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codeparrot
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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
xR86/ml-stuff	labs-neural-networks/hw-lab2/Refactor.ipynb	1	7439	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Solution of a linear system <a class=\"tocSkip\">\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "https://drive.google.com/file/d/1hBZ4tsoCt9P2v3SLft-Cn9_dhTQ9ULbz/view\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "import sys\n", "import re\n", "import numpy as np\n", "#import parser as ps" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 16\n", "-rw-r--r-- 1 root root 7419 Oct 8 16:28 Refactor.ipynb\n", "drwxrwxrwx 2 1000 1000 4096 Jun 11 17:20 \u001b[0m\u001b[34;42minput\u001b[0m/\n", "-rwxrwxrwx 1 1000 1000 49 Jun 7 19:23 \u001b[01;32minput.txt\u001b[0m\n", "\n", "total 40\n", "-rwxrwxrwx 1 1000 1000 51 Jun 7 19:23 \u001b[0m\u001b[01;32meqs0\u001b[0m\n", "-rwxrwxrwx 1 1000 1000 50 Jun 7 19:23 \u001b[01;32meqs1\u001b[0m\n", "-rwxrwxrwx 1 1000 1000 52 Jun 7 19:23 \u001b[01;32meqs2\u001b[0m\n", "-rwxrwxrwx 1 1000 1000 56 Jun 7 19:23 \u001b[01;32meqs3\u001b[0m\n", "-rwxrwxrwx 1 1000 1000 56 Jun 7 19:23 \u001b[01;32meqs4\u001b[0m\n", "-rwxrwxrwx 1 1000 1000 56 Jun 7 19:23 \u001b[01;32meqs5\u001b[0m\n", "-rwxrwxrwx 1 1000 1000 56 Jun 7 19:23 \u001b[01;32meqs6\u001b[0m\n", "-rwxrwxrwx 1 1000 1000 56 Jun 7 19:23 \u001b[01;32meqs7\u001b[0m\n", "-rwxrwxrwx 1 1000 1000 51 Jun 7 19:23 \u001b[01;32meqs8\u001b[0m\n", "-rwxrwxrwx 1 1000 1000 56 Jun 7 19:23 \u001b[01;32meqs9\u001b[0m*\n" ] } ], "source": [ "%ls -l\n", "print()\n", "%ls -l input/" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# if len(sys.argv) > 1:\n", "# #print sys.argv[1]\n", "# f = open('input/'+sys.argv[1], 'r')\n", "# else:\n", "# f = open('input.txt', 'r')\n", "\n", "f = open('input.txt', 'r')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "# pattern = re.compile(r'(?P<a>(-?\\d+/))x(?P<b>([+-]\\d+/))y(?P<c>([+-]\\d+/))(\\=)(?P<r>[0-9]+)')\n", "\n", "pattern = re.compile(r'(?P<a>(-?\\d+))x(?P<b>([+-]\\d+))y(?P<c>([+-]\\d+))(\\=)(?P<r>[0-9]+)')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "_ = \"\"\"\n", "matrixA = np.array([])\n", "matrixB = np.array([])\n", "\n", "for line in f:\n", " #line = f.readline()\n", " #newLine = ps.expr(line).compile()\n", " #match = pattern.match(line) #or findall\n", " match = re.search(pattern, line)#.groups()\n", " print match, type(match)\n", " print pattern, type(pattern)\n", " \n", " if match:\n", " a = int(match.group('a'))\n", " b = int(match.group('b'))\n", " c = int(match.group('c'))\n", " newRow = [a, b, c]\n", " matrixA = np.concatenate(matrixA, newRow) \n", " \n", " r = int(match.group('r'))\n", " newRow = [r]\n", " matrixB = np.concatenate(matrixB, newRow)\n", " else:\n", " raise Exception('Pattern not matched')\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "matrixA:\n", " [[2 3 6]\n", " [0 4 2]\n", " [1 3 5]]\n", "\n", "matrixB:\n", " [[2]\n", " [3]\n", " [1]]\n" ] } ], "source": [ "matrixA = np.array([[2, 3, 6], [0, 4, 2], [1, 3, 5]])\n", "matrixB = np.array([[2], [3], [1]])\n", "\n", "print('matrixA:\\n %s\\n' % matrixA)\n", "print('matrixB:\\n %s' % matrixB)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.86 ms, sys: 33.9 ms, total: 37.7 ms\n", "Wall time: 44.3 ms\n" ] } ], "source": [ "%%time\n", "detA = np.linalg.det(matrixA)\n", "\n", "if detA == 0:\n", " print('Determinant is 0. Program will stop')\n", " quit()\n", "\n", "matrixAinv = np.linalg.inv(matrixA)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--- Inverse matrix is: \n", "[[ 1.4 0.3 -1.8]\n", " [ 0.2 0.4 -0.4]\n", " [-0.4 -0.3 0.8]]\n", "\n", "Is matrixAinv ok ? True\n" ] } ], "source": [ "print('---', 'Inverse matrix is: ')\n", "print(matrixAinv)\n", "checkInv = np.allclose(np.dot(matrixA, matrixAinv), np.eye(3))\n", "\n", "print(\"\\nIs matrixAinv ok ?\", checkInv)\n", "\n", "#x = np.linalg.solve(a, b, c)\n", "\n", "#Check that the solution is correct:\n", "#np.allclose(np.dot(a, x), b)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "---\n" ] } ], "metadata": { "celltoolbar": "Slideshow", "hide_input": false, "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" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "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
xqding/StatisticsExamples	Rao-Blackwellization/main.ipynb	1	258448	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Rao-Blackwellization\n", "Rao-Blackwellization is a statistical method for constructing a better estimator, in the term of its mean square error, by transforming a crude estimator. The transformed estimator is often called a Rao-Blackwellized estimator; its mean square error is always smaller than or equal to that of the crude estimator, which is guaranteed by the Rao-Blackwell Theorem (https://en.wikipedia.org/wiki/Rao–Blackwell_theorem)\n", "\n", "In this example, Rao-Blackwellization is applied for both point estimation and density estimation. In both cases, the Rao-Blackwellized estimator is much better than the crude estimator used for constructing the Rao-Blackwellized estimator." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Point Estimator\n", "Random variable $T$ has a standard Student's t-distribution with degree of freedom of $\\nu\\ $ ($\\mathcal{T}(\\nu)$) and it has probability density function:\n", "$$p(t\\ \|\\ \\nu)=\\frac{\\Gamma(\\frac{\\nu+1}{2})}{\\sqrt{\\pi\\nu}\\ \\Gamma(\\frac{\\nu}{2})} (1+\\frac{t^2}{\\nu})^{-\\frac{\\nu+1}{2}}, t \\in (-\\infty,\\infty).$$\n", "The transformation $X = \\mu + \\sigma T$ generates a new random variable $X$,which has non-standard Student's t-distribution ($\\mathcal{T}(\\nu,\\mu,\\sigma^2)$) with density function:\n", "$$p(x\\ \|\\ \\nu,\\mu,\\sigma^2)=\\frac{\\Gamma(\\frac{\\nu+1}{2})}{\\sigma\\sqrt{\\pi\\nu}\\ \\Gamma(\\frac{\\nu}{2})} (1+\\frac{1}{\\nu}(\\frac{x-\\mu}{\\sigma})^2)^{-\\frac{\\nu+1}{2}}, x \\in (-\\infty,\\infty),$$\n", "where $\\mu$ is the location parameter and $\\sigma$ is the scale parameter.\n", "\n", "Using Dickey's decomposition, we can simulate the non-standard Student's t-distribution as a gamma mixture of normal distribution:\n", "$$X\\ \|\\ y \\sim \\mathcal{N}(\\mu, \\sigma^2y) \\text{ and } Y^{-1}\\sim \\Gamma(\\frac{\\nu}{2},\\frac{2}{\\nu}) \\Rightarrow X \\sim \\mathcal{T}(\\nu,\\mu,\\sigma^2) $$\n", "\n", "In this example, we are interested in estimating the expected value of $h(x)=\\exp(-x^2)$ for $X \\sim \\mathcal{T}(\\nu,\\mu,\\sigma^2)$ using samples $\\{(y_i,x_i),i=1,2,...n\\}$ generated from the distribution. We would compare two different estimators: naive estimator and Rao-Blacwellized estimator based on the naive estimator.\n", "\n", "1. Naive Estimator\n", "$$\\hat{h}=\\frac{1}{n}\\sum_{i=1}^{n}{\\exp(-x_i^2)}$$\n", "\n", "2. Rao-Blackwellized Estimator\n", "$$\\hat{h}^* =\\frac{1}{n}\\sum_{i=1}^{n}{\\mathbb{E}[\\exp(-X^2)\\ \|\\ y_i]} \n", "= \\frac{1}{n}\\sum_{i=1}^{n}{\\frac{1}{\\sqrt{2\\sigma^2y_i+1}}}$$" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.stats as stats\n", "from sklearn.neighbors import KernelDensity" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## parameters for the non-standard Student's t-distribution\n", "nu = 4.6\n", "mu = 0.0\n", "sigma = 1.0\n", "## the true value of the expectation of exp(-x^2) calculated \n", "## using numerical integration\n", "trueValue = 0.537332 " ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## repeat the estimations of exp(-x^2) to get the variance \n", "## of the estimation\n", "R = 50 # num of repeats\n", "naive = []\n", "RaoBlackwellization = []\n", "native = []\n", "r = 1\n", "while r <= 50:\n", " ## simulate (X,Y) from the joint distribution\n", " N = 500\n", " data = []\n", " for i in range(N):\n", " y = 1/np.random.gamma(nu/2.0, 2.0/nu)\n", " x = np.random.normal(mu, np.sqrt(sigma*2y))\n", " data.append((x,y))\n", "\n", " ## naive estimate exp(-x^2)\n", " naiveEstimate = []\n", " naiveCumsum = 0.0\n", " for i in range(N):\n", " naiveCumsum += np.exp(-data[i][0]*2)\n", " naiveEstimate.append(naiveCumsum / float(i+1))\n", " naive.append(naiveEstimate);\n", "\n", " ## Rao-Blackwellization estimation\n", " RaoBlackwellizedEstimate = []\n", " RaoBlackwellizedCumsum = 0.0\n", " for i in range(N):\n", " RaoBlackwellizedCumsum += 1.0/np.sqrt(2.0 data[i][1] + 1)\n", " RaoBlackwellizedEstimate.append(\n", " RaoBlackwellizedCumsum/float(i+1))\n", " RaoBlackwellization.append(RaoBlackwellizedEstimate)\n", " r+=1\n", "\n", " ## simulate X from the native T distribution\n", " nativeX = stats.t.rvs(nu, loc = mu, scale = sigma, size = N)\n", " nativeEstimate = []\n", " nativeCumsum = 0\n", " for i in range(N):\n", " nativeCumsum += np.exp(-nativeX[i]2)\n", " nativeEstimate.append(nativeCumsum / float(i+1))\n", " native.append(nativeEstimate);" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1112cd7b8>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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B633ttdeoVKkSvXr1AmDPnj1kZGTwv//9jyFDhtC4cWM++eQThg8fzqZNmxg/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\nwm+za5f/UAlrpIqK/De+HTs6huwIH8tUUNAxr6uDTZv8h159vX9dV+e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"text/plain": [ "<matplotlib.figure.Figure at 0x11152b748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## plot the results\n", "naive = np.array(naive)\n", "naiveMean = naive.mean(0)\n", "naiveStd = np.sqrt(((naive - trueValue)2).mean(0))\n", "\n", "RaoBlackwellization = np.array(RaoBlackwellization)\n", "RaoBlackwellizationMean = RaoBlackwellization.mean(0)\n", "RaoBlackwellizationStd = \n", " np.sqrt(((RaoBlackwellization - trueValue)2).mean(0))\n", "\n", "native = np.array(native)\n", "nativeMean = native.mean(0)\n", "nativeStd = np.sqrt(((native - trueValue)2).mean(0))\n", "\n", "plt.plot(naiveStd, label = \"naive\", color = \"red\", linewidth = 1.5)\n", "plt.plot(nativeStd, label = \"native\", color = \"black\", linewidth = 1.5)\n", "plt.plot(RaoBlackwellizationStd, label = \"Rao-Blackwellization\", \n", " color = \"blue\", linewidth = 1.5)\n", "plt.ylim(-0.05)\n", "plt.legend()\n", "plt.title(\"Estimation of exp(-X^2) for Student's t distribution\",\n", " fontsize = 15)\n", "plt.xlabel(\"num of draws\", fontsize = 15)\n", "plt.ylabel(\"RMSE\",fontsize = 15)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.3, 0.7)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EmA+8KyJd6pjsVeA4YAwwAMgHlrryHA68BDwFHAK8DrwmIoPTLZ9SSimlmk9D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\n/Y2COSHOu/00ppzn2hDDYdi4EX76Cda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"text/plain": [ "<matplotlib.figure.Figure at 0x1118c0b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## standard variation \n", "plt.errorbar(range(500)[::10],nativeMean[::10], \n", " yerr = nativeStd[::10], color = \"black\")\n", "plt.errorbar(range(500)[::10],naiveMean[::10], \n", " yerr = naiveStd[::10], color = \"red\")\n", "plt.errorbar(range(500)[::10],RaoBlackwellizationMean[::10], \n", " yerr = RaoBlackwellizationStd[::10], color = \"blue\")\n", "plt.hlines(y=trueValue,xmin = 0,xmax = 500)\n", "plt.title(\"Standard Variation for Estimators across Repeating Runs\")\n", "plt.ylim((0.3,0.7))" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x110eede80>" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lOhu1OTqDEHSG6HB0RmYmGk7lCxHZ1+kKOoZhGIZhGLs8JlyrnyQ0PMq5ToNv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\nqkBycmkhT7huyd+bha5BtMU1IFwDvx7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KaNdOhXBeRLzRkpKfgf3YsmULnHiiWq/8SE2F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\nC2STGlzjr+szBdchQ8J9Y3Wl8etxEF2+PATebAsFpJcKlJWF+9ZaFUtER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"text/plain": [ "<matplotlib.figure.Figure at 0x110c46c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## plot some sample estimations\n", "plt.clf()\n", "plt.plot([trueValue for i in range(N)], \n", " label = \"True Value\", color = \"cyan\")\n", "plt.plot(naive[0,:], label = \"naive\", color = \"red\")\n", "plt.plot(naive[10,:], color = \"red\")\n", "plt.plot(native[0,:], label = \"native\", color = \"black\")\n", "plt.plot(native[10,:], color = \"black\")\n", "plt.plot(RaoBlackwellization[0,:], \n", " label = \"Rao-Blackwellization\", color = \"blue\")\n", "plt.plot(RaoBlackwellization[10,:], color = \"blue\")\n", "plt.ylim((0.40, 0.65))\n", "plt.legend()\n", "plt.title(\"Examples for Estimation of exp(-X^2) of \\\n", " Student's t distribution\",\n", " fontsize = 15)\n", "plt.xlabel(\"num of draws\")\n", "plt.ylabel(\"estimated value\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 2.Density Estimation\n", "In this example, we still work with the non-standard Student's t-distribution: $\\mathcal{T}(\\nu,\\mu,\\sigma^2)$. The difference is that here we are now interested in estimating the density function using samples $\\{(y_i,x_i),i=1,2,...n\\}$ generated from the distribution. Again we would compare two different estimators: naive estimator and Rao-Blacwellized estimator based on the naive estimator.\n", "\n", "1.Naive Estimator\n", "\n", "Kernel based density estimation with Gaussian Kernel is used.\n", "$$\\hat{p}(x)= \\frac{1}{n}\\sum_{i=1}^{n}{f(x\\ \|\\ x_i,w)},$$\n", "where $f(x\\ \|\\ x_i,w) = \\frac{1}{\\sqrt{2\\pi}w}\\exp(-\\frac{1}{2}(\\frac{x-x_i}{w})^2)$, and $w$ is the kernel width.\n", "\n", "2.Rao-Blackwellized Estimator\n", "$$\\hat{p}^(x)=\\frac{1}{n}\\sum_{i=1}^{n}{p(x\\ \|\\ y_i)}=\\frac{1}{\\sqrt{2\\pi\\sigma^2y}}\\exp(-\\frac{1}{2}(\\frac{x-\\mu}{\\sigma\\sqrt{y}})^2)$$\n" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## true underlying density \n", "X = np.linspace(-10,10,200)\n", "original_den = stats.t.pdf(X, nu, loc = mu, scale = sigma)\n", "\n", "#### calcuate absolute error (AE) for naive method and \n", "#### Rao-Blackwellization method \n", "## simulate (X,Y) from the joint distribution \n", "N = 1000\n", "data = []\n", "X_sample = []\n", "Y_sample = []\n", "for i in range(N):\n", " y = 1.0/np.random.gamma(nu/2.0, 2.0/nu)\n", " x = np.random.normal(mu, np.sqrt(sigma2y))\n", " X_sample.append(x)\n", " Y_sample.append(y)\n", " data.append((x,y))\n", "X_sample = np.reshape(X_sample, (-1,1))\n", "\n", "naive_AE = []\n", "RaoBlackwellized_AE = []\n", "for k in range(50,N+1,50):\n", " ## print(k)\n", " ## density estimation using kernel method \n", " kde = KernelDensity(kernel = \"gaussian\", \n", " bandwidth = 0.5).fit(X_sample[0:k])\n", " log_den = kde.score_samples(np.reshape(X, (-1,1)))\n", " naive_den = np.exp(log_den)\n", " naive_AE.append(np.sum(np.abs(naive_den - original_den)))\n", "\n", " ## density estimation using Rao-Blackwellization \n", " RaoBlackwellized_den = []\n", " for x in X:\n", " cumsum = 0.0\n", " for y in Y_sample[0:k]:\n", " cumsum += stats.norm.pdf(x, loc = mu, \n", " scale = np.sqrt(sigma*2y))\n", " RaoBlackwellized_den.append(cumsum / k)\n", " RaoBlackwellized_den = np.array(RaoBlackwellized_den)\n", " RaoBlackwellized_AE.append(np.sum(np.abs(RaoBlackwellized_den \n", " - original_den)))" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x112ef8128>" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QNmnSJI488khuu+02atWqxSeffMK9997Lrl27ePjhhwPqDR06lFNOOYWxY8fy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\nmMGhh3qz1J13wmWXwcKFqY+dNw8efdS3xHu32652IPP97/s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"text/plain": [ "<matplotlib.figure.Figure at 0x112ed0898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(range(50,N+1,50), naive_AE, label = \"naive\", \n", " color = \"red\", linewidth = 3)\n", "plt.plot(range(50,N+1,50), RaoBlackwellized_AE, \n", " label = \"Rao-Blackwellization\", \n", " color = \"blue\", linewidth = 3)\n", "plt.xlabel(\"num of samples\", fontsize = 15)\n", "plt.ylabel(\"absolute error\", fontsize = 15)\n", "plt.title(\"Absolute Error for Density Estimation\", \n", " fontsize = 15)\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## density estimation using Rao-Blackwellization\n", "RaoBlackwellized_den = []\n", "for x in X: \n", " cumsum = 0.0 \n", " for y in Y_sample: \n", " cumsum += stats.norm.pdf(x, loc = mu, \n", " scale = np.sqrt(sigma*2y))\n", " RaoBlackwellized_den.append(cumsum / N) " ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "naive: 1.0511, Rao-Blackwellization: 0.0329\n" ] }, { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x1144cc2e8>" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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f//oXF110kS+te7YN2K4CYwybN2+mf//+fvs2b95Mhw52XcehQ4ey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\n1YiIZBeFEck/u3dnzXyRqAE9IwojIiIxCiOSf3bs6BdGEhc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"text/plain": [ "<matplotlib.figure.Figure at 0x114464390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## plot\n", "naive_AE = np.sum(np.abs(naive_den - original_den)) \n", "RaoBlackwellized_AE = np.sum(np.abs(RaoBlackwellized_den - original_den)) \n", "print(\"naive: {0:.4f}, Rao-Blackwellization: {1:.4f}\".format(\n", " naive_AE, RaoBlackwellized_AE))\n", "plt.subplot(121) \n", "plt.fill(X, original_den, label = \"original\", fc='black', \n", " alpha=0.2) \n", "plt.plot(X, naive_den, label = \"naive:{0:.2f}\".format(naive_AE), \n", " color = \"red\", linewidth = 2)\n", "plt.plot(X, RaoBlackwellized_den, \n", " label = \"Rao-Blackwell:{0:.2f}\".format(RaoBlackwellized_AE),\n", " color = \"blue\", linewidth = 2)\n", "plt.title(\"Estimated Density with 500 Samples\", fontsize = 15)\n", "plt.xlim((-12,12))\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Summary\n", "As shown in both point estimation and density estimation examples, the Rao-Blackwellized estimators are better than the original estimator used for constructing the Rao-Blackwellized estimators." ] } ], "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": 1 }	gpl-3.0
Boussau/Notebooks	Notebooks/Plot with figures.ipynb	1	4576810	null	gpl-2.0
stechma2/PECAN-MP-Analysis	_deprecated/_writePECANprms_old-with-PIP.ipynb	1	24049	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-10-22T15:22:11.138368", "start_time": "2017-10-22T15:22:10.132885" }, "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "from netCDF4 import Dataset\n", "import numpy as np\n", "import sys\n", "import time" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-10-22T15:22:11.154882", "start_time": "2017-10-22T15:22:11.140182" }, "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "flt = ['20150706']\n", "\n", "flts = ['20150617','20150620','20150701','20150702','20150706','20150709']\n", "\n", "savePath = '/Users/danstechman/GoogleDrive/PECAN-Data/'\n", "\n", "# Recreate all parameter files\n", "runAll = True" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-10-22T15:22:11.171057", "start_time": "2017-10-22T15:22:11.157489" }, "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "if runAll:\n", " runFlights = flts\n", "else:\n", " runFlights = flt" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-10-22T15:22:20.073242", "start_time": "2017-10-22T15:22:11.176391" }, "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "for flight in runFlights:\n", " if flight == '20150617':\n", " ## Flight-level data parameters\n", " flFile = '20150617I1_AXC.nc'\n", " dewPtSens = 'TDM.1'\n", " altSens = 'AltGPS.2'\n", " wsSens = 'WS.dX'\n", "\n", "\n", " ## Microphysics parameters\n", " # Start and end times of spirals in seconds\n", " startT = np.array([14203, 16750, 18576, 20353, 21846, 23588, 24995],dtype=float)\n", " endT = np.array([14917, 17948, 19577, 21440, 22905, 24570, 25895],dtype=float)\n", " \n", " # Start and end times of PDDs in seconds\n", " PDDstartT = np.array([9272, 9671, 10827, 12996, 14925, 16023, 19682, 21499, 24672, 26046],dtype=float)\n", " PDDendT = np.array([9647, 10433, 11843, 14107, 15484, 16602, 20322, 21846, 24995, 26376],dtype=float)\n", "\n", " # Time/[temp at same time] when last evidence of ice was observed\n", " mlBotTemp = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " mlBotTime = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", "\n", " # Time/[temp at same time] when first evidence of liquid water was observed\n", " mlTopTemp = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " mlTopTime = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", "\n", " # MCS stage of evolution. \n", " # F = Formative, M = Mature, W = Weakening, U = Unclassified\n", " mcsStg = np.array(['U', 'U', 'U', 'U', 'U', 'U', 'U'],dtype=str)\n", "\n", " # Location of spiral relative to MCS structure. \n", " # T = Transition Zone, S = Enhanced Stratiform Region, A = Rear Anvil, U = Unclassified\n", " sprlZone = np.array(['U', 'U', 'U', 'U', 'U', 'U', 'U'],dtype=str)\n", "\n", " # Thresholds of particle interarrival time (in seconds) for each spiral \n", " # below which particles are to be considered shattered\n", " CIP_intArvThrsh = np.array([1.9e-5, 1.2e-5, 7.8e-5, 3.7e-6, 7e-6, 8.7e-6, 3e-6],dtype=float)\n", " PIP_intArvThrsh = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " \n", " PIP_acptStartT = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], dtype=float)\n", " PIP_acptEndT = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " PIP_rjctStartT = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " PIP_rjctEndT = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", "\n", " elif flight == '20150620':\n", " ## Flight-level data parameters\n", " flFile = '20150620I1_AXC.nc'\n", " dewPtSens = 'TDM.1'\n", " altSens = 'AltGPS.2'\n", " wsSens = 'WS.dX'\n", "\n", "\n", " ## Microphysics parameters\n", " startT = np.array([17757, 18230, 19284, 20845, 21877, 23890, 27947],dtype=float)\n", " endT = np.array([18229, 18783, 20227, 21826, 22968, 24451, 28702],dtype=float)\n", " \n", " # Start and end times of PDDs in seconds\n", " PDDstartT = np.array([15109, 17115, 18841, 20289, 22907, 24433, 25740, 26898],dtype=float)\n", " PDDendT = np.array([17002, 17715, 19295, 20838, 23876, 25709, 26774, 27973],dtype=float)\n", "\n", " mlBotTemp = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " mlBotTime = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", "\n", " mlTopTemp = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " mlTopTime = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", "\n", " mcsStg = np.array(['U', 'U', 'U', 'U', 'U', 'U', 'U'],dtype=str)\n", "\n", " sprlZone = np.array(['U', 'U', 'U', 'U', 'U', 'U', 'U'],dtype=str)\n", "\n", " CIP_intArvThrsh = np.array([np.nan, 7e-6, 3e-5, 6e-6, 6e-6, 1.5e-5, 3e-6],dtype=float) # Need value for new first spiral\n", " PIP_intArvThrsh = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " \n", " PIP_acptStartT = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], dtype=float)\n", " PIP_acptEndT = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " PIP_rjctStartT = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " PIP_rjctEndT = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", "\n", " elif flight == '20150701':\n", " ## Flight-level data parameters\n", " flFile = '20150701I1_AXC.nc'\n", " dewPtSens = 'TDM.2X'\n", " altSens = 'AltGPS.2'\n", " wsSens = 'WS.d'\n", "\n", "\n", " ## Microphysics parameters\n", " startT = np.array([21882],dtype=float)\n", " endT = np.array([22838],dtype=float)\n", " \n", " # Start and end times of PDDs in seconds\n", " PDDstartT = np.array([17421, 21247, 22838, 23775, 25230, 25899, 26398, 27598, 28944, 30158, 31008],dtype=float)\n", " PDDendT = np.array([20037, 21901, 23699, 25152, 25819, 26318, 27519, 28871, 30052, 30978, 31920],dtype=float)\n", "\n", " mlBotTemp = np.array([np.nan],dtype=float)\n", " mlBotTime = np.array([np.nan],dtype=float)\n", "\n", " mlTopTemp = np.array([np.nan],dtype=float)\n", " mlTopTime = np.array([np.nan],dtype=float)\n", "\n", " mcsStg = np.array(['U'],dtype=str)\n", "\n", " sprlZone = np.array(['U'],dtype=str)\n", "\n", " CIP_intArvThrsh = np.array([3e-5],dtype=float)\n", " PIP_intArvThrsh = np.array([np.nan],dtype=float)\n", " \n", " PIP_acptStartT = np.array([np.nan], dtype=float)\n", " PIP_acptEndT = np.array([np.nan],dtype=float)\n", " PIP_rjctStartT = np.array([np.nan],dtype=float)\n", " PIP_rjctEndT = np.array([np.nan],dtype=float)\n", "\n", " elif flight == '20150702':\n", " ## Flight-level data parameters\n", " flFile = '20150702I1_AXC.nc'\n", " dewPtSens = 'TDM.2X'\n", " altSens = 'AltGPS.3'\n", " wsSens = 'WS.d'\n", "\n", "\n", " ## Microphysics parameters\n", " startT = np.array([13336, 15350, 16892],dtype=float)\n", " endT = np.array([14551, 16501, 18196],dtype=float)\n", " \n", " # Start and end times of PDDs in seconds\n", " PDDstartT = np.array([10203, 12967, 14692, 16500, 18177, 20573, 22860],dtype=float)\n", " PDDendT = np.array([11734, 13335, 15350, 16932, 20497, 22783, 25177],dtype=float)\n", "\n", " mlBotTemp = np.array([np.nan, np.nan, np.nan],dtype=float)\n", " mlBotTime = np.array([np.nan, np.nan, np.nan],dtype=float)\n", "\n", " mlTopTemp = np.array([np.nan, np.nan, np.nan],dtype=float)\n", " mlTopTime = np.array([np.nan, np.nan, np.nan],dtype=float)\n", "\n", " mcsStg = np.array(['U', 'U', 'U'],dtype=str)\n", "\n", " sprlZone = np.array(['U', 'U', 'U'],dtype=str)\n", "\n", " CIP_intArvThrsh = np.array([1.7e-4, 3.9e-5, 3.9e-5],dtype=float)\n", " PIP_intArvThrsh = np.array([np.nan, np.nan, np.nan],dtype=float)\n", " \n", " PIP_acptStartT = np.array([np.nan, np.nan, np.nan], dtype=float)\n", " PIP_acptEndT = np.array([np.nan, np.nan, np.nan],dtype=float)\n", " PIP_rjctStartT = np.array([np.nan, np.nan, np.nan],dtype=float)\n", " PIP_rjctEndT = np.array([np.nan, np.nan, np.nan],dtype=float)\n", "\n", " elif flight == '20150706':\n", " ## Flight-level data parameters\n", " flFile = '20150706I1_AC.nc'\n", " dewPtSens = 'TDM.2'\n", " altSens = 'AltGPS.2'\n", " wsSens = 'WS.d'\n", "\n", "\n", " ## Microphysics parameters\n", " startT = np.array([11987, 12653, 15824, 16805, 20419, 21384, 22950, 23879],dtype=float)\n", " endT = np.array([12652, 13534, 16804, 17615, 21349, 22393, 23878, 24875],dtype=float)\n", " \n", " # Start and end times of PDDs in seconds\n", " PDDstartT = np.array([6988, 10269, 11536, 13570, 14475, 15460, 17760, 19098, 22517, 25204],dtype=float)\n", " PDDendT = np.array([8340, 11435, 12063, 14172, 15380, 15820, 18897, 20401, 22950, 27063],dtype=float)\n", "\n", " mlBotTemp = np.array([np.nan, 1.9535, 3.7707, 4.9644, 1.9219, 3.5011, 3.0759, 3.6202],dtype=float) \n", " mlBotTime = np.array([np.nan, 13099, 16228, 17252, 20851, 21872, 23420, 24383],dtype=float) \n", "\n", " mlTopTemp = np.array([np.nan, 0.8741, 0.0271, 0.2957, 0.0111, 0.3134, 0.1221, 0.0554],dtype=float) \n", " mlTopTime = np.array([np.nan, 13054, 16336, 17156, 20917, 21792, 23492, 24281],dtype=float)\n", "\n", " mcsStg = np.array(['F','F', 'F', 'F', 'M', 'M', 'M', 'M'],dtype=str)\n", "\n", " sprlZone = np.array(['T','T', 'T', 'T', 'S', 'S', 'S', 'S'],dtype=str)\n", "\n", " CIP_intArvThrsh = np.array([np.nan, 2.7e-5, 2.7e-5, 3.4e-5, 3.3e-6, 9e-6, 2.5e-5, 2.5e-5],dtype=float) # Need value for new first spiral\n", " PIP_intArvThrsh = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " \n", " PIP_acptStartT = np.array([startT[0], startT[1], startT[2], startT[3], startT[4], startT[5],\n", " np.nan, np.nan], dtype=float)\n", " PIP_acptEndT = np.array([endT[0], 12798, endT[2], 17067, endT[4], 21659,\n", " np.nan, np.nan],dtype=float)\n", " PIP_rjctStartT = np.array([np.nan, 12799, np.nan, 17068, np.nan, 21660,\n", " startT[6], startT[7]],dtype=float)\n", " PIP_rjctEndT = np.array([np.nan, endT[1], np.nan, endT[3], np.nan, endT[5],\n", " endT[6], endT[7]],dtype=float)\n", "\n", " elif flight == '20150709':\n", " ## Flight-level data parameters\n", " flFile = '20150709I1_AC.nc'\n", " dewPtSens = 'TDM.2'\n", " altSens = 'AltGPS.3'\n", " wsSens = 'WS.d'\n", "\n", "\n", " ## Microphysics parameters\n", " startT = np.array([8857, 9584, 10643, 11371, 12769, 13665, 14517, 15379, \n", " 16585, 17482,19045, 19960, 20891, 21834, 22729, 23546],dtype=float)\n", " endT = np.array([9582, 10417, 11369, 12160, 13532, 14516, 15377, 16150, \n", " 17481, 18226, 19959, 20687, 21833, 22363, 23544, 24074],dtype=float)\n", " \n", " # Start and end times of PDDs in seconds\n", " PDDstartT = np.array([5975, 7245, 8433, 10404, 12117, 16351, 18316, 18695, 20694, 22451, 24082],dtype=float)\n", " PDDendT = np.array([7161, 8330, 8847, 10656, 12720, 16602, 18574, 18876, 20885, 22717, 24732],dtype=float)\n", "\n", " mlBotTemp = np.array([0.827, 2.76, 1.584, 2.2527, 2.8054, 4.7628, 3.823, 5.7988, 3.7277, \n", " 5.6487, 2.3316, 4.7067, 3.6857, 4.9586, 2.5909, 5.7844],dtype=float)\n", " mlBotTime = np.array([9166, 10102, 10997, 11769, 13072, 14202, 14867, 15861, 16956, 17932, \n", " 19429, 20445, 21312, 22169, 23075, 23942],dtype=float)\n", "\n", " mlTopTemp = np.array([0.0104, 0.0034, 0.0423, 0.1119, 0.4757, 3.4426, 1.3057, 2.5534, 0.4398, \n", " 2.0643, 0.0656, 1.5315, 0.6095, 2.740, 0.6454, 2.5692],dtype=float)\n", " mlTopTime = np.array([9185, 10028, 11036, 11711, 13146, 14163, 14903, 15820, 17038, \n", " 17866, 19494, 20391, 21391, 22136, 23121, 23882],dtype=float)\n", "\n", " mcsStg = np.array(['F', 'F', 'F', 'F', 'F', 'M', 'M', 'M', 'M',\n", " 'M', 'M', 'M', 'M', 'M', 'M', 'M'],dtype=str)\n", "\n", " sprlZone = np.array(['T', 'T', 'T', 'T', 'S', 'S', 'S', 'S', 'S', \n", " 'S', 'S', 'S', 'S', 'S', 'S', 'S'],dtype=str)\n", "\n", " CIP_intArvThrsh = np.array([6.9e-5, 5.4e-5 ,6.9e-5, 3.4e-5, 3.4e-5, 2.7e-5, 1e-4, 8.7e-5, 4.3e-5, \n", " 1e-4, 3.4e-5, 3.4e-5, 3.4e-5, 2.7e-5, 3.4e-5, 3.4e-5],dtype=float)\n", " PIP_intArvThrsh = np.array([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, \n", " np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],dtype=float)\n", " \n", " PIP_acptStartT = np.array([startT[0], startT[1], 11042, startT[3], startT[4], startT[5], 14891, startT[7], startT[8], \n", " startT[9], startT[10], startT[11], 21125.96, startT[13], 22825, startT[15]], dtype=float)\n", " PIP_acptEndT = np.array([endT[0], 9916, endT[2], 11708, endT[4], 14186, endT[6], 15778, endT[8], \n", " 17845, endT[10], 20372, endT[12], 21986, endT[14], 23677],dtype=float)\n", " PIP_rjctStartT = np.array([np.nan, 9933, startT[2], 11711, np.nan, 14235, startT[6], 15779, np.nan, \n", " 17845, np.nan, 20372, startT[12], 21986, startT[14], 23678],dtype=float)\n", " PIP_rjctEndT = np.array([np.nan, endT[1], 10996, endT[3], np.nan, endT[5], 14878, endT[7], np.nan, \n", " endT[9], np.nan, endT[11], 21120, endT[13], 22818, endT[15]],dtype=float)\n", "\n", " else:\n", " sys.exit('The flight string provided is not valid')\n", "\n", " numSprls = len(startT)\n", " numPDDs = len(PDDstartT)\n", "\n", " \n", " ## Define flight-independent parameters\n", " # Variables used from flight-level data\n", " latSens = 'LatGPS.1'\n", " lonSens = 'LonGPS.1'\n", " tempSens = 'TA.d'\n", " rhSens = 'HUM_REL.d'\n", " spSens = 'PSM.2'\n", " dpSens = 'PQM.2'\n", " wSens = 'DPJ_WSZ'\n", " wind_xSens = 'UWX.d'\n", " wind_ySens = 'UWY.d'\n", " relWind_xSens = 'URX.d'\n", " relWind_ySens = 'URY.d'\n", " wdSens = 'WD.d'\n", " \n", "\n", " \n", " ## Create the netCDF file and define global/variable attributes\n", " rootGrp = Dataset((savePath + flight + '_PECANparams.nc'),'w',format='NETCDF4')\n", " rootGrp.set_fill_on()\n", "\n", " sprls = rootGrp.createDimension('sprls',numSprls)\n", " pdds = rootGrp.createDimension('pdds',numPDDs)\n", "\n", " sT = rootGrp.createVariable('startT','f8',('sprls',))\n", " eT = rootGrp.createVariable('endT','f8',('sprls',))\n", " PsT = rootGrp.createVariable('PDDstartT','f8',('pdds',))\n", " PeT = rootGrp.createVariable('PDDendT','f8',('pdds',))\n", " mlBtmp = rootGrp.createVariable('mlBotTemp','f8',('sprls',))\n", " mlBtime = rootGrp.createVariable('mlBotTime','f8',('sprls',))\n", " mlTtmp = rootGrp.createVariable('mlTopTemp','f8',('sprls',))\n", " mlTtime = rootGrp.createVariable('mlTopTime','f8',('sprls',))\n", " stg = rootGrp.createVariable('mcsStg','S1',('sprls',))\n", " zone = rootGrp.createVariable('sprlZone','S1',('sprls',))\n", " CiaT = rootGrp.createVariable('CIP_intArvThrsh','f8',('sprls',))\n", " PiaT = rootGrp.createVariable('PIP_intArvThrsh','f8',('sprls',))\n", " PAcptST = rootGrp.createVariable('PIP_acptStartT','f8',('sprls',))\n", " PAcptET = rootGrp.createVariable('PIP_acptEndT','f8',('sprls',))\n", " PRjctST = rootGrp.createVariable('PIP_rjctStartT','f8',('sprls',))\n", " PRjctET = rootGrp.createVariable('PIP_rjctEndT','f8',('sprls',))\n", "\n", " # Global attributes\n", " rootGrp.description = 'Flight- and spiral-specific PECAN microphysics parameters'\n", " rootGrp.flight = flight\n", " rootGrp.history = 'Created ' + time.asctime(time.gmtime()) + ' UTC'\n", " rootGrp.FL_rawFile = flFile\n", " rootGrp.FL_procFile = flight + '_FltLvl_Processed.nc'\n", " rootGrp.FL_dwptSens = dewPtSens\n", " rootGrp.FL_altSens = altSens\n", " rootGrp.FL_latSens = latSens\n", " rootGrp.FL_lonSens = lonSens\n", " rootGrp.FL_tempSens = tempSens\n", " rootGrp.FL_rhSens = rhSens\n", " rootGrp.FL_spSens = spSens\n", " rootGrp.FL_dpSens = dpSens\n", " rootGrp.FL_wSens = wSens\n", " rootGrp.FL_wind_xSens = wind_xSens\n", " rootGrp.FL_wind_ySens = wind_ySens\n", " rootGrp.FL_relWind_xSens = relWind_xSens\n", " rootGrp.FL_relWind_ySens = relWind_ySens\n", " rootGrp.FL_wdSens = wdSens\n", " rootGrp.FL_wsSens = wsSens\n", "\n", "\n", " sT.description = 'Time spiral began'\n", " sT.units = 'Seconds since midnight UTC'\n", " sT._Fill_Value = np.nan\n", "\n", " eT.description = 'Time spiral ended'\n", " eT.units = 'Seconds since midnight UTC'\n", " eT._Fill_Value = np.nan\n", " \n", " PsT.description = 'Time PDD began'\n", " PsT.units = 'Seconds since midnight UTC'\n", " PsT._Fill_Value = np.nan\n", "\n", " PeT.description = 'Time PDD ended'\n", " PeT.units = 'Seconds since midnight UTC'\n", " PeT._Fill_Value = np.nan\n", "\n", " mlBtmp.long_name = 'Melting layer bottom temperature'\n", " mlBtmp.description = 'Temperature where last evidence of ice was observed in spiral'\n", " mlBtmp.units = 'deg C'\n", " mlBtmp._Fill_Value = np.nan\n", "\n", " mlBtime.long_name = 'Melting layer bottom time'\n", " mlBtime.description = 'Time when last evidence of ice was observed in spiral'\n", " mlBtime.units = 'Seconds since midnight UTC'\n", " mlBtime._Fill_Value = np.nan\n", "\n", " mlTtmp.long_name = 'Melting layer top temperature'\n", " mlTtmp.description = 'Temperature where first evidence of liquid water was observed in spiral'\n", " mlTtmp.units = 'deg C'\n", " mlTtmp._Fill_Value = np.nan\n", "\n", " mlTtime.long_name = 'Melting layer top time'\n", " mlTtime.description = 'Time when first evidence of liquid water was observed in spiral'\n", " mlTtime.units = 'Seconds since midnight UTC'\n", " mlTtime._Fill_Value = np.nan\n", "\n", " stg.description = 'MCS stage of evolution at time of spiral'\n", " stg.units = 'F = formative; M = mature; W = weakening; U = Unclassified'\n", "\n", " zone.description = 'Location of spiral relative to MCS structure'\n", " zone.units = 'T = Transition Zone; S = Enhanced Stratiform Region; A = Rear Anvil; U = Unclassified'\n", "\n", " CiaT.description = 'Thresholds of CIP particle interarrival time (in seconds) for each spiral below which particles are to be considered shattered'\n", " CiaT.units = 'seconds'\n", " CiaT._Fill_Value = np.nan\n", "\n", " PiaT.description = 'Thresholds of PIP particle interarrival time (in seconds) for each spiral below which particles are to be considered shattered'\n", " PiaT.units = 'seconds'\n", " PiaT._Fill_Value = np.nan\n", " \n", " PAcptST.description = 'Start time of good PIP data for each spiral'\n", " PAcptST.units = 'Seconds since midnight UTC'\n", " PAcptST._Fill_Value = np.nan\n", " \n", " PAcptET.description = 'End time of good PIP data for each spiral'\n", " PAcptET.units = 'Seconds since midnight UTC'\n", " PAcptET._Fill_Value = np.nan\n", " \n", " PRjctST.description = 'Start time of bad PIP data for each spiral'\n", " PRjctST.units = 'Seconds since midnight UTC'\n", " PRjctST._Fill_Value = np.nan\n", " \n", " PRjctET.description = 'End time of bad PIP data for each spiral'\n", " PRjctET.units = 'Seconds since midnight UTC'\n", " PRjctET._Fill_Value = np.nan\n", "\n", " \n", " ## Write variables to file\n", " sT[:] = startT\n", " eT[:] = endT\n", " PsT[:] = PDDstartT\n", " PeT[:] = PDDendT\n", " mlBtmp[:] = mlBotTemp\n", " mlBtime[:] = mlBotTime\n", " mlTtmp[:] = mlTopTemp\n", " mlTtime[:] = mlTopTime\n", " stg[:] = mcsStg\n", " zone[:] = sprlZone\n", " CiaT[:] = CIP_intArvThrsh\n", " PiaT[:] = PIP_intArvThrsh\n", " PAcptST[:] = PIP_acptStartT\n", " PAcptET[:] = PIP_acptEndT\n", " PRjctST[:] = PIP_rjctStartT\n", " PRjctET[:] = PIP_rjctEndT\n", "\n", " rootGrp.close()" ] } ], "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": 1 }	gpl-3.0
phievo/phievo	Examples/immune/Analyse_pMHC.ipynb	1	6680	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse Run #\n", "\n", "This is a template notebook to browse the results of a evolution simulation.\n", "\n", "WARNING: THIS IS THE IMMUNE ADD-ON. THIS NOTEBOOK SHOULD BE MOVED TO BE RUN IN THE SAME DIRECTORY AS run_evolution\n", "\n", "\n", "Please _Restart & Run All_ to make shure you start with a clean notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Enter the path of the project here (necessary to load the addons)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os,sys\n", "#sys.path.append(\"immune\")\n", "sys.path.append(\"example_immune\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import required libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from ipywidgets import widgets \n", "from ipywidgets import interact, interactive, fixed\n", "from IPython.display import display,HTML,clear_output\n", "\n", "HTML('''<script>code_show=true;function code_toggle() {if (code_show){$('div.input').hide();} else {$('div.input').show();} code_show = !code_show} $( document ).ready(code_toggle);</script><form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')\n", "from Immune import Add_ons_pMHC\n", "import phievo.AnalysisTools as AT\n", "from phievo.AnalysisTools.Notebook import Notebook\n", "\n", "notebook = Notebook()\n", "notebook.run_dynamics_pMHC = Add_ons_pMHC.Run_Dynamics_pMHC(notebook)\n", "notebook.plot_pMHC = Add_ons_pMHC.Plot_pMHC(notebook)\n", "notebook.plot_layout_immune = Add_ons_pMHC.Plot_Layout_Immune(notebook)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Select the Project" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "notebook.select_project.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Select Seed" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "notebook.select_seed.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot observable\n", "\n", "For our immune simulations, the fitness is the mutual information between output concentrations (taken as a probability distribution) and binding time. An ideal fitness is $-1$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "notebook.plot_evolution_observable.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Select Generation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "notebook.select_generation.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PLot Layout\n", "\n", "The Layout of the network for immune accounts for the new interactions defined. $0$ represents the ligand, $1$ the receptor. They interact to form complex, that can be phosphorylated/dephosphorylated (black arrows, indexed with the corresponding kinase or phosphatase). All other species are either kinases or phosphatastases. Arrows with $1/\\tau$ correspond to kinetic proofreading steps. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "notebook.plot_layout_immune.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run Dynamics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "notebook.run_dynamics_pMHC.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Response function\n", "\n", "The response function for Immune displays the concentration of all species at the end of simulation as a function of the number of ligands presented. The output is the solid line. Left column is for binding time $\\tau=3s$, right column for binding time $\\tau=10s$. The ideal case such as ``adaptive sorting\" corresponds to horizontal lines for the Output, at different levels for different the $\\tau$ s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "notebook.plot_pMHC.display()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": 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": "3.6.1" }, "widgets": { "state": { "3598eb7646394352ab73d6907bfa607d": { "views": [ { "cell_index": 6 } ] }, "4118ded886f54c2eb0ca23145229c187": { "views": [ { "cell_index": 10 } ] }, "8dfc65a8698340cbb702064232e2b908": { "views": [ { "cell_index": 14 } ] }, "90d3134d143546638aa15b9d929a17b5": { "views": [ { "cell_index": 16 } ] }, "bd2b7b131f324aba8d386ed46df23009": { "views": [ { "cell_index": 8 } ] }, "c12b3fde939e49b08792c5cfa1558d2f": { "views": [ { "cell_index": 12 } ] }, "dc4fac0bb3134c8ea22646980d8fc8a9": { "views": [ { "cell_index": 18 } ] }, "f4663082efc347929023025c4e326e5d": { "views": [ { "cell_index": 4 } ] }, "fcedbb95c6f940e28fc262dab1634ce5": { "views": [ { "cell_index": 20 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }	lgpl-3.0
JasonSanchez/w261	exams/MIDS-MidTerm.ipynb	1	339969	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MIDS Machine Learning at Scale\n", "## MidTerm Exam \n", "\n", "4:00PM - 6:00PM(CT)\n", "October 19, 2016 \n", "Midterm\n", "\n", "MIDS Machine Learning at Scale\n", "\n", "\n", "\n", "### Please insert your contact information here\n", "__Insert you name here__ : Jason Sanchez \n", "__Insert you email here__ : [email protected] \n", "__Insert your UC Berkeley ID here__: 26989981" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from __future__ import division\n", "\n", "%reload_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exam Instructions\n", "\n", "1. : Please insert Name and Email address in the first cell of this notebook\n", "2. : Please acknowledge receipt of exam by sending a quick email reply to the instructor\n", "3. : Review the submission form first to scope it out (it will take a 5-10 minutes to input your \n", " answers and other information into this form): \n", "\n", " + [Exam Submission form](http://goo.gl/forms/ggNYfRXz0t) \n", "\n", "4. : Please keep all your work and responses in ONE (1) notebook only (and submit via the submission form)\n", "5. : Please make sure that the NBViewer link for your Submission notebook works \n", "6. : Please submit your solutions and notebook via the following form:\n", "\n", " + [Exam Submission form](http://goo.gl/forms/ggNYfRXz0t)\n", "\n", "7. : For the midterm you will need access to MrJob and Jupyter on your local machines or on AltaScale/AWS to complete some of the questions (like fill in the code to do X).\n", "8. : As for question types:\n", " + Knowledge test Programmatic/doodle (take photos; embed the photos in your notebook) \n", " + All programmatic questions can be run locally on your laptop (using MrJob only) or on the cluster\n", "\n", "9. : This is an open book exam meaning you can consult webpages and textbooks, class notes, slides etc. but you can not discuss with each other or any other person/group. If any collusion, then this will result in a zero grade and will be grounds for dismissal from the entire program. Please complete this exam by yourself within the time limit. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exam questions begins here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "===Map-Reduce===" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT1. Which of the following statememts about map-reduce are true? \n", "\n", "(I) If you only have 1 computer with 1 computing core, then map-reduce is unlikely to help \n", "(II) If we run map-reduce using N single-core computers, then it is likely to get at least an N-Fold speedup compared to using 1 computer \n", "(III) Because of network latency and other overhead associated with map-reduce, if we run map-reduce using N computers, then we will get less than N-Fold speedup compared to using 1 computer \n", "(IV) When using map-reduce for learning a naive Bayes classifier for SPAM classification, we usually use a single machine that accumulates the partial class and word stats from each of the map machines, in order to compute the final model.\n", "\n", "Please select one from the following that is most correct:\n", "\n", "* (a) I, II, III, IV\n", "* (b) I, III, IV\n", "* (c) I, III\n", "* (d) I,II, III" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# C" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "===Order inversion===\n", "\n", "### MT2. normalized product co-occurrence \n", "\n", "Suppose you wish to write a MapReduce job that creates normalized product co-occurrence (i.e., pairs of products that have been purchased together) data form a large transaction file of shopping baskets. In addition, we want the relative frequency of coocurring products. Given this scenario, to ensure that all (potentially many) reducers\n", "receive appropriate normalization factors (denominators)for a product\n", "in the most effcient order in their input streams (so as to minimize memory overhead on the reducer side), \n", "the mapper should emit/yield records according to which pattern for the product occurence totals: \n", "\n", "(a) emit (\\,product) count \n", "(b) There is no need to use order inversion here \n", "(c) emit (product,\\) count \n", "(d) None of the above " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# A" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "===Map-Reduce===\n", "\n", "### MT3. What is the input to the Reduce function in MRJob? Select the most correct choice. \n", "\n", " \n", "(a) An arbitrarily sized list of key/value pairs. \n", "(b) One key and a list of some values associated with that key \n", "(c) One key and a list of all values associated with that key. \n", "(d) None of the above " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# C\n", "\n", "(Although it is not a list, but a generator)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "===Bayesian document classification=== \n", " \n", "### MT4. When building a Bayesian document classifier, Laplace smoothing serves what purpose? \n", "\n", "(a) It allows you to use your training data as your validation data. \n", "(b) It prevents zero-products in the posterior distribution. \n", "(c) It accounts for words that were missed by regular expressions. \n", "(d) None of the above " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# B " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT5. Big Data\n", "Big data is defined as the voluminous amount of structured, unstructured or semi-structured data that has huge potential for mining but is so large that it cannot be processed nor stored using traditional (single computer) computing and storage systems. Big data is characterized by its high velocity, volume and variety that requires cost effective and innovative methods for information processing to draw meaningful business insights. More than the volume of the data – it is the nature of the data that defines whether it is considered as Big Data or not. What do the four V’s of Big Data denote? Here is a potential simple explanation for each of the four critical features of big data (some or all of which is correct):\n", "\n", "__Statements__ \n", "* (I) Volume –Scale of data\n", "* (II) Velocity – Batch processing of data offline\n", "* (III)Variety – Different forms of data\n", "* (IV) Veracity –Uncertainty of data\n", "\n", "Which combination of the above statements is correct. Select a single correct response from the following :\n", "\n", "* (a) I, II, III, IV\n", "* (b) I, III, IV\n", "* (c) I, III\n", "* (d) I,II, III" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# B" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT6. Combiners can be integral to the successful utilization of the Hadoop shuffle. \n", "Using combiners result in what? \n", "\n", "* (I) minimization of reducer workload \n", "* (II) minimization of disk storage for mapper results \n", "* (III) minimization of network traffic \n", "* (IV) none of the above \n", "\n", "Select most correct option (i.e., select one option only) from the following:\n", "\n", "* (a) I \n", "* (b) I, II and III \n", "* (c) II and III \n", "* (d) IV \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# B (uncertain)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pairwise similarity using K-L divergence\n", "\n", "In probability theory and information theory, the Kullback–Leibler divergence \n", "(also information divergence, information gain, relative entropy, KLIC, or KL divergence) \n", "is a non-symmetric measure of the difference between two probability distributions P and Q. \n", "Specifically, the Kullback–Leibler divergence of Q from P, denoted DKL(P\\‖Q), \n", "is a measure of the information lost when Q is used to approximate P:\n", "\n", "For discrete probability distributions P and Q, \n", "the Kullback–Leibler divergence of Q from P is defined to be\n", "\n", " + KLDistance(P, Q) = Sum_over_item_i (P(i) log (P(i) / Q(i)) \n", "\n", "In the extreme cases, the KL Divergence is 1 when P and Q are maximally different\n", "and is 0 when the two distributions are exactly the same (follow the same distribution).\n", "\n", "For more information on K-L Divergence see:\n", "\n", " + [K-L Divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence)\n", "\n", "For the next three question we will use an MRjob class for calculating pairwise similarity \n", "using K-L Divergence as the similarity measure:\n", "\n", "* Job 1: create inverted index (assume just two objects)\n", "* Job 2: calculate/accumulate the similarity of each pair of objects using K-L Divergence\n", "\n", "\n", "Using the following cells then fill in the code for the first reducer to calculate \n", "the K-L divergence of objects (letter documents) in line1 and line2, i.e., KLD(Line1\|\|line2).\n", "\n", "Here we ignore characters which are not alphabetical. And all alphabetical characters are lower-cased in the first mapper." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the MRJob Class below calculate the KL divergence of the following two string objects." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing kltext.txt\n" ] } ], "source": [ "%%writefile kltext.txt\n", "1.Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from large volumes of data in various forms (data in various forms, data in various forms, data in various forms), either structured or unstructured,[1][2] which is a continuation of some of the data analysis fields such as statistics, data mining and predictive analytics, as well as Knowledge Discovery in Databases.\n", "2.Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[2] Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions,[3]:2 rather than following strictly static program instructions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MRjob class for calculating pairwise similarity using K-L Divergence as the similarity measure\n", "\n", "Job 1: create inverted index (assume just two objects) <P>\n", "Job 2: calculate the similarity of each pair of objects " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0986122886681098" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "np.log(3)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from large volumes of data in various forms (data in various forms, data in various forms, data in various forms), either structured or unstructured,[1][2] which is a continuation of some of the data analysis fields such as statistics, data mining and predictive analytics, as well as Knowledge Discovery in Databases.\r\n", "2.Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[2] Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions,[3]:2 rather than following strictly static program instructions." ] } ], "source": [ "!cat kltext.txt" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting kldivergence.py\n" ] } ], "source": [ "%%writefile kldivergence.py\n", "# coding: utf-8\n", "\n", "from __future__ import division\n", "from mrjob.job import MRJob\n", "from mrjob.step import MRStep\n", "import re\n", "import numpy as np\n", "\n", "class kldivergence(MRJob):\n", " # process each string character by character\n", " # the relative frequency of each character emitting Pr(character\|str)\n", " # for input record 1.abcbe\n", " # emit \"a\" [1, 0.2]\n", " # emit \"b\" [1, 0.4] etc...\n", " def mapper1(self, _, line):\n", " index = int(line.split('.',1)[0])\n", " letter_list = re.sub(r\"[^A-Za-z]+\", '', line).lower()\n", " count = {}\n", " for l in letter_list:\n", " if count.has_key(l):\n", " count[l] += 1\n", " else:\n", " count[l] = 1\n", " for key in count:\n", " yield key, [index, count[key]1.0/len(letter_list)]\n", "\n", " # on a component i calculate (e.g., \"b\")\n", " # Kullback–Leibler divergence of Q from P is defined as (P(i) log (P(i) / Q(i))\n", " def reducer1(self, key, values):\n", " p = 0\n", " q = 0\n", " for v in values:\n", " if v[0] == 1: #String 1\n", " p = v[1]\n", " else: # String 2\n", " q = v[1]\n", " \n", " if p and q:\n", " yield (None, pnp.log(p/q))\n", "\n", " #Aggegate components \n", " def reducer2(self, key, values):\n", " kl_sum = 0\n", " for value in values:\n", " kl_sum = kl_sum + value\n", " yield \"KLDivergence\", kl_sum\n", " \n", " def steps(self):\n", " mr_steps = [self.mr(mapper=self.mapper1,\n", " reducer=self.reducer1),\n", " \n", " self.mr(reducer=self.reducer2)]\n", "# mr_steps = [MRStep(mapper=self.mapper1, reducer=self.reducer1)]\n", " return mr_steps\n", "\n", "if __name__ == '__main__':\n", " kldivergence.run()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(u'KLDivergence', 0.08088278445318145)\n" ] } ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "from mrjob.job import MRJob\n", "from kldivergence import kldivergence\n", "\n", "#dont forget to save kltext.txt (see earlier cell)\n", "mr_job = kldivergence(args=['kltext.txt'])\n", "with mr_job.make_runner() as runner: \n", " runner.run()\n", " # stream_output: get access of the output \n", " for line in runner.stream_output():\n", " print mr_job.parse_output_line(line)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Questions:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT7. Which number below is the closest to the result you get for KLD(Line1\|\|line2)? \n", "(a) 0.7 \n", "(b) 0.5 \n", "(c) 0.2 \n", "(d) 0.1 " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# D" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT8. Which of the following letters are missing from these character vectors? \n", "(a) p and t \n", "(b) k and q \n", "(c) j and q \n", "(d) j and f " ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "q\n", "j\n" ] } ], "source": [ "words = \"\"\"\n", "1.Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from large volumes of data in various forms (data in various forms, data in various forms, data in various forms), either structured or unstructured,[1][2] which is a continuation of some of the data analysis fields such as statistics, data mining and predictive analytics, as well as Knowledge Discovery in Databases.\n", "2.Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.[2] Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions,[3]:2 rather than following strictly static program instructions.\"\"\"\n", "\n", "for char in ['p', 'k', 'f', 'q', 'j']:\n", " if char not in words:\n", " print char" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# C" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing kldivergence_smooth.py\n" ] } ], "source": [ "%%writefile kldivergence_smooth.py\n", "from __future__ import division\n", "from mrjob.job import MRJob\n", "import re\n", "import numpy as np\n", "class kldivergence_smooth(MRJob):\n", " \n", " # process each string character by character\n", " # the relative frequency of each character emitting Pr(character\|str)\n", " # for input record 1.abcbe\n", " # emit \"a\" [1, (1+1)/(5+24)]\n", " # emit \"b\" [1, (2+1)/(5+24) etc...\n", " def mapper1(self, _, line):\n", " index = int(line.split('.',1)[0])\n", " letter_list = re.sub(r\"[^A-Za-z]+\", '', line).lower()\n", " count = {}\n", " \n", " # (ni+1)/(n+24)\n", " \n", " for l in letter_list:\n", " if count.has_key(l):\n", " count[l] += 1\n", " else:\n", " count[l] = 1\n", " \n", " for letter in ['q', 'j']:\n", " if letter not in letter_list:\n", " count[letter] = 0\n", " \n", " for key in count:\n", " yield key, [index, (1+count[key]1.0)/(24+len(letter_list))]\n", "\n", " \n", " def reducer1(self, key, values):\n", " p = 0\n", " q = 0\n", " for v in values:\n", " if v[0] == 1:\n", " p = v[1]\n", " else:\n", " q = v[1]\n", "\n", " yield (None, pnp.log(p/q)) \n", "\n", " # Aggregate components \n", " def reducer2(self, key, values):\n", " kl_sum = 0\n", " for value in values:\n", " kl_sum = kl_sum + value\n", " yield \"KLDivergence\", kl_sum\n", " \n", " def steps(self):\n", " return [self.mr(mapper=self.mapper1,\n", " reducer=self.reducer1),\n", " self.mr(reducer=self.reducer2)\n", " \n", " ]\n", "\n", "if __name__ == '__main__':\n", " kldivergence_smooth.run()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(u'KLDivergence', 0.06791349183751216)\n" ] } ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "\n", "from kldivergence_smooth import kldivergence_smooth\n", "mr_job = kldivergence_smooth(args=['kltext.txt'])\n", "with mr_job.make_runner() as runner: \n", " runner.run()\n", " # stream_output: get access of the output \n", " for line in runner.stream_output():\n", " print mr_job.parse_output_line(line)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT9. The KL divergence on multinomials is defined only when they have nonzero entries. \n", "For zero entries, we have to smooth distributions. Suppose we smooth in this way: \n", "\n", "(ni+1)/(n+24) \n", "\n", "where ni is the count for letter i and n is the total count of all letters. \n", "After smoothing, which number below is the closest to the result you get for KLD(Line1\|\|line2)?? \n", "\n", "(a) 0.08 \n", "(b) 0.71 \n", "(c) 0.02 \n", "(d) 0.11 \n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# A" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT10. Block size, and mapper tasks\n", "Given ten (10) files in the input directory for a Hadoop Streaming job (MRjob or just Hadoop) with the following filesizes (in megabytes): 1, 2,3,4,5,6,7,8,9,10; and a block size of 5M (NOTE: normally we should set the blocksize to 1 GigB using modern computers). How many map tasks will result from processing the data in the input directory? Select the closest number from the following list.\n", "\n", " (a) 1 map task \n", " (b) 14 \n", " (c) 12 \n", " (d) None of the above " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# B" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### MT11. Aggregation\n", "Given a purchase transaction log file where each purchase transaction contains the customer identifier, item purchased and much more information about the transaction. Which of the following statements are true about a MapReduce job that performs an “aggregation” such as get the number of transaction per customer.\n", "\n", "__Statements__\n", "* (I) A mapper only job will not suffice, as each map tast only gets to see a subset of the data (e.g., one block). As such a mapper only job will only produce intermediate tallys for each customer. \n", "* (II) A reducer only job will suffice and is most efficient computationally\n", "* (III) If the developer provides a Mapper and Reducer it can potentially be more efficient than option II\n", "* (IV) A reducer only job with a custom partitioner will suffice.\n", "\n", "Select the most correct option from the following:\n", "\n", "* (a) I, II, III, IV\n", "* (b) II, IV\n", "* (c) III, IV\n", "* (d) III" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# C" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT12. Naive Bayes\n", "Which of the following statements are true regarding Naive Bayes?\n", "\n", "__Statements__\n", "* (I) Naive Bayes is a machine learning algorithm that can be used for classifcation problems only\n", "* (II) Multinomial Naive Bayes is a flavour of Naive Bayes for discrete input variables and can be combined with Laplace smoothing to avoid zero predictions for class posterior probabilities when attribute value combinations show up during classification but were not present during training. \n", "* (III) Naive Bayes can be used for continous valued input variables. In this case, one can use Gaussian distributions to model the class conditional probability distributions Pr(X\|Class).\n", "* (IV) Naive Bayes can model continous target variables directly.\n", "\n", "Please select the single most correct combination from the following:\n", " \n", "\n", "* (a) I, II, III, IV\n", "* (b) I, II, III\n", "* (c) I, III, IV\n", "* (d) I, II\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# B" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT13. Naive Bayes SPAM model\n", "Given the following document dataset for a Two-Class problem: ham and spam. Use MRJob (please include your code) to build a muiltnomial Naive Bayes classifier. Please use Laplace Smoothing with a hyperparameter of 1. Please use words only (a-z) as features. Please lowercase all words." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# Training Data\n", "# Record format\n", "# Class docID:\"doc contents string\"\n", "ham d1:\t “good.”\n", "ham d2: “very good.”\n", "spam d3: “bad.”\n", "spam d4: “very bad.”\n", "spam d5: “very bad, very BAD.”" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting spam.txt\n" ] } ], "source": [ "%%writefile spam.txt\n", "0002.2001-05-25.SA_and_HP\t0\t0\tgood\n", "0002.2001-05-25.SA_and_HP\t0\t0\tvery good\n", "0002.2001-05-25.SA_and_HP\t1\t0\tbad\n", "0002.2001-05-25.SA_and_HP\t1\t0\tvery bad\n", "0002.2001-05-25.SA_and_HP\t1\t0\tvery bad, very BAD" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting spam_test.txt\n" ] } ], "source": [ "%%writefile spam_test.txt\n", "0002.2001-05-25.SA_and_HP\t1\t0\tgood? bad! very Bad! " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting NaiveBayes.py\n" ] } ], "source": [ "%%writefile NaiveBayes.py\n", "\n", "import sys\n", "import re\n", "from mrjob.job import MRJob\n", "from mrjob.step import MRStep\n", "from mrjob.protocol import TextProtocol, TextValueProtocol\n", "\n", "# Prevents broken pipe errors from using ... \| head\n", "from signal import signal, SIGPIPE, SIG_DFL\n", "signal(SIGPIPE,SIG_DFL) \n", "\n", "def sum_hs(counts):\n", " h_total, s_total = 0, 0\n", " for h, s in counts:\n", " h_total += h\n", " s_total += s\n", " return (h_total, s_total)\n", "\n", "\n", "class NaiveBayes(MRJob):\n", " MRJob.OUTPUT_PROTOCOL = TextValueProtocol\n", "\n", " def mapper(self, _, lines):\n", " _, spam, subject, email = lines.split(\"\\t\")\n", " words = re.findall(r'[a-z]+', (email.lower()+\" \"+subject.lower()))\n", " \n", " if spam == \"1\":\n", " h, s = 0, 1\n", " else:\n", " h, s = 1, 0 \n", " yield \"*Total Emails\", (h, s)\n", " \n", " for word in words:\n", " yield word, (h, s)\n", " yield \"Total Words\", (h, s)\n", " \n", " def combiner(self, key, count):\n", " yield key, sum_hs(count)\n", " \n", " def reducer_init(self):\n", " self.total_ham = 0\n", " self.total_spam = 0\n", " \n", " def reducer(self, key, count):\n", " ham, spam = sum_hs(count)\n", " if key.startswith(\"\"):\n", " if \"Words\" in key:\n", " self.total_ham, self.total_spam = ham, spam\n", " elif \"Emails\" in key:\n", " total = ham + spam\n", " yield \"_\", \"Priors\\t%.10f\\t%.10f\" % (ham/total, spam/total)\n", " else:\n", " pg_ham, pg_spam = ham/self.total_ham, spam/self.total_spam\n", " yield \"_\", \"%s\\t%.10f\\t%.10f\" % (key, pg_ham, pg_spam)\n", " \n", "if __name__ == \"__main__\":\n", " NaiveBayes.run()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*Priors\t0.0000000000\t0.0000000000\r\n", "bad\t0.0000000000\t0.0000000000\r\n", "good\t0.0000000000\t0.0000000000\r\n", "very\t0.0000000000\t0.0000000000\r\n" ] } ], "source": [ "!cat spam.txt \| python NaiveBayes.py --jobconf mapred.reduce.tasks=1 -q \| head" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__QUESTION__\n", "\n", "Having learnt the Naive Bayes text classification model for this problem using the training data and classified the test data (d6) please indicate which of the following is true:\n", "\n", "__Statements__\n", " (I) P(very\|ham) = 0.33\n", "* (II) P(good\|ham) = 0.50\n", "* (I) Posterior Probability P(ham\| d6) is approximately 24%\n", "* (IV) Class of d6 is ham\n", "\n", "Please select the single most correct combination of these statements from the following:\n", " \n", "\n", "* (a) I, II, III, IV\n", "* (b) I, II, III\n", "* (c) I, III, IV\n", "* (d) I, II" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# C (wild guess)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT14. Is there a map input format (for Hadoop or MRJob)? \n", "\n", "(a) Yes, but only in Hadoop 0.22+. \n", "(b) Yes, in Hadoop there is a default expectation that each record is delimited by an end of line charcacter and that key is the first token delimited by a tab character and that the value-part is everything after the tab character. \n", "(c) No, when MRJob INPUT_PROTOCOL = RawValueProtocol. In this case input is processed in format agnostic way thereby avoiding any type of parsing errors. The value is treated as a str, the key is read in as None. \n", "(d) Both b and c are correct answers. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# D" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT15. What happens if mapper output does not match reducer input? \n", " \n", "(a) Hadoop API will convert the data to the type that is needed by the reducer. \n", "(b) Data input/output inconsistency cannot occur. A preliminary validation check is executed prior to the full execution of the job to ensure there is consistency. \n", "(c) The java compiler will report an error during compilation but the job will complete with exceptions. \n", "(d) A real-time exception will be thrown and map-reduce job will fail." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# D" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### MT16. Why would a developer create a map-reduce without the reduce step? \n", " \n", "(a) Developers should design Map-Reduce jobs without reducers only if no reduce slots are available on the cluster. \n", "(b) Developers should never design Map-Reduce jobs without reducers. An error will occur upon compile. \n", "(c) There is a CPU intensive step that occurs between the map and reduce steps. Disabling the reduce step speeds up data processing. \n", "(d) It is not possible to create a map-reduce job without at least one reduce step. A developer may decide to limit to one reducer for debugging purposes. \n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# C" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "===Gradient descent===\n", "### MT17. Which of the following are true statements with respect to gradient descent for machine learning, where alpha is the learning rate. Select all that apply\n", "\n", "* (I) To make gradient descent converge, we must slowly decrease alpha over time and use a combiner in the context of Hadoop.\n", "* (II) Gradient descent is guaranteed to find the global minimum for any unconstrained convex objective function J() regardless of using a combiner or not in the context of Hadoop\n", "* (III) Gradient descent can converge even if alpha is kept fixed. (But alpha cannot be too large, or else it may fail to converge.) Combiners will help speed up the process.\n", "* (IV) For the specific choice of cost function J() used in linear regression, there is no local optima (other than the global optimum).\n", "\n", "\n", "Select a single correct response from the following:\n", "* (a) I, II, III, IV\n", "* (b) I, III, IV\n", "* (c) II, III\n", "* (d) II,III, IV" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# D" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "===Weighted K-means===\n", "\n", "Write a MapReduce job in MRJob to do the training at scale of a weighted K-means algorithm.\n", "\n", "You can write your own code or you can use most of the code from the following notebook:\n", "\n", "* http://nbviewer.jupyter.org/urls/dl.dropbox.com/s/oppgyfqxphlh69g/MrJobKmeans_Corrected.ipynb\n", "\n", "Weight each example as follows using the inverse vector length (Euclidean norm): \n", "\n", "weight(X)= 1/\|\|X\|\|, \n", "\n", "where \|\|X\|\| = SQRT(X.X)= SQRT(X1^2 + X2^2)\n", "\n", "Here X is vector made up of two component X1 and X2.