <span class=\"kt\">unsigned</span> <span class=\"kt\">int</span> <span class=\"nf\">start</span> <span class=\"o\">=</span> <span class=\"mf\">1</span></pre>\n", "<pre class='cython code score-0'> __pyx_v_start = 1;\n", "</pre><pre class='cython line score-5' onclick='toggleDiv(this)'>+10: <span class=\"n\">minimosx</span> <span class=\"o\">=</span> <span class=\"p\">[]</span></pre>\n", "<pre class='cython code score-5'> __pyx_t_1 = <span class='py_c_api'>PyList_New</span>(0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_v_minimosx = ((PyObject)__pyx_t_1);\n", " __pyx_t_1 = 0;\n", "</pre><pre class='cython line score-5' onclick='toggleDiv(this)'>+11: <span class=\"n\">minimosy</span> <span class=\"o\">=</span> <span class=\"p\">[]</span></pre>\n", "<pre class='cython code score-5'> __pyx_t_1 = <span class='py_c_api'>PyList_New</span>(0);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 11; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_v_minimosy = ((PyObject)__pyx_t_1);\n", " __pyx_t_1 = 0;\n", "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+12: <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">start</span><span class=\"p\">,</span> <span class=\"n\">ii</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-0'> __pyx_t_2 = __pyx_v_ii;\n", " for (__pyx_t_3 = __pyx_v_start; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {\n", " __pyx_v_i = __pyx_t_3;\n", "</pre><pre class='cython line score-0' onclick='toggleDiv(this)'>+13: <span class=\"k\">for</span> <span class=\"n\">j</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">start</span><span class=\"p\">,</span> <span class=\"n\">jj</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-0'> __pyx_t_4 = __pyx_v_jj;\n", " for (__pyx_t_5 = __pyx_v_start; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {\n", " __pyx_v_j = __pyx_t_5;\n", "</pre><pre class='cython line score-4' onclick='toggleDiv(this)'>+14: <span class=\"k\">if</span> <span class=\"p\">(</span><span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"ow\">and</span></pre>\n", "<pre class='cython code score-4'> __pyx_t_7 = __pyx_v_j;\n", " __pyx_t_8 = __pyx_v_i;\n", " __pyx_t_9 = -1;\n", " if (unlikely(__pyx_t_7 >= (size_t)__pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (unlikely(__pyx_t_8 >= (size_t)__pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 14; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_10 = (__pyx_v_j - 1);\n", " __pyx_t_11 = (__pyx_v_i - 1);\n", " __pyx_t_9 = -1;\n", " if (__pyx_t_10 < 0) {\n", " __pyx_t_10 += __pyx_pybuffernd_malla.diminfo[0].shape;\n", " if (unlikely(__pyx_t_10 < 0)) __pyx_t_9 = 0;\n", " } else if (unlikely(__pyx_t_10 >= __pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (__pyx_t_11 < 0) {\n", " __pyx_t_11 += __pyx_pybuffernd_malla.diminfo[1].shape;\n", " if (unlikely(__pyx_t_11 < 0)) __pyx_t_9 = 1;\n", " } else if (unlikely(__pyx_t_11 >= __pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 14; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_12 = (((__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_7, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_8, __pyx_pybuffernd_malla.diminfo[1].strides)) < (__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_10, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_11, __pyx_pybuffernd_malla.diminfo[1].strides))) != 0);\n", " if (__pyx_t_12) {\n", " } else {\n", " __pyx_t_6 = __pyx_t_12;\n", " goto __pyx_L8_bool_binop_done;\n", " }\n", "</pre><pre class='cython line score-4' onclick='toggleDiv(this)'>+15: <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"ow\">and</span></pre>\n", "<pre class='cython code score-4'> __pyx_t_13 = __pyx_v_j;\n", " __pyx_t_14 = __pyx_v_i;\n", " __pyx_t_9 = -1;\n", " if (unlikely(__pyx_t_13 >= (size_t)__pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (unlikely(__pyx_t_14 >= (size_t)__pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 15; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_15 = (__pyx_v_j - 1);\n", " __pyx_t_16 = __pyx_v_i;\n", " __pyx_t_9 = -1;\n", " if (__pyx_t_15 < 0) {\n", " __pyx_t_15 += __pyx_pybuffernd_malla.diminfo[0].shape;\n", " if (unlikely(__pyx_t_15 < 0)) __pyx_t_9 = 0;\n", " } else if (unlikely(__pyx_t_15 >= __pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (unlikely(__pyx_t_16 >= (size_t)__pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 15; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_12 = (((__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_14, __pyx_pybuffernd_malla.diminfo[1].strides)) < (__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_15, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_16, __pyx_pybuffernd_malla.diminfo[1].strides))) != 0);\n", " if (__pyx_t_12) {\n", " } else {\n", " __pyx_t_6 = __pyx_t_12;\n", " goto __pyx_L8_bool_binop_done;\n", " }\n", "</pre><pre class='cython line score-4' onclick='toggleDiv(this)'>+16: <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"ow\">and</span></pre>\n", "<pre class='cython code score-4'> __pyx_t_17 = __pyx_v_j;\n", " __pyx_t_18 = __pyx_v_i;\n", " __pyx_t_9 = -1;\n", " if (unlikely(__pyx_t_17 >= (size_t)__pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (unlikely(__pyx_t_18 >= (size_t)__pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 16; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_19 = (__pyx_v_j - 1);\n", " __pyx_t_20 = (__pyx_v_i + 1);\n", " __pyx_t_9 = -1;\n", " if (__pyx_t_19 < 0) {\n", " __pyx_t_19 += __pyx_pybuffernd_malla.diminfo[0].shape;\n", " if (unlikely(__pyx_t_19 < 0)) __pyx_t_9 = 0;\n", " } else if (unlikely(__pyx_t_19 >= __pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (__pyx_t_20 < 0) {\n", " __pyx_t_20 += __pyx_pybuffernd_malla.diminfo[1].shape;\n", " if (unlikely(__pyx_t_20 < 0)) __pyx_t_9 = 1;\n", " } else if (unlikely(__pyx_t_20 >= __pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 16; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_12 = (((__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_17, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_18, __pyx_pybuffernd_malla.diminfo[1].strides)) < (__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_19, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_20, __pyx_pybuffernd_malla.diminfo[1].strides))) != 0);\n", " if (__pyx_t_12) {\n", " } else {\n", " __pyx_t_6 = __pyx_t_12;\n", " goto __pyx_L8_bool_binop_done;\n", " }\n", "</pre><pre class='cython line score-4' onclick='toggleDiv(this)'>+17: <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"ow\">and</span></pre>\n", "<pre class='cython code score-4'> __pyx_t_21 = __pyx_v_j;\n", " __pyx_t_22 = __pyx_v_i;\n", " __pyx_t_9 = -1;\n", " if (unlikely(__pyx_t_21 >= (size_t)__pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (unlikely(__pyx_t_22 >= (size_t)__pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 17; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_23 = __pyx_v_j;\n", " __pyx_t_24 = (__pyx_v_i - 1);\n", " __pyx_t_9 = -1;\n", " if (unlikely(__pyx_t_23 >= (size_t)__pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (__pyx_t_24 < 0) {\n", " __pyx_t_24 += __pyx_pybuffernd_malla.diminfo[1].shape;\n", " if (unlikely(__pyx_t_24 < 0)) __pyx_t_9 = 1;\n", " } else if (unlikely(__pyx_t_24 >= __pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 17; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_12 = (((__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_21, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_22, __pyx_pybuffernd_malla.diminfo[1].strides)) < (__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_23, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_24, __pyx_pybuffernd_malla.diminfo[1].strides))) != 0);\n", " if (__pyx_t_12) {\n", " } else {\n", " __pyx_t_6 = __pyx_t_12;\n", " goto __pyx_L8_bool_binop_done;\n", " }\n", "</pre><pre class='cython line score-4' onclick='toggleDiv(this)'>+18: <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"ow\">and</span></pre>\n", "<pre class='cython code score-4'> __pyx_t_25 = __pyx_v_j;\n", " __pyx_t_26 = __pyx_v_i;\n", " __pyx_t_9 = -1;\n", " if (unlikely(__pyx_t_25 >= (size_t)__pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (unlikely(__pyx_t_26 >= (size_t)__pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 18; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_27 = __pyx_v_j;\n", " __pyx_t_28 = (__pyx_v_i + 1);\n", " __pyx_t_9 = -1;\n", " if (unlikely(__pyx_t_27 >= (size_t)__pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (__pyx_t_28 < 0) {\n", " __pyx_t_28 += __pyx_pybuffernd_malla.diminfo[1].shape;\n", " if (unlikely(__pyx_t_28 < 0)) __pyx_t_9 = 1;\n", " } else if (unlikely(__pyx_t_28 >= __pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 18; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_12 = (((__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_25, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_26, __pyx_pybuffernd_malla.diminfo[1].strides)) < (__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_28, __pyx_pybuffernd_malla.diminfo[1].strides))) != 0);\n", " if (__pyx_t_12) {\n", " } else {\n", " __pyx_t_6 = __pyx_t_12;\n", " goto __pyx_L8_bool_binop_done;\n", " }\n", "</pre><pre class='cython line score-4' onclick='toggleDiv(this)'>+19: <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"ow\">and</span></pre>\n", "<pre class='cython code score-4'> __pyx_t_29 = __pyx_v_j;\n", " __pyx_t_30 = __pyx_v_i;\n", " __pyx_t_9 = -1;\n", " if (unlikely(__pyx_t_29 >= (size_t)__pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (unlikely(__pyx_t_30 >= (size_t)__pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 19; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_31 = (__pyx_v_j + 1);\n", " __pyx_t_32 = (__pyx_v_i - 1);\n", " __pyx_t_9 = -1;\n", " if (__pyx_t_31 < 0) {\n", " __pyx_t_31 += __pyx_pybuffernd_malla.diminfo[0].shape;\n", " if (unlikely(__pyx_t_31 < 0)) __pyx_t_9 = 0;\n", " } else if (unlikely(__pyx_t_31 >= __pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (__pyx_t_32 < 0) {\n", " __pyx_t_32 += __pyx_pybuffernd_malla.diminfo[1].shape;\n", " if (unlikely(__pyx_t_32 < 0)) __pyx_t_9 = 1;\n", " } else if (unlikely(__pyx_t_32 >= __pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 19; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_12 = (((__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_29, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_30, __pyx_pybuffernd_malla.diminfo[1].strides)) < (__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_31, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_32, __pyx_pybuffernd_malla.diminfo[1].strides))) != 0);\n", " if (__pyx_t_12) {\n", " } else {\n", " __pyx_t_6 = __pyx_t_12;\n", " goto __pyx_L8_bool_binop_done;\n", " }\n", "</pre><pre class='cython line score-4' onclick='toggleDiv(this)'>+20: <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"ow\">and</span></pre>\n", "<pre class='cython code score-4'> __pyx_t_33 = __pyx_v_j;\n", " __pyx_t_34 = __pyx_v_i;\n", " __pyx_t_9 = -1;\n", " if (unlikely(__pyx_t_33 >= (size_t)__pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (unlikely(__pyx_t_34 >= (size_t)__pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 20; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_35 = (__pyx_v_j + 1);\n", " __pyx_t_36 = __pyx_v_i;\n", " __pyx_t_9 = -1;\n", " if (__pyx_t_35 < 0) {\n", " __pyx_t_35 += __pyx_pybuffernd_malla.diminfo[0].shape;\n", " if (unlikely(__pyx_t_35 < 0)) __pyx_t_9 = 0;\n", " } else if (unlikely(__pyx_t_35 >= __pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (unlikely(__pyx_t_36 >= (size_t)__pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 20; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_12 = (((__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_33, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_34, __pyx_pybuffernd_malla.diminfo[1].strides)) < (__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_35, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_36, __pyx_pybuffernd_malla.diminfo[1].strides))) != 0);\n", " if (__pyx_t_12) {\n", " } else {\n", " __pyx_t_6 = __pyx_t_12;\n", " goto __pyx_L8_bool_binop_done;\n", " }\n", "</pre><pre class='cython line score-4' onclick='toggleDiv(this)'>+21: <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\"><</span> <span class=\"n\">malla</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">,</span> <span class=\"n\">i</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">]):</span></pre>\n", "<pre class='cython code score-4'> __pyx_t_37 = __pyx_v_j;\n", " __pyx_t_38 = __pyx_v_i;\n", " __pyx_t_9 = -1;\n", " if (unlikely(__pyx_t_37 >= (size_t)__pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (unlikely(__pyx_t_38 >= (size_t)__pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_39 = (__pyx_v_j + 1);\n", " __pyx_t_40 = (__pyx_v_i + 1);\n", " __pyx_t_9 = -1;\n", " if (__pyx_t_39 < 0) {\n", " __pyx_t_39 += __pyx_pybuffernd_malla.diminfo[0].shape;\n", " if (unlikely(__pyx_t_39 < 0)) __pyx_t_9 = 0;\n", " } else if (unlikely(__pyx_t_39 >= __pyx_pybuffernd_malla.diminfo[0].shape)) __pyx_t_9 = 0;\n", " if (__pyx_t_40 < 0) {\n", " __pyx_t_40 += __pyx_pybuffernd_malla.diminfo[1].shape;\n", " if (unlikely(__pyx_t_40 < 0)) __pyx_t_9 = 1;\n", " } else if (unlikely(__pyx_t_40 >= __pyx_pybuffernd_malla.diminfo[1].shape)) __pyx_t_9 = 1;\n", " if (unlikely(__pyx_t_9 != -1)) {\n", " <span class='pyx_c_api'>__Pyx_RaiseBufferIndexError</span>(__pyx_t_9);\n", " <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " }\n", " __pyx_t_12 = (((__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_37, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_38, __pyx_pybuffernd_malla.diminfo[1].strides)) < (__Pyx_BufPtrStrided2d(double , __pyx_pybuffernd_malla.rcbuffer->pybuffer.buf, __pyx_t_39, __pyx_pybuffernd_malla.diminfo[0].strides, __pyx_t_40, __pyx_pybuffernd_malla.diminfo[1].strides))) != 0);\n", " __pyx_t_6 = __pyx_t_12;\n", " __pyx_L8_bool_binop_done:;\n", " if (__pyx_t_6) {\n", "</pre><pre class='cython line score-5' onclick='toggleDiv(this)'>+22: <span class=\"n\">minimosx</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">i</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5'> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyInt_From_unsigned_int</span>(__pyx_v_i);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 22; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_t_41 = <span