\n", "\n", "Using the following data to answer the following TWO questions:\n", "\n", "* https://www.dropbox.com/s/ai1uc3q2ucverly/Kmeandata.csv?dl=0" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.19611613513818404" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def inverse_vector_length(x1, x2):\n", " norm = (x12 + x22)*.5\n", " return 1.0/norm\n", "\n", "inverse_vector_length(1, 5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "0 --> .2" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import pylab \n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = pd.read_csv(\"Kmeandata.csv\", header=None)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Ul19DNPpu4vHBac2fTmb9+mra2/cTDted8oxyuWGWLl09WbwplzNwnOnNqoaHE1hWNYFA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FLioq8vTUfHi1RX917QOt+8uYpTpwYYGekkGNxEMvk0mmYjSbXE4ydQlEYKCtqR5b0UFa2hu/sF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\ncellaChOI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EBgGw0NlbPOtXr1zezf/xix2PVzFArLtozetcskEDjN5s0PIEmOOcFLJpPh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usPiGJyRqrf8BqaH5la1XU0Lyah/EqyEVO3YM0Ni4GdM8iFIx2tpuYXJyF9PTu6itTVIuf42G\nhqUkEgV8v0I8Xkbr/fh+O5XKg/h+1HzofyAJR0swDEUQrAbaMIx7CIJHwpKpJxGlGuUDTCAuv6cR\nxb8O8QwoJN5ZRNz2DyDWxjsRYb8AAZBhJLtfIR4HGyEVLQjIrECpBrReiVJL0HpP+Pp/Q7q/LUZI\nwhakynUjkh8RuT4PIzkAGxGrZwZR7GlE4UclilEpmBt+lsjdWgrP/zRCNkYxDButHZRahu//jGTy\n/Sg1hu/PkErto1jcT6mkcN2b8X0ZdFVTU0cqlcLzuikWl3Do0Cnmz7+RRGIujjOF1jLJzjBiuG6F\nYnGaUukJfH+MXO4YlUorlgX19ZdiWX+N5y1Da8nf0Frjur2kUseZM2cda9d2UF9/iv7+3bN9DpYs\naZ8dy1wsnu3pejsH4fVdLzWS/fmk/+KLW1m1qpc9e3bR1DTCqVOPsWZNC6OjU1Qq32Jq6gjl8nkY\nxm9h2w/g++uBBjzvFGLtTxIEz9LaeozDhwcplRbj+x247qPAd1Eqi5DeJCJnJ8O7iaamRm7/LFUv\n4AcRpb4VIelLEcu9DwkJNCAy8QDw24gn4jBC9jVCGn6GkPI00ta8E1H630CIxx1UhzIpxID4GNK5\n8auIZ+F3w+NMhAxsR0jDeqrNlqK5KT6GcQKt54Tn7MLz9gJgmmlGRrLYtiKZ3ILnKRxnN76/CcOY\noqEhSW1tC1NTBxgfX0Jt7QepqSmTSFz9orjrOA62PcV9992LbS85Y9piG93dO8lmV7JyZcvsPngx\n7P9VNjf61+6w+KasVnit69WUkLzaB/HAAzteNqmIsqCjzXVmH/3m5s00N8t7SiVFU5NPT8+DDA3N\npVCoIZGopa6uzNiYiQi/g+QafAHoIQgeRAa+pPG8dmKxGzGMUzjOvQRBGRHwCiKIzyDNkpYhW2AM\nUc6HEMF9AnEBLkLch9EmX4Ak/I0iwFWHJD91IUC1H+m7bhIEMwRBInwNBCD6EaukGSEm54X3UKQa\nE12JEI5Hw3soIAB5OxKOeAohBFH1QdSgZQHVBi/d05Y6SwAAIABJREFUVDOtTxIECUzTpqVlcVjX\nXELrerSupbGxwNjY+yiVfhKO5e3A88rkclnK5efIZA6STF7M1NRDLF6cJplsxnX7ws6VOnyuLrnc\ndwCTROI/k0z24TgP4nkW+fw0HR3rmJjYRT7/DOXyEpRyse0iyeR5zJnzBIlEB7W1m1i+/IXJh5Gn\ny3Gct+x0tzfj+mXevnOR/iAI2Lq1h5aWXfzFX9yK1povfen7DA+v4/DhpxgYiFFT83sEQRGtDwIb\nSSbbqa9PUSjEEbf9PCYnV+H7D1JffxGe100Q1OF5AVongRNofRNCBK5HwnUKUdpRTP8kIlfRcLFW\nRIZOIoQ+QXXw0g6ETEcNlUByi6L+JX+H5CxEw9huRcqOp8N7WInI7DhRCE6u+afALgzjAYKgEzEe\nEgjJOIUYI/nwuv8PhvE7BEEHkCIIChjG02j9L0A7nvdNpAnaScbHT+N5Pfh+N/H4XBwnwLKSeF6R\nWMzEdU8yM3OIUkmjdQc1NRezePGlDA19m1WrXjgcTynF9PQ0H//4lzhypIGxsW34PmQyq3GcGkZG\nniGX20dj49V0dkYVV9X1fOz/VTU3ejN0WPy1JAevtITklT6IM4Hl4YcPE4u9n46OXjo727Cs6lea\nTnfx2GNbiTrkRSDU33+cmpryi/TRLzM4uJ3h4WY87w+Jxy8ln59mejpPLrcV368AN6PUQkQxa7SW\nNrtaj+H70mnQsrqBHKbZhHQTG0dAJqpj/igixJcg1kUfYpUXEGCIko+iWQlN4bEeYiVcQrUskPBY\nG5iD51UwjB4MY1HozcggIYdBJAxwPQI8i6jWXNdTTZiKh/dwHAl35BEAs8L7OICAT5RAFSAgdgQB\nzyhMMRN+1hl8/ygzMya1tWU6O+dSKh1jZqbC4OB5+L6BYcwDjqHUTiCO1jm0riGZvIqZmYcol3s4\nfPhLFIs9BMFhDGM+WttovY4gOIBlrcOyjlJT00gqtZggWInWNsmkxjRz1NSsxXX/FzU1Jr7v0NhY\nxDQn0VqzceNCtm4VT9fzBX1qqptNm+a+pae7vdnWmYq/oWETyaTxgu8z8iTW1bVx7NhjDAwMzHp1\nMhmD++9/nJtv3sK///c38Ad/8HmOHp1gYqJCEMSxbYVpjpFMXkUyGWd6uojvt6DUGI2NHcAKfH+K\nurpLaG5eRm/vblx3Bs8r4XkXAsdx3UhOuxALfhKRDQ8hwVEX0Kh1ejRyPR7+XEM1NOAiHoY+pLJo\nDVJFVIPIz7HwvR9EsKAXCRdsQSz+OKL0nwX+EPEi/AKlhgiC5xByclH4niuQcEIKIc9PEo/71NTU\nkM3+MCRNI2Fexk3hcZFH5FF8/3N43oWY5m2USn+HUnehdYEgGEbrNIbxH4A+LCsFzOC6v0BGsws+\n+77D6OgAf/mXP+Phh4exrBJPPLGbbPbDNDZupLbWYWJiJ5OTP2F6eoLm5npisTEuu+z8s7A7WudS\nwr+K5kZvhg6Lv5bkAF5ZCckreRDPBxbL+h62vZaeniyjo8+yceN5s5vM9x127TqC615JY2MV1F33\nMR555B+55pqPn9VH3/cNxsfvI5VaSCZzK0NDDeTz0l9dqXqCYBOiJA+i9VyUAq2/i1KXIrPbH0ay\n/vsoFv8K+DhK/SZVgNiKZCB/DAGL6fB3G3EDxsLz1yEuRpNqjkA0CCmJgJKJKOIS1Zg+iIIfAjoJ\ngjqUclAqRRD4KBUL3YhfRCyQDgSoolrduvA+FobXGQI+g4Qk+hGQylFtM5sKj3sIScLajoQ4ysjW\njnIs0sBKisXHyGQ8crkfMDl5L5VKLZa1gIaGNYyPl/H9RSi1HqViBIFLpfIUPT3fwrbXEgTvxHE2\nYdsNVCpP4PsHiMUuw3HuxbKSWNZCLGsX8Xgdrlukrk4aObluA6Ojx4nF1lBXt4z58zeSSu3isss+\nimXFyWaPo1QvLS27XtTTpfWct/R0tzfbuu++x9izxwiHJT0x2666q2vj7Pe5Y8cAtbUb2Lnz7tmm\nVlEVydhYN1/5yte54ooLue22zzM+/n66ui7CML6HZb2PqaknKZVOkcnEKZUqVCoQBBW0rjAxcQzD\nmCGRWMj4uEEQ3E+lcgmGcT6u24/vx9H6p0hlTiPicVtDVTZSCDk+hCjydci+70JksBh+ShshFDWI\nfJYRDPgdxPMI1byExUiy8T8iYcd3cXYjMRPBlWYEQ44Ca9D63UjEeA5C/K8kGq4kxxoo1Y7vX4rW\n+7GsqbB0OR1OZZ1PNE5eqRhKrUHr1UA3QdCD1kmUeheG8SBwG0EgUyMFow0Mowvfn2FychfptIvv\nO7PPCz5EQ8Nqjh3r5ciRBaRSJ0in12MYcVparqKl5SqKxedYsmSI3t6fYpqRkXP2OpcS/lU1N/rX\n7rBo/PK3vPXXy211OTV14px/m5rq5vLL5UGcmZtgGAam6QKQTGYoFBZy4sTg7HHd3TuoVK4kk1l6\nVixr7dorgMUcOPAvmKbJ8uWL2Lx5Oc3NB6hUnqNQcBge3s309GN4HsRiCQzDD2ujmxG3foDWxzCM\nDdj2CoSPeEAmHOv8n4AhtN6P1hGYxJG44D8hQvxHiHXwB4g78uuIEp5AAOe7SCLi/0Lc+VNUJzF+\nF2nYMoS48YepEoY8QSCuShmbaqBUL0q1otTNCPFoQojE3yLVB+9GwgjvJSJAUmZVQkjJFBJnXYmE\nIqKkx92IO/MCBOx+m2o3RZOqB6GbILgT0xynq2srN974KRYtuoxkcgGGYVFbexnS0rkfx3GB1rBL\n4tV4HljWPIJAesInEpdgmuvxvB3Y9nxM8yCNjYeYMyeD5+2lsdGhvb2V9vZWGhsdyuXTBMEvqK09\nwZIlQ1x22a2zmdLpdBd79ozwqU/dwrXXDlEu30Eudxfl8h1ce+0Qn/zkLezdO0o6HWWsn73E7dl/\nzr+9vV64KpUKX/nKA4yNXYxlfYhE4jYs60P09raxc+fd1Na2sW1bH5WKzYkT1TkLZ8pvMrmU6elN\n3HTTH3Pw4BwmJk7Q1/dtKpUTBIFDLLYYz5vi1KlhJiamqVQcXFfj+wWCYATPy5DPj+A4JyiVLsYw\nFuM44wRBXUieL0IKu76P7F2b6oyUHkQ5/xdEgc9DZHQvst8PIRb7CJJ8aCDyZCDy00a1o2k0xr0X\nIQhlhFB3IDjwKGLRDyKY4CBV6SkMoxjeSxzBnVEk3yCGeAFrgSRaJ/D9uUxPN+L7H0Gp74bnvgAZ\nTuUCRbQ+jmGswvcXoPUBtB7BMBowzSxKgWGsRqlyGA6soJQmCDySyRVMTh6gvb19di5GItGJZUXt\nsKex7Qtw3Q1MTu46ay8kk0sZGBigtbWGbLb7nPvlTOyPVuSZfjF5fb28eNdfv5GWll1ks8dnQ9Ja\na7LZ4yEJeWOnEPzaeg5e6Xq5bPD5uQltbW309kqjjUQiQ3//IMvD5tDd3QdZuvSmF1zLsiyuvvpd\nHD36Bcrlb1EsKvbv3086/R7mzftjhofN0JJ4miD4HsnkB4nHbVy3ENb6Goj1AKYpVkAQ9CNCr/D9\nGSQn4DBiUcxBXPP9CFOfH74eDTtSSPyxC0lA6kMal7wTEfYaJE/hzvC1qDLBQ2KfX0UsnCsQ1+M0\nEuu8D8v6SJhotDycRNePJEmuQUjGTUjCpI0AWlt4X+8L35tH3JSPI0C4EgGp2vDv+5Hs7HkIYUki\niZV7ESsnhQDcfGABmcx/ZPfuT/HhD3dhWU8jJEuTTDaQy9kEwQ+BIp5Xj9bdGMZaEolGksnzKJWG\nCYJplLKxrAXE4ztZtOjfMTKylQ9/+HrAZ9eu71MoNKNUAzJ1sZ6x/5+9Nw+P47zOfH/fV9XVC9Bo\n7ARBgAQBkuBOarMkSjKpxbJsyYqt8b5kmdxsziR35iaTTJI7Sa5zE+cmuVl8ZzxJ7GzWxI7kxE7k\nTZK1keIuiRJ3UgRBEAsJgFgavXdt3/3jVBOkTEmk7YztGOd5+iGJru4qsOqc7/3Oec97JqdYs0Zz\n222/cREU1KyWtnQc54qZru+H2uO/BauVAv/2b5/m5MkbSCRKZDJDNDd3o7VNItFHsWg4dWoHlcoZ\nJiaKDA8vwrLW0Ng4S3NzBq01YRgyNTXNyIhHudwesd+TJBINhOHnmZz8Mo5zP/H4LVQqBxFOTAic\nIxZLYVm9VKtfIx7vo1w+imW9D61LBEE9YRggvqgQn7CRzN6XkYVcIUqjH0T8dgbZ0f8espAbJOV/\nE/OiYxMIeP8GsrF4EiEgxpmf+rgG8fu7kczDW5AYM4r4cQIBLBlkk/ARwnAaGfa0mvnpkrVSAghQ\nyUfnaCIIJrDtIsasIgiOAs9hTAIBP4uAVsKwiGi6hBhzFKV+GmM8jFFovYQgOIEx7ViWTSymcN05\nHAe0nmH58pvZvv2LJBLbLkpeS5nBQmuDUn3kcntorc2oA2rj0Fes6KK5eS8XLnDVmYDvprjR69m/\ntsLim9kCOIjsam7ElQL1ihVbmJx8NOINrCAIJIBkswM4zgwrVnR/y7l832dwcJSzZ12WLLEZGztO\nY+O72LTpDmZmjqJUAEAs1k+1qnHdZzDmDmSWlY9S0yh1hDBcShDMoLWFMSORymI1IgLGmBczqY1F\ndZAdeCvi8CVkgbeiY2qkxfuRVLyKvqNWdqgRpVykhAHi3Dci7U7PIwDjHBJo7qZare1GwugcnwF+\nDAlAn0TaDm3mdy2vItmIW5BdSiK6nocRgFBkXg/eZh7UZJGAV2vZrOk11BTbpoBHOH/eMDPTzZe+\n9PuE4WIKhVP4fgljngZuwrI+iDGTUafHw3jeNGEYx/MCkkmHjo4UuZyLMZowhBUrSixa1E0+P0Rz\n80q2bHk/p0/vYXh4D0EQw/cn6Os7wpYt/+1bgAFcOW352r9/r2uPP+h2aSlwYmIl8fjdaN3BzMwF\nJiefx3EaARtjPE6ffpSurvvo72/i7NmTWFYHs7MVisVJlixpZWxsitnZUWKxG6hUhlGqg3K5iute\nwLbvwZiH8f0XkMX5syjVA7ShVAWt+4AslnUY192G52l8v4hSLmGYRvxtMvrzMeS5/wnms2ejCLAG\n4e68i/nOgQQC4m9BAPOXEeCwCMkE/Hz0fgIRAXsv4v8mOn5b9N7LiJ/60Wd7kUW+9ppDMhcZhHeU\nRTYDh6L3Msx3VoxG17YROI/v90Xn+ROU2grMYMyJ6LzDiKS6j8SP6wiCWoeHTxD4aN2B1ieJxWZp\naWmmVCpFXIMiTz/9ZYaHj5JOP0ZnZyd9fdfh+z5zczOUy2kqlfNAlnR6kJaWpWgtqpe+P8mdd27m\n3ntv/bYX4X9N3/tfAUJezxbAwSX2ZjfiSoG6prAlC8JufH+AanUlb3vbUhyn51tqWb7vs3PnYcbG\nNKVShf37NSMjVVKpZvL5wyxZkmZyssjMzHmq1QTGrMF1d6DUOpQqoNQujHkWY0T3IAxbMSaFMSVs\nuwutz0U7kIB5st448/MQakJDxejlIES+c0hgspCFtQ1x+tHomJeRVsRTSNDwEMdNIfXQJLJQ/yMS\neDoQsFFE+ApLkDpqIrqONFIKKCGkQ838NMU+5vUSQMDJR5CAuIx52eiaImOS+YFLe5AMRjz6nB1d\nw26gEaWaCII2hoc9WlttGhoGuHDhVTzvNozpxLJsgmAWrQsYsxjL2owxx/D9OvL5GerrqyxfLorg\nQdBIf38PhcIymprmdx79/dtYtUrSf4sW7WP16vddFM95rV1N7fB7XXv8QbdaKVAGK71AY2OCmZkC\npdL5qMU0RSqVJps9TrFYjzH7iMdXEASv4HkXiMXacF3D8PA4rqswZpa6us0UChWUUmidJAgMrjtJ\nPP4L+P7v4nm7EL/7NMbE0bqXIHiG+vqbqFR+iWr1D4AUtl3C9+NRRnAa0f+YZL6VtwZ6A8S/QLIE\nRxHibScygfEuBAyDlAq/Ep3/YwjhcIp5GeQmZFBSN1JqGEIyb5no/QGk7KiQEkE+Os8YontwM5KR\nqAky3YNkFB5H4oaDxBGZ1mrMK8zPVXkeUUf8f6Lf145eAcbcg/CNmlFqM8YcwbIktok0dQ9tbX10\ndU1y110b2LnzMMPDszjOWpTagFIthGEDFy78M9/85kHOnStRLLrALFrfjjF1ZLMZSqWjdHWtI58/\ncHFWwvdyEb5a+199TQvg4HXs9W7ElQK1ZTn092+jvf0Ud9+9lAcfvBPgIgP60mNffXWIgYEqvq9p\narqHfD7G3FyCuTmX8fELrF9fwPNyVKs5LKuXIEhjTB2ikPg8tj2FMb+F7/8NEgRWY0wnkCUMHcLw\nJEoZjHkcWRTHkJ3HIsTZT0d/nwMGUUrEgIx5C+LoLUja8QiyoC9FAEUn4vQVxOlrgiy10c8VZNG+\nHgESM4hgSW2nH4vOXRvJHENAyjIkoNRF758DFErVY8w40IBSogQnQVYDSzFGR+1PNY2GYeazDSDZ\nhAYEbBwH/h7L+gTZ7LM4zlJgFs+7ncbGIYrFfZRKZarVo/i+j9YBsdiNKFUmCLIEQQDMEYaGyckS\nhcIojjPKypUOU1MnuO++vivuPN72tqUXU5KXiudcK4HpB3262/fadu0aIZORtjbL8mhqamBy8hC+\n34lltVOtnsPzhikWM1jWOhKJ91Ao5Mlk4szOPoHW61HKZmbmNMuWbSKbbScMB8lkFlMuT2Lbi9A6\nSaUSEovFCcN2jOnAtjeTSj1ApeJGY4hfwRjJZlnWSizrn9B6JWHYFtWUk8hO/SWkNJBiXo+gpu3R\ni5QaziEcnb1IiW8dAn4VMkq6B+Hy1LhGNyNxQNp1RaxoCZKdOI2AgSKSncsiPrUUAe0pJDOwHRFQ\nOoT48HIkY9gcXUspus51zG9AhhHQ4EbnvQtRO51GMogrER/NY8xOZGy6cIq0bqGz863AneRyj+L7\nLVSrCWzb5eTJM5w9O0sQvEhr691YVsDy5UnOnj1IofBucrkEYbiC+nqHbHY/QfCXNDevATxKpTjn\nz3+eW289c3FWwqX2/QgMvhe2AA6u0WqB+vz5KtPTE4yMjOL7NkEwyerVM2zb9ssXj7333ls5dOiL\nlwX1Q4fO4vtpEokjkfDOHWi9AqVuJAzLHD58lEzGoru7lcnJE+RyLwLPYFkHCcMetP55wvCLaP3j\nWNYmguBRjDEY42BMK4LUX0Sc/0NIu6BCdiGDyNyBbdi2j++XIhVFPxJROonswI8wL0gUIsHlHLJb\nqBGRrot+ywbmWc8ro889C9yP1hMEwZeiz+9GQEQGARkBEpxOIkHDRwCLEBuN2Y9S49h2L1q/iuMc\nw/ffR7W6jzB0EAATIIHzMDJtbi3zddXHkADmRtd2O1pXcd3HaG//Q/L5TwPN5HK7SSZXUF9/A/n8\nGQqFV0kkbkGpBuA2wvBRlKrH9/ei9fooQJ0mnT5FNtvHyZOf4ZOf/O033Xl8J7XD73Xt8fvRrmZ3\nV61WefzxXTz99Cls+yiWFWJZMSqVARxHYUwD1WoR1x3Btpdg25Ok0yuw7RZyuTGWL/8QlcqnSaVm\n6ejYxsmTU0CGubl9aD1Dff3bKRQO4nl5pF22TLEIYXgC2z5GOv1JXNcnkXDwvCZ8v5Ni8SiW1QEc\nYMmSX8H3L5DLLWJ2tgPxrSPIbv4wkkEbRXbc44gfWYiPggDgQwgojgMVjHExplb660J88d3R8bWM\n36nou9dE7zcimT4bWdRrpb2noms5j7Qf/yLzQ5cK0bWdR0BAHMkOZJGBaFUk41CKrv3tSGx4CvHL\nd0bXk42OaUB4DQo4gdZ7UaoFAK0TpNPvI5//W+LxQfJ5i8HBCZLJ99DV9YtROdUwNvZlZmZ6UKoH\n1z1GMjlHPB6nvr6PanWAVOoFmpoqWFaVjo5BPvvZ3/ih9J2rtQVwcI0Wj8f5xV/8ET7+8T/l1KnN\n2PZmbDtk+fJbaWys8sd//E9s2NDJ/v0TVKsxLMslnf4GxWI9vp8gm32KlpaPYcwS5ub6sO0+4vER\nKpXTaN1HuZwGWrGsAuvWbeXcuacZHS3hus0RmSdE61G0FjARhi1I14CJ0m9JxDF/AVnE/w5Z2Fei\n9Vps+88x5gs4Tgml6nGcTbjuOYyZioSUbkcCwErm2czHkd3DcWQRtxGikoyqlZ1HDkkZnkYCzZMY\n8wEkGL2Teb2Czui6ViNlhc8jZYAAyTxsRNKahzHmdizrCWIxG9d9kCCYQ6kNyG5kP0rNYtu9BEEj\nYfh/I8CnE0mlVpHAdRIRfMqi9WqCYClBMMSSJdehdYGZmdMY4wJr6OhIMzxcIgzzWFYGpeLEYu8l\nDH8S2+7Bso4RBMPEYnEWL15LT49Pc/NPsn37gctaCV9vfOt3krb8QUh7/mvbtciUX8oziMdXYNvr\nASiV2pmaehjPW00qtYlUCqanfTKZMvn8PpLJfx9lZjRKOSxf/nNMTf0enneWYnE7St1MInGeQqGF\nqamv4/sjSHZrJVInrxCPN1Jff4gg2IfrtiBzPzySyThB4GNZjxOP30ln59sIwyonT34e2TH3IAvt\nDNIWOBn9fT3zZb8WxAfvQfzAID5W6+hpRXwphfhCGmk9XhIdK62DAtIvIH45EH3XWqQ0sTv6/jii\nNNoGvId5LkI98+OgR5EBaYsR0L8V4RU8iYD+IrL4e9Hxh6Prqo1zrmOeKJ2M3jtJEOTR+htMTn6T\nMLSxLEUyqbj55gcpFLZjzE00Nj548X4bY7hwYRz4MLZdwHUrhOE4+fw4WrtkMuspFA5x3XWLWLHi\nNorFL+E4zlU8dT+8tgAOvg177rmXaGx8P6tWxRkeniMILEZGcoRhHS+80MRLL01z/fUfu6hrkM2e\npq1tD7/0S+9h587jJJN3cPz4p6lWN6LUJMb0EoZPYExAGGpsu4WZmbOcO7efQuEYYfhfonbETkBF\n5YMcElBuQ3bxEjSUOoIxf4KoBta00adRygHOEQRnCMN6ZBraLL7/DcBCqRVRGSCGkA1rO/uW6JVE\nFtkfZ37HDuLYU0iK8Xj0/hngAmFoI0FrmvnJjdPI/INpZOfyMYQpXWaemJhBBiDFcV2DMXtIpcYp\nFs/geaMI0LkOpe7Bth2C4AKSdbgLAUo1kmUZCVC/TCz2TRzn7ZTLLxEEj9La+mso5ZDJrKS7u4sz\nZ+pJJHrJZk/hulO47jTGWBgzSCLRSmPj/4VkNA7R3Q333rsZkKC0c+fD1yRj+p0u7D+swOBahKAu\nbTnu7h652FFUV7cY+Cjnz/8RYTiL6wY4ziGamt5NJvMQc3MGpQxKhVEZIkEms4aWlnZWrVrCxEQ7\nxWKIMd0EQTewCGPqMOYUsdjXCILFGNPPzEweY/4G2/5VoA+lcpTLz+P7O4nHs9TV/e/MzIwQhgFh\neDOx2L14nkbKBHuQ7oIlyGJvYVlvIQhOIoC3gICFrzI/AG0I4fhMIAt2CwImQsQHRHtEduZDiN++\nFQEeHgLci4iw2hySNZxAMpD3ICC7pqgaROc/AjSh1E9gTBqooPUFjPkrIIExNyAxawMCOs4y33VU\n01EIor9rJMZkkBhwM2EYYMxW4vGbMGYG2z7D0FCG8+fP0t3dexlInp7OEgT1xGIZjKmQSNRhWaDU\nW4AEQeCi9XUMDnYzMfEI11+/QOJ9M1sAB9+G7dhxhqNHmyiV2i8bsfvKK0MUi4vQ+vhlfdE1sZqn\nntpHV5fF/v37qVTqsayOi/VjY95PEHyTWOwwvt/P7OwzKHUDsdjPUK3uRBjFM0iwGEECyG1IanEy\nujILSc3dj22X8f23Ic59M8bcRW3eum0fA3ayfPmPksuVyOW+hOtOodRSpDfZjj5XIy9ayC7jVxDC\noUFSgmVkd7ESCTJrkOxBL7JAF5EOhy8hgeAhJOV4DxLkDiPEwjPRcV0IG/tutG7HsjSu24XrnsOy\nNG1tv8nY2K9h2z+NqBmepFr1o7aqVMSfWAncizFLkMCTi84TEIZfIZOJoXUjWscpl09FMwxqM91n\n6OtrZGKikUJhEUqNEovN4Xk3oXUMz5umrk6zbFnDxWfh22kl/GHd+X8ndq2zTy5tOX5tR1EqtZhU\nahWeVwFyaF3P3NxZ0umAWGwFlYpNS4vc4zAM8f0JcrkX6Om5k7Nnn8aYjxCPb8DznoxKeUUgjTG3\nA+P4fhvG/DuUmsWYR7GsdmCIWOxubPvdxGJHiMVuYHZ2lkLhhag9L4PvfxmlbiUMlyILbyOycJ7A\ntm1s+wy+f4YgeDviK0uRmXY9CLnwHJIx6EUA+/bof6M5em8XUoZ4hflsRE2RtAasGhGOQG1Q0mqE\nl7AnetUIvl1Ix9HLyNySl1Dqukgw7DDG/Ci+34L4eY1/sBTZJOxBQEttnHsQXUetqygNDKDUuwmC\nLnx/jni8jlhsBXNz53CcB7DtC1QqAvgAcjkXrU10z+ZIJn3K5cXYdjMiST9HIuGTSq1kdnaWVGrf\n1Tx2P9S2AA6u0YwxnDw5Ram0lGTy8hG75XISWMXMzPPfsgDUNLpXr06zb18+Ig2aS0BEHZa1maVL\n63HdVWj9Ekq9k2r1K4ThHcguohFxyhTCRv4zakNMAGzbwpgLGNONMU+h9XOE4U1obdA6E3UxlNE6\ng1I3o7XNihXvZnQ0xfnzjxKLVahUSkAvormxCtFT6EFqhTXxoo8itcgGZIcxyXzXwNcR8tNZ5hUM\n4wi7egfzpKdNSNAqM6/MeAy4HqXaAQhDKWEo1YXrrqZa3U8sJoNZgqAvklTN4DhJjGnH988jddcR\ntG7AmBbgBErJiGnPe5Z0+joKhZc4c+Z/0NPj0Nf3UbSOsWbN9eRyj9DS0s3evV+lUIBU6iY6Ot7P\n0NAjuO4UWp+iszNxmQb71bYSfi/nsv9bsGuZffLaluPLO4r24HmQz++goeGdtLS8h0qlyMxMA9ns\nNNXqP6PUEmZmGpmdPU0qNcZ73xtQKGzm5ZdnSSY3onUrxaJ0v2jdBBQxphNjulHq84RhA7atUKoL\nx3kXYfgorvtuwvACtn2WYvE4vv8CqVQbntc33f93AAAgAElEQVSFMVmkTHYjlrUC2z6CZX2AanUf\nxhwFpqirO4DnjRAEH0ZAeALpOPhtJCuQQRbrLLW2QAHF3Yjy4RFE/+NdSOnuJmST8U2ED5BANgC3\nIZmHX0W4AvsQ0uF9SJxZjizog8yLIP0LicQteN5zuO6rUew4H11XLftR6z7qjr7vxej38JGywxiS\nvTiI1t2E4RTQE8XICg0NzQSBplqdoLOzH98vU1e3h2LREI/3YYwiHu+mXH4By5rFcdrwvBRB4EVt\ni0Ok092Uy9M0NTmUy3XX8vj9UNoCOLhGU0oxOXmBeLzxsp/L7l9hWU0UCheu2AbpujG0zrBq1TlO\nnQrI5Q4hO3+D4+Spry+QTrcxPPwstm3hugcIw7cgi/QLyC74HIK+jyIL98boDEXCsEosdppkshvX\nXYTrTqBUO5Y1h++PR3XUCeLxEdrb7+X8+U9TKIwTBDaWBYnEEEqlcd0JPG8ccVYPCTQlJJNQm8rm\nIM5eyy5MRe+1RtcIUrv8EDLHoTP62fMI4WkTErBakd3MMLWuBWMqBMF5BIBowvAAkKFSOYdSIbY9\nhlJd+P4kWteTSHRQrR7FssqE4RywC2P2IgFnmlSqGdhMENxFPv8K0EylMsmRI0cYGXmOxsYuli9P\n8eEP38YDD2zDGMPXv76Dv//7fZw9+yLxuLRa3nDDFlauXHaZBvvVtBJ+r+ey/6DbtQpBvV7LcX//\nNvr74fjxpwmCD/KOd7yT06dHGRqaJZt9lbm5dizrJjo7y7S13UW5/CqWdYREognXdfG8JBCjrm45\nrpsjCKSG7/sFlEpijIdlJQlDmyAoEYu5eF5jlJU7i1I3YtvvJQgewfMOMjdXwfdFnty2NanUr+P7\n0ygVx7IMtn0dsAylTlAoPIVl/Uz0XNchC/NNiJ7IHKKmeAoJ6RcQv13D/MyDe4EHkR17DvGtdoTv\nUxu61I6AhLbofQfJHCyOjklFnx1DYlI3kg3sxPe/iFLrozT+K4gMcon5WJCLPrMZ4QY9hcSOZUhG\nwUPiwUm0vplYzMWywPOqaO1hzCRNTQlSqXaWLWvg5MkK27Z9kKGh/QwP7yEMs8TjKYw5QWPjfZTL\nNplMPaVShXL5CEo9TkPDA/T05OnrW0+xeHwhg/cmtgAOrtGMMbS31zE5OXgxpQVEQckQBAPU19d/\ny4NnjCEWc3HdOu644yEWLdrO3r2fw3W3YdtLaWxspKlpPfn84yxdepRstoEzZw4RhhuR2p+NLKin\nEEGgC0jtcBBJ+xu0XoLjrMXzZmhsrKdY9HHdo2hdTxhmsSyD44wSBGeYmNhOpbIauIl0eiWOU6Vc\nVihlo9QUWm8gDFcju49JpF45jez8febnymsEHNRHf84iwWoc4QAsi665AQE0vdFnnkGyDSUkyNQW\nx2R03DIEUIwA9xKGMQqFx9G6Hc/7BFLHvJ14HIzRxGIOSn0OYwKUsvF9RWPjQwTBqySTd2Pb3czN\nPYLn9bNs2T2Uyx6lko9lvUpj49hFLYLjx2Wxfuihe3nPe2TEcrVa5Y/+6ItcuBBiWRY1KdOrbSX8\nXs9l/0G3b0cI6o20IU6fPszKlT+Cbdv09/fQ39/DsWMDvPJKmVIpRTb7GE1NY/T1LaWv7+eYnR0m\nnf4GYVhEqUbCMMQYjWXV4/vTGCO6IsaE0TFgzGl830FrG/EHjTEvUKm8hO8/D9yKUg+gdR1BcJ4w\n3E61+gK2XYlS4UPEYhWCYJZ4XFGp5FFqGstaShDsQoi/9yE7cgfpWGhnfgbKw8giX0R8sQXJ3GUQ\nIL8Y8WOH+a6k9UgGz0c2BouRjUEBKf3dhfh4HikVlhF/3o8x92Dba9D6IFrXUS57iH970ef7kYzE\naQQUnAc+FZ2vgXkSZQNBcAClBonFIJGIkckk6evriEBihZGRwwwMbOeznx3GcQJ6e7u4+eatnDnj\n0tioaW8P2bXrYXz/OpJJn6amRjZv/hBr165+3edlwb7VFsDBNZpSilWruikUdlMqSQ2zxhtIJkcp\nlV6kubnrWx482WEuY+fOYYwxaB1jyZJ1TE8foFh8Htdtw/db6esboqdnPaOjPYyO7sT3Yyi1OCoX\nBEg6biuyWLcAa9B6DqUGaWhooVI5hjGHaWg4R0ODQ2vr3WhtMzBwlmJxB0GwhSCQISfxeD1aG7LZ\nvWidxvOex/c3I7KmZaQ2eRohGv4MsmNpQQJFW3RMNvrzZebbrj6GpCMfQhZ3omNGkd3DjUj24H4k\neOyKPl+bMNeBgI0BhC19EwIUPojjpHFdjzDcC3yKMGzDdZMkk2tpbl6L72/GstaRzf4NWqcIwyq2\n3Uep9Gw07S0V7TQzJBIJKpVpisVuBgf30t+/jfPnq/zO7/wFntd4Wfr/Z37mPv7sz77A44+P4Lp1\nOE6Rt72tm1/4hR9/013/93ou+78Fu1YhqNfThpidPXVF5dKxsSKLF29EKUWlcox77vnQRR9ubFxB\nsVhPf/8Q09MJqtWBKIO1JEr9NyMcnNPACow5CzyD1j+KLL45ZHZJD77/GPB/AC0YcziaQXKeIBgj\nDNdgWX0otRxjfIJgGstKkk5XALDtU1Qq9ShVR7XajPhKOvr+DOKXPvM6Ii6y6FoIz+cUsjDHmO9g\naEGpOmrlSmOGkAW/jAD9+uh3mwD+EAHzH0K6F2pCSV/G91/G99vRuoTjxBGthpoMc01crYwAkRRS\nquxBfNxBSh6rsO3zQBtBsJNq9b9TX/9RMhmZBZHPjzI29jhBcBddXb+HZbWQzZY5ceIwyeTvc8st\nS2lr+zFaW9eidYzBwSUo1UJd3SirVs0/NwvCYVdnC+Dg27CtW3upVNqZmRm7KJNrWR6bNy/h7NlW\nWlurF3c5rxWrcd0qf/RHf04YPkAisY3OThURZgaAr/KRj9yObcd44ol2tD6NUjMYU0PXTyBpwiSS\ninsYpZYQhr2Aw+zsQRxnDPgqqdQnKBRe5vTpJ9C6i7m5/VSra4nFWgiCCSyrk1TKI5nMMD1dxLLy\nwA0YM4Ms7JuQHU8MyRjsQwLcGmTXv5b5QS5rEacfQWYv/Abzokc1zfWnkJRiV/ReN/OM6nuQwPZ5\nJFOxLDq3jYCQASRIrcV1/xrL+kmM2YpS57DtE2g9i2UdoavrvzI8PAQ4pFJraG7+Z86fzzMz8zTV\n6h4s66MkEoa5uSq23XSxZS0e72N4eA+9vRWOHXuJXK6HBx5418X0/xNPHONTn/okq1f/NPfd13/x\nOchmT/OpT/3LG5YFFmYjfHfsWoWgXk8b4krKpaLBry8Siy3Lv+xeyDRWmwceuIkDB/6Fycl9+P7t\nhOEcsdhaPO8EYfhFlHoBY1aj9SLgemTU8PMI2XYJSmUj/1qFLJabUSqL4xiMieN5Ib7vYttDxOOK\neDwkmVzN7Ow3MSZJELwPy+pAa0O1+klkoT+OLOadiG+NIr64C8kavBtZhEFKCKNIWeA4ki1IIC3Q\nk8yTd38cmERrizAsIgDAR2LQSiTzsBNpV3Si814HPEsYrsJ1b0fkm2eja7OYV0IdRTY290ff+xxw\nCqUasCwX2x7F876GMQl8fxrX/QM87x5Onhwmnz9CEGwhnfZpaenCtuO0tjZiTAdzc4309e1ky5YL\n7Nz5MG1tMDb2FRobH2Djxjuwbft1n5cF37uyLYCDb8Nqgcq2b2HVqq0Xf57NDrBu3TAbNixh376H\n30CsphdJp82bUs3Rz+X7Dx58hJaWFixrnHx+kDCMIwv0uuizHnAXxrwMPI5MONuHZS3Htt/B+PgI\nlqWZmztGGHYQBIoguB7fL6OURzKZI5FYRD6fxXUtfN/BsvrxPIMs5u9HAkIW4UU8jhAJp9C6Aalj\nrsa2VxIEMxizDMu6C897AiE9vYAs7BuRYDQX/bumlugiadA4EpSmEPDxXPT5PuZn1J8HHkHr+7Cs\nPhxnBbFYCaWWo9Qw1133AOPjO5iZ+X9JJuvJZr/BqlVdWNYqZmdz2PadzM1dwJgujCmQy83Q2BhQ\nKuWpVM5z9GiVavUgZ86cIAzfTiZjEwQBtm2jlGJ6epKpqQ8xPZ2gtXU+iFxNWWBhNsJ3x74dIajX\n04Z4rXKptCzKAK5KZYDe3st3lZ5X4ZVXXkHrj/PQQ/dx6tQgBw8+z5kz/0gQaGy7C8uaA34c329E\nqUkcZx3l8l+h1H6MeT8wErX21mZ9ADRGo4kVsVg3QXACrRXp9EYsC4rFMsXiAZQ6SzzeS7WaR6nF\n0Wd7Eb85xXxpoREB3bV2xzJatyNDjYaQxf3SuSQu89mEA0jGro9ah4Ftx/B9jTEvY8wYkoX4MCLt\nXBNCmkZIyL+IlDhbCcNRBJgUELBR62qq6RhcD+xB61sxJollbcOynsD35/C8O4Efw7YnqauLUyp9\njbGxJ+noeJBCYZy6uvuIxULGxk7Q3b0OpSyUUjQ03Mgzz3yRT35y/n677gei5+ULlEqXPy8Ajz32\n7AJB+A1sARx8G/bGgerDxONxHnroyoh0//4J7rrrQwwOjjE8PEoQaCwrpLc3Q2/vO9m37wvcfz+s\nXbsI285RKv0jSvWgVDvGZBAHHY5evVjWW9G6GfAwxkFrQzp9J+XynxAED2LbG6lU9hKGg1jWCWSc\nco5qNYsx4Pt54vFlVKtDJBLLqFROIsFCMz9+9XqENPh+4JdYvHgF09MhyeSdJJMe5fIWksl2qtVd\nVKvvpFBYEl3fBWqdFPI9U0gq8xQSxBSSPRijFsxkTPNy4AJKGcCgVBfGvIMw3IVlxUilQhKJBnK5\nEq4rUyoXL74T3x9h06YbePXVv8JxFjM9fTstLaPMzg5iTAmtK9TXNzI97TM9PQLEMKabarUPrY+Q\nzzej9RBKLWf37qNs2bIO27YZGRmhoeGjDA8fvjhxs2ZXUxZYmI3w3bHvRAjq0mOvlIXo7k5z4sQL\nNDefpK/v8izEoUP/SFPTuy7ev7Vr+1mzZhVf//pBstlppqf/hWTyRlz3HwlDG2Na8LzncJxdwGZ8\nXwS1XLdG7PVqVwWA76cIAhv4EbT+ErYtJOFqdQJjDC0tP0sYfhnPGyQIWpCwvR+p/38ISfvX2gU/\ni2QF7gV2Y1nP4nlnkczAB4AetI4Thmuin40i46Fd4P/jUrEzY85j2+dJp18ln9e47t1IxnI0uvZW\n5nkJ30SARwuyMdiGkCVHmZ+B0oJkEwdR6izQi1JJlLqFMHyCZPIngC6Umqa7ezFKKaamtrB06TtY\nvnyU7dubSSSkNTGXczl27DCJRCdKGTKZeEQGDdFaR6D8ys/LAkH46mwBHHyb9kaB6lLW9KU2n2KO\nRUSob/1sLgd/8AePMDW1hY6O/41croN8/gxBMI3v78GYLUgw2AwMYFn9gIXnuWgdEIYWmUwDsJG5\nORdjzqB1I55n4TiNOM5iIIfvXyCT6SCbraC1A4RobaHUOMbEmRdBCZDAM4CMRF5EsdhOKnWWFSsW\nYdsOtr0BYwwHD05gzLtIJAyVSj9Sd1wbvfII2NiHBIgPRL9xiICCAYQZvQTJFiQwJodl2VhWM2GY\nwZhHyGTuJpWSGmRDQ4pisYzvH8L3FTMzLzI+fpaVKzfx9NO70bqL+voGLOvLpNONwHmUWoXWDqVS\ngXjcxbLWAWcjgNRKEPQQix2nWFzH6dOjrFq1jCCIEYtpXFd/y/26mrLAwmyE7759J5mWK4H7rq4y\ntj1EJvMBLEuU82r3aG7uEHfc8YHLvkMpheMoFi++k1Lp5aglVl/8HIDrNjIwcIhCoZ5q9SWElNeN\nAOcVyKI5i1LrCcOdWFYJ216JMd2AJpnsJZ/PUi4XSaUCurpWMD4+RC73N0jNP4kA8KPRVeURUN+G\nAPppwvBGlOoCzqP104BHEIwiwGAZ4pvPIyWCRdG1jQEuQdBBMnkex9kUtSfegGQXatoEtWFn1yPC\nabWso48A/GEEaGSYJzMvQbKDDsZMIdmTGGEYAkuJx2tiSDA3VyUWi5FMrmR0dC+W5RIEAfl8mSBo\nQ6lJ6usXYYxhZqaE1iN4nnfFhf3SrNECQfjqbAEcfBdMapJv3sd+pRTza0HF2NgptP4xmppWEIZ5\nYBbLWofjtOO6VapVSb+L5K9LGI6gdQ8wiDFdwAmamxvIZuN4Xh1hWMWYZchUxxJK9RAEdxAEf8rU\nlMaYxYThDJalCYJXicdfpVKpDVW6mfk06EHgLFp3kEqtJAynmJkp0NzcSiymmJqaJQjq8X2ZPKlU\nA8Z8DJl5cBAJKCWkzLCWeU31EMkk3IaAhi5EI0H4BmFYiRQQXWCcmZlVFIuzpFIKz3uSdHoQ388x\nPn4Ox7FZs+ZXsCyHxsYYSl1HKjXCPff8PIODu9i798+pVO4kDJtwnCLGbASGsax9JJPvo1x+Bcta\nRhgeIJFoZnh4lP5+GdYThiGWFV4R8L1ZWWBhNsL3n10J3Fer1egezZcE7767mzDcQCwW+5bvWLq0\ngcHBLPX19VQqs6RSMgtANE9O0du7gvHxcYLgAJXKSmplOeHs1AYXrYtaIkvAsxhzJ1qvwbKk1JHL\nHSOXe5L29g5aW3u4cOFzwFKUugVjjiBy5/XMzybJIyW/fuCbBEEvWntY1nJ8/60YcwC4Dq3TQJkw\n3IqMTn8/Ui7QSFnhKGH4FMXiEiqVFsLQQ6k0xswhMaG2dFxASJE2khH5THQdr0TXsC1q85xDMgzH\nAQ+l5qiru5Nq9SSuWyMtV9Fakck0k8tN4nlztLbKMKkgiNHb28nhw68QBBvR2qZaDZmZyQOKIHiJ\nxYub+NrXtvPQQ/dedp9eG5v37DnA8uU/RzrtX9aWDAsE4UttARx8F+xa0lRvlmJWyqGxsQ9jDGFo\n4zgBtQExlnUf8Kto3YPWm/C8RoLgNErNodRzxGJ3kEhIp0Q+P4frlrHtG6Kyw634/tMEgUcisRHb\nvgutD2DbI/i+IZWqJwyXM78beRFRMutE2qN6kBkJLzE39xJLl25gevoMtq05d26cbLZMpZJDJiVO\nY0wTWtcDN2FMOzKn/Q+RgFhmfrLjISRr8CNIq1UNWIwhKUkbARZLo2uZw3UbqFQ+Syy2mrVr/xP5\nfIlKpYDnvcxf/uV/wbI6KBSmiMcLNDUtoq0twdq197Jq1VZOndrFjh17gSzV6n607iIe34gxJ2lp\nUfh+TTEOgkAyBd3d3Zw48RJr1rRd8Z5dTVlgYTbC96/V7sXr3aNduz53xXvW19fN+PgRPK9EXd0I\npRLE401Uq6dJpfbQ3Hw9ixZN4XnjZLMjwIeQOQsZhJx3AJEObwRexnGux3GKlEqzVKsuxhANLzuM\n63YzMeFQKp1FqUVoXY5KEbXSXW3XnkW6imQao1IJwtAlDBWwAcs6RjLZQ7m8nCD4NAIGbkE2AlWk\nNFDzt7uAxwjD6zBmGgEBJ5FSQnN0ntpwpTHg94ETwBcQ0mUvSjnR/1sCmS4bAP+A42xCa4dEYg3G\nfC0C30lEbVHhunPE42doaronIol63Hbbv+Pgwd/FdQ/ieQA5wvAQWleJx4/S0PCf+JM/+R/cf//W\ni/H2tbE5kQAwnDnTyOTkfOnw0mdhgSAstgAOrtLe6GG5ljTVG6WY29r2EIY9F89jWQHGWNTVJamr\nA9FwvwFjXsb392FZoPUADQ3vIAjeQhC8SCLxDmZm5tA6iVIjBEE3YfhPKFUTNfkC5fJnqK9fgjED\n/OzPrueZZ0KC4B5On36YSuVeZKEWrXQRJvkH4P8EUoThflz3AsnkuwiCz1CpfJggsFGqDWNWYswh\njKkiYigzaJ0BMlFvd005cTfSujiMAIC3ImnHdyBB00KpJWhdAjIYsxxj9qLUaYz5fYxZDGzD95fz\n4otnCIIsMiLaAd5OLDaOUrdSKKRxXdi9+8vAuxkbqxIEbRjTTG9vB9PTHZRKrYShkLTq6looFIbx\nvIkoIIUAtLS009b2VzQ3/9TrdqG82TNyqf2wB50fBLsazQTbtlm7Nk5zc4xy+QCnTj3J+PgFFi2q\nZ+XKLrZtmyIeX89jjw1h2+sIgiJKlQnDw4gvXocAg704zgpaWu6lXD5EpXIApdIo5ZNOd1Is3s7E\nxLP097+XxsZz5PMeYZjEdRsIgmG0ThOGdUB9RHqcJBarYsx1hKHCmA6kBOAShiGl0jksK00QNCOL\n+Z3MdxTkkKyGRkDADpRqwZhG4FWUuh5jziEgoaaqmGd+MmQ5+o4ycAFjmjAmiXAUSoi/nyMW+0ik\nF3GGVGqGIOigWv0KltVFGMZpapphw4YVnD2bA6bp7V2KZVmk02k8rwVj6rAsD9DEYjkymQ3U13eS\nz/fwxBO7efDBO4Erx2bb9rHtJopFw+nTo/T391x8b4EgPG8L4OAN7Golb6+mj/2BB8xFkszrp5g/\nwG/+5iMXF5mlSzOcOTNx2aKjNTQ1/SjF4jGMmcLzXiIWO0x7+3qKxeupVotcuDBMQ0OFfP4pwvA8\n8J6oxUpjzBhh+FUSiSo33LCS3/7tn8e2/45/+If/jufdiu/nkR1NGkH57cBPITv8twLgeYZy+Thd\nXTewalWCvXv/gCBYFAGSKTzvDoy5gFJZlFoLDKK1gAORRK5E/zubkBKDi4ACH3geY+5FqTa03ozn\nLULrl1FqN52dn8b3S4yPfxx4K8acib6vADRgzFpgCb7/d6RSt1OtDlCpTOG6m3jllVfp6HgA3z9N\na+sQg4M34Xk2jtNLEMSoVHxmZ+eA/SxdOkOh8Kd0d/tUKge5776lfPKTv8327QfYufOFy+7Z1q0/\nsiCL/G/c3gjQd3a+yC//8k8Tj8cvA46X1rg///lDpNN3UiiAMQ3Y9nrCcJQgmEWpZcAXyGQWUV/f\nShC8h7q65ovf4XklkslTZLNxCoV/wJjzxGIrgRM0NW2mWGykXD5JteohPrsLy8qj9Raq1QJSxnCo\nlQeNUcDtBMFBpH24HVm4LaT1MMe8LgHAFGHooPU2wvBZoBWlVmPMIgQUnIyO34KUDF5A/HgXQopM\nR99L9P5XcJwWbPtpPG83icQmUqkPAzAz8xfYtsb3OwjDOCMjObLZ3TQ2TtLb+x84fXoPYbgVy+oi\nk5mloUH0DAA87xTT07tpbKxj164RHowGNl4pNnd3d3PmzGkSib6odDj/3gJBeN4WwMHr2NWWCt6o\nj933qwwM7GZg4Bj/8T9+gUTCv7hwvF6K+dJdSl9fN0eODHHhwjni8U58f4CmpqW47gQNDYa2tsUs\nW/Y+bNthaGiAUuk4Wk9SLFq4ro2UCCzga8DThKGL1kuArbjuSWKxF1BKYdsOsIympnfiuq/i+8uQ\n3YPLfD1xH0ImehmtLc6de4r16ztZtWorq1ZtZfv2L3HgwDgyvOkRfB+0vhmlDqJ1A5Z1B8YsIwgO\n4PvLkRrnAQQUaCQoNSOEra9iWa/g+zLV0ZhutH4v5fI+crkdSCC6K7q+OqSWq5CyQ0gYxvH9kERi\nJeWyhecNUSgcJgiy9PYuxXXfSza7mzC8jrm5w2i9Aa3rUWocGKdUaqezc5DPfe63yGQyF+/NlWrU\nC6znfxv2Rlmfq+WMXIlHdO+9t5LJfIlstkIiYeG6RcLQRqlmYrEGEoksixa10drqMDg4STzed/Gz\nnlfCcXK4bkhDwyqamtbR3Lyd48ehWHyMXC6LbfdSV9dJc3MCzxtmbu5JLOvXIkLwIcR3lyH+cRqZ\nV3AmOsM4Ika2HOELzCKAvT36twUolBrAmDXAe7Ht4wTBdoyxEG7CMSQD8tfYdie2fR2VyitIp8I4\nAhYcBEiESDfSl0kkPoTvfw3fT1Mq7aRSOUO5fAzHmcG2j9DQ0IPWIStWaBoaclQqf8/AwCkymfso\nl0fIZG5Gqfnly7ZXMDPzNTZu3Izrnr9ICr1SbL50EBeoi8cuEIQvtwVw8Dp2taWC1+tj9/0qu3c/\nSrF4C47zQZqaNl9x4XhtQHrtLuWBB7bw1a/uYWLiEI5zmM7OrRSLJ1EqRTL5ErCEoaEBhoZeRqkb\nue222xkbK3D2bEA+/xJK5VHq4wg7GGTa2Q5sexXg8IlP/Dnf+MYy8vk8QVBFBh250bEphFyURGqZ\nU8BtxGKLKRbPcepUHcPDv8aNN95Je3uGVGqAQiFFPH4jlrUTrfsiQPAKQVCHbZ9AqS8BfTjO48Ad\neN7dkcSsIgj2ICNrN6P1T6CUwffbEJGUnRQKA3jeBiSI1dTf5JqFv+BR698OAoPjaIxpIx5voLt7\nA9u2vZ/BwVH27j2FMe+kVNpNEOwmmTyM6xZwnBYSidtJJp+gv/8n2bHj5Suylmv37NJnpBZgFljP\nPzh2LcOwrpUzUqlUePLJPezaNUIqlWB6+gBKNZFMLsbzFI4Tw3FKtLSco7e3BaU8jhw5RrG4CMeJ\nkUrFaGpK0tTUxqlTL9Dc3Mnw8FOATRDkiMXeSxheIAieIZvN09AwRjw+Rjr9cQqFExgziygUHkNA\n9xTiWx8AXsKYP8ey/jNB8BjSkjiJEAiTzEujHwf6ozJCgCz4dyGZukNoPUsYfhCt16H1PlKpjyFa\nEU8hcaYdY2SUu1JFREnyBVz3MMbM0NDwYaan/5gg2AS4xGLvYfnym6irC6mrG+W229Zj2zazs6fY\nunUU30+STt/O5z73NJ6XJRZruZip8f0ZbHuG5ctvxvcfvYRH8q2xuTaIa2BgN2fOfJ1c7voFgvAV\nbAEcvI5di+TtlWqSAwO7KRZvBZpYtkxSX1ezcFxpl/L2t5dJJouUSs2RXkGZWCzLjh1DDAy0Uy4r\n0ul/z6JFmxkenqNUGqFY9NB6W0QE3I1t3w2AMSvR2sPznubo0Qvs2uVhWe8nmz2LUjW29XhELFyG\n1BFrgie7kECisKxNxOOr8f0VbN/+GZLJO1mx4j8zOHieQsFCqZBE4tN4nsZ1e9B6EfX13QTBf6BY\nfAbPS6B1F0o5UX0/SxDEgHdgWYMYk0apLFqXotauW6lWn0KU1SaAZ6PrC6JriyOAYQhjlmAMBEEJ\nrYV/ofUUe/Yco1jsAhbjOEvR2iEWc+dWgAsAACAASURBVKmvv4dly2R8tsjnnqalZQ07d770hqzl\nHTvOMDGxjBdfPHhRr2Lp0gb6+roXWM/f5/adZH1eDxjUwMb27YPs2XOcavWtrFx5OytWdGLbXeRy\nccrlk3R0LCYWM3R1pRkaymHMajZseIiXX/51fP8OoAPbztPUlMZ1B3GcZwnDt1EqNaD11qjDaD9B\nMBZl/UJ6e5dQKJxnYqILy3or8FsIj2cc8ZUQyfz9DuIvqwjDJ5D2xcOIXyeRzcAUQjDcj5CQlyDD\n0wbxPGlVFE7QzyKkwBBjnqFYnEKpBFDEsrYjGgwDWNa9BMFyjJklDIeBTorFv8RxGrGs29C6Ect6\nhQ0bfupiV0ippC7yARobV7Br1x5iMZ+BgWFSqTjj44cJAg/HcUgmG8hkGggCzZNP/gWdnR6/+quf\n47bbunnLWxaxY8eV+CJxFi3q4iMfuZ8HHti6wDG4gi2AgyvYtUreXqkmOTIyjDEbqK8fpa9v3WWf\nf7OF42o0FB577FlKpQdpbOzjqacexravRylFMtlMGK6nWn0eY+qxrMUYsw8ZKxuilE9dXS+l0iPM\nznbhOEtx3TQi3HIQCQSLkJ3EfiSQDAE5lLoex+knHu+jWh0hm53BceK4bg+JxG3k8+P09y9jfPws\nlrWCajWJUmNY1u10dGxBKcXp00fw/WX4fj9B8BRSRvCx7caI+JRF6wRKeYRhCcfx8P2maBzzcgSg\nvBMpSaxB5FyPI0THv0bqqGvw/ZMkkz1Y1mbi8XFisSTFYjfJZDNajyJTNIXD4fuNzMzkIilWYUZr\nrd+QtVypVNi1a4gwbCSRWH5RendwcJaJCWFBL7Cev3/tu93rfinYmJgwBMFWksklHDjwTcrlYxjz\nOEFwB/X1PfT01LFmTR8HDjyL1kNs2vRz2Hacm29+OwcPvkSlso9y2WdqqsDmzRtZuvTtPPfcASqV\nFMb0YowN3I1SEAQFGhqyxGJZUqlz1NUN4bollGrFmLsQAuByhHPwT8jck3GgA2NSSKnhecRvZhGA\nMIKA7npEk6D289sQFdMYWs8RhsfRuiNSeVxPEOQiEm8XQeADn0CpH8OYGYyZwrIWYVkbCYIiYegT\nhjtobFxCJpNhZqblsnbR+VZiuS+lkmJ6+gwnTmygre0OqtULuG4DUMa2h5mbO4LjdJJOv4WNG9dj\nWVa0YXuepqYxZmdfX2Pkavzzh9GP/1XAgRKWzX9FclAdCAz9e+B3jWxJv6/tWiVvX7vbr1ZtqtUB\nVq4s0Nd3eatM7fuvduF47fvz7VXzmQ0R6Zk/LpVqI5FI4Psmkly1cBxDMhkjmUxTKGQJAkNb21vI\n5YaYmzuGMXeh1OOEYQXhGajoNQjsx7Z/FsjiOD2EoU863Uq1Ok2xOIVtt2JZTczNjVBXl6arK8eW\nLW/lmWcOo1QIpC/uyo3xcF2Dba/H86YQ3RgbY4RNHYs1EY9PE4/HKRYVWhu0Po+AgUmko2EZ0iq1\nL3rNRe9vAn4ZpdpJJDS+P0AYfprm5gKe96MkEk0YY8hkGpidncWYQRKJbmw7ydxcjtZWLsrnvhlr\n+ckn9+B5LdTVNV1Wa04mmykWDQMDIyxbtsB6/n617/YwrBrYSKe7efLJvyOfX04uNxQNVlqL4/QA\nA+RyX2N6+hSdnVtwnBHuuuvXsW3JUPT3b2N6+lGKxVtxnF58/wDG5BkYOEQudwjPux2tbSxLFlFj\nyihVoVKp5+zZAYIgTxjWYds5tA4i/YRmJMw/j0xdzCELfTtKZdB6SdS18BmEB5RFZpzcj0i1FxBO\n0DeA/wm0odQmjKnHmDZ8fzcCJpJAf6SbMIsIMf010IllXY/vFwmCMyg1gVI+oskSo7d3LVrHmZ31\nL6obgvhSrZUY4Ny5ITo6/v/27j08qus+9P537b3nqpFGGkmAkARCEggwBgN2bLATsIOx4zjNSRrb\ntXPsvE3fxr2nSdwkJ22fOk3TJI1bN27ftK972iZxU4LTJsdJnARssLGNhR2DDdjcdEMaCZCQZjSS\n5r73XuePPRKSEKArEnh9nkfPY4vR3kua2Xv/1lq/9Vu/QSh0gHi8kIqKaqLRfnp7M0QiRzGMN7j5\n5odZtqxm6H5bVFRLNCrZtKkFl6tjwjVGJjLtdDWaqZGD5ThPlt/GCU1X4dT19AOfn6FzTquJlrwd\n3dv/4hefwutdPK7gYqJGj2wMFukZfmF5vS7cbo1IJAYkCYUCAJhmknS6GY+nnZKSmxkYOISUFppW\njG2DM2TfmjvTYOayB02rxbbbSae78PkC+P35+HwL6exsBWLY9llsO0J1dZCammvQdT23rMpNVVU+\nLS3RXK99ANM8hpTbcCq4dSDEQqS8Hil/iml2YppR0uk0uh4kFPogptlPX58Ly8pDiCpSqTjOevFN\nOB+z53FuZCkgjKY1oWk+3O40xcV1rFnzNvv3HyObdQE6UmZIpV7B7W7EMB5GCIFtQyJxgkDgNWpq\n7r1k1vLevWFqa6+lpaVpxNbdUkq83hANDa/w8Y+rrOe5aDo2wxr9b3v3hgkENrB373ai0RuxrJVI\nGSSbDSDlSbLZXSxY8Fvk53+EePy/6Oo6RFnZUlwu79AxBufCm5rqOXnyJU6efJlM5iMsW/brtLa2\n0teXwbbPIqXEMAxcLi+GMZ9kMkU6PUB1dRmxWB4+3/UkEocYzBlw8g6acILqeTijCRpSOkt1XS6n\nZoqz8qATZ3SgAE3rw7YHEMKLlNfhLG38KULU4+wsC5Z1BE37NaRcjMuVRdd9pNN52HYEKEPKBUjZ\nlgv+Q0hp4fMtQNf7SKdfp7X1ByxadB/BYIh0unnoWhpcSiyEIBptALKUlKxk48ZamprqaWurJxBw\nEQxmiUROEAz+BitXjqptjhPovfZaPd/4xkMTqjGiko1nKDiQUu7A2UJw0EkhxGPA73CFBAdTKXkr\nhDgvuBj+oZzqchmndGuGbDZLc3M7kYhJNPo6LlcVwaCHYDAPXU+SyfSh62cwzSj9/T/D4/FTWGjg\ncrXi8ZTn5vvzEeIw2ewxpLwJp6xrEojhdvdgmrFcUuMOhFiFpgVwuazcSICOYbRTWbmc0tJ5mOZp\n6uqWDP2ummYiRIba2kV0db1Df3+KWKweIW7Bttfi5Aj4kbIB0/wScA9S3ozLlUTKEFK20Nv7BBUV\nH8a2IZmsIZttxe9fQTI5gG3HcIKDw8BWNK0Tt7uTa65ZgscjWLRoAYsX38jLL/8ZmjaYVOVB0zIU\nFYHbnaK//+9IJqsxjDg1NTdQXX0P/f1tF32PBx8utbU309X1NAMDWeJxD319/di2wLZbCAZ/zPve\n961Jv8fKzJnsZlgX6klu3bqBdNpFa2s9icRGXK44iUQfllWJlBqaVodtJ0kkXsXv34zXu5QTJzpY\nvLiR4uLzk+Xq6jZjmim6uurwemOcPLmPeLwbTatF1weQshrDsDAMQTK5g2z2VeAsnZ0alvUt4Dbc\n7psR4giQnwuKYzg7pJbiBNG9aFoRUtq56cZlwEtImUbT/gAnb6CUc4nI1TgrmI4TCPwuiUQTmhZA\nSgspT+MMEh/BstIYxnyyWYmUccCFaR5CiBVIGUDT4ui6zK30KiCdXkFn506uu+5murvricclXm8t\nqVSEJUsKiEYbKC2tx7Kce/Dg32ew9DzAjh0/AHxjvp+jA73xdshUieXLm3NQiPPpvCJMteTtHXds\n5MCB77N//z4iEQvLcqPrGUIhneuvz7B168cn3KbhN6d33jnFm2/+JYHADZSW/jqp1E/IZEL09Cyg\nre0lSktXkE4fJRBopqrqj3PrpcNcc42Hw4efYf78rbS2NmLbXgzjJrLZp3D2azgORNG0UmAJmnaE\ngoJNZLNfwTAWk8lkSKdN/H6JaTbg871KJnM/x48fRtdf4sknd+aWGJlkswMEAh6amsLceONy9u79\nEZZ1PU61xSacGSc/zjKnh3CqvL2NENfh97soKLgFy1qAaf6K0tIY3d2F9PcvwOeLo2kx0mmLbDaN\nlBZgoOtpioryqK0tpLa2EsMwOH68hUSimhtuuJOWlkK83nPTAMlkA6tWtZBIdOHx9LBgwWlM8+lL\nvseDDxddd/Oe93yYZ599jEikGE0rRddNgsEKPJ4P8Q//8JN3RQ/jSjTRkcGL9yR/iK5nCIfb8Ho3\nU1Bwkmi0G8uSCDE4NF5DOv1j3O4IRUWFGMY8pOyjt/f8NjgrnZ4hL+//xTDWYRhgGGVkMm9gWd9B\n1x8ilSpGymdwtnN/CMiSl1eHZTXS2/tdTPM5XK73k05/C10vxrIGqxO6gGuBA9i2M71g22ZuV8pq\npPwlmnYMKVNY1mlsuw8hbkbXQUo3tu0k+hqGG8sCKdM4OQi96HoBljVYj0XHsspwZpR9aNqi3HWX\nQNf7cbl6KSy8hljMZGDgdZYu/QDLllXlln4/i9sdoaKiive9bwl33DGy/sugc8XiMkhpTeso7XRP\nO12JLktwIISoBf4A+OzlON90mXrJW4EQ5bkLWM99gKNIefK8V17q+KNvTnl5TQhxlr4+g0zmGSoq\n/gfR6H5On/5XTNOHy1XE+vUrgGvo6PghQrgYGOikqEjj05++g927K+jp2UcyeRIhfg1NqwHW5KJ9\nP4ZRjW2n0bQUXq+B17uagoIsp079DdlsEMsqx+crpKBgK9Ho8yQSCfLyHiKRKMQ089C0KPn5b2Lb\nL/DOOx2cORMjm03j8VSRyei5TGUPltVKNnsEKX8DId4GUng8LQQCIWz7LEJY6HozH/zgIzz77GPE\n4w24XLeSzZbm9mg/iWVl0bQQmmZjmj6am/OHSqO2tsbw+/OGRi+cnkko94Cvobn5WT7ykWIeeeTz\nuN3ucb/Hgw+Xzs4wfv9D1NXVDr2HyWQP1dX9nD2bfVf0MK5EEx0ZvFRPMhD4OfG4i8JCQXFxJe3t\nDaTTSTTNhZRODX/bNnG5whQVXYNtm5SXL6Gk5FW6u0e24c03n8a2N7Jgwdqhz6PPV4gQH8c0d5LN\nPo5t9wHvxQm0nfX6nZ2tBIOlFBY+RDr9U9LpJjye30HT/GhaL6lUnGw2n3PVGU/gPLzDSHkCt9si\nm81H03RcrhsxzX5MswcpYwhRgmV5cDZwCiNEEMtqQtcXYlmt6HoHLtcqLOsspumMGBjGWoR4GtN0\nlmpqmoXbncTrbUXT6gkGN7NqVRGJRB7Z7H+SzbpZvDjLvfeuxLIs3njjLLt3n2Hv3u0YRpTu7iOU\nlo5M7gYIhXScCrDnm8wo7XRMO10NJhQcCCG+BnzhIi+RwAop5YlhP1OOk82yXUr5b+M5z2c+85kR\nxWcA7r//fu6///6JNHdaTfRDsGPHq0Sj72XduvOnFaLRBnburGfr1g3jTngZfXPq6IhTXX0D0Wg7\nPT2n6Ox8DMOQCNFLMLiK/v5ONM1Fbe1Gli2Dxsa9hMMZfvnLRlKpAL29e1m27COcOPEi2exudL0T\ny+pA10EIF5DBMGL4/UFcrhjpdJzS0tspKdnE2bN/RSDQy8DAfny+akIhP11dG4lGu7CsgdxOjV6K\nim4hL68Ot/sXtLW10tOTIJUKYds+XK4luFyLMQwNyzqElGVI2YmmJXG5qqmuXjw0DJhKHcXjyeeu\nuz7HM8/8Hu3tDSSTyxBCR0oNXfcBL+H1bsAwCkkksghRQVNTmEQizIoVizEMg40bnZ0WW1vD2LaO\nrtuUl2f43OfumXDvfvDhsndvD37/pqHvJ5M95OW1D+VdvBt6GFeiiY4MXqonGY/nk59/jESiG5+v\nmPLyapqbnZ63bafIZgWG0YFpXkdn58usWbMAv/8Mn//8fezcWc+LL75EQ0OYzs44PT0JhPg1IpEY\noVAQTdOYP7+ScPgkcCeaZiNlB1LW4KzWASHcWJZOb28vfn+Q/PwSTPMofn8ZLlcaITbT2flVnCWL\nN6BphUhZgRBN2PZrQA2atoNgsIN4/FY0rQq3W+LxBEilGpEyi6a1U1jopbi4nTNnXkCIl/D5ViBl\na65GisC249i2BqTQtDB5eRvIZv8DTdMxzQzBYISNG29Ayg9x6lSa1tYEEOGWW25i06Z1PP/8Ph5/\n/Hn6+6vIy8ujsnIBNTUbiMVOcvz4k8BvUVKyckQwd/31GaQ8STTaMC27nk522uly27ZtG9u2bRvx\nvVgsNm3Hn+jIwWM4O4VcTPPgfwghFuJsQfaKlPLh8Z7k8ccfZ926dRNs2twy+mYy/INUWFjLnj0v\nc/BgeNwJL8OPJ6XEsjRcLoOSkiqKisppbX2FUOhT9PUlcblWk82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g6yUEgxAK2WQyn6K5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"text/plain": [ "<matplotlib.figure.Figure at 0x10fe5a450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pylab.plot(data[0], data[1], 'o', linewidth=0, alpha=.5);" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting Kmeans.py\n" ] } ], "source": [ "%%writefile Kmeans.py\n", "from numpy import argmin, array, random\n", "from mrjob.job import MRJob\n", "from mrjob.step import MRStep\n", "from itertools import chain\n", "import os\n", "\n", "#Calculate find the nearest centroid for data point \n", "def MinDist(datapoint, centroid_points):\n", " datapoint = array(datapoint)\n", " centroid_points = array(centroid_points)\n", " diff = datapoint - centroid_points \n", " diffsq = diffdiff\n", " # Get the nearest centroid for each instance\n", " minidx = argmin(list(diffsq.sum(axis = 1)))\n", " return minidx\n", "\n", "#Check whether centroids converge\n", "def stop_criterion(centroid_points_old, centroid_points_new,T):\n", " oldvalue = list(chain(centroid_points_old))\n", " newvalue = list(chain(centroid_points_new))\n", " Diff = [abs(x-y) for x, y in zip(oldvalue, newvalue)]\n", " Flag = True\n", " for i in Diff:\n", " if(i>T):\n", " Flag = False\n", " break\n", " return Flag\n", "\n", "class MRKmeans(MRJob):\n", " centroid_points=[]\n", " k=3 \n", " def steps(self):\n", " return [\n", " MRStep(mapper_init = self.mapper_init, mapper=self.mapper,combiner = self.combiner,reducer=self.reducer)\n", " ]\n", " #load centroids info from file\n", " def mapper_init(self):\n", "# print \"Current path:\", os.path.dirname(os.path.realpath(__file__))\n", " \n", " self.centroid_points = [map(float,s.split('\\n')[0].split(',')) for s in open(\"Centroids.txt\").readlines()]\n", " #open('Centroids.txt', 'w').close()\n", "# print \"Centroids: \", self.centroid_points\n", " \n", " #load data and output the nearest centroid index and data point \n", " def mapper(self, _, line):\n", " D = (map(float,line.split(',')))\n", " yield int(MinDist(D, self.centroid_points)), (D[0],D[1],1)\n", " \n", " #Combine sum of data points locally\n", " def combiner(self, idx, inputdata):\n", " sumx = sumy = num = 0\n", " for x,y,n in inputdata:\n", " num = num + n\n", " sumx = sumx + x\n", " sumy = sumy + y\n", " yield idx,(sumx,sumy,num)\n", " \n", " #Aggregate sum for each cluster and then calculate the new centroids\n", " def reducer(self, idx, inputdata): \n", " centroids = []\n", " num = [0]self.k \n", " for i in range(self.k):\n", " centroids.append([0,0])\n", " for x, y, n in inputdata:\n", " num[idx] = num[idx] + n\n", " centroids[idx][0] = centroids[idx][0] + x\n", " centroids[idx][1] = centroids[idx][1] + y\n", " centroids[idx][0] = centroids[idx][0]/num[idx]\n", " centroids[idx][1] = centroids[idx][1]/num[idx]\n", "\n", " yield idx,(centroids[idx][0],centroids[idx][1])\n", " \n", "if __name__ == '__main__':\n", " MRKmeans.run()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iteration0:\n", "0 [5.029576650922463, -0.0029051016453652692]\n", "1 [-5.056032748447057, -0.030107844551825306]\n", "2 [-0.022086029177989085, 4.90174519728454]\n", "\n", "\n", "iteration1:\n", "0 [5.0402327160888465, -0.026294229978289455]\n", "1 [-4.988208799410262, -0.0016052213546992817]\n", "2 [0.05043492418549814, 4.985354277214307]\n", "\n", "\n", "iteration2:\n", "0 [5.0402327160888465, -0.026294229978289455]\n", "1 [-4.98580568889943, 0.0009376094363626959]\n", "2 [0.053065423788147964, 4.987793423944292]\n", "\n", "\n", "Centroids\n", "\n", "[[5.0402327160888465, -0.026294229978289455], [-4.98580568889943, 0.0009376094363626959], [0.053065423788147964, 4.987793423944292]]\n" ] } ], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "from numpy import random\n", "from Kmeans import MRKmeans, stop_criterion\n", "mr_job = MRKmeans(args=['Kmeandata.csv', '--file=Centroids.txt'])\n", "\n", "#Geneate initial centroids\n", "centroid_points = []\n", "k = 3\n", "for i in range(k):\n", " centroid_points.append([random.uniform(-3,3),random.uniform(-3,3)])\n", "with open('Centroids.txt', 'w+') as f:\n", " f.writelines(','.join(str(j) for j in i) + '\\n' for i in centroid_points)\n", "\n", "# Update centroids iteratively\n", "i = 0\n", "while(1):\n", " # save previous centoids to check convergency\n", " centroid_points_old = centroid_points[:]\n", " print \"iteration\"+str(i)+\":\"\n", " with mr_job.make_runner() as runner: \n", " runner.run()\n", " # stream_output: get access of the output \n", " for line in runner.stream_output():\n", " key,value = mr_job.parse_output_line(line)\n", " print key, value\n", " centroid_points[key] = value\n", " \n", " # Update the centroids for the next iteration\n", " with open('Centroids.txt', 'w') as f:\n", " f.writelines(','.join(str(j) for j in i) + '\\n' for i in centroid_points)\n", " \n", " print \"\\n\"\n", " i = i + 1\n", " if(stop_criterion(centroid_points_old,centroid_points,0.01)):\n", " break\n", "print \"Centroids\\n\"\n", "print centroid_points" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Ul19DNPpu4vHBac2fTmb9+mra2/cTDted8oxyuWGWLl09WbwplzNwnOnNqoaHE1hWNYFA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8jlWg8LIRXu3//OrI18+/YNyHKE3bufRJZLZjTGkWUno6Ot+P3jPProD2cd9+STv1gQiHE6ndxx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"text/plain": [ "<matplotlib.figure.Figure at 0x11870d7d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pylab.plot(data[0], data[1], 'o', linewidth=0, alpha=.5);\n", "for point in centroid_points:\n", " pylab.plot(point[0], point[1], '',color='pink',markersize=20)\n", " \n", "for point in [(-4.5,0.0), (4.5,0.0), (0.0,4.5)]:\n", " pylab.plot(point[0], point[1], '',color='red',markersize=20)\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT18. Which result below is the closest to the centroids you got after running your weighted K-means code for K=3 for 10 iterations? \n", "(old11-12)\n", "\n", " (a) (-4.0,0.0), (4.0,0.0), (6.0,6.0) \n", "* (b) (-4.5,0.0), (4.5,0.0), (0.0,4.5) \n", "* (c) (-5.5,0.0), (0.0,0.0), (3.0,3.0) \n", "* (d) (-4.5,0.0), (-4.0,0.0), (0.0,4.5) " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# B" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MT19. Using the result of the previous question, which number below is the closest to the average weighted distance between each example and its assigned (closest) centroid?\n", "\n", "The average weighted distance is defined as \n", "sum over i (weighted_distance_i) / sum over i (weight_i)\n", "\n", "\n", "* (a) 2.5 \n", "* (b) 1.5 \n", "* (c) 0.5 \n", "* (d) 4.0 " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# C" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# END of Exam" ] } ], "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
peterwilletts24/IPython-Notebooks	Moisture Flux Divergence.ipynb	2	19376	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import os, sys\n", "sys.path.append('/nfs/see-fs-01_users/eepdw/python_scripts/modules')\n", "\n", "from update_pp_cube_coords import update_coordsimport iris\n", "\n", "import iris.analysis.cartography\n", "\n", "import h5py\n", "\n", "import numpy as np\n", "\n", "import pdb\n", "\n", "import scipy\n", "\n", "#Load specific humidity and wind\n", "\n", "#/nfs/a90/eepdw/Data/EMBRACE/Pressure_level\\_means/sp_hum_pressure_levels_interp_djzns_mean_masked/\n", "\n", "#experiment_ids = ['djznw', 'djznq', 'djzny', 'djzns', 'dkmbq', 'dklyu', 'dklwu', 'dklzq']\n", "experiment_ids = ['dkmbq', 'dklyu']\n", "p_levels = [1000, 950, 925, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10]\n", "\n", "for experiment_id in experiment_ids:\n", " \n", " expmin1 = experiment_id[:-1]\n", "\n", " fname_h = '/nfs/a90/eepdw/Data/EMBRACE/On_Heights_Interpolation_Data/sp_hum_pressure_levels_interp_%s' % (experiment_id)\n", "\n", " with h5py.File(fname_h, 'r') as i:\n", " \n", "# q = i['%s' % 'mean'][. . .]\n", " q = i['%s' % 'sh_on_p'][. . .]\n", " pdb.set_trace()\n", "\n", "\n", " f_oro = '/nfs/a90/eepdw/Data/EMBRACE/Mean_State/pp_files/%s/%s/33.pp' % (expmin1, experiment_id)\n", " oro = iris.load_cube(f_oro)\n", " oro,lats,lons = update_coords(oro)\n", "\n", " fu = '/nfs/a90/eepdw/Data/EMBRACE/Mean_State/pp_files/%s/%s/30201.pp' % (expmin1, experiment_id)\n", " \n", " u_wind,v_wind = iris.load(fu)\n", "\n", " u_wind,lats_w,lons_w = update_coords(u_wind)\n", " v_wind,lats_w,lons_w = update_coords(v_wind)\n", "\n", " print u_wind\n", " print u_wind.coord('pressure')\n", "\n", " qu_div = np.empty((u_wind.shape[1], u_wind.shape[2], u_wind.shape[0]))\n", " qv_div = np.empty((u_wind.shape[1], u_wind.shape[2], u_wind.shape[0]))\n", " \n", " fl_la_lo = (lats.flatten(),lons.flatten())\n", "\n", " p_lev_delta = np.diff( np.append(u_wind.coord('pressure').points, u_wind.coord('pressure').points[-1]))\n", " \n", " for p, pressure_cube in enumerate(u_wind.slices(['grid_latitude', 'grid_longitude'])):\n", " \n", " print p\n", " s = np.searchsorted(p_levels[::-1], p)\n", " #sc = np.searchsorted(p_levs, p)\n", "\n", " q_slice = q[:,:,-(s+1)]\n", " q_interp = scipy.interpolate.griddata(fl_la_lo, q_slice.flatten(), (lats_w, lons_w), method='linear')\n", "\n", " #pdb.set_trace()\n", " qu_div[:,:,pn] = (u_wind.coord('pressure').points[pn]q_interp)/9.81\n", " qv_div[:,:,pn] = (v_wind.coord('pressure').points[pn]q_interp)/9.81\n", "\n", " Qu_div = np.sum(qu_divp_lev_delta, axis=-1)\n", " Qv_div = np.sum(qv_divp_lev_delta, axis=-1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "q.shape\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "(504, 600, 600, 17)" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "print q[p,:,:,-(s+1)].shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(600, 600)\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "pressure_cube.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "(599, 600)" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "q_interp = scipy.interpolate.griddata(fl_la_lo, q[p,:,:,-(s+1)].flatten(), (lats_w, lons_w), method='linear')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "print q_interp.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(599, 600)\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "qu_div[:,:,pn] = (q_interp)/9.81\n", "qv_div[:,:,pn] = (v_wind.coord('pressure').points[pn]q_interp)/9.81" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'pn' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-9-cd1c87289d7b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mqu_div\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpn\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mu_wind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pressure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoints\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0mq_interp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m9.81\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mqv_div\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpn\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mv_wind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pressure'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoints\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0mq_interp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m9.81\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'pn' is not defined" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "print u_wind" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "x_wind / (m s-1) (forecast_period: 6; forecast_reference_time: 84; pressure: 12; grid_latitude: 599; grid_longitude: 600)\n", " Dimension coordinates:\n", " forecast_period x - - - -\n", " forecast_reference_time - x - - -\n", " pressure - - x - -\n", " grid_latitude - - - x -\n", " grid_longitude - - - - x\n", " Auxiliary coordinates:\n", " time x x - - -\n", " Attributes:\n", " STASH: m01s30i201\n", " source: Data from Met Office Unified Model 8.02\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "print time_cube" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "x_wind / (m s-1) (pressure: 12; grid_latitude: 599; grid_longitude: 600)\n", " Dimension coordinates:\n", " pressure x - -\n", " grid_latitude - x -\n", " grid_longitude - - x\n", " Scalar coordinates:\n", " forecast_period: 6.0 hours\n", " forecast_reference_time: 2011-09-07 18:00:00\n", " time: 2011-09-08 00:00:00\n", " Attributes:\n", " STASH: m01s30i201\n", " source: Data from Met Office Unified Model 8.02\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "print q.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(504, 600, 600, 17)\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "print pressure_cube" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "x_wind / (m s-1) (grid_latitude: 599; grid_longitude: 600)\n", " Dimension coordinates:\n", " grid_latitude x -\n", " grid_longitude - x\n", " Scalar coordinates:\n", " forecast_period: 6.0 hours\n", " forecast_reference_time: 2011-09-07 18:00:00\n", " pressure: 1000.0 hPa\n", " time: 2011-09-08 00:00:00\n", " Attributes:\n", " STASH: m01s30i201\n", " source: Data from Met Office Unified Model 8.02\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "pressure_cube.coord('pressure').points[0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "100.0" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "pq= iris.Constraint(pressure=pressure_cube.coord('pressure').points[0])\n", "v_slice = time_cube.extract(pq)\n", "print v_slice" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "x_wind / (m s-1) (grid_latitude: 599; grid_longitude: 600)\n", " Dimension coordinates:\n", " grid_latitude x -\n", " grid_longitude - x\n", " Scalar coordinates:\n", " forecast_period: 0.999999996275 hours\n", " forecast_reference_time: 2011-08-18 00:00:00\n", " pressure: 100.0 hPa\n", " time: 2011-08-18 01:00:00\n", " Attributes:\n", " STASH: m01s30i201\n", " source: Data from Met Office Unified Model 8.02\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "print qu_div" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[[ 2.35882378e-310 2.35885344e-310 2.35885344e-310 2.35885344e-310\n", " 0.00000000e+000 7.95445690e-322]\n", " [ 2.35885346e-310 2.35885346e-310 3.64908000e+005 6.37344683e-322\n", " 2.35885346e-310 2.35882378e-310]\n", " [ 3.64912000e+005 4.79243676e-322 2.35885346e-310 2.35885345e-310\n", " 3.64912000e+005 3.21142670e-322]\n", " ..., \n", " [ 2.35885345e-310 2.35885345e-310 2.37151510e-322 3.16696079e-321\n", " 2.35885345e-310 2.35882378e-310]\n", " [ 0.00000000e+000 0.00000000e+000 2.35885346e-310 2.35882795e-310\n", " 5.65176949e-315 4.79243676e-322]\n", " [ 2.35885346e-310 2.35885345e-310 1.58101007e-322 1.77863633e-322\n", " 2.35885346e-310 2.35882795e-310]]\n", "\n", " [[ 3.16202013e-322 1.77863633e-322 2.35885345e-310 2.35882795e-310\n", " 7.90505033e-322 1.77863633e-322]\n", " [ 2.35885346e-310 2.35882795e-310 9.48606040e-322 1.77863633e-322\n", " 2.35885346e-310 2.35882795e-310]\n", " [ 3.64908000e+005 7.95445690e-322 2.35885346e-310 2.35885346e-310\n", " 3.64908000e+005 6.37344683e-322]\n", " ..., \n", " [ 2.35885346e-310 2.35882378e-310 3.64912000e+005 1.63041663e-322\n", " 2.35885346e-310 2.35885346e-310]\n", " [ 6.32404027e-322 1.77863633e-322 2.35885346e-310 2.35882795e-310\n", " 5.45448473e-321 2.56914136e-322]\n", " [ 2.35885346e-310 2.35885345e-310 2.35885345e-310 2.35885345e-310\n", " 2.37151510e-322 2.77170827e-321]]\n", "\n", " [[ 2.35885345e-310 2.35882378e-310 0.00000000e+000 0.00000000e+000\n", " 2.35885346e-310 2.35882795e-310]\n", " [ 5.61841903e-315 1.63041663e-322 2.35885346e-310 2.35885346e-310\n", " 4.74303020e-322 2.56914136e-322]\n", " [ 2.35885346e-310 2.35885345e-310 2.35885345e-310 2.35885345e-310\n", " 2.37151510e-322 2.06025374e-321]\n", " ..., \n", " [ 2.35885346e-310 2.35885346e-310 4.00000000e+000 4.79243676e-322\n", " 2.35885346e-310 2.35885346e-310]\n", " [ 3.64908000e+005 3.21142670e-322 2.35885346e-310 2.35882378e-310\n", " 3.64913000e+005 1.63041663e-322]\n", " [ 2.35885346e-310 2.35885346e-310 8.45840386e-321 2.56914136e-322\n", " 2.35885346e-310 2.35885345e-310]]\n", "\n", " ..., \n", " [[ 3.64926000e+005 1.63041663e-322 2.35885346e-310 2.35885346e-310\n", " 3.25688074e-320 2.56914136e-322]\n", " [ 2.35885346e-310 2.35885346e-310 2.35885346e-310 2.35885346e-310\n", " 2.35885346e-310 6.24993042e-321]\n", " [ 2.35885346e-310 2.35885348e-310 0.00000000e+000 0.00000000e+000\n", " 2.37151510e-322 1.77863633e-322]\n", " ..., \n", " [ 7.11454530e-322 2.56914136e-322 2.35885346e-310 2.35885346e-310\n", " 2.35885346e-310 2.35885346e-310]\n", " [ 1.97626258e-321 1.77863633e-322 2.35885346e-310 2.35882795e-310\n", " 6.00000000e+000 4.79243676e-322]\n", " [ 2.35885348e-310 2.35885346e-310 1.58101007e-322 1.77863633e-322\n", " 2.35885346e-310 2.35882795e-310]]\n", "\n", " [[ 3.64926000e+005 1.63041663e-322 2.35885346e-310 2.35885346e-310\n", " 2.60866661e-321 1.77863633e-322]\n", " [ 2.35885346e-310 2.35882795e-310 6.00000000e+000 2.53455676e-321\n", " 2.35885346e-310 2.35885346e-310]\n", " [ 0.00000000e+000 0.00000000e+000 2.35885346e-310 2.35882795e-310\n", " 3.64926000e+005 1.63041663e-322]\n", " ..., \n", " [ 2.35885346e-310 2.35885346e-310 6.32404027e-322 2.56914136e-322\n", " 2.35885346e-310 2.35885346e-310]\n", " [ 2.35885346e-310 2.35885346e-310 8.69555537e-322 2.56914136e-322\n", " 2.35885346e-310 2.35885346e-310]\n", " [ 2.35885346e-310 2.35885346e-310 2.37151510e-322 1.63041663e-322\n", " 2.35885346e-310 2.35885346e-310]]\n", "\n", " [[ 5.29638372e-321 1.77863633e-322 2.35885348e-310 2.35882795e-310\n", " 5.45448473e-321 1.77863633e-322]\n", " [ 2.35885348e-310 2.35882795e-310 3.64926000e+005 6.37344683e-322\n", " 2.35885348e-310 2.35885348e-310]\n", " [ 3.64927000e+005 4.79243676e-322 2.35885348e-310 2.35885348e-310\n", " 1.58101007e-322 1.77863633e-322]\n", " ..., \n", " [ 1.02765654e-321 2.56914136e-322 2.35885346e-310 2.35885346e-310\n", " 2.35885346e-310 2.35885346e-310]\n", " [ 2.37151510e-322 1.42784972e-321 2.35885346e-310 2.35885346e-310\n", " 1.58101007e-322 1.77863633e-322]\n", " [ 2.35885346e-310 2.35882795e-310 3.16202013e-322 1.77863633e-322\n", " 2.35885346e-310 2.35882795e-310]]]\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "u_wind.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "(6, 84, 12, 599, 600)" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "u_wind" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "<iris 'Cube' of x_wind / (m s-1) (forecast_period: 6; forecast_reference_time: 84; pressure: 12; grid_latitude: 599; grid_longitude: 600)>" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
moizumi99/CVBookExercise	Chapter-8/CV Book Chapter 8 Exercise 1-2.ipynb	1	195840	{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import \n", "from PIL import \n", "import pickle\n", "from pylab import *" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import knn\n", "knn = reload(knn)\n", "import imtools\n", "imtools = reload(imtools)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('points_normal.pkl', 'r') as f:\n", " class_1 = pickle.load(f)\n", " class_2 = pickle.load(f)\n", " labels = pickle.load(f)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = knn.KnnClassifier(labels, vstack((class_1, class_2)))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('points_normal_test.pkl', 'r') as f:\n", " class_1 = pickle.load(f)\n", " class_2 = pickle.load(f)\n", " labels = pickle.load(f)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "print model.classify(class_1[0])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb792f01510>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Am6CzY26PtT099uwnVsw51t22UsmVHPBjSXU49YW5r6bHYxeJ7X36FRNzqs3s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\nBvLL7pNFpEusjWIKu+vOSr0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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb7955371d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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2CJfkaHZOJoX9nwC8Ynp9O4Db3fahsOc2yfwn3nlH16NyiqcnKi6Y7FJZ2Xbi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\nyKCwE0JIyKCwE0JIyKCwE0J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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb792e3d410>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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nnzsPJDrsEXJOhgEDdAi4uVm/bm7Wdu3bp8PWl1yi33/99eDOmSyl5aUoKS3B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\n9E0ihBCSDukKey2As5VSbym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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb790c965d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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En/13gI6550zcjRj29u2pc72tjXXffbryY01N8naDBzuXEhDRcfXevd2F0zphdnMzsGdP\n+8qQ5s/xyCPAzJn2nyHdTpBMct9zfZUvBry49X4vDMWEB7/CmNncdRt33NZQc21tbu7EfQnLpMpD\n91Jf3CmUUlaW2K6mpn1s/eyzEyEcax/AhAntz1tX5x7/N2N8Dmu+/Jo1SnXtqtRFF+UnPGKtGR+S\ncAzyFWPPZKGwhwc/w5jp/hfTDSH7OV7GEPfzu8xQU/v8k7rlu79XrZ+3ej+A0XBOghmNtm8Es9jP\nnq0FOxJJ7GMI+NFHJ/YxJtOIRvX6ESPcr3h2X6hTQ0ciqQc6GZNz+PWlePUkQjqAicJOckouOivT\n/S86efkNDfmZMGfV8nXq1svvVDde9Bs1TqaoB3/+WOqdsunQNIuu0VhTprjvN2GC9s6XL3f3up3s\nKi9PNLR1//r69l9GOp8p1ZdiJ+Ih7BBNB6/Czhg7yQi/w5g7d+qpPG+4wXvJ7EzTx/3ixLHH4so7\nZuGGRT/Akf26Y+fW3e4f8IwzdKzY2nDmPHQRPXuReX0koj+EuQPi8cd1nPuzz/SAor59k883aBCw\ndq1+3L0beOwxYMcO5y/N+oUac5pOm5ZoaKOmu4guNHbANA7R2MaoDGng1nmb6ksx96SzQzQtKOwk\nI/yuw5RpNoxT31q+J8wRq6BZMT7gnXe2b7jWVj3Sc8UK/djamrz+kkvsLwaxmB5hOm8e8Mknyed7\n9119hTMLYb9+uvqjXQ+ziLarqUnvb5QXuPfexHR4c+fqYmNz5gDDhrW/8u7eDQy31P+z67wdPFgf\ny+lLsRPxQ4f0RaKUO0TTgBNtkIwxlwtZuFA7pU5ettOcDLmYfyEILu5/OUZ9fSR+eNec5BVOHzAa\n1SJp13B2DdupE3D//e5GdO+uvfgbb9QiKwJ8+qluSKN+y759uuyucewlS3QmzuWXa6Ht0kVnvSxd\nmihXUFurj11Wpu8U7K7eqerliABf+Qqwd6/29I3qk3bs3AlcfTXwpz8l156JRoEHHiiJCTWc8For\nhjF2khecQqNGaLayUoddKyu9x8MLaWDhtJpZ6tf/dHv7FX4N2Kmt1fufe673eLz5tdH4RqOVlzvv\n6xSLB5xj23afc+hQ3WGbSWaKtSd95kyljjxSqUsvDV2HaDqAMXZSCKQKjRohHcM7d5pW0y79uShK\niGQbszIa0Ag5PP2087aRSHI83uDQIX2MXbsSjfbNbzrH28ePd67R7hTbtvuc5hBTuvEwu9oz+/fr\n85bIvKVZ4UX9/V7osZcGjY1KjRmjHSsnh9UtGcOM2eMvK3N2NoPC0WNXKrvUOztPuKZGqWHDkvPT\njefDhtnfITh56GaP3px77ua1T55sf8fhZ4qhlzuLEgRMdyRBY4jx8OGJu2oRnX1n6IJVtww9MTLp\nvGQH5iqdMR1chT1b7BL8DRG1Dj6yE2wjBGN3gTCHNsz57RMm6FCKncDnI9XQPNApH7mrRYJXYe8Q\n9B0DCR/WfrSNG/VjLKaTJjZs0OETo9/rxRcTyRhGf9299+qlrEyXQjH6AO1oagIefFDP6xBKdu8G\nZswA3ngDOPZYHdIwwhDXX9++o/GII4CxY4Grrkp0vtqFSs47T38J1i/MCONEo4m67UoBX/iC/hLT\nTTFy6jm3w2rLffclnnMaPM8wxk58xy7HPRLRiQwbNuj3jFBt//76fz9sGDBhQiJ92gj5XnyxTqRo\nbtYib0d1fHKjvMfaH3xQ54lHIvjdzv/FMVtey815Fi/WDdLQoB/NsWUnwb7nnvaxaHPcesYM4OGH\n3ecwPeMMffxly3R64nHHJYqPpVPzJZ3OEDtbamqASy/NX+5qGPDi1vu9MBQTfuySGsx31KmSOtIZ\nwOhX+DWtLJsHHmj3YQ5Hy/T7ftHYmFwywLyIJAw1wjKpyviamTMnOS5mF+5xG+XpZQRopsOTQ1rn\nxQ/AGDvJJ1ZRtOtHs5srwgjhGiHf+nq9T329fm0NrRrbV1crNXBgoqSKU6pkOmKd1mj1gQNtRSs2\nYEDabZeE2WBDfLt0SZzDnEaY7qTZSrlfNevr9cXB7YrpJtbWybZPOcW959yJkNZ58QMKO8krXjTF\nrUggoFSvXglNOP107eVbLwSpFj+1ztWxdOi0jAHq88OfO++X6kpjzj/PdrH7AI2Nzh2u0WjiYjJk\niP3gAre8fPNdQH19+55zp4EMhTIYoQigsJO8kK4oLl2qVMeOiSy2aFSpAQMS+02dqtcDyWV4p051\nvyh4dSytaZRKZTiGyMFjb4Wohlk/dt7PqTyvWzqisZSVKdWpU+IWxVr1zGigaDT5A1jFM5OLhFmQ\nraESL19MWZm9913iRb3ShcJO8oK18J9VU6yMGJGZrrgt5nCOk2NpaOFFF9nblXZY1ybGbiyfdyxv\nH2t3K2srom9XzGEL40MZhhsxJzsPONXV1fCkUzWkUzzfejxzqKS+Xqnu3d2Pb3eRYX56RlDYSU5J\n9/+ZjXCfeKLWkkwcS6ftrJ57RmHdBx5w9FbbavonN5S1lnAqAQX0VfCssxKPc+fqMIfV0DVrtEds\n2NKhg+6g8HIX4CbwHTqopCtmQ4NzGd1hw+yPY+0LYH56VlDYSU4x/z9rahIaYGiK9f+5Zo1j9MJ1\n6dw5oQ9O23TooNTEiXqsjVmQ3WL6xvwPWeMSa2/+7FByqMF8WyDi/KGcwhZ2NDZqsTdfFMyDklJ5\n4U7DeM3Hs8bN3e4SzMvUqYnP4bY9s188Q2EnOSHd/6eRHDFmjL1T59fiVptqyBB3m53289Sn55Qd\n43TCaDThbdfUJK/r0CE9r9VtZqMjj9THmTGj/fqhQ/XEG0ZczPhizB66OUXJ6bOYL0x2I1nNFyan\nka/GObLJfimhDlgKO8kJbv9PuzTqmTOT//teQr1OWtW5c/v3BwzQ53bThEGDEscw2+KmoZ779Gxi\n7W3lFeo2jFQfnTXRPdRQV6fFVSRxa+HVa3UTdbNnbXcOL52dXr1y60XLjVzlp5dQB6xXYefIU5IW\nbgMdFy/WgxsHDUoUJTSPCG9q0gqQCbEYcPBg+/ffe0+XHliyxHkw5Akn6IGTkyfr10YBRLuR6cYk\nRp4n6pk+XQ/bHzhQbzhwIN6/5kY8JUPQVlXtXtVx8WI95HbOHF2b3W3yCSvGCM1otH1D3Xtv8kxL\nb76ZfI7x4/W+1g8VjQJnnQX07q0n6rCOAjW2MWZ5Ms5tPPbr526z37OfcFYlZ7yov98LPfbixtrR\nmCqMa10y7dOzOqaGkzh5sg71mB1VL3bbefnGHYa13zCdu/x1L21U42SK2nfa2NwOtLH2Dnftqkdu\npbotMeLyVi8+EtHevbkRDS/baPDzztMNnapaZCr8CJ/4Veu+iABDMSRf2P2/Jk9OzA2RyeI1ZON2\nUUknc84t6pDuHf66lzaqizBJHTj2pNyKjJcrql14xBgEZc66cTpWJKIvSlOn6te1tYn9jz5ax93s\n8k1T4Vf4pMTKD3gVdoZiSNaYwzPl5Trk0rlz5mEXIPW+1dVAx472U2oC2p50psO0Rh2iUWDiRKC+\nPv2IweZVWzEdb6LT+tU4eNU1OLDPJobkhtcCW++/3z5UYhCJ6Mkyxo9PvGcOXQC6ItsLLwBbtgCj\nR+vp56yFwDp21BUgH3lEv79tW2L/TZt0DKytzXvlRb/DJ/me3LZY8KL+fi/02Isbu7toI8xhOHaZ\ndpJ6XYxsvrq61I6rV6c5lfPnJXrQ2tE+fbCtrMx7A6fjzRpGO314c8K+U+jC+NKGDWvfANZ9zEuq\nTBg7SjB84idgKIbkCjvdybYiYyZLRYUeiOm0vlMn+1pZTqSKwafS29bWVjWjarJajv6qGTo80VpW\nrv6v2zFqemWdWv38OncD0q3P0NjoraHMVyS7SmzWxZpHn8kcqG6UWPjET/Iq7ADOAbAJwBYA16ba\nnsJenKQq7Gd1xJxi7D166H6+dDtdzUsm+2Y6Wj0dvX1m4TL1JAarGETF4sLVfOll6jvHXqUmVX1L\nNfxlvfOJ0vVmjVIBRnGdVIt55qWGBudGNLx844Jw1lk6991oCBH7EWFeYfXGjMmbsAOIAngHwGAA\nZQDWAhjutg+FvThJpTtWR6y2tn1tmBEjkkv4ZirsM2d6D/cYnbljxmR2x5+u3rZMOlfF5sxJEq59\nuz9WF/e/XF17znz3k3nxZv0qWG83emvo0MQHcxo167eXHeQAoyIb3ORV2P2YGm80gC1Kqa0AICIP\nAbgAwEYfjk0KCGsOe3Oz7nszMPqxZs1KzMgG6H7AujrgkkuAxkY98dDIkdnZYs6Pd6O8XNu7aZNO\n5zZPyecVu9x9tz7CsqefSrxYsAAA0B1An9peaGk+7H4yp0Y0s3Wrng5v8eLkaeQA3RHZrRuwf3/i\nvQ4dgNZW3SFaV5eYQ7BvX/0+oKenOnxYv66tdZ4qb8UKZ7vSxZgyr7YWePll4MQTgdWr8zvtnXl2\np3R/GIWMF/V3WwBMAXCX6fUMAP9ls90sACsBrByQ7WQEJDDMd9GGN+7FeTOXGTf269QpO8fTaTE8\n+e7dnUuhpBuW8SN6cNuchWqcTFHPLFyW/s5WnG55qqp0CqL1S3LytO0+mNMtil0RsGxwit3no8Jj\nprM7BQzyGIrxJOzmhaGY4iad/0QQnarWJRIpjCSMlkOH1Y8n/dwfcTcEuUcPfQU7+WT9aL3SOgl3\nKoFOd6q8dPDyozD/mHIRLinS7Jx8CvupAJ4zvb4OwHVu+1DYi5t0/hNr1uh6VE7x9FTFBdNd6ura\nT/xjzMRkDJasr897k/2DluYW/8TdIJ0rbWOjrv1ubQi7uQ3r63XNZKcvL5vJZZ1SKO0KoeWqFkwR\nZud4FXY/Bii9DmCoiNSKSBmAaQCe9OG4pEBJJ+a8cCHw0UfOA4kOpwg5p8PgwToE3NysXzc3a7sO\nHtRh6/PP1++/9JJ/50yXsooy/PTRqzF64gm49fI7se4lH7qirKOrjMFF1hFalZW6nsuePfr1vfcm\nBgeZY82Ajt9XVQENDbpzxMvxvWL+AUVMEhSN6hi/8WPKdS2YMA9u8qL+qRYAEwG8DZ0dMy/V9vTY\ni59UMedUd9si2ZUc8GMJMpy6+729apxMUU/fudSfA6byPv2KiTnVZnYKlTitM/+Aamv1Yv0xFWm4\nJJeAA5RIkBj/SSMsEvQydGhh6cP+PR+rcTJF3fztBSoWi2V/wFRXWmOYrrVhjjoqeUo+IzfU+p7b\nCFO3UEm2YZQiDJfkEq/C7ke6IyH/yFxbtEgrxrRpQK9eOhwSidiHYqqqdF2ZfLB5c+K5l5ImuaZb\nz6648MpJeOy3z6CqSyXm3HopRMR5B3MD2xm+eHHieTzFMom+fXU5XisHDuj3zXG13r31l2itzWyk\nAxrHr6xsnxb5+9/rfQDndUaszAte0j9Je7yov98LPfbwYXbMjOfGxBhHHZVI2Ahyqa3VEwcVymDH\nWCymbpv732qcTFHvrH3XfWM/OhDdRoSZZ3aqq/N2B3DKKe09e6fZxAvhNikEgKEYkg/SCd1GIjrF\n2qh1nu+lEO/iX3+uQY2TKWr9396y38CpgcvL008BNMIx5vxxo6C9l+OY4+XGhWb4cOdQCcMovuNV\n2Fm2l2SFXblb66Q+lZU6iWL1ap1kYQx2dKKysv0xMuULXwAuukgPbizKpAenjJdp05KzWJwwlwA2\nwjFtbYn1bW36PS9xqfnzdUpR376JTJWNG/VjLNY+s8Qp68RrWWKSMYyxk6ywS30EtDC3tenHlha9\nzZ13eguRphOCHThQu57vvadfi+hzHXsscNxx+nyLFiVC1Lt2JTQsVdi6ILA2cFOTrslgYMSuy8uB\nU05p/2GsQ+Z379ZXuZNP1utffz21wFpj6WbMZQqsjegU9w/rMP5Cwotb7/fCUEy4sMtcMybmOess\nb3Mn19YmwrHRqM5icQvZTJ2aCP3a3fFbs+zsQtSBzoH8wANKDRyoYiJqF6rUez+92XlbcwPX1+sM\nFWvs2jovoNtk1+nGua3xcuMLLS9PrwGLdBh/IQHG2Emh4KQLkYjOZx8xQqlBg5LFeebMxABJczh4\nwACl+vXTo1kNfZowQU/hae4YNUTby0Ul7/rywAPtRl22lZfrqea8iK6Xmurl5e07L4cOTa9Avds5\nR4xIv3DmxWERAAAP3UlEQVQOO1SzxquwM8ZOco41mtDWBowYoWPuc+YAw4YBJ5yQCMeK6OqNxgBJ\ng7Y2YNIk4IILdPFCI7w8aJCOMDz2GHD33cDjjydCwOZwMqAjB5Mn288Cl+lAyrSZN69dnmekpUWX\noEwVMweSY9f19UBNTfsP8+67iUYH9Pk2b9ayn8kITvM5587VX9rxx+sQiznk4ka6ZTJJ5nhRf78X\neuzhxctAQzcnz80J9ep9W4t+GY6qOVQTaMKGl0Ly6dw+OH0Yo9GXLdONYKQ7BuUpNzbqW610ptIj\nSYAeOwkCa8kRg8WLtXPXqxewfr1zn5mRBGJHW5se7GSeu7lTp4TjGYkAEyYAPXroDlhjLMx772nH\ndsUKYMYM4OGHge3bAywTMmCA87pMbh+csk+MRh83Dhg7Vr8XpKc8f76+1aqsTN/bJ+nhRf39Xuix\nhw+v/WJeOixnz7b31qdPTz3rkjEQyggBW2vGB9phamATY48Burc4V8YFOR1dqh9Hkc1iFCRg5ynJ\nJ6n6xdJJiDA0aMqURGjF0Lszz9T7GCV4s10CS8gwZcU0I6oOjDrVf9EtFMG0/jgqK3XP+Nq1en1B\nXG2LA6/CzlAM8YVU/WJeK8sCiQhCW5vup1u9OhFhOOYYfeyWlkTF19pa3RdopqJCd6oaYZrKyuTX\nee8wtRLv4Fz15zU4T+rw3m1/8D88YY6LBTkoyG5OxT179FR4uSzLW8JQ2IlvuJW3dhJ+pZz1ZsEC\nHY/v3Ts528UgFtM68OGH+jgG0aiu815VpS8AFRX60fy60BIylPkDZItdHfN+/fSoUS9ZN6nI5CKx\ne7e2yTzQyS5lKdCrbXigsBPfMDxtJ8fTTvidOluB5HVOnapK6Yk0zLS16ePv3598PuvrQhjR3rPm\nCIgI7vvZwzjU1OLPQa23R2b88IrdvjQnFi8Gduxof9s2dKi2pxCvtsWMl3iN3wtj7MQt5u60LhrV\nmYLmDlS3FMhiGdC49L6/qLMjU9V1E/7dn9rsSiVSII3OCGMYr1uqY6qYvB8jR62pmbW1wXXqFiFg\njJ0UMm4xd6d148frAU2TJ+v3I5HE3bx5hrViu6M/e8YZ+OY1F+D1Pzeg6WAahXLcMG6PXn1VjwZr\nbU3tFafyxNPpKElll3HbNHKk+20eyQgWASN5xVx4y62z1drX9sILuhO1Tx8t7HPn6rkX6ur09ied\nBDz6qBb4Yryj79qzi78HNAvksGE6Ju40WYXbhBnmimx+jBxNNSEI8QV67CSvmJ1Cs/NmDBwy4t7m\ndcOHay0yz7NsOHlbt+qlrU0PQho5Epg5szDi5wVDqs6PdDzxME8AHSLosZO8YOcUAtrxW7BAa4lR\n/+X227X2VFYmj1B1m11t8WLtxTc06Oq1f/hD7j9TaEjHE6fHXRTQYyd5wckpVMo5lXnrVmDIkMQx\nnBxJu+w+pkM74JSqSE88VFDYSV5wcgq3bXMW/H79gC1bEsdoagIeeqi9I+lHn16QtH7eioYX1yMS\njaBDR5+mjnIiVTEfdmKGAgo7yRt2TmEqwTemyKuo0CnP48e3P24xV4Nta23DL6bfhlefWY3Lb56J\n8sry3JyItzUlBYWd5A0np9BN8JXSYn34sC5S+Oyz9scu1kjChr9vwsuPrsCl86dh8pWTcneiYr+t\nIWnBzlMSOE79cYZYO2XpeTlGoXP40OcAgJFnfim3Jyrm2xqSNhR2UrAUq1gXLOlcKUlRQ2EnpFTg\nlbJkyCrGLiI3i8hbIrJORB4XkW5+GUYIISQzsu08XQbgS0qp4wC8DeC67E0ipPTYt+vjoE0gISIr\nYVdKLVVKtcZfrgBQk71JhJQOx4wegn5f6I1fX7YAb766OWhzSEjwM93xMgBLfDweIaGnU7dq3PzC\nzwAAN5z3S8RisWANIqEgZeepiCwHYJcTNU8p9UR8m3kAWgE86HKcWQBmAcAAt1naCSkhlFJ4/LZn\n0XSgGZOvmIRIhENLSPakFHal1Di39SJyKYBzAYyNF4J3Os5CAAsBYNSoUT7OA0ZI8fLWa1vw6C1P\n4bzZ4zH7lvqgzSEhIat0RxE5B8A1AM5QSjX5YxIhpcNnn+i/zdhLTodYZ+QmJEOyve/7LwCdASwT\nkQYRucMHmwghhGRBVh67UmpI6q0IIYTkE/bUEEJIyKCwE0JIyKCwE0JIyKCwE0JIyKCwE0JIyKCw\nExIgb698BwByP9cpKSko7IQExJO3P4c/XP9HnHr+KAw5sTZoc0iIoLATEgAH93+KBf/yPxg98QTc\n8PAPEI3SYyf+QWEnJABamg8jFlP48vkno2NZx6DNISGDwk4IISGDwk5IAMRa24I2gYQYCjsheaal\nuQW/+e4dEBEMHM5Jx4j/UNgJyTO/mvmfWLP8Dfzgrjn40mlfDNocEkIo7ITkmYYX1uPrl34N53z7\nzKBNISGFwk5IAFRUVwRtAgkxFHZCCAkZFHZCCAkZFHZCCAkZFHZC8sj/PbUSn33ShOquVUGbQkIM\nhZ2QPPHakjW4ccqvMfSkwZjyw/OCNoeEGAo7IXnikV8/gR41R+Km525Ap27VQZtDQgyFnZA80dJ8\nGH0G9aSok5xDYSckDzzym6fw5orNGHLC4KBNISUAhZ2QHPPCH1/Bwn+9D2dcdCq+c9P0oM0hJQCF\nnZAcs/6Vt1DdtQrXPXAFoh04oQbJPRR2QnJILBZD4zu70LG8I0Wd5A0KOyE5IhaL4Zbv3IFVS9di\n8hWTgjaHlBAUdkJyxMuPrsBz97yI6ddfiIuvqwvaHFJC+CLsIvJDEVEi0sOP4xESBg58dBAAcME/\nnxOwJaTUyFrYRaQ/gPEA3sveHEIIIdnih8d+K4BrACgfjkVIaFD8R5CAyErYReQCAB8opdZ62HaW\niKwUkZV79+7N5rSEFDx73/8Ij936FDp3r0ZVl8qgzSElRodUG4jIcgB9bFbNA/Bj6DBMSpRSCwEs\nBIBRo0bRlyGh5bMDTbj6zJ/i470H8Ms/X4/yyvKgTSIlRkphV0qNs3tfRI4FUAtgrYgAQA2A1SIy\nWim1y1crCSkitq7djsZ3dmPeH6/E8DHDgjaHlCAphd0JpdQbAHoZr0XkXQCjlFIf+mAXIUVPlx5d\ngjaBlCjMYyeEkJCRscduRSk1yK9jEUIIyRx67IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo\n7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQ\nEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo7IQQEjIo\n7IQQEjIo7IQQEjIo7IQQEjKyFnYR+b6IvCUiG0TkP/wwihBCSOZ0yGZnETkTwAUAjldKtYhIL3/M\nIoQQkinZeuxzANyklGoBAKXUnuxNIoQQkg3ZCvswAF8VkVdF5K8icrIfRhFSzFR2qsDQE2tR1bki\naFNIiSJKKfcNRJYD6GOzah6AnwN4EcC/ADgZwCIAg5XNQUVkFoBZ8ZdHA9gEoAeADzM1PkCK0e5i\ntBmg3fmkGG0GSsvugUqpnqk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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb790ada490>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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85dLT8OAtj6G6bxXm/vwCKKXcd7A2sJPhzc3J5wsXpq4fOlSX47WzbZt+3xpX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\ntYW3sps9w5VSf1NKvZkop3G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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb7cca82910>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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CCjunxiOhYGaA27FDP585U4dZDhzofrdtxT5LXCHZsCE9TZ5X6KiYVNdUo/kL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\nUo/9XPZtSW2zQSnVkrMRIhL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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb790bf0d10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb793177f90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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BAuCLL+zj6UdsQs2pMGSICgEfPKgeHzyo7Nq/X4WFL71UPf/aa/4dM1W69zkG\n9y6/A/2P74vbL/0FPt/+ReqN6Zi7W23zsjJgwABV+x0AHntMxc3LyjrGmuvrVTsNDcBVV/lbO934\nA4oYJCgaVTF+/WMqK+sY69f2+kHSRfjzCC/q77YBGA/gI6jsmFvc9qfHnv+4xZzdrraFSK/kgB9b\nLoRTV72wVo4Wk+R7/+/D9Bpy8z79ionZ1Wa2C5W4VWZraFA/hKqqjj+mPA2XZBJwghIJEv2fNBcQ\nDGqrrs5NfWhY8Z4cLSbJJQ8sTa8htzOtnqZr7piBAzuWEqit7fic0wxTp1BJumGUPAyXZBKvwl4U\n4MUCCRFNTcCUKcDixUoxpkwB+vRR4ZBIxDoUU14eX7ou02zcGL/vmK6YZWrOPgFnjP8GHrrhcZR3\nLcP4a0db72jsYCvD6+vj9+fO7fh6//4q39TMvn3qeWNcrW9f9SUan5swQS2JZ2y/rEy9pvn979Wm\n0zDtXtOxMi/ocMmMGSqu19Tk/b2FjBf193ujxx4+jI6Zvq8Xxhg4MJ6wEeRWVaUWDsq1yY6HDx6W\nN150lxxbfIU81HLIeic/BhCdZoQZV3aaONHbFcCZZ1oXDrNaTTyXLpPyGDAUQ7JBMqHbSESlWOta\n59necvkqfskDS+VoMUke2PtV4gtOFRmTTQHU4Rhj/rguaO+lHWO8XJ9ohg+3D5UwjOI7XoXdj6wY\nUsCYEzKiUbUZKStTSRRr16okCz3Z0Y6yso5tpMrxxwOXX64mU+Zl0oNdxsuUKd5mTOrZnTt2xMMx\nbW3x19va1HNe4lJz5qiUov7945kq69er2/b2jpkldlknRptIRmCMnaSFVeojoIS5rU3dHj6s9nno\nIW8h0mRCsIMHK9fzk0/UYyHUsU46CTj5ZHW8xYvjIeodO+Ia5ha2zgnMHdzSAixaFH9dx65LSoAz\nz+z4YYxpjPPmKbGtqgJOP129/tZb7gJrjqUbKS8HJk4E7r+/Yyfaxf3NNhH/8eLW+70xFBMurDLX\n9MI8o0Z5W5azqipxkYvqaueQzeTJ8dCv1RW/OcvOKkQd6BrIpnVSV175v61DMVImdnBdncpQMceu\nzesCOi12nWyc22oVEh0OSqYD83Qafy4BxthJrmCnC5GI0rWaGrXaklGcp0+PT5A0hoMHDZJywAA1\nm1Xr07hxaglP48CoFm0vJ5Ws64tFBcj22NY6sOKflSBt8VJTvaSk4+BldXVyBeqdjllTk/oC3BxQ\nTRmvws6vAg0eAAAQPUlEQVQYO8k45mhCWxtQU6Ni7rNmqQqQ3/hGPBwrhKreqCdIatragIsvBi67\nDNizJx5erqxUEYannwYeeQR45pl4CNgYTgZU5KC2VkUP/JpImTS33NIhz1PEtuj2zyCvvTYx3GLG\nGLuuqwMqKjp+mK1b450OqONt3KhkP5UZnMZjzp6tvrRTTlEhFmPIxYmkymSStPCi/n5v9NjDi5eJ\nhk5OnpMT6tX7Nhf90o6qMVQTaMKGl0Lygwd7b8/uw+hOX7ZMdYJOdwzKU25sVJdaySylRxIAPXYS\nBHblrevrlXPXpw/w3nv2Y2Y6CcSKtjY12Uk7pwDQpUvc8YxEgHHjgF691ACsngvzySfKsV25Epg2\nDXjqKWDbtgDLhAwa5L6PHg32gl32ie700aOBCy9UzwXpKc+Zoy61ysqS9/ZJcnhRf783euzhw+u4\nmJcBy5kzrb31qVPdV13SE6F0CNhcMz7QAVON1SpL6XjsXghyOTq3H0eerWIUJODgKckmbuNiySRE\naA2aNCkeWtFifMEF6j26BG+6W2AJGTorBugQmjkkitwHUL2QK4Jp/nGUlamR8XXr1Os5cbbND7wK\nO0MxxBfcxsW8VpYF4hGEtjY1Trd2bTzCcOKJqu3Dh+MVX/VKbkZKS9Wgqg7TlJUlPs76gKkZPcAp\nJfDEE/9cSm9v5+54uMd5/1xKLy2McbEgJwVZram4c6daCi+TZXkLGAo78Q2n8tZ2wi+lvd7Mnavi\n8X37Jma7aNrblQ58/rlqRxONqjrv5eXqBFBaqm6Nj3MqIUOLfHs7Flw+B290qU6vPas65gMGqFmj\nfqztmcpJorlZ2WSc6GSVshTo2TY8UNiJb2hP225czEr4ndYSNr5mN6gqpVpIw0hbm2p/z57E45kf\nh3ZGu/nyyIgfXnEqC0DX1wOfftrxsq26WtmTc2fbPMdLvMbvjTF24hRzt3stGlXhaOMAqlMKZD5O\naLz3ew/KKwfPTL8hnQKpByP0NF6nVEe3mLwfM0fNqZlVVcEN6uYhYIyd5DJOMXe718aMUROaamvV\n85FI/GreuMIar+gRvzxatUrNBmttdfeK3TzxZAZK3OzSl02nnup8mUdSgkXASFYxFt5yGmw1j7W9\n+qoaRO3XTwn77Nlq7YWJE9X+p50G/PnPSuB5RY9EgRw2TMXE7RarcFoww1iRzY+Zo24LghBfoMdO\nsorRKTQ6b3rikI57G18bPlxpkXGdZe3kbd6strY2NQnp1FOB6dNDHD9PBbfBj2Q88TAvAB0i6LGT\nrGDlFALK8Zs7V2mJrv8yb57SnrKyxBmqTqur1dcrL76hQVWv/eMfM/+ZQkMynjg97ryAHjvJCnZO\noZT2qcybNwNDh8bbsHMkrbL7mA5tg12qIj3xUEFhJ1nBzincssVe8AcMADZtirfR0gI8+WRHR9KP\nMb1coaS0GHt37cOmtzNkvFsxHw5ihgIKO8kaVk6hm+DrJfJKS1XK85gxHdsNUzXYy2+8DMf07oYb\nR9+FTQ0+ijsvawoKCjvJGnZOoZPgS6nE+sgRVaTwhRes2w5LJKFfZR/cv+JOiEgEi372tH8Nh+my\nhrjCwVMSOHbjcVqs7bL0vLSRj/Sv6ou+lb1x5NARHxsN0WUNcYXCTnKWMIl1Mrz0yKvYtHYLLvvB\nWH8bTuZMSfIaCjshOcTr9avwq2vn47QxJ+Pae6/yt/FCPVMWIGnF2IUQ9wkhPhRCvCOEeEYIcaxf\nhhFSiKx8fjW69eyCu565EcWlxUGbQ/KUdAdPlwH4upTyZAAfAbg5fZMIKWxKykso6iQt0hJ2KeUr\nUsrW2MOVACrSN4kQQkg6+JnueA2AF31sjxBCSAq4Dp4KIZYDsMqJukVK+Vxsn1sAtAJY5NDODAAz\nAGCQl1XaCSGEpISrsEspRzu9LoS4GsAlAC6MFYK3a2cBgAUAMHLkSNv9CClU9n6+Dx+8+RFKO5cE\nbQrJc9JKdxRCjAVwI4DzpJQt/phESOGxb/d+3Dj6bjRv24U5S28K2hyS56Sbx/4ggBIAy4RaJn6l\nlHJm2lYRUmC8+qe/Y/M723DPf/8nRow+OWhzSJ6TlrBLKYe670UIcaP1iEouqzn7hIAtIWGARcAI\nISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRk\nUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJISRkUNgJ\nyQGkDNoCEiYo7IQEzK7PvsDz819Gt55dUVzaKWhzSAgoCtoAQgqZA19+hRsuuANf7tqHX7x8GzoV\nU9hJ+tBjJyRAPvzHJjR+3IwbHvkBvnZmddDmkJBAYSckB+jR79igTSAhgsJOCCEhg8JOSIBs39gE\nABAREbAlJExQ2AkJiL8teRPzrv8jas4+AdUjqoI2h4QICjshAfDVvhb84qr/g6+dVY2fv3ALs2GI\nr/gi7EKInwghpBCilx/tERJ2Dh44hNajbbho2nko71oWtDkkZKQt7EKI4wCMAfBJ+uYQQghJFz88\n9l8DuBEAJ0UTQkgOkJawCyEuA7BdSrnOw74zhBCrhRCrd+3alc5hCSGEOOBaUkAIsRxAP4uXbgHw\nn1BhGFeklAsALACAkSNH0rsnhJAM4SrsUsrRVs8LIU4CUAVgnRACACoArBVCnCGl3OGrlYQQQjyT\nchEwKeW7AProx0KIrQBGSik/98EuQgghKcI8dkIICRm+le2VUlb61RYhhJDUocdOCCEhg8JOSACs\nfqkBAFBSXhKwJSSMUNgJyTLLnvgbfnXtfJw66us45ztnBm0OCSEUdkKyyLIn/ob7rp6LUy6owZyl\nN6GkjB478R8KOyFZ4m9L3kwQ9VKGYUiGoLATkiWWznsJA6v7UdRJxqGwE5IlZLtEr4E9KOok41DY\nCSEkZFDYCckCrUdbsffzfUGbQQoECjshGab1aCvu+e5v8MkH23Hh1HODNocUABR2QjLMkvv/gr/X\nr8Ls33wPY68ZFbQ5pACgsBOSYXZ99gW69eyKiT8aH7QppECgsBOSBdSSBYRkBwo7IYSEDAo7IRmm\ne59jsPfz/XjpkVeDNoUUCL7VYyeEWHPFTy/D+pUb8Ktr56OkvAQXTDk7aJNIyKHHTkiGKS4txl3P\n3IgBQ/vhhYeXB20OKQAo7IRkgeLSYvTodywgZdCmkAKAwk4IISGDwk5IthDAnua9OHrkaNCWkJBD\nYSckS4z93ihsW/8Z7p78AI4cpriTzEFhJyRLjKk7Hz+a+29Y+Zc1+PMDfwnaHBJiKOyEZJEJs76N\nrt07Y8+OL4M2hYQYCjshhIQMCjshhIQMCjshWabngB7425I3sO2Dz4I2hYQUCjshWea2JT8BAPzH\nqDux85NdAVtDwkjawi6E+HchxIdCiPeFEPf6YRQhYWbQiQMxZ+lN2NO8F6tfXhe0OSSEpFUETAhx\nAYDLAJwipTwshOjjj1mEhJueA3sAACRLDJAMkK7HPgvAL6SUhwFASrkzfZMIIYSkQ7rCPgzAOUKI\nVUKIvwkhTvfDKELCTlGnKKp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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb790c4a090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for k in arange(1, 10):\n", " def classify(x, y, model=model, k=k):\n", " return array([model.classify([xx, yy], k) for (xx, yy) in zip(x, y)])\n", " imtools.plot_2D_boundary([-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])\n", " show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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oK9PLqlUivXuLjB1rfzy3jk23xdxoyTasnwW30j2WH1f5LMOrsKcdihGRN0REicjZIjIk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\nyKCwE0JIyKCwE0JIyKCwE0J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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2bdf39ce90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with open('points_ring.pkl', 'r') as f:\n", " class_1 = pickle.load(f)\n", " class_2 = pickle.load(f)\n", " labels = pickle.load(f)\n", "model = knn.KnnClassifier(labels, vstack((class_1, class_2)))\n", "\n", "with open('points_ring_test.pkl', 'r') as f:\n", " class_1 = pickle.load(f)\n", " class_2 = pickle.load(f)\n", " labels = pickle.load(f)\n", "\n", "def classify2(x, y, model=model):\n", " return array([model.classify([xx, yy]) for (xx, yy) in zip(x, y)])\n", "\n", "imtools.plot_2D_boundary([-6, 6, -6, 6], [class_1, class_2], classify2, [1, -1])\n", "\n", "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }	unlicense
gbrammer/eazy-py	docs/examples/Riverside-demo.ipynb	1	330296	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Running Eazy-py on the Riverside test catalogs" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import glob\n", "import os\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import MultipleLocator\n", "\n", "from astropy.table import Table\n", "\n", "from astropy.utils.exceptions import AstropyWarning\n", "import warnings\n", " \n", "np.seterr(all='ignore')\n", "warnings.simplefilter('ignore', category=AstropyWarning)\n", "\n", "# https://github.com/gbrammer/eazy-py\n", "import eazy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare catalogs" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## Replace extraneous tabs with spaces\n", "os.system('perl -pi -e \"s/\\t/ /g\" CANDELS_GDSS_worksho[p1].dat')\n", "\n", "## Make flux columns\n", "files = glob.glob('[p1].dat')\n", "for file in files:\n", " cat = Table.read(file, format='ascii.commented_header')\n", "\n", " for err_col in cat.colnames:\n", " if err_col.startswith('e'):\n", " mag_col = err_col[1:]\n", "\n", " flux = 10*(-0.4(cat[mag_col]-25))\n", " flux_err = np.log(10)/2.5cat[err_col]\n", "\n", " bad = (cat[mag_col] < -90)\n", " bad \|= cat[mag_col] > 90\n", "\n", " flux[bad] = -99\n", " flux_err[bad] = -99\n", "\n", " cat['flux_'+mag_col] = flux\n", " cat['err_'+mag_col] = flux_err\n", "\n", " # For translate file\n", " #print('flux_{0:<11s} F00\\nerr_{0:<12s} E00'.format(mag_col))\n", "\n", " cat.write(file.replace('.dat','.flux.fits'), overwrite=True)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/usr/local/share/eazy-photoz/templates -> ./templates\n", "/usr/local/share/eazy-photoz/filters/FILTER.RES.latest -> ./FILTER.RES.latest\n" ] } ], "source": [ "# Link templates and filter files \n", "# EAZYCODE is an environment variable that points to the the eazy-photoz distribution\n", "eazy.symlink_eazy_inputs(path='EAZYCODE', path_is_env=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### filter translation file\n", "\n", "# Not sure about CTIO_U\n", "trans = \"\"\"ID id\n", "zz z_spec\n", "flux_CTIO_U F107\n", "err_CTIO_U E107\n", "flux_VIMOS_U F103\n", "err_VIMOS_U E103\n", "flux_ACS_F435W F233\n", "err_ACS_F435W E233\n", "flux_ACS_F606W F236\n", "err_ACS_F606W E236\n", "flux_ACS_F775W F238\n", "err_ACS_F775W E238\n", "flux_ACS_F814W F239\n", "err_ACS_F814W E239\n", "flux_ACS_F850LP F240\n", "err_ACS_F850LP E240\n", "flux_WFC3_F098M F201\n", "err_WFC3_F098M E201\n", "flux_WFC3_F105W F202\n", "err_WFC3_F105W E202\n", "flux_WFC3_F125W F203\n", "err_WFC3_F125W E203\n", "flux_WFC3_F160W F205\n", "err_WFC3_F160W E205\n", "flux_ISAAC_KS F37\n", "err_ISAAC_KS E37\n", "flux_HAWKI_KS F269\n", "err_HAWKI_KS E269\n", "flux_IRAC_CH1 F18\n", "err_IRAC_CH1 E18\n", "flux_IRAC_CH2 F19\n", "err_IRAC_CH2 E19\n", "flux_IRAC_CH3 F20\n", "err_IRAC_CH3 E20\n", "flux_IRAC_CH4 F21\n", "err_IRAC_CH4 E21\"\"\"\n", "\n", "fp = open('zphot.translate.gdss','w')\n", "fp.write(trans)\n", "fp.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run the photo-z fits" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "####\n", "\n", "Read default param file: /Users/brammer/.local/lib/python3.5/site-packages/eazy/data/zphot.param.default\n", "Read CATALOG_FILE: CANDELS_GDSS_workshop_z1.flux.fits\n", "flux_CTIO_U err_CTIO_U (107): ESO/wfi_BB_U38_ESO841.res\n", "flux_VIMOS_U err_VIMOS_U (103): ESO/vimos_u.res\n", "flux_ACS_F435W err_ACS_F435W (233): hst/ACS_update_sep07/wfc_f435w_t81.dat\n", "flux_ACS_F606W err_ACS_F606W (236): hst/ACS_update_sep07/wfc_f606w_t81.dat\n", "flux_ACS_F775W err_ACS_F775W (238): hst/ACS_update_sep07/wfc_f775w_t81.dat\n", "flux_ACS_F814W err_ACS_F814W (239): hst/ACS_update_sep07/wfc_f814w_t81.dat\n", "flux_ACS_F850LP err_ACS_F850LP (240): hst/ACS_update_sep07/wfc_f850lp_t81.dat\n", "flux_WFC3_F098M err_WFC3_F098M (201): hst/wfc3/IR/f098m.dat\n", "flux_WFC3_F105W err_WFC3_F105W (202): hst/wfc3/IR/f105w.dat\n", "flux_WFC3_F125W err_WFC3_F125W (203): hst/wfc3/IR/f125w.dat\n", "flux_WFC3_F160W err_WFC3_F160W (205): hst/wfc3/IR/f160w.dat\n", "flux_ISAAC_KS err_ISAAC_KS ( 37): ESO/isaac_ks.res\n", "flux_HAWKI_KS err_HAWKI_KS (269): VLT/hawki_k_ETC.dat\n", "flux_IRAC_CH1 err_IRAC_CH1 ( 18): IRAC/irac_tr1_2004-08-09.dat\n", "flux_IRAC_CH2 err_IRAC_CH2 ( 19): IRAC/irac_tr2_2004-08-09.dat\n", "flux_IRAC_CH3 err_IRAC_CH3 ( 20): IRAC/irac_tr3_2004-08-09.dat\n", "flux_IRAC_CH4 err_IRAC_CH4 ( 21): IRAC/irac_tr4_2004-08-09.dat\n", "Read PRIOR_FILE: templates/prior_F160W_TAO.dat\n", "Process template tweak_fsps_QSF_12_v3_001.dat.\n", "Process template tweak_fsps_QSF_12_v3_002.dat.\n", "Process template tweak_fsps_QSF_12_v3_003.dat.\n", "Process template tweak_fsps_QSF_12_v3_004.dat.\n", "Process template tweak_fsps_QSF_12_v3_005.dat.\n", "Process template tweak_fsps_QSF_12_v3_006.dat.\n", "Process template tweak_fsps_QSF_12_v3_007.dat.\n", "Process template tweak_fsps_QSF_12_v3_008.dat.\n", "Process template tweak_fsps_QSF_12_v3_009.dat.\n", "Process template tweak_fsps_QSF_12_v3_010.dat.\n", "Process template tweak_fsps_QSF_12_v3_011.dat.\n", "Process template tweak_fsps_QSF_12_v3_012.dat.\n", "Process templates: 2.934 s\n", "Fit 3.0 s (n_proc=4, NOBJ=371)\n", "`error_residuals`: force uncertainties to match residuals\n", "Fit 3.2 s (n_proc=4, NOBJ=371)\n", "`error_residuals`: force uncertainties to match residuals\n", "Get physical parameters\n", "Rest-frame filters: [153, 154, 155, 161]\n", "Process template tweak_fsps_QSF_12_v3_001.dat.\n", "Process template tweak_fsps_QSF_12_v3_002.dat.\n", "Process template tweak_fsps_QSF_12_v3_003.dat.\n", "Process template tweak_fsps_QSF_12_v3_004.dat.\n", "Process template tweak_fsps_QSF_12_v3_005.dat.\n", "Process template tweak_fsps_QSF_12_v3_006.dat.\n", "Process template tweak_fsps_QSF_12_v3_007.dat.\n", "Process template tweak_fsps_QSF_12_v3_008.dat.\n", "Process template tweak_fsps_QSF_12_v3_009.dat.\n", "Process template tweak_fsps_QSF_12_v3_010.dat.\n", "Process template tweak_fsps_QSF_12_v3_011.dat.\n", "Process template tweak_fsps_QSF_12_v3_012.dat.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/brammer/.local/lib/python3.5/site-packages/eazy/photoz.py:1742: RuntimeWarning: covariance is not positive-semidefinite.\n", " coeffs_draw[:, ok_temp] = np.random.multivariate_normal(coeffs_i[ok_temp], covar, size=get_err)\n" ] } ], "source": [ "# Galactic extinction\n", "EBV = {'aegis':0.0066, 'cosmos':0.0148, 'goodss':0.0069, 'uds':0.0195, 'goodsn':0.0103}['goodss']\n", " \n", "roots = ['CANDELS_GDSS_workshop', 'CANDELS_GDSS_workshop_z1'][1:]\n", "\n", "for root in roots:\n", " print('\\n####\\n')\n", " params = {}\n", "\n", " params['CATALOG_FILE'] = '{0}.flux.fits'.format(root)\n", " params['MAIN_OUTPUT_FILE'] = '{0}.eazypy'.format(root)\n", "\n", " params['PRIOR_FILTER'] = 205\n", " params['PRIOR_ABZP'] = 25\n", " params['MW_EBV'] = EBV\n", "\n", " params['Z_MAX'] = 12\n", " params['Z_STEP'] = 0.01\n", "\n", " params['TEMPLATES_FILE'] = 'templates/fsps_full/tweak_fsps_QSF_12_v3.param'\n", " \n", " params['VERBOSITY'] = 1\n", " \n", " ez = eazy.photoz.PhotoZ(param_file=None,\n", " translate_file='zphot.translate.gdss',\n", " zeropoint_file=None, params=params,\n", " load_prior=True, load_products=False)\n", "\n", " for iter in range(2):\n", " ez.fit_parallel(n_proc=4)\n", " ez.error_residuals()\n", "\n", " print('Get physical parameters')\n", " ez.standard_output()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['id', 'z_spec', 'nusefilt', 'numpeaks', 'z_phot', 'z_phot_chi2', 'z_phot_risk', 'z_min_risk', 'min_risk', 'z_chi2_noprior', 'chi2_noprior', 'z025', 'z160', 'z500', 'z840', 'z975', 'restU', 'restU_err', 'restB', 'restB_err', 'restV', 'restV_err', 'restJ', 'restJ_err', 'Lv', 'MLv', 'Av', 'mass', 'SFR', 'LIR', 'line_flux_Ha', 'line_EW_Ha', 'line_flux_O3', 'line_EW_O3', 'line_flux_Hb', 'line_EW_Hb', 'line_flux_O2', 'line_EW_O2', 'line_flux_Lya', 'line_EW_Lya', 'ssfr']\n" ] } ], "source": [ "# Outputs for the z=1 catalog\n", "zout = Table.read('{0}.zout.fits'.format(params['MAIN_OUTPUT_FILE']))\n", "zout['ssfr'] = zout['SFR']/zout['mass']\n", "print(zout.colnames)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Diagnostic plots" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/brammer/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['Courier'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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Ri8W47rrrNtRusyn5+9Kd3blzcN99cPjw5hhWAnb5nSrGshGe3SutBINBs00o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\nO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"text/plain": [ "<matplotlib.figure.Figure at 0x112c5aa20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = ez.zphot_zspec(zmin=0.7, zmax=1.4, minor=0.1, skip=0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "### UVJ\n", "uv = -2.5np.log10(zout['restU']/zout['restV'])\n", "vj = -2.5np.log10(zout['restV']/zout['restJ'])\n", "\n", "uverr = 2.5np.sqrt((zout['restU_err']/zout['restU'])2+(zout['restV_err']/zout['restV'])2)\n", "vjerr = 2.5np.sqrt((zout['restV_err']/zout['restV'])2+(zout['restJ_err']/zout['restJ'])*2)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/brammer/anaconda3/lib/python3.5/site-packages/matplotlib/figure.py:1999: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", " warnings.warn(\"This figure includes Axes that are not compatible \"\n", "/Users/brammer/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['Courier'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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+cy8ebWGeulKKxTrN9KXUURHBt0DNiwW77kHXoNdh3759js/FvJg0NqIr2eXo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"text/plain": [ "<matplotlib.figure.Figure at 0x1176d5eb8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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BoXfeRaSZAEYPK+AQKA6JoRmR1nhkaCfSAKRESgj1KJEomNijISgpe+gxOv7n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D+Aza5w5Ni58d5z2bp1K3V1dQAJFwJBaLz8rAtOY1xpaIgn05I5xLehaZjuB+Y7WuUnCT0B48aN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\nY8emO4SUY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"text/plain": [ "<matplotlib.figure.Figure at 0x11403c710>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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72kFRCT2WwueVgC5x7duOa/8OmL8c3vNxcGauL/eJltGJ2j1xNFJ1D1oOPQm1\ntbUsWTL6vJFmwOwaI/qO1xga31Vw3z/bcAAlccPFpQz7WAM06ePSv5RweFMvXt8gudnZ9L3pItAe\nxOgxRiY3B4RDIXfu8eumtW4/+376Op4tLUgJpWdNY9atq3BNjc3IF3FaNTU1LL7523i3vsaBn24c\nrn0xAHQGN9dStvo0Si4dWZeuiyX0GMOvIqF2UQEIBBIMHeqrsT/3Rwo+d/dx7U4n/y5l9FSxHHoS\n5syZk2kT0o7ZNY5FX/ywa4MjSPRR658VF0y/MJ8FS+eyYOnwA2H+t+YD0Nc0yKaPv4zRH4w5hupQ\nWfGZVWSVJs7dIXWDqi8/g//oIFIPhefurS1033yM1Y+tG9ou+uGUl5dHZ3c3wfyZaH6V+Loiwxek\n9R9vIlfMSNi4WjprMTTuQ+jBsDOPQ9cINNTirquFwuNHvumKjK0yOjYsh56E1tZW5s2bl2kz0orZ\nNUb0JZrJfjQHtPIjPdT8NvHoPQlofqj9tQ+1tZNrfrxyxJD70lIYfHg1B/7fXvq39YKErHk5rLr3\nbAbEIAPuwYTHbtp4AH+nNzYvuiHRvUEa/7kX9SN2ILZqpqmpiZkzZ6J3dCFFwhcDDN/o6Vnlez+H\n2Pw0xq7XQR8lH4yqQm9XUoeeLlJdRqX/IM7BxxB4MQavQWSdk7hKapxIlT7LoSehuHhitPKnE7Nr\njNbnafTSur2HaYsM5l5QhqIkvonP+/Q8tv35MNk+Y2iMfqR+XEMQlGDrl+x8/Ai1/zrKF946m+yS\n2LlHbfl2lnx/JUbQwNPpoaSyhPySguPaGmgdRCbIACb9Bv7mgaHv0XXwqqpSWFiILCpi0G6DQGyE\nLuw2yi454/hD39//edwXvJ/B5/9Cds1boaqW6GMYOsWLlqHkZKYrYyrLqN79Vwz3D3BKDdDRW59B\n5FyGWvnTjDn1VOmzHHoSBgcHTZ2JEMyvcXBwkIKCQp74/DtU/eUwwgZC1JJb5uJLr1xM0cycoW3d\nbjfV/zjK+q/uI+jX6TJCmRQloYZRJSr+NQilyfX1BHn44re47tnFMQ4z2ukqdmVMA4a6l03FbavD\nCMb2K1dcKqXLpybc3+/3Dy333nIVxx54OtSgqhsoLjuOiiIqrls7pt/Kd/q5ZNftAL93ONmM3YHr\nnCsy5swhdWVU6l0Y7h+A9A9fSelFDmxEDr6JyDn/lM9xMqRKn+XQk6CYKB/GaJhdo6IobHu4gZ1/\nayLo18EfavLzeAd5+IObuPXtK4CQM29vOcb6W/cR9BqAGEqLmyxuG+jQ2PHzJirvLx9aFl0t4vF4\nxpSmt+CMCrKm5+Ft7Bly6kKHkXgtAAAgAElEQVQV2PKdlF40c1R9EbJXzGHqjz5B38vV6J19lK09\nneILl6E47EnPDSCz8+ATd+J850X8+3eCK4eci6/FteaSMe2fLsZaRpN1n7QHXiBLKiOup5SDeN3/\nwOtdfNz909VGkKp70HLoSbDbx3YjTGYmk8axTEaRaNtnv7+f9oHgUHOhC0mOLmjd3c3216rJrXTg\n8Xg4WuUDkbivYPzS6FtQSGh7aYDOb4TOGe28T2TCCKEITn/wMtr/uI8jz+xH6pKpl81jymeWoDjV\nhPvEXz97eSHFHw5FmiflgApKyPvwl/GHnWPWBOgCmKoyKrGT+PGsIIUzwfLxIVX6LIeehP7+/knX\np/VEMbvGxp2dHG4ciHHIPkAiKVRA84Yi4aKiIvxlAyCPkawDuEKsW3AIsNnVIeed5Xfh8Njpbw/y\n/P1v01nTjbPIwZqvL+XMLy8+bl2tLcfOinsuYdoty4HkTjlV18/oH4AjLRh2R6hVdwIxVo3JtpHG\n1WiH7ht5eYWD7PKPkuvKjO5UXUPLoSfBzI4uwmTSGN+bZCwjFHf/tT+he/YDapagYNZwZFZxejbO\nfJXg4MjcKDZCWRSzshX8AwZChBx7lgL2LMGc64rxtXupvnM73bVdBDRBl9seSrUI+DoDbLlvNz5P\ngPPuSd1grlO9ftIwOPbwo/S9sRlVVenTDYxVK1CvvwZhmxguIlVlVCjZqFN+jt72JQwpCCVrMFCL\nb0FxLUvJOU6GVOkzd+VpCmhubs60CWnH7Bpba3oTBtwCWHHzFERUTxehCK59aO6IDIo2QjMTOVS4\n8ekzKS6G/GxJnhOcOQoly3NYeGMpW7/4Jl173Oh+nT6POuK82qDOjp/XEhxMkHvlJEl0/U5kMofu\n516kf9PboOkIfwA0jYEdu/E9/ULKbDxVUllGlZzzsM3djC/nm/iyv4Z99gbU4ptTdvyTIVX6Jsbj\ndwIzf/78TJuQdiazxuM5rUgd+uLzprGl8TCGHutdFbtgzoJpTMmvJH/K8LD2RYug7gMD7HqyGWmE\ne7eEHXzx/GzKl+Wz6o/5tLzcS3nWVOaeNwX7XI2jW1vxtYcGBEkJmh4acRmPRFK9eS85M1xR51x0\nQtqiGe36jTW/Su/zG5CBuP7nwSD+Le8weP7ZYzpGut/yUl1GhZJL0PHu0P/2zL+hpkqfFaEnYe/e\nvZk2Ie2YXePC61TsWbENikKAUwrWf6WK7895hmfu3IWMmhNu3U9Wkltkx5WloAhQbODIUbnqgdDw\nbMUumPHuAs79+mKmnFWIEAKtJwiKQNcFPr8t/BBI0KfckLjKUtcQfarXT3p9iVfoGugJMpRlALOX\n0VTpsyL0JKxYsSLTJqQds2u8aN1ZzHl9AX/9ehV1W9xITeLUJa6gxB8MVX28+d8HmX1OKadfG5pT\nqGhWDp/ddA47H2mhcdMxiuZlcfXdZ1E0K4e+Ni/G9jyEIshenUd2aagOPvvSLNofbCEQVAGBw27g\n9cc+SFSXwvLPL2DJiuPn7TiRvPCjXb+xRs3+hfPw1ewfsVwpL6OwomJCtLGYvYymSp/l0JNg9sT6\nYF6NkfwqGzduZMWKFXzu8RXUbW3hyesPoEUNrZeAf0DnqTt3YK/UKZkXyrPipY+1t85h0Y35QMjJ\nb3voIC/evhMZ7tpYdV8zFe8q4shb3Wheg9LyHHIJzU2nqhKXU8cfUJEyFNUvuWU2S780K6UTeZzq\n9Sv96AdpuffHyGAQDAOEQNhtZH/w2lO2LVWYtYxGsCa4GCfMXIgimFWjf8BA8xssX7N8aFmgX0dE\nBc2S4Tzm7fsH+PVFb7PgilKu+9XSoW0iPWu66vp48fadaL7YaoiWVzsJPR8EA1062fnDde42VWLL\n0pASyi8vYeX/WZBynad6/ZwzpzPj+9/B89yLDByoQ62soPID6+h1jXHqujFwqg8ws5bRCNYEF+PE\n9u3bM21C2jGbxr5OP3ef/wL/855DPHRtI99avpGOGp3S0lIWnjsdIypPVUznRAM0n8HBl9xs/HEt\nHo+Hurq6oc9rv6pC1xLUKcvhG8kXTHxLKS6BXGlQV1eXKplDRF+/zs7OExp8FcFeUUb5pz5G3m23\nkP2x63FMn5pKE08Zs5XReFKlz4rQk2D2yADMpVFKyW1Ln6brWKihTwK9HRr/8e6N3PPGebh7mtF1\nA8noQ4c0r0H1XzooOD+Ho4966X49gJSQNT1r1DmUI31ZJIJjvQ7KCwModoHUJYpNUHhxAa7TUxfx\nRhN9/U5kVOpkwkxlNBGp0mc59CRUV1ezbFnmBhyMBxNVY6LJkUf7HnFk2x5rwXNsZK8NLWDwxPd3\nsOA8FcUBWpJu4NqgQd1dffibdWQ4oh844EXIxA+C6Ljdj43K9y+gcolA69dYdM1CZIVBZ2fnmBJ0\nnSjV1dWcvnQpAzv2MlC9D7W4EOPyC1CyXMl3niRM1DKaKlKlz3LoSVi4cGGmTUg7k0lj/MxC8d93\n/XP0ARrte/wsvToHI4kzFypMX55NsHZgyJkDSE1icwgMFPRAaEaiyFhDoo5pc6mc/60lFM2KzeKY\nLubPnMXhb/8/AkePIX1+cNipf2oDM7/3dZzTKtN23vFkMpXRkyFV+tLu0IUQlwF/AN4vpXwnavkd\nwIeBbmCrlPKb6bblZGhqamLBgtQ3ZE0kJqrG6Eg28n98RA6xTr2oInGCJQGUT8mi4x+SbBWCCgQS\nTNlpcyk481Tmn13C3l19I46j6JJFH6mkX+sHIZm52MmxzR4atgTp7VGYelYRF9+3ED3Hi9vtHdqv\ns7NzxMPneHrHgtR1mrfs4dhzr5DTfHT4tSMQxAgEafvvPzL7/jtO6JgTlYlaRlNFqvSl1aELIcqB\ny4BNQHyqOBdwq5Ty1XTacKpUVFRk2oS0M5k0xudyiV9+/s1Otvytg37diKRQCTVaCghuH6Q1CBhg\nB2wKBJ0CQ0Ll6hxUh8K888s44+PTaHu9E5tLJTgQ2whqy1aYclYhxmzovn8v/TUaWbpkyUyBa0EB\ny3+6ctSsiIkwgjqdT2yl9+W91AcNslbOpOzGc7GXhnKPjxbZe/e30vbjpzGCGviDBMghN28Au3P4\nVcF/pI2OhkaUvNyEx4gwEfqZJ2MyldGTIVX60urQpZQdwB1CiD+m8zzppLu7m/z8/EybkVYmgkYt\naPDqQ3W8+tABDAMu+PQ8Lv0/Y3sNja6XLj2vlOvv7uTpew/glzpOJVQlMnNqLr4jvpiIXADTF+Tx\nxbevwO6KdcJTZpSz9d6DaF59uCFUAXu2ndWfWMJrn3kUo8sf003Gd6CHrvWHOO2Wd43JVoCaO/5M\nz44GZCDkiPvfqsO7p5nZD96ImhN624h/KzG8AVp/sB45NK1cKMVAf28uBcU9KGpEpWREUppJykQo\no+kkVfoy2W3xGHC/EGKLEOKm+JVHjhxBCDHic9ddd1FTU4NhGFRVVQHDXX6qqqowDIOamhq8Xi/1\n9fV4PB5aWlpoa2vD7XbT2NhIf38/tbW1aJrGrl27Yo4R+VtdXY3f76ezs5Pe3l6ampro6Oigo6OD\npqYment7OXjwIH6/n+rq6oTH2LVrF5qmUVtbS39/P42Njbjdbtra2mhpacHj8VBfX4/X6x1XTQcP\nHozR5PP5Mqqprq6On733ZR7/xg5a9/RztKafJ7+zmx9dvIED+w/EaIrXVlNTM0LTjPc4WHOGixV5\ngtPyBUvzwNY+QKLmzGM1vWx66w00TWPLli00NjZSVVVFfWMdZz00i6IV2aF+6yqUrMhlyU+LcXe0\nM7i3M67PI8iATt2jVRw5coR33nmHuro6qqur2bNnD3V1dezdu5empqah67TjX6/Tu3PYmQNgSIKD\nfpp+9hL7fvQsfW8cYrC3n87OTo4dO0ZzczPu1/cgjcTdbfy+UE8aAxBTyunyeXG5XPT39wMQDAbx\n+/2oqkpPTw85OTkjrlP0b52KsieDA3RW/R57/e9p2f4YvT2eE76fXC5XWspeQ0NDyu+nk/ERx44d\nG7Om4yGi81eki3CE/isp5VsJ1uUB24AzpZT9keWrV6+W27ZtS7ttyejo6KC8vDz5hpOYTGus2+Lm\np5e/in8gtrXSmWvj479bxqJLSkakyy0tLeXovl5e/VUNgQGNsz40n0WXVSCE4H9v2EDLxg6M4HDZ\nlsCAX+DTYiNxZ6GNL+6/MGYC6Xjaj3QgJVTODP1GWp+fd973d9BH3ju2Qidr1n9wpMa6Orq7u5kz\nZ85QpN37xn7af/1KVKQNhiHQgrZQZC1BcdmwFWVTdNdFlM2cAkD3cztw/2UTjOgTL3Fm+ckqDqI4\nHBTc9hkqFo9M+hVP9G862v8ni+xtQL7+aaQeQBg+ULMhdwbigt8ibNljPk6my2i6ORF9QojtUsrV\nidZlrJeLEMIupQwSmmvAD4w+LXkG8flGSVxkIpJpTGcPDYCdLzSGpoaLw9+vUfXcIdSZnqFqh0jD\n4uu/3M/r97VgaBKpw46/tjHrgjwu+8EMmje0I+N6sgggyyHxRS1XXYJFHw051+guktENrokaZikF\n54xc/I1xjaY2hWlXLEzoABMd215ZMDxvZxgt8sCRoCg6uVnduGx+5O860W++noplp5N93jK6HtuC\nEefQhcNO3kULyTp9BlMuOW/M086lE7ntLgj2ISJvR/og9DUg9/8OsfTLYz6O2e/DVOlLd6NoIfAo\nsBxYJIT4BbBGSvkV4H+EEIvCNvxQSulPpy0nS2FhYaZNSDuZ1phX7sTmVAjEOShblkJ22cgGRn+P\nzuv3taD7h52h5jVoeqOPw6/2jnoe1SFQgiAcAhmE2VcVsezmk+vWV/nl5Ry5++1Qqly/juKyYS9y\ncdpXRk7G7Ha78Xg8dHd3D/VFB3DNr8AxrQj/4U7QjZBvl6H6cFXVmDKrDaEYKArI/lYG/usXHP3k\nx7AtXkTO2gX0bz6I9IfiIOG0kbVkOtrVZ+EsLR0XZ57sQS8C3RT21g878whGAL3xGboqPpL0HJGH\nY6bLaLpJlb50N4p2A1fGLX4kvO4L6Tx3qmhvbzd1Ywwk15juXhAXfbKQp79zkEBcjxJVVbj8C8vw\n6r0xke3BtzyodgU9LqoPDhq0bPGTNcXJ4JHY+ECosOi6WVR+uoD+IwHOuGQJOWWJB96MRa9zdj5z\nfnEh2tZufM295C4ppeTiWXQeGGDLDzZzrMZDwfxcLrjnTBzzEjdMCiGY+s2rqb//aeThrrDjC22b\nX9KDohpDbZpCAJqG74knyfnOtyj/wmXkrJrD0We24h3wMvt955J77kK6knSNjCfyZpKWEabHa5A9\nwcZas9+HqdJnDSxKwsyZiWdaNxOZ1ujMsfGtVy/hwWtfo+eoDyEEeWVOvvTEeWQXSrxxqUlUh5LY\nHwjIUXyUnqmxv01iGAIMUJ0Kjlw7a+9ZSdtgM1kl9lGd+Ymg5jkov2F4lvijWzp59bM70LyhB433\nqJ8n121k3WMXkudwoB/V0fIGIJLs64V9tP5mSyhzo+7EWZmPzSHQGj1k53gTapQDgxiebtSSYnLP\nmk/RvCJyAgHypoTq1yOO2e12J30w6d2d6C89SU7jQYxpM9He8wFQhtMTJNs/+YOvFKNgIbJ7X0yU\nLhUH6uxrTyhQyHQZTTep0mc59CQcOHDA1EOOYWJonLG8kG9uXUvd9lYKCgpZfM5MhBC43e4RXf3y\nrylk4x1HRhxj7pR+Ko8cQeoG82YodPXmEVCyKL2mjCvuvoCsEhdt4bTfozX49bX6kO5eKPYjFHFC\nTmfbvbVDzjyC7tPYc+ffyXb2g6pwTN+Nf81siq8+PeTM/VH9xlt6UEuzUYpcGFIhNqFACKnpDL74\nNnkfGX7xbWhoYErYoY+VYGsTrd+7D39faJo8X81etKod2D/5BcSMeSd0rOMh1tyH8eqnMDQfigyA\n6kTkz0MsvOmEjjMRymg6SZW+cenlcjJMlF4uFukj3qlGv/4n6tUS/b1zt85vrn0TKQ2kASoGVyxr\nQsR363Ko5C2eTsv+AfY0ZOP2qGSV2rnoq3NZfeNUysrKQsc70MM/rn8B79FB/LqdoN3F5Q+ezuob\nFiWsK45v5Ozs7OT5s7eN6Mo4tbKL0uJelOgOwnYFpSQHva0Pm01DUUM7GYaCJuw4bllNUVMTrr3b\nEFHZwKQEQ1cIaHnMf/BOHOXFJ90T5fCt38DfPkDsFHmSvAV27F+5O6XVbO6OVvyNL1BgHyRn2hoo\nW4MwSf/4TDAhe7lMFsyeWB8mp8aS5Spfrz6X7U82onkN5s9UOfbbFozBOI8a0Gne0srLtVPQDQFI\nAk0Bnv3OfrqbvXzkZ2V07T3Gi+v+l2xDkpsLhvTiCwzy/Gd2MeeMqRh5Bi3Pt9Dw5CEUu0LFeytg\niYhxSh6PB1u+itYdG1WXFPXFOnOAoIHR0YfdEUQIOVS1oigGDvx0HDxC36ppzD5cj6N3uL7JMBT8\nXhc4BO1bqsg6dyWdnZ3s3buXCy64IOYUiRzy/v378Xg8iIFBikY48xC+5gGaavfRWRbqQpdortMT\nRnHQX7AWZ0kJuSf5oJiMZfREsCa4GCfMXIgiTFaNjlwbC99bBICzJcCxBC+bUkLt0YJQfXqUA9O8\nkk2/aOL8L89i66dfQEg55HhVAVkOjXzHIK/9524cx7roqupCD1en9OzsIf+CAqbcHFtvPe+Tfup+\n3YrujTxUJIoyyhuwJMaZQ7jruYR8DXKKihicMQd9mw8FDSkVpKEMbSiyh9sAli5dyomg9vSHfooR\npgn0oA2pjD11wXgxWcvoWLEmuBgnzJ5YH8ZHo9vtTml/9tLS0pgoNGdpBYozcXziGXAiE0SjUjF4\n8y9vE/D4RjRAKgoUZvvp3HWMzqrOIWcOYPgMel/twdnrHKoeKikpYc7HKlj8qVnYslXUbBXVpSLz\n8hLa5KjMSxQgIwRk9RsUFRWRfe4KUGwYum3YmQOKTWXqhWcPnbe5uXno94j/XaJZtGgR8+fPZ+aq\nFcRM2zT8i6BWFDF/4UIWLVqUkuhca66Hf/ySor//DNY/hNbacFLHMft9mCp9lkNPgtkjA5g4Gg1D\ncrJtOkJVmPv9d6PmOxEuGzgVJKDrCjlOjUTD/qUmKJ2ZjxjtLhCQm2tgeEcOt5ZIeqp6gOG6/+7u\nblbevpCbG67n6mfX8sHtl7Dg3isQThtR2VUQDhtlHzoj4QNI2hWYEnoIiNJC8m+6Bhw2DIcN6bCh\nFuQy+54vImwKWvUzZP3r25zT+ijazieR+tjG5im5ORRcvDb0KhKlCFUh6xMfG9MxxkKwfg89D30X\n6nej9nVB3S56fnk3wcMjJ6ROxkQpo+nCitDHiUgeBzOTaY1tB3u577KNfG3qS9y3Ygd/+lI1A92B\nMe1bUlIyFCXPPHsRF77wZeZ851Jmff1Cij6/GsPuYEF5H6qIdeiqE+Zdns+KK5eRO2VkFG0YMKhm\nM2NtBYp95G0iVIEtL/EbgS3LxpwzZ1I5o4LSWdPw+vMIBOxomkLAb2egLxs1mEf2zLIRTlXRdApL\nHJSUlFCgODH2dxIw8gnKfJznrGHhQ/fgmjud4LP3oL3xS9SuQ6ieRrTNvyOw/g7kaFMqxVH+6Q9R\nesO1iLwcUBVsc2cw6747sU1NXf70gad+D8HA0IuIQELQz8DTfzjhY2W6jKabVOmz6tCTcKL1k5OR\nTGoc7A7y/bWvMtAVmuZNNyQ7/nmU++o3ct+295zw8RSbSv6Z0wEwOjvR+4MofzvIaqWH3Y15+DUV\nYVdYeUMFa79RjhCCtT9/N6989J8EfRoyaCClwFaWw3v/eDHFBYUc+lPdiMQUQlGYf818SktL6a3v\npvF7tXRtP0aTcx85s0uwlxcx+93TGdzbQHBAR2rRedoN9j2wmUue+Sh7PvVjgt5Q9YfNrpGV40U+\nv4nAorm0/+czGP1e0ENOum9jNU26wsyPLME4shO0qMFTmh/ZcQCjqQp1VsIOECGkDnoQhKB43eUY\na8+gs7OT/JISnKWl9J1gtdio1WjSgPYjiWqV0Fobjlv9lqjKyOz3Yar0WQ49CXV1dZx22mmZNiOt\njKfG6Bu5s7OTtx5uxz+oxaQ00YOS1v09bH22jsIFY4s4R6PwqjnkXTqDJX4H7y50oQsb9mwVm0MZ\n6npYtKSUa968kX2P78LT1EnF0hKyZAfs2Y3j4nO4+FcX89otr2HI0IwYtiwbax5Ygy3bxuDRAV54\n33qC/QGQoPkNuncdJRA8RsPzR1AIUl5gjKijF0qAum/9jCyXF5czJF4IiaoaGFKj+ffPogx4EXpU\nt8WARufLOwmW1jFNC4x4vZZBLwN1bxHImT3yhzA0nHsfIa/hBYSh4c8uw3H+1yErTZNGCAWcWeD3\njlznyhm5LAlmvw9Tpc9y6EmYPn16pk1IO5nUeHS/l2CiOmoJHfUDFC7IOu7+x+svPZQzxeUiNzc0\nyYPb7UZKSf+rO/D9801a+r10zapk6qfXMXXdHIL/W4vx5IsMhLuc1K/fSPnHruEj1R/l4MaDKHbB\n/EsW0OXpAmD/H/ag+7WYKnohwGHX8fYF0YVCv81JXm50KgJJZVkr9Grh7QWKTaNsXivZRaGEoz5v\nM0c9M/H1xWYklIqgr93AcKkocdUrUrEjs4pilkUeWtObHsPe/CbCCFVlqYPt6Bu/S/FVD0DJyU+u\ncLzff/C89+J9/SkIRlWf2Z1kX7iO7BPsvmj2+zBV+iyHngS32z3kDMzKeGqMdwDzV/dT+0I3/sHY\n/tuKIjjt7OkUlcTudzI9ZeL19T//Nn1PbYJAqB7Fe6iVQ/f+jqLPvhfXKztAH7ZFAu1/fopjRdmI\neQVI4GDdwaGsjx1bDmMEEj+QVFWiaTDoj3XojuwANocRVR0hmbL0MI4s/1ADbVaOl5lnHuTQ5iVo\ngeFEWwLIWr4WUb8FtBH1QARnnTvCFkUbxH7kdYQRu73U/Pje/g2dC784tCx+Em44+Vw+WZdejzHY\nj2/rBlBUhDRwnXMFWeevO+Fjmf0+TJU+y6EnwcyFKEImNZ71oam89EAjQb+BEc4vrjoEM5YVMv/s\nkoQO5kTx+/1DDwKp6fQ9sxkZiHVuulfj0AOb8HeXkuXyU1Q0PPOPNAwGt+0hcNaSoe27u7sBUKba\nYC8jRukrimRqZSfBoI3BnEKQAl03EAbYZqgIR3hSU8CZ68XuCozobSOEpHCaG3dDqL+7VAT2yiLE\nrErai79K5e7fIXy9GFIiHDn4zv8aOEdeS9XvAcUGcQ5dAEp/a3oScwFCUcm99jP4Vl8BvV0Uz12I\ncB7/jWs0zH4fpkqf5dCTEAxOyDTtKSWTGrPy7dy79T386avb2PWvVhS7YM0Hp/KZB991ysPDIw+D\njo6OoWWyZwCpx2VpDNhoODQdwwgN3hGKTntLKfMXN+JwaAgEJcXF5MyfP+LYWTe72PLahph+6iBx\nOILk5vqR0k+xw8+c71/F3vp6RI7KsjNP59idPx/a2uYKIuXIkT6KKnEV63BEBSlxLplB+Zeuojvg\nJZg/g8F1DyJ6W+g81sGCsy8je7T+lwXZiF2Jul4KbBWLE0bgKc2w6cyCsmkn7czB/PdhqvRZDj0J\nyaZ8MgOZ1lg2K4fb1l8Yk8vFlXvy+bwjzijidAsLC4eiUJlfQJuiIKNC6qNtpeiaSmSkjzRUNEOh\npamSOfObETaVyovfhXMUx5f96DVs+dYr9B3oASnJyvJRUDAAhEd/BjXaf/4ac8/LBlcBZdOnYlx1\nPl0vbEb6AwQGXAiRoP+9zUnRde+n6GuXoLgc2HJDDlGNnlVICPyDKiLOmWvudgZe+DvGwb2QV4A2\n7xJs3a+ANjyRgrA5Uc+46SR+4fEn02U03aRKn+XQk5CdPfZpsiYrk1FjsqRUkfUejwclKpmKsNvI\nuWIN/f/aCsHwxMx9OSiKJCd7EIcjFC17fQ76enLAbiP7PefS51RjuvR1dnbi8YRnUsqCsu/NolQz\n8H7jtRETOghhYLjbGHg2dL7dj22l9I4PkV9xFYMb32Gw3Y/naA+FFV2otkiiLkA6sC+7AuE8fq+Q\nrKzYyFdzt+P52Z1Ivz/UfbDXQ1+Hk+w1l2H3v40I9KKUL0E9+0uIojnJfuoJwWQsoydCqvRZA4uS\n0NXVlWkT0o7ZNfb09MR8z7/2fNTLViKddiSgqAZFhX04nUEUJdSYmZ3tJz/fS/GdnyXnyvPHdB5h\nU1Cy42MkSU7uIDabTugNQKD7oeO+J3CuWEDJHZ9kUK9g/6alNO+biX/QQdBv49jhCna/fBY4kt/o\nnrhJLQZf/N9hZx4h6Me7fR+9l/4Pvdc8iu29/4UoTUHirXHC7GU0VfqsCD0JU6dOzbQJaceMGqMj\n92nTpo2I5MUHL8N98UpKiorp/fKLBBr6YvqKKwKcTo38ggqyS4uHlh/rOEbrMw0c+ksNht/Ae0El\nzssK6A300dPTQ/GqYmxvH4NgyJnabNqIJFwgkJpBy3/8jvZrzkBp6EIYCs1759C8NypiViRvvbYJ\n4bJRVFRESUnJ0JsBDL8l+Hw+9u8fHk6fv78aNcGIUcMw6G2sxygqG/V3S9vsRaeIGctoNKnSZ0Xo\nSWhoOLlkQpOJE9WY6kRb6cTj8bB79+4hmyOfSO6Vrm4PzpJRZkRzKLRW1cfst/O7m9j/kx0EGr1o\nbX46nmyi5a4aZMCgoKCA3PctRswuQIrQ9KCKMlrdqEC09VC0tRYKo7olCgOHy4+i6uBQwJE882Fr\na2vMdyO3IPGGhoHMOvFBPafK8RKGjRWz34ep0mdF6Ekw8+i0CJNFo687QN3jzQT7NaacV0rhwrF1\n9Zo7d+5x12fPLaF/Vytocc5XhrMihhls7qf7tXZkdL/zoET06ZRs1iheU8HATjfeQ4MYwVCspCk2\nnCTKSyNRFY38uhby338lzb/eTmFpN6WVnaEEXgL07HLmnrkKxTmcNqC0tDSm/cDtdjN37lzKy8uH\ntvFf/WF6/vifMQN6pAFjyIMAACAASURBVGrDsXQVxdNmDO07mZgsZfRkSZU+y6EnYefOnaxatSrT\nZqSViahxaGaicE+Vts2dvPq5HUgpkZoE5QDT15VywY/OGLV7Y2Tfffv2Dc1MFE2kGiNwtRP303sx\nohy6VAVKRRbeIkGkFrt3TyfCNtx/HMDl8pGb46PvtW76N9dieCMNoiGbgn47RpaCqupDy0CGhvnb\ndKQm0A/soWzKMQpKeof6vgMIbzutD/0a10c/klBX5P8RE1yUTYfLPwAb1yMNHQwDx+lnUviRL9LZ\n25fwt5roTMQymkpSpc9y6EkwcyGKMNE16gGD127ejRY3mrTlmU5ar3Iz7aKRzjrQG2TnD47Q9Fwn\nAoFYV8NZdy7AWTiyO6SjLJd5P7yKIw+8ge9INwiwzSvBN7uc7m0eii8vRigCR2lsbxJV1cnNDedS\nlxLpG274HEbQ15NLTvEAdkK9XFSbhsMVQAjQCnMxmjrIK+qLceYQitK1PdXU16xAOoYnb44MaorU\npU+dOnVE3beyci1y2dmIni7IzqFoemQS4snp0Cd6GT1VUqXPcuhJMPvUVzB+GuNHfSb6HnFS0c6p\nq6oPEuRJ170G9X9vGeHQDV3y1HXb6KkfwAiG9jvwt1ba3vJw/cZ3odiGm46Gqh5KS5l57hL2vr2L\n/ffUMviWF2NzJ8eUThofrOe8P11I0aoy7AUOdJ8GBjidcVUpCWcBAkMo9M+eRmXPfoRuoBCqX5eq\niv26Swk88QaKMUoeeEWhyOlEFhYmXk+oDn3+/PkJq1Hcamwd/GSraolg9vvQmoJunDBzIYow0TXK\nqMBcSggaAt0IpRLX/SMbHZtfddN/xDvkzAGMoGSwzc/hl9zMubJ8xD4RWh5vo3//ADIQHvYP+Nq8\nvHPbZko/V8Gc+5bT/OP99Nd1I8Z49yg2lUVfuJzW9vk43tlHfp8XyovJvuJc2HuEDk8/WrYNuxIc\nOXOSw8GC1asRUY45PmNlUVFsQi4zMtHL6KmSKn1Ji6QQYqaUsiklZ5uEVFVVmf51b7w0RiaiiCd+\nWfx2BZcXsuvbDRgS+vwqkWBWAPWbB1jT5iN3yvAcm101/Wi+uOQqQHBAp2tfX4xD379/f8zbQMfz\nR4ecOYR6qTjtAYK1xzj6bTddZVks/9H59Ab6EVtbcD9ZjdQj6W8BRSINgXCqIEBIweJvv5spZ8xn\nYL8Oc2Yyd9Ei3G43vt11eP7xKugG/gEndkdwqEEUALud4g99KMaZj8DQadu8kZJZ09AWr8Q27fgN\nwJMVs9+HqdI3lhjjT0KIBuBeKeWhUz7jJGPlypWZNiHtTHSNtv/P3nnHx3WVef97zr1TVUfNRW5y\nje04cRzbaQRCCGm00MuGXpaFl7bvwrLLsmw2LMsL7BJgYekhlEBCC+mQQqpjEte425Il2SqWLGlG\n0mjavfec9487I81oRpbtjGNp0O/zcWLfueU855773Oc+5fcEDK75wSXc+dZNaWWeLtEH4pEUj35m\nP6/92ZgMFQsDmH4DayRXqZtlksoFAWIdUbrvakMnFVWX1FF76ZhC13a260Pj96XGFGxKk+qMseMj\nf2b+NQbOziMYQmKPPkYCw29QuTpEsqsXpKD2Nesx1zfQ19c36k7q6+ujubkZ7z1PYiZdt41SBsPh\nCvzBBIbXgcZZlL36VSSWLiUxLkU046qSwwOE7v8hNVYCWjWDj/8Wz5JzqXjnpxFGaX18T/U1+kJR\nLPkmzUPXWl8B/Bz4rhDix0KI0jQBJsD+/fvP9hDOOE5FRqU0HbuG6Nw9jJrI73sK6O/vz8lrz1jL\n2fni/f39hC4J4hhjfCsZaAfaH+0fZWoEWHRNA55yM7cPsgTDL/GpOJvf9jDdv2jn2B1HOPSPO9n3\nle2jvUzLL6qE9HFudWeBOUjZDD15DFIWPn8SfyCB6bHw+FKUzYJUSwdiOIEYjBO+8xmO3fIA4GbV\nhEIhrL2HqPrVfXjau3NlUZJ4NMjgcDXJy6/CyCIDK4SKx36FqQbwmFGEnQIrhdWym8SmB0b3KUYO\n+FRAqT+HxZLvpF7jWutHgEeEEJcA/y2E6Ae+qLUu7Wx/oKlpenBdvBCcrIwHNx3nm296kviw6xoo\nq97BJ397OUs2vjgK42TZFyPRMC/96TKev7mL7mfckuqatWWs/Uwjh/5xWw5/uUo4dN3dRvCiSsrP\nrcb3Gj9yu8QJqwIVnuljUsrt/uZ33SOm6WCaDkpInMGRnHx2nbKJbW8jeaQPygQjz+1APLQJ07bR\nhoHtmIx/SUlANJ54Tr3hZkLV25F1zmggNtZVjT0MI5seZGTlJZPO03RS9KX+HBZLvpOuFBVCzMZd\nOncAlbgs0CWP8VV4pYiTkTE6kOQr1/6ZyLEEyRGH1IhDuDPOl6/+M7Gh06f+zPjLJ/qT3QR61Rvn\nIz3jo4aw4IoapJG7PTjHy6vuuJDXbl7LBXfW8rJbV2B3RhFGvoZWKUX3n466RF6VBr5XVWFjkLKM\nQsk16e5qBYqFNPnFSYBG07+jmXA4jP+J5xC2m75oepw0y2I6ACsEeAxS116I8Exsa+lkkjmHv4X0\n2AipEYb7J9gYQXqtnAYdpYJSfw6LJd/JBEWfB7xAB9ACNAO/BG4qygimOGpqaibfaZpDaz1pKf9T\ntx7FcfKVlWM7/Pm2vVz09sYTHl8MjpDrvraW9qd7GOlJYcUcjIDAX+XhFf+1asJjDJ8k1OCm/Akp\nJqjxB38wMOoSad/UxYAdxXK8+Dw2ppnVE1QKgisr8caO5TW1AMCU+UpdCGR1EJTCHIlnb8YXTGFb\nBsqRcOEqRlbPJ+wXVKb3yU7t1Mf6cX77OAHrIHJDND/LRmg8NXGcBflNoqeTNV4Ipf4cFku+k3G5\nrNNa20W52jRELBYr+bSweDxO9QnynAGG+5IFe3/aKcXw8UKl7cVHsNbHjU9cTOtD/fTvj1K9OMjG\nd56DOQHfSUaJpVIpamtrqby2gqPfPJi3n+EzWPe+jahaV2tXv66G9p90Y8dsItEgAV8Kv88GAY03\nLOWyL11J291P0f+jR1BpRS80BN96MfE7t6CzFboAI+hj1mXn4gkPoLw+UmGwkl5A4wuk8PpTUB9E\nv+ll6HCYwfZ2wuFwzgtQR+PY37sbkYpTfW6/e8FxEAJkwISLrjnNGZ66KPXnsFjyTarQ/5qVOZDD\npV2qOJnA2YXXOjz2nSMkR3KXg8dncOF1iyY9PhPczOxnJx3CzSME670wwaHZDS8ykKZkyXX1LLnO\nLSYar8wLHZPxvXsqvFzy9St55lOPogGtNEIIVn54LTVr6ke/UhrW1THvpbPpeOIYdswmnvSRkkFq\n1lSy9P+sRpiSypetxrd4Fkd/8yR4TSpfvQ5Z7ie0pJHubzyAMxxHaPDMq2Hu37/apW9sPYKdkIBC\nK4HWBvGogW17CbzuooJzkJEj+twzRB1FWXUUJ+4t2BBDCQ/Wxne6HYIKzOXJYLKWf2fL0i/157BY\n8pVWbtMZgMdz+p1zpgtORsaVVzSw/LI6Dj51fLShszdosOrK2Sy9+NQe8mdu2c/jN+1Ga42yNavf\nvIBXf3cDpm9yZsHTQbZ8869dTP2GOez/3W5UymH5a1ZRvqAyZ38hBNf85Ar2/7KFXT/Zh7YVs5o8\nDO7o4JnrjuKrDVJ3oR/2709X+guGtnZQ/vGrCKxdRuXNNxBp6aSqrob6JfPRjkPsh7dhHjqM6XHQ\nJgQrYgwPVGJbHizLi7dhDhNJ39/fj93WCZaNN5hA2yaJvgr8tVHEaN9TgRWo55j/XJiiFLgvBKX+\nHBZLvlNW6EKIv9Na/29Rrj4NEI1Gp73/cTKcjIxCCP7vPVfw+I9bePSHB0HAK//2HC5/d9Mp9f7c\n+9ujPPaFXVhZvCx7f3ME6ZG89vsbR7fFIym2/eAonTv7WLAxzuV/W4237PTsj5GRkRwF568NMO/1\nblZBeV1lwWOkKVn1zmU0XBei5Ts76f5dM07c/TpJ9I7Q+WCUmloDv991N+kkRL/9KA3fW+LOR22Z\n6zcHUs9uxWlpQziOW2wkADTloWEivSEwJE7rcYy5E39yi/kN6F2H0Y4Ej0O8uxo75sNXO4yQmtRQ\nOak33Uxtlfvlcrprdqqu9VJ/Dosl3+k8IW8H/moUeikvogxOVsaWw4eYdwW86wo3P7q2topwpHCn\nlcyne0aRZopqtt18KEeZA9gJxa7bW9nwLwvxBA3Ch2Pcft1zWHEHJ6E5fE+EZ77Wyo1/2kjSEz3h\nGMe7DOrq6pBRh2N37UFbDv5rz6O8qW50v+z9sxtHZDDQ20/3rw/mUuYCWguGBsvx+8fkd+Ipdtz9\nZwYqYGhoiMrKSqqqqpj/5ycIFGgCLITCMB0c4WFEOoyEw0QiEYaGhohEIjnjkBcsh8d2EBsup8Ib\nQUqwBoNYg0GUkHiaVlC/ZOUJ52Y6o9Sfw2LJV9qOqSKgo6PjbA/hjOPFlHGkZ4IAqhAkI67S+9Pf\n7yMRsXESrjvBjitix1M89vn8gOZk6H5wL1vf/lO6fvwcXbdt5S833sah/3n8pI9XUXu06Gg8HCfX\nSSKEoMIboKpqrMFEdUUFMjlxWqcGpN9L9cYVhEIhmpqamD9//mhj69G0zbmzWfZf/4B/3WXEY5Vo\nLdDCRBkmyep6Qu/+1EnLNB1R6s9hseQ7HQv95L+vSwBLJ6nWKwWcrIzj/bITWRXDrRHCj/fgbfCz\n/HXLEUKMWpsLLrM4cHdnHiuhGZAEZ/lwLEXn5kje79qBlof6uOTLY626spkZs8eSuVYqEmfvzQ+A\npUZPp2zFkV9tYekF11O2oj7nuEKNp5ctWcY9/gPY1vgXkcb05AaIDSG58M3XEB4ZpqmpiSptEPna\nzyHuoI38jEmtJd65c1h2041E0z0sVNLCtgNUi3Jqa2oQ44Jl8z95I31916LjI9DTQcTWVCxaiiwv\n7DoqFZT6c1gs+U5Hof+2KFeeJtizZw/nn3/+2R7GaaGQgiqEYsmobMXmv3+Yrofb0BLQ0PPdw7z8\n568d3efKm8+j9ZEerJiDTlMHmAHJS/59BdIQKNx8cV2AVkAYuQ2RM7zgGYx0xdnxtR1E9g5RtjRE\n04ZgwW9QlbTpun83NXXn5WyfKMNj4ftW0fq9Xagswi8hBVV1CfcfUiA8Bk2ffA1GwAcjLuf44A//\ngB0ZBiWQfgMjQyVgSIRh4NxwPXMu34i/ro5oXx/Rp/fR/6OH0QISQH9lGctufjeBRbPy5yJQBotW\noPr72b9/P7Nnzy449lLBdH4OTwbFku9kCouuB3ZnGBe11re84KtOI5TyIsqgWDIevO15uh5px0mO\nKb6hwxGe+NiDLPy31QDUr6ziA5uv5vGbd9P+dC+V8/1c+YW1LH7FmEJa+bpG9t/diZNFf2v4JKvf\nkt9IN9N16PiTXRz43GNI4RAC1PPH2f5kFfX1XoJmgczbU6ChmfeWFZiVPlp/+DxWOEXNynrmfWAl\n8eMdpHYeRZb7aXjVesT82tG0yXDXMcrbu3HZxASphA9pKKThoP1+5MfeRSQeQ6RfIr27m4n94E9g\nOaPDSyUiHPjsjzj/F/94QsbFc8899+SFmaYo9eewWPKdjIV+CfBRIcRCoA94Drhda729KCOY4ih1\nYn04PRkLWbMHf/q82/whG44mvK0XZ4+P0FxX4VELL7tlObAcyP+CeP3/ruf7+4YIt4/g2A5SCuac\nV80NX99IW6dL+FlbWzuabx6qqGbzTbdjSGfUrSHRNFRH6O6qoml+LMfdIX0mc69fTVnaVTOeGKwg\nxe+NdXg2uP1FV6Tpb51+A++6hQD4xrmjRN4XhkA5hvvH6yMWj9He3j76lWE9fQgKVeLGk3Q8sYPA\nmoWj28bP/c6dO0cVQqkGD0v9OXzRGlxorT+f+bsQogbYAHxRCHG71voXL3gEUxylvIgyeCEyZmez\nOAU4yDPQ1olN4vGFLxd/v5q2JxwGmkeoWxnk/FfNp63z8Gh39IGBASIDEcLhMOa+BMpSFKrNCPgt\nlGEghUJoDYak7BWL6DaHoHkop0tSIZxIQWbHFDL7DW49ROK792N0D6D9EteJlDUPhsS4YBmhUIhI\nJDIa/OxNOVgF3EwacKLxvO3ZKHXrFUr/OXzRGlxkQ2s9APxRCPEo8AhQ8gq91C0DOD0Zxwci6+rq\nmH/1Yg7/ej/aylVinjofoQW1hEKhgkHIQhBSEFprIpugqsoczXXXStNxR4JnfxvBTkAwcJzlq0cw\nC1ROSgmGoel9W4hZYS8+00vNpU14F1TlKHFtKazN3dg7+hBBk+G3l1Fx3pxTmg+AwecO0nzzL9Dp\nrJaULfEGQJiGS5jl8yCqyql/41XIoJ9wODw6J+Jla2nf0YZK5AZfhaOYe+l5eGsnDnq2t7fPrNFp\njhfNQhdCvBvYjetHTwJorS0hRGmXbqVRyosog1OVcSKr9dxPbKDrkXaSgwk3gGgKpCkJXLeI7i0J\nKl+W71KY7JyZL4AVK1YAsP3rXXTdMYhjuwo+Fvfx/DYPy+YNUFWWqwyVEoTWNbLxXZeMumcy18m8\nTEIV1Wz+wpNYHYPopOsuat3/EE3vv5Sm91x8SnPR8aMHwUoSrBzB9Dikkl4SUT+eCpOKq87Fu6SR\n6PxaZNCfd57Q5efSe9cmYq3H0Cl3HMLnYdYbLjuhMoeZNVoKeDEt9Grg48C5QggDl3GxGthSlBFM\ncezatYs1a9ac7WGcURRLRn9dkOv++DZ2/3QrR59sRfuCPH1vkvj/O47Wmj9znA/dUcWaV7nBzYmy\nSjJukFAolNPlx044HPhZ76gyz0ApSfdAGZVlqdGcWqVA+31c8LUL2bRpEw0NDXnXAEhu6iKZpcwB\nVMLi8A+eIvDSeZiVY8o3eyyFxm71dNKwoAcESKkJqBgVIUlfVwMVr7+CgaFBBKAi/STv+BEL+zrQ\nSDpnnYPnrTdS+9k3YD/wLPFnD+EpD7L4ba+gasPyCec78zKZWaPTH8WS72R86N/I/F0IYQKL3c36\n0Au++jTA8uUTP1ClgoyMJ5vmeCJ4KrzMf+syhldo7nxdB1ZsjO8bNN9/y9N8sfnVVM3JJ5DKRiZY\nmPl/X18fPQ/0pFvE5ZdCJFMm5YtsBpMBSGiMC0LUv2kxB7cdYuR3UVqODyG0h1SXhTY1ZVcEmP/m\nRgb/1IpKFsiCkXBsczOBC8dcL5mxTOR3r24Iu/zkmcCs1AjToaI+Sv9gBCEEIh5F/+IWvI6FkAAK\n3bOb5C1fwv+ZL+B9yQpGVjcQDIWoWnFya++vaY2WKool36n60G3g1Mv1pjGOHDnCsmXLzvYwTgl9\nfX10tA6zY1MvlSEvr3pLNT7/xLf6hchYyFLt7+9n3/3HC1ZYOo7izz/cy0v+bmFOYVAGdXV1pKI2\ng0+ZDEWT+Jt81K330fvtFnof7QBVSb5C11SUJwiurSZ52XwAlw74ELT9ayt2ykHZEhhT3EO/H6Hr\naC9NK/3u6fKGKqieU0t51tgyAdmCSMQxzVQeq62Q4K9MEgeG2w5TvWszOFZO1o2Q4BVDmNuep/aS\nDXlzMhmm4xo9VZS6jMWSb4ZtcRLMmpVf1DGVobXmls9t5b5fHkYKkIbgq5/ewo8fvIGVF9QXPOZM\nyJiKukyK4+EkNbHwxKXwx3ZH+P5Ln8ZJKey4wgxIquZ7WCR7IelQXxXn+FAApcZSWqTUrFzRx4K3\nv5lqw1XaNaEaHvnww6ikYpS0PAs6qRncOkT9ezcQfvwwOpmboeOp8LPwZee6TTHSyHDGF1S2Qrql\noIVoAjwGvlu/Q2AkisdI5DemwOWGCW/5C731bvZLOBwu+LLMpGvC2JfUdFujp4NSl7FY8p1xhS6E\nuAq4FXiD1vq5rO1LcLNkbOBerfWXz/RYTgeRSITKyulTVv3o3a08cMdhUjkphDYfef29PHL4PUiZ\nr9xeiIwT5W5f8OpFHPj5QdS4jBdvmcGG1y+mrq4uTzEBfO3ye0lGxixpO6YItyTxVprMqbapr0pg\nSM3xIT+WbeA1HWorU/SGGzj0zAG6y90qzTme2SSHkoCrLAu5aZRStOzvxfP6xaR+2wKGAKWRFV5C\nf7+BY62dzF40F2G4L49MefZ4Wfv7+8HnQy5oQrUfdtMj09CGgbAtzETc/RAQAq3zaQCE0KjQ6bm6\nptsaPR2UuozFku+MKnQhRANwFfA05NE9fxX4AG5v0meFED/WWveeyfGcDvz+/IyEFxsn25wA4Bff\n2U4ilp8PPjyY5OlHDrLygnzrcmRkpGCg74X40s2GBCteFeLg/RFS6fGYAcHqa+ZQfY6gr68vJ8gI\nMNyVINIWyzuXsuH4oJ/ZVQmEgFB5Cp8J2Uo6NaA5cvNR5l81wLErFyErjTH6AKHTLpX8fqSpQBLV\n6KXyixejjgyDz8BoS9Hx+WfosBWG38OyD1/KondcUFDO7BeT543vIPXD/0HFouA4CGkgQjWISFaK\nZJr+Nhtag2N7qLj+Whzt/hYKhUYzeybDVFijZxqlLmOx5DujCj2toD8rhPhJgZ+Xaq13AwghngLO\nBx46k+P5a0ByfKVmGkJAKnl6zYNP5YWSjVd8YR6zV1Wz5/5upEex9i2zefXHNjIQLky5e6ICn5Qj\n2dVdwblzhrHsXNtAabBtg0TKpOWBeuamktT9cx29y3oZ3BtBCnDyCzfxBAyMPxxAD8UZMSR115+D\nEfLS/Yf96PQ82laSg996EjPoIXD5iXPTRVU13k/+E4Pbt5A41o1vYRMV8SjOH+/J2ktiJz0Y6QbP\nALYKIt/0PoyaapikY9AMZnAinE363Ow89iGgIvvHo0ePulkB4/587nOfY+/evSil2LZtG+Am5QNs\n27YNpRR79+4lHo/T0tJCOByms7OT7u5u+vr6aGtrIxqNsn//fmzbZufOnTnnyPx/165dJJNJ2tvb\nGRoa4siRI/T29tLb28uRI0cYGhri0KFDJJNJdu3aVfAcO3fuxLZt9u/fTzQapa2tjb6+Prq7u+ns\n7CQcDtPS0kI8Hj+hTDU1NfT29lJWVsbg4CCGYZBMJrHSHNvRaBS/309fXx+vu3EVHm++e0FIQWiO\nRUVFBeFwGK/XSywWQymF4zjEYjG8Xi+RSIRkMsnevXvzZAHYvXt3jkxHjhyhv78/R6bW1laanx7k\n/126nfv+u4XDe6IcbU7Sm+pAGpIjR44QCoVoe7KX44+kaPtLN4Zh4KmB8nnePENaa7C0YCjh4Vjc\nixJjLhStIWUZqLRbJZn00v4gPHfT84QPDo26tKVIZ9sI0FLjmSuoLevBHoiBrVFJh5579tJ129Y8\n+gInYbPnW4/T2dnJn/70J/bt28cTTzxBf38/jzzyCOCSK9XU1HC8vx/f6vPoblyImL8QZ+58tALH\nljh2+nHTEodyWhZcSOSNH+PYjR9hzktfQnt7O7W1taRSKRYtWsShQ4cKrr3h4WFaWlpG114ikThj\na6/Yz9NEMk32PCUSiZKTKfs+tba2nrRMJ4KYiOu5mEhb6N/VWm/O2nZQa708/fcvAY9orR/J/L5+\n/Xq9ZcvZT3XPNCqYDoiNWHz0Tffy7KNj9LRCgNdv8LWfXcOVr11c8LiMjOMt8fFFONnbJ9oXoGVP\nJzdteAI7kbu2vOWS/9h1BSqhuOtN2xhsd90rAsGci6pZ87EadnyumePNNgGPRgBxSzCYNBjRrgJv\nWKw4Z8kQTpsiFTdJJD04qoCPXIAhFSI9EZlEFqPOIPBJLxX39+A5ksybC61JB1zHnc8Q1Hz70tFy\n/Qyyc+UzPvZsOoTAQIzWf/1f7JgFGgyPTeWsGIGFcwm/7q0gZY5r5WRSR8fvM53W6Omi1GU8FfmE\nEFu11usL/XY2s1w6hRCrgb3AxcBXzuJYJkRPT8+0WUj/8X8fZ8tTXdhaua0uERhS8Mb3rZpQmUOu\njOM7DQFYCYc/fOkQm37RQSrusGBdORe816Dp/LrRTuXZxz1+WzOOrQCBT2jm+xReLbBtxe//+SlU\nuyB8yIZRD5CmY9MAA1t68aOo8EFGoZZ5FT5T0xY1qa9IcF4wjOgF4YMyL0RHfESGy/KF0mm/eYYy\nIL3ZGVAILxjhCXqfC9d9o5TEMBRSKoQAOStAJBJhcHAw75CMks/MQUbB65EEyf+8M+2+ccfhWCZD\n/XXM+9pnCHccnfCenAqm0xo9XZS6jMWS70wHRauBXwLnASuEEN8BNmitPw58FvgxEMNlb4xMfKaz\nhwULFpztIUyIbCvZcRR3/2I/qaT7SeY6GDTK0dzzq/387edW5xybbQGeSMa+vj6+8+7nOLx5GCvh\nnrttS5TOPYL33F5NKKsNZkaRDXRH0TYEpGKhlFhJD4m0jdxzh40pAZ1rAesUxCwDX9B9EWQghMCQ\nUOV1OG9+2I2sZ14EAsrKksSTXpIp77iRF0wuRxhQVVuFmB+BfUP5u2iwLBMQOI6BlApfucOs96wh\nWe0+LtXV1Xlc7IVgbz2EdsbHLQRKQ9eT2wlXu7GA7Pt4OoHpqbxGi4VSl7FY8p3poGgEuG7c5p+l\nf/sLcNGZvH4xcPDgwWlRcqwcjWUV9q/FRiawRtM4kYy9rSO0PDOEnczSfBqUpdl3T4ILvpSbNVNb\nW8uyi8McuGOIOqWJ2xKJSBvKbsqe1rpw26sJvH9SQHXAcdMKx7kIpYBgMJmj0IUHAo0+nONJnMTY\nnAgv1F5RQ9PSJsKrHIb27s1JIdQaUikP2aEljcHCd6xn9VsuHc0Eys4FH//3bBiJdqJWfiBa2won\nEiXUtKiwwKeI6bJGXwhKXcZiyTdTWDQJpvIiGm+9nbOmjn07c33bQsBFV8zLOzbbKpwzZ85YY4Z0\no+JMYcv2xzsQRoECIQsOPtvD+DjHk7e2sedrYcocQXLU8QN+snKvC1XvC/B6CmfhaKGZfYkf47iA\nRP5YytcEGX7ecJW3o5lz5Sw2fvE89tyyn/bfHQUTDDuFz69JPN3Nrjd34jeSCGViGM7ogCzHxLJy\nOee0gmNbOpE/+YN8GwAAIABJREFUeozeu3djRRP0rApR9+4NGBW+PMbJbJjrVhJ74vl8BkVDUr9u\nJfGaYMHjJto2EabyGi0WSl3GYsk30yR6EmSi0dMB//6dKwmWeTA9rrb0eCVlFR7+6asvPeFxO3fu\nZPsfjvHNq3bxP5cd5nfvCrP33iG01ni9AlWgsFMLRU+7zXdv7OEH7+/lyZ8O8dwvo2z/6gA4IMnK\nRAGy6dAdwAhIMnyd0g/+OpO65TJ9SLbS1hgByWVf3IjU+Xa98Bk03riCV/58Iys/VM/amxbymluv\nYk7THNZ+fg3XPvIKgi8X+IMabAUavE4S4WjQAsc2sS0PtmXCBAkEqqubrh8/i90dRQzbWH/ppefj\n92If6DzhvFZdvBrf3DqEd8xuEl4P5ecuoWz5Aurq6k4r13/8cdNpjZ4uSl3GYsn3omS5nA6mSpbL\ndMPRw4N872ubadkbZvW6Ot74vuWsPPfE/rlnf3OE777nmdECIADDI6gsM5FxTb9lY2mdo2aHsbDS\naYAAhimQluYcFUjb5PkISgCNt1zw7s0v4an/2U344AgLL23gwvcu5cBNf6Tt0TgjQ8F0dSf4/Dbn\nf30lF739Yrr+dJAd//wgSmlwFNJrUnfNYpIRm/DTXW5KlyEwfSbnfesVxCtcV9PzNz6N3Tf2Viov\ni+VVarojy7hcxn40A1AeHHFfBjlQlIWSrP/1Z4ik3AYU47OC6urqcBIpen/3GP2PbgEpabj+Uupf\nfZnLkV4AxSBIm0FpY6pmuUwLTEVi/RMV+gQq4Z2fWAQsSm+JT1oY9JNP5SpzAMfShMMW5UgqkUSF\nIil02jmhsMexUDlp3pYxb4pOhyXH/CxCgDTholtmkZBRal+jMQctlqyq5Y+f3M2Be7wI4frCm+aE\nWTR7CI8fjM4aAOZevRwW+Dnwm634pZdVb9jA0b+00n3L1pwGzlbCYe/nnuTCn78KIQT2QO4nRiaD\nJQ9+iRDSfXHZGmFIypf7oSNaYNYkVlyw/5f3Ez3fJQQbn+WSgefqdcy+eh1wZhT1VFyjxUapy/ii\nNbj4a0epLaJC1K/RnokrSKMoqpBUawPhUYSU4jgQ1vkxTEdACocFQU25x/3VUtAbM/DUa2pemWL5\nm2ZT1eB26xkcHGRoaIgnbjpM+4NRlJajJ23tDhHw2cyrj9K3rZXNmze7Od+RMM2z+qisrGRZneTo\nb/ejkxY+r4VhKBxHkrI8JHtixI8OE1xQiW9OgGTnWBu3ZNJDIJDMtdI9kvI3L8K3oY7+JzthRFF9\nYQMyliDxvZ4CM6ORUmEdbCOyMKcmLodmNxunwqB4Kii1NVoIpS7jWWlB99eI7Aa8UwWnauUV2j9D\nBVtdXY2/ThM7VsAHIaAOQZC05W0Logi6UQWpUQSwoFxRZowFQL0GzC13sKLgsWA4PoSMuD8ODQ0x\nFB6m994oOjduiKMkh7tCzJ0VJVauIYsffXjYJeDq7+/HicYpCybc6wuQ0sHjcUg4crQVXvCNNVj/\n2zkaC3CUQSLpobwijlImot5PwztWsug159Lf34+82iAUClFbW4t2FHtv24M9MpZLnoE/kKRy7iKC\nTU3AmMKeKOvlTLlRpuIaLTZKXcZiyTej0CfB6tWrJ99pimKi7Ins7TU1NTSs6KX12HCO71ujCSEJ\nkKZdAEDgQVKLyTCpvGyVSgllhptKmA0BmBJSf/TguSgIVWO/6aRMByM11cEU1WUpbEdyfNhPypYg\nNIPn+JlbXT1axFRR4VrE4XAYv5HMGUPmReLzJAk2uRda+/b1HHl8G50Hq0gmvHh9KebO66G6Zhgt\nJYMffQsV9WNzklHm4GakLPqHi2j7z8exU+7jIqWirCqK6bcJXLya4XFzPJElfqb849N5jZ4sSl3G\nYsk3o9AnQXNzM+ecc87ZHkZBTOQbn6i1W/bvmRZv2+7pofnZIRTuYsiU4zhoKhDIcdFDiaAGg14M\ntFDE015yD4JVwkOhVBEh0oSHFhz55hDmSoOKSyGSiBFtdpA4LGscxu9xGBwKolImdX6HmuooKMHC\nuyN4PH1wtavQMxY6AEOJgjJK7dA/0I8QgsOHD9NUOUjlmuP5O7q1/iecr+DGlcx/xV+I7jmGHZdI\nQ+EJKMzFdXiWL4aBwmRjLxam8hotFkpdxmLJN6PQJ8G8efk53NMVGUXf2trK4OAgkUiEe78RwU4A\nAlLZXvECnN0ZSFzFvhI/oFGAH4Gh3YDpeGjtMiIKBNYxGOmEwUfA0rOo8Nt4y5MEvTY9fZWj3OVa\nCwYi5aQskyWLehm6fSeqdYTK966hUc6CoCAUCjHi96Bj+XmVImCOWsI+nw91bjupbXvzCpOMufWE\nZjUQqqym7/aDtN65E5V0GLpsEas/fTnBRtfKr/v8p+i86wFiT2/BMAx8F63Fd/lG6uvrEemJOluZ\nKaW0RidCqctYLPlmFPok6Ovro7y8/GwPoyAmUiCTKZZwOIxWmv6dHsKtha1TYYBZI3H68tNa42nF\nH0bRgBy14pWGpC3wmnrU7aLSmY1Ku1WiCSfdREIITA1SQU11kljcl9eIQmtJdMRPIuHB77cY3tRC\n58PHELaBUIIDd2+mYn0DzjPdbvQ1A4+k8Q1rR+chGo0y//1vpa35qzixOFg22pBIj4fKG29gEDj4\nr08wuO0YKk0xfOzRFvq3dHDlve/GWx1AmCb+Ky5hZM1yqs5QcPN0MZXXaLFQ6jIWS74ZhT4JSmkR\nZRScUorv3bSFlieOY8XTinCcNe4rM7jq64t5+IOHcZIKtHC5YYBjaTKVXm1RLbz4tMu5orRmMGFg\nmw51Po0hYNBy3TEeAVGl2We7qY+zhKABiVICpQUpy8gfBO5XQiLlKnQhNB5hY1smsYSH3k0C9ZTG\nMBqorx2iqjaO0Br/utnUvm3NqEsqmUwyWO5Q9S8fIbFpG5Hd+/E2zmbxW1/HoLJI7WzPUebuJGmc\nuE37b3az7AMbinwniotSWqMTodRlLJZ8Mwp9EmQ4x6caTjfA9vRth/nlp7YzErYQaLxIEmQpdQmm\nR/DG/2hi1oUVXP79WWz/6BGSCYk2FIcSGqUFPgRCSsIVNtVRE0dpYsCw1qiU5Eg6a8UAmryadqXp\nd8ZSHXu0xkTRCAyP+PB7bRLJfE4ArcHvde+BUgIhNPGEyeBwYHRfxzHo6a0mkfLge5uP89+7kYHB\nsdTMgwcPUl+f7qe6qIF2EWfhwoUMptNePAO4PDHjoJI2Pc+1E7rBzWIplPI5FTBV12gxUeoyFku+\nGYU+CSYjlJ9qKKToM9se+U4rf7ypBRRZWSsAEguNRlO12OEV/1BLYH6EA1sGOPz5AeZUxjGqXeV6\nrMePSgkUApQgGgOnXFAd00QtneNBF0ANgj2WystZV0AvmjpbQsLE57ERQqdd3OnRCUV5WRK/30oT\nZ5k4jmQ45icTvs0EcTWCocEgsx+Ood+jR/3a4JKBZVCIJTEwr8L1DY2D8EiCi6vztk81TLc1ejoo\ndRmLJd+MQp8EwWDwbA9hUpyIfjVjVapEgHtvOoRH5dL3iLRS92mBH4GvTxLdr6hq1By4aQSOA2mK\n3L6kZMiSrjJPw7EgZWnWfamBskVJWm6T7L1/AFNBFQIpJiRRxAJsRyCEJDwcwGM6COkSYgmhCVVF\nmTs7gmUZxOM+HMdAKYHjCMoCSSrK3R6jSgmGoj7iCS9q2KF1VwuiYqy0PplM5iny7H+XLashuCTE\nyMGB0dx1AOkxWPmeiwnU5X4OZ6clFmr28WJjOqzRF4pSl7FY8s2Qc02CgbOcklYMhEIhnrpzAEMV\nTlsRCIIITC1wBgz2fDvOn983RLTVIZ40R9V3JGngaGjwaNaVay6q0Jwb1HiTDj37kjQ01vDqf17J\nfEMy25AEpMDLxFZDENf3bjsSMd+D84EIgx86Qv3Lh5g3Z5DyoMPQUAXDw+XYtsf1tzuC8jJXmUvp\n+tgNQ1NVkSAYSOek+3PlFEJQXV1NdXU1TU1NVFVV5Y1l5f+7krmvXIYwJUhB5cp6LrvtzQRmTX3f\nbSms0clQ6jIWS74ZC30SzJ0796xc91R85BMVEGVbjs3bw1gofOSTQmWz2gKoJCR6NEoqeoTDzm4v\nIa9Dg0cxzwuNvjGXc6UJqw0wUnEikQibPnMUtHaLlISrTOci6BAa5eRec7YYG0u8XVHWaEPUwRwp\nzJcuBCSSJuXBFHKcKSIlVJYnMS4sI1Rfk/Pb6tWrCQQCo/8u5Ac3y70s+MwGgu9ZhHY09XMaKOTV\nrK2tnRJWeTbO1hp9MVHqMhZLvhkLfRJkSuSnMvr6+goqmObmZlpbW2ltbaVqLgyYFuO92RqNHyDd\nfVPj+reTKPaRok87xDR0Jg12RQ0a09kr2ZAA+6Ps2bqP6EE7rxtRNQZLyiSh5Q4eAQ0SzvdBpTk2\nFrNMUlFRQXl5OXJlADyFvyYs5wT58Qas/OxGQqFQzp8jR46cYPZyIQyJ9BZmQpyqmA5r9IWi1GUs\nlnwzFvokmCrVaaeT1RLK6g/3mg/NZsdD2zk2kqRWefAgsYUmYGqEla/ALNI1n1nK0zuBP1wIUIcM\ngkY5MSEK7lPpMXjpV0N0fjaM6k25wU/h+tAHUz7WfnAhi1/qBiCdmE3r7mdJ9cbQTuZsmoqKKI3z\nonR0zAadb4tULq1h5ZpVeduXLVuGzDLpM3GGE83ldKKvnSpr9Eyi1GUslnwzFvok2LFjx9keQg4y\n1nh/fz/Nzc0cOHCA5ubmgn/f9fQA3/nEYf7zbw7w0O2dfOArK6hd6afTk+CIfwRd7uCxjIL85YEC\nSyOpC2WKu3AciD7kI7BQUrBRp4CeHw3DoJUm0XI5XzyGZtZCuPATTaN7DyWHCf3LMs75uw145gYR\nIS8VG+Yz7/olSK9BqH4IMY6+V/oM1nz60oJjm2r3sNgodfmg9GUslnwzFvokWLdu3dkewgmRiDl0\ntyWorvfkNGx++PZO7vzvVlLpnpp/vrObzff3cvPvLqQv0snD/zZA7/MTK+hMzWa22nSAo5ZmgUfk\nEHBpDfGkgbPFZOG/KA5+HoQNHjLuEYE1oDl6n6K2AjxZrhYhQIUTLsuh6b5EMhkoy9+2nsH1ikgk\nwoKmJmpra6nuWUf7Y9sQRx1GnkuQOp7A11jGsv9zAbNftrCgLKdyD8+2ZX4615/qa7QYKHUZiyXf\njEKfBFONWD/zwGutuet7Xdz1o11IQ+BYite/fxXv+acV2CnFP33jvlFlDm4DinjU4b4fHWXlRk3P\ndne7hcYLeVZ6waxYDUcTggYEgTTfudIQS0lsJZEajv3exNIWZeT7urWGaNygpiL37NrRaFtTO8tN\nBzxR8Y5nVjVcuIDAhTD3vWNvsJoTlONn7qFK2bT9+HF679qCTjlYG5ew5ONXE5gbmvDY6YCptkbP\nBEpdxpkGFy8Spuoi+v0Pm7nrR4dIxseU41237iNlD3PRVQ0Fj3Fsza6n+um4e8xnPoLCg4FbmuOW\n9xtegaOhwjYYTpf5ayCoJbO1STQliafcDkS2hmFHYAFiWOPfbOEDkIU7QVuOzFP0QoBKnLiw4kTZ\nJZnfJkLmHu793K+JbG1Dp9zWdANPH2Lo+aNsuP0jeKpPPQ84NRBnx3/dx7HHmhFS0PiqVZzziZdi\nlnlP+VwvBFN1jRYTpS5jseSb8aFPgm3btp3tIYyiv79/1Id+x//sy1HmAMm4w/0/O4ojR7CtAgpS\ngz8qSA6ObVICIjjE0VgozNma638yh9d+ezG1Pi8LPD5mCw/zpY95Hh8eIYkrV/3bCsKOcHuL4m6L\na4hPyNSoMY0CFZmm4Pgjblzg2O52IrfvY+TW/fQ/0oLO6+XpIpuzfDJs27aNWNtxItvaUGll7g5H\no5IW3fec+j12EjZ7PnIvXX86gIrbOCMWHXftZvMH7+TF7tM7ldbomUKpy1gs+WYs9Emwdu3asz2E\nghiOFOZ+SCYUCxbXs+LCHg5sGcS20spFQ4PwQb8czyCLEhBDIaRm4UaTA78aYtFlHt50ZyMHH+3H\nKwKsunoW2tI8+qk2Yh0xHA0pJdDjU2EQJBnLTBlvpVcE8setLM3xrf0YNVFitx4AR2EpaN8VRtWY\nDL5tzPru7+8f9bGP7xA0EebNm0f3ppb8zhu4fC1921sJXjd5lkH2dY7cuxs7mgRnbDJVymGkdYCB\nrR3Urp8/6fmKham6RouJUpexWPLNKPRJsH//flatyk+FOxvIdjssWllB8/NDefs0LirD9Ej+5dbL\n+MrfPseuZ45jmOB1DAKOiUpqXF5D172SgdBQowx6HxBoa4QjD8Xw1Qku/no5njLFrqPPoneU4+9R\n+E1XTXfFc+luR8+F64rxCHKu4TVUTkB0FBoGN0ep6j6SQ4OrEjYcd/DujMK1Y3OQjZMhy2pubmZh\nWS3KKWDtmxI9KzjpS2H8tYb296IT+b1YleNwbEcbelEg77czhQMHDrBixYqzHtA9k5hKz+GZQLHk\nm1Hok6CpqWnync4C3vnppXzpQztJJRx02sXh9Ru867NL092I4BPfPofnt5iMDDoM7yrjye8fA5Hp\n8aBHA6Faa2owMLVApw1oO65xuhWH70yw4r0BdBKGf6ZcH12mzZsBcVXIVw6mcNvOZb4GhACtBZYl\n8XrUqEtGp5tNm6ZV2L1iaeSuYZqbm09K6UK+0g8GgwQCAeKLdxFv7oWs6wiPwdzXr8dTU3ZS586c\nf3BJLalnetHJXKUuDImvsfKkz1UMLFiw4EW93tnAVH0Oi4ViyTej0CdBV1cXS5YsOWvXb90T5rb/\n2M7+Lb00LqvgA/+2kfomQWWFh49/4QKefKiDlv0Rms6p5qM3XYK3KtcdMX+Ja1X+4du9zNEGBm5n\noggKO209WzjIApQA2ha03BPjeP1RKsMmUjGqu7WGKo9i0DLSVvgYgYBfpkv3DZ1uV5T+TUA0buJz\nFD6PgxB6VNFra2IWL+2dKLkSli5dmrdtvKXa0tLC/Pnzqf7Gu2j+7wfofWQPOJqyFbNZ8Y+vpnzZ\n7AnPXwh1dXU4r11D5NeHsFNq7K1lCPz15Sy55jxEAffOmUJGvlLG2X4OzzSKJd+MQp8ENTU1k+9U\nZBzY2scdt2yns2WY5p2DOLZCKzjWNsKuJ++joc7LYE8Kj9fETimufudc3vnva9n29CA/+aftxGI2\nr3/Hat7+gTWEQiE239JFal8Mb1rpBgA/Bt04pNAIw1W6hWJ5Vlyy7ccVLKzWLNZwPCWJWG6jOb/U\n1Pkchi1JIk3JW2FqgoCsVYQ+FWfoaQdrZxmJYwb+dOZLypKgwecds5S1x4c3FCTVH81V7F5JzfXL\nuPjii0c3nWrVbOYemmU+zvn8DdR+6FJQmvo5s07+poyDUeZl1beup+NbzzGwrRME1F/exHn/es2L\nqszh7KzRFxulLmOx5JtR6JMgFovllNCfaTxw20Fu+fgmUgknr3ex1kBM05/uHmEn3YyNx3/ezc7d\nx9m6O0wi7roAmvc9zW9u3cX1V9Wz9YcDBJEuu2FWydAcbZCQYJgmKlnIQNak0DhJQdegQXlKErUF\nGeqshBL0JAULAw4eCY4Cy5ZIv+T8j8xnyQ31PNfRxsGHB3FszUg6waS2wsLv06PJ7mbQYP6VjWz4\n/Bqe+9s7sWMptKNQKcVAb4CDXxhEdG7j0s+fj+E1TtlXPP4eCkNS4INkUoy/rr+xkot/+FZ6j/Ui\nBNTPKpwueqbxYq/Rs4FSl7FY8s0o9Ekgx9P6nSZOhp0vGXe45eObSMbyg20A6MJ5plZC07M5iZJj\ntfkyBuzUPLi7F5SgBk0QlwpXoRnSmiiuUvUlBW7/n0xtaMbCFPiR1CqTcMJmWOUHQTXQmzSo8Wrw\nuI0laq7wMLSsg9bWKId/M4STzH1V9A+bxG3NostMvB4PS9+ymHnXziEpYc3P30Tzb3ax/yttRIdM\nbNtdotu/e4C+ljCXf/P8U1boxbqHE57fPLvZv2davqmAUpexWPLNKPRJ4PF4XtDxGUV+MgG9Q1sH\nESe4ryf6kJdastypptkYJIHDPKcciSBdF0QIA5nObOlFkco6YVxrLDSVyLyKUY+A2VIyolw3S/7w\nBKrBQb3xCIFUBXPX1WKGLKqqXP5xOzaYdwQI4pag8v1l1M0PEazxMRAe44NufWqEyKB/dOwATkJx\n5I89dO7rhpUnmIgCGBoaYs6cOad20DTCC12j0wGlLmOx5JtR6JMgGo0WJR3sZIpghueZ6bzuwpiw\nXEWDgUACs1WQYzLmKvMsZP6VwGVSHJc6jq3BBjLLSghNUyhOTdBGa1gNtEcEvSO+vMvPXVvBwsvd\nxhGZaH0mxXLbZX10PjQyjktAM2+Bg/Xlo3QOt9LfWM6Sj59P/UvnuWNsVTnKPAPplRiD+defDCMj\nI6d8zHRCsdboVEapy1gs+WYU+iR4oZN8KsfXXl5LfeM2OluG8hS76QUnCaaWOBnVnsWe5Ulb12W6\n8C0dRFGHIJWTGZ4LG40nrembQnFCAdutxRHpZs/VSaJOurenkug0h/rsc6tYuNBtwjx0OMHgwTgD\n80aofUMt1315LT977i/YIw5OUiM9MKsqSYM3jhp2tXaiM8r+L/wF48smNRfNpmFtiKH9I+hxSl1b\nmkXr5lFed2pl+n6//5T2n24oZUWXQanLWCz5StsxVQR0dHSc0fP39fVx4MABDhw4wMGDB/nwN5ZR\nPcuDxy/w+sH0wJXvrGX9KyuoxuUx92G4FrkGE4GfMQpcG0VSO3lquw+HJBrJxK4bdzFoDKGoCdoY\n41aHKWFhpcUgipW1MeaUJ3GkIhaPo2zN3pvCPPyW/Tz3hXYee+8hvrf+floPtbLgXSazrwhSv9HP\ninfUMXdWApXK1dZOwubQt7fT39/P6g82Yfhyo5ZGwGD+NQ0kvLFTnuMzfQ/PNkpdPih9GYsl34yF\nPgkK5TkXG9kViGYFfOaOZezZ3Ev/sSjL1lUzd2Etv/pMG0ILRFp1ewvljaOx0TTqsnRnIo0wQDtQ\nb0jKpYNfS2wFwyrnQMANljqAz9CjDSjGw29obA2HR0xWV9rYUjB7jY+2XwzS81RuAPTYrmE63hbF\nyOrxueads4hsLuxXSna5yrpiYZCr79jIc1/Yx/EdEYyAZN2HVrD87xpPckZz8WLcw7OJUpcPSl/G\nYsk3o9AnwZ49ezj//PPP2Pkn+tSqqamhtbWV6upqli5dyvL1I7Q/3VVw34w1bqEQCPzp2xoAhBIs\nNSWVUiLTVaKLDeix4VjGSBbuQrC0QCKI24W7DikN4aRr4w+kJIaEeWUWiapjtP/OdQll75up4rdj\nY2d74jNHOX+JibZSeecvW1Q56nuvu6KOFY8vZvPmzQBcfPEFp93H80zfw7ONUpcPSl/GYsk3o9An\nwQud5JMpgqmrq6O39zh2SjF33lixS6bP4J6tR3nsvi5sNCa53OUajYMmicrr9tyESbkQeKQe5aUS\nbu9mZpkQcdxA6Oj5BNhaYyI5EvGxsDo56nZR2uWhOjTshk1N6V7EFJquri6cxGyyPXgTkCSiUNir\n52Du6kBlcaFIn0HdOxblZQNliLheCEpZEUDpywelL2Ox5JtR6JPghRDPJxM2zz1yjKHBIS671sYf\nzJ9ux1b87D/3ct+tLVhJRV2jn498eR2VC4Y5evQofT0Rfvr3MeJDDoaQVGsvGj2agpghrjUQOOlS\nTwnUYVAmDUypMApw2WqgQkJEiZyNmX/1jPhIOZLZlUl8hmYgKTk07CFmSySaBQFXGZvVBqtWreLw\nxhEGnrIKZqeMv3BgVYiFL6/j8I92Y4dTBBdWsORja2H5meERn2mOMP1R6jIWSz7xYnM3nyzWr1+v\nt2zZcraHcdrY/NBRPvvWhwCN1hqt4B++tYFLr8/1A3/n09v582+PkoqPaUKvX/I3N9ehAr10bPfz\n1C9SWAn3PgkNPu0GRS2hsLTDLOnHcsbSGmuFYIEwkEJgSoUp8/nJHQ0dFgwrl3ex0gCP0KQciaMy\nlramzOMQdiCm9ajLZm7AYUWFmwETWhrg8t9fTbQ7wW+veRZrxMaOK7QBiVT+i0R6Be/bfCkVjf68\nr5ZCXzNjLpeLT6tRdiEU4zzZ5yjWuGYwg5OBEGKr1np9od9mLPRJcCpvzsyDPTSQ5NNv/CPJeK65\n+pWPPscPN9VQN8elVo0Opnj010ewkrn+iVRSce/XwlT2BRmwLayshshaQEK4WSxaQyMBTCUx0+p8\nthBUCYEUbk6jEBM1m4Co4/KxLA+qMZcMDkOWojcpMQVUIqmQgpTWYCjqghZlHoVSAr/fxlvhBmfL\n5/h525OXsO+XXRzd1Ev5Ih+DLYqOTRGsmANCI72SNR+cRUXji5tGOGPdTX+UuowzLeheJJyKMs/4\nfx/9dVfBbG+lFA/+8gDXvdtlxus4NILpkXkK3dSCwR6HIa0w0uX+Iq2V1Si3ocArBV4hUdaYH3xM\nmbt+7vFV6RrXyu5Kgh/BkoCDOU7pV3o0lV4LgcBxJBrw2oKkLQkP+zmuxuqE5jUEOW/AIlDjwVfl\nYe2HF7L2w26z5pqaWg7e382fvvocdjLFlf98Hmtfu2LCOdRKo53ceSsGv8WZUARTyRovZUWXQanL\nWCz5ZhT6JNi1axdr1qw5qX0z1aAG/Sg7/3fb0gwcHx5NUzSDDs646KE3HVgUuNFLO+0vH+33iUBp\n1/JeuNCH0wkqzWE+nmnFY4wp6rgDI47AUm5VaEpLfFLjKeCOcV0rEqXHgqgaULiMjIZwK1MtDZ3P\nx7j91c/yuj+syGMZHGwb4YEPP0u010IIuO8tWzn+d0e54HO53VnsuMO2/9hH6+86UZYmtLqSDTev\npnZN1ehcZb8ws3EyivVU7uF0RKnLB6UvY7HkmyksmgTLly8/qf3q6upG/7zihnMwzHw/h9c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"text/plain": [ "<matplotlib.figure.Figure at 0x119181630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for show in ['ssfr', 'MLv', 'Av']:\n", " \n", " fig = plt.figure(figsize=[5,5])\n", " \n", " ax = fig.add_subplot(111)\n", " ax.errorbar(vj, uv, xerr=vjerr, yerr=uverr, color='k', \n", " alpha=0.1, marker='.', capsize=0, linestyle='None')\n", " \n", " if show == 'ssfr':\n", " sc = ax.scatter(vj, uv, c=np.log10(zout['ssfr']), \n", " vmin=-13, vmax=-8.5, zorder=10, cmap='RdYlBu')\n", " label = 'log sSFR'\n", " ticks = np.arange(-13,-8,2)\n", " \n", " elif show == 'MLv':\n", " sc = ax.scatter(vj, uv, c=np.log10(zout['MLv']), \n", " vmin=-1, vmax=1, zorder=10, cmap='magma')\n", " label = r'$\\log\\ M/L_V$'\n", " ticks = np.arange(-1,1.1,1)\n", "\n", " elif show == 'Av':\n", " sc = ax.scatter(vj, uv, c=zout['Av'], vmin=0, \n", " vmax=2.5, zorder=10, cmap='plasma')\n", " label = r'$A_V$'\n", " ticks = np.arange(0,2.1,1)\n", " \n", " # Colorbar\n", " cax = fig.add_axes((0.18, 0.88, 0.2, 0.03))\n", " cb = plt.colorbar(sc, cax=cax, orientation='horizontal')\n", " cb.set_label(label)\n", " cb.set_ticks(ticks)\n", " \n", " ax.set_xlim(-0.3, 2.6)\n", " ax.set_ylim(-0.3, 2.6)\n", " \n", " ax.grid()\n", " \n", " ax.set_xlabel(r'$V-J$'); ax.set_ylabel(r'$U-V$')\n", " ax.set_title('Riverside z=1 sample')\n", " \n", " fig.tight_layout(pad=0.1)\n", " \n", " plt.savefig('Riverside_z1_{0}.pdf'.format(show))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/brammer/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['Courier'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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qoXL9zA4ePFiR88wX1WkX1emeiq4yU+zwkQceCxLMye84H7TX+HFeY9Wp/caK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\nqSChfa6GqDZcSzQUZSiUP2Vmz9iuWbPG2lguUZ1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"text/plain": [ "<matplotlib.figure.Figure at 0x11791dda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Show SED\n", "id_i = ez.cat['id'][4]\n", "fig = ez.show_fit(id_i, show_fnu=0)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/brammer/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['Courier'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "text/plain": [ "(0, 2)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ibbeD60iVkG8/9Roee6XwcKWeh8dgNb5daGQLOhnMYOaKXKKv6QZUXcepkQja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"text/plain": [ "<matplotlib.figure.Figure at 0x11729b6d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# nu-Fnu scaling for far-IR \n", "fig = ez.show_fit(id_i, show_fnu=2)\n", "\n", "fig.axes[0].set_xlim(0.2, 1200)\n", "fig.axes[0].set_ylim(1, 100)\n", "fig.axes[0].loglog()\n", "fig.axes[0].set_xticklabels([0.1, 1, 10, 100, 1000])\n", "fig.axes[1].set_xlim(0, 2)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:anaconda3]", "language": "python", "name": "conda-env-anaconda3-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 }	mit
camillescott/ucd-ecs253	notes.ipynb	1	17126	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import networkx as nx\n", "import pandas as pd\n", "import seaborn as sns\n", "import scipy as sp\n", "import scipy.stats as sps" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns.set_style('white')\n", "sns.set_context('notebook')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lecture 3\n", "### Power laws, Preferential Attachment, and master equations\n", "### April 5, 2016" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Emergenceo of a Giant Component in Erdos-Renyi Random Graphs" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "G = nx.random_graphs.erdos_renyi_graph(100, .1)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def random_graph_maxc(N=100):\n", " P = np.arange(0,1,.001)\n", " graphs = [nx.random_graphs.erdos_renyi_graph(N, p) for p in P]\n", " c_max = [float(len(next(nx.connected_components(g)))) for g in graphs]\n", "\n", " return pd.Series(c_max, index=PN)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "maxc_df = pd.DataFrame([random_graph_maxc() for _ in xrange(10)])" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x153d49c90>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { 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"text/plain": [ "<matplotlib.figure.Figure at 0x15da8a710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "maxc_df.median().plot(logx=True, logy=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Preferential Attachment\n", "\n", " $P_r(t+1 \\rightarrow n_j) = d_j / \\sum_i{}{d_i}$" ] }, { "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": "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 }	cc0-1.0
nfaggian/pyconau	2014/notebooks/Interpolate Point Observations.ipynb	1	685338	{ "metadata": { "name": "", "signature": "sha256:1bb02e4199f779f3b9eeb3baa845572950fbda3daa7de7690a7cd900f8badfd1" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Note:\n", "\n", "sampler.py requirements:\n", " - sklearn (kd-tree)\n", " - cartopy (plotting)\n", " - matplotlib \n", " " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os \n", "import sampler\n", "import numpy as np\n", "\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Retrieve and parse a text product containing observations: \n", "\n", "Retrieve and parse a text product from the Australian Bureau of Meterology.\n", "~~~\n", "ftp://ftp2.bom.gov.au/anon/gen/fwo/IDY03021.txt\n", "~~~" ] }, { "cell_type": "code", "collapsed": false, "input": [ "obs = sampler.dataframe_observations()\n", "print(obs.head())\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " station lat lon day hour vis cld wind_dir wind_mag temp \\\n", "2 Albury AP -36.07 146.95 2 100 10 NaN NE 003 7 \n", "3 Albury AP -36.07 146.95 2 100 NaN NaN NE 003 7 \n", "5 Armidale -30.52 151.67 1 2300 50 NaN SW 006 2 \n", "6 Armidale A -30.53 151.62 2 100 10 NaN S 008 6 \n", "7 Armidale A -30.53 151.62 2 100 NaN NaN S 008 6 \n", "\n", " rh bar rain_mm rain_hr weather tx tn \n", "2 74 1030.0 0.0 9> NaN NaN NaN \n", "3 74 1030.3 NaN NaN NaN NaN NaN \n", "5 67 NaN 0.0 24 Clr 17 -1 \n", "6 45 1023.6 0.0 9> NaN NaN NaN \n", "7 45 1023.4 NaN NaN NaN NaN NaN \n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "sampler.plot_data(obs.lat, obs.lon, obs.temp, overlay=False, S=50)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "<matplotlib.axes.GeoAxesSubplot at 0x112fb0950>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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o9rje3t6ws7PD999/j++//14I47lx4wbq16+Pq1evYtmyZVBSUhIeBIyNjbFkyRIEBQXB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nkf/bEvK6ABERERERERERERERERERERERERERERERERERERERERERERERsUb/D16MZc3j8kxrAAAA\nAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x112fc02d0>" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Form a fast nearest neighbour sampler (kd-tree)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tree = sampler.sample_tree(obs.lat.values, obs.lon.values)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Form a high-resolution lat/lon grid" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lons, lats = np.meshgrid(np.linspace(105.5, 160.4375, 1000), \n", " np.linspace(-49.5, -4.0625, 1000))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate the (whole) temperature grid" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def estimate(tree, obs, coords):\n", " \n", " distance, obs_values, _ = sampler.nearest_neighbour(\n", " tree, \n", " obs.temp.values, \n", " coords[0], \n", " coords[1], \n", " K=10)\n", " \n", " weights = 1. / distance*2\n", " \n", " temp_vector = (np.nansum((weights obs_values), axis=1) / \n", " np.nansum(weights, axis=1))\n", " \n", " return temp_vector, obs_values" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "%%time \n", "\n", "temp_vector, _values = estimate(tree, obs, (lats, lons))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 3.61 s, sys: 297 ms, total: 3.9 s\n", "Wall time: 4.09 s\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "ax = sampler.plot_data(lats, lons, np.reshape(temp_vector, lons.shape))\n", "\n", "sampler.plot_data(obs.lat, obs.lon, obs.temp, S=50, ax=ax, cbar=False)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 22, "text": [ "<matplotlib.axes.GeoAxesSubplot at 0x10d9d2c50>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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RDIS+4NlXD0qJfQDSU0hKTqfNwFV0bePKpZs+3H74iub1y3HvcRAuZa0oaWWM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\nSmEJeBNH+wl78A2KxUBXA4FAQFJKOvWqmdOghjmZmVIWj31fplkikbJ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KpTIrqZmYnMaR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raNqkn1KafwC4liSOOi6OuJ2Hr5Hftvf+TUlAZa5dv/Waya0pzuUw7RsWFxDA1+nyxQsbFx+PiH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GwbNMLib294rfVCAC1gAPgfky+zPJ/H8T4J62vrQ/JQ0x0f/hJJbof/j4HQA9Osf9KDwrFuDf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JK8YNZuLa7dgWy4yPb1vMFZVShblTedKT48hISSTQ5wxRz4JoNPjXHEJZo9Hw+OZl+sxc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\n1mRnpHJx/wrizm4lOzMd34AgoirX5NKpw2RlpKPx8KTtkLGg0ZCVkca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ASUVO\nRK5CYII=\n", "text": [ "<matplotlib.figure.Figure at 0x10a525550>" ] } ], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chunk the input grid" ] }, { "cell_type": "code", "collapsed": false, "input": [ "N = 20\n", "\n", "lat_chunks = np.array_split(lats, N)\n", "lon_chunks = np.array_split(lons, N)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit estimate(tree, obs, (lat_chunks[0], lon_chunks[1]))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 221 ms per loop\n" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.parallel import Client\n", "\n", "c = Client(profile='default')\n", "\n", "direct = c.direct_view()\n", "\n", "balanced = c.load_balanced_view()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make a remote function" ] }, { "cell_type": "code", "collapsed": false, "input": [ "@balanced.remote()\n", "def parallel_estimate(tree, obs, coords):\n", " \n", " import sys; sys.path.append(notebook_path)\n", " import sampler\n", " import numpy as np\n", " \n", " distance, obs_values, _ = sampler.nearest_neighbour(\n", " tree, \n", " obs.temp.values, \n", " coords[0], \n", " coords[1],\n", " K=10)\n", " weights = 1. / distance*2\n", " \n", " temp_vector = (np.nansum((weights obs_values), axis=1) / \n", " np.nansum(weights, axis=1))\n", " \n", " return temp_vector" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Push shared state and process chunks" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "\n", "direct.push({'notebook_path': os.getcwd()})\n", "\n", "parallel_result = [parallel_estimate(tree, obs, x) for x in \n", " zip(lat_chunks, lon_chunks)]\n", "\n", "temp_vector = np.c_[[x.result for x in parallel_result]]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 766 ms, sys: 129 ms, total: 895 ms\n", "Wall time: 2.22 s\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "ax = sampler.plot_data(lats, lons, np.reshape(temp_vector, lons.shape))\n", "\n", "sampler.plot_data(obs.lat, obs.lon, obs.temp, S=50, ax=ax, cbar=False)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "<matplotlib.axes.GeoAxesSubplot at 0x10c2b9650>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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gVLe19G5enrZ1y9CvVYXCNver5o3gt7EwJCwqCblcgYZG8Q89W7nrDhXLWOLlH8k9rzCl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\nSmEJeBNH+wl78A2KxUBXA4FAQFJKOvWqmdOghjmZmVIWj31fplkikbJ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KpTIrqZmYnMaR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raNqkn1KafwC4liSOOi6OuJ2Hr5Hftvf+TUlAZa5dv/Waya0pzuUw7RsWFxDA1+nyxQsbFx+PiH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GwbNMLib294rfVCAC1gAPgfky+zPJ/H8T4J62vrQ/JQ0x0f/hJJbof/j4HQA9Osf9KDwrFuDf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JK8YNZuLa7dgWy4yPb1vMFZVShblTedKT48hISSTQ5wxRz4JoNPjXHEJZo9Hw+OZl+sxc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\n1mRnpHJx/wrizm4lOzMd34AgoirX5NKpw2RlpKPx8KTtkLGg0ZCVkca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"text": [ "<matplotlib.figure.Figure at 0x10c51d1d0>" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 } ], "metadata": {} } ] }	apache-2.0
balouf/INF674	2016-2017/07-Markov-Chains/07-Markov-Chains-TP.ipynb	1	21452	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# INF 674 S7: Markov Chains\n", "\n", "## Céline Comte & Fabien Mathieu\n", "\n", "### 2016-2017" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remark: the number of this Session is 7, and not 6, because the sessions numbers have been renumbered:\n", "- S0 Python Introduction\n", "- S1 Galton-Watson\n", "- S2 Erdös-Rényi\n", "- S3 Competitive Epidemics\n", "- S4 Small-Worlds\n", "- S5 Power Laws\n", "- S6 Distances and Clustering\n", "- S7 Markov Chains\n", "- S8 PageRank on a real dataset\n", "- S9 Navigability" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The objective of this practical is to make you see a Markov chain in action. In particular, you will observe what happens if the conditions of the Perron-Frobenius theorem are not met." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Things to know" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Right stochastic matrix\n", "Let $A = (a_{i,j})$ be a $n\\times n$ matrix. We say that $A$ is a right stochastic matrix if:\n", "- The coefficients of $A$ are non-negative: $\\forall i, j, a_{i,j}\\geq 0$.\n", "- Each row sums to 1: $\\forall i, \\sum_j a_{i,j} = 1$.\n", "\n", "\n", "Homogeneous Markov chain: A right stochastic matrix $A$ can define a Markov Chain that describes the evolution of a distribution over $n$ states as follows: if one is in state $i$ at step $k$ of the process, the probability to be in state $j$ at step $k+1$ is $a_{i,j}$.\n", "\n", "With this notation, the evolution of a Markov chain is easy to study: if $P_k$ is the probability distribution at step $k$ ($P_k\\geq 0, \\sum_{i}P_k[i]=1$), then if have\n", "\n", "$$P_{k+1}=P_k A$$\n", "\n", "irreducibility: Let $A$ be a non-negative matrix $A$ ($\\forall i,j, A[i,j]\\geq 0$). Let $G=(V,E)$ be the oriented graph associated to $A$: $(i,j)\\in E$ if, and only if $A[i,j]>0$. The following propositions are equivalent:\n", "- $A$ is irreducible\n", "- $\\forall i,j, \\exists k>0, A^k[i,j]>0$\n", "- $G$ is strongly connected: $\\forall (i,j)\\in V^2$, there exists an oriented path in $G$ from $i$ to $j$.\n", "Intuitively, the irreducibility property indicates that starting from any state, any state can be reached with a positive (e.g. >0) probability after some steps.\n", "\n", "aperiodicity: (from wikipedia) For an index $i$, the period of $i$ is the greatest common divisor of all natural numbers $k$ such that $A^k[i,i]>0$. When $A$ is irreducible, the period of every index is the same and is called the period of $A$. \n", "\n", "If the period is 1, $A$ is aperiodic.\n", "\n", "Intuitively, a period $k>1$ indicates that the length of any cycle must be a multiple of $k$.\n", "\n", "\n", "Perron-Frobenius Theorem (a variant, actually)\n", "If $A$ is right stochastic, irreducible and aperiodic, then $A^k\\xrightarrow[k\\to \\infty]{} B$, where $B$ is the right stochastic matrix having all its rows equal to the same row vector $Q$ defined as the unique normalized solution to the equation $QA = Q$.\n", "\n", "Interpretation: when the condition of the Perron-Frobenius theorem are met, the process will eventually converge to a unique distribution, which does not depend of its initial state, which is forgotten." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Markov chains animations " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To start with, a small experiment that plays with the assumptions of Perron-Frobenius theorem.\n", "\n", "Consider the following game: you have a circular game board made of $ n = 36 $ squares numbered from $ 0 $ to $ 35 $. At (discrete) turn $ t=0 $, a player stands on square $ 0 $. Between two turns, the player moves from a certain number of squares. Remember that the board is circular: if the player is in square $35$ and moves one square forward, she lands in square $0$. We propose to visualize the evolution of the (probabilistic) position of the player on the board." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We give you the code to visualize the game where at each turn, the player moves one square forward." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "First, evaluate the following cells. Note that you should install ffmpeg if it is not available in your system (https://ffmpeg.org/ ). If you are on a TPT machine, ffmpeg is not available, use the writer avconv instead." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from matplotlib import animation, rc\n", "from IPython.display import HTML\n", "\n", "# use writer = 'avconv' on TPT computers\n", "rc('animation', html='html5', writer = 'ffmpeg')\n", "\n", "xkcd()\n", "\n", "n = 36" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remark: the use of xkcd() is just to have funny movies. You can remove it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following function will be used to display the evolution of a distribution. It takes two arguments:\n", "- next_step is a function that takes a distribution as input and ouputs the resulting distribution after one step of the Markov process.\n", "- k_max indicates the number of steps you want to watch." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def evolution(next_step, k_max):\n", " # Turn interactive plotting off\n", " fig, ax = subplots()\n", " # Initiate probability distribution: the position of the player is known.\n", " P = zeros(n)\n", " P[0] = 1\n", " # Display probability\n", " pbar = ax.bar(range(n),P,1)\n", " xlim([0,n])\n", " #Init only required for blitting to give a clean slate.\n", " def init():\n", " for rect, y in zip(pbar, P):\n", " rect.set_height(y) \n", " return pbar\n", " def animate(i):\n", " P[:] = next_step(P) # Update the values using the next_step function\n", " for rect, y in zip(pbar, P):\n", " rect.set_height(y) \n", " return pbar\n", " ani = animation.FuncAnimation(fig, animate, frames = k_max, init_func=init,\n", " interval=25, blit=True)\n", " return ani" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The rule for the toy example is to move one case forward. This can be easily done with the np.roll function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def next_step(P):\n", " # Roll, baby!\n", " return roll(P, 1)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now call evolution. The %%capture command hides a static picture that would be shown otherwise." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%capture\n", "ani = evolution(next_step,180)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And... Showtime (can take a few seconds to initiate, depending on the tmax you set)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ani" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are the condition of the Perron-Frobenius met? Justify your answers:\n", "- using the theoretical definitions\n", "- by commenting the animation above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now change the rules. At each turn, the player tosses an unbiased coin. If it is a head, she moves forward $ a = 1 $ step. If it is a tail, she moves forward $ b = 2 $ steps." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we are now playing with more general Markov chains, roll may not be enough. You may want to use dot(P, A), which multiplies a vector P and a matrix A" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def next_stepA(P):\n", " return dot(P, A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize the evolution of the position of the player. Comment what you observe." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New rules. We assume now $ a = 1 $, $ b = i^2 $, where $ i $ is the number of the square where the player stands." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize the evolution of the position of the player. Comment what you observe." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is convergence. Note compared to previous case that:\n", "- It is much faster (if you want to go deeper in that direction, the reason is that the spectral gap is greater with the new rules).\n", "- The asymptotic distribution is very heterogeneous." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New rules. We assume now $ a = 1 $, $ b = 7 $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize the evolution of the position of the player. Comment what you observe." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have not the aperiodicity condition. In fact it is quite easy to see that the period of the graph is 6: consider a loop. It is mad of a certain number of $a$ jumps, say $i$, and a certain number of $b$, say $j$. The fact that it is a loop means that $i a + j b = 36 k$ for some $k$, i.e. $i+7j = 36k$. Hence we have\n", "$$(i+7j)\\%6 = (i+j)\\%6 = 36k\\%6 = 0$$\n", "\n", "In other words, $i+j$, the length of the loop, is a multiple of 6.\n", "\n", "This cycle of 6 is indeed easy to see on the animation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New rules. We assume now $ a = 2 $, $ b = 4 $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize the evolution of the position of the player. Comment what you observe." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is like question 2 except that only even squares are covered.\n", "- Regarding Perron-Frobenius, the irreducible property is lost. you have two strongly connected components: even squares and odd squares (there are indeed disconnected from each other).\n", "- The practical effect of loosing irreducibility is that you can have states with 0 probability (the odd squares), whereas when all conditions of Perron-Frobenius are met, all states have a positive (e.g. >0) probability in the steady state distribution.\n", "- While $2 \\wedge 4 = 2$, 2 is NOT a period of the graph (the graph is actually aperiodic on both of its strongly connected components)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With probability $ 1/5 $, the coin lands on its edge (it is a very thick coin). Default procedure: toss again until is does not land on its edge. Exception: if it lands on its edge while the player stands on square 35, she realizes that the game is pointless and quits the game. Redo questions 2 to 5 with this new rule." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A remark first: many of you have assumed that if you toss an edge on a different square than 35, you have to skip a square. What was intended for squares less than 35 was that a step was the action of tossing until you have head or tail, then move. Indeed the problem was probably not enough precise on that point, so it is OK if you implemented loops for squares less than 35." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# PageRank on a small graph (if time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal of this last part is to test a few variants of PageRank on small graphs and see how they differ.\n", "\n", "It is entirely optional, but it can help you before computing PageRank on bigger graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let $ h $ be an integer (use $ h $ = 3 for toy evaluation, $ h = 9 $ for complete evaluation).\n", "\n", "Let $ G $ be the following graph:\n", "- There are $ n = 2^{h+1}-1 $ nodes numbered from $0$ to $ 2^{h+1}-2 $.\n", "- The oriented edges are as follows:\n", " - For all nodes $ i\\geq 1 $, there is an edge from $ i $ to $ \\lfloor (i-1) / 2 \\rfloor $.\n", " - For all nodes $ i\\geq 1 $, there is an edge from $ i $ to $ i - 1 $.\n", " - For all nodes $j$ such that $ j \\in [2^h-1, 2^{h+1}-2] $, there is an edge from $0$ to $j$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build the stochastic matrix $A$ associated to $G$, defined by $ A(i,j) = 1/deg_+(i)$ if there is an edge from $ i $ to $ j $, 0 otherwise. ($deg_+$: outdegree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try to guess which nodes of the graph are important. Justify your answer (e.g. copying/pasting the answer from next question is not enough)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the PageRank $P$ which is a solution of $ P = P A $. Proceed iteratively starting from $ P = \\mathbf{1}/n $ and updating $ P \\leftarrow P A $ until $ \|\|P-PA\|\|_1<\\epsilon $ (recommendation: $ \\epsilon = 10^{-8} $). Display for the $10$ first iterations the current top ten (the ten nodes that have highest value in current $P$), the total number of iterations and the final top ten.\n", "\n", "Configure a maximal number of authorized iterations to avoid infinite loops (recommendation: 2000 iterations). When reaching the maximal number of iterations, your function should display a message saying the process has not converged and give the past top ten obtained." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe the expected ranking." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We add to $b$ new nodes to $G$ ($ b = 10 $) numbered from $ n $ to $ n+b-1 $. For each new node $ i $, we add an edge from $ i-1 $ to $i$.\n", "- Do Questions 1, 2, 3 over with this new graph. What happens (in theory and in practice)?\n", "- Use $ P \\leftarrow P A / \|\|PA\|\|_1 $ for updating $P$. What happens?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let us do the modification, then compute the PageRank." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems the convergence cannot be observed. The reason is that we introduced a leak on the last node. In theory, $P$ should converge towards 0, but\n", "- the leak is big enough to prevent $\|\|P-PA\|\|<\\epsilon$ when $P$ stays large enough\n", "- the leak is small enough so that $P$ stays large enough for a long time\n", "\n", "However, notice that the ranking after the maximum allowed number of iterations seems to be correct.\n", "\n", "Now, change the algorithm so that $P$ stays normalized." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Convergence works again! (with a similar number of iterations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We add one edge to the previous graph, from $ n+b-1 $ to $ n+b-2 $.\n", "- Do Questions 1, 2, 3 over with this new graph, along with the update proposed in Question 4. What happens (in theory and in practice)?\n", "- Use $ P \\leftarrow d P A + (1-d)\\mathbf{1} $ for updating $P$, wirh $d = 0.85$. What happens?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, change the graph by adding one last edge." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- No convergence.\n", "- In theory, the nodes 1031 and 1032 form a probability trap: it is a strongly connected component that can be reached from any other node and from which you cannot exit. So they should be ranked first, with all other nodes ranked last.\n", "- Yet, the returned ranking is [0, 1031, 1032, ...]: 1031 and 1032 did not have enough time to converge.\n", "- Remark that normalizing does not change anything: there is no leak to compensate for.\n", "- It is not very satisfying that one single edge completely changes the ranking\n", "- Now, let see what happens with damping." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Faster convergence\n", "- The ranking [0, 1, 2, ...] is returned: the effect of local traps is ignored." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Question 6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare and discuss the convergence and rankings you observed in this practical." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See the markdown above." ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
arne-cl/alt-mulig	python/rstdt-batch-tokenization.ipynb	1	18164	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tokenization of RST-DT files using off-the-shelf tokenizers\n", "\n", "* CoreNLP: failed\n", "* nltk's TreebankWordTokenizer: failed, but might be adaptable\n", "* let's try the preprocessing provided by the [Educational Testing Service](https://github.com/EducationalTestingService)'s [RST discourse parser](https://github.com/EducationalTestingService/discourse-parsing), \n", " cf. [rstdt-fixing-tokenization.ipynb](rstdt-fixing-tokenization.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ImportError", "evalue": "No module named stanford_corenlp_pywrapper", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-4-b2b6b3b69279>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mstanford_corenlp_pywrapper\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msockwrap\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mCORENLP_PYWRAPPER_DIR\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpanduser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'~/repos/stanford_corenlp_pywrapper'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m jars = (\"stanford-corenlp-full-2014-08-27/stanford-corenlp-3.4.1.jar\",\n\u001b[0;32m 5\u001b[0m \"stanford-corenlp-full-2014-08-27/stanford-corenlp-3.4.1-models.jar\")\n", "\u001b[1;31mImportError\u001b[0m: No module named stanford_corenlp_pywrapper" ] } ], "source": [ "from stanford_corenlp_pywrapper import sockwrap\n", "\n", "CORENLP_PYWRAPPER_DIR = os.path.expanduser('~/repos/stanford_corenlp_pywrapper')\n", "jars = (\"stanford-corenlp-full-2014-08-27/stanford-corenlp-3.4.1.jar\",\n", " \"stanford-corenlp-full-2014-08-27/stanford-corenlp-3.4.1-models.jar\")\n", "\n", "p=sockwrap.SockWrap(\"pos\",\n", " corenlp_jars=[os.path.join(CORENLP_PYWRAPPER_DIR, jar) for jar in jars])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Couldn't import dot_parser, loading of dot files will not be possible.\n" ] } ], "source": [ "import re\n", "import discoursegraphs as dg\n", "\n", "# a string enclosed in '_!', possibly with '<P>' before the closing '_!' \n", "RST_DIS_TEXT_REGEX = re.compile(\"_!(.?)(\\<P\\>)?_!\", re.DOTALL)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'p' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-7-4ec65294dfcd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcorenlp_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_doc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\"\"that its money would be better spent \"in areas such as research\" and development.\"\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m' '\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtok\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msent\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcorenlp_result\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sentences'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtok\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msent\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'tokens'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'p' is not defined" ] } ], "source": [ "corenlp_result = p.parse_doc(\"\"\"that its money would be better spent \"in areas such as research\" and development.\"\"\")\n", "\n", "print ' '.join(tok for sent in corenlp_result['sentences'] for tok in sent['tokens'])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "import glob\n", "import os\n", "import codecs\n", "\n", "RSTDT_MAIN_ROOT = os.path.expanduser('~/repos/rst_discourse_treebank/data/RSTtrees-WSJ-main-1.0')\n", "RSTDT_TOKENIZED_ROOT = os.path.expanduser('~/repos/rst_discourse_treebank/data/RSTtrees-WSJ-main-1.0-tokenized')\n", "\n", "RSTDT_TEST_FILE = os.path.join(RSTDT_MAIN_ROOT, 'TEST', 'wsj_1306.out.dis')\n", "\n", "def tokenize_rst_file(rst_input_path, rst_output_path):\n", "# edus = {}\n", " with open(rst_input_path, 'r') as rstfile, codecs.open(rst_output_path, 'w', encoding='utf-8') as outfile:\n", " rstfile_str = rstfile.read()\n", " input_file_onset = 0\n", " edu_matches = RST_DIS_TEXT_REGEX.finditer(rstfile_str)\n", "\n", " for edu in edu_matches:\n", " doc_onset = edu.start()\n", " doc_offset = edu.end()\n", " doc_untokenized_str = edu.groups()[0]\n", " corenlp_result = p.parse_doc(doc_untokenized_str)\n", " corenlp_tokenized_str = u' '.join(tok for sent in corenlp_result['sentences'] for tok in sent['tokens'])\n", " outfile.write(rstfile_str[input_file_onset:doc_onset])\n", " outfile.write(u'\"{}\"'.format(corenlp_tokenized_str))\n", " input_file_onset = doc_offset\n", " outfile.write(rstfile_str[input_file_onset:])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "global name 'p' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-9-6d2e76672065>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# print f.read()[325]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mtokenize_rst_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRSTDT_TEST_FILE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'/tmp/1306.dis'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-8-41190de0ed14>\u001b[0m in \u001b[0;36mtokenize_rst_file\u001b[1;34m(rst_input_path, rst_output_path)\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[0mdoc_offset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0medu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[0mdoc_untokenized_str\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0medu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroups\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mcorenlp_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_doc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdoc_untokenized_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 23\u001b[0m \u001b[0mcorenlp_tokenized_str\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mu' '\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtok\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msent\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcorenlp_result\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sentences'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtok\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msent\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'tokens'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[0moutfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrstfile_str\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minput_file_onset\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mdoc_onset\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: global name 'p' is not defined" ] } ], "source": [ "# with open(RSTDT_TEST_FILE, 'r') as f:\n", "# print f.read()[325]\n", "\n", "tokenize_rst_file(RSTDT_TEST_FILE, '/tmp/1306.dis')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%time\n", "for folder in ('TEST', 'TRAINING'):\n", " for rst_fpath in glob.glob(os.path.join(RSTDT_MAIN_ROOT, folder, '.dis')):\n", " out_fpath = os.path.join(RSTDT_TOKENIZED_ROOT, folder, os.path.basename(rst_fpath))\n", " out_dir, _fname = os.path.split(out_fpath)\n", " dg.util.create_dir(out_dir)\n", " tokenize_rst_file(rst_fpath, out_fpath)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# tokenize using nltk.tokenize.treebank.TreebankWordTokenizer" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from nltk.tokenize.treebank import TreebankWordTokenizer" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['that',\n", " 'its',\n", " 'money',\n", " 'would',\n", " 'be',\n", " 'better',\n", " 'spent',\n", " '``',\n", " 'in',\n", " 'areas',\n", " 'such',\n", " 'as',\n", " 'research',\n", " \"''\",\n", " 'and',\n", " 'development',\n", " '.']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenizer = TreebankWordTokenizer()\n", "tokenizer.tokenize(\"\"\"that its money would be better spent \"in areas such as research\" and development.\"\"\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import re\n", "\n", "ENDS_WITH_COMMA = re.compile('(.),$')\n", "ENDS_WITH_PUNCTUATION = re.compile('(.)(,\|.\|!\|:\|;)$')\n", "\n", "foo = \"Cummins Engine Co. , Columbus , Ind.,\"\n", "bar = ENDS_WITH_COMMA.sub(r'\\1 ,', foo)\n", "\n", "BRACKETS = {\n", " '(': '-LRB-', # round brackets\n", " ')': '-RRB-',\n", " '[': '-LSB-', # square brackets\n", " ']': '-RSB-',\n", " '{': '-LCB-', # curly brackets\n", " '}': '-RCB-'\n", "}\n", "\n", "def fix_tokenized_sentence(tokenized_sentence):\n", " # If an EDU ends with a comma, we'll have to tokenize it,\n", " # e.g. \"when it ends,\" -> \"when it ends ,\"\n", " tokenized_sentence[-1] = ENDS_WITH_PUNCTUATION.sub(r'\\1 \\2', tokenized_sentence[-1])\n", " for i, token in enumerate(tokenized_sentence):\n", " if token in BRACKETS:\n", " tokenized_sentence[i] = BRACKETS[token]\n", " return tokenized_sentence" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('Cummins Engine Co. , Columbus , Ind.', ',')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ENDS_WITH_PUNCTUATION = re.compile('(.)(,\|\\.\|!\|:\|;)$')\n", "\n", "ENDS_WITH_PUNCTUATION.match(foo).groups()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from nltk.tokenize.treebank import TreebankWordTokenizer\n", "from nltk.tokenize import sent_tokenize\n", "\n", "TOKENIZER = TreebankWordTokenizer()\n", "\n", "def tokenize_rst_file_with_nltk(rst_input_path, rst_output_path, tokenizer):\n", "# edus = {}\n", " with open(rst_input_path, 'r') as rstfile, codecs.open(rst_output_path, 'w', encoding='utf-8') as outfile:\n", " rstfile_str = rstfile.read()\n", " input_file_onset = 0\n", " edu_matches = RST_DIS_TEXT_REGEX.finditer(rstfile_str)\n", "\n", " for edu in edu_matches:\n", " doc_onset = edu.start()\n", " doc_offset = edu.end()\n", " doc_untokenized_str = edu.groups()[0]\n", " untokenized_sents = sent_tokenize(doc_untokenized_str)\n", " tokenized_sents = tokenizer.tokenize_sents(untokenized_sents)\n", " fixed_tokenized_sents = [fix_tokenized_sentence(sent) for sent in tokenized_sents]\n", " tokenized_str = u' '.join(tok for sent in fixed_tokenized_sents for tok in sent)\n", "\n", " outfile.write(rstfile_str[input_file_onset:doc_onset])\n", " outfile.write(u'\"{}\"'.format(tokenized_str))\n", " input_file_onset = doc_offset\n", " outfile.write(rstfile_str[input_file_onset:])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.18 s, sys: 864 ms, total: 4.04 s\n", "Wall time: 4.05 s\n" ] } ], "source": [ "%%time\n", "\n", "RSTDT_NLTK_TOKENIZED_ROOT = os.path.expanduser('~/repos/rst_discourse_treebank/data/RSTtrees-WSJ-main-1.0-nltk-tokenized')\n", "\n", "for folder in ('TEST', 'TRAINING'):\n", " for rst_fpath in glob.glob(os.path.join(RSTDT_MAIN_ROOT, folder, '.dis')):\n", " out_fpath = os.path.join(RSTDT_NLTK_TOKENIZED_ROOT, folder, os.path.basename(rst_fpath))\n", " out_dir, _fname = os.path.split(out_fpath)\n", " dg.util.create_dir(out_dir)\n", " tokenize_rst_file_with_nltk(rst_fpath, out_fpath, TOKENIZER)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['on',\n", " 'Monday',\n", " 'the',\n", " 'small',\n", " '(',\n", " 'investors',\n", " ')',\n", " 'are',\n", " 'going',\n", " 'to',\n", " 'panic',\n", " 'and',\n", " 'sell']" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TOKENIZER.tokenize(\"on Monday the small ( investors ) are going to panic and sell\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Treebank tokenizer uses regular expressions to tokenize text as in Penn Treebank. \n", "This is the method that is invoked by word_tokenize(). \n", "It assumes that the text has already been segmented into sentences, e.g. using sent_tokenize()." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from nltk.tokenize import sent_tokenize" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sents = sent_tokenize(\"a tree. You are a ball.\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'a tree . You are a ball .'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokenized_sents = TOKENIZER.tokenize_sents(sents)\n", "u' '.join(tok for sent in tokenized_sents for tok in sent)" ] }, { "cell_type": "code", "execution_count": 19, "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": "2.7.5+" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
GoogleCloudPlatform/practical-ml-vision-book	09_deploying/09e_tflite.ipynb	1	9477	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 72 }, "id": "hiQ6zAoYhyaA", "outputId": "0acee878-1207-42c3-9bee-a594acd44365" }, "outputs": [ { "data": { "text/markdown": [ "\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://console.cloud.google.com/ai-platform/notebooks/deploy-notebook?name=Edge ML with TensorFlow Lite&url=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fpractical-ml-vision-book%2Fblob%2Fmaster%2F09_deploying%2F09e_tflite.ipynb&download_url=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fpractical-ml-vision-book%2Fraw%2Fmaster%2F09_deploying%2F09e_tflite.ipynb\">\n", " <img src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/practical-ml-vision-book/master/logo-cloud.png\"/> Run in AI Platform Notebook</a>\n", " </td>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/GoogleCloudPlatform/practical-ml-vision-book/blob/master/09_deploying/09e_tflite.ipynb\">\n", " <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/09_deploying/09e_tflite.ipynb\">\n", " <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://raw.githubusercontent.com/GoogleCloudPlatform/practical-ml-vision-book/master/09_deploying/09e_tflite.ipynb\">\n", " <img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", " </td>\n", "</table>\n" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Markdown as md\n", "\n", "### change to reflect your notebook\n", "_nb_loc = \"09_deploying/09e_tflite.ipynb\"\n", "_nb_title = \"Edge ML with TensorFlow Lite\"\n", "\n", "### no need to change any of this\n", "_nb_safeloc = _nb_loc.replace('/', '%2F')\n", "md(\"\"\"\n", "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://console.cloud.google.com/ai-platform/notebooks/deploy-notebook?name={1}&url=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fpractical-ml-vision-book%2Fblob%2Fmaster%2F{2}&download_url=https%3A%2F%2Fgithub.com%2FGoogleCloudPlatform%2Fpractical-ml-vision-book%2Fraw%2Fmaster%2F{2}\">\n", " <img src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/practical-ml-vision-book/master/logo-cloud.png\"/> Run in AI Platform Notebook</a>\n", " </td>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/GoogleCloudPlatform/practical-ml-vision-book/blob/master/{0}\">\n", " <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/GoogleCloudPlatform/practical-ml-vision-book/blob/master/{0}\">\n", " <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://raw.githubusercontent.com/GoogleCloudPlatform/practical-ml-vision-book/master/{0}\">\n", " <img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", " </td>\n", "</table>\n", "\"\"\".format(_nb_loc, _nb_title, _nb_safeloc))" ] }, { "cell_type": "markdown", "metadata": { "id": "a8HQYsAtC0Fv" }, "source": [ "# Edge ML with TensorFlow Lite\n", "\n", "In this notebook, we convert the saved model into a TensorFlow Lite model\n", "so that we can run it on Edge devices.\n", "\n", "In order to do edge inference, we need to handle raw image data from the camera\n", "and process a single image (not a batch of images)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "import os, shutil\n", "\n", "MODEL_LOCATION='export/flowers_model3' # will be created\n", "# load from checkpoint and export a model that has desired signature\n", "CHECK_POINT_DIR='gs://practical-ml-vision-book/flowers_5_trained/chkpts'\n", "model = tf.keras.models.load_model(CHECK_POINT_DIR)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n", "INFO:tensorflow:Assets written to: export/flowers_model3/assets\n" ] } ], "source": [ "IMG_HEIGHT = 345\n", "IMG_WIDTH = 345\n", "IMG_CHANNELS = 3\n", "CLASS_NAMES = 'daisy dandelion roses sunflowers tulips'.split()\n", " \n", "# a single image of any size\n", "@tf.function(input_signature=[tf.TensorSpec([None, None, 3], dtype=tf.float32)])\n", "def predict_flower_type(img):\n", " img = tf.image.resize_with_pad(img, IMG_HEIGHT, IMG_WIDTH)\n", " batch_pred = model(tf.expand_dims(img, axis=0))\n", " top_prob = tf.math.reduce_max(batch_pred, axis=[1])\n", " pred_label_index = tf.math.argmax(batch_pred, axis=1)\n", " pred_label = tf.gather(tf.convert_to_tensor(CLASS_NAMES), pred_label_index)\n", " return {\n", " 'probability': tf.squeeze(top_prob, axis=0),\n", " 'flower_type': tf.squeeze(pred_label, axis=0)\n", " }\n", "\n", "shutil.rmtree('export', ignore_errors=True)\n", "os.mkdir('export')\n", "\n", "\n", "model.save(MODEL_LOCATION,\n", " signatures={\n", " 'serving_default': predict_flower_type\n", " })" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert to TFLite\n", "\n", "This will take a while to do the conversion" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "converter = tf.lite.TFLiteConverter.from_saved_model(MODEL_LOCATION)\n", "tflite_model = converter.convert()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "with open('export/model.tflite', 'wb') as ofp:\n", " ofp.write(tflite_model)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 jupyter jupyter 8.8M Jan 26 05:31 export/model.tflite\n" ] } ], "source": [ "!ls -lh export/model.tflite" ] }, { "cell_type": "markdown", "metadata": { "id": "Duu8mX3iXANE" }, "source": [ "## License\n", "Copyright 2020 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "5UOm2etrwYCs" ], "name": "03a_transfer_learning.ipynb", "provenance": [], "toc_visible": true }, "environment": { "name": "tf2-2-3-gpu.2-3.m59", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf2-2-3-gpu.2-3:m59" }, "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": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
smcl/ProjectEulerJupyter	Problem 021 - Amicable numbers.ipynb	1	1844	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Let d(n) be defined as the sum of proper divisors of n (numbers less than n which divide evenly into n).\n", "If d(a) = b and d(b) = a, where a ≠ b, then a and b are an amicable pair and each of a and b are called amicable numbers.\n", "\n", "For example, the proper divisors of 220 are 1, 2, 4, 5, 10, 11, 20, 22, 44, 55 and 110; therefore d(220) = 284. The proper divisors of 284 are 1, 2, 4, 71 and 142; so d(284) = 220.\n", "\n", "Evaluate the sum of all the amicable numbers under 10000.\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "31626" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "let isDivisor x y = x % y = 0\n", "\n", "let d n = \n", " [1 .. (n-1)] \n", " \|> List.filter (isDivisor n)\n", " \|> List.sum \n", "\n", "let isAmicable n = \n", " let d_n = d n\n", " let d_d_n = (d(d_n))\n", " (n = d_d_n) && (n <> d_n)\n", "\n", "[1..10000]\n", "\|> List.filter isAmicable\n", "\|> List.sum" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "F#", "language": "fsharp", "name": "ifsharp" }, "language": "fsharp", "language_info": { "codemirror_mode": "", "file_extension": ".fs", "mimetype": "text/x-fsharp", "name": "fsharp", "nbconvert_exporter": "", "pygments_lexer": "", "version": "4.3.1.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
Computing4Physics/C4P	Resources/Schedule/DayByDay/Day06.ipynb	1	3826	{ "metadata": { "name": "", "signature": "sha256:e1bf40ce931238f649a7748bf642cc0b7c5c63608c2488278ef9598ae6ebf279" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "<img src=\"../../../C4PLogo.png\" width=300 style=\"display: inline;\"> Day 6 - Thursday, April 17, 2014" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summary: Self-guided tour of Data I/O followed by self-guided tour of Counting Stars lesson from Software Carpentry." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`1.` Open terminal, create new directories for DataIO and CountingStars work, practice workflow:\n", "\n", "```bash\n", "$ cd PHYS202-S14\n", "$ git pull origin master\n", "$ git status\n", "$ mkdir DataIO\n", "$ mkdir CountingStars\n", "$ ipynb\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`2.` Navigate to the `DataIO` directory and create a new notebook called `DataIOTour` with title cell (Heading 1) Data I/O Tour." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`3.` Open \"DataIO Intro\" webpage on PolyLearn in one Browser Window. Follow the self-guided tour, entering code into your `DataIOTour` notebook as you go. Add markdown cells with your notes, if you like. \n", "\n", "<b><font color=\"red\">No copy/paste!</font></b>\n", "\n", "Type the commands yourself to get the practice doing it. This will also slow you down so you can think about the commands and what they are doing as you type them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`4.` Before starting the exercise for the Data I/O notebook, switch to the \"Counting Stars\" lesson on PolyLearn. It is a separate notebook on data I/O with image files that builds on the \"Data I/O Tour\". \n", "\n", " * Navigate to the `CountingStars` directory and create a new notebook called `CountingStarsTour` for the self-guided tour. Enter code into the notebook as you go, adding markdown cells with your notes, if you like." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`5.` At the end of the two self-guided tours create new notebooks for the exercises. Open those pages on PolyLearn and complete them in the appropriate notebooks. \n", "\n", "\n", "```\n", "```\n", "<font color=\"red\"><b>They will be due as HW by Wednesday, April 23, 2014 at 11:59pm.</b></font>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`6.` Before leaving, save your work in the notebook, then practice your workflow:\n", "\n", "```bash\n", "$ git status\n", "$ git add .\n", "$ git commit -m \"<summary message goes here>\"\n", "$ git push origin master\n", "$ git status\n", "```\n", "\n", "Check github to be sure the work got there so you have it for the weekend." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"../../../C4PLogo.png\" width=200 style=\"display: inline;\"> All content is under a modified MIT License, and can be freely used and adapted. See the full license text [here](../../../LICENSE)." ] } ], "metadata": {} } ] }	mit