class='pyx_c_api'>__Pyx_PyList_Append</span>(__pyx_v_minimosx, __pyx_t_1);<span class='error_goto'> if (unlikely(__pyx_t_41 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 22; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre><pre class='cython line score-5' onclick='toggleDiv(this)'>+23: <span class=\"n\">minimosy</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">j</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-5'> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyInt_From_unsigned_int</span>(__pyx_v_j);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 23; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_t_41 = <span class='pyx_c_api'>__Pyx_PyList_Append</span>(__pyx_v_minimosy, __pyx_t_1);<span class='error_goto'> if (unlikely(__pyx_t_41 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 23; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", " goto __pyx_L7;\n", " }\n", " __pyx_L7:;\n", " }\n", " }\n", "</pre><pre class='cython line score-0'> 24: </pre>\n", "<pre class='cython line score-66' onclick='toggleDiv(this)'>+25: <span class=\"k\">return</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">(</span><span class=\"n\">minimosx</span><span class=\"p\">),</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">(</span><span class=\"n\">minimosy</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-66'> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n", " __pyx_t_42 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_np);<span class='error_goto'> if (unlikely(!__pyx_t_42)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 25; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_42);\n", " __pyx_t_43 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_42, __pyx_n_s_array);<span class='error_goto'> if (unlikely(!__pyx_t_43)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 25; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_43);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_42); __pyx_t_42 = 0;\n", " __pyx_t_42 = NULL;\n", " if (CYTHON_COMPILING_IN_CPYTHON && unlikely(<span class='py_c_api'>PyMethod_Check</span>(__pyx_t_43))) {\n", " __pyx_t_42 = <span class='py_macro_api'>PyMethod_GET_SELF</span>(__pyx_t_43);\n", " if (likely(__pyx_t_42)) {\n", " PyObject* function = <span class='py_macro_api'>PyMethod_GET_FUNCTION</span>(__pyx_t_43);\n", " <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_t_42);\n", " <span class='pyx_macro_api'>__Pyx_INCREF</span>(function);\n", " <span class='pyx_macro_api'>__Pyx_DECREF_SET</span>(__pyx_t_43, function);\n", " }\n", " }\n", " if (!__pyx_t_42) {\n", " __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_CallOneArg</span>(__pyx_t_43, __pyx_v_minimosx);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 25; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " } else {\n", " __pyx_t_44 = <span class='py_c_api'>PyTuple_New</span>(1+1);<span class='error_goto'> if (unlikely(!__pyx_t_44)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 25; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_44);\n", " <span 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cdef unsigned int start = 1\n", " minimosx = []\n", " minimosy = []\n", " for i in range(start, ii):\n", " for j in range(start, jj):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vemos que muchas de las partes oscuras ahora son más claras!!! Pero parece que sigue quedando espacio para la mejora." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cythonizando, que es gerundio (toma 5)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a ver si definiendo el tipo del resultado de la función como un numpy array en lugar de como una tupla nos introduce alguna mejora:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython --name probandocython5\n", "import numpy as np\n", "cimport numpy as np\n", "\n", "cpdef np.ndarray[int, ndim = 2] busca_min_cython5(np.ndarray[double, ndim = 2] malla):\n", " cdef list minimosx, minimosy\n", " cdef unsigned int i, j\n", " cdef unsigned int ii = malla.shape[1]-1\n", " cdef unsigned int jj = malla.shape[0]-1\n", " cdef unsigned int start = 1\n", " minimosx = []\n", " minimosy = []\n", " for i in range(start, ii):\n", " for j in range(start, jj):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array([minimosx, minimosy])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 137 ms per loop\n" ] } ], "source": [ "%timeit busca_min_cython5(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vaya, parece que con respecto a la versión anterior solo obtenemos una ganancia de un 2% - 4%." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Cythonizando, que es gerundio (toma 6)." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Vamos a dejar de usar listas y vamos a usar numpy arrays vacios que iremos 'rellenando' con `numpy.append`. A ver si usando todo numpy arrays conseguimos algún tipo de mejora:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%cython --name probandocython6\n", "import numpy as np\n", "cimport numpy as np\n", "\n", "cpdef tuple busca_min_cython6(np.ndarray[double, ndim = 2] malla):\n", " cdef np.ndarray[long, ndim = 1] minimosx, minimosy\n", " cdef unsigned int i, j\n", " cdef unsigned int ii = malla.shape[1]-1\n", " cdef unsigned int jj = malla.shape[0]-1\n", " cdef unsigned int start = 1\n", " minimosx = np.array([], dtype = np.int)\n", " minimosy = np.array([], dtype = np.int)\n", " for i in range(start, ii):\n", " for j in range(start, jj):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " np.append(minimosx, i)\n", " np.append(minimosy, j)\n", "\n", " return minimosx, minimosy" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 5.59 s per loop\n" ] } ], "source": [ "%timeit busca_min_cython6(data)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.append?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En realidad, en la anterior porción de código estoy usando algo muy ineficiente. La función `numpy.append` no funciona como una lista a la que vas anexando elementos. Lo que estamos haciendo en realidad es crear copias del array existente para convertirlo a un nuevo array con un elemento nuevo. Esto no es lo que pretendiamos!!!!" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Cythonizando, que es gerundio (toma 7)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En Python existen [arrays](https://docs.python.org/3.4/library/array.html) eficientes para valores numéricos (según reza la documentación) que también pueden ser usados de la forma en que estoy usando las listas en mi función (arrays vacios a los que les vamos añadiendo elementos). Vamos a usarlos con Cython." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython --name probandocython7\n", "import numpy as np\n", "cimport numpy as np\n", "from cpython cimport array as c_array\n", "from array import array\n", "\n", "cpdef tuple busca_min_cython7(np.ndarray[double, ndim = 2] malla):\n", " cdef c_array.array minimosx, minimosy\n", " cdef unsigned int i, j\n", " cdef unsigned int ii = malla.shape[1]-1\n", " cdef unsigned int jj = malla.shape[0]-1\n", " cdef unsigned int start = 1\n", " minimosx = array('L', [])\n", " minimosy = array('L', []) \n", " for i in range(start, ii):\n", " for j in range(start, jj):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 98.1 ms per loop\n" ] } ], "source": [ "%timeit busca_min_cython7(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parece que hemos ganado otro 25% - 30% con respecto a lo anterior más eficiente que habíamos conseguido. Con respecto a la implementación inicial en Python puro tenemos una mejora de 30x - 35x veces la velocidad inicial.\n", "\n", "Vamos a comprobar si seguimos teniendo los mismos resultados." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1 1 1 ..., 1998 1998 1998]\n", "[ 1 3 11 ..., 1968 1977 1985]\n", "[ 1 1 1 ..., 1998 1998 1998]\n", "[ 1 3 11 ..., 1968 1977 1985]\n", "True\n", "True\n" ] } ], "source": [ "a, b = busca_min(data)\n", "print(a)\n", "print(b)\n", "aa, bb = busca_min_cython7(data)\n", "print(aa)\n", "print(bb)\n", "print(np.array_equal(a, aa))\n", "print(np.array_equal(b, bb))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "¿Qué pasa si el tamaño del array se incrementa?" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 24.6 s per loop\n", "1 loops, best of 3: 687 ms per loop\n" ] } ], "source": [ "data2 = np.random.randn(5000, 5000)\n", "%timeit busca_min(data2)\n", "%timeit busca_min_cython7(data2)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1 1 1 ..., 4998 4998 4998]\n", "[ 7 12 18 ..., 4975 4978 4983]\n", "[ 1 1 1 ..., 4998 4998 4998]\n", "[ 7 12 18 ..., 4975 4978 4983]\n", "True\n", "True\n" ] } ], "source": [ "a, b = busca_min(data2)\n", "print(a)\n", "print(b)\n", "aa, bb = busca_min_cython7(data2)\n", "print(aa)\n", "print(bb)\n", "print(np.array_equal(a, aa))\n", "print(np.array_equal(b, bb))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Parece que al ir aumentando el tamaño de los datos de entrada a la función los números son consistentes y el rendimiento se mantiene. En este caso concreto parece que ya hemos llegado a rendimientos de más de ¡¡35x!! con respecto a la implementación inicial." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Cythonizando, que es gerundio (toma 8)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Podemos usar [directivas de compilación](http://docs.cython.org/src/reference/compilation.html#compiler-directives) que ayuden al compilador a decidir mejor qué es lo que tiene que hacer. Entre ellas se encuentra una opción que es `boundscheck` que evita mirar la posibilidad de obtener `IndexError` asumiendo que el código está libre de estos errores de indexación. Lo vamos a usar conjuntamente con `wraparound`. Esta última opción se encarga de evitar mirar indexaciones relativas al final del iterable (por ejemplo, `mi_iterable[-1]`). En este caso concreto, la segunda opción no aporta nada de mejora de rendimiento pero la dijamos ya que la hemos probado." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython --name probandocython8\n", "import numpy as np\n", "cimport numpy as np\n", "from cpython cimport array as c_array\n", "from array import array\n", "cimport cython\n", "\n", "@cython.boundscheck(False) \n", "@cython.wraparound(False)\n", "cpdef tuple busca_min_cython8(np.ndarray[double, ndim = 2] malla):\n", " cdef c_array.array minimosx, minimosy\n", " cdef unsigned int i, j\n", " cdef unsigned int ii = malla.shape[1]-1\n", " cdef unsigned int jj = malla.shape[0]-1\n", " cdef unsigned int start = 1\n", " minimosx = array('L', [])\n", " minimosy = array('L', []) \n", " for i in range(start, ii):\n", " for j in range(start, jj):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 94.3 ms per loop\n" ] } ], "source": [ "%timeit busca_min_cython8(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parece que hemos conseguido arañar otro poquito de rendimiento." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Cythonizando, que es gerundio (toma 9)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En lugar de usar numpy arrays vamos a usar [memoryviews](http://docs.cython.org/src/userguide/memoryviews.html#typed-memoryviews). Los memoryviews* son arrays de acceso rápido. Si solo queremos almacenar cosas y no necesitamos ninguna de las características de un numpy array pueden ser una buena solución. Si necesitamos alguna funcionalidad extra siempre lo podemos convertir en un numpy array usando `numpy.asarray`." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython --name probandocython9\n", "import numpy as np\n", "cimport numpy as np\n", "from cpython cimport array as c_array\n", "from array import array\n", "cimport cython\n", "\n", "@cython.boundscheck(False) \n", "@cython.wraparound(False)\n", "#cpdef tuple busca_min_cython9(np.ndarray[double, ndim = 2] malla):\n", "cpdef tuple busca_min_cython9(double [:,:] malla):\n", " cdef c_array.array minimosx, minimosy\n", " cdef unsigned int i, j\n", " cdef unsigned int ii = malla.shape[1]-1\n", " cdef unsigned int jj = malla.shape[0]-1\n", " cdef unsigned int start = 1\n", " #cdef float [:, :] malla_view = malla\n", " minimosx = array('L', [])\n", " minimosy = array('L', []) \n", " for i in range(start, ii):\n", " for j in range(start, jj):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 97.6 ms per loop\n" ] } ], "source": [ "%timeit busca_min_cython9(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parece que, virtualmente, el rendimiento es parecido a lo que ya teniamos por lo que parece que nos hemos quedado igual." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Bonus track" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voy a intentar usar pypy (2.4 (CPython 2.7)) conjuntamente con numpypy para ver lo que conseguimos." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1.76405235 0.40015721 0.97873798 ..., 0.15843385 -1.14190142\n", " -1.31097037]\n", " [-1.53292105 -1.71197016 0.04613506 ..., -0.03057244 1.57708821\n", " -0.8128021 ]\n", " [ 0.61334917 1.84369998 0.27109098 ..., -0.53788475 0.39344443\n", " 0.28651827]\n", " ..., \n", " [-0.17117027 0.57332063 -0.89516715 ..., -0.01409412 1.28756456\n", " -0.6953778 ]\n", " [-1.53627571 0.57441228 -0.20564476 ..., 0.90499929 0.51428298\n", " 0.72148202]\n", " [ 0.51262101 -0.90758583 1.78121159 ..., -1.12554283 0.95170926\n", " -1.15237806]]\n", "(443641, 443641)\n", "[ 1 1 1 ..., 1998 1998 1998]\n", "[ 1 3 11 ..., 1968 1977 1985]\n", "0.3795211339\n" ] } ], "source": [ "%%pypy\n", "import numpy as np\n", "import time\n", "\n", "np.random.seed(0)\n", "data = np.random.randn(2000,2000)\n", "\n", "def busca_min(malla):\n", " minimosx = []\n", " minimosy = []\n", " for i in range(1, malla.shape[1]-1):\n", " for j in range(1, malla.shape[0]-1):\n", " if (malla[j, i] < malla[j-1, i-1] and\n", " malla[j, i] < malla[j-1, i] and\n", " malla[j, i] < malla[j-1, i+1] and\n", " malla[j, i] < malla[j, i-1] and\n", " malla[j, i] < malla[j, i+1] and\n", " malla[j, i] < malla[j+1, i-1] and\n", " malla[j, i] < malla[j+1, i] and\n", " malla[j, i] < malla[j+1, i+1]):\n", " minimosx.append(i)\n", " minimosy.append(j)\n", "\n", " return np.array(minimosx), np.array(minimosy)\n", "\n", "resx, resy = busca_min(data)\n", "print(data)\n", "print(len(resx), len(resy))\n", "print(resx)\n", "print(resy)\n", "\n", "t = []\n", "for i in range(100):\n", " t0 = time.time()\n", " busca_min(data)\n", " t1 = time.time() - t0\n", " t.append(t1)\n", "print(sum(t) / 100.