koulakis/amazon-review-qa-analysis	modules/notebooks/Summarize reviews.ipynb	1	35843	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Summarize the reviews" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "all_reviews = (spark\n", " .read\n", " .json('../../data/raw_data/reviews_Home_and_Kitchen_5.json.gz'))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.sql.functions import col, expr, udf, trim\n", "from pyspark.sql.types import IntegerType\n", "import re\n", "\n", "remove_punctuation = udf(lambda line: re.sub('[^A-Za-z\\s]', '', line))\n", "make_binary = udf(lambda rating: 0 if rating in [1, 2] else 1, IntegerType())\n", "\n", "reviews = (all_reviews\n", " .na.fill({ 'reviewerName': 'Unknown' })\n", " .filter(col('overall').isin([1, 2, 5]))\n", " .withColumn('label', make_binary(col('overall')))\n", " .select(col('label').cast('int'), remove_punctuation('summary').alias('summary'))\n", " .filter(trim(col('summary')) != ''))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Splitting data and balancing skewness" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train, test = reviews.randomSplit([.8, .2], seed=5436L)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def multiply_dataset(dataset, n):\n", " return dataset if n <= 1 else dataset.union(multiply_dataset(dataset, n - 1))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reviews_good = train.filter('label == 1')\n", "reviews_bad = train.filter('label == 0')\n", "\n", "reviews_bad_multiplied = multiply_dataset(reviews_bad, reviews_good.count() / reviews_bad.count())\n", "\n", "\n", "train_reviews = reviews_bad_multiplied.union(reviews_good)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Benchmark: predict by distribution" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Always predicting 5 stars accuracy: 0.87139780791\n" ] } ], "source": [ "accuracy = reviews_good.count() / float(train.count())\n", "print('Always predicting 5 stars accuracy: {0}'.format(accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learning pipeline" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.ml.feature import Tokenizer, HashingTF, IDF, StopWordsRemover\n", "from pyspark.ml.pipeline import Pipeline\n", "from pyspark.ml.classification import LogisticRegression\n", "\n", "tokenizer = Tokenizer(inputCol='summary', outputCol='words')\n", "\n", "pipeline = Pipeline(stages=[\n", " tokenizer, \n", " StopWordsRemover(inputCol='words', outputCol='filtered_words'),\n", " HashingTF(inputCol='filtered_words', outputCol='rawFeatures', numFeatures=120000),\n", " IDF(inputCol='rawFeatures', outputCol='features'),\n", " LogisticRegression(regParam=.3, elasticNetParam=.01)\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing the model accuracy" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = pipeline.fit(train_reviews)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9168045600888572" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n", "\n", "prediction = model.transform(test)\n", "BinaryClassificationEvaluator().evaluate(prediction)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using model to extract the most predictive words" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.sql.functions import explode\n", "import pyspark.sql.functions as F\n", "from pyspark.sql.types import FloatType\n", "\n", "words = (tokenizer\n", " .transform(reviews)\n", " .select(explode(col('words')).alias('summary')))\n", "\n", "predictors = (model\n", " .transform(words)\n", " .select(col('summary').alias('word'), 'probability'))\n", "\n", "first = udf(lambda x: x[0].item(), FloatType())\n", "second = udf(lambda x: x[1].item(), FloatType())\n", "\n", "predictive_words = (predictors\n", " .select(\n", " 'word', \n", " second(col('probability')).alias('positive'), \n", " first(col('probability')).alias('negative'))\n", " .groupBy('word')\n", " .agg(\n", " F.max('positive').alias('positive'),\n", " F.max('negative').alias('negative')))\n", "\n", "positive_predictive_words = (predictive_words\n", " .select(col('word').alias('positive_word'), col('positive').alias('pos_prob'))\n", " .sort('pos_prob', ascending=False))\n", "\n", "negative_predictive_words = (predictive_words\n", " .select(col('word').alias('negative_word'), col('negative').alias('neg_prob'))\n", " .sort('neg_prob', ascending=False))" ] }, { "cell_type": "code", "execution_count": 12, "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>positive_word</th>\n", " <th>pos_prob</th>\n", " <th>negative_word</th>\n", " <th>neg_prob</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>toxic</td>\n", " <td>0.702056</td>\n", " <td>worst</td>\n", " <td>0.693118</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>perfect</td>\n", " <td>0.702056</td>\n", " <td>za</td>\n", " <td>0.681185</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>excellent</td>\n", " <td>0.698975</td>\n", " <td>disappointed</td>\n", " <td>0.681185</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>awesome</td>\n", " <td>0.695059</td>\n", " <td>disappointing</td>\n", " <td>0.677256</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>fantastic</td>\n", " <td>0.690034</td>\n", " <td>disappointment</td>\n", " <td>0.669026</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>dalla</td>\n", " <td>0.689843</td>\n", " <td>terrible</td>\n", " <td>0.667437</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>amazing</td>\n", " <td>0.689843</td>\n", " <td>poor</td>\n", " <td>0.667185</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>wonderful</td>\n", " <td>0.687460</td>\n", " <td>useless</td>\n", " <td>0.660330</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>five</td>\n", " <td>0.683558</td>\n", " <td>bango</td>\n", " <td>0.660330</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>fabulous</td>\n", " <td>0.678689</td>\n", " <td>worthless</td>\n", " <td>0.658721</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>bailey</td>\n", " <td>0.677647</td>\n", " <td>gingergarlic</td>\n", " <td>0.658486</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>handy</td>\n", " <td>0.677647</td>\n", " <td>flimsy</td>\n", " <td>0.658486</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>blox</td>\n", " <td>0.677111</td>\n", " <td>grabber</td>\n", " <td>0.658486</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>regime</td>\n", " <td>0.677111</td>\n", " <td>returned</td>\n", " <td>0.658471</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>love</td>\n", " <td>0.677111</td>\n", " <td>poorly</td>\n", " <td>0.652594</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>best</td>\n", " <td>0.673670</td>\n", " <td>junk</td>\n", " <td>0.652316</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>great</td>\n", " <td>0.670816</td>\n", " <td>jarsgreat</td>\n", " <td>0.652202</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>expectedgot</td>\n", " <td>0.670816</td>\n", " <td>hamiliton</td>\n", " <td>0.652202</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>perfection</td>\n", " <td>0.667853</td>\n", " <td>defective</td>\n", " <td>0.652202</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>silex</td>\n", " <td>0.666016</td>\n", " <td>awful</td>\n", " <td>0.651954</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>loves</td>\n", " <td>0.666016</td>\n", " <td>infuse</td>\n", " <td>0.651954</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>needed</td>\n", " <td>0.664291</td>\n", " <td>coctails</td>\n", " <td>0.651954</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>perfectly</td>\n", " <td>0.663811</td>\n", " <td>meh</td>\n", " <td>0.651746</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>outstanding</td>\n", " <td>0.662990</td>\n", " <td>ok</td>\n", " <td>0.651042</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>wowloving</td>\n", " <td>0.662849</td>\n", " <td>microbopper</td>\n", " <td>0.651042</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>exactly</td>\n", " <td>0.662849</td>\n", " <td>broke</td>\n", " <td>0.648492</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>saves</td>\n", " <td>0.660796</td>\n", " <td>negive</td>\n", " <td>0.647956</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>terrific</td>\n", " <td>0.660496</td>\n", " <td>postal</td>\n", " <td>0.647956</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>classy</td>\n", " <td>0.659689</td>\n", " <td>horrible</td>\n", " <td>0.647956</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>beat</td>\n", " <td>0.659624</td>\n", " <td>cheaply</td>\n", " <td>0.644965</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>solved</td>\n", " <td>0.659568</td>\n", " <td>dangerous</td>\n", " <td>0.644139</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>simple</td>\n", " <td>0.658571</td>\n", " <td>breaks</td>\n", " <td>0.642654</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>finally</td>\n", " <td>0.658427</td>\n", " <td>eh</td>\n", " <td>0.642366</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>ruffled</td>\n", " <td>0.658241</td>\n", " <td>charges</td>\n", " <td>0.642366</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>beautiful</td>\n", " <td>0.658241</td>\n", " <td>okay</td>\n", " <td>0.642030</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>yay</td>\n", " <td>0.655354</td>\n", " <td>mediocre</td>\n", " <td>0.641426</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>easy</td>\n", " <td>0.655322</td>\n", " <td>flawed</td>\n", " <td>0.641148</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td>fun</td>\n", " <td>0.654575</td>\n", " <td>weak</td>\n", " <td>0.639523</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td>pleasantly</td>\n", " <td>0.652444</td>\n", " <td>managing</td>\n", " <td>0.639406</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>sooner</td>\n", " <td>0.649271</td>\n", " <td>lousy</td>\n", " <td>0.639406</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>versatile</td>\n", " <td>0.649171</td>\n", " <td>broken</td>\n", " <td>0.637405</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>sharpened</td>\n", " <td>0.649171</td>\n", " <td>beware</td>\n", " <td>0.636521</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>gift</td>\n", " <td>0.648284</td>\n", " <td>doesnt</td>\n", " <td>0.636136</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>gorgeous</td>\n", " <td>0.646563</td>\n", " <td>akrobins</td>\n", " <td>0.636136</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>mugthermos</td>\n", " <td>0.646563</td>\n", " <td>madewelding</td>\n", " <td>0.634321</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>wow</td>\n", " <td>0.643426</td>\n", " <td>rusted</td>\n", " <td>0.634321</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>casingborder</td>\n", " <td>0.643426</td>\n", " <td>uncomfortable</td>\n", " <td>0.634118</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>nice</td>\n", " <td>0.643066</td>\n", " <td>nightmare</td>\n", " <td>0.633125</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>ont</td>\n", " <td>0.642420</td>\n", " <td>shoddiest</td>\n", " <td>0.633116</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>solid</td>\n", " <td>0.642420</td>\n", " <td>cracked</td>\n", " <td>0.633116</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>favorite</td>\n", " <td>0.641204</td>\n", " <td>soso</td>\n", " <td>0.633033</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>elegant</td>\n", " <td>0.641035</td>\n", " <td>handheal</td>\n", " <td>0.633033</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>charm</td>\n", " <td>0.640674</td>\n", " <td>garbage</td>\n", " <td>0.632779</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>value</td>\n", " <td>0.640382</td>\n", " <td>overpriced</td>\n", " <td>0.631248</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>yummy</td>\n", " <td>0.639512</td>\n", " <td>fail</td>\n", " <td>0.630755</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>kiss</td>\n", " <td>0.639512</td>\n", " <td>died</td>\n", " <td>0.630731</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>penny</td>\n", " <td>0.638611</td>\n", " <td>short</td>\n", " <td>0.628559</td>\n", " </tr>\n", " <tr>\n", " <th>57</th>\n", " <td>cozy</td>\n", " <td>0.638148</td>\n", " <td>frustrating</td>\n", " <td>0.628401</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>superb</td>\n", " <td>0.636925</td>\n", " <td>rusts</td>\n", " <td>0.628361</td>\n", " </tr>\n", " <tr>\n", " <th>59</th>\n", " <td>affordable</td>\n", " <td>0.636830</td>\n", " <td>noisy</td>\n", " <td>0.628036</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>heaven</td>\n", " <td>0.636549</td>\n", " <td>lasted</td>\n", " <td>0.627848</td>\n", " </tr>\n", " <tr>\n", " <th>61</th>\n", " <td>exceeded</td>\n", " <td>0.635176</td>\n", " <td>stinks</td>\n", " <td>0.627412</td>\n", " </tr>\n", " <tr>\n", " <th>62</th>\n", " <td>saver</td>\n", " <td>0.634595</td>\n", " <td>pumped</td>\n", " <td>0.626616</td>\n", " </tr>\n", " <tr>\n", " <th>63</th>\n", " <td>comfy</td>\n", " <td>0.634482</td>\n", " <td>theory</td>\n", " <td>0.626616</td>\n", " </tr>\n", " <tr>\n", " <th>64</th>\n", " <td>accessorie</td>\n", " <td>0.634482</td>\n", " <td>dissapointed</td>\n", " <td>0.626493</td>\n", " </tr>\n", " <tr>\n", " <th>65</th>\n", " <td>tool</td>\n", " <td>0.634073</td>\n", " <td>bad</td>\n", " <td>0.626172</td>\n", " </tr>\n", " <tr>\n", " <th>66</th>\n", " <td>sturdy</td>\n", " <td>0.633408</td>\n", " <td>seniors</td>\n", " <td>0.626172</td>\n", " </tr>\n", " <tr>\n", " <th>67</th>\n", " <td>nonbasic</td>\n", " <td>0.633408</td>\n", " <td>helpers</td>\n", " <td>0.626172</td>\n", " </tr>\n", " <tr>\n", " <th>68</th>\n", " <td>lovely</td>\n", " <td>0.632788</td>\n", " <td>nori</td>\n", " <td>0.626172</td>\n", " </tr>\n", " <tr>\n", " <th>69</th>\n", " <td>kitchen</td>\n", " <td>0.632656</td>\n", " <td>cornerssee</td>\n", " <td>0.625675</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td>canning</td>\n", " <td>0.631700</td>\n", " <td>awkward</td>\n", " <td>0.625675</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td>highly</td>\n", " <td>0.629741</td>\n", " <td>mata</td>\n", " <td>0.625675</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td>addition</td>\n", " <td>0.629598</td>\n", " <td>unreliable</td>\n", " <td>0.625524</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td>measuring</td>\n", " <td>0.629445</td>\n", " <td>uneven</td>\n", " <td>0.625510</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td>storage</td>\n", " <td>0.628716</td>\n", " <td>simpleyet</td>\n", " <td>0.624851</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>essential</td>\n", " <td>0.628671</td>\n", " <td>difficult</td>\n", " <td>0.624851</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td>unique</td>\n", " <td>0.626989</td>\n", " <td>leaked</td>\n", " <td>0.623439</td>\n", " </tr>\n", " <tr>\n", " <th>77</th>\n", " <td>must</td>\n", " <td>0.626507</td>\n", " <td>worse</td>\n", " <td>0.623415</td>\n", " </tr>\n", " <tr>\n", " <th>78</th>\n", " <td>complaints</td>\n", " <td>0.626157</td>\n", " <td>windw</td>\n", " <td>0.623415</td>\n", " </tr>\n", " <tr>\n", " <th>79</th>\n", " <td>joining</td>\n", " <td>0.625883</td>\n", " <td>concept</td>\n", " <td>0.623266</td>\n", " </tr>\n", " <tr>\n", " <th>80</th>\n", " <td>cake</td>\n", " <td>0.625883</td>\n", " <td>waste</td>\n", " <td>0.622812</td>\n", " </tr>\n", " <tr>\n", " <th>81</th>\n", " <td>mom</td>\n", " <td>0.625141</td>\n", " <td>windowmounted</td>\n", " <td>0.622812</td>\n", " </tr>\n", " <tr>\n", " <th>82</th>\n", " <td>beats</td>\n", " <td>0.624665</td>\n", " <td>leaks</td>\n", " <td>0.622331</td>\n", " </tr>\n", " <tr>\n", " <th>83</th>\n", " <td>aquality</td>\n", " <td>0.624426</td>\n", " <td>leaky</td>\n", " <td>0.622287</td>\n", " </tr>\n", " <tr>\n", " <th>84</th>\n", " <td>organized</td>\n", " <td>0.624426</td>\n", " <td>misleading</td>\n", " <td>0.622105</td>\n", " </tr>\n", " <tr>\n", " <th>85</th>\n", " <td>husband</td>\n", " <td>0.624266</td>\n", " <td>fragile</td>\n", " <td>0.621708</td>\n", " </tr>\n", " <tr>\n", " <th>86</th>\n", " <td>helps</td>\n", " <td>0.624006</td>\n", " <td>disapointed</td>\n", " <td>0.621552</td>\n", " </tr>\n", " <tr>\n", " <th>87</th>\n", " <td>pleasure</td>\n", " <td>0.622396</td>\n", " <td>loveable</td>\n", " <td>0.621552</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td>loving</td>\n", " <td>0.622124</td>\n", " <td>crap</td>\n", " <td>0.621500</td>\n", " </tr>\n", " <tr>\n", " <th>89</th>\n", " <td>convenient</td>\n", " <td>0.621260</td>\n", " <td>warped</td>\n", " <td>0.621298</td>\n", " </tr>\n", " <tr>\n", " <th>90</th>\n", " <td>works</td>\n", " <td>0.620345</td>\n", " <td>yuck</td>\n", " <td>0.620694</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>baking</td>\n", " <td>0.620343</td>\n", " <td>nystrip</td>\n", " <td>0.620396</td>\n", " </tr>\n", " <tr>\n", " <th>92</th>\n", " <td>fits</td>\n", " <td>0.619780</td>\n", " <td>rusty</td>\n", " <td>0.620396</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td>windtunel</td>\n", " <td>0.619480</td>\n", " <td>rip</td>\n", " <td>0.620298</td>\n", " </tr>\n", " <tr>\n", " <th>94</th>\n", " <td>stylish</td>\n", " <td>0.619480</td>\n", " <td>inaccurate</td>\n", " <td>0.619848</td>\n", " </tr>\n", " <tr>\n", " <th>95</th>\n", " <td>circulonthis</td>\n", " <td>0.618929</td>\n", " <td>hate</td>\n", " <td>0.619539</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td>workhorse</td>\n", " <td>0.618929</td>\n", " <td>asteroid</td>\n", " <td>0.619539</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>wife</td>\n", " <td>0.618862</td>\n", " <td>dud</td>\n", " <td>0.618871</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>delight</td>\n", " <td>0.618565</td>\n", " <td>crappy</td>\n", " <td>0.618867</td>\n", " </tr>\n", " <tr>\n", " <th>99</th>\n", " <td>emglish</td>\n", " <td>0.618565</td>\n", " <td>skip</td>\n", " <td>0.618234</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " positive_word pos_prob negative_word neg_prob\n", "0 toxic 0.702056 worst 0.693118\n", "1 perfect 0.702056 za 0.681185\n", "2 excellent 0.698975 disappointed 0.681185\n", "3 awesome 0.695059 disappointing 0.677256\n", "4 fantastic 0.690034 disappointment 0.669026\n", "5 dalla 0.689843 terrible 0.667437\n", "6 amazing 0.689843 poor 0.667185\n", "7 wonderful 0.687460 useless 0.660330\n", "8 five 0.683558 bango 0.660330\n", "9 fabulous 0.678689 worthless 0.658721\n", "10 bailey 0.677647 gingergarlic 0.658486\n", "11 handy 0.677647 flimsy 0.658486\n", "12 blox 0.677111 grabber 0.658486\n", "13 regime 0.677111 returned 0.658471\n", "14 love 0.677111 poorly 0.652594\n", "15 best 0.673670 junk 0.652316\n", "16 great 0.670816 jarsgreat 0.652202\n", "17 expectedgot 0.670816 hamiliton 0.652202\n", "18 perfection 0.667853 defective 0.652202\n", "19 silex 0.666016 awful 0.651954\n", "20 loves 0.666016 infuse 0.651954\n", "21 needed 0.664291 coctails 0.651954\n", "22 perfectly 0.663811 meh 0.651746\n", "23 outstanding 0.662990 ok 0.651042\n", "24 wowloving 0.662849 microbopper 0.651042\n", "25 exactly 0.662849 broke 0.648492\n", "26 saves 0.660796 negive 0.647956\n", "27 terrific 0.660496 postal 0.647956\n", "28 classy 0.659689 horrible 0.647956\n", "29 beat 0.659624 cheaply 0.644965\n", "30 solved 0.659568 dangerous 0.644139\n", "31 simple 0.658571 breaks 0.642654\n", "32 finally 0.658427 eh 0.642366\n", "33 ruffled 0.658241 charges 0.642366\n", "34 beautiful 0.658241 okay 0.642030\n", "35 yay 0.655354 mediocre 0.641426\n", "36 easy 0.655322 flawed 0.641148\n", "37 fun 0.654575 weak 0.639523\n", "38 pleasantly 0.652444 managing 0.639406\n", "39 sooner 0.649271 lousy 0.639406\n", "40 versatile 0.649171 broken 0.637405\n", "41 sharpened 0.649171 beware 0.636521\n", "42 gift 0.648284 doesnt 0.636136\n", "43 gorgeous 0.646563 akrobins 0.636136\n", "44 mugthermos 0.646563 madewelding 0.634321\n", "45 wow 0.643426 rusted 0.634321\n", "46 casingborder 0.643426 uncomfortable 0.634118\n", "47 nice 0.643066 nightmare 0.633125\n", "48 ont 0.642420 shoddiest 0.633116\n", "49 solid 0.642420 cracked 0.633116\n", "50 favorite 0.641204 soso 0.633033\n", "51 elegant 0.641035 handheal 0.633033\n", "52 charm 0.640674 garbage 0.632779\n", "53 value 0.640382 overpriced 0.631248\n", "54 yummy 0.639512 fail 0.630755\n", "55 kiss 0.639512 died 0.630731\n", "56 penny 0.638611 short 0.628559\n", "57 cozy 0.638148 frustrating 0.628401\n", "58 superb 0.636925 rusts 0.628361\n", "59 affordable 0.636830 noisy 0.628036\n", "60 heaven 0.636549 lasted 0.627848\n", "61 exceeded 0.635176 stinks 0.627412\n", "62 saver 0.634595 pumped 0.626616\n", "63 comfy 0.634482 theory 0.626616\n", "64 accessorie 0.634482 dissapointed 0.626493\n", "65 tool 0.634073 bad 0.626172\n", "66 sturdy 0.633408 seniors 0.626172\n", "67 nonbasic 0.633408 helpers 0.626172\n", "68 lovely 0.632788 nori 0.626172\n", "69 kitchen 0.632656 cornerssee 0.625675\n", "70 canning 0.631700 awkward 0.625675\n", "71 highly 0.629741 mata 0.625675\n", "72 addition 0.629598 unreliable 0.625524\n", "73 measuring 0.629445 uneven 0.625510\n", "74 storage 0.628716 simpleyet 0.624851\n", "75 essential 0.628671 difficult 0.624851\n", "76 unique 0.626989 leaked 0.623439\n", "77 must 0.626507 worse 0.623415\n", "78 complaints 0.626157 windw 0.623415\n", "79 joining 0.625883 concept 0.623266\n", "80 cake 0.625883 waste 0.622812\n", "81 mom 0.625141 windowmounted 0.622812\n", "82 beats 0.624665 leaks 0.622331\n", "83 aquality 0.624426 leaky 0.622287\n", "84 organized 0.624426 misleading 0.622105\n", "85 husband 0.624266 fragile 0.621708\n", "86 helps 0.624006 disapointed 0.621552\n", "87 pleasure 0.622396 loveable 0.621552\n", "88 loving 0.622124 crap 0.621500\n", "89 convenient 0.621260 warped 0.621298\n", "90 works 0.620345 yuck 0.620694\n", "91 baking 0.620343 nystrip 0.620396\n", "92 fits 0.619780 rusty 0.620396\n", "93 windtunel 0.619480 rip 0.620298\n", "94 stylish 0.619480 inaccurate 0.619848\n", "95 circulonthis 0.618929 hate 0.619539\n", "96 workhorse 0.618929 asteroid 0.619539\n", "97 wife 0.618862 dud 0.618871\n", "98 delight 0.618565 crappy 0.618867\n", "99 emglish 0.618565 skip 0.618234" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.set_option('display.max_rows', 100)\n", "\n", "pd.concat(\n", " [ positive_predictive_words.limit(100).toPandas(),\n", " negative_predictive_words.limit(100).toPandas() ],\n", " axis=1)" ] } ], "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
Amarchuk/2FInstability	notebooks/.ipynb_checkpoints/test-checkpoint.ipynb	1	2683	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR:root:File `u'../../utils/load_notebook.py'` not found.\n" ] } ], "source": [ "%run ../../utils/load_notebook.py" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "ename": "ImportError", "evalue": "No module named n338_test", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-ff89180d896f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[1;32mfrom\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn338_test\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mImportError\u001b[0m: No module named n338_test" ] } ], "source": [ "from test.n338_test import " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print incl" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "os.chdir('../../notebooks/')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "from n3898_test import *" ] }, { "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": "2.7.12" }, "nav_menu": { "height": "271px", "width": "242px" }, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
AllenDowney/ThinkBayes2	examples/shuttle_soln.ipynb	1	185439	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Think Bayes\n", "\n", "Copyright 2018 Allen B. Downey\n", "\n", "MIT License: https://opensource.org/licenses/MIT" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Configure Jupyter so figures appear in the notebook\n", "%matplotlib inline\n", "\n", "# Configure Jupyter to display the assigned value after an assignment\n", "%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "# import classes from thinkbayes2\n", "from thinkbayes2 import Pmf, Cdf, Suite, Joint\n", "\n", "import thinkplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Space Shuttle problem\n", "\n", "Here's a problem from [Bayesian Methods for Hackers](http://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter2_MorePyMC/Ch2_MorePyMC_PyMC2.ipynb)\n", "\n", ">On January 28, 1986, the twenty-fifth flight of the U.S. space shuttle program ended in disaster when one of the rocket boosters of the Shuttle Challenger exploded shortly after lift-off, killing all seven crew members. The presidential commission on the accident concluded that it was caused by the failure of an O-ring in a field joint on the rocket booster, and that this failure was due to a faulty design that made the O-ring unacceptably sensitive to a number of factors including outside temperature. Of the previous 24 flights, data were available on failures of O-rings on 23, (one was lost at sea), and these data were discussed on the evening preceding the Challenger launch, but unfortunately only the data corresponding to the 7 flights on which there was a damage incident were considered important and these were thought to show no obvious trend. The data are shown below (see [1](https://amstat.tandfonline.com/doi/abs/10.1080/01621459.1989.10478858)):\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# !wget https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/data/challenger_data.csv" ] }, { "cell_type": "code", "execution_count": 3, "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>Date</th>\n", " <th>Temperature</th>\n", " <th>Damage Incident</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1981-04-12</td>\n", " <td>66</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1981-11-12</td>\n", " <td>70</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1982-03-22</td>\n", " <td>69</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1982-01-11</td>\n", " <td>68</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1983-04-04</td>\n", " <td>67</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1983-06-18</td>\n", " <td>72</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1983-08-30</td>\n", " <td>73</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1983-11-28</td>\n", " <td>70</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1984-02-03</td>\n", " <td>57</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1984-04-06</td>\n", " <td>63</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>1984-08-30</td>\n", " <td>70</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1984-10-05</td>\n", " <td>78</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1984-11-08</td>\n", " <td>67</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>1985-01-24</td>\n", " <td>53</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1985-04-12</td>\n", " <td>67</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1985-04-29</td>\n", " <td>75</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1985-06-17</td>\n", " <td>70</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>1985-07-29</td>\n", " <td>81</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1985-08-27</td>\n", " <td>76</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>1985-10-03</td>\n", " <td>79</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1985-10-30</td>\n", " <td>75</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1985-11-26</td>\n", " <td>76</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>1986-01-12</td>\n", " <td>58</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Date Temperature Damage Incident\n", "0 1981-04-12 66 0\n", "1 1981-11-12 70 1\n", "2 1982-03-22 69 0\n", "4 1982-01-11 68 0\n", "5 1983-04-04 67 0\n", "6 1983-06-18 72 0\n", "7 1983-08-30 73 0\n", "8 1983-11-28 70 0\n", "9 1984-02-03 57 1\n", "10 1984-04-06 63 1\n", "11 1984-08-30 70 1\n", "12 1984-10-05 78 0\n", "13 1984-11-08 67 0\n", "14 1985-01-24 53 1\n", "15 1985-04-12 67 0\n", "16 1985-04-29 75 0\n", "17 1985-06-17 70 0\n", "18 1985-07-29 81 0\n", "19 1985-08-27 76 0\n", "20 1985-10-03 79 0\n", "21 1985-10-30 75 1\n", "22 1985-11-26 76 0\n", "23 1986-01-12 58 1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns = ['Date', 'Temperature', 'Incident']\n", "df = pd.read_csv('challenger_data.csv', parse_dates=[0])\n", "df.drop(labels=[3, 24], inplace=True)\n", "df" ] }, { "cell_type": "code", "execution_count": 4, "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>Date</th>\n", " <th>Temperature</th>\n", " <th>Damage Incident</th>\n", " <th>Incident</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1981-04-12</td>\n", " <td>66</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1981-11-12</td>\n", " <td>70</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1982-03-22</td>\n", " <td>69</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1982-01-11</td>\n", " <td>68</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1983-04-04</td>\n", " <td>67</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1983-06-18</td>\n", " <td>72</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1983-08-30</td>\n", " <td>73</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1983-11-28</td>\n", " <td>70</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1984-02-03</td>\n", " <td>57</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1984-04-06</td>\n", " <td>63</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>1984-08-30</td>\n", " <td>70</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1984-10-05</td>\n", " <td>78</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1984-11-08</td>\n", " <td>67</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>1985-01-24</td>\n", " <td>53</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1985-04-12</td>\n", " <td>67</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1985-04-29</td>\n", " <td>75</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1985-06-17</td>\n", " <td>70</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>1985-07-29</td>\n", " <td>81</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>1985-08-27</td>\n", " <td>76</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>1985-10-03</td>\n", " <td>79</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>1985-10-30</td>\n", " <td>75</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>1985-11-26</td>\n", " <td>76</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>1986-01-12</td>\n", " <td>58</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Date Temperature Damage Incident Incident\n", "0 1981-04-12 66 0 0.0\n", "1 1981-11-12 70 1 1.0\n", "2 1982-03-22 69 0 0.0\n", "4 1982-01-11 68 0 0.0\n", "5 1983-04-04 67 0 0.0\n", "6 1983-06-18 72 0 0.0\n", "7 1983-08-30 73 0 0.0\n", "8 1983-11-28 70 0 0.0\n", "9 1984-02-03 57 1 1.0\n", "10 1984-04-06 63 1 1.0\n", "11 1984-08-30 70 1 1.0\n", "12 1984-10-05 78 0 0.0\n", "13 1984-11-08 67 0 0.0\n", "14 1985-01-24 53 1 1.0\n", "15 1985-04-12 67 0 0.0\n", "16 1985-04-29 75 0 0.0\n", "17 1985-06-17 70 0 0.0\n", "18 1985-07-29 81 0 0.0\n", "19 1985-08-27 76 0 0.0\n", "20 1985-10-03 79 0 0.0\n", "21 1985-10-30 75 1 1.0\n", "22 1985-11-26 76 0 0.0\n", "23 1986-01-12 58 1 1.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Incident'] = df['Damage Incident'].astype(float)\n", "df" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(df.Temperature, df.Incident, s=75, color=\"k\",\n", " alpha=0.5)\n", "plt.yticks([0, 1])\n", "plt.ylabel(\"Damage Incident?\")\n", "plt.xlabel(\"Outside temperature (Fahrenheit)\")\n", "plt.title(\"Defects of the Space Shuttle O-Rings vs temperature\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grid algorithm\n", "\n", "We can solve the problem first using a grid algorithm, with parameters `b0` and `b1`, and\n", "\n", "$\\mathrm{logit}(p) = b0 + b1 * T$\n", "\n", "and each datum being a temperature `T` and a boolean outcome `fail`, which is true is there was damage and false otherwise.\n", "\n", "Hint: the `expit` function from `scipy.special` computes the inverse of the `logit` function." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from scipy.special import expit\n", "\n", "class Logistic(Suite, Joint):\n", " \n", " def Likelihood(self, data, hypo):\n", " \"\"\"\n", " \n", " data: T, fail\n", " hypo: b0, b1\n", " \"\"\"\n", " return 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Solution\n", "\n", "from scipy.special import expit\n", "\n", "class Logistic(Suite, Joint):\n", " \n", " def Likelihood(self, data, hypo):\n", " \"\"\"\n", " \n", " data: T, fail\n", " hypo: b0, b1\n", " \"\"\"\n", " temp, fail = data\n", " b0, b1 = hypo\n", " \n", " log_odds = b0 + b1 * temp\n", " p_fail = expit(log_odds)\n", " if fail == 1:\n", " return p_fail\n", " elif fail == 0:\n", " return 1-p_fail\n", " else:\n", " # NaN\n", " return 1" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "b0 = np.linspace(0, 50, 101);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "b1 = np.linspace(-1, 1, 101);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<itertools.product at 0x7f25c84d75e8>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from itertools import product\n", "hypos = product(b0, b1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "suite = Logistic(hypos);" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(66, 0.0)\n", "(70, 1.0)\n", "(69, 0.0)\n", "(68, 0.0)\n", "(67, 0.0)\n", "(72, 0.0)\n", "(73, 0.0)\n", "(70, 0.0)\n", "(57, 1.0)\n", "(63, 1.0)\n", "(70, 1.0)\n", "(78, 0.0)\n", "(67, 0.0)\n", "(53, 1.0)\n", "(67, 0.0)\n", "(75, 0.0)\n", "(70, 0.0)\n", "(81, 0.0)\n", "(76, 0.0)\n", "(79, 0.0)\n", "(75, 1.0)\n", "(76, 0.0)\n", "(58, 1.0)\n" ] } ], "source": [ "for data in zip(df.Temperature, df.Incident):\n", " print(data)\n", " suite.Update(data)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "thinkplot.Pdf(suite.Marginal(0))\n", "thinkplot.decorate(xlabel='Intercept',\n", " ylabel='PMF',\n", " title='Posterior marginal distribution')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "thinkplot.Pdf(suite.Marginal(1))\n", "thinkplot.decorate(xlabel='Log odds ratio',\n", " ylabel='PMF',\n", " title='Posterior marginal distribution')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to the posterior distribution, what was the probability of damage when the shuttle launched at 31 degF?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9909003512180481" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Solution\n", "\n", "T = 31\n", "total = 0\n", "\n", "for hypo, p in suite.Items():\n", " b0, b1 = hypo\n", " log_odds = b0 + b1 * T\n", " p_fail = expit(log_odds)\n", " total += p * p_fail\n", " \n", "total" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9909003512180481" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Solution\n", "\n", "pred = suite.Copy()\n", "pred.Update((31, True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MCMC\n", "\n", "Implement this model using MCMC. As a starting place, you can use this example from [the PyMC3 docs](https://docs.pymc.io/notebooks/GLM-logistic.html#The-model).\n", "\n", "As a challege, try writing the model more explicitly, rather than using the GLM module." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from warnings import simplefilter\n", "simplefilter('ignore', FutureWarning)\n", "\n", "import pymc3 as pm" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "logp = -18, \|\|grad\|\| = 11.915: 100%\|██████████\| 27/27 [00:00<00:00, 1502.12it/s] \n", "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [Temperature, Intercept]\n", "Sampling 2 chains: 100%\|██████████\| 4000/4000 [00:06<00:00, 594.28draws/s]\n", "The estimated number of effective samples is smaller than 200 for some parameters.\n" ] } ], "source": [ "# Solution\n", "\n", "with pm.Model() as model:\n", " pm.glm.GLM.from_formula('Incident ~ Temperature', df, \n", " family=pm.glm.families.Binomial())\n", " \n", " start = pm.find_MAP()\n", " trace = pm.sample(1000, start=start, tune=1000)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pm.traceplot(trace);" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [Temperature, Intercept]\n", "Sampling 2 chains: 0%\| \| 0/4000 [00:00<?, ?draws/s]\n" ] }, { "ename": "RuntimeError", "evalue": "Chain 0 failed.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mRemoteTraceback\u001b[0m Traceback (most recent call last)", "\u001b[0;31mRemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/home/downey/anaconda3/lib/python3.6/site-packages/pymc3/parallel_sampling.py\", line 73, in run\n self._start_loop()\n File \"/home/downey/anaconda3/lib/python3.6/site-packages/pymc3/parallel_sampling.py\", line 113, in _start_loop\n point, stats = self._compute_point()\n File \"/home/downey/anaconda3/lib/python3.6/site-packages/pymc3/parallel_sampling.py\", line 139, in _compute_point\n point, stats = self._step_method.step(self._point)\n File \"/home/downey/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/arraystep.py\", line 247, in step\n apoint, stats = self.astep(array)\n File \"/home/downey/anaconda3/lib/python3.6/site-packages/pymc3/step_methods/hmc/base_hmc.py\", line 117, in astep\n 'might be misspecified.' % start.energy)\nValueError: Bad initial energy: inf. The model might be misspecified.\n\"\"\"", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mValueError\u001b[0m: Bad initial energy: inf. The model might be misspecified.", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-20-18c7e811f775>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m family=pm.glm.families.Binomial())\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtune\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pymc3/sampling.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, nuts_kwargs, step_kwargs, progressbar, model, random_seed, live_plot, discard_tuned_samples, live_plot_kwargs, compute_convergence_checks, use_mmap, kwargs)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0m_print_step_hierarchy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_mp_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0msample_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPickleError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0m_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Could not pickle model, sampling singlethreaded.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pymc3/sampling.py\u001b[0m in \u001b[0;36m_mp_sample\u001b[0;34m(draws, tune, step, chains, cores, chain, random_seed, start, progressbar, trace, model, use_mmap, **kwargs)\u001b[0m\n\u001b[1;32m 997\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 998\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 999\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdraw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1000\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraces\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchain\u001b[0m \u001b[0;34m-\u001b[0m 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timeout)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'error'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0mold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Chain %s failed.'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mproc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0;32melif\u001b[0m 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"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 }	mit
xiaodongpang23/anomaly_detection	anomaly_detection.ipynb	1	10251	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import networkx as nx\n", "import json\n", "import sys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step1: build the initial state of the entire user network, as well as the purchae history of the users\n", "Input: sample_dataset/batch_log.json" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "batchlogfile = 'sample_dataset/batch_log.json'\n", "df_batch = pd.read_json(batchlogfile, lines=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "index_purchase = ['event_type','id','timestamp','amount']\n", "index_friend = ['event_type','id1','id2','timestamp']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#df_batch.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#df_batch.describe()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Read D and T\n", "df_DT=df_batch[df_batch['D'].notnull()]\n", "df_DT=df_DT[['D','T']]\n", "D = df_DT.values[0][0]\n", "T = df_DT.values[0][1]\n", "#print(D)\n", "#print(T)\n", "#df_DT.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# check D and T values\n", "if D < 1:\n", " print('Program terminated because of D < 1')\n", " sys.exit()\n", "if T < 2:\n", " print('Program terminated because of T < 2')\n", " sys.exit()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#for possible_value in set(df['event_type'].tolist()):\n", "# print(possible_value)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_purchase = df_batch[df_batch['event_type']=='purchase']\n", "df_purchase = df_purchase[index_purchase]\n", "df_purchase = df_purchase.dropna(how='any')\n", "# If sort on the timestamp is needed, commentout the following line\n", "# df_purchase = df_purchase.sort_values('timestamp')\n", "#df_purchase.shape" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_friend=df_batch[(df_batch['event_type']=='befriend') \| (df_batch['event_type']=='unfriend')]\n", "df_friend=df_friend[index_friend]\n", "df_friend=df_friend.dropna(how='any')\n", "# If sort on the timestamp is needed, commentout the following line\n", "#df_friend=df_friend.sort_values('timestamp')\n", "#df_friend.shape" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "G = nx.Graph()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "idlist = set(df_purchase.id.tolist())\n", "G.add_nodes_from(idlist)\n", "#len(list(G.nodes()))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def Add_edges(data):\n", " for row in data.itertuples():\n", " id10 = row.id1\n", " id20 = row.id2\n", " event_type0 = row.event_type\n", " if event_type0 == 'befriend':\n", " G.add_edge(id10,id20)\n", " if event_type0 == 'unfriend':\n", " if G.has_edge(id10,id20):\n", " G.remove_edge(id10,id20) " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Add_edges(df_friend)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#len(list(G.edges()))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#G[10.0]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#G.number_of_nodes()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#G.number_of_edges()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# define a function to calcualte the mean and sd for userid's network\n", "def Get_Mean_SD(userid):\n", " Nodes = list(nx.ego_graph(G, userid, D, center=False))\n", " df_Nodes = df_purchase.loc[df_purchase['id'].isin(Nodes)]\n", " if len(df_Nodes) >= 2: \n", " if len(df_Nodes) > T:\n", " df_Nodes = df_Nodes.sort_values('timestamp').iloc[-int(T):]\n", " #df_Nodes.shape\n", " #the std from pd is different from np; np is correct\n", " #mean = df_Nodes.amount.mean()\n", " #sd = df_Nodes.amount.std()\n", " mean = np.mean(df_Nodes['amount'])\n", " sd = np.std(df_Nodes['amount'])\n", " mean = float(\"{0:.2f}\".format(mean))\n", " sd = float(\"{0:.2f}\".format(sd))\n", " else:\n", " mean=np.nan\n", " sd=np.nan\n", " \n", " return mean, sd" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Get_Mean_SD(0.0)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#df_purchase.head()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#df_purchase.tail()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#df_purchase.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step2: Determine whether a purchase is anomalous \n", "input file: sample_dataset/stream_log.json" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# read in the stream_log.json\n", "streamlogfile = 'sample_dataset/stream_log.json'\n", "df_stream = pd.read_json(streamlogfile, lines=True)\n", "# If sort on the timestamp is needed, commentout the following line\n", "#df_stream = df_stream.sort_values('timestamp')\n", "\n", "# open output file flagged_purchases.json\n", "flaggedfile = 'log_output/flagged_purchases.json'\n", "f = open(flaggedfile, 'w')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Determine whether a purchase is anomalous; update purchase history; update social network\n", "for i in range(0, len(df_stream)):\n", " datai = df_stream.iloc[i]\n", " event_type = datai['event_type']\n", " if (event_type == 'purchase') & (not datai[index_purchase].isnull().any()):\n", " # update purchase history\n", " df_purchase = df_purchase.append(datai[index_purchase])\n", " timestamp = datai['timestamp']\n", " timestamp = str(timestamp)\n", " userid = datai['id']\n", " if (not G.has_node(userid)):\n", " G.add_node(userid)\n", " amount = datai['amount']\n", " mean, sd = Get_Mean_SD(userid)\n", " if mean != np.nan:\n", " mean_3sd = mean + (3*sd)\n", " if amount > mean_3sd:\n", " f.write('{{\"event_type\":\"{0:s}\", \"timestamp\":\"{1:s}\", \"id\": \"{2:.0f}\", \"amount\": \"{3:.2f}\", \"mean\": \"{4:.2f}\", \"sd\": \"{5:.2f}\"}}\\n'.format(event_type, timestamp, userid, amount, mean, sd))\n", " # update social network\n", " if (event_type == 'befriend') & (not datai[index_friend].isnull().any()):\n", " df_friend=df_friend.append(datai[index_friend])\n", " id1 = datai['id1']\n", " id2 = datai['id2']\n", " G.add_edge(id1,id2)\n", " if (event_type == 'unfriend') & (not datai[index_friend].isnull().any()):\n", " df_friend=df_friend.append(datai[index_friend])\n", " id1 = datai['id1']\n", " id2 = datai['id2']\n", " if G.has_edge(id1,id2):\n", " G.remove_edge(id1,id2) \n", " \n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "f.close() " ] }, { "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": 0 }	mit
GAMPTeam/vampyre	demos/sparse/sparse_lin_inverse.ipynb	1	62859	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sparse Linear Inverse Demo\n", "\n", "In this demo, we illustrate how to use the `vampyre` package for a simple sparse linear inverse problem. The problem is to estimate a sparse vector $z$ from linear measurements of the form $y=Az+w$ where $w$ is Gaussian noise and $A$ is a known linear transform -- a basic problem in compressed sensing. By sparse, we mean that the vector $z$ has few non-zero values. Knowing that the vector is sparse can be used for improved reconstruction if an appropriate sparse reconstruction algorithm is used.\n", "\n", "There are a large number of algorithms for sparse linear inverse problems. This demo uses the Vector Approximate Message Passing (VAMP) method, one of several methods that will be included in the `vampyre` package. In going through this demo, you will learn to:\n", "* Load the `vampyre` package\n", "* Create synthetic data for a sparse linear inverse problem\n", "* Set up the VAMP method in the `vampyre` package to perform the estimation for the linear inverse problem\n", "* Measure the mean squared error (MSE) and compare the value to the predicted value from the VAMP method.\n", "* Using the `hist_list` feature to track variables per iteration of the algorithm." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing the Package \n", "\n", "\n", "First we need to import the `vampyre` package. Since `python` does not have relative imports, you need to add the path location for the `vampyre` package to the system path. In this case, we have specified the path use a relative path location, but you can change this depending on where `vampyre` is located." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/naxos2-raid9/hbraun/lib/vampyre/vampyre/trans/tflintrans.py:11: UserWarning: Tensorflow not installed. Some functionality may not be available\n", " 'Some functionality may not be available')\n" ] } ], "source": [ "import os\n", "import sys\n", "vp_path = os.path.abspath('../../')\n", "if not vp_path in sys.path:\n", " sys.path.append(vp_path)\n", "import vampyre as vp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will also load the other packages we will use in this demo. This could be done before the above import." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating Synthetic Data\n", "\n", "We begin by generating synthetic data $z$ and measurements $y$ that we will use to test the algorithms. First, we set the dimensions and the shapes of the vectors we will use." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Parameters\n", "nz = 1000 # number of components of z\n", "ny = 500 # number of measurements y \n", "\n", "# Compute the shapes\n", "zshape = (nz,) # Shape of z matrix\n", "yshape = (ny,) # Shape of y matrix\n", "Ashape = (ny,nz) # Shape of A matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate the synthetic data for this demo, we use the following simple probabilistic model. For the input $z$, we will use Bernouli-Gaussian (BG) distribution, a simple model in sparse signal processing. In the BG model, the components $z_i$ are i.i.d. where each component $z_i=0$ with probability $1-\\rho$ and $z_i \\sim {\\mathcal N}(0,1)$ with probability $\\rho$. The parameter $\\rho$ is called the sparsity ratio and represents the average number of non-zero components. When $\\rho$ is small, the vector $z$ is sparse. The components on which $z_i$ are non-zero are called the active components. We set the parameters below. We also set the SNR for the measurements." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "sparse_rat = 0.1 # sparsity ratio\n", "zmean1 = 0 # mean for the active components\n", "zvar1 = 1 # variance for the active components\n", "snr = 30 # SNR in dB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using these parameters, we can generate random sparse `z` following this distribution with the following simple code." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Generate the random input\n", "z1 = np.random.normal(zmean1, np.sqrt(zvar1), zshape)\n", "u = np.random.uniform(0, 1, zshape) < sparse_rat\n", "z = z1u" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To illustrate the sparsity, we plot the vector `z`. We can see from this plot that the majority of the components of `z` are zero." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x214cdb72898>]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ind = np.array(range(nz))\n", "plt.plot(ind,z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we create a random transform $A$ and output $y_0 = Az$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "A = np.random.normal(0, 1/np.sqrt(nz), Ashape)\n", "y0 = A.dot(z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we add noise at the desired SNR" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "yvar = np.mean(np.abs(y0)2)\n", "wvar = yvarnp.power(10, -0.1snr)\n", "y = y0 + np.random.normal(0,np.sqrt(wvar), yshape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating the Vampyre estimators\n", "\n", "Now that we have created the sparse data, we will use the `vampyre` package to recover `z` from `y`. In `vampyre` the methods to perform this estimation are called solvers. For this demo, we will use a simple solver called VAMP described in the paper:\n", "\n", " Rangan, Sundeep, Philip Schniter, and Alyson Fletcher. \"Vector approximate message passing.\" arXiv preprint arXiv:1610.03082 (2016).\n", "\n", "\n", "Similar to most of the solvers in the `vampyre` package, the VAMP solver needs precise specifications of the probability distributions of `z` and `y`. The simplest way to use VAMP is to specify two densities:\n", "* The prior $p(z)$; and\n", "* The likelihood $p(y\|z)$.\n", "\n", "Each of the densities are described by estimators. \n", "\n", "We first describe the estimator for the prior $p(z)$. The `vampyre` package will eventually have a large number of estimators to describe various densities. In this simple demo, $p(z)$ is what is called a mixture distribution since $z$ is one distribution with probability $1-\\rho$ and a second distribution with probability $\\rho$. To describe this mixture distribution in the `vampyre` package, we need to first create estimator classes for each component distribution. The following code creates an estimator, `est0`, for a discrete distribution with a probability of 1 at a 0 and a second estimator, `est1`, for the Gaussian distribution with the active components." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "est0 = vp.estim.DiscreteEst(0,1,zshape)\n", "est1 = vp.estim.GaussEst(zmean1,zvar1,zshape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We next use the `vampyre` class, `MixEst`, to describe a mixture of the two distributions. This is done by creating a list, `est_list`, of the estimators and an array `pz` with the probability of each component. The resulting estimator, `est_in`, is the estimator for the prior $z$, which is also the input to the transform $A$. We give this a name `Input` since it corresponds to the input. But, any naming is fine. Or, you can let `vampyre` give it a generic name." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "est_list = [est0, est1]\n", "pz = np.array([1-sparse_rat, sparse_rat])\n", "est_in = vp.estim.MixEst(est_list, w=pz, name='Input')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we describe the likelihood function, $p(y\|z)$. Since $y=Az+w$, we can first use the `MatrixLT` class to define a linear transform operator `Aop` corresponding to the matrix `A`. Then, we use the `LinEstim` class to describe the likelihood $y=Az+w$." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "Aop = vp.trans.MatrixLT(A,zshape)\n", "est_out = vp.estim.LinEst(Aop,y,wvar,map_est=False, name='Output')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the VAMP method needs a message handler to describe how to perform the Gaussian message passing. This is a more advanced feature. For most applications, you can just use the simple message handler as follows." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "msg_hdl = vp.estim.MsgHdlSimp(map_est=False, shape=zshape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running the VAMP Solver\n", "\n", "Having described the input and output estimators and the variance handler, we can now construct a VAMP solver. The construtor takes the input and output estimators, the variance handler and other parameters. The paramter `nit` is the number of iterations. This is fixed for now. Later, we will add auto-termination. The other parameter, `hist_list` is optional, and will be described momentarily. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [], "source": [ "nit = 20 # number of iterations\n", "solver = vp.solver.Vamp(est_in,est_out,msg_hdl,\\\n", " hist_list=['zhat', 'zhatvar'],nit=nit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can print a summary of the model which indicates the dimensions and the estimators." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variable: shape: (1000,), var_axes: (0,)\n", "est0: Input (Mixture)\n", "est1: Output (LinEstim)\n" ] } ], "source": [ "solver.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now run the solver by calling the `solve()` method. For a small problem like this, this should be close to instantaneous. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "solver.solve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The VAMP solver estimate is the field `zhat`. We plot one column of this (`icol=0`) and compare it to the corresponding column of the true matrix `z`. You should see a very good match." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x214cda8a978>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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x448AADvWLPUk/wyKWKU8DMWYEWupwm5QH9SRU5FoVFreeoRX2C3eaFoi67QourE+jRs993I9dr+mBWgtnSBEaY+KEZGy9H8vPXYzzpzEe9qoPqj7EbGwW8aqF6w1TFJd4Mkp3Bg7706nsD9lTXaLS6uOeDcqxifjcKJqKMY7j92U8yKx38W8xx6Tlrce4RV2ixdO9byySVkcYWOUVMJ5L8cNYZcroPIES/YLQLTS8yoU4xvUuLGHwm6m/slswRpNS3U2WdhtYFVkUhphEec9djdCMS4Iu8zZojLF2I3lhEt8gpLaeUoehmJM9atJvB8Kao0Qmb4n9eHPMXYbWJ+gpBGKcVgU3ZiO7srsOJkxdqnnXPZwR42Zp6LEJyhFVLHycNilGWE3sW+x1n/BsO8dNfj0vmnKfsqomAh77NaxKgxao2IcFt6EC8IuXAj3yBV2a+dk8Uc7caCzd1ldWQRMUiiGAurzC+WRRh6OjHIsxl6kTCmN8G02o9qWKTsqHnuMPXbLWI2xazXnnA7FuOGxJ7qDJexWRlds3nMIC/7wJOb+eXnvtFyceRpMWe7NBy/+Bri9Pw53Gp/6nrnfbHRKvrvoYXy86h+Wjzc1jt2M41fUYzc3jj3CHrt1rMfY3R/uqBXXl02iwHs2V25uwbl3PoW2Dh0xNToBQ2pc3Pw56W7ZjGfK78QFTf/V+wcXvEghKRSjer5WWywyGLriHgDAvuYdxg9SQ5jKubbyMD111Y9w7KILTB+36/dXYveS+abmK5hz/Aqna/QhwZ2nErAqxtrj2J2NGyZdCJMkC3jsHy/8f3gpdQ02rFmhs4exG0bqpA8LD7tIV3rBtjHJT3qnJX3ZXo1x7EbqmwkxcWOZCako4QhSx/O7aP/QTX/BkKXzzF1nU969jRi7xn5RFvbi6J50q8Ku1THmmEeteDcujFhJFpgROKEzPQO24tA2zd/J6P3igxi7Ji6sx27IkTDVYWes/E7G5E21enOF3YOXrpgLxSgdmYZ21j7HHV1JvPNJi+GHsOqsUIRj7EXRu5iWx7FrpOf0GGWnY/jpPPQ9dkFq9dau5umx/cUrr0zPmKwIu044RGtugh00Q05GRMXEA5yM7uuIgKbPYMpMP4dqr3LdvBB2U+fC1Hh77XTvevofeOPRW7Fz30FDqbgZinE+B4dJpZKaTyerMXbNUIxjHjspeboh7PoV2dBIBln7GMXCOUnqXXM31ooxIMRmxM74pBfnBDRhZnE61WNXrpsXL58wlaeZh5bOdfvapz/BxPhHWNYyXuewZG8nQzlHsRgvAlYUvYtp3WP3YOapC7MWbcXxhTDkDQmZY5gthKfUlk+ujMuPxOiv7ljwOEPCo3ZCGlx/xMEJQWbqjNrCIIseu4xWq6k8TQm7dgWqFulRQ3qTsrpz+rVUbSGeoFQcPU/J8loxWh1jsoS3ZRPwz1/lVRQnb85MHlkeu274Su9gkTIWZvHYY08pI3/yA0rOT1AyFO4xExIy6n1KDjNlkzSztroaBlP6Bsy2JLotruOefZ+b8thTJkIxOg8MtbbrjcZJ5JQp03nKwx2Lo9sEthyK0XqfpRyP/fDvvg688XOk2rbn5Gn/5tz0/lKsWjRf9/fs9Wh0RTqnnD0hGmFMQKTG2M0/7FLdOjerG+PYk7I8djU8Z3CkRZEw4a7VS9C6ZbWhtHIpFL7LhTKdp6rHbu6cJ/SuXRG6u7KPM56nqaUPik1Q0jlPuR67en9FYizsRdH12H3YedrWtg8AsOdA74kfMmL4Y54/H5NWzdP9PVvY9c6ZXtObjIZiZOqnhYddSqdDzJUXgBh68JkfZVI8ycL7DX32q6h5/PPG880iaUZslbJFFHuy65iRB5qpeH4WXV09byMy1Uow4awV1ZLuDp0scs5fZrgjh2KKotv8sthE1V7dUe508UTnIYj9O1BGarNVXgxfr3Ink8U99rzXmikVWoiUwYW05IU8yOBwv165Kw+m/Bi75GV7LcbYzdRJwyLlYP+PqVVHMx67cg2yzpGRB6tVYc8+zswD3FyLsEi63e2am3PnjmRCMUHpPCWi6US0gYg2EtEcGWkaRS/+nTLQNNZMT/MNSnLjmB2HDmDb47N70pc46ubwYW3vQRjw2PNfRJwditGalJOzzeNFwIRejN2kXamUQGt7AW9VM8Zu4BqaGcductKLE6QKzFbORe08jQjVY+8pqxHHKGHxzUvZwm7GATMl7BY99rwyiQBNUKL060DmAzgXwDgAlxDROLvpGkW6x64ZipEjvOp08a6Og+jc35yVvrybs33/Ps3t2WEKXc8mN5SRWf9DR9hz0pE6jt2Sx64nDuYe8r9eshH1d76KvYeMh3aMzLQ04oVnJhwZfQgU8Ni7u+y9NNnUa+4yoZj8GLuTHnt3d1YZzdQ/M/d0kXQjCW1hz+ujUK5/IIQdwGkANgohPhFCdAF4CoD5BR8sIj3GbnW6uAm62/fnpC/PY+841Ja/sXM/Kts2ZeWnfW7ybmS1QgvtztO8G1Zq56kVj11PiM2l89x7TQCAz/ZqN7Gz2arsY2xJATMLT9n32A/tbzWenwZmQjFq52lEfSD3Gq1SuCz7DnXh5Q8/M28gene6mhkVE1FDRkbmnhapQKQj7LkL76lDQd1Yj53sellEdBGA6UKIK5Xv3wJwuhDi+3rHNDQ0iBUr9NYl0eft3/0Egz57qde2fqlWDE2lvd/G6DEYm9wIANhOQ3Eo2q/XvnXdnyBOSTTGxurmUZE8hBGi96iVrXQkRojt2BQdjZTO+wr7J1owBHsLpj020QgA2EZDcZTY1eu3xthYjE004qCoxI74cN00iqWt0oYq7I4diTLRhaOTW3r9tjE6BoIiecfuxCAciA3IbK9N7MJA9H4IZbMfVeiHno7gJhqGjmhfAMDgxA7U4CA2RseAAByZ2IpK6sIODMYwNGfOkwDh2MTHme+qLWrareiL5tgwVCX340ixS/f8jk00okvEMv0WjbGxiKW6MCq1BU00DMPFjsz2YrR3pYWoPBZBNJJ/45elOnF0aisA4IPUaPQpi/Y6/3p5qHXkgKhENXXgk8jR6JdqwyCkBfiwiKOc0mKwGwPRFhuo5HcYR6e24pPoaESQyvx1I46KRBuG0V4cFnFsiY8C0OP1R1PdGJ3abLjcQO96tJ2GoD1SjWOSm3BYxPFZ/OjMbwMTzahFWyZd9Xq3i3Jsi49ERCQza/bk1rfc/LaJWhwSFTg2kl7SYnNkJOpSnyl2H5PZN3siXW2yGQOwH9siw3BUKn1tP43UYZRSXr0yq+VrQxX64xA6RRzbo0dhdGqz7jmqSezBYPS0hHPraiv6ogYH8/KMpw5nlWMsBiSaUSP2I3aHdqvaCES0UgjRUGw/GW0CrUde3tOCiK4GcDUAjBw50lJGkfK+aI8P6LWtHQMgOoDDkXK0lw3E6tQknHh4FZorjs47Pp5I37C5afRKLz4AHd190RmrRlmyA8O7PkUiEgNEenGpDypP0zxOvchJiuNwrK/mPqujJ+PEw+9hT8XRaBEjMLGz5+FWiXRTtC91FLRPj0aM7XVT9schbI4PwEFEcXRHj7Br2f9B7FSc1PkudlWO7rW9PT4AAzuWY115PQDghMPv9/pdFfXG6DEYkdiClsoRmd9qFFsOR/ugO1qFymS6xTAM6YdwChF0xvtjaMen6fInD2Bv+QhsEqNRk2zB1srjUd+xDFsq01E9tWwChI54TS87yhJpO1RRX11xCgQimNj5LgCgM9IHXYkYGsvHIRGtKHQa03nEgL2HulBbXqb5ezuA/h378FlqMLorapFM9Uwp/6jsJCSi5drHxQdgSMdy7I4NQ3XyE4xO9X7gqqIOANsrewRibEd6Pe8kIthXNgwpikAggpjoQio6CMO69mJDZX3vMijLROzpaMX+SI2hOjVEuRY7MQiDxF7srhyNaKoLSKZta48PzHi4YxNpBypBcXTF+mJLfACau/aivWxgTxk6DyGJKA6V1WrmV9GddhqOohYsSdXjWKSFXRVDAOibaMupl+n8j+lI16dasRf70A8t0cHpfDo2Z/asTB5ES8UIZNOIsRiS2I7NleNxUsdybKisx0lKPYmlurG/fEiene3xARjc0bMUdFekDxLRcnwYa8DEzhXYUjkOZe0fYEvZMeiKVfU6dl/HPrREh6A9PgDt8QFoHHA8pmieDbnI8NinALhdCDFN+T4XAIQQv9A7xqrHbpvb+yv/NcIVBVj29K9w+tqfY318HI6/9e2CaW+68O8YM/EMQ+l++tEyjPrLOQCAHZcvx7DHT7Nkn8ran5+Bcd1rsmxKp7Pswctx+p7n8PZRl2PKVfdZSltl+bP34bTVt2Fl9Vk45cASAMDWb/4bI445sfeOyvnYeulSjBh7Us+5V/jonD9h/Odm9Ng24kpMueIe/YyV49dM/R0mfKF3pO+zj9/HyD9+Sdmv59ztuP0YDEMzPjjzMZx05tesFNcQG1YswXEvfjUvfz1W/u1/cMq7P8Qe1GS89Tyy0jlw2xGopg68N+UBnDztMhkma7Js/hU4vfkZvD3s25hyza8BAG379qD//WPybOqp7y9hzMTPWcpv/fJXcfzfL8qkPWPufIyPbMbNsacxhNLn5Z0h/wuTr1uQf7CS/6dfX4xR40/PbH77kRswZdsT6c9HX4spl/+yqB1b7hyPo1NNeLf+5zj1P3QDDZk891z9IQYdme84uoFRj11GjP1dAGOJaBQRlQG4GMAiCen6B51mpBaxuLa3VixdOXG3IvFCkvDitohic1ZfRCSif35IJ3RFuQuP6eyncWDepoqqGo0deyjrO7Dg73YxvaiT0bLKOs4gmXhz1imOFZlMY6fe5q5yuIFG4S/JM9Er+lXk3st/aUXWwQbre88Kmcb2j5cXb/V5je1QjBAiQUTfB/AKgCiAx4QQH9m2zAHu6jcPIpnAXJPHkQlhj8S0m+/a6fZUJBlvVRGDxgI7tGYZKq8tk/JGTq00Cgi7XrlybzqjDx2N/Sr79tPYscfScoeFnUwKO2nE7Y0d53Cnm8Yqn8XGXNuxKfeB+PINX8DG3QeBZ3tlUCyRHIOyhd2c30oFHJRsSkLYAUAI8XcAf5eRlpPM+b8/snScmcobi5sQ9qwbyGilKsQJs+dj8xuTUPfOj3MyUqapS/DY1Ydc9jrgVCDdSFT7t7xjDJ5j0niI9OnbX2NP4NPIcByRakZZH23hl4XpmYQWPW+rDwQTOeRtiRdpgdp5MXPufTV2aDXGDq3GnixhL1Znc1sMvZwXg+e5p6ViUNjL/C/sgZ956gZ6PfpamAvFZHnsJr0LLSLlfVA18fy87T1DuiQIe+YBlNU3U+Dm03so5rUebIQZ9IR1+aS7cUXXD9FviPlRRmYw29oq9CAsfKDzw+TS+WS3JIuFQqzXqaiENVNy7ct+EJg+zwb3d2PZXbsEfj12N1C9VCPdzGamC2d76TIqeTrNAje/jBi7KshZJ6PQzR/RsyfHFsMtFp0y3Ju4CAOPOwPZXYs/mNGAA2fVo1+Fszcimbx2VsMXZkKCFjNQP/RsKnJd9PpQjKBbN0yQ32KwEYoxuL+M1rXTsLAbwMyFjJUb99h7eRSS4qfas9rkeeww6bHrtkTyYuwGbyqdvG782aP5eUcI/fu4sC6HWY/dojDYCXsYzEH5Z7ye2HkbkNm+Cc38C51Lsw9Ct1pELuD/R48fyMSVixOPWRwVI8kbK3ijORVjL3Rz6cWf84Td+qgYrzErbla9XOdj7Eo+JhwAOzbJ8Njzzr2dzlP/VS3LsLAbwYzHXmat8zQak+MtON5MVGt/1vyHQg+lqM7Nm3eMTY/dS0wLu8Y1ei4yDX+s/UHh45z2KNWQo0seu4x3f+bXPfPCrjopQQixGCU8JXGQiIkbKmZmuGOWtyPDewGKjNCQ6LFnh2IKeXi6TWWLLRQ5QzblYnpRJ6U+ZZfk1P/9E1zyvTsKH+aa8JgQdlvDHSXE2HPTIOP9AxqJ2bbHL7CwG8CMl2iuMmWFYhyNsafRGipoFq3yiULNcb1RMZZHhvhP2M2+EUd9oGd3xhOoaKeqrDqiR8ZTN1Xf7XSeyoixF3JkjNV3QwuBBQzuPDWCQzdU7wlKcvLQbN6q0RPqGPxcAAAPtElEQVQJ9VdoPBwKhWL0PfbwCLvZd1hqhVSyW29tfcdAc2S+Lz126zbJ8Njz67udUExhezZdsAid+5sx3oyBHsHCbgSnhplliZSs12VpeeyZjk4J5VAfRoYnKJWAx04mxzVrt+qUbd99G/37DdM+zunhjj05Gd7TztricoY75vbVZH00PfO0cLnHnPwlU+l5CQu7AZyayp3t7cjKQ8uDUvs5ZcSnVTspa62YQrbr37wWp9X7sNls9qGsJeyZbUP131Hj/HBHNSMT+9qotzLmbuSe+17hRoNOQE8oJjwxdhZ2AxgZ0tV5zXIc3v2xdhNaN2HzCxYVTbLguF4JeWikESm4pIC2PVY9dj+OXCi2UFYuWp6kkdOhdy5lQRZWerXT0pQzjr13/tnhRrOl8eOIK6uwsBtBiYkWqigVw45DxbDjzCXrdkWSml/2BCUrM0/9J9BWiURNToLS8tgNnA/XQjFmhjvaeNDqDYU1Q36c3nod96PTYJXwlMRBIiYmKJnBvZhpJkf7Kag2i15rCujuLz3G7stQjDn/SPOcGDgfjq/uqOZjQhbsxMnlrBWTO9zRxj0VouGO7LEbwKkbKpAee6bztIeCoRgd0bfed+o/YTe7EJZ2KMY/wp47empVdCI6Y/2h9ToNWxOUJPQZyHwxtA+rlmVY2A3glJi43xHoTCim0PnRbd5a9ax8ePeZrR+anacGvEU7YQ9z9C7PpJ/8S3dPW6EYCR577rm3VTs4FFNiOOQpFZzY4wRSZp6qaWQLu/lqZLnz1IfCbhqN+lSo1aPi/HIRmYwMH2JvuKMDE+Zs1A/Hl2xwERZ2Azg23DGAHYhqKyN7BIWVG9R6ayX4wq553Q085N0KxZjBzsPGkfLYEvbg1y2V4CmLBzjlKblXkSTmo+mxW0jf6loxIbj5MnMBem8tepzjSwr45KGZJDtLLZsvAy8CVqI4FQt3f1SMBDRmnloRacvLvfr0nH2/6wf4Xs1DhvbVauH4qfPUc761EAmrk4Vseez+rFtW4M5TDwmm96mxVoyVUEzIYuw/mfNjVFcYu51Uz7DXImAGRMW9zlNvGTr6RLw9+nuY8skDFo42Xz963nnqz7plBRZ2D/GrSBVCIxLjcijG0mGOM7Sf8Rcca3neRkQ7TKECNzA7kzZMHnt4SuIo5qdaGyKIFSkTislaK8ZKKMZqeCuI5ywHq6EYGeO+/cihkWfJS8xOKCZEoa5w1hTJOCTrLo6blVgCjRvHiidpNcbuU4fdFJrD6owsKeBwfbGyVoxt5m1HVdTE6ySLwqNiABZ2TzEydtkKjRUTcGDgSZiU94uEceyZRl72q/GspBueCUpm0XyoGfLYg99ayaOsSnNzVVn64denzD2JClOoi4XdAM6NinEm3bFz3nQkXQA9oRhh8GXWusmEZ9les2g1+Y2cQ6eHO2b6EJ3NxRATzpmNRONDmHDeda7lGaYYOwu7hwSx6SfN5hC9aMMsWgJtbOapO7erBwGZPCID6xD5yS53Mw2RsIenJAEkkMKe+W/v9leLPnDSLADAUSd92eCBwa+yWtfdUF1wewkKH7PktEfw/BHXS03T7GJufoY9dg8JZNMvkh9jt4Ja9rGf+w9g8l6MMBhmCOCzMA+tcJKhCUpOGGOR98bPQdln//bs/Z9nzfgGgG9IScvoO0+DBAu7h+TdzJMuAwaO8sYYg2TWirHbYM8uu4kbKoitHD2yS+KHh7yZJQVO/vpcBy3xhjD036h4X5tKmdybedYDwOdv9MYWo2hMULKC5QXQfCCATlCo8/SfAy8CAJRV9HHLnJIijDNPw3mXBIRgDl+T47GX8qgYld5LCuiX6+zr7sem721DRUWl80YxoYBDMQYY3C99Q/WvMj5t3AhBDCv0jMEuLOx/O+sV7Nq9A9/RS8fqkgIh6uDKptD5qIhHMWZwXxetYYKOLZeRiL5ORB8RUYqIGmQZ5TdqjzsD3addhyNm/05quu4Lu4yBbMY89vO+OBnfueir+jtYFuggtnJ6U15dAwBYObjn/ATxIc/4F7t3yRoAFwJYKsEW/xKJID7jF6CaEVKTda/DTKZoyErL6uqOkrL3kOrq/mi6fhu+fPU9mW1+EHZPlhRgHMFWKEYIsQ7wR6UMIkE8b7KmXVsve/DOmRbDB/YOrfhiqJ2PZp7KoHz8eV6b4BnBb9cGmCAKeyYUY9O7s1z2QJ6z4ji1bpAVwuC3L+l/IU464TivzfCMoh47Eb0G4AiNn24VQrxgNCMiuhrA1QAwcuRIwwaGGfcWHZJ3q6qCnL1sr7V0wrUeu13CtACVH+hT5oMWkIcUFXYhxNkyMhJCLACwAAAaGhrC4BQEEAmqKElZrb9BiQWQYYrBdwljCmldpxyK8R1+eZk1Yx+7wx2/SkRNAKYA+BsRvSLHLMa3KB6z/SUFrIZiWHwYphh2R8UsBLBQki1MAMjE2IW9GLtVz5tDMYxskiGcp8l3CWOKfgOHAABaB060lY71GDt77Ixcyi79I9464lsYeWy916ZII3yPKsZRBg8/BjsvXoyJoyYCv1hkOR2ra1+Haa0Yxh8MHzMew8c86LUZUmFhZ0xzxPGn207DskCHdK0YP8FnOPhwKIbxBOvjtll2nIbHIgcfFnbGE3iCkv+wPdKJ8Q0s7IwnlPpaMQzjJCzsJYWPPDIeFcMwjsHCzngCLynAMM7Bd0lJ4D8v13qM3X9lCQu8pEB4YGEvCbwPwZzZdR+mHb4r850nKDGMc/A49pLCO1F84cffxOFkErhnTtoSy4uAsS/CMMVgYWdcoX+fOIB45rvlWDl77AxTFHZ/GMssTp6CVlFl6VgOxTCMc7CwM5bZNv1R/Kre2krNLOwM4xwcimEsc/kZoywfa3VJARZ2hikOe+xMoGBZdw5eUiA8sLAzwYI9doYpCgs7Eyg4FMMwxWFhZwIFLyngHDzzNDzwXcIEirB57L8++gH8OHGF12YwIYNHxTDBImTC/oPLL/PaBCaEsMfOBIqweewM4wTssXvM7xNnY1ffE3CTK7kFfzgbx9gZpjgs7B4z69anUB5jsTIKe+wMUxwWdo/pXxkvvpM0gi+KLOwMUxx2FZlAwaEYhikO3yUlwMF4LQDgcLzGY0uAncfPtnU8++sMUxwOxZQAJ1/8Uzzx5EB8bebVXpuCIy6+H8D9lo+nCEu7U/BaMeGBhb0EqO1Xhdnf/ZHXZkiChZ1hisGhGCZQWF3ulykOLykQHvguYRiGCRks7AzDMCHDlrAT0d1EtJ6IPiSihUTk/bALhmGYEseux/4qgAlCiIkAPgYw175JDMMwjB1sCbsQYrEQIqF8fQfAcPsmMQzDMHaQGWP/DoCX9H4koquJaAURrWhubpaYLcMwDJNN0XHsRPQagCM0frpVCPGCss+tABIAntRLRwixAMACAGhoaOCZEAzDMA5RVNiFEGcX+p2ILgMwE8BUIQQLNsMwjMfYmnlKRNMB/AjAl4QQ7XJMYhiGYexgN8b+IIBqAK8S0ftE9LAEmxgmj80Nt+JAfLDXZjBMILDlsQshjpFlCMMUom7mLcDMW7w2I9S8N+RCjNj+MhqHnIspXhvD2IJnnjIMAwA4/0tTcEnVIzhnyiSvTWFswqs7MgwDABgxsA/+dctZXpvBSIA9doZhmJDBws4wDBMyWNgZhgkR6ak0pf42KBZ2hmGYkMHCzjBMaNgx9MsAgNVDL/DYEm/hUTEMw4SGGV+agl8cfgc/mDrWa1M8hYWdYZjQUB6LYu6ME7w2w3M4FMMwDBMyWNgZhmFCBgs7wzBMyGBhZxiGCRks7AzDMCGDhZ1hGCZksLAzDMOEDBZ2hmGYkEFevH+aiJoBbLF4+CAAeySaEwS4zKUBl7k0sFPmo4UQRd8R6Ymw24GIVgghGry2w024zKUBl7k0cKPMHIphGIYJGSzsDMMwISOIwr7AawM8gMtcGnCZSwPHyxy4GDvDMAxTmCB67AzDMEwBAiXsRDSdiDYQ0UYimuO1PTIgohFE9AYRrSOij4joBmX7QCJ6lYgalf8DlO1ERA8o5+BDIprkbQmsQ0RRInqPiF5Uvo8iomVKmf9MRGXK9nLl+0bl9zov7bYKEdUQ0TNEtF653lPCfp2J6EalXq8hoj8RUUXYrjMRPUZEu4loTdY209eViC5T9m8kosvs2BQYYSeiKID5AM4FMA7AJUQ0zlurpJAA8EMhxAkAJgP4nlKuOQBeF0KMBfC68h1Il3+s8nc1gIfcN1kaNwBYl/X9lwDuVcq8D8AVyvYrAOwTQhwD4F5lvyByP4CXhRDHAzgJ6bKH9joT0VEArgfQIISYACAK4GKE7zo/AWB6zjZT15WIBgK4DcDpAE4DcJv6MLCEECIQfwCmAHgl6/tcAHO9tsuBcr4A4CsANgAYpmwbBmCD8vm3AC7J2j+zX5D+AAxXKvxZAF4EQEhP2ojlXm8ArwCYonyOKfuR12UwWd5+AD7NtTvM1xnAUQC2AhioXLcXAUwL43UGUAdgjdXrCuASAL/N2t5rP7N/gfHY0VNJVJqUbaFBaXqeDGAZgKFCiB0AoPwfouwWlvNwH4BbAKSU77UAWoUQCeV7drkyZVZ+b1P2DxKjATQDeFwJP/0PEVUhxNdZCLENwH8B+AzADqSv20qE+zqrmL2uUq93kISdNLaFZkgPEfUF8CyA/yOE2F9oV41tgToPRDQTwG4hxMrszRq7CgO/BYUYgEkAHhJCnAzgEHqa51oEvsxKKOECAKMAHAmgCulQRC5hus7F0Cuj1LIHSdibAIzI+j4cwHaPbJEKEcWRFvUnhRDPKZt3EdEw5fdhAHYr28NwHs4AMIuINgN4CulwzH0AaohIfcF6drkyZVZ+7w9gr5sGS6AJQJMQYpny/RmkhT7M1/lsAJ8KIZqFEN0AngPwOYT7OquYva5Sr3eQhP1dAGOVHvUypDthFnlsk22IiAA8CmCdEOK/s35aBEDtGb8M6di7uv3bSu/6ZABtapMvKAgh5gohhgsh6pC+jkuEEJcCeAPARcpuuWVWz8VFyv6B8uSEEDsBbCWi45RNUwGsRYivM9IhmMlE1Eep52qZQ3udszB7XV8BcA4RDVBaOuco26zhdaeDyQ6KGQA+BrAJwK1e2yOpTJ9Husn1IYD3lb8ZSMcWXwfQqPwfqOxPSI8O2gRgNdIjDjwvh43ynwngReXzaADLAWwE8BcA5cr2CuX7RuX30V7bbbGs9QBWKNf6eQADwn6dAdwBYD2ANQB+D6A8bNcZwJ+Q7kPoRtrzvsLKdQXwHaXsGwFcbscmnnnKMAwTMoIUimEYhmEMwMLOMAwTMljYGYZhQgYLO8MwTMhgYWcYhgkZLOwMwzAhg4WdYRgmZLCwMwzDhIz/Dwuk6M97ewiUAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "zhat = solver.zhat\n", "ind = np.array(range(nz))\n", "plt.plot(ind,z)\n", "plt.plot(ind,zhat)\n", "plt.legend(['True', 'Estimate'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can measure the normalized mean squared error as follows. The VAMP solver also produces an estimate of the MSE in the variable `zhatvar`. We can extract this variable to compute the predicted MSE. We see that the normalized MSE is indeed low and closely matches the predicted value from VAMP." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized MSE (dB): actual -36.299375 pred -36.400120\n" ] } ], "source": [ "zerr = np.mean(np.abs(zhat-z)2)\n", "zhatvar = solver.zhatvar\n", "zpow = np.mean(np.abs(z)2)\n", "mse_act = 10np.log10(zerr/zpow)\n", "mse_pred = 10np.log10(zhatvar/zpow)\n", "print(\"Normalized MSE (dB): actual {0:f} pred {1:f}\".format(mse_act, mse_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can plot the actual and predicted MSE as a function of the iteration number. When `solver` was contructed, we passed an argument `hist_list=['zhat', 'zhatvar']`. This indicated to store the value of the estimate `zhat` and predicted error variance `zhatvar` with each iteration. We can recover these values from `solver.hist_dict`, the history dictionary. Using the values we can compute and plot the normalized MSE on each iteartion. We see that VAMP gets a low MSE in very few iterations, about 10." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Compute the MSE as a function of the iteration\n", "zhat_hist = solver.hist_dict['zhat']\n", "zhatvar_hist = solver.hist_dict['zhatvar']\n", "nit = len(zhat_hist)\n", "mse_act = np.zeros(nit)\n", "mse_pred = np.zeros(nit)\n", "for it in range(nit):\n", " zerr = np.mean(np.abs(zhat_hist[it]-z)*2)\n", " mse_act[it] = 10np.log10(zerr/zpow)\n", " mse_pred[it] = 10*np.log10(zhatvar_hist[it]/zpow)\n", " \n", "plt.plot(range(nit), mse_act, 'o-', linewidth=2)\n", "plt.plot(range(nit), mse_pred, 's', linewidth=1)\n", "plt.xlabel('Iteration')\n", "plt.ylabel('Normalized MSE (dB)')\n", "plt.legend(['Actual', 'Predicted'])\n", "plt.grid()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": { "collapsed": 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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
nasa-nccs-cds/EDAS	python/test/Untitled.ipynb	1	3469	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sending request on port 5670, server localhost: [domain=[{\"name\":\"d0\",\"lat\":{\"start\":5,\"end\":40,\"system\":\"values\"},\"lon\":{\"start\":80,\"end\":120,\"system\":\"values\"}}],variable=[{\"uri\":\"http://esgf.nccs.nasa.gov/thredds/dodsC/CMIP5/NASA/GISS/historical/E2-H_historical_r1i1p1/tas_Amon_GISS-E2-H_historical_r1i1p1_185001-190012.nc\",\"name\":\"tas:v1\",\"domain\":\"d0\"}],operation=[{\"name\":\"CDSpark.average\",\"input\":\"v1\",\"domain\":\"d0\",\"axes\":\"xy\"}]]\n", "Waiting for a response from the server\n", ".\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Traceback (most recent call last):\n", " File \"<ipython-input-2-14284e5c3212>\", line 25, in <module>\n", " timeSeries = responses[0](squeeze=1)\n", "IndexError: list index out of range\n" ] } ], "source": [ "from pyedas.portal.edas import \n", "import time, sys, cdms2, os\n", "import pandas as pd\n", "from pyedas.portal.edas import \n", "import matplotlib.pyplot as plt\n", "\n", "startServer = False\n", "portal = None\n", "request_port = 5670\n", "response_port = 5671\n", "host = \"cldra\"\n", "server = \"localhost\"\n", "\n", "try:\n", "\n", " portal = EDASPortal( server, request_port, response_port)\n", " response_manager = portal.createResponseManager()\n", "\n", " t0 = time.time()\n", " datainputs = '[domain=[{\"name\":\"d0\",\"lat\":{\"start\":5,\"end\":40,\"system\":\"values\"},\"lon\":{\"start\":80,\"end\":120,\"system\":\"values\"}}],variable=[{\"uri\":\"http://esgf.nccs.nasa.gov/thredds/dodsC/CMIP5/NASA/GISS/historical/E2-H_historical_r1i1p1/tas_Amon_GISS-E2-H_historical_r1i1p1_185001-190012.nc\",\"name\":\"tas:v1\",\"domain\":\"d0\"}],operation=[{\"name\":\"CDSpark.average\",\"input\":\"v1\",\"domain\":\"d0\",\"axes\":\"xy\"}]]'\n", " print \"Sending request on port {0}, server {1}: {2}\".format( portal.request_port, server, datainputs ); sys.stdout.flush()\n", "\n", " rId = portal.sendMessage( \"execute\", [ \"CDSpark.workflow\", datainputs, '{ \"response\":\"object\" }'] )\n", " responses = response_manager.getResponseVariables(rId)\n", " timeSeries = responses[0](squeeze=1)\n", "\n", " timeSeries -= 273.15\n", " datetimes = pd.to_datetime(timeSeries.getTime().asdatetime())\n", "\n", " plt.plot_date( datetimes, timeSeries.data )\n", " plt.gcf().autofmt_xdate()\n", " plt.show()\n", "\n", "except Exception, err:\n", " traceback.print_exc()\n", "\n", "finally:\n", "\n", " portal.shutdown()" ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:edas]", "language": "python", "name": "conda-env-edas-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-2.0
guidj/xlab	lab/exp/.ipynb_checkpoints/kidney-chronic-disease-checkpoint.ipynb	1	5407	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import requests\n", "\n", "import os.path\n", "\n", "def download_file(url, filepath=None, override=False):\n", " \n", " if filepath:\n", " if filepath.endswith('/'):\n", " filename = url.split('/')[-1]\n", " filepath = os.path.join(filepath, filename)\n", " else:\n", " filepath = url.split('/')[-1]\n", " \n", " if override is False:\n", " if os.path.exists(filepath):\n", " return filepath\n", " \n", " res = requests.get(url, stream=True)\n", " with open(filepath, 'wb') as fp:\n", " for chunk in res.iter_content(chunk_size=1024): \n", " if chunk: # filter out keep-alive new chunks\n", " fp.write(chunk)\n", " return filepath" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "request() got an unexpected keyword argument 'override'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-15-db099a8efd63>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'http://archive.ics.uci.edu/ml/machine-learning-databases/00336/Chronic_Kidney_Disease.rar'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfilepath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/guilherme/anaconda/envs/ipy3/lib/python3.5/site-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, kwargs)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\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 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/guilherme/anaconda/envs/ipy3/lib/python3.5/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, kwargs)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\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 58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: request() got an unexpected keyword argument 'override'" ] } ], "source": [ "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00336/Chronic_Kidney_Disease.rar'\n", "filepath = download_file(url, override=False)" ] }, { "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 }	apache-2.0
saketkc/hatex	2018_Fall/EE-546/HW04/HW04-aca5.ipynb	1	56695	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "load('aca5.mat')\n", "X = transpose(X);\n", "s = transpose(s);" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ans =\n", "\n", " 1747 42\n", "\n" ] } ], "source": [ "size(X)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ans =\n", "\n", " 1747 1\n", "\n" ] } ], "source": [ "size(s)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ans =\n", "\n", " 1.0000\n", " 1.0000\n", "\n" ] } ], "source": [ "a = [1 2 3; 4 5 6];\n", "a_row_norm = sqrt(sum(a.^2,2));\n", "a_normalized = bsxfun(@rdivide, a, a_row_norm); \n", "sqrt(sum(a_normalized.^2,2))\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "X_truncated = X(1:2:end, :);\n", "s_truncated = s(1:2:end);\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "X_col_norm = sqrt(sum(X_truncated.^2,1)); \n", "X_truncated = bsxfun(@rdivide,X_truncated, X_col_norm); \n", "X_col_norm = reshape(X_col_norm,[],1);\n", "X_truncated(isnan(X_truncated)) = 0;\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "gamma =1 ;\n", "k = 2;\n", "min_error = 1e9;\n", "min_gamma = 1;\n", "min_k = 2;\n", "\n", "errors_laplacian_unnormalized = zeros(100, 49);\n", "errors_laplacian_normalized = zeros(100, 49);\n", "errors_laplacian_rw = zeros(100, 49);\n", "\n", "errors_minimum = zeros(100, 49);\n", "errors_minimum_index = zeros(100, 49);\n", "\n", "for gamma=1:100 \n", " for k=2:50\n", " [idx1, idx2, idx3] = SpectralClustering(gamma, k, X_truncated);\n", " missclassification_error = Misclassification([idx1 idx2 idx3], s_truncated);\n", " [min_value, min_index] = min(missclassification_error);\n", " if min_value < min_error\n", " min_error = min_value;\n", " min_gamma = gamma;\n", " min_k = k;\n", " end\n", " errors_laplacian_unnormalized(gamma, k-1) = missclassification_error(1);\n", " errors_laplacian_normalized(gamma, k-1) = missclassification_error(2);\n", " errors_laplacian_rw(gamma, k-1) = missclassification_error(3);\n", " \n", " errors_minimum(gamma, k-1) = min_value;\n", " errors_minimum_index(gamma, k-1) = min_index;\n", " end \n", "end\n", " " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "gammas = 1:100;" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "dlmwrite('aca5_gammas.txt', gammas);\n", "dlmwrite('aca5_errors_laplacian_unnormalized.txt', errors_laplacian_unnormalized);\n", "dlmwrite('aca5_errors_laplacian_normalized.txt', errors_laplacian_normalized);\n", "dlmwrite('aca5_errors_laplacian_rw.txt', errors_laplacian_rw);\n", "dlmwrite('aca5_errors_minimum.txt', errors_minimum);\n", "dlmwrite('aca5_errors_minimum_index.txt', errors_minimum_index);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "set(gcf, 'PaperPositionMode', 'manual');\n", "set(gcf, 'PaperUnits', 'inches');\n", "set(gcf, 'PaperPosition', [1 1 10 5]);\n", "subplot(2,2,1);\n", "plot(errors_minimum(17,:));\n", "grid on\n", "\n", "title('aca5: \\gamma=2 \| Minimum error');\n", "xlabel('K');\n", "ylabel('Misclassification Error');\n", "\n", "subplot(2,2,2);\n", "plot(errors_laplacian_normalized(17,:))\n", "grid on\n", "title('aca5: \\gamma=2 \| Laplacian Normalized error')\n", "xlabel('K')\n", "ylabel('Misclassification Error')\n", "\n", "subplot(2,2,3);\n", "plot(errors_laplacian_unnormalized(17,:))\n", "grid on\n", "title('aca5: \\gamma=2 \| Laplacian Unnormalized error')\n", "xlabel('K')\n", "ylabel('Misclassification Error')\n", "\n", "subplot(2,2,4);\n", "\n", "plot(errors_laplacian_rw(17,:))\n", "grid on\n", "title('aca5: \\gamma=2 \| Laplacian RW error')\n", "xlabel('K')\n", "ylabel('Misclassification Error')\n", "\n", "\n", "saveas(gcf,'aca5_minimum_error.pdf')\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "min_gamma =\n", "\n", " 2\n", "\n" ] } ], "source": [ "min_gamma" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "min_k =\n", "\n", " 6\n", "\n" ] } ], "source": [ "min_k" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "min_error =\n", "\n", " 0.3043\n", "\n" ] } ], "source": [ "min_error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Spectral Clustering Function" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[0;31mError: Function definitions are not permitted in this context.\n", "\n", "\u001b[0m" ] } ], "source": [ "function [idx1, idx2, idx3] = SpectralClustering(gamma, k, X)\n", " K = zeros(size(X, 1), size(X, 1));\n", " for i=1:size(X,1)\n", " for j=1:size(X,1)\n", " K(i,j) = exp(-gamma* (norm( X(i, :) - X(j, :), 2).^2) );\n", " end\n", " end\n", " K_maxk = maxk(K, k);\n", " W = K;\n", " for i=1:size(X,1)\n", " for j=1:size(X,1)\n", " if K(i,j) < min(K_maxk(:, j))\n", " W(i,j) = 0;\n", " end\n", " end\n", " end\n", " W = (W+transpose(W))/2;\n", " D = diag(Wones(size(W,1),1));\n", " I = eye(size(W,1));\n", " laplacian_unnormalized = D-W;\n", " laplacian_normalized = I-W;\n", " laplacian_rw = I-inv(D)W;\n", " [V1, D1] = eigs(laplacian_unnormalized, k);\n", " [V2, D2] = eigs(laplacian_normalized, k);\n", " [V3, D3] = eigs(laplacian_rw, k);\n", "\n", "\n", " V1_row_norm = sqrt(sum(V1.^2,2));\n", " Y1 = bsxfun(@rdivide, V1, V1_row_norm);\n", "\n", " V2_row_norm = sqrt(sum(V2.^2,2));\n", " Y2 = bsxfun(@rdivide, V2, V2_row_norm);\n", "\n", " V3_row_norm = sqrt(sum(V3.^2,2));\n", " Y3 = bsxfun(@rdivide, V3, V3_row_norm);\n", "\n", " idx1 = kmeans(Y1, k);\n", " idx2 = kmeans(Y2, k);\n", " idx3 = kmeans(Y3, k);\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-octave", "name": "matlab", "version": "0.15.2" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
magic2du/contact_matrix	Contact_maps/DeepLearning/DeepLearningTool/DL_contact_matrix_load2-new10fold_01_26_2015_parallel.ipynb	1	97889	{ "metadata": { "name": "", "signature": "sha256:e9aa010abbd58dbf7c8fd1eac71bbad7147507698f5cc0f92d1cb0118b191eef" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sys, os\n", "sys.path.append('../../../libs/')\n", "import os.path\n", "import IO_class\n", "from IO_class import FileOperator, saveAsCsv\n", "from sklearn import cross_validation\n", "import sklearn\n", "import csv\n", "from dateutil import parser\n", "from datetime import timedelta\n", "from sklearn import svm\n", "import numpy as np\n", "import pandas as pd\n", "import pdb\n", "import pickle\n", "import numpy as np\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.cross_validation import KFold\n", "from sklearn import preprocessing\n", "import sklearn\n", "import scipy.stats as ss\n", "from sklearn.svm import LinearSVC\n", "import random\n", "from DL_libs import *\n", "from itertools import izip #new\n", "import math\n", "from sklearn.svm import SVC" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#filename = 'SUCCESS_log_CrossValidation_load_DL_remoteFisherM1_DL_RE_US_DL_RE_US_1_1_19MAY2014.txt'\n", "#filename = 'listOfDDIsHaveOver2InterfacesHave40-75_Examples_2010_real_selected.txt' #for testing\n", "\n", "# set settings for this script\n", "settings = {}\n", "settings['filename'] = 'ddi_examples_40_60_over2top_diff_name_2014.txt'\n", "settings['fisher_mode'] = 'FisherM1ONLY'# settings['fisher_mode'] = 'FisherM1ONLY'\n", "settings['with_auc_score'] = False\n", "settings['reduce_ratio'] = 1\n", "settings['SVM'] = 0\n", "settings['DL'] = 1\n", "settings['SAE_SVM'] = 1\n", "settings['SAE_SVM_COMBO'] = 1\n", "settings['SVM_RBF'] = 1\n", "settings['SAE_SVM_RBF'] = 1\n", "settings['SAE_SVM_RBF_COMBO'] = 1\n", "settings['SVM_POLY'] = 0\n", "settings['DL_S'] = 1\n", "settings['DL_U'] = 0\n", "\n", "settings['finetune_lr'] = 1\n", "settings['batch_size'] = 100\n", "settings['pretraining_interations'] = 5001\n", "settings['pretrain_lr'] = 0.001\n", "settings['training_epochs'] = 20001\n", "settings['hidden_layers_sizes'] = [100, 100]\n", "settings['corruption_levels'] = [0, 0]\n", "\n", "\n", "filename = settings['filename']\n", "file_obj = FileOperator(filename)\n", "ddis = file_obj.readStripLines()\n", "import logging\n", "import time\n", "current_date = time.strftime(\"%m_%d_%Y\")\n", "\n", "logger = logging.getLogger(__name__)\n", "logger.setLevel(logging.DEBUG)\n", "\n", "logname = 'log_DL_contact_matrix_load' + current_date + '.log'\n", "handler = logging.FileHandler(logname)\n", "handler.setLevel(logging.DEBUG)\n", "\n", "# create a logging format\n", "\n", "formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n", "handler.setFormatter(formatter)\n", "\n", "# add the handlers to the logger\n", "\n", "logger.addHandler(handler)\n", "\n", "logger.info('Input DDI file: ' + filename)\n", "#logger.debug('This message should go to the log file')\n", "for key, value in settings.items():\n", " logger.info(key +': '+ str(value))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:Input DDI file: ddi_examples_40_60_over2top_diff_name_2014.txt\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:DL: 1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:SVM: 0\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:fisher_mode: FisherM1ONLY\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:SAE_SVM_RBF_COMBO: 1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:SAE_SVM_COMBO: 1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:DL_U: 0\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:DL_S: 1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:corruption_levels: [0, 0]\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:SVM_RBF: 1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:SVM_POLY: 0\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:reduce_ratio: 1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:filename: ddi_examples_40_60_over2top_diff_name_2014.txt\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:pretraining_interations: 5001\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:batch_size: 100\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:SAE_SVM_RBF: 1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:with_auc_score: False\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:pretrain_lr: 0.001\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:training_epochs: 20001\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:finetune_lr: 1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:hidden_layers_sizes: [100, 100]\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:SAE_SVM: 1\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "number of lines in ddi_examples_40_60_over2top_diff_name_2014.txt:136\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "class DDI_family_base(object):\n", " #def __init__(self, ddi, Vectors_Fishers_aaIndex_raw_folder = '/home/du/Documents/Vectors_Fishers_aaIndex_raw_2014/'):\n", " #def __init__(self, ddi, Vectors_Fishers_aaIndex_raw_folder = '/home/sun/Downloads/contactmatrix/contactmatrixanddeeplearningcode/data_test/'):\n", " def __init__(self, ddi, Vectors_Fishers_aaIndex_raw_folder = '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/'):\n", " \"\"\" get total number of sequences in a ddi familgy\n", " Attributes:\n", " ddi: string ddi name\n", " Vectors_Fishers_aaIndex_raw_folder: string, folder\n", " total_number_of_sequences: int\n", " raw_data: dict raw_data[2]\n", " LOO_data['FisherM1'][1]\n", "\n", " \"\"\"\n", " self.ddi = ddi\n", " self.Vectors_Fishers_aaIndex_raw_folder = Vectors_Fishers_aaIndex_raw_folder\n", " self.ddi_folder = self.Vectors_Fishers_aaIndex_raw_folder + ddi + '/'\n", " self.total_number_of_sequences = self.get_total_number_of_sequences()\n", " self.raw_data = {}\n", " self.positve_negative_number = {}\n", " self.equal_size_data = {}\n", " for seq_no in range(1, self.total_number_of_sequences+1):\n", " self.raw_data[seq_no] = self.get_raw_data_for_selected_seq(seq_no)\n", " try:\n", " #positive_file = self.ddi_folder + 'numPos_'+ str(seq_no) + '.txt'\n", " #file_obj = FileOperator(positive_file)\n", " #lines = file_obj.readStripLines()\n", " #import pdb; pdb.set_trace()\n", " count_pos = int(np.sum(self.raw_data[seq_no][:, -1]))\n", " count_neg = self.raw_data[seq_no].shape[0] - count_pos\n", " #self.positve_negative_number[seq_no] = {'numPos': int(float(lines[0]))}\n", " #assert int(float(lines[0])) == count_pos\n", " self.positve_negative_number[seq_no] = {'numPos': count_pos}\n", " #negative_file = self.ddi_folder + 'numNeg_'+ str(seq_no) + '.txt'\n", " #file_obj = FileOperator(negative_file)\n", " #lines = file_obj.readStripLines()\n", " #self.positve_negative_number[seq_no]['numNeg'] = int(float(lines[0]))\n", " self.positve_negative_number[seq_no]['numNeg'] = count_neg\n", " except Exception,e:\n", " print ddi, seq_no\n", " print str(e)\n", " logger.info(ddi + str(seq_no))\n", " logger.info(str(e)) \n", " # get data for equal positive and negative\n", " n_pos = self.positve_negative_number[seq_no]['numPos']\n", " n_neg = self.positve_negative_number[seq_no]['numNeg']\n", " index_neg = range(n_pos, n_pos + n_neg)\n", " random.shuffle(index_neg)\n", " index_neg = index_neg[: n_pos]\n", " positive_examples = self.raw_data[seq_no][ : n_pos, :]\n", " negative_examples = self.raw_data[seq_no][index_neg, :]\n", " self.equal_size_data[seq_no] = np.vstack((positive_examples, negative_examples))\n", " def get_LOO_training_and_reduced_traing(self, seq_no, fisher_mode = 'FisherM1ONLY' , reduce_ratio = 4):\n", " \"\"\" get the leave one out traing data, reduced traing\n", " Parameters:\n", " seq_no: \n", " fisher_mode: default 'FisherM1ONLY'\n", " Returns:\n", " (train_X_LOO, train_y_LOO),(train_X_reduced, train_y_reduced), (test_X, test_y)\n", " \"\"\"\n", " train_X_LOO = np.array([])\n", " train_y_LOO = np.array([])\n", " train_X_reduced = np.array([])\n", " train_y_reduced = np.array([])\n", "\n", " total_number_of_sequences = self.total_number_of_sequences\n", " equal_size_data_selected_sequence = self.equal_size_data[seq_no]\n", " \n", " #get test data for selected sequence\n", " test_X, test_y = self.select_X_y(equal_size_data_selected_sequence, fisher_mode = fisher_mode)\n", " total_sequences = range(1, total_number_of_sequences+1)\n", " loo_sequences = [i for i in total_sequences if i != seq_no]\n", " number_of_reduced = len(loo_sequences)/reduce_ratio if len(loo_sequences)/reduce_ratio !=0 else 1\n", " random.shuffle(loo_sequences)\n", " reduced_sequences = loo_sequences[:number_of_reduced]\n", "\n", " #for loo data\n", " for current_no in loo_sequences:\n", " raw_current_data = self.equal_size_data[current_no]\n", " current_X, current_y = self.select_X_y(raw_current_data, fisher_mode = fisher_mode)\n", " if train_X_LOO.ndim ==1:\n", " train_X_LOO = current_X\n", " else:\n", " train_X_LOO = np.vstack((train_X_LOO, current_X))\n", " train_y_LOO = np.concatenate((train_y_LOO, current_y))\n", "\n", " #for reduced data\n", " for current_no in reduced_sequences:\n", " raw_current_data = self.equal_size_data[current_no]\n", " current_X, current_y = self.select_X_y(raw_current_data, fisher_mode = fisher_mode)\n", " if train_X_reduced.ndim ==1:\n", " train_X_reduced = current_X\n", " else:\n", " train_X_reduced = np.vstack((train_X_reduced, current_X))\n", " train_y_reduced = np.concatenate((train_y_reduced, current_y)) \n", "\n", " return (train_X_LOO, train_y_LOO),(train_X_reduced, train_y_reduced), (test_X, test_y)\n", " \n", " #def get_ten_fold_crossvalid_one_subset(self, start_subset, end_subset, fisher_mode = 'FisherM1ONLY' , reduce_ratio = 4):\n", " def get_ten_fold_crossvalid_one_subset(self, train_index, test_index, fisher_mode = 'FisherM1ONLY' , reduce_ratio = 4):\n", " \"\"\" get traing data, reduced traing data for 10-fold crossvalidation\n", " Parameters:\n", " start_subset: index of start of the testing data\n", " end_subset: index of end of the testing data\n", " fisher_mode: default 'FisherM1ONLY'\n", " Returns:\n", " (train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y)\n", " \"\"\"\n", " train_X_10fold = np.array([])\n", " train_y_10fold = np.array([])\n", " train_X_reduced = np.array([])\n", " train_y_reduced = np.array([])\n", " test_X = np.array([])\n", " test_y = np.array([])\n", "\n", " total_number_of_sequences = self.total_number_of_sequences\n", " \n", " #get test data for selected sequence\n", " #for current_no in range(start_subset, end_subset):\n", " for num in test_index:\n", " current_no = num + 1\n", " raw_current_data = self.equal_size_data[current_no]\n", " current_X, current_y = self.select_X_y(raw_current_data, fisher_mode = fisher_mode)\n", " if test_X.ndim ==1:\n", " test_X = current_X\n", " else:\n", " test_X = np.vstack((test_X, current_X))\n", " test_y = np.concatenate((test_y, current_y))\n", " \n", " #total_sequences = range(1, total_number_of_sequences+1)\n", " #ten_fold_sequences = [i for i in total_sequences if not(i in range(start_subset, end_subset))]\n", " #number_of_reduced = len(ten_fold_sequences)/reduce_ratio if len(ten_fold_sequences)/reduce_ratio !=0 else 1\n", " #random.shuffle(ten_fold_sequences)\n", " #reduced_sequences = ten_fold_sequences[:number_of_reduced]\n", " \n", " number_of_reduced = len(train_index)/reduce_ratio if len(train_index)/reduce_ratio !=0 else 1\n", " random.shuffle(train_index)\n", " reduced_sequences = train_index[:number_of_reduced]\n", "\n", " #for 10-fold cross-validation data\n", " #for current_no in ten_fold_sequences:\n", " for num in train_index:\n", " current_no = num + 1\n", " raw_current_data = self.equal_size_data[current_no]\n", " current_X, current_y = self.select_X_y(raw_current_data, fisher_mode = fisher_mode)\n", " if train_X_10fold.ndim ==1:\n", " train_X_10fold = current_X\n", " else:\n", " train_X_10fold = np.vstack((train_X_10fold, current_X))\n", " train_y_10fold = np.concatenate((train_y_10fold, current_y))\n", "\n", " #for reduced data\n", " for num in reduced_sequences:\n", " current_no = num + 1\n", " raw_current_data = self.equal_size_data[current_no]\n", " current_X, current_y = self.select_X_y(raw_current_data, fisher_mode = fisher_mode)\n", " if train_X_reduced.ndim ==1:\n", " train_X_reduced = current_X\n", " else:\n", " train_X_reduced = np.vstack((train_X_reduced, current_X))\n", " train_y_reduced = np.concatenate((train_y_reduced, current_y)) \n", "\n", " return (train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y)\n", " \n", " def get_total_number_of_sequences(self):\n", " \"\"\" get total number of sequences in a ddi familgy\n", " Parameters:\n", " ddi: string\n", " Vectors_Fishers_aaIndex_raw_folder: string\n", " Returns:\n", " n: int\n", " \"\"\"\n", " folder_path = self.Vectors_Fishers_aaIndex_raw_folder + self.ddi + '/' \n", " filename = folder_path +'allPairs.txt'\n", " all_pairs = np.loadtxt(filename)\n", " return len(all_pairs)\n", "\n", " def get_raw_data_for_selected_seq(self, seq_no):\n", " \"\"\" get raw data for selected seq no in a family\n", " Parameters:\n", " ddi: \n", " seq_no: \n", " Returns:\n", " data: raw data in the sequence file\n", " \"\"\"\n", " folder_path = self.Vectors_Fishers_aaIndex_raw_folder + self.ddi + '/' \n", " filename = folder_path + 'F0_20_F1_20_Sliding_17_11_F0_20_F1_20_Sliding_17_11_ouput_'+ str(seq_no) + '.txt'\n", " data = np.loadtxt(filename)\n", " return data\n", " def select_X_y(self, data, fisher_mode = ''):\n", " \"\"\" select subset from the raw input data set\n", " Parameters:\n", " data: data from matlab txt file\n", " fisher_mode: subset base on this Fisher of AAONLY...\n", " Returns:\n", " selected X, y\n", " \"\"\"\n", " y = data[:,-1] # get lable\n", " if fisher_mode == 'FisherM1': # fisher m1 plus AA index\n", " a = data[:, 20:227]\n", " b = data[:, 247:454]\n", " X = np.hstack((a,b))\n", " elif fisher_mode == 'FisherM1ONLY': \n", " a = data[:, 20:40]\n", " b = data[:, 247:267]\n", " X = np.hstack((a,b))\n", " elif fisher_mode == 'AAONLY':\n", " a = data[:, 40:227]\n", " b = data[:, 267:454]\n", " X = np.hstack((a,b))\n", " else:\n", " raise('there is an error in mode')\n", " return X, y\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": true, "input": [ "import sklearn.preprocessing\n", "\n", "\n", "\n", "def LOO_out_performance_for_all(ddis):\n", " for ddi in ddis:\n", " try:\n", " one_ddi_family = LOO_out_performance_for_one_ddi(ddi)\n", " one_ddi_family.get_LOO_perfermance(settings = settings)\n", " except Exception,e:\n", " print str(e)\n", " logger.info(\"There is a error in this ddi: %s\" % ddi)\n", " logger.info(str(e))\n", "\n", " \n", "class LOO_out_performance_for_one_ddi(object):\n", " \"\"\" get the performance of ddi families\n", " Attributes:\n", " ddi: string ddi name\n", " Vectors_Fishers_aaIndex_raw_folder: string, folder\n", " total_number_of_sequences: int\n", " raw_data: dict raw_data[2]\n", "\n", " \"\"\"\n", " def __init__(self, ddi):\n", " self.ddi_obj = DDI_family_base(ddi)\n", " self.ddi = ddi\n", "\n", " def get_LOO_perfermance(self, settings = None):\n", " fisher_mode = settings['fisher_mode']\n", " analysis_scr = []\n", " with_auc_score = settings['with_auc_score'] \n", " reduce_ratio = settings['reduce_ratio'] \n", " for seq_no in range(1, self.ddi_obj.total_number_of_sequences+1):\n", " print seq_no\n", " logger.info('sequence number: ' + str(seq_no))\n", " if settings['SVM']:\n", " print \"SVM\"\n", " (train_X_LOO, train_y_LOO),(train_X_reduced, train_y_reduced), (test_X, test_y) = self.ddi_obj.get_LOO_training_and_reduced_traing(seq_no,fisher_mode = fisher_mode, reduce_ratio = reduce_ratio)\n", " standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)\n", " scaled_train_X = standard_scaler.transform(train_X_reduced)\n", " scaled_test_X = standard_scaler.transform(test_X)\n", " Linear_SVC = LinearSVC(C=1, penalty=\"l2\")\n", " Linear_SVC.fit(scaled_train_X, train_y_reduced)\n", " predicted_test_y = Linear_SVC.predict(scaled_test_X)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", "\n", " predicted_train_y = Linear_SVC.predict(scaled_train_X)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", " # Deep learning part\n", " min_max_scaler = Preprocessing_Scaler_with_mean_point5()\n", " X_train_pre_validation_minmax = min_max_scaler.fit(train_X_reduced)\n", " X_train_pre_validation_minmax = min_max_scaler.transform(train_X_reduced)\n", " x_test_minmax = min_max_scaler.transform(test_X)\n", " pretraining_X_minmax = min_max_scaler.transform(train_X_LOO)\n", " x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax = train_test_split(X_train_pre_validation_minmax, \n", " train_y_reduced\n", " , test_size=0.4, random_state=42)\n", " finetune_lr = settings['finetune_lr']\n", " batch_size = settings['batch_size']\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " #pretrain_lr=0.001\n", " pretrain_lr = settings['pretrain_lr']\n", " training_epochs = cal_epochs(settings['training_epochs'], x_train_minmax, batch_size = batch_size)\n", " hidden_layers_sizes= settings['hidden_layers_sizes']\n", " corruption_levels = settings['corruption_levels']\n", " if settings['DL']:\n", " print \"direct deep learning\"\n", " # direct deep learning \n", " sda = trainSda(x_train_minmax, y_train_minmax,\n", " x_validation_minmax, y_validation_minmax , \n", " x_test_minmax, test_y,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda.predict(x_train_minmax)\n", " y_train = y_train_minmax\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_train, training_predicted).values()))\n", "\n", " test_predicted = sda.predict(x_test_minmax)\n", " y_test = test_y\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_test, test_predicted).values()))\n", "\n", " if 0:\n", " # deep learning using unlabeled data for pretraining\n", " print 'deep learning with unlabel data'\n", " pretraining_epochs_for_reduced = cal_epochs(1500, pretraining_X_minmax, batch_size = batch_size)\n", " sda_unlabel = trainSda(x_train_minmax, y_train_minmax,\n", " x_validation_minmax, y_validation_minmax , \n", " x_test_minmax, test_y, \n", " pretraining_X_minmax = pretraining_X_minmax,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs_for_reduced, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda_unlabel.predict(x_train_minmax)\n", " y_train = y_train_minmax\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))\n", "\n", " test_predicted = sda_unlabel.predict(x_test_minmax)\n", " y_test = test_y\n", "\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))\n", " if settings['DL_S']:\n", " # deep learning using split network\n", " print 'deep learning using split network'\n", " # get the new representation for A set. first 784-D\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " hidden_layers_sizes= settings['hidden_layers_sizes']\n", " corruption_levels = settings['corruption_levels']\n", " \n", " x = x_train_minmax[:, :x_train_minmax.shape[1]/2]\n", " print \"original shape for A\", x.shape\n", " a_MAE_A = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_A = a_MAE_A.transform(x_train_minmax[:, :x_train_minmax.shape[1]/2])\n", " x = x_train_minmax[:, x_train_minmax.shape[1]/2:]\n", " \n", " print \"original shape for B\", x.shape\n", " a_MAE_B = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_B = a_MAE_B.transform(x_train_minmax[:, x_train_minmax.shape[1]/2:])\n", " \n", " new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax[:, :x_test_minmax.shape[1]/2])\n", " new_x_test_minmax_B = a_MAE_B.transform(x_test_minmax[:, x_test_minmax.shape[1]/2:])\n", " new_x_validation_minmax_A = a_MAE_A.transform(x_validation_minmax[:, :x_validation_minmax.shape[1]/2])\n", " new_x_validation_minmax_B = a_MAE_B.transform(x_validation_minmax[:, x_validation_minmax.shape[1]/2:])\n", " new_x_train_minmax_whole = np.hstack((new_x_train_minmax_A, new_x_train_minmax_B))\n", " new_x_test_minmax_whole = np.hstack((new_x_test_minmax_A, new_x_test_minmax_B))\n", " new_x_validationt_minmax_whole = np.hstack((new_x_validation_minmax_A, new_x_validation_minmax_B))\n", "\n", " finetune_lr = settings['finetune_lr']\n", " batch_size = settings['batch_size']\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " #pretrain_lr=0.001\n", " pretrain_lr = settings['pretrain_lr']\n", " training_epochs = cal_epochs(settings['training_epochs'], x_train_minmax, batch_size = batch_size)\n", " hidden_layers_sizes= settings['hidden_layers_sizes']\n", " corruption_levels = settings['corruption_levels']\n", " \n", " sda_transformed = trainSda(new_x_train_minmax_whole, y_train_minmax,\n", " new_x_validationt_minmax_whole, y_validation_minmax , \n", " new_x_test_minmax_whole, y_test,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " \n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda_transformed.predict(new_x_train_minmax_whole)\n", " y_train = y_train_minmax\n", " \n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))\n", "\n", " test_predicted = sda_transformed.predict(new_x_test_minmax_whole)\n", " y_test = test_y\n", "\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, seq_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))\n", " \n", " \n", " \n", " report_name = filename + '_' + '_'.join(map(str, hidden_layers_sizes)) + \\\n", " '_' + str(pretrain_lr) + '_' + str(finetune_lr) + '_' + str(reduce_ratio)+ \\\n", " '_' +str(training_epochs) + '_' + current_date\n", " saveAsCsv(with_auc_score, report_name, performance_score(y_test, test_predicted, with_auc_score), analysis_scr)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": true, "input": [ "#for 10-fold cross validation\n", "\n", "def ten_fold_crossvalid_performance_for_all(ddis):\n", " for ddi in ddis:\n", " try:\n", " process_one_ddi_tenfold(ddi)\n", " except Exception,e:\n", " print str(e)\n", " logger.debug(\"There is a error in this ddi: %s\" % ddi)\n", " logger.info(str(e))\n", "def process_one_ddi_tenfold(ddi):\n", " \"\"\"A function to waste CPU cycles\"\"\"\n", " logger.info('DDI: %s' % ddi)\n", " try:\n", " one_ddi_family = {}\n", " one_ddi_family[ddi] = Ten_fold_crossvalid_performance_for_one_ddi(ddi)\n", " one_ddi_family[ddi].get_ten_fold_crossvalid_perfermance(settings=settings)\n", " except Exception,e:\n", " print str(e)\n", " logger.debug(\"There is a error in this ddi: %s\" % ddi)\n", " logger.info(str(e))\n", " return None\n", "class Ten_fold_crossvalid_performance_for_one_ddi(object):\n", " \"\"\" get the performance of ddi families\n", " Attributes:\n", " ddi: string ddi name\n", " Vectors_Fishers_aaIndex_raw_folder: string, folder\n", " total_number_of_sequences: int\n", " raw_data: dict raw_data[2]\n", "\n", " \"\"\"\n", " def __init__(self, ddi):\n", " self.ddi_obj = DDI_family_base(ddi)\n", " self.ddi = ddi\n", " def get_ten_fold_crossvalid_perfermance(self, settings = None):\n", " fisher_mode = settings['fisher_mode']\n", " analysis_scr = []\n", " with_auc_score = settings['with_auc_score']\n", " reduce_ratio = settings['reduce_ratio']\n", " #for seq_no in range(1, self.ddi_obj.total_number_of_sequences+1):\n", " #subset_size = math.floor(self.ddi_obj.total_number_of_sequences / 10.0)\n", " kf = KFold(self.ddi_obj.total_number_of_sequences, n_folds = 10, shuffle = True)\n", " #for subset_no in range(1, 11):\n", " for ((train_index, test_index),subset_no) in izip(kf,range(1,11)):\n", " #for train_index, test_index in kf;\n", " print(\"Subset:\", subset_no)\n", " print(\"Train index: \", train_index)\n", " print(\"Test index: \", test_index)\n", " #logger.info('subset number: ' + str(subset_no))\n", " (train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y) = self.ddi_obj.get_ten_fold_crossvalid_one_subset(train_index, test_index, fisher_mode = fisher_mode, reduce_ratio = reduce_ratio)\n", " standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)\n", " scaled_train_X = standard_scaler.transform(train_X_reduced)\n", " scaled_test_X = standard_scaler.transform(test_X)\n", " \n", " if settings['SVM']:\n", " print \"SVM\" \n", " Linear_SVC = LinearSVC(C=1, penalty=\"l2\")\n", " Linear_SVC.fit(scaled_train_X, train_y_reduced)\n", " predicted_test_y = Linear_SVC.predict(scaled_test_X)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", "\n", " predicted_train_y = Linear_SVC.predict(scaled_train_X)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values())) \n", " if settings['SVM_RBF']:\n", " print \"SVM_RBF\"\n", " L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(scaled_train_X, train_y_reduced)\n", "\n", " predicted_test_y = L1_SVC_RBF_Selector.predict(scaled_test_X)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", "\n", " predicted_train_y = L1_SVC_RBF_Selector.predict(scaled_train_X)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", " if settings['SVM_POLY']:\n", " print \"SVM_POLY\"\n", " L1_SVC_POLY_Selector = SVC(C=1, kernel='poly').fit(scaled_train_X, train_y_reduced)\n", "\n", " predicted_test_y = L1_SVC_POLY_Selector.predict(scaled_test_X)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", "\n", " predicted_train_y = L1_SVC_POLY_Selector.predict(scaled_train_X)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", " # direct deep learning \n", " min_max_scaler = Preprocessing_Scaler_with_mean_point5()\n", " X_train_pre_validation_minmax = min_max_scaler.fit(train_X_reduced)\n", " X_train_pre_validation_minmax = min_max_scaler.transform(train_X_reduced)\n", " x_test_minmax = min_max_scaler.transform(test_X)\n", " \n", " x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax = train_test_split(X_train_pre_validation_minmax, \n", " train_y_reduced\n", " , test_size=0.4, random_state=42)\n", " finetune_lr = settings['finetune_lr']\n", " batch_size = settings['batch_size']\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " #pretrain_lr=0.001\n", " pretrain_lr = settings['pretrain_lr']\n", " training_epochs = settings['training_epochs']\n", " hidden_layers_sizes= settings['hidden_layers_sizes']\n", " corruption_levels = settings['corruption_levels']\n", " \n", " #### new prepresentation\n", " x = X_train_pre_validation_minmax\n", " a_MAE_A = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_A = a_MAE_A.transform(X_train_pre_validation_minmax)\n", " new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax)\n", " standard_scaler = preprocessing.StandardScaler().fit(new_x_train_minmax_A)\n", " new_x_train_scaled = standard_scaler.transform(new_x_train_minmax_A)\n", " new_x_test_scaled = standard_scaler.transform(new_x_test_minmax_A)\n", " new_x_train_combo = np.hstack((scaled_train_X, new_x_train_scaled))\n", " new_x_test_combo = np.hstack((scaled_test_X, new_x_test_scaled))\n", " \n", " \n", " if settings['SAE_SVM']: \n", " print 'SAE followed by SVM'\n", "\n", " Linear_SVC = LinearSVC(C=1, penalty=\"l2\")\n", " Linear_SVC.fit(new_x_train_scaled, train_y_reduced)\n", " predicted_test_y = Linear_SVC.predict(new_x_test_scaled)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", " predicted_train_y = Linear_SVC.predict(new_x_train_scaled)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", " if settings['SAE_SVM_RBF']: \n", " print 'SAE followed by SVM RBF'\n", " x = X_train_pre_validation_minmax\n", " L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_scaled, train_y_reduced)\n", " predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_scaled)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", " predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_scaled)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))\n", " if settings['SAE_SVM_COMBO']: \n", " print 'SAE followed by SVM with combo feature'\n", " Linear_SVC = LinearSVC(C=1, penalty=\"l2\")\n", " Linear_SVC.fit(new_x_train_combo, train_y_reduced)\n", " predicted_test_y = Linear_SVC.predict(new_x_test_combo)\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_COMBO', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", " predicted_train_y = Linear_SVC.predict(new_x_train_combo)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_COMBO', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values())) \n", " if settings['SAE_SVM_RBF_COMBO']: \n", " print 'SAE followed by SVM RBF with combo feature'\n", " L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_combo, train_y_reduced)\n", " predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_combo) \n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF_COMBO', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new\n", " predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_combo)\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF_COMBO', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values())) \n", " \n", " if settings['DL']:\n", " print \"direct deep learning\"\n", " sda = trainSda(x_train_minmax, y_train_minmax,\n", " x_validation_minmax, y_validation_minmax , \n", " x_test_minmax, test_y,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda.predict(x_train_minmax)\n", " y_train = y_train_minmax\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_train, training_predicted).values()))\n", "\n", " test_predicted = sda.predict(x_test_minmax)\n", " y_test = test_y\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_test, test_predicted).values()))\n", " \n", " if settings['DL_U']:\n", " # deep learning using unlabeled data for pretraining\n", " print 'deep learning with unlabel data'\n", " pretraining_X_minmax = min_max_scaler.transform(train_X_10fold)\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " sda_unlabel = trainSda(x_train_minmax, y_train_minmax,\n", " x_validation_minmax, y_validation_minmax , \n", " x_test_minmax, test_y, \n", " pretraining_X_minmax = pretraining_X_minmax,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda_unlabel.predict(x_train_minmax)\n", " y_train = y_train_minmax\n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))\n", "\n", " test_predicted = sda_unlabel.predict(x_test_minmax)\n", " y_test = test_y\n", "\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))\n", " if settings['DL_S']:\n", " # deep learning using split network\n", " y_test = test_y\n", " print 'deep learning using split network'\n", " # get the new representation for A set. first 784-D\n", " pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)\n", " \n", " x = x_train_minmax[:, :x_train_minmax.shape[1]/2]\n", " print \"original shape for A\", x.shape\n", " a_MAE_A = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_A = a_MAE_A.transform(x_train_minmax[:, :x_train_minmax.shape[1]/2])\n", " x = x_train_minmax[:, x_train_minmax.shape[1]/2:]\n", " \n", " print \"original shape for B\", x.shape\n", " a_MAE_B = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size, \n", " hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)\n", " new_x_train_minmax_B = a_MAE_B.transform(x_train_minmax[:, x_train_minmax.shape[1]/2:])\n", " \n", " new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax[:, :x_test_minmax.shape[1]/2])\n", " new_x_test_minmax_B = a_MAE_B.transform(x_test_minmax[:, x_test_minmax.shape[1]/2:])\n", " new_x_validation_minmax_A = a_MAE_A.transform(x_validation_minmax[:, :x_validation_minmax.shape[1]/2])\n", " new_x_validation_minmax_B = a_MAE_B.transform(x_validation_minmax[:, x_validation_minmax.shape[1]/2:])\n", " new_x_train_minmax_whole = np.hstack((new_x_train_minmax_A, new_x_train_minmax_B))\n", " new_x_test_minmax_whole = np.hstack((new_x_test_minmax_A, new_x_test_minmax_B))\n", " new_x_validationt_minmax_whole = np.hstack((new_x_validation_minmax_A, new_x_validation_minmax_B))\n", "\n", " \n", " sda_transformed = trainSda(new_x_train_minmax_whole, y_train_minmax,\n", " new_x_validationt_minmax_whole, y_validation_minmax , \n", " new_x_test_minmax_whole, y_test,\n", " hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \\\n", " training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, \n", " pretrain_lr = pretrain_lr, finetune_lr=finetune_lr\n", " )\n", " \n", " print 'hidden_layers_sizes:', hidden_layers_sizes\n", " print 'corruption_levels:', corruption_levels\n", " training_predicted = sda_transformed.predict(new_x_train_minmax_whole)\n", " y_train = y_train_minmax\n", " \n", " isTest = False; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))\n", "\n", " test_predicted = sda_transformed.predict(new_x_test_minmax_whole)\n", " y_test = test_y\n", "\n", " isTest = True; #new\n", " analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))\n", " \n", " \n", " report_name = filename + '_' + '_test10fold_'.join(map(str, hidden_layers_sizes)) + \\\n", " '_' + str(pretrain_lr) + '_' + str(finetune_lr) + '_' + str(reduce_ratio)+ \\\n", " '_' + str(training_epochs) + '_' + current_date\n", " saveAsCsv(with_auc_score, report_name, performance_score(test_y, predicted_test_y, with_auc_score), analysis_scr)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "#LOO_out_performance_for_all(ddis)\n", "#LOO_out_performance_for_all(ddis)\n", "from multiprocessing import Pool\n", "pool = Pool(2)\n", "pool.map(process_one_ddi_tenfold, ddis[:])\n", "pool.close()\n", "pool.join()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb1_5_int_RNA_pol_Rpb5_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/ERAP1_C_int_Peptidase_M1/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/NTP_transf_2_int_tRNA_NucTransf2/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/DNA_PPF_int_gp45-slide_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Pkinase_int_TGF_beta_GS/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Asn_synthase_int_GATase_7/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Eno-Rase_FAD_bd_int_Enoyl_reductase/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Hemocyanin_M_int_Hemocyanin_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/C2-set_2_int_V-set/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Colicin-DNase_int_Colicin_Pyocin/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/LRR_1_int_LRR_7/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/FERM_C_int_FERM_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/PSII_int_PsbU/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/FERM_C_int_FERM_M/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Skp1_int_Skp1_POZ/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/TetR_C_2_int_TetR_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Lyase_8_int_Lyase_8_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/FG-GAP_int_Integrin_beta/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Lyase_8_int_Lyase_8_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Trypsin_int_V-set/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/OKR_DC_1_int_OKR_DC_1_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Arg_repressor_int_Arg_repressor_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/SUFU_int_SUFU_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Peptidase_S9_int_Peptidase_S9_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Alpha-amylase_int_DUF3459/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GAF_int_PHY/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Sigma70_r2_int_Sigma70_r3/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb2_6_int_Sigma70_r4/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/FXa_inhibition_int_Ldl_recept_b/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/PQQ_int_PQQ_2/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Fn3-like_int_Glyco_hydro_3/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/MutS_II_int_MutS_III/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GT36_AF_int_Glyco_transf_36/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/EFG_IV_int_Exotox-A_cataly/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Alpha-amylase_C_int_TIG/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/IlvC_int_IlvN/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb2_7_int_RNA_pol_Rpb6/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Cytochrom_B_N_2_int_Rieske/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GCV_T_int_GCV_T_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb2_7_int_Sigma70_r3/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/EF-hand_7_int_Pkinase/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/ABC_tran_int_CFTR_R/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb1_5_int_RNA_pol_Rpb2_5/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/CBM_X_int_GT36_AF/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Pkinase_int_Ribonuc_2-5A/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/EFG_II_int_GTP_EFTU_D2/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/HATPase_c_int_HisKA/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Amidase_int_Glu-tRNAGln/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/PolyA_pol_int_PolyA_pol_RNAbd/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Glyco_hydro_4_int_Glyco_hydro_4C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Bromodomain_int_PHD/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/ATLF_int_Anthrax-tox_M/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GTP_EFTU_int_eIF2_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Transglut_N_int_Transglut_core/allPairs.txt'\n", "[Errno 2] No such file or directory: 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'/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/PNPase_int_RNase_PH/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/LRR_4_int_LRR_7/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/AAA_int_CDC48_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_L_2_int_RNA_pol_Rpb8/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/CK_II_beta_int_Pkinase/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb1_5_int_RNA_pol_Rpb2_45/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Fucose_iso_N1_int_Fucose_iso_N2/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/G6PD_C_int_G6PD_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/POR_N_int_TPP_enzyme_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Fn3-like_int_Glyco_hydro_3_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Pkinase_int_Pkinase_Tyr/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Hemocyanin_C_int_Hemocyanin_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/MutS_I_int_MutS_II/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/ATP-sulfurylase_int_PUA_2/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/PGM_PMM_III_int_PGM_PMM_IV/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/SNARE_int_Synaptobrevin/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Cbl_N2_int_Cbl_N3/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/V-set_int_VWA/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/MaoC_dehydrat_N_int_MaoC_dehydratas/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Cytochrom_B559a_int_Photo_RC/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/UreE_C_int_UreE_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GST_C_2_int_GST_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/DUF1205_int_Glyco_transf_28/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb2_1_int_Sigma70_r3/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Synapsin_int_Synapsin_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Topoisom_bac_int_Toprim/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Pyr_redox_int_Pyr_redox_dim/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb1_3_int_RNA_pol_Rpb6/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/EF-hand_7_int_GFP/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Molybdop_Fe4S4_int_Molydop_binding/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/BCDHK_Adom3_int_HATPase_c/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Peptidase_S41_int_Tricorn_C1/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/UCR_hinge_int_UcrQ/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/UvrD-helicase_int_UvrD_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Dynamin_M_int_Dynamin_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Neur_chan_LBD_int_V-set/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Cytochrom_D1_int_Cytochrome_CBB3/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Lipase3_N_int_Lipase_3/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/THDPS_M_int_THDPS_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb1_1_int_Sigma70_r1_2/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/End_beta_propel_int_End_tail_spike/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Fimbrial_int_PapD_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Ephrin_int_Ephrin_lbd/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Fimbrial_int_PapD_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/MSP_int_Photo_RC/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GHMP_kinases_C_int_GHMP_kinases_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Arrestin_C_int_Arrestin_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Med11_int_Med22/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/UCR_TM_int_UCR_UQCRX_QCR9/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Cohesin_int_Dockerin_1/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Mur_ligase_int_Mur_ligase_M/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/AlkA_N_int_HhH-GPD/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/AMNp_N_int_PNP_UDP_1/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/PDGF_int_V-set/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Ribosomal_S11_int_Ribosomal_S7/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/EFG_II_int_GTP_EFTU/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Photo_RC_int_PsbT/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Adaptin_N_int_Clat_adaptor_s/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Cbl_N_int_Cbl_N3/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GP120_int_ig/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Cbl_N_int_Cbl_N2/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/FAD_binding_1_int_NAD_binding_1/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Amidase_int_GatB_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Ecotin_int_Trypsin/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/MutS_I_int_MutS_III/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/OTCace_int_PyrI_C/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Photo_RC_int_PsbL/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Lipoprot_C_int_Sushi/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Photo_RC_int_PsbJ/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Pre-SET_int_SET/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/6PGD_int_NAD_binding_2/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/LRRNT_int_LRR_8/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Phosphodiest_int_Somatomedin_B/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/UBACT_int_UBA_e1_thiolCys/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/NUDIX_2_int_RRM_6/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/QueF_int_QueF_N/allPairs.txt'\n", "[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GFRP_int_GTP_cyclohydroI/allPairs.txt'\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:__main__:DDI: ERAP1_C_int_Peptidase_M1\n", "INFO:__main__:DDI: RNA_pol_Rpb1_5_int_RNA_pol_Rpb5_N\n", "DEBUG:__main__:There is a error in this ddi: ERAP1_C_int_Peptidase_M1\n", "DEBUG:__main__:There is a error in this ddi: RNA_pol_Rpb1_5_int_RNA_pol_Rpb5_N\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/ERAP1_C_int_Peptidase_M1/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb1_5_int_RNA_pol_Rpb5_N/allPairs.txt'\n", "INFO:__main__:DDI: DNA_PPF_int_gp45-slide_C\n", "INFO:__main__:DDI: NTP_transf_2_int_tRNA_NucTransf2\n", "DEBUG:__main__:There is a error in this ddi: DNA_PPF_int_gp45-slide_C\n", "DEBUG:__main__:There is a error in this ddi: NTP_transf_2_int_tRNA_NucTransf2\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/DNA_PPF_int_gp45-slide_C/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/NTP_transf_2_int_tRNA_NucTransf2/allPairs.txt'\n", "INFO:__main__:DDI: Asn_synthase_int_GATase_7\n", "INFO:__main__:DDI: Pkinase_int_TGF_beta_GS\n", "DEBUG:__main__:There is a error in this ddi: Asn_synthase_int_GATase_7\n", "DEBUG:__main__:There is a error in this ddi: Pkinase_int_TGF_beta_GS\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Asn_synthase_int_GATase_7/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Pkinase_int_TGF_beta_GS/allPairs.txt'\n", "INFO:__main__:DDI: Hemocyanin_M_int_Hemocyanin_N\n", "INFO:__main__:DDI: Eno-Rase_FAD_bd_int_Enoyl_reductase\n", "DEBUG:__main__:There is a error in this ddi: Hemocyanin_M_int_Hemocyanin_N\n", "DEBUG:__main__:There is a error in this ddi: Eno-Rase_FAD_bd_int_Enoyl_reductase\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Hemocyanin_M_int_Hemocyanin_N/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Eno-Rase_FAD_bd_int_Enoyl_reductase/allPairs.txt'\n", "INFO:__main__:DDI: Colicin-DNase_int_Colicin_Pyocin\n", "INFO:__main__:DDI: C2-set_2_int_V-set\n", "DEBUG:__main__:There is a error in this ddi: Colicin-DNase_int_Colicin_Pyocin\n", "DEBUG:__main__:There is a error in this ddi: C2-set_2_int_V-set\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Colicin-DNase_int_Colicin_Pyocin/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/C2-set_2_int_V-set/allPairs.txt'\n", "INFO:__main__:DDI: FERM_C_int_FERM_N\n", "INFO:__main__:DDI: LRR_1_int_LRR_7\n", "DEBUG:__main__:There is a error in this ddi: FERM_C_int_FERM_N\n", "DEBUG:__main__:There is a error in this ddi: LRR_1_int_LRR_7\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/FERM_C_int_FERM_N/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/LRR_1_int_LRR_7/allPairs.txt'\n", "INFO:__main__:DDI: FERM_C_int_FERM_M\n", "INFO:__main__:DDI: PSII_int_PsbU\n", "DEBUG:__main__:There is a error in this ddi: FERM_C_int_FERM_M\n", "DEBUG:__main__:There is a error in this ddi: PSII_int_PsbU\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/FERM_C_int_FERM_M/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/PSII_int_PsbU/allPairs.txt'\n", "INFO:__main__:DDI: TetR_C_2_int_TetR_N\n", "INFO:__main__:DDI: Skp1_int_Skp1_POZ\n", "DEBUG:__main__:There is a error in this ddi: TetR_C_2_int_TetR_N\n", "DEBUG:__main__:There is a error in this ddi: Skp1_int_Skp1_POZ\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/TetR_C_2_int_TetR_N/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Skp1_int_Skp1_POZ/allPairs.txt'\n", "INFO:__main__:DDI: FG-GAP_int_Integrin_beta\n", "INFO:__main__:DDI: Lyase_8_int_Lyase_8_N\n", "DEBUG:__main__:There is a error in this ddi: FG-GAP_int_Integrin_beta\n", "DEBUG:__main__:There is a error in this ddi: Lyase_8_int_Lyase_8_N\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/FG-GAP_int_Integrin_beta/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Lyase_8_int_Lyase_8_N/allPairs.txt'\n", "INFO:__main__:DDI: Trypsin_int_V-set\n", "INFO:__main__:DDI: Lyase_8_int_Lyase_8_C\n", "DEBUG:__main__:There is a error in this ddi: Trypsin_int_V-set\n", "DEBUG:__main__:There is a error in this ddi: Lyase_8_int_Lyase_8_C\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Trypsin_int_V-set/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Lyase_8_int_Lyase_8_C/allPairs.txt'\n", "INFO:__main__:DDI: Arg_repressor_int_Arg_repressor_C\n", "INFO:__main__:DDI: OKR_DC_1_int_OKR_DC_1_N\n", "DEBUG:__main__:There is a error in this ddi: Arg_repressor_int_Arg_repressor_C\n", "DEBUG:__main__:There is a error in this ddi: OKR_DC_1_int_OKR_DC_1_N\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Arg_repressor_int_Arg_repressor_C/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/OKR_DC_1_int_OKR_DC_1_N/allPairs.txt'\n", "INFO:__main__:DDI: Peptidase_S9_int_Peptidase_S9_N\n", "INFO:__main__:DDI: SUFU_int_SUFU_C\n", "DEBUG:__main__:There is a error in this ddi: Peptidase_S9_int_Peptidase_S9_N\n", "DEBUG:__main__:There is a error in this ddi: SUFU_int_SUFU_C\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Peptidase_S9_int_Peptidase_S9_N/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/SUFU_int_SUFU_C/allPairs.txt'\n", "INFO:__main__:DDI: GAF_int_PHY\n", "INFO:__main__:DDI: Alpha-amylase_int_DUF3459\n", "DEBUG:__main__:There is a error in this ddi: GAF_int_PHY\n", "DEBUG:__main__:There is a error in this ddi: Alpha-amylase_int_DUF3459\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GAF_int_PHY/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Alpha-amylase_int_DUF3459/allPairs.txt'\n", "INFO:__main__:DDI: RNA_pol_Rpb2_6_int_Sigma70_r4\n", "INFO:__main__:DDI: Sigma70_r2_int_Sigma70_r3\n", "DEBUG:__main__:There is a error in this ddi: RNA_pol_Rpb2_6_int_Sigma70_r4\n", "DEBUG:__main__:There is a error in this ddi: Sigma70_r2_int_Sigma70_r3\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb2_6_int_Sigma70_r4/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Sigma70_r2_int_Sigma70_r3/allPairs.txt'\n", "INFO:__main__:DDI: PQQ_int_PQQ_2\n", "INFO:__main__:DDI: FXa_inhibition_int_Ldl_recept_b\n", "DEBUG:__main__:There is a error in this ddi: PQQ_int_PQQ_2\n", "DEBUG:__main__:There is a error in this ddi: FXa_inhibition_int_Ldl_recept_b\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/PQQ_int_PQQ_2/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/FXa_inhibition_int_Ldl_recept_b/allPairs.txt'\n", "INFO:__main__:DDI: MutS_II_int_MutS_III\n", "INFO:__main__:DDI: Fn3-like_int_Glyco_hydro_3\n", "DEBUG:__main__:There is a error in this ddi: MutS_II_int_MutS_III\n", "DEBUG:__main__:There is a error in this ddi: Fn3-like_int_Glyco_hydro_3\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/MutS_II_int_MutS_III/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Fn3-like_int_Glyco_hydro_3/allPairs.txt'\n", "INFO:__main__:DDI: EFG_IV_int_Exotox-A_cataly\n", "INFO:__main__:DDI: GT36_AF_int_Glyco_transf_36\n", "DEBUG:__main__:There is a error in this ddi: EFG_IV_int_Exotox-A_cataly\n", "DEBUG:__main__:There is a error in this ddi: GT36_AF_int_Glyco_transf_36\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/EFG_IV_int_Exotox-A_cataly/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GT36_AF_int_Glyco_transf_36/allPairs.txt'\n", "INFO:__main__:DDI: IlvC_int_IlvN\n", "INFO:__main__:DDI: Alpha-amylase_C_int_TIG\n", "DEBUG:__main__:There is a error in this ddi: IlvC_int_IlvN\n", "DEBUG:__main__:There is a error in this ddi: Alpha-amylase_C_int_TIG\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/IlvC_int_IlvN/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Alpha-amylase_C_int_TIG/allPairs.txt'\n", "INFO:__main__:DDI: Cytochrom_B_N_2_int_Rieske\n", "INFO:__main__:DDI: RNA_pol_Rpb2_7_int_RNA_pol_Rpb6\n", "DEBUG:__main__:There is a error in this ddi: Cytochrom_B_N_2_int_Rieske\n", "DEBUG:__main__:There is a error in this ddi: RNA_pol_Rpb2_7_int_RNA_pol_Rpb6\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/Cytochrom_B_N_2_int_Rieske/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb2_7_int_RNA_pol_Rpb6/allPairs.txt'\n", "INFO:__main__:DDI: RNA_pol_Rpb2_7_int_Sigma70_r3\n", "INFO:__main__:DDI: GCV_T_int_GCV_T_C\n", "DEBUG:__main__:There is a error in this ddi: RNA_pol_Rpb2_7_int_Sigma70_r3\n", "DEBUG:__main__:There is a error in this ddi: GCV_T_int_GCV_T_C\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/RNA_pol_Rpb2_7_int_Sigma70_r3/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/GCV_T_int_GCV_T_C/allPairs.txt'\n", "INFO:__main__:DDI: ABC_tran_int_CFTR_R\n", "INFO:__main__:DDI: EF-hand_7_int_Pkinase\n", "DEBUG:__main__:There is a error in this ddi: ABC_tran_int_CFTR_R\n", "DEBUG:__main__:There is a error in this ddi: EF-hand_7_int_Pkinase\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/ABC_tran_int_CFTR_R/allPairs.txt'\n", "INFO:__main__:[Errno 2] No such file or directory: '/big/du/Protein_Protein_Interaction_Project/Contact_Matrix_Project/Vectors_Fishers_aaIndex_raw_2014_paper/EF-hand_7_int_Pkinase/allPairs.txt'\n", "INFO:__main__:DDI: CBM_X_int_GT36_AF\n", "INFO:__main__:DDI: RNA_pol_Rpb1_5_int_RNA_pol_Rpb2_5\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "x = logging._handlers.copy()\n", "for i in x:\n", " log.removeHandler(i)\n", " i.flush()\n", " i.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 } ], "metadata": {} } ] }	gpl-2.0
kdmurray91/kwip-experiments	writeups/reviews/2017-03-26.ipynb	1	13241	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "library(tidyr)\n", "library(dplyr, warn.conflicts=F, quietly=T)\n", "library(ggplot2)\n", "library(Cairo)\n", "library(scales)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "unlink(\"plots/\", recursive = T)\n", "dir.create(\"plots\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "results.all = read.delim('2017-03-26.tsv', header=F,\n", " col.names=c(\"seed\", \"metric\", \"sketchsize\", \"cov\", \"var\", \"rho\"))\n", "str(results.all)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Take the best sketch sizes\n", "results = results.all %>%\n", " filter(sketchsize == 1e4 & metric %in% c(\"mashec\", \"mash\")\n", " \| sketchsize==1e7 & metric %in% c(\"ip\", \"wip\"))\n", "table(results$sketchsize)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Remove redundant mash datapoints\n", "#results = results %>% filter(cov > 8 \| (cov <= 8 & metric != \"mashec\"))\n", "results$metric = factor(results$metric, labels=c(\"IP\", \"Mash (min. abund. 1)\", \"Mash (min. abund. 2)\", \"WIP\"))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Parameter ranges" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "ggplot(results, aes(x=cov, y=var)) +\n", " geom_point() +\n", " scale_x_log10() +\n", " scale_y_log10() +\n", " theme_bw()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# PI\n", "\n", "Performance across $\\pi$ at both 8x and 32x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "pi.dat = results %>%\n", " filter(cov==32 \| cov==8) %>%\n", " select(rho, metric, var, cov, seed) %>%\n", " mutate(cov.f = as.factor(cov),\n", " var.f = as.factor(var),\n", " seed = as.factor(seed))\n", "\n", "pi.dat.summ = pi.dat %>%\n", " group_by(cov, metric, var) %>%\n", " summarise(rho_av=mean(rho),\n", " rho_sd=sd(rho),\n", " rho_med=median(rho),\n", " rho_25=quantile(rho, p=c(1/4)),\n", " rho_75=quantile(rho, p=c(3/4))) %>%\n", " mutate(cov.f = as.factor(cov),\n", " var.f = as.factor(var))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "str(pi.dat)\n", "summary(pi.dat)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "str(pi.dat.summ)\n", "summary(pi.dat.summ)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [], "source": [ "p = ggplot(pi.dat, aes(x=var.f, y=rho, fill=metric)) +\n", " geom_boxplot(aes(fill=metric)) +\n", " xlab(expression(paste('Mean pairwise nucleotide divergence (', pi, ')'))) +\n", " ylab(expression(paste(\"Spearman's \", rho, \" +- SD\"))) +\n", " facet_wrap(~cov.f ) + \n", " ylim(0, 1) +\n", " theme_classic() + \n", " theme(axis.text.x=element_text(angle = 45, hjust = 1, vjust=1),\n", " legend.position = \"bottom\")\n", "\n", "pdf(\"plots/pi_both_box.pdf\", width=7, height = 3)\n", "print(p)\n", "dev.off()\n", "svg(\"plots/pi_both_box.svg\", width=7, height = 3)\n", "print(p)\n", "dev.off()\n", "\n", "print(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "p = ggplot(pi.dat.summ, aes(x=var, y=rho_med)) +\n", " geom_line(aes(linetype=metric)) +\n", " geom_ribbon(aes(fill=metric, ymin=rho_25, ymax=rho_75), alpha=0.2) +\n", " scale_x_log10() +\n", " facet_wrap(~cov) + \n", " xlab(expression(paste('Mean pairwise nucleotide divergence (', pi, ')'))) +\n", " ylab(expression(paste(\"Spearman's \", rho))) +\n", " ylim(0, 1) +\n", " theme_bw()\n", "\n", "pdf(\"plots/pi_both_quartline.pdf\", width=7, height = 3)\n", "print(p)\n", "dev.off()\n", "svg(\"plots/pi_both_quartline.svg\", width=7, height = 3)\n", "print(p)\n", "dev.off()\n", "print(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "p = ggplot(filter(pi.dat, cov==8), aes(x=var.f, y=rho, fill=metric)) +\n", " geom_boxplot(aes(fill=metric)) +\n", " xlab(expression(paste('Mean pairwise nucleotide divergence (', pi, ')'))) +\n", " ylab(expression(paste(\"Spearman's \", rho, \" +- SD\"))) +\n", " ylim(0, 1) +\n", " theme_bw() + \n", " theme(axis.text.x=element_text(angle = 45, hjust = 1, vjust=1))\n", "\n", "pdf(\"plots/pi_8x_box.pdf\", width=4, height = 3)\n", "print(p)\n", "dev.off()\n", "svg(\"plots/pi_8x_box.svg\", width=4, height = 3)\n", "print(p)\n", "dev.off()\n", "\n", "# print(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "math_format(frac(1, .x))(1:10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "?trans_breaks" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "p = ggplot(filter(pi.dat.summ, cov==8), aes(x=var, y=rho_av, fill=metric)) +\n", " geom_line(aes(linetype=metric)) +\n", " geom_ribbon(aes(fill=metric, ymin=rho_av - rho_sd, ymax=rho_av + rho_sd), alpha=0.2) +\n", " xlab(expression(paste('Mean pairwise nucleotide divergence (', pi, ')'))) +\n", " ylab(expression(paste(\"Spearman's \", rho, \" +- SD\"))) +\n", " ylim(0, 1) +\n", " scale_x_continuous(trans = 'log10',\n", " breaks = trans_breaks('log10', function(x) 10^x),\n", " labels = trans_format('log10', math_format(10^.x))) +\n", " theme_bw()# + \n", " #theme(axis.text.x=element_text(angle = 45, hjust = 1, vjust=1))\n", "\n", "pdf(\"plots/pi_8x_avgsd.pdf\", width=6, height = 3)\n", "print(p)\n", "dev.off()\n", "svg(\"plots/pi_8x_avgsd.svg\", width=6, height = 3)\n", "print(p)\n", "dev.off()\n", "print(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "?tran" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Coverage" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "cov.dat = results %>%\n", " filter(var %in% c(0.002, 0.005, 0.01)) %>%\n", " select(rho, metric, cov, var, seed) %>% \n", " mutate(cov.f = as.factor(cov),\n", " var.f = as.factor(var))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "cov.dat.summ = cov.dat %>%\n", " group_by(cov, metric, var) %>%\n", " summarise(rho_av=mean(rho),\n", " rho_sd=sd(rho),\n", " rho_med=median(rho),\n", " rho_25=quantile(rho, p=c(1/4)),\n", " rho_75=quantile(rho, p=c(3/4))) %>%\n", " mutate(cov.f = as.factor(cov),\n", " var.f = as.factor(var))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "p = ggplot(cov.dat, aes(x=cov.f, y=rho, fill=metric)) +\n", " geom_boxplot(aes(fill=metric)) +\n", " scale_fill_discrete(guide = guide_legend(title = \"Metric\")) +\n", " xlab(expression(paste('Mean sequencing depth'))) +\n", " ylab(expression(paste(\"Spearman's \", rho, \" +- SD\"))) +\n", " facet_wrap(~var.f ) + \n", " ylim(0, 1) +\n", " theme_classic() +\n", " theme(legend.position = \"bottom\")\n", "\n", "pdf(\"plots/cov_all_box.pdf\", width=7, height = 3)\n", "print(p)\n", "dev.off()\n", "# print(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "p = ggplot(filter(cov.dat, var==0.002), aes(x=cov.f, y=rho, fill=metric)) +\n", " geom_boxplot(aes(fill=metric)) +\n", " scale_fill_discrete(guide = guide_legend(title = \"Metric\")) +\n", " xlab(expression(paste('Mean sequencing depth'))) +\n", " ylab(expression(paste(\"Spearman's \", rho, \" +- SD\"))) +\n", " ylim(0, 1) +\n", " theme_classic() + \n", " theme(axis.text.x=element_text(angle = 45, hjust = 1, vjust=1)) +\n", " theme(legend.position = \"bottom\")\n", "\n", "pdf(\"plots/cov_500_box.pdf\", width=7, height = 3)\n", "print(p)\n", "dev.off()\n", "# print(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "p = ggplot(cov.dat.summ, aes(x=cov, y=rho_av)) +\n", " geom_line(aes(linetype=metric)) +\n", " geom_ribbon(aes(fill=metric, ymin=rho_av - rho_sd, ymax=rho_av + rho_sd), alpha=0.2) +\n", " scale_x_log10() +\n", " facet_wrap(~var) + \n", " xlab(expression(paste('Mean sequencing depth'))) +\n", " ylab(expression(paste(\"Spearman's \", rho))) +\n", " ylim(0, 1) +\n", " theme_bw()\n", "\n", "pdf(\"plots/cov_all_avgsd.pdf\", width=7, height = 3)\n", "print(p)\n", "dev.off()\n", "\n", "print(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "p = ggplot(filter(cov.dat.summ, var==0.005), aes(x=cov, y=rho_av)) +\n", " geom_line(aes(linetype=metric)) +\n", " geom_ribbon(aes(fill=metric, ymin=rho_av-rho_sd, ymax=rho_av+rho_sd), alpha=0.2) +\n", " scale_x_log10() +\n", " xlab(expression(paste('Mean sequencing depth'))) +\n", " ylab(expression(paste(\"Spearman's \", rho, \" +- SD\"))) +\n", " ylim(0, 1) +\n", " theme_bw()\n", "\n", "pdf(\"plots/cov_500_avgsd.pdf\", width=6, height = 3)\n", "print(p)\n", "dev.off()\n", "svg(\"plots/cov_500_avgsd.svg\", width=6, height = 3)\n", "print(p)\n", "dev.off()\n", "\n", "print(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": "3.3.3" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
JannerM/gamma-models	scripts/gamma-pendulum-local.ipynb	1	1325841	null	mit
cmgerber/CensusMapper	old/colorbrewer_reformat.ipynb	1	38339	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "from pandas.io.excel import ExcelFile" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "infile = ExcelFile('ColorBrewer_all_schemes_RGBonly3.XLS')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "df = infile.parse(infile.sheet_names[0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "df_fill = df.fillna(method = 'ffill')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "df_fill.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ColorName</th>\n", " <th>NumOfColors</th>\n", " <th>Type</th>\n", " <th>CritVal</th>\n", " <th>ColorNum</th>\n", " <th>ColorLetter</th>\n", " <th>R</th>\n", " <th>G</th>\n", " <th>B</th>\n", " <th>SchemeType</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> Accent</td>\n", " <td> 3</td>\n", " <td> qual</td>\n", " <td>NaN</td>\n", " <td> 1</td>\n", " <td> A</td>\n", " <td> 127</td>\n", " <td> 201</td>\n", " <td> 127</td>\n", " <td> Qualitative</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td> Accent</td>\n", " <td> 3</td>\n", " <td> qual</td>\n", " <td>NaN</td>\n", " <td> 2</td>\n", " <td> B</td>\n", " <td> 190</td>\n", " <td> 174</td>\n", " <td> 212</td>\n", " <td> Qualitative</td>\n", " </tr>\n", " <tr>\n", " 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qual NaN 3 C 253 192 134 \n", "3 Accent 4 qual NaN 1 A 127 201 127 \n", "4 Accent 4 qual NaN 2 B 190 174 212 \n", "\n", " SchemeType \n", "0 Qualitative \n", "1 Qualitative \n", "2 Qualitative \n", "3 Qualitative \n", "4 Qualitative " ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "df_fill = df_fill.drop(['Type', 'ColorLetter'], axis = 1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "df_fill.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>ColorName</th>\n", " <th>NumOfColors</th>\n", " <th>CritVal</th>\n", " <th>ColorNum</th>\n", " <th>R</th>\n", " <th>G</th>\n", " <th>B</th>\n", " <th>SchemeType</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " 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Qualitative</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ " ColorName NumOfColors CritVal ColorNum R G B SchemeType\n", "0 Accent 3 NaN 1 127 201 127 Qualitative\n", "1 Accent 3 NaN 2 190 174 212 Qualitative\n", "2 Accent 3 NaN 3 253 192 134 Qualitative\n", "3 Accent 4 NaN 1 127 201 127 Qualitative\n", "4 Accent 4 NaN 2 190 174 212 Qualitative" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "#df_key = pd.factorize(pd.lib.fast_zip([df_fill.ColorName, df_fill.NumOfColors]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "#df_key[0]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "#df_fill.index = df_key[0]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 58 }, { 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buckleylab/Buckley_Lab_SIP_project_protocols	sequence_analysis_walkthrough/QIIME2_Processing_Pipeline.ipynb	1	31482	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "> ### Pipeling to Process Raw Sequences into Phyloseq Object with DADA2 ###\n", "* Prep for Import to QIIME2 (Combine two index files)\n", "* Import to QIIME2\n", "* Demultiplex\n", "* Denoise and Merge\n", "* Prepare OTU Tables and Rep Sequences (Note: sample names starting with a digit will break this step)\n", "* Classify Seqs\n", "\n", "100% Appropriated from the \"Atacama Desert Tutorial\" for QIIME2\n", "\n", "### Pipeline can handle both 16S rRNA gene and ITS sequences (in theory)####\n", "* Tested on 515f and 806r\n", "* Tested on ITS1\n", "\n", "### Commands to Install Dependencies ####\n", "##### \|\| QIIME2 \|\|\n", "* conda create -n qiime2-pipeline --file https://data.qiime2.org/distro/core/qiime2-2017.11-conda-linux-64.txt\n", "* source activate qiime2-pipeline\n", "\n", "** Note: QIIME2 is still actively in development, and I've noticed frequent new releases. Check for the most up-to-date conda install file <https://docs.qiime2.org/2017.11/install/native/#install-qiime-2-within-a-conda-environment>\n", "\n", "\n", "##### \|\| Copyrighter rrn Database \|\|\n", "* The script will automatically install the curated GreenGenes rrn attribute database\n", "* https://github.com/fangly/AmpliCopyrighter\n", "\n", "##### \|\| rpy2 (don't use conda version) \|\|\n", "* pip install rpy2 \n", "\n", "##### \|\| phyloseq \|\|\n", "* conda install -c r r-igraph \n", "* Rscript -e \"source('http://bioconductor.org/biocLite.R');biocLite('phyloseq')\" \n", "\n", "##### \|\| R packages \|\|\n", "* ape (natively installed with in conda environment)\n", "\n", "\n", "### Citations ###\n", "* Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., et al. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature methods, 7(5), 335-336.\n", "\n", "\n", "* McMurdie and Holmes (2013) phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE. 8(4):e61217\n", "\n", "\n", "* Paradis E., Claude J. & Strimmer K. 2004. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20: 289-290.\n", "\n", "\n", "* Angly, F. E., Dennis, P. G., Skarshewski, A., Vanwonterghem, I., Hugenholtz, P., & Tyson, G. W. (2014). CopyRighter: a rapid tool for improving the accuracy of microbial community profiles through lineage-specific gene copy number correction. Microbiome, 2(1), 11.\n", "\n", "###### Last Modified by R. Wilhelm on October 12th, 2017 ######\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 1: User Input" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os, re\n", "\n", "# Provide the directory for your index and read files (you can do multiple independently in one go)\n", "bioblitz = '/home/roli/BioBlitz.2017/SV_based/'\n", "\n", "# Prepare an object with the name of the library, the name of the directory object (created above), and the metadatafile name\n", "#datasets = [['name',directory1,'metadata1','domain of life'],['name',directory2,'metadata2','domain of life']]\n", "datasets = [['bioblitz',bioblitz,'metadata.tsv','bacteria']]\n", "\n", "# Ensure your reads files are named accordingly (or modify to suit your needs)\n", "readFile1 = 'read1.fq.gz'\n", "readFile2 = 'read2.fq.gz'\n", "indexFile1 = 'index_read1.fq.gz'\n", "indexFile2 = 'index_read2.fq.gz'\n", "\n", "## Enter Minimum Support for Keeping QIIME Classification\n", "# Note: Classifications that do not meet this criteria will simply be retained, but labeled 'putative'\n", "min_support = 0.8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 2: Concatenate Barcodes for QIIME2 Pipeline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Note: QIIME takes a single barcode file. The command 'extract_barcodes.py' concatenates the forward and reverse read barcode and attributes it to a single read.\n", "\n", "# See http://qiime.org/tutorials/processing_illumina_data.html\n", "\n", "for dataset in datasets:\n", " directory = dataset[1]\n", " index1 = directory+indexFile1\n", " index2 = directory+indexFile2\n", " \n", " # Run extract_barcodes to merge the two index files\n", " !python2 /opt/anaconda2/bin/extract_barcodes.py --input_type barcode_paired_end -f $index1 -r $index2 --bc1_len 8 --bc2_len 8 -o $directory/output\n", "\n", " # QIIME2 import requires a directory containing files names: forward.fastq.gz, reverse.fastq.gz and barcodes.fastq.gz \n", " !ln -s $directory$readFile1 $directory/output/forward.fastq.gz\n", " !ln -s $directory$readFile2 $directory/output/reverse.fastq.gz\n", " \n", " # Gzip the barcodes files (apparently necessary)\n", " !pigz -p 5 $directory/output/barcodes.fastq\n", "\n", " # Removed orphaned reads files (not needed)\n", " !rm $directory/output/reads?.fastq\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 3: Import into QIIME2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for dataset in datasets:\n", " name = dataset[0]\n", " directory = dataset[1]\n", " \n", " os.system(' '.join([\n", " \"qiime tools import\",\n", " \"--type EMPPairedEndSequences\",\n", " \"--input-path \"+directory+\"output/\",\n", " \"--output-path \"+directory+\"output/\"+name+\".qza\"\n", " ]))\n", " \n", " # This more direct command is broken by the fact QIIME uses multiple dashes in their arguments (is my theory)\n", " #!qiime tools import --type EMPPairedEndSequences --input-path $directory/output --output-path $directory/output/$name.qza\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 4: Demultiplex" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "########\n", "## Note: The barcode you supply to QIIME is now a concatenation of your forward and reverse barcode.\n", "# Your 'forward' barcode is actually the reverse complement of your reverse barcode and the 'reverse' is your forward barcode. The file 'primers.complete.csv' provides this information corresponding to the Buckley Lab 'primer number'\n", "# This quirk could be corrected in how different sequencing facilities pre-process the output from the sequencer\n", "\n", "##\n", "## SLOW STEP (~ 2 - 4 hrs)\n", "##\n", "\n", "for dataset in datasets:\n", " name = dataset[0]\n", " directory = dataset[1]\n", " metadata = dataset[2]\n", " \n", " os.system(' '.join([\n", " \"qiime demux emp-paired\",\n", " \"--m-barcodes-file \"+directory+metadata,\n", " \"--m-barcodes-category BarcodeSequence\",\n", " \"--i-seqs \"+directory+\"output/\"+name+\".qza\",\n", " \"--o-per-sample-sequences \"+directory+\"output/\"+name+\".demux\"\n", " ]))\n", " \n", " # This more direct command is broken by the fact QIIME uses multiple dashes in their arguments (is my theory)\n", " #!qiime demux emp-paired --m-barcodes-file $directory/$metadata --m-barcodes-category BarcodeSequence --i-seqs $directory/output/$name.qza --o-per-sample-sequences $directory/output/$name.demux\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 5: Visualize Quality Scores and Determine Trimming Parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## Based on the Graph Produced using the Following Command enter the trim and truncate values. Trim refers to the start of a sequence and truncate the total length (i.e. number of bases to remove from end)\n", "\n", "# The example in the Atacam Desert Tutorial trims 13 bp from the start of each read and does not remove any bases from the end of the 150 bp reads:\n", "# --p-trim-left-f 13 \\ \n", "# --p-trim-left-r 13 \\\n", "# --p-trunc-len-f 150 \\\n", "# --p-trunc-len-r 150\n", "\n", "for dataset in datasets:\n", " name = dataset[0]\n", " directory = dataset[1]\n", " \n", " os.system(' '.join([\n", " \"qiime demux summarize\",\n", " \"--i-data \"+directory+\"/output/\"+name+\".demux.qza\",\n", " \"--o-visualization \"+directory+\"/output/\"+name+\".demux.QC.summary.qzv\"\n", " ]))\n", " \n", " ## Take the output from this command and drop it into:\n", " #https://view.qiime2.org\n", "\n", "wait_for_user = input(\"The script will now wait for you to input trimming parameters in the next cell. You will need to take the .qzv files for each library and visualize them at <https://view.qiime2.org>. This is hopefully temporary, while QIIME2 developers improve on q2view.\\n\\n[ENTER ANYTHING. THIS IS ONLY MEANT TO PAUSE THE PIPELING]\")\n", "print(\"\\nThe script is now proceeding. Stay tuned to make sure trimming works.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 6: Trimming Parameters \| USER INPUT REQUIRED" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## User Input Required\n", "trim_dict = {}\n", "\n", "## Input your trimming parameters into a python dictionary for all libraries\n", "#trim_dict[\"LibraryName1\"] = [trim_forward, truncate_forward, trim_reverse, truncate_reverse]\n", "#trim_dict[\"LibraryName2\"] = [trim_forward, truncate_forward, trim_reverse, truncate_reverse]\n", "\n", "## Example\n", "trim_dict[\"bioblitz\"] = [1, 240, 1, 190]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 7: Trim, Denoise and Join (aka 'Merge') Reads Using DADA2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Hack for Multithreading\n", "# I hardcoded 'nthreads' in both versions of 'run_dada_paired.R' (find your versions by running 'locate run_dada_paired.R' from your home directory)\n", "# I used ~ 20 threads and the processing finished in ~ 7 - 8hrs\n", "\n", "##\n", "## SLOW STEP (~ 6 - 8 hrs, IF multithreading is used)\n", "##\n", "\n", "\n", "for dataset in datasets:\n", " name = dataset[0]\n", " directory = dataset[1]\n", " \n", " os.system(' '.join([\n", " \"qiime dada2 denoise-paired\",\n", " \"--i-demultiplexed-seqs \"+directory+\"/output/\"+name+\".demux.qza\",\n", " \"--o-table \"+directory+\"/output/\"+name+\".table\",\n", " \"--o-representative-sequences \"+directory+\"/output/\"+name+\".rep.seqs.final\",\n", " \"--p-trim-left-f \"+str(trim_dict[name][0]),\n", " \"--p-trim-left-r \"+str(trim_dict[name][2]),\n", " \"--p-trunc-len-f \"+str(trim_dict[name][1]),\n", " \"--p-trunc-len-r \"+str(trim_dict[name][3])\n", " ]))\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 8: Create Summary of OTUs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for dataset in datasets:\n", " name = dataset[0]\n", " directory = dataset[1]\n", " metadata = dataset[2]\n", " \n", " os.system(' '.join([\n", " \"qiime feature-table summarize\",\n", " \"--i-table \"+directory+\"/output/\"+name+\".table.qza\",\n", " \"--o-visualization \"+directory+\"/output/\"+name+\".table.qzv\",\n", " \"--m-sample-metadata-file \"+directory+metadata\n", " ]))\n", "\n", " os.system(' '.join([\n", " \"qiime feature-table tabulate-seqs\",\n", " \"--i-data \"+directory+\"/output/\"+name+\".rep.seqs.final.qza\",\n", " \"--o-visualization \"+directory+\"/output/\"+name+\".rep.seqs.final.qzv\"\n", " ])) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 9: Make Phylogenetic Tree" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Hack for Multithreading\n", "# I hardcoded 'n_threads' in '_mafft.py' in the directory ~/anaconda3/envs/qiime2-2017.9/lib/python3.5/site-packages/q2_alignment\n", "# I used ~ 20 threads and the processing finished in ~ 15 min\n", "\n", "for dataset in datasets:\n", " name = dataset[0]\n", " directory = dataset[1]\n", " metadata = dataset[2]\n", " domain = dataset[3]\n", "\n", " if domain != \"fungi\":\n", " # Generate Alignment with MAFFT\n", " os.system(' '.join([\n", " \"qiime alignment mafft\",\n", " \"--i-sequences \"+directory+\"/output/\"+name+\".rep.seqs.final.qza\",\n", " \"--o-alignment \"+directory+\"/output/\"+name+\".rep.seqs.aligned.qza\"\n", " ]))\n", "\n", " # Mask Hypervariable parts of Alignment\n", " os.system(' '.join([\n", " \"qiime alignment mask\",\n", " \"--i-alignment \"+directory+\"/output/\"+name+\".rep.seqs.aligned.qza\",\n", " \"--o-masked-alignment \"+directory+\"/output/\"+name+\".rep.seqs.aligned.masked.qza\"\n", " ])) \n", "\n", " # Generate Tree with FastTree\n", " os.system(' '.join([\n", " \"qiime phylogeny fasttree\",\n", " \"--i-alignment \"+directory+\"/output/\"+name+\".rep.seqs.aligned.masked.qza\",\n", " \"--o-tree \"+directory+\"/output/\"+name+\".rep.seqs.tree.unrooted.qza\"\n", " ])) \n", "\n", " # Root Tree\n", " os.system(' '.join([\n", " \"qiime phylogeny midpoint-root\",\n", " \"--i-tree \"+directory+\"/output/\"+name+\".rep.seqs.tree.unrooted.qza\",\n", " \"--o-rooted-tree \"+directory+\"/output/\"+name+\".rep.seqs.tree.final.qza\"\n", " ])) \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 10: Classify Seqs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for dataset in datasets:\n", " name = dataset[0]\n", " directory = dataset[1]\n", " metadata = dataset[2]\n", " domain = dataset[3]\n", "\n", " # Classify\n", " if domain == 'bacteria':\n", " os.system(' '.join([\n", " \"qiime feature-classifier classify-sklearn\",\n", " \"--i-classifier /home/db/GreenGenes/qiime2_13.8.99_515.806_nb.classifier.qza\",\n", " \"--i-reads \"+directory+\"/output/\"+name+\".rep.seqs.final.qza\",\n", " \"--o-classification \"+directory+\"/output/\"+name+\".taxonomy.final.qza\"\n", " ]))\n", "\n", " if domain == 'fungi':\n", " os.system(' '.join([\n", " \"qiime feature-classifier classify-sklearn\",\n", " \"--i-classifier /home/db/UNITE/qiime2_unite_ver7.99_20.11.2016_classifier.qza\",\n", " \"--i-reads \"+directory+\"/output/\"+name+\".rep.seqs.final.qza\",\n", " \"--o-classification \"+directory+\"/output/\"+name+\".taxonomy.final.qza\"\n", " ]))\n", "\n", " # Output Summary\n", " os.system(' '.join([\n", " \"qiime metadata tabulate\",\n", " \"--m-input-file \"+directory+\"/output/\"+name+\".taxonomy.final.qza\",\n", " \"--o-visualization \"+directory+\"/output/\"+name+\".taxonomy.final.summary.qzv\"\n", " ])) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 11: Prepare Data for Import to Phyloseq" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Make Function to Re-Format Taxonomy File to Contain Full Column Information \n", "# and factor in the certain of the taxonomic assignment\n", "\n", "def format_taxonomy(tax_file, min_support):\n", " output = open(re.sub(\".tsv\",\".fixed.tsv\",tax_file), \"w\")\n", " output.write(\"\\t\".join([\"OTU\",\"Domain\",\"Phylum\",\"Class\",\"Order\",\"Family\",\"Genus\",\"Species\"])+\"\\n\")\n", " \n", " with open(tax_file, \"r\") as f:\n", " next(f) #skip header\n", "\n", " for line in f:\n", " line = line.strip()\n", " line = line.split(\"\\t\")\n", "\n", " read_id = line[0]\n", " tax_string = line[1]\n", "\n", " # Annotate those strings which do not meet minimum support\n", " if float(line[2]) < float(min_support):\n", " tax_string = re.sub(\"__\",\"__putative \",tax_string)\n", "\n", " # Remove All Underscore Garbage (gimmie aesthetics)\n", " tax_string = re.sub(\"k__\|p__\|c__\|o__\|f__\|g__\|s__\",\"\",tax_string) \n", "\n", " # Add in columns containing unclassified taxonomic information\n", " # Predicated on maximum 7 ranks (Domain -> Species)\n", " full_rank = tax_string.split(\";\")\n", " last_classified = full_rank[len(full_rank)-1]\n", "\n", " count = 1\n", " while last_classified == \" \":\n", " last_classified = full_rank[len(full_rank)-count]\n", " count = count + 1\n", "\n", "\n", " for n in range(full_rank.index(last_classified)+1, 7, 1):\n", " try:\n", " full_rank[n] = \"unclassifed \"+last_classified\n", " except:\n", " full_rank.append(\"unclassifed \"+last_classified)\n", "\n", " output.write(read_id+\"\\t\"+'\\t'.join(full_rank)+\"\\n\")\n", " \n", " return()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#####################\n", "## Export from QIIME2\n", "\n", "for dataset in datasets:\n", " name = dataset[0]\n", " directory = dataset[1]\n", " metadata = dataset[2]\n", " domain = dataset[3]\n", "\n", " ## Final Output Names\n", " fasta_file = directory+\"/output/\"+name+\".rep.seqs.final.fasta\"\n", " tree_file = directory+\"/output/\"+name+\".tree.final.nwk\"\n", " tax_file = directory+\"/output/\"+name+\".taxonomy.final.tsv\"\n", " count_table = directory+\"/output/\"+name+\".counts.final.biom\"\n", "\n", " # Export Classifications\n", " os.system(' '.join([\n", " \"qiime tools export\",\n", " directory+\"/output/\"+name+\".taxonomy.final.qza\",\n", " \"--output-dir \"+directory+\"/output/\"\n", " ]))\n", " \n", " # Reformat Classifications to meet phyloseq format\n", " format_taxonomy(directory+\"/output/taxonomy.tsv\", min_support)\n", "\n", " # Export SV Table\n", " os.system(' '.join([\n", " \"qiime tools export\",\n", " directory+\"/output/\"+name+\".table.qza\",\n", " \"--output-dir \"+directory+\"/output/\"\n", " ]))\n", "\n", " # Export SV Sequences\n", " os.system(' '.join([\n", " \"qiime tools export\",\n", " directory+\"/output/\"+name+\".rep.seqs.final.qza\",\n", " \"--output-dir \"+directory+\"/output/\"\n", " ]))\n", " \n", " # Export Tree\n", " os.system(' '.join([\n", " \"qiime tools export\",\n", " directory+\"/output/\"+name+\".rep.seqs.tree.final.qza\",\n", " \"--output-dir \"+directory+\"/output/\"\n", " ]))\n", " \n", " # Rename Exported Files\n", " %mv $directory/output/dna-sequences.fasta $fasta_file\n", " %mv $directory/output/feature-table.biom $count_table\n", " %mv $directory/output/taxonomy.fixed.tsv $tax_file\n", " \n", " if domain == \"bacteria\":\n", " %mv $directory/output/tree.nwk $tree_file\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 13: Get 16S rRNA Gene Copy Number (rrn)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## This step is based on the database contructed for the software 'copyrighter'\n", "## The software itself lacked information about datastructure (and, the import of a biom from QIIME2 failed, likely because there are multiple versions of the biom format)\n", "downloaded = \"N\"\n", "for dataset in datasets:\n", " name = dataset[0]\n", " directory = dataset[1]\n", " domain = dataset[3]\n", "\n", " if domain == 'bacteria':\n", " if downloaded == \"N\":\n", " ## Download copyrighter database\n", " !git clone https://github.com/fangly/AmpliCopyrighter $directory/temp/\n", "\n", " ## There are multiple GreenGenes ID numbers for a given taxonomic string.\n", " ## However, the copyrighter database uses the same average rrn copy number.\n", " ## We will therefore just use the taxonomic strings, since QIIME2 does not output the ID numbers\n", "\n", " !sed -e '1,1075178d; 1078115d' $directory/temp/data/201210/ssu_img40_gg201210.txt > $directory/output/copyrighter.tax.strings.tsv\n", "\n", " ## Create Dictionary of rrnDB\n", " rrnDB = {}\n", "\n", " with open(directory+\"/output/copyrighter.tax.strings.tsv\", \"r\") as f:\n", " for line in f:\n", " line = line.strip()\n", " line = line.split(\"\\t\")\n", "\n", " try:\n", " rrnDB[line[0]] = line[1]\n", "\n", " except:\n", " pass\n", "\n", " downloaded = \"Y\"\n", " \n", " ## Attribute rrn to readID from taxonomy.tsv\n", " output = open(directory+\"/output/\"+name+\".seqID.to.rrn.final.tsv\",\"w\")\n", " output.write(\"Feature ID\\trrn\\n\")\n", "\n", " with open(directory+\"/output/taxonomy.tsv\", \"r\") as f:\n", " missing = 0\n", " total = 0\n", " next(f) # Skip Header\n", "\n", " for line in f:\n", " line = line.strip()\n", " line = line.split(\"\\t\")\n", "\n", " seqID = line[0]\n", "\n", " try:\n", " rrn = rrnDB[line[1]]\n", "\n", " except:\n", " rrn = \"NA\"\n", " missing = missing + 1\n", "\n", " total = total + 1\n", " output.write(seqID+\"\\t\"+rrn+\"\\n\")\n", "\n", " print(\"\\nPercent of OTUs Missing {:.1%}\".format(float(missing)/total))\n", " print(\"Don't Panic! The majority of missing OTUs could be low abundance.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 14: Import into Phyloseq" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## Setup R-Magic for Jupyter Notebooks\n", "import rpy2\n", "%load_ext rpy2.ipython\n", "\n", "def fix_biom_conversion(file):\n", " with open(file, 'r') as fin:\n", " data = fin.read().splitlines(True)\n", " with open(file, 'w') as fout:\n", " fout.writelines(data[1:])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "%R library(phyloseq)\n", "%R library(ape)\n", "\n", "\n", "for dataset in datasets:\n", " name = dataset[0]\n", " directory = dataset[1]\n", " metadata = dataset[2]\n", " domain = dataset[3]\n", " \n", " #### IMPORT DATA to R\n", " ## For '.tsv' files, use Pandas to create a dataframe and then pipe that to R\n", " ## For '.biom' files, first convert using 'biom convert' on the command-line\n", " ## Had problems importing the count table with pandas, opted for using read.table in R\n", " \n", " # Import Taxonomy File\n", " tax_file = pd.read_csv(directory+\"/output/\"+name+\".taxonomy.final.tsv\", sep=\"\\t\")\n", " %R -i tax_file\n", " %R rownames(tax_file) = tax_file$OTU\n", " %R tax_file$OTU <- NULL\n", " %R tax_file <- tax_file[sort(row.names(tax_file)),] #read names must match the count_table\n", "\n", " # Import Sample Data\n", " #sample_file = pd.read_csv(directory+\"/\"+metadata, sep=\"\\t\")\n", " sample_file = pd.read_table(directory+metadata, keep_default_na=False)\n", " %R -i sample_file\n", " %R rownames(sample_file) = sample_file$X.SampleID \n", " %R sample_file$X.SampleID <- NULL\n", " %R sample_file$LinkerPrimerSequence <- NULL ## Clean-up some other stuff\n", " \n", " # Import Count Data\n", " os.system(' '.join([\n", " \"biom convert\",\n", " \"-i\",\n", " directory+\"/output/\"+name+\".counts.final.biom\",\n", " \"-o\",\n", " directory+\"/output/\"+name+\".counts.final.tsv\",\n", " \"--to-tsv\"\n", " ]))\n", " \n", " # The biom converter adds a stupid line that messes with the table formatting\n", " fix_biom_conversion(directory+\"/output/\"+name+\".counts.final.tsv\")\n", "\n", " # Finally import\n", " count_table = pd.read_csv(directory+\"/output/\"+name+\".counts.final.tsv\", sep=\"\\t\")\n", " %R -i count_table\n", " %R rownames(count_table) = count_table$X.OTU.ID \n", " %R count_table$X.OTU.ID <- NULL \n", " %R count_table <- count_table[sort(row.names(count_table)),] #read names must match the tax_table\n", " \n", " # Convert to Phyloseq Objects\n", " %R p_counts = otu_table(count_table, taxa_are_rows = TRUE) \n", " %R p_samples = sample_data(sample_file) \n", " %R p_tax = tax_table(tax_file)\n", " %R taxa_names(p_tax) <- rownames(tax_file) # phyloseq throws out rownames\n", " %R colnames(p_tax) <- colnames(tax_file) # phyloseq throws out colnames\n", " \n", " # Merge Phyloseq Objects\n", " %R p = phyloseq(p_counts, p_tax)\n", "\n", " # Import Phylogenetic Tree\n", " if domain == \"bacteria\":\n", " tree_file = directory+\"/output/\"+name+\".tree.final.nwk\"\n", " %R -i tree_file \n", " %R p_tree <- read.tree(tree_file)\n", " \n", " # Combine All Objects into One Phyloseq\n", " %R p_final <- merge_phyloseq(p, p_samples, p_tree)\n", " \n", " else:\n", " # Combine All Objects into One Phyloseq\n", " %R p_final <- merge_phyloseq(p, p_samples)\n", " \n", " # Save Phyloseq Object as '.rds'\n", " output = directory+\"/output/p_\"+name+\".final.rds\"\n", " %R -i output\n", " %R saveRDS(p_final, file = output)\n", " \n", " # Confirm Output\n", " %R print(p_final)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 15: Clean-up Intermediate Files and Final Outputs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for dataset in datasets:\n", " directory = dataset[1]\n", " metadata = dataset[2]\n", " \n", " # Remove Files\n", " if domain == \"bacteria\":\n", " %rm -r $directory/output/tree.unrooted.qza \n", " %rm -r $directory/output/aligned.masked.qza \n", " \n", " %rm $directory/output/.biom \n", " %rm -r $directory/temp/\n", " %rm $directory/output/barcodes.fastq.gz \n", " %rm $directory/output/taxonomy.tsv\n", " %rm $directory/output/forward.fastq.gz # Just the symlink\n", " %rm $directory/output/reverse.fastq.gz # Just the symlink\n", " %rm $directory/output/copyrighter.tax.strings.tsv\n", " \n", " # Separate Final Files\n", " %mkdir $directory/final/ \n", " %mv $directory/output/.final.rds $directory/final/\n", " %mv $directory/output/.taxonomy.final.tsv $directory/final/ \n", " %mv $directory/output/.counts.final.tsv $directory/final/\n", " %mv $directory/output/.final.fasta $directory/final/\n", " %cp $directory$metadata $directory/final/\n", " %mv $directory/output/.seqID.to.rrn.final.tsv $directory/final/ \n", " %mv $directory/output/.nwk $directory/final/ \n", " \n", " # Gzip and Move Intermediate Files\n", " !pigz -p 10 $directory/output/.qza\n", " !pigz -p 10 $directory/output/.qzv\n", " \n", " %mv $directory/output/ $directory/intermediate_files\n", "\n", "print(\"Your sequences have been successfully saved to 'final' and 'intermediate_files'\")" ] } ], "metadata": { "anaconda-cloud": {}, "hide_input": true, "kernelspec": { "display_name": "Environment (conda_qiime2-pipeline)", "language": "python", "name": "conda_qiime2-pipeline" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