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El último valor del output anterior es el tiempo promedio después de repetir el cálculo 100 veces.\n", "\n", "Wow!! Parece que sin hacer modificaciones tenemos que el resultado es 10x - 15x veces más rápido que el obtenido usando la función inicial. Y llega a ser solo 3.5x veces más lento que lo que hemos conseguido con Cython." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Resumen de resultados." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vamos a ver los resultados completos en un breve resumen. Primero vamos a ver los tiempos de las diferentes versiones de la función `busca_min_xxx`:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 3.67 s per loop\n", "1 loops, best of 3: 5.34 s per loop\n", "1 loops, best of 3: 3.41 s per loop\n", "1 loops, best of 3: 3.54 s per loop\n", "1 loops, best of 3: 3.65 s per loop\n", "10 loops, best of 3: 139 ms per loop\n", "10 loops, best of 3: 136 ms per loop\n", "1 loops, best of 3: 5.65 s per loop\n", "10 loops, best of 3: 95.4 ms per loop\n", "10 loops, best of 3: 89 ms per loop\n", "10 loops, best of 3: 92.3 ms per loop\n" ] } ], "source": [ "funcs = [busca_min, busca_min_numba, busca_min_cython1,\n", " busca_min_cython2, busca_min_cython3,\n", " busca_min_cython4, busca_min_cython5,\n", " busca_min_cython6, busca_min_cython7,\n", " busca_min_cython8, busca_min_cython9]\n", "t = []\n", "for func in funcs:\n", " res = %timeit -o func(data)\n", " t.append(res.best)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAA1gAAAGqCAYAAAAWWuWTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAGFdJREFUeJzt3X+M7Xl91/HXm70gv0sBRRDMVhMarFR+h/CjXNq1bgm0\n", "0WCUUOEShaS2dG1Sk9bEuMZY0qoVUkO0wGJBwB+rbaS0gRZ2GpCGFtjdLixFpWAWkR8t5XdQYN/+\n", "cc5lhztz78y9933mfGfu45FM7pmZM9/7Oe97z5l5nu/3fKe6OwAAAFy+u217AQAAACeFwAIAABgi\n", "sAAAAIYILAAAgCECCwAAYIjAAgAAGHJgYFXVA6rqxqr6UFXdXlVPPoqFAQAAHDenDnGdVyT5te5+\n", "blWdSnKfDa8JAADgWKoL/aLhqvq2JDd39587uiUBAAAcTwcdIvgdST5TVa+tqvdX1auq6t5HsTAA\n", "AIDj5qDAOpXkcUle2d2PS/LlJD+18VUBAAAcQwe9BuvjST7e3b+7fv/GnBNYVXX+YwwBAABOqO6u\n", "cz92wcDq7k9W1R1V9cju/u9JrknywcNseFOq6vruvv6o/r7jwlz2MpP9mcteZrKXmezPXPYyk/2Z\n", "y15msj9zOb7Ot6PpMGcRfGmSN1TVPZJ8JMmLJhcGAABwUhwYWN19a5InHsFaAAAAjrUDf9HwAu1s\n", "ewELtbPtBSzQzrYXsFA7217AAu1sewELtLPtBSzUzrYXsEA7217AQu1sewELtLPtBSzUzrYXwKwL\n", 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Recordemos que la versión de pypy ha tardado unos 0.38 segundos." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Y ahora vamos a ver los tiempos entre `busca_min` (la versión original) y la última versión de cython que hemos creado, `busca_min_cython9` usando diferentes tamaños de la matriz de entrada:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 67.9 µs per loop\n", "The slowest run took 4.77 times longer than the fastest. This could mean that an intermediate result is being cached \n", "100000 loops, best of 3: 5.13 µs per loop\n", "100 loops, best of 3: 8.65 ms per loop\n", "10000 loops, best of 3: 177 µs per loop\n", "1 loops, best of 3: 223 ms per loop\n", "100 loops, best of 3: 5.51 ms per loop\n", "1 loops, best of 3: 890 ms per loop\n", "10 loops, best of 3: 26.6 ms per loop\n", "1 loops, best of 3: 3.64 s per loop\n", "10 loops, best of 3: 92.8 ms per loop\n", "1 loops, best of 3: 22.8 s per loop\n", "1 loops, best of 3: 605 ms per loop\n" ] } ], "source": [ "tamanyos = [10, 100, 500, 1000, 2000, 5000]\n", "t_p = []\n", "t_c = []\n", "for i in tamanyos:\n", " data = np.random.randn(i, i)\n", " res = %timeit -o busca_min(data)\n", " t_p.append(res.best)\n", " res = %timeit -o busca_min_cython9(data)\n", " t_c.append(res.best)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 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np.array(t_p) / np.array(t_c)\n", "plt.figure(figsize = (10,6))\n", "plt.plot(tamanyos, ratio, 'bo-')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parece que conseguimos rendimientos que son 40 veces más rápidos que con Python puro que usa un numpy array de por medio (excepto para tamaños de arrays muy pequeños en los que el rendimiento no sería una gran problema)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Apuntes finales" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Después de haber probado Python, Cython, Numba y Pypy:\n", "\n", "Numba:\n", "\n", "* Numba no parece fácilmente generalizable a día de hoy (experiencia personal) y no soporta ni parece que soportará todas las características del lenguaje. La idea me parece increible pero creo que le falta todavía un poco de madurez.\n", "\n", "* Me ha costado instalar numba y llvmlite en linux sin usar conda (con conda no lo he probado por lo que no puedo opinar).\n", "\n", "(Creo que JuanLu estaba preparando un post sobre Numba. Habrá que esperar a ver sus conclusiones).