rsignell-usgs/notebook	.ipynb_checkpoints/cmg_ts2cf_loop-checkpoint.ipynb	1	66250	{ "metadata": { "gist_id": "bc431264273be627a4f5", "name": "", "signature": "sha256:45f710d66ea52ec67e6cd94e63f19b917ecf213bb0a6babdf9883c994cb4f612" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Convert existing time series NetCDF files in 4D (T,Z,Y,X) \"grid\" format to CF-1.6 format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the USGS time series data has traditionally been stored in PMEL EPIC-compliant NetCDF files. These files use EPIC codes for " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import glob\n", "import os\n", "import netCDF4\n", "import StringIO\n", "import pandas as pd\n", "import re" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "projs = '''\n", "ARGO_MERCHANT:B. Butman:Argo Merchant Experiment:A moored array deployed after the ARGO MERCHANT ran aground onNantucket Shoals designed to help understand the fate of the spilled oil.\n", "BUZZ_BAY:B. Butman:Currents and Sediment Transport in Buzzards Bay:Investigation of the near-bottom circulation in Buzzards Bay and consequent transport of fine-grained sediments that may be contaminated with PCBs from inner New Bedford Harbor.\n", "CAMP:B. Butman:California Area Monitoring Program (CAMP):A four-year multi-disciplinary field and laboratory study to investigate the sediment transport regime in the vicinity of production drilling rigs in the Santa Barbara Basin\n", "CAPE_COD_BAY:B. Butman:Currents and Sediment Transport in Cape Cod Bay:A pilot study to determine the effect of winter storms on sediment movement at two potential dredge spoil disposal areas.\n", "CC_MISC:B. Butman:Transport studies - Nauset Inlet:Part of a collaborative study of sediment movement in Nauset Inlet.\n", "DEEP_REEF:J. Lacey:Gulf of Mexico - Pinnacles:Pressure data from the Gulf of Mexico\n", "DWDS_106:B. Butman:Sediment Transport at Deep Water Dump Site 106:Near-bottom current measurements to understand the fate and transport of sludge from the New York Metropolitan region discharged at the sea surface.\n", "ECOHAB_II:R. Signell:Ecology of Harmful Algal Blooms (ECOHAB-II):A field program to continue investigating the transport and fate of toxic dinoflagellate blooms in the western Gulf of Maine.\n", "ECOHAB_I:R. Signell:Ecology of Harmful Algal Blooms (ECOHAB-I):A field program to study the transport and fate of toxic dinoflagellate blooms in the western Gulf of Maine.\n", "EUROSTRATAFORM:C. Sherwood:EuroSTRATAFORM:The EuroSTRATAFORM Po and Apennine Sediment Transport and Accumulation (PASTA) experiment was an international study of sediment-transport processes and formation of geological strata in the Adriatic Sea.\n", "FARALLONES:M. Noble:Farallons:Program to measure the currents and circulation on the continental slope off San Francisco CA and thus infer the transport of dredged materialat the newly-established deep-water disposal site.\n", "GB_SED:B. Butman:Georges Bank Current and Sediment Transport Studies:A series of studies to assess environmental hazards to petroleum development in the Georges Bank and New England Shelf region\n", "GLOBEC_GB:R. Schlitz:GLOBEC Georges Bank Program:A moored array program to investigate the circulation and mixing of plankton on Georges Bank.\n", "GLOBEC_GSC:R. Schlitz:GLOBEC Great South Channel Circulation Experiment:A moored array program to investigate the recirculation of water and plankton around Georges Bank\n", "GULF_MAINE:B. Butman:Deep Circulation in the Gulf of Maine:A two-year field study to investigate the deep flow between the major basins in the Gulf of Maine and the effects on the distribution of suspended sediments.\n", "HUDSON_SVALLEY:B. Butman:Circulation and Sediment Transport in the Hudson Shelf Valley:Field experiments have been carried out to understand the transport of sediments and associated contaminants in the Hudson Shelf Valley offshore of New York.\n", "KARIN_RIDGE:M. Noble:Karin Ridge Experiment:Current measurements collected at 2 sites in Karin Ridge Seamount.\n", "LYDONIA_C:B. Butman:Lydonia Canyon Dynamics Experiment:A major field experiment to determine the importance of submarine canyons in sediment transport along and across the continental margin.\n", "MAB_SED:B. Butman:Sediment Transport Observations in the Middle Atlantic Bight:A series of studies to assess environmental hazards to petroleum development in the Middle Atlantic Bight. \n", "MAMALA_BAY:D. Cacchione:Mamala bay Experiment:Current measurements collected at 350-450 meters in Mamala Bay near Waikiki Beach. \n", "MBAY_CIRC:R. Signell: Massachusetts Bay Circulation Experiment:Current measurements collected at 6 sites in Massachusetts Bay throughout the year to map the tidal wind and density driven currents. \n", "MBAY_IWAVE:B. Butman:Massachusetts Bay Internal Wave Experiment:A 1-month 4-element moored array experiment to measure the currents associated with large-amplitude internal waves generated by tidal flow across Stellwagen Bank.\n", "MBAY_LTB:B. Butman:Long-term observations in Massachusetts Bay; Site B-Scituate:Measurements of currents and other oceanographic properties were made to assess the impact of sewage discharge from the proposed outfall site.\n", "MBAY_LT:B. Butman:Long-term observations in Massachusetts Bay; Site A-Boston Harbor:Measurements of currents and other oceanographic properties were made to assess the impact of sewage discharge from the proposed outfall site.\n", "MBAY_STELL:R. Signell:Monitoring on Stellwagen Bank:A year-long series of current measurements on the eastern flank of Stellwagen Bank to document the currents at the mouth of Massachusetts Bay driven by the Maine Coastal current.\n", "MBAY_WEST:B. Butman:Currents and Sediment Transport in Western Massachusetts Bay:A pilot winter-time experiment to investigate circulation and sediment transport. Designed to provide information to aid in citing the new ocean outfall for the Boston sewer system.\n", "MOBILE_BAY:B. Butman:Mobile Bay Study:Measure currents and transport out of Mobile Bay.\n", "MONTEREY_BAY:M. Noble:Monterey Bay National Marine Sanctuary Program:Part of a large multi-disciplinary experiment to characterize the geologic environment and to generate a sediment budget.\n", "MONTEREY_CAN:M. Noble:Monterey Canyon Experiment: A program to determine the mechanisms that govern the circulation within and the transport of sediment and water through Monterey Submarine Canyon.\n", "MYRTLEBEACH:J. Warner:Myrtle Beach Experiment SC:Measurements collected as part of a larger study to understand the physical processes that control the transport of sediments in Long Bay South Carolina. \n", "NE_SLOPE:B. Butman:Currents on the New England Continental Slope:A study designed to describe the currents and to investigate the transport of sediment from the shelf to the slope. \n", "OCEANOG_C:B. Butman:Oceanographer Canyon Dynamics Experiment:A field experiment to determine the importance of submarine canyons in sediment transport along and across the continental margin.\n", "ORANGE_COUNTY:M. Noble:Orange County Sanitation District Studies:Observations to monitor coastal ocean process that transport suspended material and associated comtaminants across the shelf\n", "PONCHARTRAIN:R. Signell:Lake Ponchartrain Project:A series of moored array studies to investigate the circulation and particle transport in Lake Pontchartrain.\n", "PV_SHELF04:M. Noble:Palos Verdes Shelf 2004:Additional observations to estimate the quantity and direction of sediment erosion and transport on the shelf near the White Point ocean outfalls.\n", "PV_SHELF07:M. Noble:Palos Verdes Shelf 2007:Follow-up observations to evaluate how often coastal ocean processes move the DDT contaminated sediments near the White Point ocean outfalls.\n", "PV_SHELF:M. Noble:Palos Verdes Shelf Study:Initial observations of currents and circulation near the White Point ocean outfalls determine how often coastal ocean processes move the DDT contaminated sediments in this region.\n", "SAB_SED:B. Butman:Sediment Transport Observations in the Southern Atlantic Bight:A series of studies to assess environmental hazards to petroleum development in the South Atlantic Bight.\n", "SOUTHERN_CAL:M. Noble:Southern California Project:A series of moorings were deployed to understand how coastal ocean processes that move sediments change with location on the shelf.\n", "STRESS:B. Butman:Sediment Transport on Shelves and Slopes (STRESS):Experiment on the California continental margin to investigate storm-driven sediment transport.\n", "WRIGHTSVILLE:R. Thieler:Wrightsville Beach Study: Measurements of bottom currents and waves to investigate the flow field and sediment transport in a rippled scour depression offshore of Wrightsville Beach NC.\n", "DIAMONDSHOALS:J. Warner:Cape Hatteras- Diamond Shoals:This experiment was designed to investigate the ocean circulation and sediment transport dynamics at Diamond Shoals NC.\n", "CHANDELEUR:C. Sherwood:Chandeleur Islands Oceanographic Measurements:A program to measure waves water levels and currents near the Chandeleur Islands Louisiana and adjacent berm construction site.\n", "WFAL:N. Ganju:West Falmouth Harbor Fluxes:Oceanographic and water-quality observations made at six locations in West Falmouth Harbor and Buzzards Bay.\n", "BW2011:N. Ganju: Blackwater 2011: Oceanographic and Water-Quality Measurements made at several sites in 2 watersheds in Blackwater National Wildlife Refuge.\n", "MVCO_11:C. Sherwood: OASIS MVCO 2011: Near-seabed Oceanographic Observations made as part of the 2011 OASIS Project at the MVCO.\n", "HURRIRENE_BB:B. Butman: Observations in Buzzards Bay during and after a Hurricane: Oceanographic data collected in Buzzards Bay MA during Hurricane Irene August 2011.\n", "FI12:J. Warner:Fire Island NY - Offshore: Oceanographic and meteorological observations were made at 7 sites on and around the sand ridges offshore of Fire Island NY in winter 2012 to study coastal processes.\n", "BARNEGAT:N. Ganju:Light attenuation and sediment resuspension in Barnegat Bay New Jersey: Light attenuation is a critical parameter governing the ecological function of shallow estuaries. Near-bottom and mid-water observations of currents, pressure, chlorophyll, and fDOM were collected at three pairs of sites sequentially at different locations in the estuary to characterize the conditions.\n", "'''" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 81 }, { "cell_type": "code", "collapsed": false, "input": [ "project = pd.read_csv(StringIO.StringIO(projs.strip()), \n", " sep=':',index_col='project_id',\n", " names=['project_id', 'project_pi', 'project_name','project_summary'])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 82 }, { "cell_type": "code", "collapsed": false, "input": [ "project.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>project_pi</th>\n", " <th>project_name</th>\n", " <th>project_summary</th>\n", " </tr>\n", " <tr>\n", " <th>project_id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>ARGO_MERCHANT</th>\n", " <td> B. Butman</td>\n", " <td> Argo Merchant Experiment</td>\n", " <td> A moored array deployed after the ARGO MERCHAN...</td>\n", " </tr>\n", " <tr>\n", " <th>BUZZ_BAY</th>\n", " <td> B. Butman</td>\n", " <td> Currents and Sediment Transport in Buzzards Bay</td>\n", " <td> Investigation of the near-bottom circulation i...</td>\n", " </tr>\n", " <tr>\n", " <th>CAMP</th>\n", " <td> B. Butman</td>\n", " <td> California Area Monitoring Program (CAMP)</td>\n", " <td> A four-year multi-disciplinary field and labor...</td>\n", " </tr>\n", " <tr>\n", " <th>CAPE_COD_BAY</th>\n", " <td> B. Butman</td>\n", " <td> Currents and Sediment Transport in Cape Cod Bay</td>\n", " <td> A pilot study to determine the effect of winte...</td>\n", " </tr>\n", " <tr>\n", " <th>CC_MISC</th>\n", " <td> B. Butman</td>\n", " <td> Transport studies - Nauset Inlet</td>\n", " <td> Part of a collaborative study of sediment move...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 84, "text": [ " project_pi project_name \\\n", "project_id \n", "ARGO_MERCHANT B. Butman Argo Merchant Experiment \n", "BUZZ_BAY B. Butman Currents and Sediment Transport in Buzzards Bay \n", "CAMP B. Butman California Area Monitoring Program (CAMP) \n", "CAPE_COD_BAY B. Butman Currents and Sediment Transport in Cape Cod Bay \n", "CC_MISC B. Butman Transport studies - Nauset Inlet \n", "\n", " project_summary \n", "project_id \n", "ARGO_MERCHANT A moored array deployed after the ARGO MERCHAN... \n", "BUZZ_BAY Investigation of the near-bottom circulation i... \n", "CAMP A four-year multi-disciplinary field and labor... \n", "CAPE_COD_BAY A pilot study to determine the effect of winte... \n", "CC_MISC Part of a collaborative study of sediment move... " ] } ], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "project.ix['PV_SHELF'].project_pi" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 85, "text": [ "'M. Noble'" ] } ], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "len(project)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Process only these projects:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "proj=project.ix[['FI12','BARNEGAT','WFAL']]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 88 }, { "cell_type": "code", "collapsed": false, "input": [ "len(proj)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 89, "text": [ "3" ] } ], "prompt_number": 89 }, { "cell_type": "code", "collapsed": false, "input": [ "for index,row in proj.iterrows():\n", " print index,row['project_pi']" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "FI12 J. Warner\n", "BARNEGAT N. Ganju\n", "WFAL N. Ganju\n" ] } ], "prompt_number": 90 }, { "cell_type": "code", "collapsed": false, "input": [ "\"\"\" this is Ellyn's old Matlab code:\n", "\n", "function nname=lookup_cf(long_name)\n", "% LOOKUP_CF Get CF equivalent name for EPIC variable long_name\n", "% return the new name string or [] if there's no equivalent\n", "%\n", "\n", "if(strfind(lower(long_name),'temp'))\n", " nname='sea_water_temperature';\n", "elseif (strfind(lower(long_name),'cond'))\n", " nname='sea_water_electrical_conductivity';\n", "elseif (strfind(lower(long_name),'sal'))\n", " nname='sea_water_salinity';\n", "elseif (strfind(lower(long_name),'sigma'))\n", " nname='sea_water_sigma_theta';\n", "% also have to deal with the min, max std of vels for burst stats files\n", "elseif (strfind(lower(long_name),'east'))\n", " nname='eastward_sea_water_velocity';\n", "elseif (strfind(lower(long_name),'north'))\n", " nname='northward_sea_water_velocity';\n", "elseif (strfind(lower(long_name),'vertical'))\n", " nname='upward_sea_water_velocity';\n", "elseif (strfind(lower(long_name),'pitch'))\n", " nname='platform_pitch_angle';\n", "elseif (strfind(lower(long_name),'roll'))\n", " nname='platform_roll_angle';\n", "elseif (strfind(lower(long_name),'head'))\n", " nname='platform_orientation';\n", "elseif (strfind(lower(long_name),'pres'))\n", " if ~isempty(strfind(lower(long_name),'dev')) \|\| ~isempty(strfind(lower(long_name),'std'))\n", " nname=[];\n", " else\n", " nname='sea_water_pressure';\n", " end\n", "elseif (strfind(lower(long_name),'cond'))\n", " nname='sea_water_electrical_conductivity';\n", "elseif (strfind(lower(long_name),'speed'))\n", " if (strfind(lower(long_name),'rotor'))\n", " nname=[];\n", " else\n", " nname='sea_water_speed';\n", " end\n", "elseif (strfind(lower(long_name),'direction'))\n", " nname='direction_of_sea_water_velocity';\n", "else\n", " nname=[];\n", "end\n", "disp([long_name ' : ' nname])\n", "\"\"\";" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 91 }, { "cell_type": "code", "collapsed": false, "input": [ "#os.chdir('/usgs/data2/emontgomery/stellwagen/Data/ARGO_MERCHANT')\n", "\n", "root_dir='/usgs/data2/emontgomery/stellwagen/Data/'\n", "#root_dir='/usgs/data2/emontgomery/stellwagen/Data/MVCO_11'\n", "odir='/usgs/data2/emontgomery/stellwagen/CF-1.6/'\n", "os.chdir(root_dir)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 92 }, { "cell_type": "code", "collapsed": false, "input": [ "# now find all the unique names, long_names & units\n", "names = set()\n", "long_names = set()\n", "units = set()\n", "epic_keys = set()\n", "for path, subdirs, files in os.walk(root_dir):\n", " for name in files:\n", " file= os.path.join(path, name)\n", " try:\n", " nc=netCDF4.Dataset(file)\n", " for var in nc.variables.keys():\n", " names.add(var)\n", " try:\n", " long_names.add(nc.variables[var].long_name)\n", " except:\n", " pass\n", " try:\n", " units.add(nc.variables[var].units)\n", " except:\n", " pass\n", " try:\n", " epic_keys.add(nc.variables[var].epic_code)\n", " except:\n", " pass\n", " \n", " except:\n", " pass" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "names= list(names)\n", "long_names = list(long_names)\n", "units = list(units)\n", "epic_keys = list(epic_keys)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 72 }, { "cell_type": "code", "collapsed": false, "input": [ "print len(names)\n", "print len(long_names)\n", "print len(units)\n", "print len(epic_keys)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "509\n", "545\n", "153\n", "138\n" ] } ], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "# let's use Ellyn's approach of matching substrings in the long_names to deduce standard_names" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "# air temp, frtemp, laser temp factor, internal, temp diff\n", "filter(lambda x:re.search(r'temp',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 43, "text": [ "[u'AIR TEMPERATURE (C) ',\n", " u'instrument Transducer Temp.',\n", " u'Air Temperature (degrees C)',\n", " u'TEMPERATURE (C) ',\n", " u'FR TEMP ',\n", " u'ADP Transducer Temp.',\n", " u'ADCP Transducer Temp.',\n", " u'TEMPERATURE (C)',\n", " u'Transducer Temp. ',\n", " u'laser temperature factor',\n", " u'Transducer Temp.',\n", " u'TEMP 1 ',\n", " u'TEMP 2 ',\n", " u'Temperature',\n", " u'internal temperature',\n", " u'FRTEMP ',\n", " u'TEMP 2 Q ',\n", " u'TEMP ',\n", " u'TEMP DIFF ',\n", " u'TEMP LP ',\n", " u'Sea Surface Temperature (degrees C)',\n", " u'TEMPERATURE ']" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "# seconds, second\n", "filter(lambda x:re.search(r'cond',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 44, "text": [ "[u'SPECIFIC CONDUCTANCE ',\n", " u'CONDUCTIVITY ',\n", " u'Specific conductance',\n", " u'SECONDS SINCE START',\n", " u'second',\n", " u'CONDUCTIVITY',\n", " u'SP COND ',\n", " u'COND ',\n", " u'CTD Conductivity ',\n", " u'COND 2 ',\n", " u'COND 1 ']" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "filter(lambda x:re.search(r'sal',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 45, "text": [ "[u'SALINITY 2 Q',\n", " u'SALINITY 1 ',\n", " u'CTD Salinity, PSS-78',\n", " u'Salinity',\n", " u'SALINITY 2 ',\n", " u'SALINITY (PPT) ',\n", " u'SALINITY (PSU) ',\n", " u'SALINITY ']" ] } ], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "filter(lambda x:re.search(r'sigma',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "[u'SIGMA-THETA (KG/M3) ',\n", " u'SIGMA-T (KG/M3) ',\n", " u'SIGMA-THETA (KG/M*3) ',\n", " u'SIGMA THETA ']" ] } ], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "# deal with burst data, std dev, resolution velocity, variance\n", "filter(lambda x:re.search(r'east',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "[u'Eastward Velocity ',\n", " u'Eastward Velocity',\n", " u'Ratio of variances for burst after points replaced by delgitch: Eastward Velocity',\n", " u'EAST ',\n", " u'Maximum Eastward Velocity',\n", " u'Minimum Eastward Velocity',\n", " u'Eastward Resolution Velocity',\n", " u'Std. Dev. of east component',\n", " u'Ratio of means for burst after points replaced by delgitch: Eastward Velocity',\n", " u'EAST VELOCITY VARIANCE ',\n", " u'Mean Eastward Velocity',\n", " u'Eastward Velocity ',\n", " u'Eastward Velocity ',\n", " u'EAST LP ',\n", " u'EAST VELOCITY VARIANCE ',\n", " u'Mean Resolution VelocityEastward']" ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "filter(lambda x:re.search(r'north',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 48, "text": [ "[u'NORTH ',\n", " u'Northward Velocity ',\n", " u'Std. Dev. of north component',\n", " u'Peak Wave Direction (degrees North)',\n", " u'Wind Direction (degrees true North)',\n", " u'Ratio of means for burst after points replaced by delgitch: Northward Velocity',\n", " u'Minimum Northward Velocity',\n", " u'Maximum Northward Velocity',\n", " u'Peak Wave Direction (degrees true North)',\n", " u'Northward Velocity',\n", " u'Mean Wave Direction (degrees true North)',\n", " u'Mean Resolution VelocityNorthward',\n", " u'NORTH VELOCITY VARIANCE ',\n", " u'NORTH LP ',\n", " u'Northward Resolution Velocity',\n", " u'Mean Northward Velocity',\n", " u'Ratio of variances for burst after points replaced by delgitch: Northward Velocity',\n", " u'Northward Velocity ',\n", " u'Northward Velocity ',\n", " u'NORTH VELOCITY VARIANCE ']" ] } ], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "filter(lambda x:re.search(r'vertical',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 49, "text": [ "[u'Vertical Velocity ',\n", " u'Vertical Velocity ',\n", " u'Ratio of means for burst after points replaced by delgitch: Vertical Velocity',\n", " u'Vertical Resolution Velocity',\n", " u'Ratio of variances for burst after points replaced by delgitch: Vertical Velocity',\n", " u'Vertical Velocity',\n", " u'Maximum Vertical Velocity',\n", " u'Mean Resolution VelocityVertical',\n", " u'Vertical Velocity ',\n", " u'Mean Vertical Velocity',\n", " u'Minimum Vertical Velocity']" ] } ], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "# wind, rotor speed\n", "filter(lambda x:re.search(r'speed',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 50, "text": [ "[u'WIND SPEED (CM/S) ',\n", " u'ROTOR SPEED DIFFERENCE',\n", " u'ROTOR SPEED DIFFERENCE ',\n", " u'Current Speed',\n", " u'UPPER ROTOR SPEED ',\n", " u'Wind Speed (m/s)',\n", " u'CURRENT SPEED (CM/S) ',\n", " u'LOWER ROTOR SPEED ',\n", " u'CURRENT SPEED (CM/S) ',\n", " u'VECTOR SPEED',\n", " u'LOWER ROTOR SPEED ',\n", " u'WIND SPEED (M/S) ',\n", " u'WIND SPEED (CM/S) ',\n", " u'CURRENT SPEED (CM/S) ',\n", " u'ROTOR SPEED ',\n", " u'ROTOR SPEED ']" ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "# std dev\n", "filter(lambda x:re.search(r'pitch',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 51, "text": [ "[u'INST Pitch',\n", " u'Pitch Std. Dev. ',\n", " u'Standard Deviation of INST Pitch',\n", " u'INST Pitch ']" ] } ], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "filter(lambda x:re.search(r'roll',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 52, "text": [ "[u'Roll Std. Dev. ',\n", " u'INST Roll ',\n", " u'INST Roll',\n", " u'Standard Deviation of INST Roll']" ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "# std dev\n", "filter(lambda x:re.search(r'heading',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 53, "text": [ "[u'Median INST Heading ',\n", " u'Heading Std. Dev. ',\n", " u'INST Heading',\n", " u'INST Heading ']" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "# std, dev, wave height spectra, barometric, presscheck\n", "pres = filter(lambda x:re.search(r'press',x.lower()), long_names)\n", "pres" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 54, "text": [ "[u'STAND.DEV.(PRESS)',\n", " u'Pressure at Transducer Head',\n", " u'Pressure',\n", " u'PRESSURE (PASCALS)',\n", " u'PRESSURE (DB) ',\n", " u'STAND. DEV. (PRESS) ',\n", " u'AVERAGE BURST PRESSURE ',\n", " u'Bottom Pressure (psia) ',\n", " u'BAROMETRIC PRESSURE (MB) ',\n", " u'AVERAGE BURST PRESSURE ',\n", " u'Pressure-derived Non-directional Wave Height Spectrum (mm/sqrt(Hz))',\n", " u'STAND. DEV. (PRESS) ',\n", " u'INTERVAL PRESSURE ',\n", " u'Barometric Pressure (mbar)',\n", " u'PRESS ',\n", " u'Barometric Pressure (mb)',\n", " u'ADCP Pressure at Transducer Head',\n", " u'AVERAGE RELATIVE PRESSURE',\n", " u'PRESSURE ',\n", " u'Raw pressure Std. Dev.',\n", " u'PRESSCHECK ',\n", " u'INTERVAL PRESSURE ',\n", " u'PRESSURE ']" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "filter(lambda x:re.search(r'std\|dev',x.lower()), pres)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ "[u'STAND.DEV.(PRESS)',\n", " u'STAND. DEV. (PRESS) ',\n", " u'STAND. DEV. (PRESS) ',\n", " u'Raw pressure Std. Dev.']" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "# save only direction and current direction\n", "filter(lambda x:re.search(r'direct',x.lower()), long_names)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 56, "text": [ "[u'beginning of first direction slice, degrees',\n", " u'Surface-derived Non-directional Wave Height Spectrum (mm/sqrt(Hz))',\n", " u'Direction (degrees)',\n", " u'Peak Wave Direction (degrees North)',\n", " u'Wind Direction (degrees true North)',\n", " u'V DIRECTION',\n", " u'Peak Wave Direction (degrees true North)',\n", " u'CURRENT DIRECTION (T) ',\n", " u'WIND DIRECTION ',\n", " u'CURRENT DIRECTION (T) ',\n", " u'Pressure-derived Non-directional Wave Height Spectrum (mm/sqrt(Hz))',\n", " u'WIND DIRECTION STABILITY',\n", " u'Mean Wave Direction (degrees true North)',\n", " u'Directional Wave Energy Spectrum (mm^2/Hz/degree)',\n", " u'Velocity-derived Non-directional Wave Height Spectrum (mm/sqrt(Hz))',\n", " u'mean wave direction',\n", " u'Direction (degrees T)',\n", " u'WIND DIRECTION ',\n", " u'Current Direction',\n", " u'CURRENT DIRECTION (T) ']" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "f = open('/usgs/data2/notebook/names.txt','w')\n", "f.write(\"\\n\".join(names))\n", "f.close()\n", "f = open('/usgs/data2/notebook/long_names.txt','w')\n", "f.write(\"\\n\".join(long_names))\n", "f.close()\n", "f = open('/usgs/data2/notebook/units.txt','w')\n", "f.write(\"\\n\".join(units))\n", "f.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 57 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Create a dictionary with `standard_name` to `NetCDF variable` mapping" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d={}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 93 }, { "cell_type": "code", "collapsed": false, "input": [ "d['sea_water_temperature']=['instrument transducer temp.', 'temperature (c)','fr temp',\n", " 'adp transducer temp.','adcp transducer temp.','transducer temp.','temp 1','temp 2',\n", " 'temperature','internal temperature','frtemp','temp 2 q','temp','temp lp','sea surface temperature (degrees C)']\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 94 }, { "cell_type": "code", "collapsed": false, "input": [ "d['sea_water_salinity'] = ['salinity 2 q','salinity 1','ctd salinity, pss-78','salinity','salinity (ppt)','salinity (psu)','salinity']\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 95 }, { "cell_type": "code", "collapsed": false, "input": [ "d['northward_sea_water_velocity']=['northward velocity','north','mean northward velocity','northward velocity','north lp']\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 96 }, { "cell_type": "code", "collapsed": false, "input": [ "d['eastward_sea_water_velocity']=['eastward velocity','east','mean eastward velocity','eastward velocity','east lp']\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 97 }, { "cell_type": "code", "collapsed": false, "input": [ "def grid2dsg(ifile,ofile,coord_vars=['time','time2','depth','lat','lon'],\n", " project_name=None,project_pi=None,project_summary=None):\n", " nc = netCDF4.Dataset(ifile)\n", " id = '%s/%s' % (project_name,ifile.split('.')[0]) \n", " #id = ifile.split('.')[0]\n", " vars=nc.variables.keys()\n", " data_vars = [var for var in vars if var not in coord_vars]\n", " nt = len(nc.dimensions['time'])\n", " nz = len(nc.dimensions['depth'])\n", " \n", " # create dimensions of output file\n", " nco = netCDF4.Dataset(ofile,'w',clobber=True)\n", " nco.createDimension('time',nt)\n", " if nz > 1:\n", " nco.createDimension('depth',nz)\n", "\n", " nchar=20\n", " nco.createDimension('nchar',nchar)\n", " # create coordinate variables\n", " time_v = nco.createVariable('time', 'f8', ('time'))\n", " lon_v = nco.createVariable('lon','f4')\n", " lat_v = nco.createVariable('lat','f4')\n", " if nz > 1:\n", " depth_v = nco.createVariable('depth','f4',dimensions='depth')\n", " else:\n", " depth_v = nco.createVariable('depth','f4')\n", "\n", " station_v = nco.createVariable('site','S1',('nchar'))\n", " # write global attributes\n", " g_attdict = nc.__dict__\n", " g_attdict['Conventions'] = 'CF-1.6'\n", " if nz>1:\n", " g_attdict['featureType'] = 'timeSeriesProfile'\n", " else:\n", " g_attdict['featureType'] = 'timeSeries'\n", "\n", " g_attdict['naming_authority'] = 'gov.usgs'\n", " g_attdict['id'] = id\n", " g_attdict['source'] = 'USGS'\n", " g_attdict['institution'] = 'USGS Woods Hole Coastal and Marine Science Center'\n", " g_attdict['project'] = 'Coastal and Marine Geology Program'\n", " g_attdict['title'] = '%s/%s/%s' % (g_attdict['source'],project_name,g_attdict['id'])\n", " g_attdict['keywords']='Oceans > Ocean Pressure > Water Pressure, Oceans > Ocean Temperature > Water Temperature, Oceans > Salinity/Density > Conductivity, Oceans > Salinity/Density > Salinity'\n", " g_attdict['keywords_vocabulary']='GCMD Science Keywords'\n", " g_attdict['standard_name_vocabulary'] = 'CF-1.6'\n", " g_attdict['creator_email'] = '[email protected]'\n", " g_attdict['creator_name'] = 'Rich Signell'\n", " g_attdict['creator_phone'] = '+1 (508) 548-8700'\n", " g_attdict['creator_url'] = 'http://www.usgs.gov'\n", " g_attdict['publisher_email'] = '[email protected]'\n", " g_attdict['publisher_name'] = 'Ellyn Montgomery'\n", " g_attdict['publisher_phone'] = '+1 (508) 548-8700'\n", " g_attdict['publisher_url'] = 'http://www.usgs.gov'\n", " g_attdict['contributor_name'] = project_pi\n", " g_attdict['contributor_role'] = 'principalInvestigator' #from esip ACDD\n", " g_attdict['summary'] = project_summary\n", "\n", " nco.setncatts(g_attdict) \n", " # write station variable\n", " station_v.cf_role = 'timeseries_id'\n", " station_v.standard_name = 'station_id'\n", " data = numpy.empty((1,),'S'+repr(nchar))\n", " data[0] = ifile.split('.')[0]\n", " station_v[:] = netCDF4.stringtochar(data)\n", "\n", " # write time variable\n", " time_v.units = 'milliseconds since 1858-11-17 00:00:00 +0:00'\n", " time_v.standard_name = 'time'\n", " time_v.calendar = 'gregorian'\n", " time_v[:] = (np.int64(nc.variables['time'][:])-2400001)3600241000 + nc.variables['time2'][:]\n", "\n", " # write lon variable\n", " lon_v.units = 'degree_east'\n", " lon_v.standard_name = 'longitude'\n", " lon_v[:] = nc.variables['lon'][:]\n", "\n", " # write lat variable\n", " lat_v.units = 'degree_north'\n", " lat_v.standard_name = 'latitude'\n", " lat_v[:] = nc.variables['lat'][:]\n", "\n", " # write depth variable\n", " depth_v.units = 'm'\n", " depth_v.standard_name = 'depth' \n", " depth_v.positive = 'down'\n", " depth_v.axis = 'Z'\n", " depth_v[:] = nc.variables['depth'][:]\n", "\n", "\n", " # create the data variables\n", " var_v=[]\n", " for varname in data_vars:\n", " ncvar = nc.variables[varname]\n", " # if time series variable\n", " if size(ncvar) == nt:\n", " var = nco.createVariable(varname,ncvar.dtype,('time'))\n", " elif size(ncvar) == nz:\n", " var = nco.createVariable(varname,ncvar.dtype,('depth'))\n", " else:\n", " var = nco.createVariable(varname,ncvar.dtype,('time','depth'))\n", "\n", " # load old variable attributes and modify if necessary \n", " attdict = ncvar.__dict__\n", " # if dounpackshort and 'scale_factor' in attdict: del attdict['scale_factor']\n", " \n", " attdict['coordinates'] = 'time lon lat depth'\n", "\n", " # assign standard_name if in dictionary\n", " a =[k for (k, v) in d.iteritems() if attdict['long_name'].strip().lower() in v]\n", " if len(a)==1: attdict['standard_name']=a[0]\n", " \n", " # write variable attributes\n", " var.setncatts(attdict) \n", " # write the data\n", " # print ncvar\n", " var[:] = ncvar[:]\n", " nco.close()\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 98 }, { "cell_type": "code", "collapsed": false, "input": [ "root_idir='/usgs/data2/emontgomery/stellwagen/Data/'\n", "#root_dir='/usgs/data2/emontgomery/stellwagen/Data/MVCO_11'\n", "root_odir='/usgs/data2/emontgomery/stellwagen/CF-1.6/'\n", "os.chdir(root_dir)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 99 }, { "cell_type": "code", "collapsed": true, "input": [ "types = ('.nc', '.cdf')\n", "coord_vars = ['time','time2','depth','lat','lon']\n", "badfiles = []\n", "goodfiles = []\n", "for index,row in proj.iterrows():\n", " idir = os.path.join(root_idir,index)\n", " os.chdir(idir)\n", " odir = os.path.join(root_odir,index)\n", " if not os.path.exists(odir):\n", " os.makedirs(odir)\n", " ncfiles = []\n", " for files in types:\n", " ncfiles.extend(glob.glob(files))\n", " print index, len(ncfiles)\n", " project_name = index\n", " project_pi = project.ix[index].ix['project_pi']\n", " project_summary = project.ix[index].ix['project_summary']\n", " print project_name,project_pi,project_summary\n", " for ifile in ncfiles:\n", " ofile = os.path.join(odir,ifile)\n", " print ifile,ofile\n", " try:\n", " grid2dsg(ifile,ofile,coord_vars = coord_vars,\n", " project_name = project_name,\n", " project_pi = project_pi,\n", " project_summary = project_summary)\n", " goodfiles.append(ifile)\n", " except:\n", " badfiles.append(ifile)\n", "\n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "FI12 44\n", "FI12 J. Warner Oceanographic and meteorological observations were made at 7 sites on and around the sand ridges offshore of Fire Island NY in winter 2012 to study coastal processes.\n", "9191met-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9191met-a.nc\n", "9202sc-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9202sc-a.nc\n", "9203pcb-cal.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9203pcb-cal.nc\n", "9203pcs-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9203pcs-cal.nc\n", "9205advb-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9205advb-cal.nc\n", "9205advs-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9205advs-cal.nc\n", "9206advb-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9206advb-cal.nc\n", "9206advs-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9206advs-cal.nc\n", "9211wh-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9211wh-a.nc\n", "9221mc-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9221mc-a.nc\n", "9232sc-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9232sc-a.nc\n", "9233pcb-cal.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9233pcb-cal.nc\n", "9233pcs-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9233pcs-cal.nc\n", "9235advb-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9235advb-cal.nc\n", "9235advs-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9235advs-cal.nc\n", "9236advb-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9236advb-cal.nc\n", "9236advs-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9236advs-cal.nc\n", "9241wh-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9241wh-a.nc\n", "9241whp-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9241whp-cal.nc\n", "9261aw-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9261aw-a.nc\n", "9261awWvs-cal.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9261awWvs-cal.nc\n", "9262sc-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9262sc-a.nc\n", "9271mc-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9271mc-a.nc\n", "9281wh-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9281wh-a.nc\n", "9281whp-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9281whp-cal.nc\n", "9282mc-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9282mc-a.nc\n", "9291mc-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9291mc-a.nc\n", "9302mc-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9302mc-a.nc\n", "9303sgWvs-p-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9303sgWvs-p-cal.nc\n", "9303sgWvs-r-cal.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9303sgWvs-r-cal.nc\n", "9303sgWvs-tide-cal.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9303sgWvs-tide-cal.nc\n", "9311mc-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9311mc-a.nc\n", "9321aw-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9321aw-a.nc\n", "9321awWvs-cal.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9321awWvs-cal.nc\n", "9322sc-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9322sc-a.nc\n", "9332sc-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9332sc-a.nc\n", "9204abss1s.cdf /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9204abss1s.cdf\n", "9204abss2s.cdf" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9204abss2s.cdf\n", "9204abss3s.cdf" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9204abss3s.cdf\n", "9212fan_proc_rot.cdf" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9212fan_proc_rot.cdf\n", "9212fan_raw.cdf /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9212fan_raw.cdf\n", "9234abss1s.cdf /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9234abss1s.cdf\n", "9234abss2s.cdf" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9234abss2s.cdf\n", "9234abss3s.cdf /usgs/data2/emontgomery/stellwagen/CF-1.6/FI12/9234abss3s.cdf\n", "BARNEGAT 29\n", "BARNEGAT N. Ganju Light attenuation is a critical parameter governing the ecological function of shallow estuaries. Near-bottom and mid-water observations of currents, pressure, chlorophyll, and fDOM were collected at three pairs of sites sequentially at different locations in the estuary to characterize the conditions.\n", "9611ecp-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9611ecp-a.nc\n", "9612solot-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9612solot-a.nc\n", "9613HRaqd-cal.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9613HRaqd-cal.nc\n", "9614dw-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9614dw-a.nc\n", "9615exo-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9615exo-a.nc\n", "9616ecp-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9616ecp-a.nc\n", "9617solot-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9617solot-a.nc\n", "9621dw-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9621dw-a.nc\n", "9622ecn-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9622ecn-a.nc\n", "9631ecp-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9631ecp-a.nc\n", "9632solot-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9632solot-a.nc\n", "9633HRaqd-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9633HRaqd-cal.nc\n", "9634dw-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9634dw-a.nc\n", "9635exo-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9635exo-a.nc\n", "9636ecp-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9636ecp-a.nc\n", "9637solot-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9637solot-a.nc\n", "9641dw-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9641dw-a.nc\n", "9642ecn-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9642ecn-a.nc\n", "9771ecp-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9771ecp-a.nc\n", "9772solot-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9772solot-a.nc\n", "9773HRaqdb-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9773HRaqdb-cal.nc\n", "9773HRaqds-cal.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9773HRaqds-cal.nc\n", "9774dw-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9774dw-a.nc\n", "9774dw_trim.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9774dw_trim.nc\n", "9775exo-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9775exo-a.nc\n", "9776ecp-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9776ecp-a.nc\n", "9777solot-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9777solot-a.nc\n", "9781dw-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9781dw-a.nc\n", "9782ecn-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/BARNEGAT/9782ecn-a.nc\n", "WFAL 40\n", "WFAL N. Ganju Oceanographic and water-quality observations made at six locations in West Falmouth Harbor and Buzzards Bay.\n", "8591wh-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8591wh-a.nc\n", "8591whp-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8591whp-cal.nc\n", "8592pt-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8592pt-a.nc\n", "8593sc-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8593sc-a.nc\n", "8594bl-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8594bl-a.nc\n", "8601aqd-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8601aqd-a.nc\n", "8602wh-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8602wh-a.nc\n", "8603pt-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8603pt-a.nc\n", "8604sc-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8604sc-a.nc\n", "8621sc-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8621sc-a.nc\n", "8622pt-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8622pt-a.nc\n", "8821aqd-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8821aqd-a.nc\n", "8822pt-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8822pt-a.nc\n", "8841mc-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8841mc-a.nc\n", "8842mc-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8842mc-a.nc\n", "8843mc-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8843mc-a.nc\n", "8851aqd-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8851aqd-a.nc\n", "8852pt-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8852pt-a.nc\n", "8862rcm-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8862rcm-a.nc\n", "8871aqd-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8871aqd-a.nc\n", "8872pt-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8872pt-a.nc\n", "9001rcm-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9001rcm-a.nc\n", "938metA-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/938metA-a.nc\n", "938metB-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/938metB-a.nc\n", "9391aqd-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9391aqd-cal.nc\n", "9392ysi-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9392ysi-a.nc\n", "9394eco-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9394eco-a.nc\n", "9395eco-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9395eco-a.nc\n", "9401aqd-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9401aqd-cal.nc\n", "9402ysi-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9402ysi-a.nc\n", "9404eco-a.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9404eco-a.nc\n", "9405eco-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9405eco-a.nc\n", "9411aqd-cal.nc" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9411aqd-cal.nc\n", "9412ysi-a.nc /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/9412ysi-a.nc\n", "8602Wvs-cal.cdf" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8602Wvs-cal.cdf\n", "8831ysi-cal.cdf /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8831ysi-cal.cdf\n", "8832isus-cal.cdf" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8832isus-cal.cdf\n", "8861ysi-cal.cdf" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8861ysi-cal.cdf\n", "8881ysiA-cal.cdf" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8881ysiA-cal.cdf\n", "8881ysiB-cal.cdf" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " /usgs/data2/emontgomery/stellwagen/CF-1.6/WFAL/8881ysiB-cal.cdf\n" ] } ], "prompt_number": 100 }, { "cell_type": "code", "collapsed": false, "input": [ "len(badfiles)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 101, "text": [ "41" ] } ], "prompt_number": 101 }, { "cell_type": "code", "collapsed": false, "input": [ "len(goodfiles)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 102, "text": [ "72" ] } ], "prompt_number": 102 }, { "cell_type": "code", "collapsed": true, "input": [ "print badfiles" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['9191met-a.nc', '9202sc-a.nc', '9203pcb-cal.nc', '9203pcs-cal.nc', '9205advb-cal.nc', '9206advb-cal.nc', '9221mc-a.nc', '9232sc-a.nc', '9233pcb-cal.nc', '9233pcs-cal.nc', '9235advb-cal.nc', '9236advb-cal.nc', '9241whp-cal.nc', '9261aw-a.nc', '9261awWvs-cal.nc', '9262sc-a.nc', '9271mc-a.nc', '9281whp-cal.nc', '9282mc-a.nc', '9291mc-a.nc', '9302mc-a.nc', '9303sgWvs-p-cal.nc', '9303sgWvs-r-cal.nc', '9303sgWvs-tide-cal.nc', '9311mc-a.nc', '9321aw-a.nc', '9321awWvs-cal.nc', '9322sc-a.nc', '9332sc-a.nc', '9204abss1s.cdf', '9204abss2s.cdf', '9204abss3s.cdf', '9212fan_proc_rot.cdf', '9212fan_raw.cdf', '9234abss1s.cdf', '9234abss2s.cdf', '9234abss3s.cdf', '9773HRaqdb-cal.nc', '8591whp-cal.nc', '9411aqd-cal.nc', '8602Wvs-cal.cdf']\n" ] } ], "prompt_number": 103 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 } ], "metadata": {} } ] }	mit
maxis42/ML-DA-Coursera-Yandex-MIPT	4 Stats for data analysis/Lectures notebooks/14 regression/stat.regression.ipynb	1	692445	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Линейная регрессия" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import statsmodels\n", "import scipy as sc\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.formula.api as smf\n", "import statsmodels.stats.api as sms\n", "from statsmodels.graphics.regressionplots import plot_leverage_resid2\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Постановка" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "По 1260 опрошенным имеются следующие данные:\n", "\n", "* заработная плата за час работы, $;\n", "* опыт работы, лет;\n", "* образование, лет;\n", "* внешняя привлекательность, в баллах от 1 до 5;\n", "* бинарные признаки: пол, семейное положение, состояние здоровья (хорошее/плохое), членство в профсоюзе, цвет кожи (белый/чёрный), занятость в сфере обслуживания (да/нет).\n", "\n", "Требуется оценить влияние внешней привлекательности на уровень заработка с учётом всех остальных факторов.\n", "\n", "Hamermesh D.S., Biddle J.E. (1994) Beauty and the Labor Market, American Economic Review, 84, 1174–1194.\n", "\n", "Данные:" ] }, { "cell_type": "code", "execution_count": 3, "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>wage</th>\n", " <th>exper</th>\n", " <th>union</th>\n", " <th>goodhlth</th>\n", " <th>black</th>\n", " <th>female</th>\n", " <th>married</th>\n", " <th>service</th>\n", " <th>educ</th>\n", " <th>looks</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.73</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.28</td>\n", " <td>28</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>7.96</td>\n", " <td>35</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>10</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>11.57</td>\n", " <td>38</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>16</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>11.42</td>\n", " <td>27</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " wage exper union goodhlth black female married service educ looks\n", "0 5.73 30 0 1 0 1 1 1 14 4\n", "1 4.28 28 0 1 0 1 1 0 12 3\n", "2 7.96 35 0 1 0 1 0 0 10 4\n", "3 11.57 38 0 1 0 0 1 1 16 3\n", "4 11.42 27 0 1 0 0 1 0 16 3" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw = pd.read_csv(\"beauty.csv\", sep=\";\", index_col=False) \n", "raw.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим на матрицу диаграмм рассеяния по количественным признакам:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NljY5VWrh+Lkb83cheKvg6OaL05sDtkSqP040MWXyqaceYfdu5zVTeYE80wu/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4B/BL6wYq0Kw2AwsHt3MaWlN3G7/xcHDmSxd68Ds7mP0tJp8vI+BOo5dMhGXV05Dscwo6P/QF2d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U19fHjKG1tXXEmM7rsKOjw1bH5/PFPG9njM5yIjtxY71Xtba22s7frTtxPp/Pdj2mYieuf+ft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\nExERERERiQgFcCIiIiIiIhHx/wG8SZ4E2AP7awAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xba86470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.tools.plotting.scatter_matrix(raw[['wage', 'exper', 'educ', 'looks']], alpha=0.2, \n", " figsize=(15, 15), diagonal='hist')\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Оценим сбалансированность выборки по категориальным признакам:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 917\n", "1 343\n", "Name: union, dtype: int64\n", "1 1176\n", "0 84\n", "Name: goodhlth, dtype: int64\n", "0 1167\n", "1 93\n", "Name: black, dtype: int64\n", "0 824\n", "1 436\n", "Name: female, dtype: int64\n", "1 871\n", "0 389\n", "Name: married, dtype: int64\n", "0 915\n", "1 345\n", "Name: service, dtype: int64\n" ] } ], "source": [ "print raw.union.value_counts()\n", "print raw.goodhlth.value_counts()\n", "print raw.black.value_counts()\n", "print raw.female.value_counts()\n", "print raw.married.value_counts()\n", "print raw.service.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "У каждого признака все значения встречаются достаточно много раз, так что всё в порядке." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Предобработка" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = raw" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим на распределение целевого признака — уровня заработной платы: " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JkiSp8wy/kiSJJKuSXJfkK0luS/IbTfsFSe5JclNzO23gOWuSbElyR5JT26tekqQ9W9Z2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\nrqr3D7SN1XiS/FCaK7omORh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"text/plain": [ "<matplotlib.figure.Figure at 0xd353048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize(16,7))\n", "plt.subplot(121)\n", "data['wage'].plot.hist()\n", "plt.xlabel('Wage', fontsize=14)\n", "\n", "plt.subplot(122)\n", "np.log(data['wage']).plot.hist()\n", "plt.xlabel('Log wage', fontsize=14)\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Один человек в выборке получает 77.72\\$ в час, остальные — меньше 45\\$; удалим этого человека, чтобы регрессия на него не перенастроилась." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = data[data['wage'] < 77]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим на распределение оценок привлекательности: " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xd84c208>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize(8,7))\n", "data.groupby('looks')['looks'].agg(lambda x: len(x)).plot(kind='bar', width=0.9)\n", "plt.xticks(rotation=0)\n", "plt.xlabel('Looks', fontsize=14)\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В группах looks=1 и looks=5 слишком мало наблюдений. Превратим признак looks в категориальный и закодируем с помощью фиктивных переменных:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\riabenko\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " if __name__ == '__main__':\n", "C:\\Users\\riabenko\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " from ipykernel import kernelapp as app\n", "C:\\Users\\riabenko\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " app.launch_new_instance()\n" ] } ], "source": [ "data['belowavg'] = data['looks'].apply(lambda x : 1 if x < 3 else 0)\n", "data['aboveavg'] = data['looks'].apply(lambda x : 1 if x > 3 else 0)\n", "data.drop('looks', axis=1, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Данные теперь:" ] }, { "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>wage</th>\n", " <th>exper</th>\n", " <th>union</th>\n", " <th>goodhlth</th>\n", " <th>black</th>\n", " <th>female</th>\n", " <th>married</th>\n", " <th>service</th>\n", " <th>educ</th>\n", " <th>belowavg</th>\n", " <th>aboveavg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.73</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.28</td>\n", " <td>28</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>7.96</td>\n", " <td>35</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>10</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>11.57</td>\n", " <td>38</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>11.42</td>\n", " <td>27</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " wage exper union goodhlth black female married service educ \\\n", "0 5.73 30 0 1 0 1 1 1 14 \n", "1 4.28 28 0 1 0 1 1 0 12 \n", "2 7.96 35 0 1 0 1 0 0 10 \n", "3 11.57 38 0 1 0 0 1 1 16 \n", "4 11.42 27 0 1 0 0 1 0 16 \n", "\n", " belowavg aboveavg \n", "0 0 1 \n", "1 0 0 \n", "2 0 1 \n", "3 0 0 \n", "4 0 0 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Построение модели" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Простейшая модель" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Построим линейную модель по всем признакам." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: wage R-squared: 0.262\n", "Model: OLS Adj. R-squared: 0.256\n", "Method: Least Squares F-statistic: 44.31\n", "Date: Sun, 29 May 2016 Prob (F-statistic): 1.42e-75\n", "Time: 14:22:09 Log-Likelihood: -3402.9\n", "No. Observations: 1259 AIC: 6828.\n", "Df Residuals: 1248 BIC: 6884.\n", "Df Model: 10 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "Intercept -0.5898 0.743 -0.793 0.428 -2.048 0.869\n", "exper 0.0850 0.009 9.118 0.000 0.067 0.103\n", "union 0.4786 0.234 2.048 0.041 0.020 0.937\n", "goodhlth -0.0444 0.417 -0.107 0.915 -0.862 0.773\n", "black -0.6748 0.403 -1.674 0.094 -1.466 0.116\n", "female -2.3058 0.242 -9.522 0.000 -2.781 -1.831\n", "married 0.4569 0.240 1.905 0.057 -0.014 0.927\n", "service -0.7303 0.252 -2.896 0.004 -1.225 -0.236\n", "educ 0.4820 0.043 11.272 0.000 0.398 0.566\n", "belowavg -0.8185 0.323 -2.532 0.011 -1.453 -0.184\n", "aboveavg -0.0729 0.234 -0.311 0.756 -0.532 0.387\n", "==============================================================================\n", "Omnibus: 898.031 Durbin-Watson: 1.858\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 17969.693\n", "Skew: 3.076 Prob(JB): 0.00\n", "Kurtosis: 20.456 Cond. No. 189.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "m1 = smf.ols('wage ~ exper + union + goodhlth + black + female + married +'\\\n", " 'service + educ + belowavg + aboveavg', \n", " data=data)\n", "fitted = m1.fit()\n", "print fitted.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим на распределение остатков:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+5CPn2DBKa3IB8H2K2dnXWg5m5vyIOKuzkyLiOqAO2DIiXqD4D/dN4KaIOAFo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\n9bM4eu7XWe/UjtDrmh1ki6gOsVAdZDO9uq2p2TM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R2SMo2IqIiIiIiEiqaY6tiIiIiNRb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\nhTDV51EzG064Vv2dZC1W5e4bzWwGMMLM/h/QiXBVguzRVQsIAfkS4HX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"text/plain": [ "<matplotlib.figure.Figure at 0xd6ce240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize(16,7))\n", "plt.subplot(121)\n", "sc.stats.probplot(fitted.resid, dist=\"norm\", plot=pylab)\n", "plt.subplot(122)\n", "np.log(fitted.resid).plot.hist()\n", "plt.xlabel('Residuals', fontsize=14)\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Оно скошенное, как и исходный признак. В таких ситуациях часто помогает перейти от регрессии исходного признака к регрессии его логарифма." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Логарифмируем отклик" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: np.log(wage) R-squared: 0.383\n", "Model: OLS Adj. R-squared: 0.379\n", "Method: Least Squares F-statistic: 77.63\n", "Date: Sun, 29 May 2016 Prob (F-statistic): 1.18e-123\n", "Time: 14:22:10 Log-Likelihood: -816.90\n", "No. Observations: 1259 AIC: 1656.\n", "Df Residuals: 1248 BIC: 1712.\n", "Df Model: 10 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "Intercept 0.4515 0.095 4.737 0.000 0.265 0.639\n", "exper 0.0138 0.001 11.546 0.000 0.011 0.016\n", "union 0.1785 0.030 5.957 0.000 0.120 0.237\n", "goodhlth 0.0785 0.053 1.470 0.142 -0.026 0.183\n", "black -0.0989 0.052 -1.913 0.056 -0.200 0.003\n", "female -0.3938 0.031 -12.684 0.000 -0.455 -0.333\n", "married 0.0425 0.031 1.383 0.167 -0.018 0.103\n", "service -0.1505 0.032 -4.656 0.000 -0.214 -0.087\n", "educ 0.0799 0.005 14.581 0.000 0.069 0.091\n", "belowavg -0.1305 0.041 -3.148 0.002 -0.212 -0.049\n", "aboveavg -0.0041 0.030 -0.138 0.890 -0.063 0.055\n", "==============================================================================\n", "Omnibus: 27.318 Durbin-Watson: 1.853\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 46.550\n", "Skew: 0.159 Prob(JB): 7.80e-11\n", "Kurtosis: 3.887 Cond. No. 189.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] }, { "data": { "image/png": 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tJNfqypJUrDFj4MILmXnV1Xzue5eycOESPrj/tgnowKc+1TnXMCVJkmqZhack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\nDPwQOIuVV36JXXddh5NOchyu1Ja33nqLvfbaC4BtttmG5Zd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"text/plain": [ "<matplotlib.figure.Figure at 0xdad40f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m2 = smf.ols('np.log(wage) ~ exper + union + goodhlth + black + female + married +'\\\n", " 'service + educ + belowavg + aboveavg', data=data)\n", "fitted = m2.fit()\n", "print fitted.summary()\n", "\n", "plt.figure(figsize(16,7))\n", "plt.subplot(121)\n", "sc.stats.probplot(fitted.resid, dist=\"norm\", plot=pylab)\n", "plt.subplot(122)\n", "np.log(fitted.resid).plot.hist()\n", "plt.xlabel('Residuals', fontsize=14)\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь стало лучше. Посмотрим теперь на зависимость остатков от непрерывных признаков:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JJFpbr0Yq1YdMoRGJdGJk5JQnb+7JZNKzjRl+09PTi3h8W06FxAtJXIwoigIhhOL0cVQ7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\n9JmsX/90w05gynRxwRrPCXA2H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"text/plain": [ "<matplotlib.figure.Figure at 0xda947b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize(16,7))\n", "plt.subplot(121)\n", "scatter(data['educ'],fitted.resid)\n", "plt.xlabel('Education', fontsize=14)\n", "plt.ylabel('Residuals', fontsize=14)\n", "plt.subplot(122)\n", "scatter(data['exper'],fitted.resid)\n", "plt.xlabel('Experience', fontsize=14)\n", "plt.ylabel('Residuals', fontsize=14)\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "На втором графике видна квадратичная зависимость остатков от опыта работы. Попробуем добавить к признакам квадрат опыта работы, чтобы учесть этот эффект." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Добавляем квадрат опыта работы" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: np.log(wage) R-squared: 0.403\n", "Model: OLS Adj. R-squared: 0.398\n", "Method: Least Squares F-statistic: 76.46\n", "Date: Sun, 29 May 2016 Prob (F-statistic): 3.19e-131\n", "Time: 14:22:11 Log-Likelihood: -796.86\n", "No. Observations: 1259 AIC: 1618.\n", "Df Residuals: 1247 BIC: 1679.\n", "Df Model: 11 \n", "Covariance Type: nonrobust \n", "======================================================================================\n", " coef std err t P>\|t\| [95.0% Conf. Int.]\n", "--------------------------------------------------------------------------------------\n", "Intercept 0.3424 0.095 3.588 0.000 0.155 0.530\n", "exper 0.0404 0.004 9.290 0.000 0.032 0.049\n", "np.power(exper, 2) -0.0006 9.63e-05 -6.351 0.000 -0.001 -0.000\n", "union 0.1710 0.030 5.793 0.000 0.113 0.229\n", "goodhlth 0.0716 0.053 1.361 0.174 -0.032 0.175\n", "black -0.0831 0.051 -1.631 0.103 -0.183 0.017\n", "female -0.3936 0.031 -12.875 0.000 -0.454 -0.334\n", "married 0.0101 0.031 0.329 0.742 -0.050 0.070\n", "service -0.1599 0.032 -5.018 0.000 -0.222 -0.097\n", "educ 0.0758 0.005 13.941 0.000 0.065 0.086\n", "belowavg -0.1352 0.041 -3.313 0.001 -0.215 -0.055\n", "aboveavg -0.0025 0.030 -0.084 0.933 -0.061 0.056\n", "==============================================================================\n", "Omnibus: 30.019 Durbin-Watson: 1.849\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 56.257\n", "Skew: 0.140 Prob(JB): 6.08e-13\n", "Kurtosis: 3.997 Cond. No. 5.62e+03\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 5.62e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] }, { "data": { "image/png": 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F3HbbF12mLEmSVGFcrixJsMES5ZuBPciaSrlMuV65XLk0HJsllYPLlYvhcmVJ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\nn1Q9uLt/6e7bU/V4IlIxJbki8dQF+CDxBnffDHwGNKTiT2Rvc/fTgZO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"text/plain": [ "<matplotlib.figure.Figure at 0xd941470>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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CnNtNF3VrHYdVMwayDMB3ATxjf7083eHYPPfcV/Hqq6/h1VctvPrqa3juuecS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\n5zH8O4CXzHEuA/BPUOcJcB+ADwE4jDNn3o2TJw+b3/2Cst+fI5dbjK9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+ZhfuGhNKzu7t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\nLf5Ws0y80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vhQLpept3d9dRFc51gCBdqzZ4/vNecVhe7tXd90vomTZ4KgWU6qH5LGW3Ej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\nGIwEIgifBeGTVnIO8wyDIZCKe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"text/plain": [ "<matplotlib.figure.Figure at 0xd245128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m3 = smf.ols('np.log(wage) ~ exper + np.power(exper,2) + union + goodhlth + black + female +'\\\n", " 'married + service + educ + belowavg + aboveavg', data=data)\n", "fitted = m3.fit()\n", "print fitted.summary()\n", "\n", "plt.figure(figsize(16,7))\n", "plt.subplot(121)\n", "sc.stats.probplot(fitted.resid, dist=\"norm\", plot=pylab)\n", "plt.subplot(122)\n", "np.log(fitted.resid).plot.hist()\n", "plt.xlabel('Residuals', fontsize=14)\n", "plt.figure(figsize(16,5))\n", "plt.subplot(131)\n", "scatter(data['educ'],fitted.resid)\n", "plt.xlabel('Education', fontsize=14)\n", "plt.ylabel('Residuals', fontsize=14)\n", "plt.subplot(132)\n", "scatter(data['exper'],fitted.resid)\n", "plt.xlabel('Experience', fontsize=14)\n", "plt.ylabel('Residuals', fontsize=14)\n", "plt.subplot(133)\n", "scatter(data['exper']*2,fitted.resid)\n", "plt.xlabel('Experience^2', fontsize=14)\n", "plt.ylabel('Residuals', fontsize=14)\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Используем критерий Бройша-Пагана для проверки гомоскедастичности ошибок:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Breusch-Pagan test: p=0.000004\n" ] } ], "source": [ "print 'Breusch-Pagan test: p=%f' % sms.het_breushpagan(fitted.resid, fitted.model.exog)[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ошибки гетероскедастичны, значит, значимость признаков может определяться неверно. Сделаем поправку Уайта:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: np.log(wage) R-squared: 0.403\n", "Model: OLS Adj. R-squared: 0.398\n", "Method: Least Squares F-statistic: 87.29\n", "Date: Sun, 29 May 2016 Prob (F-statistic): 4.23e-146\n", "Time: 14:22:12 Log-Likelihood: -796.86\n", "No. Observations: 1259 AIC: 1618.\n", "Df Residuals: 1247 BIC: 1679.\n", "Df Model: 11 \n", "Covariance Type: HC1 \n", "======================================================================================\n", " coef std err z P>\|z\| [95.0% Conf. Int.]\n", "--------------------------------------------------------------------------------------\n", "Intercept 0.3424 0.104 3.282 0.001 0.138 0.547\n", "exper 0.0404 0.004 9.511 0.000 0.032 0.049\n", "np.power(exper, 2) -0.0006 9.46e-05 -6.469 0.000 -0.001 -0.000\n", "union 0.1710 0.026 6.463 0.000 0.119 0.223\n", "goodhlth 0.0716 0.064 1.123 0.262 -0.053 0.197\n", "black -0.0831 0.052 -1.599 0.110 -0.185 0.019\n", "female -0.3936 0.031 -12.702 0.000 -0.454 -0.333\n", "married 0.0101 0.030 0.340 0.734 -0.048 0.068\n", "service -0.1599 0.033 -4.786 0.000 -0.225 -0.094\n", "educ 0.0758 0.006 13.387 0.000 0.065 0.087\n", "belowavg -0.1352 0.040 -3.384 0.001 -0.214 -0.057\n", "aboveavg -0.0025 0.030 -0.083 0.934 -0.061 0.056\n", "==============================================================================\n", "Omnibus: 30.019 Durbin-Watson: 1.849\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 56.257\n", "Skew: 0.140 Prob(JB): 6.08e-13\n", "Kurtosis: 3.997 Cond. No. 5.62e+03\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors are heteroscedasticity robust (HC1)\n", "[2] The condition number is large, 5.62e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] }, { "data": { "image/png": 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F3HbbF12mLEmSVGFcrixJsMES5ZuBPciaSrlMuV65XLk0HJsllYPLlYvhcmVJ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\ntWoVZ511FkceeSRdunQhKyuLFi1a0KxZswOOW7hwIStXruS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"text/plain": [ "<matplotlib.figure.Figure at 0xcd51240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m4 = smf.ols('np.log(wage) ~ exper + np.power(exper,2) + union + goodhlth + black + female +'\\\n", " 'married + service + educ + belowavg + aboveavg', data=data)\n", "fitted = m4.fit(cov_type='HC1')\n", "print fitted.summary()\n", "\n", "plt.figure(figsize(16,7))\n", "plt.subplot(121)\n", "sc.stats.probplot(fitted.resid, dist=\"norm\", plot=pylab)\n", "plt.subplot(122)\n", "np.log(fitted.resid).plot.hist()\n", "plt.xlabel('Residuals', fontsize=14)\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Удаляем незначимые признаки" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В предыдущей модели незначимы: цвет кожи, здоровье, семейное положение. Удалим их. Индикатор привлекательности выше среднего тоже незначим, но удалять его не будем, потому что это одна из переменных, по которым на нужно в конце ответить на вопрос." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: np.log(wage) R-squared: 0.400\n", "Model: OLS Adj. R-squared: 0.397\n", "Method: Least Squares F-statistic: 121.1\n", "Date: Sun, 29 May 2016 Prob (F-statistic): 6.49e-150\n", "Time: 14:22:12 Log-Likelihood: -799.30\n", "No. Observations: 1259 AIC: 1617.\n", "Df Residuals: 1250 BIC: 1663.\n", "Df Model: 8 \n", "Covariance Type: HC1 \n", "======================================================================================\n", " coef std err z P>\|z\| [95.0% Conf. Int.]\n", "--------------------------------------------------------------------------------------\n", "Intercept 0.3906 0.084 4.674 0.000 0.227 0.554\n", "exper 0.0410 0.004 9.781 0.000 0.033 0.049\n", "np.power(exper, 2) -0.0006 9.35e-05 -6.748 0.000 -0.001 -0.000\n", "union 0.1695 0.026 6.414 0.000 0.118 0.221\n", "female -0.4043 0.030 -13.560 0.000 -0.463 -0.346\n", "service -0.1600 0.033 -4.785 0.000 -0.225 -0.094\n", "educ 0.0773 0.006 13.549 0.000 0.066 0.089\n", "belowavg -0.1307 0.040 -3.279 0.001 -0.209 -0.053\n", "aboveavg -0.0010 0.030 -0.035 0.972 -0.059 0.057\n", "==============================================================================\n", "Omnibus: 26.927 Durbin-Watson: 1.842\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 49.409\n", "Skew: 0.120 Prob(JB): 1.87e-11\n", "Kurtosis: 3.941 Cond. No. 4.49e+03\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors are heteroscedasticity robust (HC1)\n", "[2] The condition number is large, 4.49e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] }, { "data": { "image/png": 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uJwu4S4Bv8fjj3zLkliDblfPDsVlSa9mu3FK2KzuTK0nrYNKkyXw9F3D3Z78G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\nvHcxsAFId87ND7qmIxC5gE3YoB4itwMj/fbsMRKjNwYAM/s1sME5tzr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"text/plain": [ "<matplotlib.figure.Figure at 0xd75f1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m5 = smf.ols('np.log(wage) ~ exper + np.power(exper,2) + union + female + service + educ +'\\\n", " 'belowavg + aboveavg', data=data)\n", "fitted = m5.fit(cov_type='HC1')\n", "print fitted.summary()\n", "\n", "plt.figure(figsize(16,7))\n", "plt.subplot(121)\n", "sc.stats.probplot(fitted.resid, dist=\"norm\", plot=pylab)\n", "plt.subplot(122)\n", "np.log(fitted.resid).plot.hist()\n", "plt.xlabel('Residuals', fontsize=14)\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим, не стала ли модель от удаления трёх признаков значимо хуже, с помощью критерия Фишера:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "F=1.611478, p=0.184911, k1=3.000000\n" ] } ], "source": [ "print \"F=%f, p=%f, k1=%f\" % m4.fit().compare_f_test(m5.fit())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Не стала.\n", "\n", "Проверим, нет ли наблюдений, которые слишком сильно влияют на регрессионное уравнение:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0xbbb0c18>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Kqd1KqbaWdfWUUuuVUvuVUnuVUmNs0kcrpU4qpX6xLJEVXQ9xc0hKSmbw4FgiIqIZPDiW\npKTkqi6SEELcECq0l75SygAcAjoDqcAOYIDW+nebNA8Az2mtH1RK3QHM0FrfqZSqC9TVWu9WSrkB\nu4CHtda/K6WigUyt9X+v8PnSS78GSUpKpmvXmRw9Ggu4AtmEhUWzdu1oQkIaXCm7EELc9Kqyl/7t\nwGGtdbLWugBYCjxcIs3DwEIArfV2wFMpVUdr/YfWerdlfRZwAAiyyVdqhUTNFRU13ybYA7hy9Ggs\nUVHzq7BUQghxY6jogB8EnLB5f5LiQbu0NCkl0yilGgJtge02q5+z3AL4SCnlWV4FFjevlBQTF4N9\nEVdSU01VURwhhLih3PCd9izN+Z8BYy1X+gCzgVCtdVvgD+CyTfuiZggKMgDZJdZmExh4w//MhRCi\nwlX0WPopQLDN+3qWdSXT1C8tjVLKDnOwX6S1XlGUQGv9l036D4GvyypATEyM9XWnTp3o1KnTtZRf\n3ETi4obx00/Rl9zDj4sbXcUlE0KIirFhwwY2bNhwVWkrutNeLeAg5k57p4Cfgce01gds0nQHnrV0\n2rsTeFtrfadl20LgjNb6+RL7rau1/sPy+p9AuNZ6YCmfL532apikpGSiouaTmmoiMNBAXNww6bAn\nhKgxqnQsfcsjczMw3z74WGs9TSn1FKC11nMsad4FIjG3xw7TWv+qlGoPbAL2AtqyvKq1/tZyItAW\nMAHHgKe01qdL+WwJ+EIIIWoMmTxHCCGEqAFk8hwhhBCihpOAL4QQQtQAEvCFEEKIGkACvhBCCFED\nSMAXQgghagAJ+EIIIUQNIAFfCCGEqAEk4AshhBA1gAR8IYQQogaQgC+EEELUABLwhRBCiBpAAr4Q\nQghRA0jAF0IIIWoACfhCCCFEDSABXwghhKgBJOCXs1mzZhEeHo6TkxMjRowotm3dunU0b94cNzc3\nOnfuzPHjx63b8vPzGTVqFHXr1sXX15eHH36YU6dOXbL/jRs3YjAYmDhxYoXXRQghRPUhAb+cBQUF\nERUVxciRI4utP3v2LH369GHKlCmkpaVx22230b9/f+v2t99+m+3bt7Nv3z5SU1Px8vJi9OjRxfZR\nWFjIuHHjuPPOOyulLkIIIaoPCfjlrFevXvTs2RMfH59i67/44gtatWpF7969cXBwICYmhoSEBA4d\nOgTAsWPH6NatG76+vjg4ONC/f3/2799fbB//+c9/6NatG82aNau0+gghhKgeJOBXkv3799OmTRvr\nexcXFxo1amQN6iNHjmTLli2cOnWKnJwc4uPj6d69uzV9cnIy8+bNY+LEiWitK738Qgghbm52VV2A\nmiIrKwt/f/9i6zw8PMjMzASgcePG1K9fn6CgIOzs7GjdujWzZs2yph07diyTJ0/GxcWlUssthBCi\nepAr/Eri5uZGRkZGsXXp6em4u7sD8Mwzz5CXl8e5c+fIzs7mkUceITIyEoCvv/6azMxM+vbtW+nl\nFkIIUT3IFX4ladmyJQsWLLC+z87O5ujRo7Rq1QqAhIQEpk6diqenJwCjR48mOjqatLQ01q9fz65d\nuwgICADMJwp2dnbs3buX5cuXV35lhBBC3HTkCr+cGY1GLly4gNFopLCwkLy8PIxGI4888gj79+9n\n+fLl5OXlERsbS9u2bWncuDEA4eHhLFy4kIyMDAoKCpg1axaBgYH4+PgwefJkDh06REJCAgkJCfTs\n2ZMnnniCefPmVXFthRBC3Cwk4Jezovvsr7/+OvHx8bi4uDBlyhR8fX35/PPPefXVV/Hx8WHnzp0s\nXbrUmm/69Ok4OjrSuHFj6tSpw7fffmu9end1dcXf39+6ODs74+rqipeXV1VVUwghxE1GVece30op\nXZ3rJ4QQQthSSqG1VqVtkyt8IYQQogaQgC+EEELUABLwhRBCiBpAAr4QQghRA8hz+FUsKSmZqKj5\npKSYCAoyEBc3jJCQBlVdLCGEENWM9NKvQklJyXTtOpOjR2MBVyCbsLBo1q4dLUFfCCHENZNe+jeo\nqKj5NsEewJWjR2OJippfhaUSQghRHUnAr0IpKSYuBvsirqSmmqqiOEIIIaoxCfhVKCjIAGSXWJtN\nYKB8LUIIIcqXRJYqFBc3jLCwaC4GffM9/Li4YVVWJiGEENWTdNqrYkW99FNTTQQGSi99IYQQ1+9y\nnfYk4AshhBDVhPTSF0IIIWo4CfhCCCFEDSABXwghhKgBJOALIYQQNYAEfCGEEKIGkIAvhBBC1AAS\n8IUQQogaQAK+EEIIUQNIwBdCCCFqAAn4QgghRA0gAV8IIYSoASTgX6PHH3+cgIAAvLy8aNasGR9/\n/DEAS5Yswd3dHQ8PDzw8PHB1dcVgMPDrr78Wy19QUEDz5s0JDg6uiuILIYSooWTynGv022+/ERoa\nipOTE4cOHeLee+9l1apVtGvXrli6BQsWMHnyZA4fPlxs/ZQpU1i7di2JiYkcP368XMsmhBCiZpPJ\nc8pRixYtcHJyAkBrjVKKo0ePXpJuwYIFDBkypNi6pKQklixZwr/+9a9KKasQQghRRAL+dXj22Wdx\ndXWlefPmBAYG0r1792Lbk5OT2bx58yUBf8yYMbz22mvWEwYhhBCiskjAvw6zZs0iKyuLLVu20Lt3\nbxwdHYttX7hwIR06dKBBgwbWdcuXL8dkMtGzZ8/KLq4QQggh9/D/rsDAQM6cOYOTkxNaa+rVq4fR\naGTChAnk5+fz+uuvc/r0aYxGI99//z133XUXGzZs4OGHHyY3N9eaTynFnj17aNiwYYWWVwghRPV1\nuXv4dpVdmOqoU6dOfPfddwD8+OOPREZG4ufnx7Bhw9i4cSPZ2dncfvvtdOrUCR8fH/Lz88nMzMTJ\nyYl9+/ZJj30hhBAVTpr0r8G0adMICwvDycmJ4cOHs2bNGv7880+aNGlCv379CAkJ4Z577qF9+/as\nX7+efv360axZMzZt2kRgYCD5+fmYTCbat2+Pq6srPXr0oH79+jRs2BAXFxfrI32RkZFVXVUhhBDV\njAT8axAYGIizszMA8fHxvPTSSzRq1Ihly5axevVq3NzcUErxwAMPFMvXq1cvvv32W5RSvP3226Sk\npFBQUMDatWvx8/MjNTWVp556ioyMDDIyMvj222+ronpCCCGqMQn412DIkCHs27ePF198kcGDB5OQ\nkMCiRYtISkri3LlzvPjiiwDUrVuXyMhIPv30U/bt20fdunV55513MBgMZGdn4+PjQ58+fThw4AB/\n/fUXvr6+LFy4kP/9739VXEMhhBDVlQT8vyk8PBxXV1fs7e0ZMmQIjo6O/PTTT3Tu3JmYmBh69+5N\naGgoGRkZGI1GnnzySfbs2cMrr7xC3bp1UUrh6OhIQUEBQ4cOJTIykj179lR1tYQQQlQz0mmvAhQ9\nGfD000/z9NNPA3D48GG++eYbNm/ezPLly6lTp441/ZIlS/jhhx/YsWMHd9xxB926dePgwYN4eHhU\nSfmFEEJUP3KF/zekp6fz3XffkZeXh9FoJD4+nry8PG6//Xby8vLYv38/AMePH+fJJ59k3LhxtG3b\nlhYtWtCrVy/Onz8PQK1atZg1axZ9+vThlVdewcvLi82bN1dl1YQQQlQzcoX/NxQUFDBhwgQOHjxI\nrVq1aNasGb6+vtSrV48LFy4wcOBAEhMTcXd3Z8SIEUyaNMma78CBAzRq1Ij8/Hzq1avHq6++yuDB\ngwHrc5RVWTUhhBDVjAT8a2A0GikoKMBoNFJYWIi7uzvbtm2jVq1a1kfuQkNDiY39iMLC+rRu3Zsv\nvxzG+vXf07NnT5RS/Pbbb0ybNo1//OMfvPnmm5w4cYITJ04QHh5OXl4e77zzDmfPnqV9+/ZVXV0h\nhBDViIy0dw1iY2OJjY1FqYuDGEVHRzNx4kRCQkI4fvw4JpMGirbvJyzsI9q1O87mzZvIzs7Gz8+P\nRx99lEmTJuHg4MBvv/3GY489RmJiIk5OTrRt25Y33njjktn3hBBCiCu53Eh7EvDL0eDBscTHjwdc\nLWseB9Zib3+e0NCGvPjii4wcOZLt27cTFRXFrl27sLOzo1OnTrRt25YvvviCvXv30qZNG86cOcOZ\nM2eoVasWWVlZODo6opTCaDSSm5vLrl27aNeuHenp6YwdO5bVq1ejlOLpp58mOjq60uoshBDixiHT\n41aSlBQTF4M9wL+AY9xzz8usWLGCCRMm8Ouvv3Lu3DmeeuopkpOTSU5Oxs3NjU8//ZSoqChGjhxJ\ncHAwO3fuJD09ncTERDp06EBcXBwZGRnMnj2bsLAwawvAuHHjyM3N5fjx42zfvp1FixaxYMGCqqj+\nNZk1axbh4eE4OTkxYsQI6/rt27dz//33U7t2berUqUP//v35448/iuV9+eWX8fX1xc/Pj1deeaXY\nNhm1UAghSicBvxwFBRmAbJs1LQAjgYHmw6yU4ujRo0RGRtKnTx/c3NxwcnLiueee4+jRo/Ts2RMf\nHx/c3d3x9vYGzP0GDAYDR44cAWDBggXFpt395ptveOmll3B0dKRBgwaMHDmSuXPnVlKNr19QUJD1\nBMdWaSdDw4cPt27/4IMP+Oqrr9i7dy979uzh66+/Zs6cOdbtSilWrlwpoxYKIUQJEvDLUVzcMMLC\norkY9LNxc7udpUsn06xZM/LyoEWLVpfk27hxIy1btiy27pNPPsHT0xM/Pz/27NljDYKbN28uFvCB\nYj36TSYT+/btK+ealb9evXpZT3BslXYytHXrVuv2hQsX8sILLxAQEEBAQADjx49n/vz5xfZRnW9T\nCSHE9arwgK+UilRK/a6UOqSUermMNO8opQ4rpXYrpdpa1tVTSq1XSu1XSu1VSo2xSe+tlPpOKXVQ\nKbVGKeVZ0fW4GiEhDVi7djSDBk0nIiKahx+egI9PF4zGc8CPpKU9yUMPzSEpKdmaZ8+ePcTFxTF9\n+vRi+3rsscdIT0/n8OHDjBo1ijp16rBw4UI6dOhAgwYNrOkiIyN5/fXXycrK4siRI8ybN4+cnJzK\nqnKFK3kytH//ftq0aWN936ZNG+t4B0UGDRpEnTp1ZNRCIYSwUaEBXyllAN4FugEtgceUUs1KpHkA\nCNNaNwaeAt63bCoEntdatwTuAp61yfsK8L3WuimwHvPN8htCSEgDFi+OZv36WACOH/cA3gTWAkdI\nTAwiKmo+AEeOHKF79+7MnDmTu+++u9T9hYWF0aJFC55++mkWLVrEsGHDim2fOXMmjo6ONG7cmEce\neYSBAwdSr169CqtfZSrtZCgrKwtPz4vndx4eHmRlZVnfL1myhGPHjpGcnEynTp3o1q0bGRkZlVpu\nIYS4EVX0Ff7twGGtdbLWugBYCjxcIs3DwEIArfV2wFMpVUdr/YfWerdlfRZwAAiyyVPUM20B0Kti\nq3HtkpKSWbMmHfO5SSwwHtgD/EZqqonk5GS6du1KdHQ0AwcOLJb3wIEDxTq0FRQUsHfvXlJTU1m+\nfDkhISEYDAY2bdqEl5cXixcv5tSpUwwdOpT333+fY8eOERYWdkmrAcCMGTMIDQ3Fzc2Nli1bWvsG\n3GjKOhlyc3MrFsDT09Nxc3Ozvr/rrrtwdHTEyclJRi0UQggbFR3wg4ATNu9PcjFol5UmpWQapVRD\noC3wk2WVv9b6NIDW+g/Av9xKXE5efHEWFy7ca3lnArYASUA2Xl5ZdO7cmdGjR/PEE08A5s55Fy5c\nwGg0kpaWxtNPP82IESM4f/4806ZNw9nZmd69exMREUF8fDwBAQEAJCYmkpaWhslk4vfff6ewsJCE\nhARWr17Nu+++y7Jly6xl+uijj5g3bx6rV68mKyuLb775Bl9f3wo9DmX1xgdYt24d7733HosWLaJz\n584cP34cgOTkZDp06ICbmxvPPPMMoaGh1jwtW7YkISEBMJ+8REZGkpWVVebJi4xaKIQQZjf8SHtK\nKTfgM2Cs1jq7jGQ33F/0U6c0MA/4J+aA3wCYgZPTOho00KxYkURMTAwxMTForcnPz6egoAClFCaT\niZEjR2IwGHBxcWHUqFF89NFHzJgxg06dOgFgMJjP1Xbt2sW4ceNIT0+nSZMmLFu2jBYtWgDw8MMP\n8+OPP/Loo4+itWbSpEksWLCApk2bAhASElLhx6GoN/6aNWvIzc21rv/zzz/p3bs3Xbp0wdnZGX9/\nfx599FH/oJvUAAAgAElEQVS++OILOnfuTP/+/QkPDyc3N5epU6da8w0ZMoT//ve/nDt3jnnz5uHl\n5cXUqVPp0qUL2dnZbN26lfDwcEwmk4xaKIQQNio64KcAwTbv61nWlUxTv7Q0Sik7zMF+kdZ6hU2a\n05Zm/9NKqbrAn2UVICYmxvq6U6dO1oBZ0UJC3Ni6dSXFn8vP5v77D/DWW2/y1lv/veI+oqKiSElJ\n4c033+TNN98sNU2/fv3o169fqds2b97MqFGjADh58iQnT55k7969DB06FHt7ex5//PFix6ci9Opl\nvtuyY8cOUlIufvX/+Mc/yMjI4Msvv7SuMxgMTJs2jaSkJObOncvcuXMpLCzkwoUL1jRPPfUUiYmJ\nvPzyy9YWgKJWkt9++42nn3662KiF3377rfURRyGEqG42bNjAhg0bri6x1rrCFqAWcATz5a0DsBto\nXiJNd2Cl5fWdwE822xYC/y1lv68DL1tevwxMK+PzdVVJTDymw8Je0JClQWvI0mFhL+jExGNXvY8J\nEybo4cOHl7qtXr16euPGjWXmnThxom7btq3Oz8/XWmu9detWrZTSPXr00BkZGfrYsWO6SZMm+qOP\nPrq2il2nknUZO3asfuaZZ4qlad26tf7iiy+Krfv+++91SEhIsXXHjx/XSik9Y8YMXb9+fR0aGqqj\no6MrrOxCCHGzsMS9UmNyhV7ha62NSqnngO8w9xf4WGt9QCn1lKVQc7TWq5RS3ZVSRzA/wD4MQCnV\nHhgE7FVK/Yq52f5VrfW3loC/TCk1AkgGHq3IelyPokf0oqKmk5pqIjDQQFzcaEJCGlw589/07rvv\nsnjxYrZs2YK9vT0Azs7OgHmUOnd3d9zd3XnqqadYtWrVJYPfVIasrCz8/Yt3vfDw8CAzM/OKeU+e\nPAnA2rVr2b9/P2lpadx///3Ur1+/SuoihBA3gwq/h28J0E1LrPugxPvnSsn3I+YWgtL2mQZ0Kcdi\nVoiiR/Qq09y5c3njjTfYvHmztWMfQNOmTXFwcCiW1nYSoMpWsrc9mHvcu7u7XzHvjXbyIoQQNwMZ\nae8GZNtjv7CwkLy8PIxGIwD5+fnWe9p5eXnk5eVZ88XHx/Pvf/+btWvXFhucB8xBcsCAAbzxxhtk\nZWVx8uRJ5syZw0MPPVR5FbPRsmVLdu/ebX2fnZ3N0aNHLxlxsDQ32smLEELcDCTgX6fff/+dzp07\n4+XlRZMmTaydz5KTkzEYDHh4eODu7o6HhwdTpkyx5ktPT2fYsGHUqVMHPz8/brklgoiIaAYPjrWO\nwDd58mRcXFx4/fXXiY+Px8XFhfHjX2Tw4Fg8Pevg4uJCamoqkZGRuLi4WB9ni4qKIi0tjfDwcOtn\nP/PMM9bPnjlzJq6urgQGBtK+fXsGDx58yUA+5a2sk5dHHnmE/fv3s3z5cvLy8oiNjaVt27Y0adIE\nMPctycvLIz8/H5PJRF5eHgUFBcCNd/IihBA3hbJu7leHhQrqtFdYWKibNGmi3377bW0ymfT69eu1\nq6urPnz4sD527Jg2GAzaZDKVmnfYsGH60Ucf1QcOHNTBwU9oCNUw/7Kd+sqjA2BViYmJ0UopbTAY\nrEtsbKzWWut169bpZs2aaRcXFx0REaGTk5Ot+TZs2HBJvjp1GupOnSbqQYNi9J49+/SAAQO0u7u7\nDg4O1pMnT66qKgohxA2Dy3Taq/KgXJFLRQX8ffv2aXd392Lr7r//fj1x4kR97NgxrZTShYWFpeb1\n9fXVO3fu1IMGxVgC+FQNHa2BfNCgmEvyXEyrbZbS01ZHN/MJjxBCVKbLBXxp0i8nWutis9Q1bNiQ\n4OBgRowYwdmzZy9Jm5JiwvyMvgkoyudKaqrpkn1fTGur9LTVUVTUfI4ejeXiMXDl6NFY65wEQggh\nrkwC/nVo2rQp/v7+TJ8+ncLCQr777js2btxITk4Ofn5+7Ny5k+TkZHbt2kVmZiaDBg2y5i2a3c7f\nvwDz2PrzgKLZ7bIJDLz0KwkKMnBxyl0um7Y6quknPEIIUR5qRsQoZ3Z2dnz55Zd88803BAQE8NZb\nb9G/f3/q1auHi4sLt956KwaDAT8/P959912+++47srPNAbtodrsffvgIB4fOQF/MgwtmExYWTVzc\nsEs+Ly5uGGFh0VwM+mWnrUjJyck8+OCD+Pj4EBgYyOjRozGZTBQUFNCvX79ik/rYmj59Oq1bt8bD\nw6PMSX0up6af8AghRHmQv5jXqVWrVmzYsIG//vqL1atXc/ToUW6//fZS0xaNjw9YZ7f7888/+f33\nnbRsuZU6dZwYNGg6a9eWPjBP0SA+gwZNJyIi+pK0SUnJDB4ce0lv//L2zDPP4O/vz+nTp9m9ezcb\nN25k9uzZAHTo0KHYpD4lLVq0iPPnz5c6qc+V3CgnPEIIcVMr6+Z+dViowKF19+zZoy9cuKCzs7P1\nm2++qUNDQ3V+fr7evn27PnjwoDaZTPrMmTO6f//+unPnztZ8R48e1WfPntVGo1GvWrVK+/n56QMH\nDlx3OSqzQ1uLFi306tWrre9ffPFFPWrUqGJprjTkr9ZajxkzRo8ZM6bUbYmJx/SgQTHW3vhF9Sha\nHxFRfL0QQoiLkF765e/FF1/U3t7e2t3dXXfv3l0nJiZqrbX+5JNPdEhIiHZzc9OBgYF66NCh+vTp\n09Z8y5Yt04GBgdrV1VW3a9dOr1279m+VozJ78M+ZM0cPHTpU5+Tk6JMnT+pWrVrpFStWFEtzNQG/\nXbt2+oMPPrhkvfTGF0KIv0cCfjXWqdPEEsHevERETCz3zzpw4IC+7bbbtJ2dnTYYDKVO7HOtk/rY\nqumPHwohxN91uYAv9/BvcpXVoU1rTWRkJH379iUnJ4czZ86QlpbGyy+/fNX7KJrUZ9WqVdZJfWxJ\nb3whhKg4EvBvcpXVoS0tLY0TJ07w7LPPYm9vj7e3N8OHD2f16tVXlb9oUp/169eX2bFPeuMLIUTF\nkb+kN7kr9eAvL7Vr1yYkJIT3338fo9HI+fPnWbBgAW3atAGuf1IfW9IbXwghKo4yN/lXT0opXZ3r\nV9n27NnD2LFjSUhIwM7Ojvvuu4+ZM2fi5+dHSEiIdRKfIklJSQQHBxMaGkpKSgqOjo5orVFKMXjw\nYOsjfcXzJBMVNZ/UVBOBgQbi4oaV+8mLEEJUV0optNalTh8qAf8qzJo1i/nz57N3714GDhzI3Llz\nASgoKGDgwIHWkfU2bNhAx44drUFr375E/vhjEzk5Z/H19SUxMdG6zxMnTtCiRQvrtK5aa7Kzs/nP\nf/7DP//5z79dZiGEEDXP5QK+NOlfhaCgIKKiohg5cuQl20oOOJOUlEzXrjOJjx9PQsJznD4dhZPT\nnRQWFhbLV79+fTIzM8nIyODWW28lPz8frTUTJkygefPmwJWn2hVCCCGull1VF+Bm0KtXLwB27NhB\nSkqKdb29vT1jxowBwGAwnzsVn+glHAjnr7/8cXN7vMz9K6V44IEHyMzMZN26dZdsS09Pt7YECCGE\nENdDrvDLWemPljlT4gL/Etu2bWPYsGGXrNdaW4flFUIIIa6XBPxyVvqjZbnYXaYt5fz58/z11188\n//zzdOjQgY0bN1q3KaUuO9W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x3/M3N//LPPRCCFEzXe45fAn4Zfjyyy8xGAysWbOG3Nxc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xa3b99Oo9FIDw8P6vV6Wfr4b3/7G728vPjqq68yLS2NJJmYmOiO4btxW9GQZZrdhF/PUKm62Fje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\nHM5Ily+99BJJcu/evezbty8dDge7d+/u9p5v3LiR7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"text/plain": [ "<matplotlib.figure.Figure at 0xbbb05f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize(8,7))\n", "plot_leverage_resid2(fitted)\n", "pylab.show()" ] }, { "cell_type": "code", "execution_count": 22, "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>wage</th>\n", " <th>exper</th>\n", " <th>union</th>\n", " <th>goodhlth</th>\n", " <th>black</th>\n", " <th>female</th>\n", " <th>married</th>\n", " <th>service</th>\n", " <th>educ</th>\n", " <th>belowavg</th>\n", " <th>aboveavg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1122</th>\n", " <td>6.25</td>\n", " <td>47</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " wage exper union goodhlth black female married service educ \\\n", "1122 6.25 47 0 0 1 1 1 0 5 \n", "\n", " belowavg aboveavg \n", "1122 0 1 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.loc[[1122]]" ] }, { "cell_type": "code", "execution_count": 23, "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>wage</th>\n", " <th>exper</th>\n", " <th>union</th>\n", " <th>goodhlth</th>\n", " <th>black</th>\n", " <th>female</th>\n", " <th>married</th>\n", " <th>service</th>\n", " <th>educ</th>\n", " <th>belowavg</th>\n", " <th>aboveavg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>269</th>\n", " <td>41.67</td>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>13</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " wage exper union goodhlth black female married service educ \\\n", "269 41.67 16 0 0 0 0 1 0 13 \n", "\n", " belowavg aboveavg \n", "269 0 1 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.loc[[269]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Выводы" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Итоговая модель объясняет 40% вариации логарифма отклика. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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CSuV47Ms3DEGT9Ys0zStLeWYSOmdCxgWFLYUtmQ7sguxmLeiOaWH5hEWMlrTI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\nCSVtMs+i2WzqzM1f0m0s6nYdFWWQCBsOSqW98tBDH9HXPyB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8b1E68XzHyJDeM3Qe/RpG\n3QvQxDYDPONQCy0srRYSFoZcIv9Jm6r2rGi/QJ43dYWAa/lzYlEe5Yui6vWvIU9cgeXIekrEexWv\nKRBwnMInjAVu0A4QO1kUOSYFyufd0D+cR/pZ3pAI4RHhMVGnwa1Om9N8PkyMo3Xx+Lg21mLhVm00\nVPlDnTJQWb3a2qhpbHT0EbGtAahIv0deXRhb9g8gIbrp/VCR6mJxPKTWKzxsHuFV56MmXXe9dCev\nHUEi4zbMqrnyyhGKA/Aiifqvpik8sKKc4XHyPK2i5i0LBWYe1mtC5G1paan5vr7ru15JLAKtFtjL\nuBT2To7Rgw8+yA/Q7+X97uL338rHykKQZS85kOf7GU/syTSHpPq4zBEQVDpmucViXmrS/mmezw8L\ns1Z9hqICxtLSkhTuLH8vVCrMTK1ZdhwwYi7v205J+7EK/2zEvjRcLzj4HdF2vLehP6Ns0MQ2Azxj\ntRJYnFqTJxb+u5084lrlC5IQhRJiUvPNxZsR1wb/aZM/N7dCLNdFlZ/iz9M1zSHu2Y0KYxkjx9lB\nhlHkubRjfCxmYMG1pTH5Nwkqw2gY5WZOiahB14pIrbamH1NajC8e3wtQbXxcdze5rlpFsp1Ya+ER\nDY31hH4htr18rXWO7Vp7P1RrLKsoMBlY8yeoWq22zbvMCFy4nn21Wm2OS7VhVvXvD+sVB933kAij\nte1hmp6+hxPM4KH4CAEXybLK9OCDDyrIm0WWVWnaExbCG9yXiHQrmdDVCLBoaWlJ8kzvJeaNLYX6\nEXbNm5czxMgoy2f9kR95F4++EzonldD+ycvbJb5/2sX3aRViKVwWTU+fIKLw901UqhB7IBYt5x+j\nYRTJtofJdSeUQlDi88nlhsnvcZ4nL2TbH+kXZc+1HddYz2g7sQXw35JeWTrONNiOe2xrJIhmHDHw\nxAmGCbiNvHAbEYLSIH8Zn0dIqA6yhWyM2IlckS+kDgF3UlSIMBMXCBqI66W+wgS0Wq1K+SPniZHm\nsIFkglLe3zMzs0SU1DB6uSOq+cp6wh512qwqHt8r6KbHNu0mSud9aGh46EdiC+A+AAXF4w6A+7ow\nnkxznwXd8n6oyvzFeWWTrrFx72dpaYlUaUtLS0uxY1XbUJnQEbEosTBxZIKVwb3CLrKscjMM2q+o\nPKQYo4hQC+5l3kPMaztEnmDTGJnmULNtrxRR0Bvq2TW/J5tpkhhGiXtC/ZUo2N/y+/bn7XrRa+rP\nStZgUX2m09MnQoctrb6j3h5SvM8GyRF+wokQd4Cj7bjGekcniO1DgeubAL4O4H/x6z8AfAPAJ7J0\nnGmwbTSeYuERximfF7msLM9jaupY5Guq1SonEnLocZS4ADMenkJx8PlryK+e7KkOsufvpXD+RYU8\nb2uVhHS9bR/wLYKe8qGomSv6b/DFVOSvVPkCP0y2XYk0zGHDOMnbVhOprKfWqtdFFY/vJai8CWvh\nYUhr4LRSn4aGhz4ltpcBXK14/CUALndhPJnmvt8QJCxx63uStb+Vx415Jq8hf9rS5qbHNg7B/nM5\nmcTWiHlFR0N2QFXaxrIqVK/XfW2LuqumWeTpSiRdYwRMkT96je1TLGsLhT23j9DgoNucX2afwof2\nsl3zPLwTzblj86US8DzX/FuEK5fLk2TbFT4vfu9yUKBTfBaWNebbAznOePP7kPaw5cKFRX5AIJwd\nDrEUNbb/bNVmUmFPHQKr0a/oaCgygHsA/A8hXMEfcwEsAvhQlo4zDbZNxjNoTE6fPhPKlWklxMAW\npPPkF2ha5IvUBCenBcrl2OmqR4blxV8la0/kKRdfJ7Vb4Y+NEPPuWoEF0aClpSXfmMO1al8vvU6E\nugh5foeAY7HlB/y5KWvrse2Xk0iVIVkL47KWpSI0NNYT+pTYXgGwSfH4TQCeTNjGxwH8K4AvRjz/\nKn54/Zf8+umYtlb1GfQz4tb8uEoLUVE+osyOuIeVoxGH2DWfUFPSsdXrdZqZmeX7nGu4vRfRYl6a\njyCwQlW4UDjoC6cVHlURERblyczlinxvcR0xj+gdTcK4sLBA999/P9+7iOi2BolcW38k3Szf+4yR\nZZXp6NG7fARb1IEVj0WFbptm0WcbxeuWlpb44X+YyAf3f5Y1RCxSbojvyWoEWLS8vJzpeyM+n6Wl\nJTLNIjGRrtYRg/Jr4+y4DlPW6Hd0mtj+M4D9iscPAPiXLB1nGmwbjKc6Vyaofuc/+VK9xjSHeC5K\n8HSwwg1Qg4CDZJpbaGFhgZaXlxVCE1Gy9rJXVtx/kRuKi/y5sHdYVg1Ue1xtUtVq88JgPI+tgGw4\nZPIkcmxbEamsHkutJJceaQh0N+dXnyJr9BL6idgCeArAt7jHVvwurmf44x9N2NYrARxuQWwTRWRt\nZGIbRFJCEfa4CdVdT/34woVFHnK7k4ACGUYx8VodjEobGrqRDCOcEyoLU5rmNjLNIane7EsIsMlx\nDnJtj4JyLN44x3j7Bt+niHJCRR9hu/vuu0kVMnz33XcTkVzjlSk4j43t8Y1zauqYcp5VYlv5PAtR\nVqkie55skVLGDvmPHLnTN4/scEEeq9G8Xxa1Us1/Kzu3mgiqKDuuD6811gM6TWyfAnCT4vGbAHwr\nS8eZBtsG4xkV5spIqnoRiFp4qtUq3XLLrXyBE+V5ghL6NuVy1/NF/OqYe8vcAGzn9xrEclG8+wcG\nyjQ4WCBgCwVzT4Q4lRi3asyFwm6yrAOB13nhxHKOLZEcbrSnSZrlxTrpwp2VyES9ThOj9qAb86hP\nkTV6DX1GbH8UwLu4x/YY/1tc7wDwXSnb29aC2D6UsJ3VfQg9gHash2lza717w6lMq9FoiBZfjCpH\nI8QsiTyl4BqF06DUB+H1ep17Hc8R0/MIhwMbhtv0uD700EMUPmS36cyZMwoP8EXFvU7kno2F+JbJ\ni2a7xpfDq9LvCIp5yvMcDm+uhcbTKsKvkxFUqu+tTjfSWA/oNLFdAPAPAH4IwCi/fgjAEwAWsnSc\nabAd8tjKQhAqDxar5RoOVWZe2DKxurRCql7OKSmQyCvxwoHvJUZcPxC49wwBt/MFdoxYuIuQfBfK\nyQ69971T/B5VPu+7yHX3Nj2sScSMosKJvVNK79TVMIpdJ5OaGLUPa01s9SmyRi+in4ituDjpzLeh\nnW0tiO2/AfgCgP+pitqS7s3+AfQA2mVX0hIKL090NwVDaLOq6qvJm4gkq5PaY7td6meFmGc0XC3C\nfxA+Sa67WwpxFqWBVOJTY8TK8+wgwKFbbrmVcrk90vOet9qrESueW6BwtYhdZNtblXPTaDT4vqxE\ncrkhxxmJSAnbSV5NWTH3E815Doc3r4TG0yrCTxYVk22uiIg7ffpMWyOotK3VWA/oNLF1APwKgGd5\nqNNlAM/xx0LKjJ262p1jyzy1lWZ9MdVGX3gtTfM6AhxyHKbMOzV1TCoqXiZWeJzIyxnZTSwfIygY\nNULAXmLy+HuIkVw5X3aYhMR7WKxpP1lWmW677Z3kV1gukFcDzSHLGm2OMU7MKC6cOEqEIYloRaeg\nF+v2oRsHBPoUWaMX0afE9hYAb1Y8/hYA/zlFO9tiiG1R2HcAbwDwpZh2VvEJdBfttCtZ2hJ5q/7X\n1cg0WdmYpG2FxS3FOiu0Pw4RMEyDg7avHM309D2hvtN4bJm6b5BEB/U4HAIO8PZOEWAHVI1VB/U1\nSuuxFfm/rruHgqRclGJSe2wtktWJg4f8/vDmWmg8SSL8ZmZmfTb3da97A8nh1UeO3NnWg2adzqXR\n7+gosW3ezASjJvjlZulwNddqjadMXJk4QplKpYOR//Th3IohyuUcWl5eDix0Iv9VLMRyse0CNyxi\nkRMeWoMsaycx76uK/DYonHc7QsXiOFWrVfrBH/whYuR2m2LRZ8ILllXx5Zao5iHKa8fKDIRDllqV\nGegkNDFqD7p1QKAPJjR6EX1KbP86Jj3o/6RoZ1sUsVXc+xiAkYjn6OTJk82rVqtl/ThC6ERkidxm\nu+xKMKc1q66EbW8ndoh+kAyjSKY5lFjLYmjoRn4QH00aDaMc2hcED7wHB22+d9lMouKCRwCv57+b\n9OY3/4BUeUEcwm/ne5MKsQP/YNrVCAGjlM9fywnqDgp6eB1nnItV7uL3CxIo0rIsuuWWW0OH9OL9\ne/s2dagy85IzYj8wYPE92E7+ns2ms0OeXy+8uUCDgzYZRolcd7dP20R8D1R2zn8AoCbrsjBWO6DT\ntjT6CbVazWdH1orYXgXgZQCsLJ2t9loNsVWrGseHikR5LWdnZ6WFeJEvvCJv9mrFgiWfcsr3HOPE\nNRi2M8kfF4ZEqBbfRrmcy73NB4mR4g+TX5lZvJ6Fy1hWWVkgvBW0x7a96CUD080DAn2KrNFr6FNi\newnANsXjowC+naKdUQB/FfHcZun37wTweEw7mec/Dp2ILAm2GVePVoWoyK5gm1nWe1XaU1ApWTWe\n4PjzeVmfI7ombdT7CqofLywskGkKQjvOSeu9XHhJeGNZOpZhFKlYPMz3PAvkRbOJa4JYmHCDojzT\nllWhhx56iHtla/zeEt/v1Jv7Ecsq0/T0iZDX2DCKvlI6IsdWfFZCO8S7x7/POXv2bMgZwPoQXl3m\nvCgUJpTfy6Cdm5mZDdhcdXj1wsJCqu+LhsZ6RqdDkUsAfpMLVlwGsIM/PgfgZ7J0nGmwGY2nOufE\nH+arChVRFyofpbvuuouiQ3UsvvDLrxnj9c9ELTdxr0PAsnJhZYZjEwHvJ+AseV5hk5hAQ4O3ZSte\nL6Tyxc9C6FQxyZz5i7AP91SObT8Ro17LC+72AUEvkXwNjT4ltv8E4HWKx28G8K8J27jA23kOwFcB\n/BiAowDezZ9/H4D/A+DzAP4MwMti2lr15xBEu9apIGFLq7EhI0qJd7XEWCDLoaPqNa67mwqFA8Ty\naoN7FLYvSDOX9Xqd1JFhwhs7QizU2aHbbntnrCgWe828cu/lOKwEkeNs96VTFQq7KRxBNknAOTKM\nIrnuvtCcCWIeLJ/k/6zOS+2KNLJRKhTGfN+DlZUVLsQl7mudQxv/vVsbj62GRj+j08T2VwD8KVhp\ngKclYvv9AB7J0nGmwWY0nqqFPxjmGw4VeYSHtMhhwuyUjp1GCjn83YF2txMTfvKfPjJ5+x2Be3cR\n8OFmuBEbU4VY7TYhOrWVmEKyTYzUivDmIgGLlM9fQ570vCDPo/yeWckAnCfHGYmtqyej0WjQzMxs\nswh7WmLcSfQTMeo2iYxCPx4QaGh0An1KbM9x0rlbemwPf+xjXRhPxtmPRjsiS4JENOw584sOxdmV\nqLVcJUgUNc5Wh5xxfaQZl18ocpHvSXZyQraYei5XVlbIsvaSP+R4nFid2nD5RFEHl+1xirz/Sb4v\nsZT2kHmrhYfWe65er9PZs2cpfIBf4n2LiLZToTZV78P/WTV4u35yzoi3187c3Dx5RHSF3+f/vIOO\nkaDXXnz2grwPDg6RXGJoaupYos9CQ2OjoNPE9msAXsp/f0oitmMAnsrScabBttFja5pDZNuVmFAR\ntlh55Xy2k+qEzV9rVjy2hf/cT4BDc3PzdPr0GeXrRUiQTCQLhf0kQo9lcQFmEDzhBq8ckCC0txML\n0znPDYm4l+Xs2vZ2sqxKS89hMF8nSyizBkMv5wX30wGBhkan0KfEtswPm18Eq1jwD/z3PwNQ7sJ4\nMs9/FNpRBiWe8KVrM67sXxIymvT9qAQek9rscnmyabNlL7RllSmXc0OkMelcesROVHdgh/wsFNlP\n8kqlw7SyshIgqg0S6VX5vKvMG46zlY1Gg4dXF4kd3A9R0IHAnA7jsfMUH7Yd1jnxf76C/O4P3a9y\njAAOlUoHfXuoMHm/SIbh0vLycqLPQUNjI6HTxPYZiczKxPYwgG9k6TjTYFdhPFUeqlYhSp4RrBHL\niZgILeCvfe1NnNxu4T/FqWGNAItOnz4jLWb+ckBvfvMPhAxLo9Hg+SwiZzdImpfIOy1VPT9KhlHm\nYcSyynJNuRir+u9FD2O/Qs+nhkZvox+JrbgAvA7AT/HrJgADXRqHb07bdWi2msiSVuq0aduMW8uD\nZDSfd3313+PGozrkFDmuQbLUKsRZ5TUUB+ashI5X2aHV+xafoap0IODQ6dNnAp5M/xjr9TodPXoX\nWVaZisXxZj5slGhllMdWzC+LahMRbIPEPJ0ifLhBhcJBWlhYaPmdm5o6RrKndHBQ6JiQdHlRbn6P\nPOvPsraQZUU5RsSY9hOLmhsi4IYmwe3Vg24NjV5Dp4ntpwH8OP/9KQDb+e/nAPx+lo4zDXaVp8Kt\njG18wr86r2Jubp5se5gsaz8FFZBt+4Cixts8ASvkuuOxBi2XEwuvvNjuJJZvK8Jwgvm/+8i2tzZP\nilnfZXLd3WSaRXKcoIjDGM3MzPr675aHcT17D3XYr4ZG76KfiW2vXLJtbremQFbbEEdEs7YZt5YL\n2808gEME7CFgqKlNEXV4HhVmrLLFpdLhSPKWTI23RpZVbpnLKQssqfYOpdJhqlartLKyQqdPnyHL\nqlCpdLg5J1NTxyXyaNHgYDFSaEmeV9aPQ7Y9qshhDkawGeRVrEimAeK1VSOWfzxEaq0UT5ckal7l\ntC7vnmBIsxMaY/CwwrIqOrdWQ0OBThPbV3BC+yCYGuMvA/gUz7e9MUvHmQbbgXCnIGSDFz5BZOE3\nYgFnpFZWygsqINshtb844Qa5BNHgoFgUgx7Z6ziBNihcJoiFNgdzOlz3kCJfmI3VtiuZwqXaiV4T\nV+oE1jNx1+gf6O9hGP1CbAF8AIAt/R55dWFsRNR7ESqdOFSM+x9i1QQsH5kBrGY1AXk8hlEi0xxK\nlW8rwltV96uIcKFwkGx7KwWFMuMOqplwpFzmsML3G94+R1RnUOWT+oWmwuJRpjkUQQr9ZG9paak5\n10wcSrUfuuhrt9X3zD9HK+TVuhXVLZgXd3r6Hl9bSb5HKu+1vx4vI8zT0yd8ubZCKGtubj4kdqWh\nsZHRUWLL2sdBAL/GxSnqAP47gINZOs16rQWxFYg6QZyePuHziLKFaw9f/BeJhf8y76xpXkuu688/\nCUrti4Xby8HdQyx39jZiNeRGiIXFjPC/7ybgrcQUkz05e/baITp69K6AsajxBbzG67UVpPYWlUZu\nLT2MvbYR0tBYr9gIB0hZ0EfE9jEAL5F+j7q+0oWxEdHaRPykPZxZy8McVv89XOVArv8uPLtp8m1L\nJSFYGS2QFEWEmT6ISElSv06en3Cpv1PklQ4q8Cs6rYkJPY2RV+5HqAmLa4wsq9xch5iOyU6Sw4qB\nMXLdPU3CZxguhYU6d/L22d+l0mE6e/ZsSAVZfm/+OQqS7hoBZXLdvZHRdHHfI79ysjzGFd97FyWU\ngmHX7ECkQMBOX3kiDY2Nio4T21641orYqgyEYZR9izFbbOVTTSHmVCYW4lJTClWI3BQi2WiJIubB\nUBvTR0zZY3vJC3Gp8b7O8b5tKpW8MGrH2UGMwN7If24iFsLsnbxGkci12gz0sriShsZ6gT5Aika/\nENtevtbKY9vrhzOM2AbL0oz5iC1R+nzbhYUFvk/w3y/CgYNRWioiLOq+ymKQqvlkxFakOYVTsNgh\n/o7IsbM5EF5rsbeJ82LafP8S3Et5e5STJ++PaOdi4O/tBBTIMIo0NXVc+V3xe82L/HUHSdTmzfp9\njT5Y8N6rYZSb3mq1OnP7/m90dI5Gv6PTociXAVytePwlAC5n6TjTYNeI2CYpD2Sa6sLeg4M2ua6X\nS+KFp0yQUBMU+RneIrhCzFOrWrjLBKiMVImYx1aU/zF9r2Uh0qr2zpDnBS6EcmzXGnrDraHReegD\npGhoYtte29ypiJ9+sBVhbyfbF4hQZKJ0Hlv5NeHD9pKSuEURYdPcRqZZbopaCdVkVe4oE2oSe5Ng\n1NlBUpXsEZ7ScM1b4fE9RCyndXOgve0UTqsqkwifFuvUkSN3kuc5dmhgwCLDKFKhIMjzFHlpYBUK\nVqyQ51d8BtVqld7xjh/m72cHAQ4dOXJn4pKIwfuC331Rh5dpsPjDjv1zf56CmimFwsHQwUVSrOYA\nSBNijV5Bp4ntlQhiex2AS1k6zjTYNhFb1T+u/Jj65K3AF00WLmPb20ILETBGhuFXQ1xaWiLTvIaY\nOl69uVB7hmeFPx4VajNLTDBqe6gvppB8TlrsvedddzdZ1gHFa4S39nwov7ad85kGvSiupBd3jfWE\nfiAF3UK/EFsA9yW9ujA235x2Yv3s1OFMO8fK8lNF3dZxksWjiPyEQ9Svj6rUEBxbsKSPRz7D/8+N\nRiPwfI2Cub+5nKv0Aq+srEjKweoyh+94xzubYwmWJJqevkexNxolpmhcUrRnUbDiBJu7BZL1SJiT\nwCLgw3zPxMaSy72Etyki0xZ5e1sjvyvhaLlkecwCccQx+BlGqT0LcusRc//nmc+XyDSLLcei+g5m\nXet7PSJCY2OhI8RWEqO4zA2mLFDxUwA+AeDzWTrONNg2EFvVP27UY8wwyB7R28hTvLMplyuGFkRW\n0of9zQSgRN7EGF/UT5FhlOjYsePEThQPEDudvEmxwBb4c/eQ6hQYuIaEXD2rJ+ctnF4Y9EVuIC6G\naveuZtESi7dYnFe7EPYSkdSLu8Z6RC8eIPUC+ojY/lXg+hZY7dqv8utF/tgXuzC21XwEidCJw5lO\nrPVMEdjmxMqmqaljkeOXVZGDYxHePnlswk76S9BQiLh5BHuYWHSWq9xDsMizsMfWr89xL3ke1wpZ\n1pamrfZKEol7a7wKRHgvY1nXk21Xmu9LrEO3336HklwKD+rU1DGq1+s8zzZ4WH+Qwt7eYf6Y2mPr\n/xxkj7TI8R0l5lBoT0nEVvV5q9UqT2u7l5inWRBdk+S6wUm/61kPgPThp0avoVPE9jF+XeGG8zHp\n+lsAVQAvy9JxpsGu0nhGGZaocJyw4nEwX0LkZ4zzxedevqDW+WVTUBGQ3W8TO9EsEwsnPsQfM/nC\nJtefFSRWSNtP8p8m3XHHnZTPi7I/gnxvJpFjMj4+SXK9tptvfkNbhDfiTjv7fSHUi7vGekYvHSD1\nCvqF2MoXgB8DUAOwVXpsK4A/BnCkC+PJOPvp0M7DmU6s9XFttiI4rfIzgx7ZuLF7fQmyps79PXbs\neGg+1alY+ynoQRX9BLU8LGuUC1XKtWJtWlpaapL44DrEKkJUqFgUZXK8tCvDKEklE4MpWYII+t9X\nLueECLS6nrDIIQ6W6WE5vipCmJY4JvmeeeWRdhBg0cCATMprFCdqlaU/FXS6ikavodOhyDUAw1k6\naOe1WuOp+sd13d0h5WLHGacTJ05IjwvjsJ1khbtC4SCZ5nZuNN5FjKju5j/HCdhCnpy8fBro5ep6\n5YFEjkWVWL5twzcew7iWL+K7iZ2ajvLwluBJpUuMVF8k1SlomnppqtPs6NPO9bEQ6sVdQ2NjoU+J\n7WMADikePwzgiS6MJ9PcZ0G7DmdW49mK6j8teY0jvYwUrkSOTUXyRVt+DQ8iT3wynPsbrxzs7R2K\nxfHQYUI4n5bdy2r51kh4cfP5Etl2JdYzrs4NDosqsf5E+Z+7+b7Ie16uCxuVduZ/f7cr5wY4l9hj\nG1ePOOqzimvPS30TZYgYqZ+bm4/9brbqL+67qw/1NXoNWhU5BtGLvdpjyxYVceIYPMl7u2QoLALy\nJJ9MAm/gBHOUPI9tsI15aeGeJE/iXoQT+8eTyxXJskQhcWYoWFjxvoAhnCTgemLkeIGPyW8oZ2dn\naWFhIXbhF4+rFjl/CFRYMbHfF0K9uGtobCz0KbH9NoCXKx5/OYBnujCeTHPfTWRZ61uFfnd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MSyqGXruQkLS0UdiCTdg1y4sMhJdjAVbSfvL76fqDbTfGZawyMb9L6sf9ERYgvgbfy6AuDHpL/f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\nYnFO8tmXOJyXLq5rPpu1WK/PScF3RfYuJpEf5s2KBTO7DZgB7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"text/plain": [ "<matplotlib.figure.Figure at 0xc8b99b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize(16,7))\n", "plt.subplot(121)\n", "scatter(data['wage'],np.exp(fitted.fittedvalues))\n", "plt.xlabel('Wage', fontsize=14)\n", "plt.ylabel('Exponentiated predictions', fontsize=14)\n", "plt.xlim([0,50])\n", "\n", "plt.subplot(122)\n", "scatter(np.log(data['wage']),fitted.fittedvalues)\n", "plt.xlabel('Log wage', fontsize=14)\n", "plt.ylabel('Predictions', fontsize=14)\n", "plt.xlim([0,4])\n", "pylab.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "При интересующих нас факторах привлекательности стоят коэффициенты -0.1307 (ниже среднего) и -0.0010 (выше среднего). \n", "\n", "Поскольку регрессия делалась на логарифм отклика, интерпретировать их можно как прирост в процентах. С учётом дополнительных факторов представители генеральной совокупности, из которой взята выборка, получают в среднем:\n", "\n", " на 13% меньше, если их привлекательность ниже среднего (p=0.001, 95% доверительный интервал — [5,21]%);\n", "* столько же, если их привлекательность выше среднего (p=0.972, 95% доверительный интервал — [-6,6]%)." ] } ], "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
tpin3694/tpin3694.github.io	machine-learning/accuracy.ipynb	2	3015	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: Accuracy \n", "Slug: accuracy \n", "Summary: How to evaluate a Python machine learning using accuracy. \n", "Date: 2017-09-15 12:00 \n", "Category: Machine Learning \n", "Tags: Model Evaluation\n", "Authors: Chris Albon" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a alt=\"Accuracy\" href=\"https://machinelearningflashcards.com\">\n", " <img src=\"accuracy/Accuracy_print.png\" class=\"flashcard center-block\">\n", "</a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preliminaries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load libraries\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.datasets import make_classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate Features And Target Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Generate features matrix and target vector\n", "X, y = make_classification(n_samples = 10000,\n", " n_features = 3,\n", " n_informative = 3,\n", " n_redundant = 0,\n", " n_classes = 2,\n", " random_state = 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Logistic Regression" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create logistic regression\n", "logit = LogisticRegression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-Validate Model Using Accuracy" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.95170966, 0.9580084 , 0.95558223])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Cross-validate model using accuracy\n", "cross_val_score(logit, X, y, scoring=\"accuracy\")" ] } ], "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
cloudandbigdatalab/Big-Data-Machine-Learning-Doc	Raw/Ali/ML-Algorithms/KNN/KNNClassifier.ipynb	1	97850	{ "cells": [ { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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P69KF6JtvVCbi98OFNl2w2YyZgR4dOpg7LxaYzce/pBONGiE+pbwca8dmIxoy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\nVn/3kUemdy4ioqefJ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f14860845d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "training time: 0.001 s\n", "predicting time: 0.001 s\n", "accuracy: 0.92\n" ] }, { "data": { "image/png": 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0AS1EufnJTdD9rnKOIvdTT+v3nZGb6jE/B1XBDEZXrH0VJ1e4lipznI1B1vHt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\nN7abzO6qYJR1s8lVxOX1t92GlNOeQtbgzyiwe7H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"text/plain": [ "<matplotlib.figure.Figure at 0x7f148233ff90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# %load your_algorithm.py\n", "#!/usr/bin/python\n", "\n", "import matplotlib.pyplot as plt\n", "from prep_terrain_data import makeTerrainData\n", "from class_vis import prettyPicture\n", "from time import time\n", "%matplotlib inline\n", "\n", "\n", "features_train, labels_train, features_test, labels_test = makeTerrainData()\n", "\n", "\n", "### the training data (features_train, labels_train) have both \"fast\" and \"slow\"\n", "### points mixed together--separate them so we can give them different colors\n", "### in the scatterplot and identify them visually\n", "grade_fast = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==0]\n", "bumpy_fast = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==0]\n", "grade_slow = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==1]\n", "bumpy_slow = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==1]\n", "\n", "\n", "#### initial visualization\n", "plt.xlim(0.0, 1.0)\n", "plt.ylim(0.0, 1.0)\n", "plt.scatter(bumpy_fast, grade_fast, color = \"b\", label=\"fast\")\n", "plt.scatter(grade_slow, bumpy_slow, color = \"r\", label=\"slow\")\n", "plt.legend()\n", "plt.xlabel(\"bumpiness\")\n", "plt.ylabel(\"grade\")\n", "plt.show()\n", "################################################################################\n", "\n", "\n", "### your code here! name your classifier object clf if you want the \n", "### visualization code (prettyPicture) to show you the decision boundary\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n", "clf = KNeighborsClassifier()\n", "t0 = time()\n", "# features_train = features_train[:len(features_train)/100]\n", "# labels_train = labels_train[:len(labels_train)/100]\n", "clf.fit(features_train, labels_train)\n", "print \"training time: \", round(time()-t0, 3), \"s\"\n", "\n", "t1 = time()\n", "pred = clf.predict(features_test)\n", "print \"predicting time: \", round(time()-t1, 3), \"s\"\n", "\n", "from sklearn.metrics import accuracy_score\n", "acc = accuracy_score(pred, labels_test)\n", "print \"accuracy: \", acc\n", "\n", "\n", "\n", "try:\n", " prettyPicture(clf, features_test, labels_test)\n", "except NameError:\n", " pass" ] }, { "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": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
TANAV/predictorsAndDB	predictorNotebooks/SGDClassifier_Video_Games.ipynb	1	13224	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import cPickle\n", "from scipy.io import loadmat\n", "from sklearn.linear_model import SGDClassifier\n", "from sklearn.cross_validation import StratifiedKFold, train_test_split\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.grid_search import GridSearchCV" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load processed data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "productCategory='Video_Games'\n", "tfIdfArr=loadmat('/home/hencrice/Downloads/AsterixDBClassData/processedData/TfIdf_{0}.mat'.format(productCategory))['data']\n", "scores=load('/home/hencrice/Downloads/AsterixDBClassData/processedData/score_{0}.npy'.format(productCategory))\n", "scores.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "(100000,)" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split data into training+validation (used gridSearch to pick hyper-parameters), and test set (evaluate model performance)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tfIdfArr_trVaSet, tfIdfArr_teSet, scores_trVaSet, scores_teSet = train_test_split(tfIdfArr, scores, test_size=0.1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "hist(scores_teSet)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "(array([ 1991., 0., 2060., 0., 0., 1995., 0., 1995.,\n", " 0., 1959.]),\n", " array([ 1. , 1.4, 1.8, 2.2, 2.6, 3. , 3.4, 3.8, 4.2, 4.6, 5. ]),\n", " <a list of 10 Patch objects>)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x41b7250>" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pick hyper-parameters for SGDClassifier using grid search:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hyperParam={'n_iter':range(5, 20, 5),\n", " # strength of regularization\n", " 'alpha': logspace(-5, -3, 10)\n", " }\n", "clf = GridSearchCV(SGDClassifier(loss='log', class_weight={1:0.5, 2:0.4, 3:0.05, 4:0.1, 5:0.25}), hyperParam, n_jobs=8, verbose=1)\n", "clf.fit(tfIdfArr_trVaSet, scores_trVaSet)\n", "bestClf=clf.best_estimator_\n", "clf.best_params_" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "[Parallel(n_jobs=8)]: Done 1 jobs \| elapsed: 11.1s\n", "[Parallel(n_jobs=8)]: Done 50 jobs \| elapsed: 2.6min\n", "[Parallel(n_jobs=8)]: Done 76 out of 90 \| elapsed: 4.0min remaining: 44.0s\n", "[Parallel(n_jobs=8)]: Done 90 out of 90 \| elapsed: 4.5min finished\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "{'alpha': 1.0000000000000001e-05, 'n_iter': 10}" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prediction accuracy of each class:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "te_cm=confusion_matrix(scores_teSet, bestClf.predict(tfIdfArr_teSet))\n", "te_cm.diagonal()/sum(te_cm,1,dtype=float32)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "array([ 0.81291645, 0.62580967, 0. , 0.18110236, 0.80654912])" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the resulting model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with open('/home/hencrice/Downloads/AsterixDBClassData/models/clf_{0}.pkl'.format(productCategory),'wb') as fp:\n", " cPickle.dump(bestClf, fp, -1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 } ], "metadata": {} } ] }	apache-2.0
folivetti/PIPYTHON	ListaEX_01.ipynb	1	8181	{ "metadata": { "name": "", "signature": "sha256:3f205043edcd787c9dd2c8674c016bae5b11b32a3243c5e9aed690f2abd04e70" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Exerc\u00edcio 1: Dadas as seguintes vari\u00e1veis\n", "\n", "```python\n", "segundos = 19\n", "media = 2.1\n", "letraO = 'o'\n", "```\n", "\n", "Qual ser\u00e1 o resultado das seguintes opera\u00e7\u00f5es:\n", "\n", "```python\n", "segundos/2\n", "segundos/2.0\n", "media/3\n", "(segundos + media)/2\n", "letraO10+'la'\n", "```\n", "\n", "Utilize o interpretador do Notebook para verificar as respostas" ] }, { "cell_type": "code", "collapsed": false, "input": [ "segundos = 19\n", "media = 2.1\n", "letraO = 'o'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exerc\u00edcio 2:* Fa\u00e7a um algoritmo para calcular o volume de uma esfera dado por\n", "\n", "$$\\frac{4}{3} \\cdot \\pi \\cdot r^{3}$$\n", "\n", "Pegue o valor de r do usu\u00e1rio." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# EX02" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exerc\u00edcio 3: Supondo que o pre\u00e7o do rod\u00edzio de uma churrascaria est\u00e1 R\\$ 48,50 por pessoa. Eles oferecem um desconto de 25\\% para grupos maiores que 20 e o cobrado pelo couvert \u00e9 R\\$ 4,00 para a primeira pessoa e R\\$ 1,00 para as pessoas seguintes.\n", "Calcule quantos reais um grupo de 35 pessoas economizar\u00e1 ao se juntarem." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# EX03" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exerc\u00edcio 4: Se eu sair de minha casa as 06:30, correr 1 km na velocidade 10 m/s, 2 km na velocidade de 20 m/s e mais 1,5 km em 15 m/s, retornando em casa. Que horas eu chegarei para o caf\u00e9 da manh\u00e3?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# EX04" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exerc\u00edcio 5: Fa\u00e7a um algoritmo que obtenha valores de a, b, c do usu\u00e1rio e calcule as ra\u00edzes da equa\u00e7\u00e3o do segundo grau $a \\cdot x^{2} + b \\cdot x + c$.\n", "\n", "Em seguida, utilize a biblioteca sympy para obter as ra\u00edzes. Verifique como fazer isso atrav\u00e9s do [tutorial](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-5-Sympy.ipynb)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# EX05\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exerc\u00edcio 6: Dado os valores de x e y e um valor de $0 \\leq w \\leq 1$, todos eles digitado pelo usu\u00e1rio, determine a m\u00e9dia ponderada dada por $w \\cdot x + (1.0 - w) \\cdot y$." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# EX06\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exerc\u00edcio 7: Dado o c\u00f3digo abaixo que utiliza as bibliotecas requests e json para obter a temperatura atual em Fahrenheit de uma dada coordenada, converta e imprima a temperatura em Celsius." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# EX07\n", "# Conforme especificado na documenta\u00e7\u00e3o da seguinte API:\n", "# https://developer.forecast.io/docs/v2\n", "# Ao acessar a url https://api.forecast.io/forecast/APIKEY/LATITUDE,LONGITUDE\n", "# obtemos informa\u00e7\u00f5es de previs\u00e3o do tempo para as coordenadas especificadas\n", "# O formato de sa\u00edda \u00e9 similar ao do exerc\u00edcio de tradu\u00e7\u00e3o de textos, \n", "# \u00e9 conhecido como JSON.\n", "# Utilizando a biblioteca json, \u00e9 poss\u00edvel converter esses textos para \n", "# o tipo de vari\u00e1vel conhecida como dicion\u00e1rio, em que especificamos\n", "# um campo que queremos consultar atrav\u00e9s de colchetes.\n", "\n", "# No c\u00f3digo abaixo, logo ap\u00f3s obter a temperatura atual\n", "# converta para grau Celsius e imprima o resultado\n", "\n", "import requests\n", "import json\n", "\n", "APIKEY = '64f1cb760542cc081014a27410cf1bf1' # chave obtida no site, 1000 requisi\u00e7\u00f5es por dia\n", "\n", "# coordenadas de Santo Andr\u00e9\n", "LAT = '23.6572'\n", "LONG = '46.5333'\n", "\n", "# Obtendo informa\u00e7\u00f5es do site\n", "url = 'https://api.forecast.io/forecast/'+APIKEY+'/'+LAT+','+LONG\n", "saida = requests.get(url)\n", "\n", "# pegando a temperatura em Fahrenheit\n", "# a vari\u00e1vel t cont\u00e9m a temperatura em F, como um valor decimal\n", "t = json.loads(saida.text)['currently']['temperature']\n", "\n", "# O exerc\u00edcio come\u00e7a aqui!\n", "# converta para Celsius e imprima" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exerc\u00edcio 8: Converta a idade do usu\u00e1rio para segundos. Fa\u00e7a a mesma convers\u00e3o levando em conta os anos bissextos." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#EX08\n", "\n", "# Converta a idade digitada pelo usu\u00e1rio em segundos\n", "\n", "\n", "# Fa\u00e7a a mesma convers\u00e3o levando em conta os anos bissextos\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exerc\u00edcio 9: Siga as instru\u00e7\u00f5es para calcular a \u00e1rea e volume de cada forma geom\u00e9trica." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#EX09\n", "\n", "# Crie uma fun\u00e7\u00e3o que retorne as \u00e1reas da face e volume de um cub\u00f3ide.\n", "# Um cub\u00f3ide tem 6 faces:\n", "# duas delas com \u00e1rea = basealtura\n", "# duas delas com \u00e1rea = baseprofundidade\n", "# duas delas com \u00e1rea = altura * profundidade\n", "# O volume = basealturaprofundidade\n", "\n", "\n", "# Crie uma fun\u00e7\u00e3o que retorne as a\u0155eas de uma pir\u00e2mide.\n", "# A pir\u00e2mide tem 5 faces:\n", "# quatro delas com \u00e1rea = base * altura / 2\n", "# uma dela com \u00e1rea = base * largura\n", "# O volume = base * largura * altura / 3\n", "\n", "\n", "\n", "# Crie uma fun\u00e7\u00e3o que retorne a \u00e1rea e volume de uma circunfer\u00eancia/esfera de raio r\n", "# A \u00e1rea \u00e9 pi * r^2\n", "# O volume \u00e9 (4/3)pi r^3\n", "\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 } ], "metadata": {} } ] }	mit
AEW2015/PYNQ_PR_Overlay	Pynq-Z1/notebooks/examples/pmod_dac_adc.ipynb	1	94297	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "# DAC-ADC Pmod Examples using Matplotlib and Widget" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "----" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Contents\n", "\n", "\n", "[Pmod DAC-ADC Feedback](#Pmod-DAC-ADC-Feedback)\n", "\n", "[Tracking the IO Error](#Tracking-the-I/O-linearity)\n", "\n", "[Error plot with Matplotlib](#Error-plot-with-Matplotlib)\n", "\n", "[XKCD Plot](#XKCD-Plot)\n", "\n", "[Widget controlled plot](#Wideget-controlled-plot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pmod DAC-ADC Feedback" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example shows how to use the PmodDA4 DAC and the PmodAD2 ADC on the PYNQ-Z1 board, using the baord's two Pmod interfaces. The notebook then compares the DAC output to the ADC input and tracks the errors.\n", "\n", "The errors are plotted using Matplotlib and an XKCD version of the plot is produced (for fun). Finally a slider widget is introduced to control the number of samples diaplayed in the error plot.\n", "\n", "Note: The output of the DAC (pin A) must be connected with a wire to the input of the ADC (V1 input)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Import hardware libraries and classes" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pynq import Overlay\n", "from pynq.iop import Pmod_ADC, Pmod_DAC" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Program the ZYNQ PL" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ol = Overlay('base.bit')\n", "ol.download()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Instantiate the Pmod peripherals as Python objects" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "adc = Pmod_ADC(1)\n", "dac = Pmod_DAC(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Write to DAC, read from ADC, print result" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.3418]\n" ] } ], "source": [ "dac.write(0.35)\n", "sample = adc.read()\n", "print(sample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "[Contents](#Contents)\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tracking the IO Error\n", "Report DAC-ADC Pmod Loopback Measurement Error." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Value written: 0.00\tSample read: 0.00\tError: +0.0020\n", "Value written: 0.11\tSample read: 0.10\tError: -0.0037\n", "Value written: 0.21\tSample read: 0.21\tError: +0.0004\n", "Value written: 0.32\tSample read: 0.30\tError: -0.0111\n", "Value written: 0.42\tSample read: 0.43\tError: +0.0086\n", "Value written: 0.53\tSample read: 0.50\tError: -0.0278\n", "Value written: 0.63\tSample read: 0.62\tError: -0.0144\n", "Value written: 0.74\tSample read: 0.73\tError: -0.0102\n", "Value written: 0.84\tSample read: 0.81\tError: -0.0335\n", "Value written: 0.95\tSample read: 0.93\tError: -0.0177\n", "Value written: 1.05\tSample read: 1.03\tError: -0.0253\n", "Value written: 1.16\tSample read: 1.12\tError: -0.0368\n", "Value written: 1.26\tSample read: 1.24\tError: -0.0210\n", "Value written: 1.37\tSample read: 1.31\tError: -0.0598\n", "Value written: 1.47\tSample read: 1.43\tError: -0.0401\n", "Value written: 1.58\tSample read: 1.53\tError: -0.0516\n", "Value written: 1.68\tSample read: 1.62\tError: -0.0631\n", "Value written: 1.79\tSample read: 1.75\tError: -0.0434\n", "Value written: 1.89\tSample read: 1.81\tError: -0.0842\n", "Value written: 2.00\tSample read: 1.93\tError: -0.0664\n" ] } ], "source": [ "from math import ceil\n", "from time import sleep\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from pynq import Overlay\n", "from pynq.iop import Pmod_ADC, Pmod_DAC\n", "\n", "ol = Overlay('base.bit')\n", "ol.download()\n", "\n", "adc = Pmod_ADC(1)\n", "dac = Pmod_DAC(2)\n", "\n", "delay = 0.0\n", "values = np.linspace(0, 2, 20)\n", "samples = []\n", "for value in values:\n", " dac.write(value)\n", " sleep(delay)\n", " sample = adc.read()\n", " samples.append(sample[0])\n", " print('Value written: {:4.2f}\\tSample read: {:4.2f}\\tError: {:+4.4f}'.\n", " format(value, sample[0], sample[0]-value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Error plot with Matplotlib\n", "This example shows plots in notebook (rather than in separate window)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x30535b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", " \n", "X = np.arange(len(values))\n", "plt.bar(X + 0.0, values, facecolor='blue', \n", " edgecolor='white', width=0.5, label=\"Written_to_DAC\")\n", "plt.bar(X + 0.25, samples, facecolor='red', \n", " edgecolor='white', width=0.5, label=\"Read_from_ADC\")\n", "\n", "plt.title('DAC-ADC Linearity')\n", "plt.xlabel('Sample_number')\n", "plt.ylabel('Volts')\n", "plt.legend(loc='upper left', frameon=False)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "[Contents](#Contents)\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## XKCD Plot\n", "\n", "Same data plotted in XKCD format ...\n", "\n", "(http://xkcd.com)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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uXCmyd/3118fNSoSoqKjAjh07kJubyx/bs2cPnnzySRBC8Pjjj+Po0aNYtmyZ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\nW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"text/plain": [ "<matplotlib.figure.Figure at 0x30523470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", " \n", "# xkcd comic book style plots\n", "with plt.xkcd():\n", " X = np.arange(len(values))\n", " plt.bar(X + 0.0, values, facecolor='blue', \n", " edgecolor='white', width=0.5, label=\"Written_to_DAC\")\n", " plt.bar(X + 0.25, samples, facecolor='red', \n", " edgecolor='white', width=0.5, label=\"Read_from_ADC\")\n", "\n", " plt.title('DAC-ADC Linearity')\n", " plt.xlabel('Sample_number')\n", " plt.ylabel('Volts')\n", " plt.legend(loc='upper left', frameon=False)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "[Contents](#Contents)\n", "\n", "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Widget controlled plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we extend the IO plot with a slider widget to control the number of samples appearing in the output plot.\n", "\n", "We use the `ipwidgets` library and the simple `interact()` method to launch a slider bar.\n", "\n", "> The interact function (ipywidgets.interact) automatically creates user interface (UI) controls for exploring code and data interactively. It is the easiest way to get started using IPython’s widgets.\n", "\n", "For more details see [Using ipwidgets interact()](https://ipywidgets.readthedocs.io/en/latest/examples/Using%20Interact.html#)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x30205530>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from math import ceil\n", "from time import sleep\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from ipywidgets import interact\n", "import ipywidgets as widgets\n", "\n", "from pynq import Overlay\n", "from pynq.iop import Pmod_ADC, Pmod_DAC\n", "\n", "ol = Overlay('base.bit')\n", "ol.download()\n", "\n", "dac = Pmod_DAC(2)\n", "adc = Pmod_ADC(1)\n", "\n", "def capture_samples(nmbr_of_samples): \n", " # Write to DAC, read from ADC, write to OLED\n", " delay = 0.0\n", " values = np.linspace(0, 2, nmbr_of_samples)\n", " samples = []\n", " for value in values:\n", " dac.write(value)\n", " sleep(delay)\n", " sample = adc.read()\n", " samples.append(sample[0])\n", "\n", " X = np.arange(nmbr_of_samples)\n", " plt.bar(X + 0.0, values[:nmbr_of_samples+1], \n", " facecolor='blue', edgecolor='white', \n", " width=0.5, label=\"Written_to_DAC\")\n", " plt.bar(X + 0.25, samples[:nmbr_of_samples+1], \n", " facecolor='red', edgecolor='white', \n", " width=0.5, label=\"Read_from_ADC\")\n", "\n", " plt.title('DAC-ADC Linearity')\n", " plt.xlabel('Sample_number')\n", " plt.ylabel('Volts')\n", " plt.legend(loc='upper left', frameon=False)\n", " \n", "interact(capture_samples, \n", " nmbr_of_samples=widgets.IntSlider(\n", " min=5, max=30, step=5,\n", " value=10, continuous_update=False));\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "[Contents](#Contents)\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": "3.4.3+" }, "widgets": { "state": { "c1f11e63b6034c5c859c7e974b0ce83c": { "views": [ { "cell_index": 25 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }	bsd-3-clause
4DGenome/Chromosomal-Conformation-Course	Participants/george/mapping.ipynb	1	10635	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_enz = 'HindIII'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pytadbit.mapping.full_mapper import full_mapping" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mkdir -p results" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mkdir -p results/MboI/01_mapping" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mkdir -p results/HindIII/01_mapping" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34m01_mapping\u001b[0m/\r\n" ] } ], "source": [ "ls results/MboI/" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IndentationError", "evalue": "unexpected indent (<ipython-input-13-c5c61137f524>, line 4)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-13-c5c61137f524>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m windows((1,25),(1,55),(1,75))\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" ] } ], "source": [ "full_mapping('/media/storage/db/reference_genome/Homo_sapiens/hg38/hg38.gem', nthreads=2, clean=True, r_enz=r_enz, \n", " frag_map=False, out_map_dir='results/{0}/01_mapping/map{0}_r1'.format(r_enz),\n", " fastq_path='/media/storage/FASTQs/K562_HindIII_{0}_1.fastq'.format(r_enz)),\n", " windows((1,25),(1,55),(1,75))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preparing FASTQ file\n", " - conversion to MAP format\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/student/.miniconda2/lib/python2.7/site-packages/pytadbit/mapping/full_mapper.py:390: UserWarning: WARNING: only 68 Gb left on tmp_dir: /home/student/tmp\n", "\n", " warn('WARNING: only %d Gb left on tmp_dir: %s\\n' % (fspace, temp_dir))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Mapping reads in window 1-end...\n", "TO GEM /home/student/tmp/K562_HindIII_1_EFRTyb\n", "/usr/local/bin/gem-mapper -I /media/storage/db/reference_genome/Homo_sapiens/hg38/hg38.gem -q offset-33 -m 0.04 -s 0 --allow-incomplete-strata 0.00 --granularity 10000 --max-decoded-matches 1 --min-decoded-strata 0 --min-insert-size 0 --max-insert-size 0 --min-matched-bases 0.8 --gem-quality-threshold 26 --max-big-indel-length 15 --mismatch-alphabet ACGT -E 0.30 --max-extendable-matches 20 --max-extensions-per-match 1 -e 0.04 -T 2 -i /home/student/tmp/K562_HindIII_1_EFRTyb -o /home/student/tmp/K562_HindIII_1_EFRTyb_full_1-end\n" ] } ], "source": [ "full_mapping('/media/storage/db/reference_genome/Homo_sapiens/hg38/hg38.gem', \n", " nthreads=2, \n", " clean=True, \n", " r_enz=r_enz, \n", " frag_map=True, \n", " out_map_dir='results/{0}/01_mapping/map{0}_r1'.format(r_enz),\n", " fastq_path='/media/storage/FASTQs/K562_{0}_1.fastq'.format(r_enz))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/student/.miniconda2/lib/python2.7/site-packages/pytadbit/mapping/full_mapper.py:390: UserWarning: WARNING: only 123 Gb left on tmp_dir: /home/student/tmp\n", "\n", " warn('WARNING: only %d Gb left on tmp_dir: %s\\n' % (fspace, temp_dir))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Preparing FASTQ file\n", " - conversion to MAP format\n", "Mapping reads in window 1-end...\n", "TO GEM /home/student/tmp/K562_HindIII_2_72mtM9\n", "/usr/local/bin/gem-mapper -I /media/storage/db/reference_genome/Homo_sapiens/hg38/hg38.gem -q offset-33 -m 0.04 -s 0 --allow-incomplete-strata 0.00 --granularity 10000 --max-decoded-matches 1 --min-decoded-strata 0 --min-insert-size 0 --max-insert-size 0 --min-matched-bases 0.8 --gem-quality-threshold 26 --max-big-indel-length 15 --mismatch-alphabet ACGT -E 0.30 --max-extendable-matches 20 --max-extensions-per-match 1 -e 0.04 -T 2 -i /home/student/tmp/K562_HindIII_2_72mtM9 -o /home/student/tmp/K562_HindIII_2_72mtM9_full_1-end\n", "Parsing result...\n", " x removing GEM input /home/student/tmp/K562_HindIII_2_72mtM9\n", " x removing map /home/student/tmp/K562_HindIII_2_72mtM9_full_1-end.map\n", " - splitting into restriction enzyme (RE) fragments using ligation sites\n", " - ligation sites are replaced by RE sites to match the reference genome\n", " * enzyme: HindIII, ligation site: AAGCTAGCTT, RE site: AAGCTT\n", "Preparing MAP file\n", " x removing pre-GEM input /home/student/tmp/K562_HindIII_2_72mtM9_filt_1-end.map\n", "Mapping fragments of remaining reads...\n", "TO GEM /home/student/tmp/K562_HindIII_2_tM9O0g\n", "/usr/local/bin/gem-mapper -I /media/storage/db/reference_genome/Homo_sapiens/hg38/hg38.gem -q offset-33 -m 0.04 -s 0 --allow-incomplete-strata 0.00 --granularity 10000 --max-decoded-matches 1 --min-decoded-strata 0 --min-insert-size 0 --max-insert-size 0 --min-matched-bases 0.8 --gem-quality-threshold 26 --max-big-indel-length 15 --mismatch-alphabet ACGT -E 0.30 --max-extendable-matches 20 --max-extensions-per-match 1 -e 0.04 -T 2 -i /home/student/tmp/K562_HindIII_2_tM9O0g -o /home/student/tmp/K562_HindIII_2_tM9O0g_frag_1-end\n", "Parsing result...\n", " x removing GEM input /home/student/tmp/K562_HindIII_2_tM9O0g\n", " x removing failed to map /home/student/tmp/K562_HindIII_2_72mtM9_fail.map\n", " x removing tmp mapped /home/student/tmp/K562_HindIII_2_tM9O0g_frag_1-end.map\n" ] }, { "data": { "text/plain": [ "['results/HindIII/01_mapping/mapHindIII_r2/K562_HindIII_2_full_1-end.map',\n", " 'results/HindIII/01_mapping/mapHindIII_r2/K562_HindIII_2_frag_1-end.map']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "full_mapping('/media/storage/db/reference_genome/Homo_sapiens/hg38/hg38.gem', \n", " nthreads=2, \n", " clean=True, \n", " r_enz=r_enz, \n", " frag_map=True, \n", " out_map_dir='results/{0}/01_mapping/map{0}_r2'.format(r_enz),\n", " fastq_path='/media/storage/FASTQs/K562_{0}_2.fastq'.format(r_enz))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/student/Notebooks/george\r\n" ] } ], "source": [ "! pwd" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "K562_HindIII_1_frag_1-end.map K562_HindIII_1_full_1-end.map\r\n" ] } ], "source": [ "ls results/HindIII/01_mapping/mapHindIII_r1/" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NS500645:59:HCL32BGXY:1:11101:24830:1055~2~\tAAGCTTTTTTTTCCCTTTCTTTCTTTGCCTTTAGGGTTGTAGCAAAAAGGAAGATAA\tHHHHHHEEEEEEEAEEEEEEEEEEEEEEEEEEAEEEEAEEEEE/EEE6EEEEEEEEE\t1\tchr1:+:50841566:57\r\n", "NS500645:59:HCL32BGXY:1:11101:6039:1056\tGGCTTCTTCTCTTTAAGGCAACATAGTATAAAGCTT\tAAAAAEEEEEEEEEEEEEEEEEEE/EEAE6HHHHHH\t1\tchr7:+:128976029:36\r\n", "NS500645:59:HCL32BGXY:1:11101:6039:1056~2~\tAAGCTTTCGAAGGCTAGCTAGGATAGATTTGAAACCCAGCC\tHHHHHHEEEEEEEEEEEEEEEEEEEEEEEEEEE/EEEAEEE\t0:1\tchr7:-:129557448:22G18\r\n", "NS500645:59:HCL32BGXY:1:11101:19311:1056\tACCTACTTCATAAAAAGCCAAGGATGTAATTTTTTCAGTTTAGTGTAAGCTT\t/AAAAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEHHHHHH\t1\tchr19:+:41557056:52\r\n", "NS500645:59:HCL32BGXY:1:11101:19311:1056~2~\tAAGCTTAGGGTTCAAGTCTAAACAC\tHHHHHHEEEEEEAEEEEEEEEEEEE\t1\tchr19:+:41066202:25\r\n", "NS500645:59:HCL32BGXY:1:11101:17064:1056\tCAACAATCATATATTTCCTGCAAATGGAAAGGCAAATGGTACAGAGTCCCCAATGTACAAGCTT\tAAAAAEEEEEEEEEEEEEEAEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEAEEEEEEAHHHHHH\t0:1\tchr4:+:33593268:40C23\r\n", "NS500645:59:HCL32BGXY:1:11101:26187:1057\tCCCACTTACCACTGAAGTTAAAGCTT\tAAAAAEEEA<<AEEEEEEEEHHHHHH\t1\tchr10:+:63502264:26\r\n", "NS500645:59:HCL32BGXY:1:11101:26187:1057~2~\tAAGCTTCTAAGTGAAGTCTCAAAGCCACAGAATTAAGAAAGGCTGTTTGTT\tHHHHHHAEA6EEAAEEE<E/<EA/6/A6A</EA<A/A/AA//<</AA<//E\t1\tchr10:-:63481487:51\r\n", "NS500645:59:HCL32BGXY:1:11101:8250:1059\tAGCCCGCTCAAGGTCTAAACTCGGCTCGTCCCTGGGCCTTCCACCTACAAGAAGCTT\tAAAAAE666EEEEEEEEEAAAEAEEEA/EEEEEAEEE6EEEE/EEEE6EAEHHHHHH\t0:1\tchr13:+:29695513:3A53\r\n", "NS500645:59:HCL32BGXY:1:11101:17211:1059\tAGTCATAGCAACAACAGATACTTAAGCTT\tAAAAAEEEEEEEEEEEEEEEEEEHHHHHH\t1\tchr2:+:25493831:29\r\n" ] } ], "source": [ "! head results/HindIII/01_mapping/mapHindIII_r2/K562_HindIII_2_frag_1-end.map" ] }, { "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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-3.0
Cyb3rWard0g/HELK	docker/helk-jupyter/notebooks/sigma/app_sqlinjection_errors.ipynb	1	3088	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Suspicious SQL Error Messages\n", "Detects SQL error messages that indicate probing for an injection attack" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", "- title: Suspicious SQL Error Messages\n", " id: 8a670c6d-7189-4b1c-8017-a417ca84a086\n", " status: experimental\n", " description: Detects SQL error messages that indicate probing for an injection attack\n", " author: Bjoern Kimminich\n", " references:\n", " - http://www.sqlinjection.net/errors\n", " logsource:\n", " category: application\n", " product: sql\n", " service: null\n", " detection:\n", " keywords:\n", " - quoted string not properly terminated\n", " - You have an error in your SQL syntax\n", " - Unclosed quotation mark\n", " - 'near \"\": syntax error'\n", " - SELECTs to the left and right of UNION do not have the same number of result\n", " columns\n", " condition: keywords\n", " falsepositives:\n", " - Application bugs\n", " level: high\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='\\.keyword:(quoted\\ string\\ not\\ properly\\ terminated* OR You\\ have\\ an\\ error\\ in\\ your\\ SQL\\ syntax OR Unclosed\\ quotation\\ mark OR near\\ \\\"\\\"\\:\\ syntax\\ error* OR SELECTs\\ to\\ the\\ left\\ and\\ right\\ of\\ UNION\\ do\\ not\\ have\\ the\\ same\\ number\\ of\\ result\\ columns)')\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
csaladenes/csaladenes.github.io	present/mcc2/PythonDataScienceHandbook/05.09-Principal-Component-Analysis.ipynb	2	535664	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<!--BOOK_INFORMATION-->\n", "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n", "\n", "This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).\n", "\n", "The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<!--NAVIGATION-->\n", "< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) \| [Contents](Index.ipynb) \| [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) >\n", "\n", "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.09-Principal-Component-Analysis.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# In Depth: Principal Component Analysis" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Up until now, we have been looking in depth at supervised learning estimators: those estimators that predict labels based on labeled training data.\n", "Here we begin looking at several unsupervised estimators, which can highlight interesting aspects of the data without reference to any known labels.\n", "\n", "In this section, we explore what is perhaps one of the most broadly used of unsupervised algorithms, principal component analysis (PCA).\n", "PCA is fundamentally a dimensionality reduction algorithm, but it can also be useful as a tool for visualization, for noise filtering, for feature extraction and engineering, and much more.\n", "After a brief conceptual discussion of the PCA algorithm, we will see a couple examples of these further applications.\n", "\n", "We begin with the standard imports:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns; sns.set()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Introducing Principal Component Analysis\n", "\n", "Principal component analysis is a fast and flexible unsupervised method for dimensionality reduction in data, which we saw briefly in [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb).\n", "Its behavior is easiest to visualize by looking at a two-dimensional dataset.\n", "Consider the following 200 points:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rng = np.random.RandomState(1)\n", "X = np.dot(rng.rand(2, 2), rng.randn(2, 200)).T\n", "plt.scatter(X[:, 0], X[:, 1])\n", "plt.axis('equal');" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "By eye, it is clear that there is a nearly linear relationship between the x and y variables.\n", "This is reminiscent of the linear regression data we explored in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), but the problem setting here is slightly different: rather than attempting to predict the y values from the x values, the unsupervised learning problem attempts to learn about the relationship between the x and y values.\n", "\n", "In principal component analysis, this relationship is quantified by finding a list of the principal axes in the data, and using those axes to describe the dataset.\n", "Using Scikit-Learn's ``PCA`` estimator, we can compute this as follows:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "PCA(n_components=2)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.decomposition import PCA\n", "pca = PCA(n_components=2)\n", "pca.fit(X)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The fit learns some quantities from the data, most importantly the \"components\" and \"explained variance\":" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-0.94446029 -0.32862557]\n", " [-0.32862557 0.94446029]]\n" ] } ], "source": [ "print(pca.components_)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.7625315 0.0184779]\n" ] } ], "source": [ "print(pca.explained_variance_)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "To see what these numbers mean, let's visualize them as vectors over the input data, using the \"components\" to define the direction of the vector, and the \"explained variance\" to define the squared-length of the vector:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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QEARBOxBEIst/I5g9L9+wXUYLdc6PlHE9D10LUyFrlruqdYHZG7Ampy0URWnX5E/FDVRFoTAztw8LP7U3bI/JqTq+F2DqCq4HF0Yq17UpGTMYyMYZKzT4xZvjjBUaiwbXgWycqUqTC6NlXM8nEVVJxnUGsrFNS+/sNvImILpSp+beNS18jvL9AE1TcRwXw9CJJ0zeOD/CpUKd8nQDCKc4GrZHNKqTS5ntaY5r5+XLNRtD03BdiEZUDuxKo2nKqtYFZm/AKlVsepMRcpkY8agORLkyUaFS9wmCYMHNVWPFOn4QsHcgRUSfydhxfUpli1+8OUFv2my/SbWuH8jFGOhLMTZZYaxYJxE15h3QkzGDRFSnbuu4nk/U1Dk4GK6xrKa/K3m72yp7CjabBAHRlWYvYLastM5MteHQcHzS6RjTFZtsTwxQmCzWGC3UcH2fqKpwabxC0/HQNJVYRMeqOEQ0hZrloCrKdcFI1zUSUZ19A1erULYWV1ejlTvfmg4ampgEIN8TIx2P0HSCeXcKtwbJ1y8UqTUc+ntj7RHD83zGSjX6swn2xRJzcvpb/Zmd5rnYgB4AB3al2tNqq+3vQvsMFstKWs61O51MB4mutNY6M61BpOmGc//jxRqu62MYGldGK0xVLExDY3LKIh7Vcd0A2/aIR3VMIyyIZkY0hidr1y0EJ2PGdQPgtW27NrVzrFRfNNWz2nAolC2GClUUBXQdrkxWuTxR5ej+Hm47lOPwnsycANCaLutNmSgKjBZrWM2wv8WKhRnRScWNOYvK8/VnqYXtTtX8mf12t9hC90qv3ekkCIiutJqiabO1BpHSdIP/fmOESs2mMFMiIZ022wNhw3YwNBU/8PH8cKAzdAWr6bWvabp++JQ+UeX18wUujpW5MlHl/PA0tYZzXduuXc+o1Jv8v6+PMjFVZ6pi8dblKV55bYSxUn1Oe33fZ18+hRnR8P1w8E3FIlTri+8N6MtEScYMXDegMN2g6XiUaw7JmEEuHW3/N7P7M9tSA/pa/y5aVpJZ1eksrO1MpoNEV5o9Vz4xbWNZLjFTa+fPLzUlMHs6yZkZ9KamGwzkkgz2JRker8xk3RiUrCaqosLMbIfjBkQjGk3XZ3dfgumqzeS0habCVMXG8wOyKRPb8Tk3FE5RhKWSwzr+54am8f0APwgo15tMVZoYusJ0zWb/QJpM0qBuefz89TEO7U4TACOTNZqeTzYVITlzRkAQBNRtd8FBstW/mKlzeE+GaETnykSVjOOzL58kn421s4qAdn/smbeF5WQHXft3sZwCdgtZSUns1ZTP3qm6r8dCzEjGDMjGqVkOmUSsXXlyOXPD8w0ik1MNDrkesaiBoWvYjkdfT5ShsWl0XUHTVOqWix/49PfE23XwL4yUiUUcxkoNTENnMBNFUxU0VSG/O43j+FwYKberdlYbTaZrTcp1h/7eKAEBk9M2hq6yN5/CQEVRYHK6QSphcGBXCsNQGSvViWhqOwg4no+uqXPKP7cWSktlG8/36Uma1C2XYtnCcX0ODWZ479Gw8u87w9PYjjenYmcrWI0V65TrzWWneS5V82c5i7iz01OXqiK6kmt3OgkCoqutNktooUGk0XAwUhpHD2T5zTsFPD/ghv5wgfdqdlB4CHtrIAtmPm+sVMfzw+ygdCKC44YLsO+MlEnFdNKJCCgwVmrg+wGxiIbt+MSiOtM1G0VRKNdsopE4pbJNKm7geT6KorArG2eqaoeBwEiCElC3PPoyUQZmTuuavVDqeT5vX57CNFRKVRvT0DF0FUUJ+F+/uMKhwXT439WdeZ/ek3syHaukudxF3JW8UXTq7WMnkCAgutpqs4QWGkS8mamhVMLk8DIHQgW4MFpB1zQgoGG7DBdqpKIRihWLdCyC50M8qgIKcVNntFAjl4nRbLpEIwYNO5x2GStpxKMGNcthVy5G1Lw6pXN0Xw/nhso0nbCNN/Sn2sXXzg1Nzw2GEQ1dUxktNIhENEoVi0rDZW9fglzGZGK6gR8EG5JNs5JAvZIqolJxNCRBQHS1tcwNzzeIuK6H5/lomoquL71hrNpwGJmsMVqoEzU1LNujYYdTRqqmUK07xCLhU7jjBhi6QjoRoVxz8Hwfzw+o1Juk4xGmyjZTZZuRSI2eVBg4Zi/caprKzQey8563e20wLJQtetMRimWbWETD9Xx6Eip120EtK6QSAbv7EquuabQSnUjnFQuTICC62nrMDdu2SzweIRpdvHZ+a55/smxjaArTZZtixSadMOhJx9AUhf0DKVpHKLXSUeOmTiKuEwQBpqpQqTuYhsqBPSliph6WmlBV+jJRVFVZdCNYy7XB0LJddC1881CUsE6QaYTfj6LSXgvYiIFYFnHXl3yLYsdYzQ7Q9Zgbtm2HeDyCucAg1ZrjLkw3MA0Ny3JoAAO9MXxA02CgJ9zVm8tEuTxexfMCbhhIcnmsymixTiYewTQ03h6awjQ0cpkY+Z5YmHXkeDQdn5sP5LgwUubiSDgdtbsvsWB7GrbLOyNlIrqCrqqMluoEAaTiWvhWooDt+GFQ8QOikaUPiekUWcRdXxIExI6wlh2gq50bXijouK6P63oLTge15rg9P5zqyGailMoWU7UmMVOn2miGpZT7+4iZOv29McpVh5rtoahw+419ZBIRmq7PZNliMBcnFY9cvYFy9fhVPwjYP5haMPNp9ve2py/OGxdLNB2Pgd44Tdenajlk4hHsps9IoUo6aRI3NVRFWdVAvFUCtbhKgoDYETpdh38pCwWdVsZMv+1yaG8vjnt9NdHWHHc0ojFe8sJsnKTJaKGGooDnBeiaQszUmKrYjJUaZFMmDctloDdOT9Js93FXNsbEVAMzomHMHMFYtzxu6E8t6zuZfc1E02NvfxKFMD01l4lycbTC5fEq0YjGuw9nsR2fSt2h6fprOqFsowK1WJoEAbEjbPTi4XwDbMN2+e+3JzkwmKJatYGwvPT4VJ1KzWk//SqEc9rZdDjVU647VKoWyZhBJmFiRjTqtsvl8RoN2yGTiGA1XX5zeQrX9RnMxcn3xMimo+zrT1GtOwRB+B3oWrgWcGAwzfmR8pLfyezvzbJdYqaOokDd8sKMoht6cFyf5Ey6aTajc2s6XGuo1h0Getf2nbX+XAb4zSNBQOwI67F4uNjUxXxBp1J38P3wkBbb8ag1miRiESbLNppC++m3bjsEloJpqKTjBr/6bYEggBsGk2TTURQVDg6mKdfCAHB+pILt+kxXLAxDY6xUJxEzmBqZRldVzIiOpqgkEwbZdLTdTgW4MFLG8yEa0cimo2iaMuc7mf29RU0dx/NRUIjOlMJu/awTBd4ky2dr6ljtoPPnz/PQQw9xzz338NBDD3HhwoXrrvE8jyeeeILjx49z11138dxzz3Xq9qLLdar+TMtYqc4rrw3z1pUppqo2lUZzznkD8xU9qzacuWmWU2EJ6f2DmTmFyjJJE8d1ef1CgSuTNTQVYlGV6UoT1/PZ05ckk4wwPt3gneEymqoQ+D6JWARr5sStUsWiMGUxMdXg0O40A7kYZkRrB4Bqw6FuOzSaHroOrudzYbTMVKU55zuZ/b1lU+Hu4Jrl0Jsy29/h7r5ERwq8dapQnOisjn37jz/+OA8//DAnT57khRde4Itf/CLf/e5351zz4osvcunSJc6cOcPU1BT3338/H/rQh9i7d2+nmiG61FoXD2c/9Vu2y+vni6iaQipuoKkKlu2S7421py7my1hRFeYs0Ban6uzblWbfQIorI9NMTDXCKpxBwPmxCrtzCTSlCUGA5/ukExGma00UJRwcFT/A9QMSMY3JaQvf9wn8gGrdYdivsTefIhHVSczqY6t9Y8U6mWS4M7l1QlnM1ElE9QV32TYdv7272Z85OrK18NuJ7BzJ8tmaOvImUCgUOHv2LCdOnADgxIkTnD17lmKxOOe6l19+mQcffBBVVclmsxw/fpwf/ehHnWiC2MGuLZu80AlbyZjB4T2Z68oiL+fzW1U5NRV+c7lEqWoRNVR8PzzuMCCc7mkVW2sNnroaTmfoqsrt78qjqkr7baTacJgoNdA0lXLDZbRYQ1VguFBnvFjnwmiZqtUklTBRFAWr6eP7PqPFOnbToz8bQ9dUyvUmlbpNoWJju/5MHTqFdMIIS0nMmF0Fs1UlM2bq7M0nObK3hwO7ru45WOh7ax3mPvs7nK+vq9kp3KnPEZ3VkTeBkZERBgYG0LRwHlHTNPr7+xkZGSGbzc65bvfu3e3fDw4OMjo62okmiB1qPQ//aD39nxuaxtAVBrIJihUbVVFIxSJU6g75nvBs35rl4PkBu3Pz59oDJKJz30bCCpvhsPvhd+/m0sg0l8YrTJQsepIRitMW9kxOfzxuEI9o7MsncRy//TmGofJfb4xhOz5KEObnK6oCBAxNVPngLYPt+8+eWun0GkmnsnMky2fr2XaTcbnc9nx1zOdTS1+0g622/8WLRQbyKaKRq/9UraZLM1j9Z1bqTX57ucSbl6bIxA0i0bCu/rTl4gUK+d4EddtlolQnYhrEVIXxqQYoPq6iUKw7pBMRCjUH1dDwbY+hUoPRaYv3HOnj0A29JGKROdM0hqESiUbQmh653jie6zNamiIW0YgYKnbTxXN99u/JcNOBHAdvyNLXl+TNiyX6Rqv0pGI0mh5W06UnGcVxXcp1h7rjkcmEbwxK0+Po/l5S8QjRhMmbF0tEZ7JxbMeb8/ON0s3/7rdL3zsSBAYHBxkbG8PzPDRNw/M8xsfHGRwcvO664eFh3v3udwPXvxksR6FQxffne6ndujpVTXG7Wkv/h0bDNMdGXaFhuxTKFg3bJfAhojDvsYGLbUaavVtXUxSqdZuRyTrlmE6z6TM53SCdjNB0fBKmRqPRpFi2qFouHzjWT0QJmChU+a9fV8gkDSo1h3hM59Bghl19SXblEu0sGsf1GJ6o8dpbY7x1eQrHDajUbYIAhiarxCLqrOkj6OmJ8N9vTfDug7n29xXVYGraoul6pOIRDvQnaTRdTE3HUFUqVZvhsQqHdmc4MJjGqtlYtTA9NZcwGCvWGZ/1Xcz++Xrr5n/3W6nvqqos+vDckSCQy+U4duwYp0+f5uTJk5w+fZpjx47NmQoCuPfee3nuuee4++67mZqa4qc//SnPPPNMJ5ogdqjWtIbvB1wZr+IHUK5Z2E2fV14b4fZ39THQO/fErcWmjmbv1m1V5UzGDM6PlBnIRklEdRzHx2q65HuSqKpK1NS45WCWXTNTQaYR7piNRyP8zk2DDOYTaGq4vOZ6PuOFKnXL4ZdvjDFetqjXHcr1JhFdxXZcPC/AcVvHNiq4bliOIWqGUzfXnvF7cHeayekGET08vjEZj6AQsCef4OBgGtvx0FX1uoAoUy9iOTo2HfSlL32Jxx57jG984xuk02lOnToFwKOPPsrnP/95brvtNk6ePMmvfvUr7r77bgA+97nPsW/fvk41QexArYySQtnC9wMmyxYAu/MJfJ85p2eVyjbppLHoZqTZu3VbVTld3yeTjKCpGoaukO+J4Xg+qqJweE+GQiVGwtQYnqjRm4lyaG8Ptx8bxJg5ntD3Ay6Plvk/vxnjzQsFBnJJbjnQi+cHaJpCvRmuLdQsB03TcFyXnkQEFcgkTVzfJxWP0LAcBjKxdt9bAWv/rhSu52PZMymwVStMF216XBmv0psyaQZzUy+FWK6OBYHDhw/Pm/f/T//0T+1fa5rGE0880albii7QyigZLdQplq2ZYmnR9g7d2adnXRmvYjnuzLGO4T/tazcjxU2d6WoTu+kxNFklHjWoNJokowaZZIQ9fUniUb29GerwngzRyRqJRIQPD2aIzFpoPT80xW8uFPjtpSlKVQvPD9tr2zPHKwID2QQThTqqCn09MQ7tTjM0ER7Yfm5ommjUDesA2S6OG/Dem/rbn98KWEorGJUt6rZDsWJzOJcgGddx3ICLY5V2aqcQK7XtFoZF92mlMNabHoHvM1FqEDE06pYz5/SsVNzAanph8bV8OAd6bUaMqir812/GqDZcmk0X1w+nZt61L4OKwvBklaipsyef5NihHLlcYs4C33TF4uz5Aq+9PcF4qU5AAAEYmkbcVAkATQ131Goq4cliAynqdni/ouuTy4Q1/nsSEaZrDtM1i1jU4PduHcT3A157pzCnvEQrqO3NJ6nWHBRVIRJRCQLC+y9hNUXbRPeQICC2hWTcYKocDv6JqI7teIwV6+GT+swgn01HuTJRoVL3r6uh36rf//+8PspU1cZ1AlRVwdDDQm1DE1UODGY4djDLnv40yVkZNHXLYaJQ4+3LJSZKdSanLfp7otiOSW/KpGG7NB0PP4ADgymazfAtIt8Tp2K7nCs2qDRsorqGaaromkI2HeWG/jBvP27qJOPhIq7r++31jJrlotgBmaTZ3lzV9Hxu3t9LoxkePhM1dQ7sSlFruJwbmr5uoF/PFFuxM0gQENtCte5w04Fezg+XqVoucTMsv3B5vIKuq1yhSi4dpb8nTrk299xbgNfPF3n7SomLI2UazbDQWq4nyv6BFDfuz/Ked/UzMGsPgGW7lKYbRA2VNy+WwnWDRIRMIkI0UsVqeuR7Yvh+QE/KhCB8+o+bBrcezLQH4P/95jjxqIahR2nYLq43U8cnFeXWQ7n2/a473tHQ6ElFcBy/vbkqbuocGkxjGCq5zNVpqanqzGE0ych1A70UbRNLkSAgtoW6HZZRziRMCmWLiVKDWsOhZruUyjZTZYu3Lk3R1xPjAzcPtDOGAH79ToGRQpXhiRp12yERj/Dh23bzkdv3cHhPT/s6u+kyXbEpTtcpV22qDZfbDuWuK3eQjBkUyhb7d4V1+i+NVRkt1shl4uR7rhZZGyvW0TWVGwaSMLPPt+l6aKp63STOQsXVmo4/5zjI1pN9++euz1ixzkBvfN6BXoq2iaVIEBCbbjm5/aWyzZWJKqm4QSyiYzkuuUyUbBAwXbdx3YC+dDhtMlask4ga7c8Ynqzhej6339TPe48OcHR/FlUNB2W76fKrtye4MlZBJWDPPGsJ19YlSsUjfPCWXVTrDsWyhaIo3H4kTyYZmfMUXrddMskIU9MWhh7ez9BUynXnup3Hy93hO1+NpN50jExy7gaw1kAvRzOKpci/BLEhZg/0rWflgPD5uGa59KTmTmW0Dmcpli2KFZtMwsBywLI9Lo1WaNgehq5gNT0UFBJRPTzcXVMwI1p7usM0df7H+/Yx2JdE08KUTsf1+T+/GeP//uUVXjs3TipmkktH2d2fwvcDUvEIqqrMKWw2X859q5Z+OhmZ9yk8buqohs54oQaAoSs0bA9VYU4lz9nHO6ZiOv29cXRdXbC42nVtGZpecKCXom1iKRIExJosJ/OkUm+2FydVBS6OhTspD+xKMVZq0LBdUnEDxQhLLTcsl/9+eyLcAet4aKpCpeHQl4lRt1yspofjeqiqhu/7xEwd34PRYp3+3jj92TiZZJS+md276XQ4d//WpSK/enuC/3p9lKHJKmoApqmTTkTo64mSNDWqlkvddrn9XfllLZy2pltau5kt28WM6EQNjWMHshRqDvlMjEq9yXTVQVWVOZ89e+H2yJ40Y6UG54bCN4nlLt4uNtDL0YxiKRIExKotN/NkeKJ69QjDUiOsjFlr8v+dmwSgNxmlWLaIR8On00q9iR+ET9V2MyzEVmu4XBgpY0Z0fB+m602SvoGmKfh+wN6BFMcO9nHrkb45ufyO41GuWJz+z3O8eXkKq+nhej75nhgJM/zvb9zXQzSio6kKR/qT2I637FOz4qbOdK3JRKmBYYRVOxu2R90K59yP7u/lddtB01R2910fJOcs3BoaBweNBXcAL2SpgV52DovFSBDoAuuVJ77czJNqwyEys7u2XGtSrjfRNSU8y1ZTmSzXcf0Ye9vXuyTjYfuipk614TBVsWk4PlHXQ9cUHMfj8JE8h/f1cMuhPjIz5+4CVGo2juMReGG5iWrDob83zsWxKpblYlkeyYRBKm6Qy0TDU7hmjlSElS2cDmTj/Pa1KTRVJaKrOG5AQMDAzNkDB2/IzlnYvda1C7d1y6Uw3aBUbbY/fzl/VzLQi9WSILDDrWee+HIzT1QFzs0cczhaqBMxwkPRzYhKJmlyacyh1mi2c/tVNdz4BZBLR7k8ViEIwHd9+vIp3rWvlyM39NKTirbvMVWxeON8kbcvlXjvjX0komHe/ew1hX39CXpTJiOTtXbJB7vpUbPCXcazj1Rc7sJpMmbQm45h2w51yyMa0djTlyRmassKJLMXbuuWy9BkFQWF3lRYTkJy+sV6kyCww61nnvhyMk+qDYdq3WWq1sR1A0o1C9v2yPfGOLwng6oo5NImQaC0pzJuf1cfY8U6thMOqgPZBPsGUrxrf5b8rNTPctXmnStTvH25iIJCpdFEV1X+99kxIoYaVs2cWVM4P1Ihl44ykI3QsF0aTZdkzMD1Ay6Phz97196e9pGKK1k4zaVM3IQx53uwHW9ZgWT2fH5huoGCQhAE9M2UxgDJ6RfrS4LADrfWPPHFppKWk3nSCkKGpuK6LsmogaFpWE2PRtMnE9fYm0+RikfmTJuk4xF8FHp7Yrz/3VePH7WbHiMTFS6MTPPLt8YxdQ3PD8hnYhBA1FQplS3yvXEmSg2arkcsojNVtSnXbAxDJxpR0XWTiKFh1x329CXQFHXOkYorefJeSwbO7Pn8UrVJb8qkLxNdsPaREJ0mQWCHW0ue+FJTScvJPKnbLr7tkYwbZNNRcpkYE6U6tuOTjunke2PtA+EVBUzTwDR1+mYOZoewSud4scbIRIXfXCgCCn4Q0JeO4gdwcDBNoWyh6eE6g6IoqAoUK82ZIx3D3/uBQrPpYNnQl4mSTUfJ98TYk0+0N4atxlozcGbP57u+5PSLjSX/una4tTylLmcqaakFybipM1RqYMzk6EcjGr2pKNV6k1K1yd7+FDfekCWTihKJXB34gyCgON1geLxCvR4e3DJVtmnYHpV6k1Q8wmAujh+ENYAatouhqTQ9j3QywkixTtTQQA2zh5qej66Gp3i5jovrBTRdjz19yY4MtJ1YmJWcfrEZJAjscCt5Sr126qdQsclnzDnXrHR6YiAbZ3TaolH3iEfDGv6apvD+WwfZtytNric2Z+BvNt2wUuc7BTRNCQdDz2d8pIGiBNx4Q8+cAbK1qcyyPUp2k0TcoFpt4sxkERmqQippMlWzieo6iahOpRG+SezOJdA0ZcsMtJLTLzaDBIEusJyn1PmmfkrlBhFNCQukzVjpU3MyZvCBW3bxv35+kZgZ4d039rKnPzUnl9+yHZymh227BEHAuaFpNE2Z8wZSt5qgKPTPLAy3flatOwxk4xTKDSanLeJRlalyQNPxqdQauH6Aojgc2JUmQCETNyhMW7iuT832yEW21kArqZ5io0kQEMD8Uz8D2ThjxQaxqL7q6QldV8n3JnjonmPoM2mZEObyT5UtRgtVytXmnDTI+RazXS8AZW7ZtdZbyVixTiZpkopH2ieQ2Y5HLhNlVy7OSKHO0ESNiK5g6iliUZ3+3hiqokhtfdH1JAgIYP6BN5O4vpTxtU/N82UPZZImpqkTjRrtej26ruJ5PiMTVcaLNTzv6nGIs2v9wPyL2bqmYDs+VyaqWDN19JMxA11VOTc0jaJAzNTJpqPYfR6KGtYmMg2NvkyU31am0DWDqKmRS4fZN60zCeTJW3QzCQJbxGaf/rRQFlE2HV1wx+vsKaRs2qQnHSPXEyeVuFrR0vN8NE2lVKrhuj5vXSy2j0xsuXadYb4FUlVVqdQtdE0lZmo0bI/hyTq7sjEMQ0VRAjw/YGiyStMN2mf6NmyXRMxg/640ZkRrnzg2332F6EYSBLaArXD603IzU2YHq7rlcmRfD3t3zT2Jy3V9HMfFtl0cxyOfT+G64ZP/clJW51sgzaWjZBKRmYqb4SaydMzA8wN2ZeNcGa/iBy7lWpPxkkU6aXDLgSzZdLir2HE8mBV45rvvZgdiITaDBIEtYCuc/rSczJRqw+HSWIXd/UmOZROkkibqzMDqeT5TFYtCqc7QRJVbD86fc7/cYHPtAulr7xTIJCNzFqnfvjyF64VVRHOZKL9+p0C17tB0PBxH5eJYhWhEQ9NU4tEIihKuFcx3360QiIXYDBIEtoCtcvrTYpkppqmjRXR+d29v+0AWz/e5Ml5heLyKpoAfhINsLLLwP6tkzGAgG+fNi1NM1Wx6EiZH9/csOdAutE6AEu4RePvyFKWKTczU6ctE6UlFGSvVOTdU5uYDWW45mAVYMMhthUAsxGaQILAFbNXTnyIRrb2Dd3Yu/3TVojjVYGSiyvmRMp4XcNP+nmVlD7WmXAZyMfYNJNrHI84+CWw+871BxKMRbMfl/EiZQtkiGglLSNhugK6r7OtPXnc840ID+lYJxEJstDWPMo1Gg7/5m7/h9ddfR9M0vvCFL/DRj370uut+/vOf86d/+qccOHAAgEgkwnPPPbfW2+8IW2mnqGFomKaOaRrtJ34I59SHxiqMFavtKSBDV+nvjVGuOsve3LTaJ+75pqtuOZjlwkgZz/Px/fDkrmwqiqYolGtNelJXD25Zaq5/qwZiIdbbmv+Ff+tb3yKZTPKTn/yECxcu8JnPfIYzZ86QSCSuu/bw4cP84Ac/WOstd5zN3imq62r7ib+V0gnguh6W5WLbDr4fEPg+1bqDGdGuZu0oCu89urxTuGBtT9zzTVcFwIHBNH5AWCcIcD2PQsGiWNbp74lx9kKBTNJcdK5/KwViITbSmoPAv/7rv/L3f//3ABw4cIBbb72V//iP/+DjH//4mhu3VWxE1shG7xTVNPW6XH4IF3gty8G23Tm5/K02rjRYVRsOxYtFhkbLxE0dBTr6xN16gt/dl6Bhu0zXmkxONzB1jXxvDF1TmJy2SMUjKIqy4JvHZgdiITbLmoPA8PAwe/bsaf9+cHCQ0dHRea+9cOECDzzwALqu8/DDD/PAAw+s+H653MY+mVXqTS5O1kmmouSyGrbjUag59PUlSc1Ki1xKPp9ax1Z2jqapJBImiYQ578/zwMEbssv6rNZ3F41o7BvMYDseTmATKAGxuBkeH+l4KE2Po/t7V/R9tkQTJm9eLIUpo+kYv3xrHEVVOLy3l30DKS6NlsPCcr7C7p7w7TQIAsr15nV/Jyvp20psl7/79SB93/qWDAIPPPAAw8PD8/7s1VdfXfaNbrnlFn72s5+RSqW4fPkyjzzyCAMDA3z4wx9efmuBQqGK7wdLX9gh54amcX2fwNVozPyZ7Xi8/tb4oscGzpbPp5iYqKxfIxehKEr7id+Y9fTt+wG27bRz+ddD67vrSaYpTdXC9gQeTtOnisX4rDcrq2Zj1exV3SeXMNpvahFF4fbDOeJRg6bVxG26WJ6P6/lkYmH/W2f4bsTfyWb+3W826fvW6LuqKos+PC8ZBJ5//vlFf757926GhobIZsMnqJGRET7wgQ9cd10yebUR+/bt4/jx4/ziF79YcRDYaNsxaySsyx8u7hrG3PLMth3O8TebnRn4F5sqW+i7uzZjZ61mT6XFTR3XvzqNlU1HuTBaJmbq7eMrZa5fiKvUpS9Z3L333sv3v/99IJzuee211/jd3/3d664bHx8nCMIn+KmpKV555RVuuummtd5+3bXmnGfrVNZIteFwbmia194pcG5ommrDWdPnmaZOOh0ll0uSSsWIzOTr27ZDudxgcrJKpWItGgBW0qbWBivX90nG9PaZuK3/Zj2/u4UMZOPYTQ/b8QiCsGx1Lh0jn4lRbbjoqiobwISYZc3/b/zjP/5jHnvsMe666y5UVeXLX/5y+6n/6aefpr+/n09/+tOcOXOGZ599Fl3X8TyP+++/n+PHj6+5A+ttvbJGOrVDdaFc/mbTbT/1B8ucPVtpm5ZK92x9d1bT3bCn8IVSSWXQF2J+ShAsd4jYGjZ6TQDWnh003/xga7782sPJdVVdcqpksVz+1jz/ar6ja9tUt1zGijUcN+DwzKA+u9+vvVO4rhhcEARzjmqsNhyaAe3soG6rx7OV5oY3mvR9a/R9zWsCYn3SN1e61rBYLr9tu1iWc93Av9LgNbtNdctlaLKKoakoatCe6pn9VrDcYnD5fIpsvHsGfiG2kzWvCYjVWc58uaapxOMRstkEvb0J4vEImhbW5a/VbIrFGqVSnXq9OW8AWGy+fqk2FcsWEV2DmTr9pqG16/63XDv/bjte+8hHIcT2IEFgkyw0gO7JJ4nFIvT2xslmEyQSZnvgr9eblEo1isUa9Xrzus1cs82er29tkrp2EF+sTQ3bJcDHcXxyM+WYI7pK3b76ptKaf28dOiOLrkJsPzIdtElmL2Dajs/egRSD+SRR8+oAupZc/tWkts5uUxBAECjs7U8Qm3k7mS+zR87EFWJ7kyCwSRQF+npj7NmVXpdc/tUWRGsN6q3MHlVVJL9eiB1MgsAGC7N6dCIRfd6B37Y7swltramtUktHiO4gQWCDpFLRjuTyL1cnBnGZ6hFi55MgsE5m5/IDRKPh/641l38lZBAXQixFgkAHLZTLD1Cr2fPm8m8EOUBdCLEQCQJr1KrLb5oGuj63Lr9tO1iWSzaboF5vbkr75AB1IcRiJAisgqoqmKZBNKqj67PLM/vtk7hcd+Ec/o0kB6gLIRYjQWCZNrMu/1psx1LYQoiNI0FgRiJhEosZTE3V20/xigKRyNWB//qUTpdmc2sPpnKAuhBiMV0xEiy1MJpImMTjEYIgIAjWP5d/Ixdq5QB1IcRidnztoKUKqSUSkXYAcByPnp446XSsndrZbLpUKhaFQpVyudGRALDSwm5rIfV9hBCL2fFvAostjO462k8sFgYARVHaJ3GtZy7/ZizUyn4BIcRCdnwQWGhhNBE3icUiQLjoGwQBvh+0A0CnzuBdbntkoVYIsRl2/HTQQnX77aaD5/n4ftB+E9A0lWjUIJmMbnh7ZKFWCLEZdvzIs+DCaF+SYrHWvk5VwyCgaSquu36pnrJQK4TYSnb8m8ByF0ZbU0GWtb4bvWShVgixlez4NwHYegujW609QojutePfBIQQQixszUHghRde4L777uPmm2/me9/73qLX/su//At33XUXx48f58tf/jK+vzXq6wghRLdacxA4duwYTz31FCdOnFj0usuXL/M//+f/5Pvf/z5nzpzh4sWL/PCHP1zr7YUQQqzBmoPAjTfeyJEjR1DVxT/qxz/+McePHyebzaKqKg8++CAvv/zyWm8vhBBiDTZsTWBkZITdu3e3f797925GRkY26vZCCCHmsWR20AMPPMDw8PC8P3v11VfRNG3en62XXG575tPn86nNbsKm6ub+S9+703bp+5JB4Pnnn+/IjQYHB+cEk+HhYQYHB1f8OYVCdVOOaFyLfD7FxERls5uxabq5/9J36ftmU1Vl0YfnDZsOuueee/jpT39KsVjE932ee+45Pv7xj2/U7YUQQsxjzUHg9OnT3HHHHfzoRz/i6aef5o477uC3v/0tAE8//TTPPvssAPv27eOzn/0sn/zkJ7n77rvZu3cvn/jEJ9Z6eyGEEGugBEGwreZWZDpo++nm/kvfpe+bbctMBwkhhNh6JAgIIUQXkyAghBBdTIKAEEJ0MQkCQgjRxSQICCFEF5MgIIQQXUyCgBBCdDEJAkII0cUkCAghRBeTICCEEF1MgoAQQnQxCQJCCNHFJAgIIUQXkyAghBBdTIKAEEJ0MQkCQgjRxSQICCFEF5MgIIQQXUyCgBBCdDEJAkII0cUkCAghRBeTICCEEF1szUHghRde4L777uPmm2/me9/73oLX/fznP+c973kPJ0+e5OTJkzz44INrvbUQQog10tf6AceOHeOpp57iH//xH5e89vDhw/zgBz9Y6y2FEEJ0yJqDwI033giAqsrMkhBCbDcbOnJfuHCBBx54gAcffJDnn39+I28thBBiHku+CTzwwAMMDw/P+7NXX30VTdOWdaNbbrmFn/3sZ6RSKS5fvswjjzzCwMAAH/7wh1fU4FwuuaLrt4p8PrXZTdhU3dx/6Xt32i59XzIIdOqJPZm8Onjv27eP48eP84tf/GLFQaBQqOL7QUfatFHy+RQTE5XNbsam6eb+S9+l75tNVZVFH543bDpofHycIAgH76mpKV555RVuuummjbq9EEKIeax5Yfj06dM8+eSTlMtl/u3f/o1//Md/5Nvf/jZHjhzh6aefpr+/n09/+tOcOXOGZ599Fl3X8TyP+++/n+PHj3eiD0IIIVZJCVqP59uETAdtP93cf+m79H2zbZnpICGEEFuPBAEhhOhiEgSEEKKLSRAQQoguJkFACCG62JpTRDeaqiqb3YRV2a7t7pRu7r/0vTttlb4v1Y5tlyIqhBCic2Q6SAghupgEASGE6GISBIQQootJEBBCiC4mQUAIIbqYBAEhhOhiEgSEEKKLSRAQQoguJkFACCG6mASBDfLEE09w77338olPfIJPfepTvPbaa5vdpA31wgsvcN9993HzzTfzve99b7Obs+7Onz/PQw89xD333MNDDz3EhQsXNrtJG+bUqVPceeedHD16lLfeemuzm7NhSqUSjz76KPfccw/33Xcff/EXf0GxWNzsZi1JgsAGueOOO3jxxRf54Q9/yJ/92Z/xV3/1V5vdpA117NgxnnrqKU6cOLHZTdkQjz/+OA8//DA//vGPefjhh/niF7+42U3aMB/72Md45pln2LNnz2Y3ZUMpisKf/Mmf8OMf/5gXX3yRffv28Q//8A+b3awlSRDYIB/96EcxDAOA22+/ndHRUXzf3+RWbZwbb7yRI0eOoKo7/59coVDg7Nmz7YB34sQJzp49uy2eCjvhfe97H4ODg5vdjA3X09PDBz7wgfbvb7/9doaHhzexRcuz8/8fuQU988wz/N7v/V5XDIjdaGRkhIGBATRNA0DTNPr7+xkZGdnklomN4vs+zz77LHfeeedmN2VJ266U9Fb1wAMPLBj1X3311faA8NJLL/Hiiy/yzDPPbGTz1t1y+y9EN/jbv/1b4vE4f/AHf7DZTVmSBIEOef7555e85ic/+QlPPfUU3/nOd+jr69uAVm2c5fS/WwwODjI2NobneWiahud5jI+Pd+UUSTc6deoUFy9e5Jvf/Oa2eNvf+i3cIf793/+dv/u7v+Nb3/oWe/fu3ezmiHWUy+U4duwYp0+fBuD06dMcO3aMbDa7yS0T6+2rX/0qv/71r/n6179OJBLZ7OYsixwqs0E++MEPYhjGnIHgO9/5Dr29vZvYqo1z+vRpnnzyScrlMoZhEIvF+Pa3v82RI0c2u2nr4ty5czz22GOUy2XS6TSnTp3i0KFDm92sDfGVr3yFM2fOMDk5SW9vLz09Pbz00kub3ax19/bbb3PixAkOHDhANBoFYO/evXz961/f5JYtToKAEEJ0MZkOEkKILiZBQAghupgEASGE6GISBIQQootJEBBCiC4mQUAIIbqYBAEhhOhiEgSEEKKL/f8kMM8TomiosAAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def draw_vector(v0, v1, ax=None):\n", " ax = ax or plt.gca()\n", " arrowprops=dict(arrowstyle='->',\n", " linewidth=2,\n", " shrinkA=0, shrinkB=0)\n", " ax.annotate('', v1, v0, arrowprops=arrowprops)\n", "\n", "# plot data\n", "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n", "for length, vector in zip(pca.explained_variance_, pca.components_):\n", " v = vector * 3 * np.sqrt(length)\n", " draw_vector(pca.mean_, pca.mean_ + v)\n", "plt.axis('equal');" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "These vectors represent the principal axes of the data, and the length of the vector is an indication of how \"important\" that axis is in describing the distribution of the data—more precisely, it is a measure of the variance of the data when projected onto that axis.\n", "The projection of each data point onto the principal axes are the \"principal components\" of the data.\n", "\n", "If we plot these principal components beside the original data, we see the plots shown here:" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "![](figures/05.09-PCA-rotation.png)\n", "[figure source in Appendix](06.00-Figure-Code.ipynb#Principal-Components-Rotation)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This transformation from data axes to principal axes is an affine transformation, which basically means it is composed of a translation, rotation, and uniform scaling.\n", "\n", "While this algorithm to find principal components may seem like just a mathematical curiosity, it turns out to have very far-reaching applications in the world of machine learning and data exploration." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### PCA as dimensionality reduction\n", "\n", "Using PCA for dimensionality reduction involves zeroing out one or more of the smallest principal components, resulting in a lower-dimensional projection of the data that preserves the maximal data variance.\n", "\n", "Here is an example of using PCA as a dimensionality reduction transform:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "original shape: (200, 2)\n", "transformed shape: (200, 1)\n" ] } ], "source": [ "pca = PCA(n_components=1)\n", "pca.fit(X)\n", "X_pca = pca.transform(X)\n", "print(\"original shape: \", X.shape)\n", "print(\"transformed shape:\", X_pca.shape)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The transformed data has been reduced to a single dimension.\n", "To understand the effect of this dimensionality reduction, we can perform the inverse transform of this reduced data and plot it along with the original data:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_new = pca.inverse_transform(X_pca)\n", "plt.scatter(X[:, 0], X[:, 1], alpha=0.2)\n", "plt.scatter(X_new[:, 0], X_new[:, 1], alpha=0.8)\n", "plt.axis('equal');" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The light points are the original data, while the dark points are the projected version.\n", "This makes clear what a PCA dimensionality reduction means: the information along the least important principal axis or axes is removed, leaving only the component(s) of the data with the highest variance.\n", "The fraction of variance that is cut out (proportional to the spread of points about the line formed in this figure) is roughly a measure of how much \"information\" is discarded in this reduction of dimensionality.\n", "\n", "This reduced-dimension dataset is in some senses \"good enough\" to encode the most important relationships between the points: despite reducing the dimension of the data by 50%, the overall relationship between the data points are mostly preserved." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### PCA for visualization: Hand-written digits\n", "\n", "The usefulness of the dimensionality reduction may not be entirely apparent in only two dimensions, but becomes much more clear when looking at high-dimensional data.\n", "To see this, let's take a quick look at the application of PCA to the digits data we saw in [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb).\n", "\n", "We start by loading the data:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "(1797, 64)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.datasets import load_digits\n", "digits = load_digits()\n", "digits.data.shape" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Recall that the data consists of 8×8 pixel images, meaning that they are 64-dimensional.\n", "To gain some intuition into the relationships between these points, we can use PCA to project them to a more manageable number of dimensions, say two:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1797, 64)\n", "(1797, 2)\n" ] } ], "source": [ "pca = PCA(2) # project from 64 to 2 dimensions\n", "projected = pca.fit_transform(digits.data)\n", "print(digits.data.shape)\n", "print(projected.shape)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, ..., 8, 9, 8])" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digits.target" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4nli3bp1OnDih1tZWtba2aubMmXrzzTf1wAMPpDpawgoKClRWVqaTJ09K+v63O/39/Zo9e3aKkzmTdn/woaurS1u3btU333yjKVOmKBwO684770x1rIScP39eFRUVmjNnjiZM+P4vsxQVFamhoSHFybwVCoXU2Nio+fPnpzqKJ3p6elRTU6Ovv/5aWVlZevrpp7V06dJUx3Ik7QoOwDtpdYsOwFsUHDCMggOGUXDAMAoOGEbBAcMoOGAYBQcM+x+xB+Ug76KD7wAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "i=int(np.random.random()1797)\n", "plt.imshow(digits.data[i].reshape(8,8),cmap='Blues')\n", "digits.target[i]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., 14., 13., 1., 0., 0.],\n", " [ 0., 0., 4., 16., 11., 0., 0., 0.],\n", " [ 0., 0., 12., 16., 1., 0., 0., 0.],\n", " [ 0., 1., 15., 16., 14., 1., 0., 0.],\n", " [ 0., 4., 16., 12., 8., 12., 7., 0.],\n", " [ 0., 2., 15., 8., 0., 8., 16., 2.],\n", " [ 0., 0., 10., 14., 9., 15., 15., 1.],\n", " [ 0., 0., 1., 14., 16., 14., 2., 0.]])" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digits.data[i].reshape(8,8)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We can now plot the first two principal components of each point to learn about the data:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(projected[:, 0], projected[:, 1],\n", " c=digits.target, edgecolor='none', alpha=0.5,\n", " cmap=plt.cm.get_cmap('Spectral', 10))\n", "plt.xlabel('component 1')\n", "plt.ylabel('component 2')\n", "plt.colorbar();" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Recall what these components mean: the full data is a 64-dimensional point cloud, and these points are the projection of each data point along the directions with the largest variance.\n", "Essentially, we have found the optimal stretch and rotation in 64-dimensional space that allows us to see the layout of the digits in two dimensions, and have done this in an unsupervised manner—that is, without reference to the labels." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### What do the components mean?\n", "\n", "We can go a bit further here, and begin to ask what the reduced dimensions mean.\n", "This meaning can be understood in terms of combinations of basis vectors.\n", "For example, each image in the training set is defined by a collection of 64 pixel values, which we will call the vector $x$:\n", "\n", "$$\n", "x = [x_1, x_2, x_3 \\cdots x_{64}]\n", "$$\n", "\n", "One way we can think about this is in terms of a pixel basis.\n", "That is, to construct the image, we multiply each element of the vector by the pixel it describes, and then add the results together to build the image:\n", "\n", "$$\n", "{\\rm image}(x) = x_1 \\cdot{\\rm (pixel~1)} + x_2 \\cdot{\\rm (pixel~2)} + x_3 \\cdot{\\rm (pixel~3)} \\cdots x_{64} \\cdot{\\rm (pixel~64)}\n", "$$\n", "\n", "One way we might imagine reducing the dimension of this data is to zero out all but a few of these basis vectors.\n", "For example, if we use only the first eight pixels, we get an eight-dimensional projection of the data, but it is not very reflective of the whole image: we've thrown out nearly 90% of the pixels!" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "![](figures/05.09-digits-pixel-components.png)\n", "[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-Pixel-Components)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The upper row of panels shows the individual pixels, and the lower row shows the cumulative contribution of these pixels to the construction of the image.\n", "Using only eight of the pixel-basis components, we can only construct a small portion of the 64-pixel image.\n", "Were we to continue this sequence and use all 64 pixels, we would recover the original image." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "But the pixel-wise representation is not the only choice of basis. We can also use other basis functions, which each contain some pre-defined contribution from each pixel, and write something like\n", "\n", "$$\n", "image(x) = {\\rm mean} + x_1 \\cdot{\\rm (basis~1)} + x_2 \\cdot{\\rm (basis~2)} + x_3 \\cdot{\\rm (basis~3)} \\cdots\n", "$$\n", "\n", "PCA can be thought of as a process of choosing optimal basis functions, such that adding together just the first few of them is enough to suitably reconstruct the bulk of the elements in the dataset.