\n", "\n", "Pypy:\n", "\n", "* Pypy ha funcionado como un titán sin necesidad de hacer modificaciones. \n", "\n", "* Destacar que no tengo excesivas experiencias con el mismo \n", "\n", "* Instalarlo no es tarea fácil (he intentado usar PyPy3 con numpypy y he fallado vilmente). Quería usar numpypy y al final he optado por descargar una versión portable con numpy de serie que quizá afecte al rendimiento ¿?.\n", "\n", "Cython:\n", "\n", "* Me ha parecido el más generalizable de todos. Se pueden crear paquetes para CPython, para Pypy,...\n", "\n", "* No lo he probado en Windows por lo que no sé lo doloroso que puede llegar a ser. Mañana lo probaré en el trabajo y ya dejaré un comentario por ahí.\n", "\n", "* El manejo no es tan evidente como con Numba y Pypy. Requiere entender como funcionan los tipos de C y requiere conocer una serie de interioridades de C. Sin duda es el que más esfuerzo requiere de las alternativas aquí expuestas pra este caso concreto y no generalizable.\n", "\n", "* Creo que, una vez hecho el esfuerzo inicial de intentar entender un poco como funciona, se puede sacar un gran rendimiento del mismo en muchas situaciones." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Y después de haber leído todo esto pensad que, en la mayoría de situaciones, CPython no es tan lento como lo pintan (sobretodo con numpy) y que ¡¡¡LA OPTIMIZACIÓN PREMATURA ES LA RAÍZ DE TODOS LOS MALES!!!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }	bsd-2-clause
kaushikpavani/neural_networks_in_python	src/linear_regression/linear_regression.ipynb	1	25271	{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given a 2D set of points spanned by axes $x$ and $y$ axes, we will try to fit a line that best approximates the data. The equation of the line, in slope-intercept form, is defined by: $y = mx + b$. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generate_random_points_along_a_line (slope, intercept, num_points, abs_value, abs_noise):\n", " # randomly select x\n", " x = np.random.uniform(-abs_value, abs_value, num_points)\n", " # y = mx + b + noise\n", " y = slopex + intercept + np.random.uniform(-abs_noise, abs_noise, num_points)\n", " return x, y\n", "\n", "def plot_points(x,y):\n", " plt.scatter(x, y)\n", " plt.title('Scatter plot of x and y')\n", " plt.xlabel('x')\n", " plt.ylabel('y')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f20a58eb0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "slope = 4\n", "intercept = -3\n", "num_points = 20\n", "abs_value = 4\n", "abs_noise = 2\n", "x, y = generate_random_points_along_a_line (slope, intercept, num_points, abs_value, abs_noise)\n", "plot_points(x, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If $N$ = num_points, then the error in fitting a line to the points (also defined as Cost, $C$) can be defined as:\n", "\n", "\n", "$C = \\sum_{i=0}^{N} (y-(mx+b))^2$\n", "\n", "To perform gradient descent, we need the partial derivatives of Cost $C$ with respect to slope $m$ and intercept $b$.\n", "\n", "$\\frac{\\partial C}{\\partial m} = \\sum_{i=0}^{N} -2(y-(mx+b)).x$\n", "\n", "$\\frac{\\partial C}{\\partial b} = \\sum_{i=0}^{N} -2(y-(mx+b))$" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# this function computes gradient with respect to slope m\n", "def grad_m (x, y, m, b):\n", " return np.sum(np.multiply(-2(y - (mx + b)), x))\n", "\n", "# this function computes gradient with respect to intercept b\n", "def grad_b (x, y, m, b):\n", " return np.sum(-2(y - (mx + b)))\n", "\n", "# Performs gradient descent\n", "def gradient_descent (x, y, num_iterations, learning_rate):\n", " # Initialize m and b\n", " m = np.random.uniform(-1, 1, 1)\n", " b = np.random.uniform(-1, 1, 1)\n", " # Update m and b in direction opposite to that of the gradient to minimize loss \n", " for i in range(num_iterations):\n", " m = m - learning_rate grad_m (x, y, m, b)\n", " b = b - learning_rate * grad_b (x, y, m, b)\n", " # Return final slope and intercept\n", " return m, b\n", "\n", "# Plot point along with the best fit line\n", "def plot_line (m, b, x, y):\n", " plot_points(x,y)\n", " plt.plot(x, x*m + b, 'r')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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UU/kk4Vv4RcCfwuOhwLrIc+uAQ6sekYhInarYjGYzawEOLvLUFHefG15zLbDD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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f20a568a790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# In general, keep num_iterations high and learning_rate low.\n", "num_iterations = 1000\n", "learning_rate = 0.0001\n", "\n", "m, b = gradient_descent (x, y, num_iterations, learning_rate)\n", "plot_line (m, b, x, y)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	mit

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