\n", "The principal components, which act as the low-dimensional representation of our data, are simply the coefficients that multiply each of the elements in this series.\n", "This figure shows a similar depiction of reconstructing this digit using the mean plus the first eight PCA basis functions:" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "![](figures/05.09-digits-pca-components.png)\n", "[figure source in Appendix](06.00-Figure-Code.ipynb#Digits-PCA-Components)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Unlike the pixel basis, the PCA basis allows us to recover the salient features of the input image with just a mean plus eight components!\n", "The amount of each pixel in each component is the corollary of the orientation of the vector in our two-dimensional example.\n", "This is the sense in which PCA provides a low-dimensional representation of the data: it discovers a set of basis functions that are more efficient than the native pixel-basis of the input data." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Choosing the number of components\n", "\n", "A vital part of using PCA in practice is the ability to estimate how many components are needed to describe the data.\n", "This can be determined by looking at the cumulative explained variance ratio* as a function of the number of components:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pca = PCA().fit(digits.data)\n", "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n", "plt.xlabel('number of components')\n", "plt.ylabel('cumulative explained variance');" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This curve quantifies how much of the total, 64-dimensional variance is contained within the first $N$ components.\n", "For example, we see that with the digits the first 10 components contain approximately 75% of the variance, while you need around 50 components to describe close to 100% of the variance.\n", "\n", "Here we see that our two-dimensional projection loses a lot of information (as measured by the explained variance) and that we'd need about 20 components to retain 90% of the variance. Looking at this plot for a high-dimensional dataset can help you understand the level of redundancy present in multiple observations." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## PCA as Noise Filtering\n", "\n", "PCA can also be used as a filtering approach for noisy data.\n", "The idea is this: any components with variance much larger than the effect of the noise should be relatively unaffected by the noise.\n", "So if you reconstruct the data using just the largest subset of principal components, you should be preferentially keeping the signal and throwing out the noise.\n", "\n", "Let's see how this looks with the digits data.\n", "First we will plot several of the input noise-free data:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x288 with 40 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_digits(data):\n", " fig, axes = plt.subplots(4, 10, figsize=(10, 4),\n", " subplot_kw={'xticks':[], 'yticks':[]},\n", " gridspec_kw=dict(hspace=0.1, wspace=0.1))\n", " for i, ax in enumerate(axes.flat):\n", " ax.imshow(data[i].reshape(8, 8),\n", " cmap='binary', interpolation='nearest',\n", " clim=(0, 16))\n", "plot_digits(digits.data)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now lets add some random noise to create a noisy dataset, and re-plot it:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x288 with 40 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(42)\n", "noisy = np.random.normal(digits.data, 4)\n", "plot_digits(noisy)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "It's clear by eye that the images are noisy, and contain spurious pixels.\n", "Let's train a PCA on the noisy data, requesting that the projection preserve 50% of the variance:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca = PCA(0.50).fit(noisy)\n", "pca.n_components_" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Here 50% of the variance amounts to 12 principal components.\n", "Now we compute these components, and then use the inverse of the transform to reconstruct the filtered digits:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x288 with 40 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "components = pca.transform(noisy)\n", "filtered = pca.inverse_transform(components)\n", "plot_digits(filtered)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This signal preserving/noise filtering property makes PCA a very useful feature selection routine—for example, rather than training a classifier on very high-dimensional data, you might instead train the classifier on the lower-dimensional representation, which will automatically serve to filter out random noise in the inputs." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Example: Eigenfaces\n", "\n", "Earlier we explored an example of using a PCA projection as a feature selector for facial recognition with a support vector machine (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n", "Here we will take a look back and explore a bit more of what went into that.\n", "Recall that we were using the Labeled Faces in the Wild dataset made available through Scikit-Learn:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush'\n", " 'Gerhard Schroeder' 'Hugo Chavez' 'Junichiro Koizumi' 'Tony Blair']\n", "(1348, 62, 47)\n" ] } ], "source": [ "from sklearn.datasets import fetch_lfw_people\n", "faces = fetch_lfw_people(min_faces_per_person=60)\n", "print(faces.target_names)\n", "print(faces.images.shape)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Let's take a look at the principal axes that span this dataset.\n", "Because this is a large dataset, we will use ``RandomizedPCA``—it contains a randomized method to approximate the first $N$ principal components much more quickly than the standard ``PCA`` estimator, and thus is very useful for high-dimensional data (here, a dimensionality of nearly 3,000).\n", "We will take a look at the first 150 components:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "PCA(copy=True, iterated_power='auto', n_components=150, random_state=None,\n", " svd_solver='auto', tol=0.0, whiten=False)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# from sklearn.decomposition import RandomizedPCA\n", "from sklearn.decomposition import PCA as RandomizedPCA\n", "pca = RandomizedPCA(150)\n", "pca.fit(faces.data)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "In this case, it can be interesting to visualize the images associated with the first several principal components (these components are technically known as \"eigenvectors,\"\n", "so these types of images are often called \"eigenfaces\").\n", "As you can see in this figure, they are as creepy as they sound:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 648x288 with 24 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(3, 8, figsize=(9, 4),\n", " subplot_kw={'xticks':[], 'yticks':[]},\n", " gridspec_kw=dict(hspace=0.1, wspace=0.1))\n", "for i, ax in enumerate(axes.flat):\n", " ax.imshow(pca.components_[i].reshape(62, 47), cmap='bone')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The results are very interesting, and give us insight into how the images vary: for example, the first few eigenfaces (from the top left) seem to be associated with the angle of lighting on the face, and later principal vectors seem to be picking out certain features, such as eyes, noses, and lips.\n", "Let's take a look at the cumulative variance of these components to see how much of the data information the projection is preserving:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n", "plt.xlabel('number of components')\n", "plt.ylabel('cumulative explained variance');" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We see that these 150 components account for just over 90% of the variance.\n", "That would lead us to believe that using these 150 components, we would recover most of the essential characteristics of the data.\n", "To make this more concrete, we can compare the input images with the images reconstructed from these 150 components:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Compute the components and projected faces\n", "pca = RandomizedPCA(150).fit(faces.data)\n", "components = pca.transform(faces.data)\n", "projected = pca.inverse_transform(components)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x180 with 20 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the results\n", "fig, ax = plt.subplots(2, 10, figsize=(10, 2.5),\n", " subplot_kw={'xticks':[], 'yticks':[]},\n", " gridspec_kw=dict(hspace=0.1, wspace=0.1))\n", "for i in range(10):\n", " ax[0, i].imshow(faces.data[i].reshape(62, 47), cmap='binary_r')\n", " ax[1, i].imshow(projected[i].reshape(62, 47), cmap='binary_r')\n", " \n", "ax[0, 0].set_ylabel('full-dim\\ninput')\n", "ax[1, 0].set_ylabel('150-dim\\nreconstruction');" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The top row here shows the input images, while the bottom row shows the reconstruction of the images from just 150 of the ~3,000 initial features.\n", "This visualization makes clear why the PCA feature selection used in [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) was so successful: although it reduces the dimensionality of the data by nearly a factor of 20, the projected images contain enough information that we might, by eye, recognize the individuals in the image.\n", "What this means is that our classification algorithm needs to be trained on 150-dimensional data rather than 3,000-dimensional data, which depending on the particular algorithm we choose, can lead to a much more efficient classification." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Principal Component Analysis Summary\n", "\n", "In this section we have discussed the use of principal component analysis for dimensionality reduction, for visualization of high-dimensional data, for noise filtering, and for feature selection within high-dimensional data.\n", "Because of the versatility and interpretability of PCA, it has been shown to be effective in a wide variety of contexts and disciplines.\n", "Given any high-dimensional dataset, I tend to start with PCA in order to visualize the relationship between points (as we did with the digits), to understand the main variance in the data (as we did with the eigenfaces), and to understand the intrinsic dimensionality (by plotting the explained variance ratio).\n", "Certainly PCA is not useful for every high-dimensional dataset, but it offers a straightforward and efficient path to gaining insight into high-dimensional data.\n", "\n", "PCA's main weakness is that it tends to be highly affected by outliers in the data.\n", "For this reason, many robust variants of PCA have been developed, many of which act to iteratively discard data points that are poorly described by the initial components.\n", "Scikit-Learn contains a couple interesting variants on PCA, including ``RandomizedPCA`` and ``SparsePCA``, both also in the ``sklearn.decomposition`` submodule.\n", "``RandomizedPCA``, which we saw earlier, uses a non-deterministic method to quickly approximate the first few principal components in very high-dimensional data, while ``SparsePCA`` introduces a regularization term (see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)) that serves to enforce sparsity of the components.\n", "\n", "In the following sections, we will look at other unsupervised learning methods that build on some of the ideas of PCA." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "<!--NAVIGATION-->\n", "< [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb) \| [Contents](Index.ipynb) \| [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb) >\n", "\n", "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.09-Principal-Component-Analysis.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n" ] } ], "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
KrisCheng/ML-Learning	archive/MOOC/Deeplearning_AI/NeuralNetworksandDeepLearning/Vectorization.ipynb	1	6580	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2 3 4]\n" ] } ], "source": [ "import numpy as np\n", "a = np.array([1,2,3,4])\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vectorized version: 1.1131763458251953 ms\n", "For loop version: 456.8037986755371 ms\n" ] } ], "source": [ "import time\n", "\n", "a = np.random.rand(1000000)\n", "b = np.random.rand(1000000)\n", "\n", "tic = time.time()\n", "c = np.dot(a,b)\n", "toc = time.time()\n", "print(\"Vectorized version: \" + str(1000(toc-tic)) + \" ms\")\n", "\n", "c = 0\n", "tic = time.time()\n", "for i in range(1000000):\n", " c += a[i]b[i]\n", "toc = time.time()\n", "print(\"For loop version: \" + str(1000(toc-tic)) + \" ms\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 56. 0. 4.4 68. ]\n", " [ 1.2 104. 53. 8. ]\n", " [ 1.8 135. 99. 0.9]]\n" ] } ], "source": [ "A = np.array([[56.0, 0.0, 4.4, 68.0],\n", " [1.2, 104.0, 52.0, 8.0],\n", " [1.8, 135.0, 99.0, 0.9]])\n", "print(A)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 59. 239. 156.4 76.9]\n" ] } ], "source": [ "cal = A.sum(axis = 0)\n", "print(cal)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 94.91525424 0. 2.81329923 88.42652796]\n", " [ 2.03389831 43.51464435 33.88746803 10.40312094]\n", " [ 3.05084746 56.48535565 63.29923274 1.17035111]]\n" ] } ], "source": [ "percentage = 100A/cal.reshape(1,4)\n", "print(percentage)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.80643159]\n", " [ 0.16739576]\n", " [-0.64283226]\n", " [-0.71635633]\n", " [-0.78895899]]\n" ] } ], "source": [ "a = np.random.randn(5,1)\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5, 1)\n" ] } ], "source": [ "print(a.shape)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.80643159 0.16739576 -0.64283226 -0.71635633 -0.78895899]]\n" ] } ], "source": [ "print(a.T)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.65033191 0.13499323 -0.51840025 -0.57769238 -0.63624145]\n", " [ 0.13499323 0.02802134 -0.1076074 -0.11991501 -0.13206839]\n", " [-0.51840025 -0.1076074 0.41323332 0.46049696 0.50716829]\n", " [-0.57769238 -0.11991501 0.46049696 0.51316639 0.56517577]\n", " [-0.63624145 -0.13206839 0.50716829 0.56517577 0.62245629]]\n" ] } ], "source": [ "print(np.dot(a,a.T))" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1.89047446 1.81516654 -0.55663873 0.40002315]\n", " [-1.00239767 0.37057919 -0.78413048 0.38743639]\n", " [ 0.14915671 -0.11771435 -3.50683681 2.27243449]]\n", "[[ 1.89047446 1.81516654 -0.55663873 0.40002315]\n", " [-1.00239767 0.37057919 -0.78413048 0.38743639]\n", " [ 0.14915671 -0.11771435 -3.50683681 2.27243449]]\n" ] } ], "source": [ "a = np.random.randn(3,4)\n", "b = np.random.randn(4,1)\n", "c = np.zeros([3,4])\n", "# for loop\n", "for i in range(3):\n", " for j in range(4):\n", " c[i][j] = a[i][j] + b[j]\n", "print(c)\n", "\n", "# vectorization\n", "c = np.zeros([3,4])\n", "c = a + b.T\n", "print(c)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1.08871671 0.2307551 0.77398578]\n", " [-1.07901086 -1.02137179 1.42280655]\n", " [-0.62845197 0.61533219 0.87603158]]\n", "[[-0.43546332]\n", " [ 0.73977139]\n", " [-0.8067938 ]]\n", "[[-0.4740962 -0.10048538 -0.33704242]\n", " [-0.79822136 -0.75558162 1.05255158]\n", " [ 0.50703115 -0.49644619 -0.70677685]]\n" ] } ], "source": [ "a = np.random.randn(3, 3)\n", "b = np.random.randn(3, 1)\n", "c = ab\n", "print(a)\n", "print(b)\n", "print(c)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.02217948]\n", " [ 0.14539074]\n", " [ 0.1044169 ]\n", " ..., \n", " [-1.91844572]\n", " [ 0.50626723]\n", " [ 0.14971706]]\n" ] } ], "source": [ "a = np.random.randn(32,32,3)\n", "x = a.reshape((3232*3,1))\n", "print(x)" ] }, { "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": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
wcmckee/ece-display	education-counts-url.ipynb	1	5330	{ "cells": [ { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import bs4\n", "import requests\n", "import dominate\n", "from dominate.tags import *\n", "import os\n", "import shutil\n", "import json\n", "from urlparse import urlparse\n", "from bs4 import BeautifulSoup" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#eceall = requests.get('')" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "opind = open('/media/removable/WILLIAMS30G/www.educationcounts.govt.nz/index.html', 'r')" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optfz = opind.read()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "edcountz = bs4.BeautifulSoup(optfz)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "afind = edcountz.findAll('a')" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gidict = dict()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for afi in afind:\n", " gied = afi.text\n", " gifz = gied.replace(' ', '-')\n", " giflow = gifz.lower()\n", " gidict.update({giflow[:12] : afi.attrs['href']})\n", " \n", " gihrd = urlparse(afi.attrs['href'])\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [], "source": [ "doc = dominate.document(title='educount')\n", "\n", "with doc.head:\n", " link(rel='stylesheet', href='style.css')\n", " script(type='text/javascript', src='script.js')\n", "\n", "with doc:\n", " #with div(id='header').add(ol()):\n", " #for i in ['home', 'about', 'contact']:\n", " #li(a(i.title(), href='/%s.html' % i))\n", "\n", " with div(cls='row'):\n", " h1('education-counts-url')\n", " a(dominate.tags.h3('home'), href = ('http://wcmckee.com/educount/'))\n", " a(dominate.tags.h3('excel'), href = ('http://wcmckee.com/educount/excel.html'))\n", "\n", " for afi in afind:\n", " if 'https://' in afi.attrs['href']:\n", " gied = afi.text\n", " gifz = gied.replace(' ', '-')\n", " giflow = gifz.lower()\n", " a(dominate.tags.p(giflow[:12]), href = (afi.attrs['href']))\n", " #print afi.attrs['href']\n", " #p(edu.txt)\n", " #dominate.tags.p(edu.attrs['href'])\n", " #a(dominate.tags.p(edu.text), href=dominate.tags.a(edu.attrs['href']))\n", " \n", " #print edu.text\n", "\n", "#print doc" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [], "source": [ "docre = doc.render()\n", "#s = docre.decode('ascii', 'ignore')\n", "yourstring = docre.encode('ascii', 'ignore').decode('ascii')\n", "indfil = ('/media/removable/WILLIAMS30G/education-counts-url/index.html')\n", "mkind = open(indfil, 'w')\n", "mkind.write(yourstring)\n", "mkind.close()" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dicon = json.dumps(gidict)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "opeducon = open('/media/removable/WILLIAMS30G/education-counts-url/index.json', 'w')\n", "\n", "opeducon.write(dicon)\n", "\n", "opeducon.close()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rdeducon = open('/media/removable/WILLIAMS30G/education-counts-url/index.json', 'r')\n", "\n", "rdqa = rdeducon.read()" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [], "source": [ "loadic = json.loads(rdqa)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "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 }	mit
TheTrappist/tracking-and-frap	notebooks/07_Clustering_analysis_diff_treatments.ipynb	1	20178	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Figure(s) in the manuscript created by this notebook: Supp. Fig. 4\n", "\n", "This notebook extracts cluster data from a CellProfiler .csv output and plots various cluster and cell parameters over a timecourse of stress. In this dataset, stress is induced by different chemical treatments and the notebook compares between treatments." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# User-defined parameters for analysis:\n", "\n", "# Plotting and figure saving params\n", "save_figs = True\n", "save_dir = '../reports/figures/FigS4_Fixed_cell_clusters_diffTreatments'\n", "plot_settings = '../src/plotting_settings.py'\n", "\n", "# Source data metadata\n", "# CellProfiler outputs everything in pixels. Input size of pixel in microns\n", "pixel_size = 0.206 # um per pixel\n", "\n", "# Source data location\n", "data_dir = '../data/processed/FigS4_Fixed_cell_clusters_diffTreatments/csv_outputs'\n", "\n", "image_file_csv = 'FociQuant10_Image.csv'\n", "er_masks_csv = 'FociQuant10_ER_masks_accepted.csv'\n", "ire1_clust_csv = 'FociQuant10_Clusters_in_ER_masks_masked.csv'\n", "\n", "nuclei_all_csv = 'FociQuant10_Nuclei_all.csv'\n", "er_masks_all_csv = 'FociQuant10_ER_masks_all.csv'\n", "\n", "nuclei_accepted_csv = 'FociQuant10_Nuclei_accepted.csv'\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# load modules\n", "\n", "# uncomment for debugging\n", "\"\"\"\n", "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", "from IPython.core.debugger import set_trace\n", "\"\"\"\n", "\n", "import os, sys, inspect\n", "import matplotlib\n", "import matplotlib.pylab as plt\n", "import numpy as np\n", "import pandas as pd\n", "from scipy import stats\n", "import pprint\n", "import re\n", "import time\n", "import seaborn as sns\n", "import warnings\n", "\n", "# Disable future warnings for seaborn\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "\n", "\n", "# Add source code directory (src) to path to enable module import\n", "module_dir = '../src'\n", "os.sys.path.insert(0, module_dir)\n", "\n", "import cellprofiler_tools as cpt\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set up figure save dirs and load plotting style\n", "if save_figs:\n", " %matplotlib\n", " %run $plot_settings save\n", " \n", " # Make directory for saving figures\n", " save_dir_pdf = os.path.join(save_dir, 'pdf')\n", " if not os.path.exists(save_dir_pdf):\n", " os.makedirs(save_dir_pdf)\n", "else:\n", " %matplotlib inline\n", " %run $plot_settings plot_only" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Load data from CSV files\n", "\n", "# Load the image file\n", "image_full_file = os.path.join(data_dir, image_file_csv)\n", "images = cpt.get_data_cp_csv(image_full_file)\n", "#images = cpt.get_data_cp_csv(image_full_file, data_fields=['ImageNumber','FileName_DNA_DAPI'])\n", "\n", "er_masks = cpt.get_data_cp_csv(os.path.join(data_dir, er_masks_csv))\n", "ire1_clust = cpt.get_data_cp_csv(os.path.join(data_dir, ire1_clust_csv))\n", "\n", "nuclei_all = cpt.get_data_cp_csv(os.path.join(data_dir, nuclei_all_csv))\n", "er_masks_all = cpt.get_data_cp_csv(os.path.join(data_dir, er_masks_all_csv))\n", "nuclei_accepted = cpt.get_data_cp_csv(os.path.join(data_dir, nuclei_accepted_csv))\n", "\n", "\n", "print('Loaded')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create a dataframe for all cells that are included in the analysis\n", "cells = er_masks.copy()\n", "cells.index.name = 'Cell_ID'\n", "\n", "result_name = 'Intensity_IntegratedIntensity_Corr_mNeonGreen'\n", "#result_name = 'Intensity_MeanIntensity_IRE1_mNeonGreen'\n", "#result_name = 'Intensity_StdIntensity_IRE1_mNeonGreen'\n", "\n", "condition = 'Metadata_hrs_stress'\n", "condition2 = 'Metadata_condition'\n", "condition_hrs = 'Condition_hrs'\n", "cpt.add_image_prop_to_objects (cells, images, condition)\n", "cpt.add_image_prop_to_objects (cells, images, condition2)\n", "\n", "# Combine the stress condition and stress duration into a single string column\n", "cells['Stress_hrs_str'] = cells[condition].astype(str).str.zfill(4)\n", "images['Stress_hrs_str'] = images[condition].astype(str).str.zfill(4)\n", "cells[condition_hrs] = cells[condition2].astype(str)+'_'+cells['Stress_hrs_str']\n", "images[condition_hrs] = images[condition2].astype(str)+'_'+images['Stress_hrs_str']\n", "\n", "fig, ax = plt.subplots()\n", "fig.tight_layout(pad=2)\n", "\n", "order = sorted(cells[condition_hrs].unique())\n", "ax = sns.boxplot(x=condition_hrs, y=result_name, data=cells, color='steelblue', \n", " showfliers=False, ax=ax, order=order)\n", "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n", "ax.set_title(result_name)\n", "ax.set_xlabel(condition_hrs)\n", "ax.set_ylabel(result_name)\n", "ax.set_ylim(bottom=0)\n", "\n", "if save_figs:\n", " fig_filename_pdf = os.path.join(save_dir_pdf, 'Cell_Intensity_vs_condition.pdf')\n", " plt.savefig(fig_filename_pdf)\n", "\n", "plt.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Plot fraction of cells with clusters per condition\n", "condition = 'Condition_hrs'\n", "\n", "#cells['Has_IRE1_clusters'] = cells['Children_IRE1_clusters_Count'].astype('bool')\n", "cells['Has_IRE1_clusters'] = cells['Children_Clusters_in_ER_masks_masked_Count'].astype('bool')\n", "\n", "excluded_conditions = []\n", "cells_filt = cells.copy()\n", "for cond in excluded_conditions:\n", " cells_filt = cells_filt.loc[cells_filt[condition] != cond, :]\n", " order_filt = sorted(cells[condition_hrs].unique())\n", "\n", "\n", "frac_clust = cpt.bootstrap_cell_prop (cells_filt, 'Has_IRE1_clusters', condition)\n", "\n", "fig, ax = plt.subplots()\n", "fig.tight_layout(pad=2)\n", "\n", "ax = sns.barplot(data=frac_clust, color='steelblue', ci=\"sd\", order=order)\n", "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n", "ax.set_title('Fraction of cells with clusters over time')\n", "ax.set_xlabel('Hours of Tm treatment')\n", "ax.set_ylabel('Fraction of cells with clusters')\n", "ax.set_ylim(bottom=0)\n", "\n", "if save_figs:\n", " fig_filename_pdf = os.path.join(save_dir_pdf, 'Fraction_cell_with_clusters.pdf')\n", " plt.savefig(fig_filename_pdf)\n", "\n", "plt.show()\n", "print(len(frac_clust))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot cluster area distribution over conditions\n", "condition = 'Condition_hrs'\n", "result_name = 'AreaShape_Area'\n", "#result_name2 = 'Intensity_IntegratedIntensity_IRE1_mNeonGreen'\n", "#result_name = 'Intensity_IntegratedIntensity_IRE1_mNeonGreen'\n", "cpt.add_image_prop_to_objects (ire1_clust, images, condition)\n", "\n", "excluded_conditions = []\n", "\n", "clust_filt = ire1_clust.copy()\n", "for cond in excluded_conditions:\n", " clust_filt = clust_filt.loc[clust_filt[condition] != cond, :]\n", "\n", "result_name_microns = 'Cluster_area_um2'\n", "pixel_area = pixel_size*2\n", "clust_filt[result_name_microns] = clust_filt[result_name] pixel_area\n", "\n", "\n", "fig, ax = plt.subplots()\n", "fig.tight_layout(pad=2)\n", "\n", "order_clust = sorted(clust_filt[condition_hrs].unique())\n", "ax = sns.barplot(x=condition, y=result_name_microns, data=clust_filt, \n", " color='steelblue', ci=68, order=order_clust)\n", "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n", "\n", "if save_figs:\n", " fig_filename_pdf = os.path.join(save_dir_pdf, 'Cluster_areas_vs_timepoint.pdf')\n", " plt.savefig(fig_filename_pdf)\n", "\n", "plt.show()\n", "print(len(clust_filt))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Relate clusters to nuclei and add nuclear geometry params to clusters\n", "\n", "# Relate clusters to nuclei\n", "prop = 'ObjectNumber'\n", "rel_col = 'Parent_Nuclei_all'\n", "n = 'Nuclei_Accepted_ObjID'\n", "cpt.add_child_prop_to_parents (nuclei_all, nuclei_accepted, prop, rel_col, n)\n", "\n", "cpt.add_parent_prop(er_masks_all, nuclei_all, n, 'Parent_Nuclei_all', n)\n", "cpt.add_parent_prop(er_masks, er_masks_all, n, 'Parent_ER_masks_all', n)\n", "cpt.add_parent_prop(ire1_clust, er_masks, n, 'Parent_ER_masks_accepted', n)\n", "\n", "#Add nucleus coordinates and radii to clusters\n", "props = ['AreaShape_Center_X',\n", " 'AreaShape_Center_Y',\n", " 'AreaShape_MeanRadius']\n", "props_mod = []\n", "for prop in props:\n", " result_name = prop + '_Nucleus'\n", " cpt.add_parent_prop(ire1_clust, nuclei_accepted, prop, n, result_name)\n", " props_mod.append(result_name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Calculate and plot cluster to nucleus distances\n", "condition = 'Condition_hrs'\n", "result_1 = 'Dist_to_Nucleus_Edge'\n", "result_2 = 'AreaShape_Area'\n", "\n", "n_x = ire1_clust['AreaShape_Center_X_Nucleus']\n", "n_y = ire1_clust['AreaShape_Center_Y_Nucleus']\n", "c_x = ire1_clust['AreaShape_Center_X']\n", "c_y = ire1_clust['AreaShape_Center_Y']\n", "\n", "d = np.sqrt(np.square(n_x-c_x)+np.square(n_y-c_y))\n", "ire1_clust['Dist_to_Nucleus_Center'] = d\n", "ire1_clust['Dist_to_Nucleus_Edge'] = d - ire1_clust['AreaShape_MeanRadius_Nucleus']\n", "\n", "excluded_conditions = [0, 32]\n", "\n", "clust_filt = ire1_clust.copy()\n", "for cond in excluded_conditions:\n", " clust_filt = clust_filt.loc[clust_filt[condition] != cond, :]\n", "\n", "fig, ax = plt.subplots()\n", "fig.tight_layout(pad=2)\n", "\n", "#ax = sns.swarmplot(x=condition, y=result_1, data=clust_filt, color=\".25\", size=1)\n", "ax = sns.boxplot(x=condition, y=result_1, data=clust_filt, showfliers=False,\n", " order=order_clust)\n", "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n", "\n", "if save_figs:\n", " fig_filename_pdf = os.path.join(save_dir_pdf, 'Cluster_to_nucleus_distance.pdf')\n", " plt.savefig(fig_filename_pdf)\n", "\n", "cond1 = 1\n", "cond2 = 2\n", "data1 = ire1_clust[ire1_clust[condition] == cond1]['Dist_to_Nucleus_Edge']\n", "data2 = ire1_clust[ire1_clust[condition] == cond2]['Dist_to_Nucleus_Edge']\n", "\n", "print(stats.ttest_ind(data1,data2, equal_var = False))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot cluster properties per cell\n", "\n", "#prop = 'AreaShape_Compactness'\n", "#prop = 'AreaShape_Area'\n", "prop = 'Intensity_IntegratedIntensity_Corr_mNeonGreen'\n", "stat='mean'\n", "\n", "result_name = 'IRE1_clust_'+prop+'_'+stat\n", "rel_col = 'Parent_ER_masks_accepted'\n", "condition = 'Condition_hrs'\n", "\n", "excluded_conditions = []\n", "cells_filt = cells.copy()\n", "for cond in excluded_conditions:\n", " cells_filt = cells_filt.loc[cells_filt[condition] != cond, :]\n", "\n", "\n", "cpt.add_child_prop_to_parents (cells_filt, ire1_clust, prop, rel_col, \n", " result_name, statistic=stat)\n", "cells_valid = cells_filt.dropna(subset=[result_name])\n", "\n", "fig, ax = plt.subplots()\n", "fig.tight_layout(pad=2)\n", "\n", "ax = sns.barplot(x=condition, y=result_name, data=cells_valid, \n", " color='steelblue', ci=68, order=order_clust)\n", "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n", "\n", "ax.set_title(result_name)\n", "ax.set_xlabel(condition)\n", "ax.set_ylabel(result_name)\n", "ax.set_ylim(bottom=0)\n", "plt.show()\n", "\n", "\n", "if save_figs:\n", " fig_filename_pdf = os.path.join(save_dir_pdf, 'Sum_cluster_intensity_per_cell.pdf')\n", " plt.savefig(fig_filename_pdf)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot fraction of IRE1 in clusters per cell\n", "\n", "prop_parent = 'Intensity_IntegratedIntensity_Corr_mNeonGreen'\n", "prop_child = 'Intensity_IntegratedIntensity_Corr_mNeonGreen'\n", "stat='sum'\n", "\n", "child_result = 'IRE1_clust_'+prop_child+'_'+stat\n", "rel_col = 'Parent_ER_masks_accepted'\n", "group_name = 'Condition_hrs'\n", "group_str = group_name + '_str'\n", "fraction_clust = 'Fraction_IRE1_in_clusters'\n", "\n", "\n", "cpt.add_child_prop_to_parents (cells, ire1_clust, prop_child, rel_col, \n", " child_result, statistic=stat)\n", "\n", "cells[fraction_clust] = cells[child_result] / cells[prop_parent]\n", "cells[fraction_clust].fillna(0, inplace=True)\n", "\n", "excluded_conditions = [-1]\n", "cells_filt = cells.copy()\n", "for cond in excluded_conditions:\n", " cells_filt = cells_filt.loc[cells_filt[condition] != cond, :]\n", "\n", "\n", "fig, ax = plt.subplots()\n", "fig.tight_layout(pad=2)\n", "\n", "ax = sns.barplot(x=group_name, y=fraction_clust, \n", " data=cells_filt, color='steelblue', ci=68, order=order_clust)\n", "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n", "\n", "#ax = sns.swarmplot(x=group_name, y=fraction_clust, data=cells, color=\".25\")\n", "\n", "ax.set_title(fraction_clust)\n", "ax.set_xlabel(group_name)\n", "ax.set_ylabel(fraction_clust)\n", "plt.show()\n", "\n", "if save_figs:\n", " fig_filename_pdf = os.path.join(save_dir_pdf, 'Fraction_IRE1_in_clusters.pdf')\n", " plt.savefig(fig_filename_pdf)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot number of cluster per cell for cells that have clusters\n", "\n", "result_name = 'Children_Clusters_in_ER_masks_masked_Count'\n", "#result_name = 'Intensity_IntegratedIntensity_IRE1_mNeonGreen'\n", "condition = 'Condition_hrs'\n", "\n", "excluded_conditions = [-1]\n", "cells_filt = cells.copy()\n", "for cond in excluded_conditions:\n", " cells_filt = cells_filt.loc[cells_filt[condition] != cond, :]\n", "\n", "\n", "cells_valid = cells_filt.dropna(subset=[result_name])\n", "cells_valid = cells_valid.loc[cells_valid[result_name] > 0]\n", "\n", "fig, ax = plt.subplots()\n", "fig.tight_layout(pad=2)\n", "\n", "ax = sns.barplot(x=condition, y=result_name, data=cells_valid, \n", " color='steelblue', ci=68 , order=order_clust)\n", "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n", "ax.set_title(result_name)\n", "ax.set_xlabel(condition)\n", "ax.set_ylabel(result_name)\n", "ax.set_ylim(bottom=0)\n", "plt.show()\n", "\n", "if save_figs:\n", " fig_filename_pdf = os.path.join(save_dir_pdf, \n", " 'Number_IRE1_clusters_per_cell_with_clusters.pdf')\n", " plt.savefig(fig_filename_pdf)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Select a subset of conditions for generating sub-plots\n", "\n", "# Plot fraction of cells with clusters per condition\n", "condition = 'Condition_hrs'\n", "\n", "\n", "included_conditions = ['nodrug_0nan', 'Tg_04.0', 'Tg_08.0', 'Tg_12.0', 'Tg_24.0']\n", "cells_subset = pd.DataFrame()\n", "for cond in included_conditions:\n", " cells_in_cond = cells.loc[cells[condition] == cond, :]\n", " cells_subset = cells_subset.append(cells_in_cond)\n", "\n", "# Rename the no-stress condition to be alphabetically first\n", "cells_subset.replace({'nodrug_0nan':'0_no-stress'}, inplace=True)\n", "\n", "order_subset = sorted(cells_subset[condition].unique())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Plot fraction of cells with IRE1 clusters for the selected subset of conditions\n", "\n", "frac_clust = cpt.bootstrap_cell_prop (cells_subset, 'Has_IRE1_clusters', condition)\n", "\n", "fig, ax = plt.subplots()\n", "fig.tight_layout(pad=2)\n", "\n", "ax = sns.barplot(data=frac_clust, color='steelblue', ci=\"sd\", order=order_subset)\n", "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n", "ax.set_title('Fraction of cells with clusters over time')\n", "ax.set_xlabel('Hours of Tm treatment')\n", "ax.set_ylabel('Fraction of cells with clusters')\n", "ax.set_ylim(bottom=0)\n", "\n", "if save_figs:\n", " fig_filename_pdf = os.path.join(save_dir_pdf, 'Fraction_cell_with_clusters_selected_subset.pdf')\n", " plt.savefig(fig_filename_pdf)\n", "\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot fraction of IRE1 in clusters per cell for the selected subset of conditions\n", "\n", "fig, ax = plt.subplots()\n", "fig.tight_layout(pad=2)\n", "\n", "ax = sns.barplot(x=group_name, y=fraction_clust, \n", " data=cells_subset, color='steelblue', ci=68, order=order_subset)\n", "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n", "\n", "#ax = sns.swarmplot(x=group_name, y=fraction_clust, data=cells, color=\".25\")\n", "\n", "ax.set_title(fraction_clust)\n", "ax.set_xlabel(group_name)\n", "ax.set_ylabel(fraction_clust)\n", "plt.show()\n", "\n", "if save_figs:\n", " fig_filename_pdf = os.path.join(save_dir_pdf, 'Fraction_IRE1_in_clusters_selected_subset.pdf')\n", " plt.savefig(fig_filename_pdf)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Get number of cells for each data point\n", "\n", "times = 'Condition_hrs'\n", "for time in cells[times].unique():\n", " cells_in_time = cells.loc[cells[times] == time]\n", " print('Cells in condition ', time, ': ', len(cells_in_time))\n" ] } ], "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": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
Alkxzv/categorical-kernels	notebooks/Synthetic.ipynb	1	49165	{ "metadata": { "name": "", "signature": "sha256:532e83581a7d723abd6c08160a008e94ec8778aef685a543a6521e80c542747c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Synthetic Dataset Overview" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn import cross_validation as cv\n", "from sklearn import svm\n", "\n", "plt.style.use('ggplot')\n", "\n", "from kcat.datasets import Synthetic" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Define parameters\n", "sizes = (50, 100, 200, 400)\n", "ps = np.linspace(0, 1, num=11)\n", "repeat = 150\n", "# Try all possible combinations\n", "results = np.zeros((len(sizes), len(ps), repeat))\n", "for i, m in enumerate(sizes):\n", " for j, p in enumerate(ps):\n", " print(\"{} {}\".format(m, p), end=', ')\n", " for k in range(repeat):\n", " # Generate a new dataset\n", " Xq, Xc, y = Synthetic(m, n=25, c=2, p=p).data_arrays\n", " clf = svm.SVC(kernel='rbf')\n", " results[i][j][k] = cv.cross_val_score(clf, Xc, y, cv=5).mean()\n", "# Invert results to show error rate instead of success rate\n", "results = 1.0 - results" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "50 0.0, 50 0.1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 50 0.2" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 50 0.30000000000000004" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 50 0.4" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 50 0.5" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 50 0.6000000000000001" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 50 0.7000000000000001" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 50 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"text": [ ", 400 0.1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 400 0.2" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 400 0.30000000000000004" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 400 0.4" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 400 0.5" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 400 0.6000000000000001" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 400 0.7000000000000001" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 400 0.8" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 400 0.9" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", 400 1.0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ", " ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot error\n", "figure(figsize=(10, 5))\n", "styles = (':', '-.', '--', '-')\n", "for i, m in enumerate(sizes):\n", " plot(ps, results[i].mean(axis=1), styles[i], linewidth=1.5, color=(0.7, 0.4, 0))\n", "xlabel(\"P\")\n", "ylabel(\"Error\")\n", "ylim(0, 0.5)\n", "legend([\"Size {}\".format(m) for m in sizes])\n", "# title(\"Classification Error using RBF Kernel\".format(m))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "<matplotlib.legend.Legend at 0x7f4ede379dd8>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Yu3Ytbt82D9mUnZ2NHTt2WLSW3ZWQkIANGzbgyy+/hFRa0+Haz88Pffr0wbx5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\ns9Ci1o6wsWEjofihr12snRiOwz8hf9PTcBzaDENgF0QNn4kuQ25oceP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"text": [ "<matplotlib.figure.Figure at 0x7f4ede404c50>" ] } ], "prompt_number": 3 } ], "metadata": {} } ] }	mit
surchs/Logbooks	sequential_clustering_and_pheno.ipynb	1	92759	{ "metadata": { "name": "", "signature": "sha256:033ec07c196bcfb16847821630737843458f44af5989d2a06e2a503e97551886" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Pull a test set of ABIDE subjects, read in each network for each metric, compute the distance matrix and linkage and then display selected phenotype variables for each cluster" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Imports\n", "import re\n", "import numpy as np\n", "import pandas as pd\n", "import brainbox as bb\n", "from matplotlib import pyplot as plt\n", "import scipy.cluster.hierarchy as clh" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Paths\n", "debug_path = '/data1/abide/Test/Out/Debug/All'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Find the data\n", "metric = 'stability_maps'\n", "file_dict = bb.fileOps.grab_files(debug_path, '.nii.gz', metric)\n", "# Get subject IDs of the files I just read in\n", "data_subs = np.array([int64(re.search(r'(?<=\\d{2})\\d{5}', sub_id).group()) for sub_id in file_dict['sub_name']])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "I will be pulling files from /data1/abide/Test/Out/Debug/All/stability_maps\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Read the data\n", "network = 1\n", "data_dict = bb.fileOps.read_files(file_dict, network=network)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "I found 65 files to load.\n", "\r", " 1.5 % done 2.97 seconds to go." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 3.1 % done 3.86 seconds to 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"output_type": "stream", "stream": "stdout", "text": [ "\r", " 93.8 % done 0.33 seconds to go." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 95.4 % done 0.25 seconds to go." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 96.9 % done 0.17 seconds to go." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 98.5 % done 0.08 seconds to go." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\r", " 100.0 % done 0.00 seconds to go." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "We are done\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Compute linkage on the data\n", "distance, linkage = bb.dataOps.calc_link(data_dict, metric, network=0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Visualize the distance matrix and dendrogram\n", "f = plt.figure(figsize=(8,8))\n", "subdend = f.add_axes([0, 0.71, 1, 0.29])\n", "D1 = clh.dendrogram(linkage, ax=subdend)\n", "subdend.set_xticks([])\n", "\n", "submat = f.add_axes([0, 0, 1, 0.7])\n", "D2 = submat.matshow(-distance, aspect='auto')\n", "submat.set_xticks([])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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pU1K7pBmSRkl6QNK44vJPS9rXw/WEnMqvaVxyEPmwjyQYWa+puztBffWasrYn664UH+r9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\n+qAr8/V6wmZWVXyV5du/vtBmJs3wVW7vnjjPZq6ses5mVne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"text": [ "<matplotlib.figure.Figure at 0x4982990>" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get the partition of the linkage\n", "scale = 3\n", "part = clh.fcluster(linkage, scale, criterion='maxclust')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "# Grab the phenotype data\n", "pheno_path = '/home/surchs/Project/abide/pheno/pheno_full.csv'\n", "pheno = pd.read_csv(pheno_path)\n", "# Get the subject IDs of the pheno files I just read in\n", "pheno_subs = pheno['SUB_ID']\n", "# Find a mask of those pheno subs for which we have brain data\n", "pheno_mask = pheno_subs.isin(data_subs)\n", "# Get the correct pheno data\n", "pheno_data = pheno[pheno_mask]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# Define covariates of interest\n", "cov_interest = ['DX_GROUP', 'AGE_AT_SCAN', 'SITE_ID', 'SEX', 'EYE_STATUS_AT_SCAN']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# Pull up the subjects for one cluster\n", "f = plt.figure(figsize=(30,10))\n", "\n", "for clust in np.arange(1,4):\n", " clust_subs = data_subs[part == clust]\n", " clust_pheno = pheno_data[part == clust]\n", " ax_cl = f.add_subplot(1, 3, clust)\n", " ax_cl.set_xticks([])\n", " ax_cl.set_title('Cluster {}'.format(clust))\n", "\n", " lt, lb, rt, rb = bb.visuOps.add_four_grid(ax_cl, ticks=True, titles=('age', 'sex', 'dx', 'fiq'))\n", " lt.hist(clust_pheno['AGE_AT_SCAN'].values)\n", " lb.hist(clust_pheno['SEX'].values)\n", " rt.hist(clust_pheno['DX_GROUP'].values)\n", " rb.hist(clust_pheno['FIQ'].values)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x8de9ad0>" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	gpl-3.0
iancze/CfAML	Linear_Classification/linear_classification.ipynb	1	793778	{ "metadata": { "name": "", "signature": "sha256:a78bd3b18cdaac70504ebf6ba36529ec791a5a178964b36d60873836d96b5fb0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Chapter 4: Linear Classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal of linear classification is to take an input vector, $\\mathbf{x}$ and assign it to one of K discrete classes, $C_k$ where $k = 1, ..., K$. \n", "The decision surfaces are linear functions of the input vector, $\\mathbf{x}$.\n", "Data sets whose classes can be separated exactly by linear decision surfaces are said to be 'linearly separable'." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Linearly separable... (hyperplane divides up the classes)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.random.seed(0)\n", "mu1 = [1, 1]\n", "cov1 = 0.9 * np.eye(2)\n", "mu2 = [5, 3]\n", "cov2 = np.eye(2) * np.array([0.4, 0.1])\n", "X = np.concatenate([np.random.multivariate_normal(mu1, cov1, 100),\n", " np.random.multivariate_normal(mu2, cov2, 100)])\n", "Y = np.zeros(200)\n", "y[100:] = 1\n", "fig = plt.figure(figsize=(5, 3.75))\n", "ax = fig.add_subplot(111)\n", "ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.binary, zorder=2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 60, "text": [ "<matplotlib.collections.PathCollection at 0x10bb76450>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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9GRxSqZT09PQoJiaGiIqdg9u2bVvpA+W1idjYWLK0tOTWoKRSKdnZ2dWKAJqf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\nAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x10a10da50>" ] } ], "prompt_number": 60 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not linearly separable..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('9NrALgHFwTo')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=300\"\n", " src=\"https://www.youtube.com/embed/9NrALgHFwTo\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "<IPython.lib.display.YouTubeVideo at 0x109965a50>" ] } ], "prompt_number": 61 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general, there are three different approaches to the classification problem:\n", " 1. Construct a discriminant function: assign each x to a class.\n", " \n", " OR Model the conditional probability distribution, $p(C_k\|x)$ and use this distribution to make decisions by either\n", " \n", " 2. modelling the conditional probabilities directly (optimise parametric model parameters with training data) OR\n", " 3. Adopt generative approach: model the class-conditional densities, $p(x\|C_k)$, together with the priors, $p(C_k)$ and compute posteriors." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For classification the general linear model can be written" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$y(x) = f(w^Tx + w_0),$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $f(.)$ is the activation function. Decision surfaces are linear functions of x (nonlinear in the parameters), even if the function $f(.)$ is nonlinear." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note, likelihood is the probability of the label given the parameters, class-conditional density is the probability of the data given the class." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#4.1 Discriminant Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>4.1.1 Two classes</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A discriminant is a function that takes an input vector ${\\bf x}$ and assigns is to one of $K$ classes, denoted $C_k$. Consider first the case of 2 classes. The simplest representation of a linear discriminant function is obtained by taking a linear function of the input vector so that " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$y(\\mathbf{x}) = \\mathbf{w}^T\\mathbf{x} + w_0,$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\mathbf(w)$ is the weight vector and $w_0$ is the bias. In this simplest case, an input vector, $\\mathbf{x}$ is assigned to class $C_1$ if $y(\\mathbf{x}) \\geq 0$ and to $C_2$ otherwise. So the decision boundary is defined by $y(\\mathbf{x}) = 0$. The vector $\\mathbf{w}$ defines the orientation of the decision surface and $w_0$ defines the the location:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\mathbf{w}^T(\\mathbf{x_A}-\\mathbf{x_b}) = 0,$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\frac{\\mathbf{w}^T\\mathbf{x}}{\|\|\\mathbf{w}\|\|} = - \\frac{w_0}{\|\|\\mathbf{w}\|\|}.$$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d.Image(filename=\"fig4.1.png\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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REscl86WKpUtkLsqekUuWD1OwV1RRIipNKd+CmWCmxqj2Ur1YI0xTTQtoPdbO3uGhI6bz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\nJ08KVtXGU+La0ccSf/pV477z7NWUS5LDxXPJmqq6KKVoKCN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REscl86WKpUtkLsqekUuWD1OwV1RRIipNKd+CmWCmxqj2Ur1YI0xTTQtoPdbO3uGhI6bz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\nPYz9YPY0CHrVssolfe98s8pQzVl1+d6R2ezAzU7LmqXLdLBjRxdNqSQ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NdXQXjl91BL7R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\n9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"prompt_number": 81, "text": [ "<IPython.core.display.Image at 0x104f2dd50>" ] } ], "prompt_number": 81 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is why you can't construct multi-class classifiers out of 2-class classifiers!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider a K-class discriminant comprising K linear functions of the form" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$y_k(\\mathbf{x}) = \\mathbf{w_k}^T\\mathbf{x} + w_{k0}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A point $\\mathbf{x}$ will have a class $C_k$ if $y_k(\\mathbf{x}) > y_j(\\mathbf{x})$ for all $j \\ne k$. \n", "The decision boundary between class $C_k$ and class $C_j$ is therefore given by $y_k(\\mathbf{x}) = y_j(\\mathbf{x})$ and hence corresponds to a $(D-1)$ dimensional hyperplane defined by " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$(\\mathbf{w_k} - \\mathbf{w_j})^T\\mathbf{x} + (w_{k0} - w_{j0}) = 0 $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>4.1.3 Least squares for classification</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Switch to vector notation:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\mathbf{y}(\\mathbf{x}) = \\tilde{\\mathbf{W}}^T\\tilde{\\mathbf{x}}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A new input, $\\mathbf{x}$ will be assigned to the class for which $y_k = \\mathbf{\\tilde{w}}_k^T\\mathbf{\\tilde{x}}$ is largest. $\\mathbf{\\tilde{W}}$ is determined by minimising a sum-of-squares error function. Consider a training data set $\\{\\mathbf{x}_n, \\mathbf{t}_n\\}$ where $n = 1,...,N$, and define a matrix $\\mathbf{T}$ whose $n^{th}$ row is the vector $\\mathbf{t}^T_n$ and a matrix $\\tilde{\\mathbf{X}}$ whose $n^{th}$ row is $\\mathbf{x}^T_n$. The sum-of-squares error function can be written" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$E_D(W) = \\frac{1}{2}Tr\\left\\{(\\tilde{\\mathbf{X}}\\tilde{\\mathbf{W}} - \\mathbf{T})^T(\\tilde{\\mathbf{X}}\\tilde{\\mathbf{W}} - \\mathbf{T})\\right\\}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maximum likelihood solution for $\\tilde{\\mathbf{W}}$:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\tilde{\\mathbf{W}} = (\\tilde{\\mathbf{X}}^T\\tilde{\\mathbf{X}})^{-1}\\tilde{\\mathbf{X}}^T\\mathbf{T} = \\tilde{\\mathbf{X}}^{\\dagger}T,$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and so the discriminant function can be written" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\mathbf{y}(\\mathbf{x}) = \\tilde{\\mathbf{W}}^T\\tilde{\\mathbf{x}} = \\mathbf{T}^T\\left(\\tilde{\\mathbf{X}}^{\\dagger}\\right)^T\\tilde{\\mathbf{x}}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>4.1.4 Fisher's Linear Discriminant</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Want to find a way to reduce the dimensionality of your data, i.e. project it down to one dimension. Need to find a projection that will minimise overlap between classes. Maximise mean separation, minimise within-class variance, i.e. maximise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$J(\\mathbf{w}) = \\frac{(m_1-m_2)^2}{s_1^2 + s_2^2}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$J(\\mathbf{w}) = \\frac{\\mathbf{w}^T\\mathbf{S}_b\\mathbf{w}}{\\mathbf{w}^T\\mathbf{S}_w\\mathbf{w}} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where $\\mathbf{S}_b$ is the between class covariance matrix and $\\mathbf{S}_w$ is the within class covariance matrix. The projection direction is given by 'Fisher's linear discriminant':" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$w \\propto S^{-1}_W(m_2 - m_1)$$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import IPython.display as d\n", "d.Image(filename=\"fig4.6.png\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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REscl86WKpUtkLsqekUuWD1OwV1RRIipNKd+CmWCmxqj2Ur1YI0xTTQtoPdbO3uGhI6bz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LnL+s/XP44tMHgG7YuMIQojyNuRlGu19ix7805UweNnzGmMVjeLGl3wCJ00onja22knt8Rtz0vS\nGgJcKE9rb4yv1yAEalZlRusnz1L3jhRf8IjIAcc/n+nx1Daz6xsqbqAAevp8jESOoZrWJ9z6ff9B\n9WvFrBR2MIIJI9iKkMvjul0bP4JZBYElMm9yVC/4Afg+mH0ZgJeiNeHuEgQVnOwCk2/5fBo9mTl1\n2X84ikoOi6VMYYzgKNwR9+tFcG+CZfgM0jpevIVcIo8XmjgVCPYEAy8QpVLALjbO5KZRAMVV1ixZ\ngr4etJE0S3BHQtAfFU2lV6/tUql06RL/iROn2FiX+P7jv1mzZo0ZMybBcdJuA9BUtrOgP8cv0QPj\noqloxRSy71Z/ghO96pWZ+biOdOocbdvDrM/xMSiBlKKSyNGxbCla9Q/dedAh44eptAs+WJhQDsFa\n8PkNl6+w/9GQyNGrR0dGRASPZdywKOtMhDSGXgP0lRl+quV8o5+b6RzMZ2/Ri5b2FVa8611r0xMz\nLDhLSQIHXdCwgLA+OtInDCbCHwOgoAjqofYtmFNdEBRBH9dTIhcmdnRQLYBnEkDg6CnafYD90aWu\nI0ugT2pXp5hO3tCJOH3FUMR4+0QjcO/MFrugxoGZsygzFlQqFIaN8/Ydu/3ElSIuOldvUuLmNG4C\nv6V0TdamZvOY8cPDlVPmKkFTuXA5bg+9Sg4dVxappKzbSok1COnjJzbLpNnKngOUqAWjxjaHMntR\n5TBfVo+1cRZKFFLJ8APX0wAfHwAuXHlrm4TTV7ShEqsMJBknx2Y6iCtRNBXHGJrKrVu3Ro0a7eqa\n3c42Zwa7kVmdb6G7xKJK9uy5Yg1tKg+gqZxS3uqunK+iqXxT/jSVzSnZgSbJyv4rmWdU3n9o2L5w\nrgqkFIkbO2C//xC631a+AIPFTNnsufI9K+nqycbPXTLm8EThui3Ktt2VG7YJXeL7N0wZtl95BXRH\na2WbGsrR/sqjQcoQ1vjBI2WlBmz2/67G9M1TSglSIlZy8Bj7jrDIwv5t1VUZFMTagHnIUzIh8OwF\nI5TiLeNf5ONJnL6S2n+F8Pk5AnEQ8HM74bEkMPN7BYm+UEgoOWiz44nTK6oANjp3zjF3V1UqUAZH\n7a3g/OTyUXr3ntnvq2yb1m2J8qp7/AwF3kvMdaTnMHr6AkogmrOIWU0hubpQvRoss2w+s/sUAgBB\nAY84c62bsoCdgmcA1oKnpEZAFd0Dvixh6Zv+QrUnFaCdO3ceN268jcUXc5FmaHQNmkrLllPPnj07\nY8aCu3dv2lm3sBD962RT3Sz6v5iDzejXrxu9ffvWzc10/I0INBVoZ+H8FN5UjJSm8ou/CgheJlBc\nYDoPO/jfChgwHkwkz+xlhpvFi6i7wMJDCcp9k55voNOjqA2tWsSU2TgwVbRG2LJ39oDzSKah/6ME\nFcgXd1KoyuHlfS2dRECirlRrBnXLRZG+FIWmfjPo/GWWHepLp6Pj/sDhLO4zRw9mUdgEciP+3b6P\n3RXAijSuzX3cWXlJ4hDAOQxvOUj4F5hruNNJ3JjJ30uc/FPwGTgCRo3APXq1P8/HxzfyU6afNHKA\nFon87gPCp1EdgmytNWXJTE3qa62JKcRXhYb3EoHQghYQmkFb1GpuH9M/MhcSwlwoQubw7skW4+rM\nhHIkdWbO/k0E34tIzmo8HFAnEQmIp2RFoNuftPcQ+wKGCx0ukf8C1PBaWLZs5fu3ttrbRLmf06Cp\n9Oy5LTg4eAloKhMm29iUEUW4Z3ZsKRJpen22ltQwN7fz9fVdtmzZLyzHKLrCx/Y2Oi/QVJpSuUXk\nWc9IvKkkBzyQp+F5cP025jQWwqtGgonO2UvMMgcHr9aEKBDaDmS4XoFQvl4QytFRw+cJYqhBIkcC\npwVnslr6SN8hykMcf0KB7ajKVhqF0BCsHnSUletZIGTP7szVFQ5kIakfyJNGkxCDE45l1ZOqsXoh\nzychAnDS5eHO/sRq1YQppNJI4kJ5GnlRfJnJhgB8k3uYNcy8trv2GS5eYSZBUNhULkdn92tvI5TC\nfxa8aBUqwDiC+iikQXO/eJUC7jCfhiptja4JiBDmc+kq1gR2pf/MpA3/oxl/M+37EM+Yfpha+LIp\nU5L+nsZCYIDI3ryhYdqmmOF4Tm8EcMECu1iekgIBD49uAwf8o1T2CJUfCqe1QcEHK1So5uU1olCh\nQuvWbWzevJ1cbiWmjq4O18TmuXRMKBE33rJ5W9oVyhH05wwL+nMcForQ9bpTrR3k40i2OrZsIlVC\n8LK4m0HwhBLVWbTjTK7MfaEOixfEi5gylx2tOGAj7zA7UNVRtAr8byi8i1KcX5sypch3GO3YR22b\nC6ES5KRALDnI4ofoeg0qOpxatqAKVqRmfwwbkp4D2RoPHmd3oVPGMl0JHMdhRvWUKzLMGdawbgnd\nuENgvZcuzkyPeEpuBBbPJnzSVOJCeZp6XXyxSY3APeXL/YoLj7uJqUMWZg4SN8GOExI5Epx8I/wb\nNKCw1FEPzKbq0qwTc+mKhO+JXl1UxfFmcHGJkD1IGFDPpHKrImTgWHfp3Fhd1deG8fv3jlXLHzgC\naQSBVq1a9entKZPlc3HNgKA/zZpNOnPmzJTJcx89um9r3cpCtNrWupKKpqJjTxlsfd9+2XTp0qXy\n5cvraGaEVc+ZN5WT/nRUTmHwpnKF5vxGkbKdEa41aZcESRdBK6Hqhm163ATjS0jkSHAzdec+Iw2q\nH3rq7fuPok07WQHM7v+dh585KGNVKoLIpnBYPo20xX2bMIrwCQu7Qo8gi2+gM1koQzeqtZg8s1K0\nFlx9Co0DGX+Wq5mBsobqa8MNZ8c27MMTRyB+BETxV/EajoDpIzD+7TzP+S8zbzxB7XtRUJCWDePm\nS7hnBDkBEjlo3JKsTHeOUEHqCfeYKm+GCOujZ0KvDr3ZgPDhhVAXCSbEq8vhRjmzke9wzbYgwLTo\nQpZZqGVXRobhiSOQlhGws7Pbtn3zocPbFi+ee/HCtT/+KDd29NbA156ZMjy3t1piZVlZH4kcAIjF\neSzEWcaNSzPBTUBTgWfD2jS2CPW7Qy9BU3lJy6dTt/QikUPM7TmADp0giNTXIkNXavwag5MgRJqE\nshmXgUtXkl1OyluaHj7WaBglu6NU7UDuSMzlC7gouILQbE/0lj7POjm0yMOGDb4OoZ8hB8nvNi0c\nTq20S+To79mD6bwzOLBQcXHT+BlknY1KVCPo7HniCOiHANeU64cTb2WKCDA2eeaXj+e8ZZtDPDZQ\nCeOm/Hnp+XVmJgJvgy9f05KVrMm5/2jPIfqzVUxzaEH+9xeNm0a/5SePeJgwaA1SuDoxHS7DN+5g\ng+D2E5ZJCUZog1roZUDMpOo5RJKDZSfSzv0Eyk3x3xnXBQ68JvqkITqd+oZ4Pp0jUKdOnaJFyr9/\nH2yu7JzRYZqhQT0jIn6EyLbDW6JS+cXWVpNubmzYQkY8S3dBU9lMZwWayvZ0QlPReBNQKKiO4TBt\nygVcAIIoAlYe7glxYwnbSpiE4ooSJ/PcybEGmzWeeg8mCzFjlUSnNlSpP/37gj6ep/uMFx55IEtJ\nvpMuQjV+Unm7ftCHyX8HNtn71cK3Bo1NyMA0uxtdPR49duyfUilNms2+U+APd+0WFt15wkwWW7pX\nZ+qkM2Jx7GH4U3pDgAvl6e2N8/3GIAA2uad5k8xdi7HYyLgqjc//K8yGIJEjZXRhREZcm8JuMq4j\najAR8Ykv4Qq1ZnO6fov6dI3hnCBAMcJAwOLT0oIK5I2vKysPfM/09DoIlHkRQsiKuVtBSI48Oemv\npbRsLesIS9DHV1gmxRK+jcCzvB5AIwcSrhd44ggkCgF4HH/x8kFGxhrPrf8ASmWEVPXMELcAAEAA\nSURBVHEiLGLtz5DdxYqV9vLu0bbtAQcH43XtLNBU4E1FRgp4U0lHNBWtL7Vgflo4nTZsZwEfEOte\nawJNHM2EVKQQwckGEjIaqVRxunJMo8yFHOpTqb10Zb3yVOWOc8+9OrpqeJHNzezymWUFTWWN2RBX\n32Z0I5IeE3dA9bEQVAgRf2DcGV9CMAp8azx6yuox1KHjNH4my8P9ed0alCkjy6dY8p1Gm3dRi0bM\n4QxPxo0AF8qN+/3w1SUbAkxNTlcfmy2lmWpeSnRPByXN2X20dQ9VKU8liupuq1kLBTYkcqR/VxNC\nBQm2mDDSP3+Adh+kutUJgTnjS4gx0W8kCwK6YzU1qK29Fb4ezu2nA8eoYW32VRHtFS4mI3QDRQe7\n0MqJ1z6u4aVrNjO3CUh9BhMCZMSNlGT4kLxHOkTA0dGxUaNmp45tcLDx0Wf7irCHofK1iogNdnZi\n7z5d3d1v5cuXT5+OqdIGNJXIoD/HLtCDplR2IfWFsCgiTigl8u7FPnomIRRRjmzUroWePfCXD4Ty\nVRHHDk36HmJbqNO6wLMnBhWt1Tmq+8HNtGojs9fXamIkNDpzgRq2Zx5apo9jqof40oldtGYT+6bA\nFejhEzGtVIcziqDCAIkRZ3LyJYQzmzSHDQ+vAJDL044fkuSDxJhH5kK5Mb8dvrZkRMAPanJqmJn0\nlsiFteDOFBTz0xeY8iNuHDgd6wWfBPp13GZmyxLLg2HRwoSP7oSbWXSUylgkvPiEcowAzRA+QhrU\nl7nFBX0FdkuqNGsBjZpIGV3p2A4tiiVVs1/MqNT5uARIVun/F9fJuxs9An36dDt+bACRLqE8IuJb\nsGyrUrQ2VHqnZcvWffuurV49xlW5sW1RRVOBN5XClN2dam+jURnI2Nk1xgZjzHpw2oAxeOkqlSwa\noz6PqdaSQ9zTudTjTviT1mPW1Nv2ylwkptsVYtplzkQj8CunM63eFOUzcbG/LqEcPnBHDYoaCHL5\nJJ8o+gpOYCEdPEZtujMToHVLmdu+ZEowPzUXMXcFZhTvbXAyTc2HNRwBLpQbjhnvkfYRuEsvD9DV\nJ7TU4K38tYSG+bFe5y4RPILrnyAun9nH2N5tmjGdt0GpeiVGTERCRs9kZ0dLIrUj6u2nz2fCPW57\nIdxrtUxSb5zoPKIUzZ3ErgXgTJ0L5YmGkXckqlevnsg8SKa4JLHQ9J2iVIZL5UfDmLfEfX+UruDp\n5dWmTRtbMM2MNb2gD7Dg9KdjUlJ0pur/0exC6cSbSrK+EXjHatqJzQC23sub+viWtSHJYGrOHBtO\naUHlD7JDVUWG0XOpcJEukAP1P5Ax8tih7KOe5i+NEu7hSzf5hHLcwW7xZ+66mzXQwrpUXw/PGwEC\nXCg3gpfAl5DiCIynjVCTZzJUTY51PnwStViBLGjQyiuVI3wMSs9f0ujJJLGk1f8QjIpqVjWg9/HT\nNGMB5c5Oi2ZH+U2Hs4LDJ9kIyCRfwuUsrJqSPOGed8H/2HXBwL5aAjwl+XR8QCNAQCwWd+ncYe3q\n9epCuTzsrlSxVh62MUMGG+++3dzd5+TKFT/1K7V3EUIygaYCy0KBpoKgP+ZkntrrMpX5VecwbPFh\nnGPQ7SWM+BFTwqAEHjkOZFxCrlzIzIEg5uqfHj+lkRMZWWXRzKiwRziHYeKPlKwHMsZv2Zh9kjzh\nxgBh9fp208W9TPJJTX1ALpSb+hvm+4uDQOLV5BhqsAe7gkTIzKkxFv1xZki6Au8RtP8oGw4hgZbN\nN2Bc+Mdt3hlevViXuw+Zkh5p20rG9s6Vg+rXYo9pK42dSnMi3brj76I1i9PW2vlqE41Aj57dli2r\nbW81K0IZFCLdrBStk8kftW3XrnfvLS4uLv/734oaNepevnze1TWaEpDomZK6Y6Q3lWObo2kqW2kk\np6kkNcZE7ZoTjFj+u0bDvA2TyBO3FBxBC5axro+fUcAZw8boMYDOXGRdQEl/fYsZ7oNbCO4izmp1\nR16GDZp6rTfvpG7ebHqEW3pwKfXWYWozc6Hc1N4o30+CCEBN7pU4NTmGLlQw5Q4gcA0RrkhIcj28\nmKvvHB4eZfKoAjhwxKNIROC09OnGVDU79zEpv6khah71wVMlDydoQlJlUmUZfNKURaBUqVJZs2b9\n+KGeVH6zfPmqXl5Da9WqtXv3bi/PoQ8e3rW1av3958spU6bMmzcvZdcV72wCTQXODUNJDpvCSzSr\nMKLW8JRMCNjb08ndyTS2lmERrVNIhh7I6CXEPBIyCORctjQ7k9u3ZOPBJQvsf0D8S0Nm8apzGJ6C\n8VWlTxBrtlWeEkCAC+UJAMSrTQyBX1KTpyQW+4+wuEKwzfmjBMFgaMoYwyYHbX3iKBo9hYng9Wqw\n01+VfCYxM3wkNBg3TFVs7BlErgaxHn+lwF6Kp/SEwIKFMwMCbnXuvPXRo0eLFy3v7t7LxqaMKKJX\nJseWIjPb0NDA9es3pbpQrk5TaUJl/6be8KbCaSqm83v66AnVbU1vAtmBbGuTGN+Ci2dR3TYsSBzO\n899/i0EGbG+YeyIhZsWe9THlRp7r2ZkF6wB9Bd9NXCJPupfFhfKkw5KPlBYQ+CU1eUpuEHLzj59s\nQnhG37U2MTPD8B+qF1yz1qkeqztMVIWkysSqjv8B16z4RklW713xT06lS9CjyzrqeZWpItC4ceOv\nX7+XKVMlOFhpYdbJ1eGK2DwybkDkhh1sR3z4UPvt27dubm6pgoCKplKIssGbyhYa6cS9qaTKm0jW\nSeHK9sVrNgPsfD49SsxU1SvT6wDGtKlcntQtks//FzWaoQcyuoHjjvvPVEmuLnR2f6rMbNqTqunP\nTHujfHccAZCrI52uDCV9PdqmNGbwzbL7ALsKRCr2e9TsqkwiVlMgHzWso+mRAE5REOUOxqMe7gYM\nefkaZSlMjnmY5xaeOAIpi8C4sdPlIZ7ONvccbHzVJXKswsqyorm53aRJk1J2RfSSPk6hzfmpbzua\n6Uz2oKn8R3O8qBGXyFP4RSTjdA8fE5jTiBOEpDqHVZlETIyYQU3qa8aA69KOnCM988KEXf/08RP9\nXpHsc1P/kfp34i2NHwGuKTf+d8RXmGQIwDd54tnkSbaKeAZau5m6RLoC6N6BVixgXgXh2hwuBbu2\nj6dDYothVARn5/CREl8EU60DL1pBn7+yGvhV7BHpg0xrM17IEUgGBJq3qLva/5GZmXYtkqV5zS1b\nti1enBLmv6Cp7KALoIyfo3ucppIMr9pohrx9j/6oReCOFytMN0+zcxjuVqAmd++QxEssWYzZfcJ1\njEFhPrfupnuRCvuFy2nSaMrgmMSr4sOlEgJcKE8l4Pm0KY7AHXp5kK4lxje5QUtFGIunz1ngNENp\nHnDqIqQTZ9lPkMJhlJlMKREnONgjKzey5ZSOjk+UTGvjw3IE4iDQo0e3JUuq2UnmmJlJ4lSSg+2o\n958rP3v2LE+eGFpL3Ga/WAIpfCXBm8rZ3yJpKptpBFeK/yKkKdEd2m6cyVBDqEL26DkrWCWCNeet\ne/TpM+ueHF4FhcXgy8LQ7wuI8mZESqJ8ubmLWD1faZpoxoXyNPGa+CKTAIHxKaAm33eYmnRka61f\nkw5uMWzRndvShu0UEsrcvhph6t+b2SfhywmXrTxxBFIWgeLFi+fInvPbx302Vq3izmwhLiQSWYPB\nsmLFiri1v1gCmgqC/kAcDyYZvKlcoJm/U85fHJN3TyEE4Bq8RHUW3CB3Drp/kXkh1D81rseiL8Oy\ns2UjgwV6/WdJdMuKZZmj26s3mVNIdTv+RA/IOxoHAlwoN473wFeRzAgIavKn9G/yzqOy1BH80cY3\n2dt3tO8ICyRUpFBMkxpV2CXmz2DKkS2mUJW7HkCdPSgsnFb9QxXKqIpTNJN8MedSdBt8sjSJQF+P\nrlMnwzdFLKFcrrguDVsZKtuUM0fO8uXLJ+HG1GkqjanMX9SrAZXm3lSSEOGUGOrKDSaRIz1/RW/e\nUd7c2icNCaHNu5jgjkNYlaCDgGU5euXTdv0C9UTbHvTgMXPDkuQMQ9UadGdgMIoPT6aFgHaKnmnt\nke+GI0BQk3tTo4yUzMS7jm3IxYnBDb1yfAmRKcvXoz5DGGERbraEBMeF/UZSxQa0cbv2fn4zWAwg\nxM0ZM0V7A17KETBpBDp16hQUfCw84hN2GRHxNShkyXdphSB5kw6drf67fObZ8/t9+xpiJxc/VqCp\n9KaFWanbX7QHcdjfkD8cqjSmslwijx8zY61BlLSC+djiGtfVFXUSHgm796eaLQhEbVVauIxK1WQm\nNILlvapcyCxZyaLIvXvPzm2c3jxxBJIIAa4pTyIg+TDGgwA89yHBRDI6QU1+iK4nO5sc0yE826sA\n+vqN3LJGTx7nJ0I0v37HShHc5/otgoMUpIPH6J/lLDNiArVtTrnj3I+r1OfZ4x+Z9eeJI2CaCCCE\nUMWK1a9fmWRh8f1nyJ6y5Sr17+/TsmVLiUGchPixecVoKidBUwmi0M5Ug9NU4ofKwBrIrAgvkCph\ncZwy0J1z9DaQXT/CtD2+BLdXQkKmTTOWhW+TgT4UoWS6cEj2OJM1kupAhkJdx8gavfgjRyAhBGIE\nl4Raplr9tGnTFAqFr69vqq2AT5yGEIB0264nW++WFapg8gKbPNnV5AJKCZrs4DIUZqA791PRQqoV\nxgSIhqdCRKaIm2b4UuaMLCDz0MjIxnEb8BKOgKkjMGSIp7fXEPfunXr3vps7d+4k2W4o86ZyEbL4\nWboHmso86slpKkkCbNQgYHXXaUXQRMzwS52zC9qZnNkT2BEO1XFTycWZuv0Z1dJKwiwvg0PYo+Cv\nUGMIUFYQ3RMie79eGjX8kSPwKwikAfrKx48fJ0yY4ODgsGDBgl/Zqgn3PXXqVJigHjbhTeq5tVkL\nmW8pfGYvFHoIanIj8k0OtcqO1RR4l7nZUjklLP8H+S8g2HoiTpBWLwGIEOE7nCb6xHTRExCDmi1e\nQfa5qHRNAuudJ46AkSHQokWLGzcvvXzxplCh369cidZuJnaR5+leH/onC3WbR7ubU/nXtILTVBKL\nZfz9lq9jMXfCI2jirPgbpXYNogUjHtCb2zHOyO3tae96diAvmU21q2tZH45xj+40b4p2xrmWDokq\nOnmWsv5O2YuSKsBQoobhndIQAmlAKJ87d+7nz59r1KgxcOBAV1fX1atXpyF8U2Cpx44dAzi2NnZj\nxoxJgemMfYoSRaJWWKKokPGj9fBNnkJqcv3Rwb3qn73IrQhNnRfVCe5v1yyO0Z3rP1QSthw+nlma\nglSD8HU8cQSMD4Fdu3Zv23Y9PMx23LhxiVsdaCpTaUtB8mhN0x3I+jzNvExzvKkxAgAlbkDeSxcC\nJaPOYYo+kHU1TsU6ZyeaPIdJwMyePpIACaNPHMh93VNxUTRxNgV+YB5gsDae0gcCaUAox4vIkCHD\n7t27EUi5RIkS3bp1y5Yt265du9LHC0p4lyNH+GWwnWJh3nbatBm2tvbz589PuI8Jt5g5nlb8zT4w\niie6TS8O0w0jUpOrkN+xj7bsZnZCMNwMfK8qTuVMgbxRCxCso1J5NXx6joAmAkWLFjEze2dt2eXY\nsROadTqfQVNZT6fqke9v5HWNnsylnlCNz6YeRbh/Q524/Wpl+5a0Zx3Nn0q71/3qUMna/9kLmjSH\nScDrttKBo8k6lQGD8wPZALBMpGnaEMoFsLNkyQKt8OPHj3PkyIF7zLx58548edJE3kNit3HkyJF7\n957Y2Xi4OPybzeWNMqz6oEGDnZ1dN2zYkNgh03g/MAi7d2SfSEPPFGWTG4QczIOE5GDHAsUZSdq7\ngfyG09rFBDcyPHEEjA+BChUq2NlLJBaVFAr5unV6yXkCTSUruc+lXc0iaSpbaRSCcXJvKin0ehFY\nfkAfYw85iXhqKmMetywphEyC08ybTLMnEP6N1DEl2Jw3MAEE0pJQLsCdL1++ixcv3rx5087OrmbN\nmkWKFPl1cmHafZEjRvhJzIeKzKyxBZHIIWOGLW7Oj0N+Fu7YsWO2bDkgsqfdrf36yo1XTY69ValA\nG/4lrx50ZDvZ2v76ZpNmBPypMH4kdWqbNKPxUTgCyYBA587tw+mAxKLwzJm6aMqv6dM02vobebai\naaCpnKXpV2huP05TSYY3YgpDgk94aAs7kLf60x8ljWVHNjbMOnaQR+r4rjEWFNLXOtKeUC68HwR4\nCwgIOHv2rFwuL1u2bJkyZV68eJG+Xh3c6B08+PDBczvrSGcj0ZsXi7NmdjqcxfnG508u9erVK1iw\nULr9oyWFfJNHI2/wzz9b0T8zydKClvibrGFlUJDBsPAOHAGdCHTq9GeofKeNxOPWrQApvP7HTqCp\nbKDT9cmvIHlepcdzqMdr8gdNpSjlit2QP3EEYiOAQDw4kMuUZAfy3Qex60zlCWGStLpdN5X9mcA+\n0qpQLkBfuXLlR48e7du379OnT1u3bk2L72PLli1VKle7c+dOIhYfqSYfbmZmFbevpbhg5gwXMmU4\n8eJ5GP5o+eOPsk+eRMepidvaFEsENfkQiuNf1qg2e+c+la1DnsOpQn2Sy41qaUmwmJ4DyCEPlahG\n374nwWh8CI5AJAKlS5d2cXEWm8OXv3j69OkqVC7Q/b70D2gqc2hnUyr3ipYLNBUxmava8AxHQBcC\nP4KiDuQytenFK10t02LdP8vIITfzLnDzdlpcfjpZc9oWyoWX1KhRo+fPnw8dOhSPUJy7u7v//Pkz\nTbw/mUzm4THg0qX7RYsWrVixkkHK/v379z958sbOuruOnVpZls/idCuj4/ZbAW/y588P2o+OxiZW\nZexqcgHuG7coLJxlX71lNkamlOBUcQWCohMF3KVdB0xpZ3wvqY5A167t5RGrzUV5VqxYqaKptKSp\ndmo0FRdySPV18gWkMQSev6SPn9maQ6V0624aW3yCy532F/NN+eETd66VIFSp2MAUhHJ1+G7fvg2V\nuZOTk6enp/G77l6+fHm4IrubywsXh7VXrjzJnTtP3br1PnzQSzhjanIR1OQS9e1rzVtLGjjaznNx\ncYOGSWsD0ys0aja5OtyN61GR31jBny0TjnCh3tH4864ulMONLdNcRCo/lca/bL7CtIBAp04dfoTt\nzj48m82ZGgXI4wo9mk3dQVMBWYXTVNLCCzTWNSIkc+O6bHEgsdSqaqyrTOy6SpeI6lm6eGKH4P2S\nHQFTE8ohd0JNPmjQIMi79vb2iDqU7BAmdgIo9cf7TbMUjcYAtlat3JyfO9kvPHniMpzMtGrV6seP\nHzoG3rt37/NngXbW7jraqFfJI6ZNnz7B0tJSvdCE8/BN7k2NXI1fVQaT/1tn6dND2vA/U3sd+GU7\nf5C5QsO/JYuZ2u74flIVAdj3H/94WeyTJ/OliHWn3LeRD/gqnKaSqu/EJCYXiQgeqD4+oP+OEIws\nTSzBtQBiIe1eSz07m9jOTGk7piaUC+9m1qxZEM3btm07ceJE+DhftmyZEb6zFStWyGWZocZWrc3e\nuruby1tHmym7dx92cnIGDyeuGZPQGL7JLc1HmJnpJWSHyPZYWX+Ff3fVRKaduUXPj9BNY/RNrhV3\nRIZDeGeTTNndmCu0cunlfsYk36HRbkpx/uN759VWf25aNHKK0S6SLyxNIoBbPhzLppfg5guxkJrG\niBymt0UT2JFpCuV4MdAKI/bnx48f4de2T58+bm5uIGEn+Qv79u0bFN6JGBa9fH2nCmpyje4OtoOy\nuXy0tRq6Zs1Ge3uHAQMGREREqLdBHKUXLz7ZWXVVL9SRV0RMnzx5rIWFhY42plQlsMnTgJrclEDn\ne+EIpCwCMJIJCQ+bpbQ+e+WynpS/lF0gn40jwBHgCBiMgMkK5QISzs7O8BsIxyPZs2dv3LhxoUJJ\n6R9QoVDkzVsQQTRhY6ohNyf4Hvz9/eVSV2tJw/haOtmNd3P+bGXRc+HCJdbWtpMnT1a1jHS6or+a\nfJ/E6hOU7qrupp1JY2pyE3gZcLD11xIa6EMIiccTRyClEMiTJ4+bi+sJUtSQ2KVR11spBRWfJ50h\nsGUXeQ2nc5fS2bZNZLsmLpQLbwnH93+RCaIz/AOWL1/+2bNnv/4CV65cGa7IZiXuOm/e3zY2dqDK\n6DkmpHmoyS0i2eQ6uohEImf7udlc3pspy02bNkNouWPHjtevv9paddHRUb1KETFtUjpTk/dLXTb5\nph1UpBK160FpxAWQ+m9LYvJLV9LgsfT3/6hVeuFHJQYl3icZEGjcts0KkaJ9SNjGZcuTYXg+pEkg\ncPUGlatL1ZvS46cmsZ+ENnH5GrXrSYv9qX5b7os2IbCMsT5dCOUC8BDHHz58uGfPnlevXpUs+ash\nu+DahfFPzH2cHf7O5hJoTs3Gj59gZ+ewYMGCBN8zpHlpqJONpHGCLdFAJLKysvo5Zw6LXadUKkeO\nHC8RjTIz04uLEirbbyn5kN7U5EOohT7AJksbqI17DqS7D2nLblq+LlmmMLZB37yLWtHbQGNbGl+P\naSMwbty4uxGKUiS+HBDw5s0b094s310iEYDK4PJ1On2B/KIUW4kcJ610U53DwSH0XZe7iLSyofS2\nznQklAuvtkmTJm/fvn33LkqSuHTpUnzGlLp/FUBYDwm2t7ZsgmYikY2ro382l9cRimoDBgx0cnJB\nbXzdo6T5hNTkqu5MsLb62KNHD5Rs37793dsgW6tOqlrdGThdmThpTPpxugI2eSqryWG87xjtHRlx\nm9ND6t+bqleiXNlZMDyeOAIpiAAshbJnznyIFHUtbRCFLQVn5lOlHQScnaLWmk4OZLjZ7dSG3LLQ\n+BGUK0faeU98pVEIpDuhXNi3TbS3o06dOjk4OAwePNggUnh4ePi4cVMszaCxjrHRFokyZMywxc35\nsTSkKFydZMmSbdeuXXF/0VatWhUS7CBI83Fr45bII6ZOnDgagjXU5KNGjrdkanJx3GZxS0JlBywl\n7wVpPm6t6ZUIbPLUVJMDU/w+7FlHXdvRDF/q3Nb0QNayoyyZ6eRuen6D2jTTUsuLOALJiUCLDh1W\nihTNQsM5gyU5YU7LYy+eRZ7daYgnTRmTlreh99rFYlq7hN7cJr8RevfhDY0IgXQqlKveAAgtffv2\nXbhwIZyaz5ypr6pv3bp1P39YWUu00CTE4qyZMhzM6nzr21e3Fi1a5M6d9+TJk6rpoCZn0rzIR12a\nV9XGzairyWHMFBgYamvVIW4zrSVQk0+IlOa11ppeoV+qq8kFTBGgYdUiGjEAFyimBzLfEUfAqBAY\nM2bM4wiFlyjUNVMmLAzusJYsWdK8QYNPnz4Z1Tr5YlINgaxZaNEsmjMp5g4z1ZbCJ+YIJIxAepcb\nYEwJFvj379/hm8XHx8fFxWXt2rW6YYNOfeyYyRZMYx2jJtfoYiHOlznD6SxOlwLf2tSsWbNly1ZC\ngzVr1gT/tLW21Fen+Atq8oMWlu/Sj5o8gJ4fpZuprCbX+CXgjxwBjkAyI+Dq6rpt166Xb94M9BnV\nsWWrbJkybR468uWJ09CbJPPMiRn+zp07o4YN27p5c2I68z4cAY5AOkAgvQvlwisGm2Xz5s0gmpco\nUaJLly45c+Y8duxYfG9/48aN376JbCRRcnZ8zVBuaVEss9NlO9sihQqxUOogvYwdO9nSLDFqcizv\nwweZrdWfOqZTr4KafPwEH4lEgsKmTVs1bNjoy5cv6g1MLJ/6bHITA5RvhyOQRhBo0KBB8+o1ezdv\nWXDXodsK2+MhFiPl5qsXLU7c8qFzCQgISFzf+Hp9/fp18eLF5YsUrViq1PX5i8YNGx5fS17OEeAI\npHMEuFAe8wuQKVOm48eP379/H/ryOnXqFCtWLO7pjCN7zOhJlqKRZmZ6QQf+ibX19wkTJmAa6OCD\n4iG9xCxCLadSk2NSsMkl5qPNzMzV6uPNhsoOiy3f9OrVCy0OHz589MjpI0fOubpmBIE+cVat8c5k\nHBWCmjzNhPA0DtD4KjgCJoPArUcPNoWaj1da5yF2QjYny8fPnt29e9egDT569Gisj0/uTJmhmjl1\n6pRBfbU2hhYGUTL+bN48W6bMW4aN8r77PFBhvzfM+uOH99euXdPahRdyBDgC6RwBvSTLdIXRb7/9\ndv369TNnzgQHB+N0rlq16uvXr1UIgNj96bPCRqKvDZ88YgrchMNMk6nJx0yWiCBYx0t6Uc2CjDqb\nHLr5z5/DbSTt1BvoyMuVU8ePj1KTjxzhZ23hA6eNDtZjN27cYWdnHzdEqI6h0kQV1OT9qbELOaSJ\n1fJFcgQ4AkmIAE7XAoUKbTBTqMa0JrM2ZpLVK1aoSnRkfvz4sXz58qqlSpf6vciLef/4f5YPMbfd\nGL/7LB1Dqaru3bsHmkoO14z9Wrf9fc/R+2FMf9+VrGwIvmzN2kVYrF25UtWYZzgCHAGOgAoBLpSr\noIiVqVKlytOnT+Fm68GDB2CzCH5U4P9kNNTkZlCT66WxDpHtsbL5IhC7169f//27hbWkZaxp4n9Q\nV5OP9ploKRqj56ShsiPm4le9e/fG2IcOHXr48IWddQ/kHe1GuTl/tLbsgxChNja2+oc6in+NRlED\nNfkxxiZvbhSr4YvgCHAEUhyBXp6ea5UyJSlVM3eVm63zX4k7RlWJRgZV4Ch2ads2q6vryoFD3G88\nfBdmv0ZmWZssOoSLt23ZAqN8jS4JPgpmphWKFitfouSHBUs3fgt/HCLxVVrnjNTfq7p3Uog2rlmr\nY22qljzDEeAIpDcEuFCu6423adPmw4cPiPVTuXJltEM0zfeBIXoSuyHByyMmT5kyzsLCAmrySNJL\nYtjkkObBBreRtNG1ULU6qMn9/EYJbPIRI/wk5sPMzKyE+sgQobMRItScWoz3m2Bv7wimo1rXNJmN\nZJNzNXmafHd80RyBJEHA29s71IzOUYwYXY3E5qHSEydOxB3/8ePH40aPBk2lZ9PmebYfCFDYngm2\n6ElW9hR1h1mGxA7hSlAZ4/bVWoLjHeqPDi1auGVkZqZed54FKuxWyCXVyEJr+0oklsjk+o+vdRBe\nyBHgCJgkAqkjlH99cmkpHBDOnDRz5e4nQcYObNeuXWHjj1V+/vwjIiIsVLZHnxWHynba2v2Ew3I0\n1t82VBhZpSbHcR+tJtfrTYXKjpqLX/bp0wfjHDhw4PHjV4KaXH3BCBHq6rjCzfVluLyil5e3q2vm\nbdu2qTdIQ3muJk9DL4sv1WgRSFsHclwYoW4oXLzYOlEMg8WMzJrIIlb/+6+qMWgqy5YtA02lZOHf\nX8xduPKz/FmoZGKEVb7YamyhfXupcpMeDBYYIPkMH54zYybvVm0K7z5yT42mopo3bgZr6ySjtcuW\nx63iJRwBjkA6R0AvUS9pMbq/dbBz/goet2R582Y63715focSu59Ik3aKZBqtZ8+uS5ZOFtuM+RZa\nRSrXpUeBmlwWMWXaND+xWIxrykjBGmpyvdBWZ5PDsde3b2J9PL0IW4aa3Nd3pJUVU40zNblouJkZ\nc8ASN5mLnDNm2OHmfD84KC8uBHLmzHPz5s24zYy8xI/W9+NsciN/SXx5xo1A2j2Q1XH16NdvQ4Q0\nnJQfKGI+hf5hq1hjqSxYqBCO36NHj3Zu0wY0ldWDhnaPpKmsllnWIgsIx+ojqOfbR4hxLyqXy9UL\nVXnQVJYuXVqxWPFyxUu8/3vJhq9hj0IsQVPJpU2+V/VSz3SKEG/fuTM0NFS9kOc5AhwBjoBeYmIS\nwhT2dnf7tn9R8Rlv1oxr06bvzjcHilNA81az3ibhHMk2FPQxcJj48uXDyVPc5coeP6QNZYrLWmcL\nkW1zzCDv3Lkzajdt2vT1qyH8k+gQnoKaXMLY5Hq9plD5MZH5c8RCwqT79u179vSdnbW71uWpCsXi\nHJkyHIc/9dev38CGVVWeJjI36dlxCuBs8jTxsvgijROBNH0gq0MKZ1PhIlEli9Bc5kGHqpUdvmzp\n2f/+C5XLQVPp1axF3kiayulgix5qNBX17hr54iTOYmYO11Xq5ZDvI72pRNFUPG8/VdFUdMj36iMg\nH0LKtSTrb6NURIS/evVKo5Y/cgQ4AukcAb2kvSTE6MLyv+ADts+41m7CoG41RkJwDfBde+lrEs6S\nrEOBI96/f//Xb54OHlonSNbsh/RPRdh99RmVygg4XYGa3Nzc/FfU5Ig0FBSEuKEG2Iaq1OQjR463\nFIFNrl1Nrr5a5MPCn2TJkt3Pz0+j3MgfOZvcyF8QX57xI2ACB7IK5D4D+lf27htw726rLp0XTp9R\nsfQfr+b9E0VTUVprpamo+qpnZKTcQrIIueLwgQNCOaOpjBiRM2NG79ZtCu85cjfM9liwWPCmot5R\nd/48KfpYyrNa/JxfKEfLmVPfvn9fsGBB3V14LUeAI5DeEEhhofztya2wvCneslKuaKCtilVh6uSD\np25Hl6SNn7a2tr6+Y1+9etq9R8EvP6v+kPYJC49Se4TINjs7U8eOHbETBP2J9GaovwvFqRMnjoaT\nL9j+jx490dJMXxeKUvkJM9FTQU2+d+/e588CE1STC0Azg1Tl9MmTx4Jpkzagj1wlV5OnoZfFl2qs\nCJjOgQyE58ybly+rW4kiRdYMGtrj5iN4U1mVEE1F471cpjBvC1lWy+Ap+bL0nT5l8LBhAk2lbPHi\ngfOXrPsSBm8qfkrr3HrTVDD+KwqfahZawEba2lHk6NXzwo3rl+/dhWWqk5OTxuz8kSPAEeAIpKwc\nJg28ymKl5clkHzNv7rJVEFfnxMG7X0dUjXtK3bhxIzAwEH0Q0CdfvnzG9sKcnZ3nzps1bPjgMWPG\nr1tXytaqu63lUJhpTp8xCVyXaDX5KH35J7L9llYfBReKq1atCv5p52jVQs8tY9JxfiOtra3RHr7J\nLc1HmJlZ6tM3VL7H2uY7jFn1aWw8bSJ9kzfhvsmN543wlehA4PPnz0+ePBEa4FjQ0TJFqww8kPEH\nPBgX//33HxYJw5XixYun6Gr1mGzK5MkTFBbDFXB7ot3zidYx3lEEKCUrbcw/iKhjly5HevbE+1q5\nePHYUaPKWVh7hIS3IXvb+AnoWscMJeUOkmPMs/KQRvXq/uXlhcijuDvV2pgXcgQ4AimJgEwmUxnR\nISKNYIaXkgvQMVeMcKyjUZJVBX37EXcs4fB0IK1LAVFE0OA2adJk/vz5cXsbQ4mbm5u//7+jRw8f\nPnzsnj2/5c5VoF07FugHLO2Pn+RO1noH/YE0H6kmVygUY8dOthRN1zPSkFR+ksweeXp6YNI9e/a8\nePHR2VZfIVseMW1OtJo8KCjI3t7eGCDVvQZBTb6M+uluxms5AkaCwNmzZ4cMGSIsBt8HRrIqMvBA\nhlC+YcMG8KqxfkRv0OpwMHW3VrNpk10btw7X768e0FR2M7lZdFweUq9GzclengUKFFi/Zk3zevUs\nQ2XdZHQnwja3AmK0AfI9tg+aykpL5SalrEDevO79vDd27AjdTerCwmfnCHAE1BF49+5dhw4dhJI3\nb95A56tem7p5rZJwci0p6NUjkFe0Jy3SOmuIyJo4/bV3MbJSHOg7d24KCAgA+QRqcnyB+SDojxnU\n5HppR9SdrsAzekiwo6NVUz23CDX5mHEjBDV5pG9yfdXkIbK9VtZfBb+NcLXboEHD/PkKrF6zsmLF\ninpOnSrNuJo8VWDnkyYageaRSeju4GAsoWcNPZBxrI0YMWLUqFGJxiG5O/r6+hZZvx4OWDKRLmYm\naCorLcIRBDRH9uyQm+c3bQqv4bP8xt+6f6+NmdU6uVk1kuhvuylsCjSVNWbyldaiIAtxJ/du53v3\nLlKkSHLvl4/PEeAIJAKB3Llzq64u//jjj0SMkHxddJ1cST6rfYGyOuIuhiX5fKkxIK50CxUqhJlD\nQkI+ffoUoXytVOrl8FHlmzxaTQ6nK/F67FLfmVR+Smn20MvLE4WIPPrq5WdbK/3V5FPBJofpKvoO\nGzbOzsrr5UuqVKlS8eIlb982UpY/Z5Orv32e5wgkGgHTO5B/++23zBmctpJ2V4agqcyi0CI28kZ2\nEeJeXY6cPzdr8aLLp8+U+P339YOH97n1JFBh7y+3rK7TW6IG2qCpbCBZPRvFb+Lgaw1rzN204fXn\nT7P/+otL5BpA8UeOAEdAHwRSVCjHNWAkN8Je/T5Q+u07W2g2SYoq7fXB5tfawBL00KGd+X879jm4\n2M/QNXDJomM8dTX5ihUrZKEuNpImOtqrVzE1+ZjhNjY20M3D6YrEfKSZmV73rSGyfVZWn93d3TEa\nXCg+ffrWyX5yFqcbGR33Pbj3tVixYhUqVHz27Jn6XMaQh2/y/tTEWfhVMoYF8TVwBNIoAqZ4INdv\n2WKlWhQhvBnBm0pjG0VecdDZOpUnr1118uIFW3v7FvXqe7RoVXDXodsK25MhFu5kZRBx/AIp+lrK\nslj8nPNb9mYzprx+H7h13z7QLDlxPI3+b+DL5ggYAwIpK5RbFajCVOVrA57HKI9fXT+Pos61/0gD\nXGYD31iFChUuXzmzYcMiR5e/vkvLh8oYF1NrUqnJEa5i3LgplqIxWpvFLZTKTyvpvre3F6qgJn/z\n+putFfNmo09SREybOGmMoCaPJr0wF4rWkppZnO+7OKy/dvVZ3rx5a9eu8+HDB30GTIE2N+jpCbrF\nfZOnANR8CtNHwBQPZLh2vRYhf0PheH3q3lTqTB5/+/79xm3bgKZSoVSpt/MXr/mieBIqGa+0zmOI\nN5XXFD7NLPQ3G1lLBzN7z577jh+7cCugX79+nDhu+v9f+A45AsmPQMoK5WRfp3MfbGr17pvRW/t4\ndPku5NvWLBxdYmo/mzVr9ujxrTlzB4Wbe/+QNpAprmrsUF1Nvnz5crk0k7WkkUab+B7hEH10tJoc\ngrWlCGxyvdTkobIDlpIP3bt3x8hwofji+Qc7q27qs9hatcjq/MzZftHpU1ezZMnSsmVLxKlWb5Aq\neYFNztXkqQI+n9TkEDDBAzlXrlxuLq59KVigqVj07nr0wnnQVK6cOVu8cOH1QxhN5Z3CfqXMsobh\nNJX6NmEFxcFXGlSfvnb13//798GtWzWqVZsxbZrJ/WLwDXEEOAKpg0AKC+WUr81oH6ITI30OvmDK\n8ie7540MoOI+x5rlMjH2SqzXiQtNBJx79erx4KF1f0gbB0m7KMKeqlqo1ORwy+BrmJr8bATdE9Tk\nCAr97m2Q/mpyTDoh0tMLlhHpQnG4VheKcHbu5vLW0Wbanj1HnZycYRIqlcbccqi2kDIZriZPGZz5\nLOkHAZM8kD2HDnn3e4Ep61YLNJXmdev1ad6ywM6DjKYSzGgqdob4NxRoKlktf84umK3J9EkHjh7J\nkSdPn67dJnbvVf34xeVKm1VLlqafXxi+U44ARyBZEUh5UTjXxA8XqX2Fhrmt4eU2IICa+6z739Ra\nybpJIxkctG/EG/Ly8vD1nbh8WTlbK3cbySi54j+Vb/J///1XIXdztG6g54LlEZPhhxHkdYFNbikC\nm1yvFwoijYVlYM+ePTHR7t27X7z45GwbS02usQAH2wH4fPs5ce3av9av33DixPEqVeBdPqUT1OQD\nqClXk6c07nw+U0bABA9kHx8fOKXt3LLV7fv3Wous1sjMqhvuTQU0FcGbynexeefuPfe2aXP9+nX/\nBQufvXjeQWm5X2FeNtKLbzgpR379euHCBSP3WGXKv8J8bxwBE0JALxkuafcrzlh+6nHl8LcvvoSG\nWTtnc3OyStrxjXw0V1fXRYv+HjZs0JAhPgcPFBWJHOdOZyE8oYGeMGG6pWixnuuXys+FK+/067cf\n7bdv3x74LtjJhsUQ1SfJldOmTPDBpGgczSZPmPSSwc7XWtLqc1BV+GXXZ5akbSOoyVfQgKQdlo/G\nEUjnCJjkgezZu7fN7fvvlPYGKcXxmyCNCvojOo2gP3XqzvToC0eQq5curVezVlVL6xEhES3IzkpN\n0W5OZh0VotXLlnGhPJ3/P+Lb5wgkCQKpIJQL63Zyy+WUJDtIm4PAgBJOza9cubJ27TohhCfiOYcp\ncjha1dVzQ1CT+/gMS5Sa/LDY4g3oNJho586dr19/dbbpou+k4TP6effD4vVsn4TNuJo8CcHkQ3EE\nNBAwsQO5U7du/U+cNEjfc5EF/YnYqJTny5MHzsvHlymzc+tWT/fudnJFt9CIeUrb7GHaI050DRfX\n2rhx/uLFgppDA1j+yBHgCHAE9EcgpTnl+q8sPbQsU6bMX3/NE47yTRsPRYRVBBFFn41L5efDlbf7\n92chLRE39H1gqK2V/mryqX5+oyQSSZQLRUZ6SVhNjokUYfdCZAd9Ro/QZ4VJ20ZQkw+mZkk7LB+N\nI8ARMEkEYP1iJjY/RIoEdwc/LdPNQgvZyJo7kG3f7vtPnug7dMj6f/9Xv3qNTwv/t/lb+MMQyRil\ndfb4PbSUJHE2M3OYyyc4F2/AEeAIcAR0I8CFct34pFyt3/iBGVz2fZdWDJUdTnBWOF0ZNWqonZ0d\nBOtRI8dbivSPG3rUXPyqTx/mAwe2oW/ffNffNlQaNtXTwyNTpkwJLi/JG/hxNnmSY8oH5AiYNAJ/\nVKniH9thufp2QVPZSLIGNor84p//1a82Y/3aNVu2vH/7tm6NGggk5HH76TuF3XK5ZRX1mBrq/WPn\nuwaHr+bmnrEx4U8cAY5AIhDgQnkiQEuWLvXr13/69O7MWV5hIo/v0voyxeX4ppHJL4RF3BwwoD8a\nbNmy5cNHma3Vn/E11iiXK6f6+o5UqckjpXm9KEypqCa/Tk9O0i2uJtd4lfyRI8AR0IHA2HHj9kRI\nf5Lm3SNoKh4WLOjPzAJujaZNOnvxYtHSpQf06t2nRat82w8IgYS6GRJI6D1FfKaI/cePwX2WjvXw\nKo4AR4AjkCACXChPEKKUayAWi/v27fv69ZOhw+oHyZr9kHZUhD2MO70sWk0eEREBNbnEzMfMTDvZ\nUaNvqPyYyPw5pkC5YBuqP+klFdXk42kjd7qi8Sr5I0eAI6AbgVq1ajla2+wgudBMnaZi49H94OlT\nA8eM3r56TbUKFZ7OWbDik+xZqGSCIYGEFMwkVNbMWpHbPOhOrcrbduyAskP3kngtR4AjwBHQjQAX\nynXjkwq18Jw4btyYV6+ednXP+/ln5SBpv7Dwt6p1yBSXwiJuCGryzZs3f/yksLFqr6rVnYFvcqjJ\nraysDHWhmLpq8lN0m6vJdb9ZXssR4AjERaB640ZLRfJNKppKvWozN6zbtGvX9y9f6tWoscR7QMer\n996F2a+VWdY2JJBQAIUNFsvcJCFjc7lUmzD2+ZvXe44dbdq0adwF8BKOAEeAI2AQAlwoNwiulGuM\noM3z5895/Pheg8bhn36UCAodFxHxDdPLwiePGDHE3t4eanKfURMkotF6qsml8hNmoqeCmtxQ29DU\nVpM34b7JU+43j8/EETAVBCZOnHghQj4tf1bQVC5dvfpH5UpDPTw7NmzkunHnfzLrC8EWfcjKgfT9\nEgRHZSGF/mGrqG6tkHZtt+/0qTvPnw8bPjxz5symAhjfB0eAI5DKCOh7HqXyMtPr9Dly5Fi/fuW1\n65fKVnj4KajI15/DFOHXBg1ivro3bdr0+XO4jaSdnthATT527Ahra2uB9KK/bagRqMmb67lH3owj\nwBHgCKgQKFy48ItXr0ZNmrhv85YKf/xxY+rs+e+CXkmtZ4RbFYrfm4qqu5BBeKD9JG9rpcghDtpT\nufSw/y158PxZ2cqVfQYMgHZDozF/5AhwBDgCv4IAF8p/Bb0U6lukSJFDh3cfPbbn92J3J070VanJ\nLZmaXK83KJWfUpo98vLyxIoNtQ1NdTW5E9mlENB8Go4AR8C0EDh//nzfTp2bnLv2Ksx+i9SiEVki\n3I+eW7xP4aPMpTmsQgdmtS85etj9J0+G+fnu27YtT/bs/gMGZ7t0fdHMWXoOxZtxBDgCHAF9ENDL\n84Y+A/E2yY1A5cqVL148Icxy6NCh9x9+ZLRvo+ekcKE4dlyUmtwg0ku0mvypnhMlYbNr9ARs8pU0\nMAnH5ENxBDgC6QqBggULhkSEdyCJi940lR8UsYnk/rbmtxSytm1ab/LwyJgx46oVKyqXKi0OlXaV\nKgOUtvkU5l/I3O3a1cDAwCxZsqQrSPlmOQIcgeRDQC89a/JNz0dOHAJly5Z1drb+Lq0cKjuY4AhS\n+WklPRDU5CC9fPoUpj/pRV1N7unptWjRogSnS6oGkSE8m3A1eVLhycfhCKRDBEqWLJkpgxNckie4\ndyUpj5Gis0SeVRy09o9CfRbOv/fkcdkqVYZ7epUrXiJw/pI1XxRPIz205IukvjiTqLbEDhePCY7M\nG3AEOAIcAT0R4EK5nkAZVzNXV9cXLx7Mmu2tFPf/Lq0llZ/RsT6oyUePGQ6nLoJtaCTpRS8XitFq\nchbCc8+ePcuXr/P27ufsnHHdunU6pkuSKqjJT9OdwcTZ5EkCJx+EI5B+EWje4c8l8UcRAi7PKNzP\nLDSPtayHqyTv0P7X79wZPn78vq1bC+bJu23YKO87zwMVdv5yyxpxPLS0DwnbtHx5+kWW75wjwBFI\nagS4UJ7UiKbUeHBqjsCcr14/HufbVhrR6Ye0iUxxJe7kUvnZCLrn7e2Fqo0bN375orSRtI3bTGuJ\nupp81KgJdlaT3Zwfhvws1LlzZze37MkaVpqrybW+EV7IEeAIGIoAfLA8jFCAIK7RMZiUq0la01ZR\n1CL4ccsGy3bv3Hn40M+goGplyw1t36HU/hMPwmyPhlh0IYlNPDT0FmR59fbtV69eaYzMHzkCHAGO\nQOIQ4EJ54nAzll5wOj506JA3b54NGlIjSNb0h7S9POyu+uKgJvfxGWZraxseHg42uf62oepqcsjf\nL55/sLPqKhZny+x0OItzwJdPmeGXN0+e/KdPn1afLknyXE2eJDDyQTgCHAEggHvFvLnzrIqOIoSS\ns6ToaSnPahG0qEieP+fMvHn3btmqVUFTqVWhQsgS/x0/lA9CJKOV1jkS8tACd4oNLG03b9rEceYI\ncAQ4AkmCABfKkwTGVB7Ezs7Oz2/c69fPevYu/DW4epC0hyKMmWZK5efClbf79++H/IYNG75+FdlI\n9LUNVVeTjxw53tJ8uJmZpbBPS3H+zE5nMzude/fGsnr16r//XvTGjRtJCAHU5AOpKWeTJyGkfCiO\nQHpGoHc/7+Vm0pcUPtUstKCNrF0Gc9d+vc9cvjx8wvh9W7YUK1z4yBg/n8fvAuV2SxSSimShP1bl\nQ8M2r/DXvz1vyRHgCHAEdCDAhXId4KSxKicnp9mzZzx//rhdB5fPP8sFSftLw/xUavLRPhN/UU2u\nAYfEolRmp6uZMhx+/DikVKlSZcqUe/TokUabRDwKavJB1CwRfXkXjgBHgCMQF4GBAwcGm1EBUdC1\nhjXmbd64/cB+0FRqVa48oVv36scuPAuz3xdi0Y4kknhoKnEHDKSI2RRa1EY+21ZUvxmP5RkXIV7C\nEeAIJAYBLpQnBjVj7gP/XEuWLHz48G7DJuH2jm8FNTlMM799E9tIWum58lhq8hF+6mpyjRGsLKtk\ndbrj6rg14OYbeB+rXr0GfIQJbUJCQqRSqUb7BB+5mjxBiHgDjgBHwCAEYIGz+/Dh8/9dYt5UvLya\n1agp9t9wJFgcEGw5lKyz6O0tUUbKrSRrYqPIIw46XbvihNX+b798njh9ukGL4Y05AhwBjkB8CHA/\n5fEhk7bLc+XKtW5dzKXquHFTLUUj9Yw0FM0mZwQYxiZ/8dHZtqtuOGwkjfAJDl1/9ozX4MGDQZVB\n+5b1GwTcujV28qQ+fftaWOh1I3yVHsPpyioapHu65Ki9fv36gX37vPv3d3R0TI7x+ZgcAY5AKiJQ\nrFix4nnzVYwQTQtVNiIbC72V4sKaL1PYSnH4RpEie/bs7t5eKzp3zpQpUypuh0/NEeAImCQCXFNu\nkq9Vc1O1a9cPUYwKClmqVCo06+I8x1KTx2aTx2kbq8DCooSlpeWCBQtQCgPQa5cvL/kevvL/7d13\nQJPH/wfwSwhJmCK4J27cili1Veuoo7j3QESrIloVcAEqCiJa7XK1VfypVdDWDbbW8bW4q6io4AAV\nEAcuFJGVnfwOohCREUlCnoQ3f+jD89xzz+deZx4+Hvfc47PIoR79H8IuuiDjB6WL+kaPw+Rzpk4N\nXb6iYe3aq1etomP8RUWHfRCAgKEK2NjYZObkzBfQZVa56mfkz4j8eyJoaS52tpRzprmevHQxJjHB\ne+5cZOSG+u8AcUOA2QJIypndP1qKbtu2dRERYTXq7HyT0z5buF+hUBRX8fth8ty1yfMXXSmucKH9\nIumqGTM86FoHdP8S77m+Yjb9+Xc5x3T188ygadPbNWla8iqKdJj8HLnjrY/Z5P/++2/CnbhrEou/\nsk2OBa1qVKv2xg0bxGJxoQbiWwhAwEAF6HhBy/bttpW4YHl+0+g0lb1E5GwuacjJvNCn64qwHbH3\n7jZo2nTSyFGuI9V9Vj6/NmxAAAIQUFMASbmaUAZfrE+fPrfvRP/f1pV8q8DcV4GK/y2ySR8Nk8/P\nX3SlyPL5OyXS+BzRcV+/BXTP8ePHE+7cmang020WYY0kvNsCnmfS85mjxnzRrn1xqygqh8ltiGV+\nneW2EbjQZ6GIbUZYnxPTUwJu6FtZqO/ipnXqbt++nS4lWW5h4EIQgIDuBBb6+f0pF9KEu4RLRBHJ\nTFNRDW7WykY1+gYHPnj8eOqc2WEhWxrWq3d88bIZSc8PRBzOyMgooQYcggAEIFBmASTlZaYzvBNZ\nLNaYMWMePIhbuWqqRDElQ+gskkSrNqOoYXI31QIlbAulq+jLjJS/1fWfO2+JMDfHzS9vQlhTCP+e\nkD8i9v6Ivv2ce/QstIqifofJ79+546Hg5Uf7FeFG5XDXpgp+mjWnZYMGe/fuLeF3C/lnYQMCEGCy\nwMiRI3lc7l8qC5bnR5tCZKtZgubmokFWCp672+nLl//46/Czx4/bOzSnLxJyOn4mQWp5NMfUg/Db\n88wjIiLyT8QGBCAAAS0KICnXIqZhVEWfufz222+fpCR5ze2VIRyQKZwgkSYoQ9dgmPxetvDIokW5\nk14OHDhwOy7OhNAX6BUekeIT1lwFP1Fk1vHclS8/6zRu2LCEhHeXpsPkdBnEMg+TqzNhvbjuyR8m\nL1SAvq4vJofr//jNoknfODo4HDlypFABfAsBCBiWwBd9+2xRmcEiJIo/iai/uaQxJyuqb/fvdofF\nPXjQvE0bjwmundq2fbVxy/63cvoiIT+FWe33LxIaky3Zg4XJDavXES0EDEcASbnh9JVWI6Xv+KTv\nG3r0KHG8a93XWZ0zBXOE4lM5omN+eYn1+9nk6g6TC6TfTZ06la7GSGNc6r9Szuk5jyVpyHpL52Uq\nPkrN6WvwAuX8BIlF9b9PtmvRYv1PP9Fh8vMkzum87cK5c1NTU8vQ0H7duju1aEGnzXzquXQ2eaFh\nctUa2ITlQnjxAt6MeykeI0eXMPdG9SxsQwACzBQICgqKlItSifwikXhwRTVMs9Y0qeW8KujR06ez\nFi7YHxZWr2bN3XMXeNxKeiax2irmfvHRi4RGEd6/58+9efOGmQ1EVBCAgEELICk36O7TNHj6UObG\njWvpouZ9nUWpbwfO8PBQzj/Je4Wn+rPJE3OEhxcv9qHR0OHkhw9fVrU5UNnuVRr/m8n0N8KsjCNF\n/b64KmGvlfLacfgKNls5m3zpFM/TG35rXK9e4NKlWVlZ6rftzJkzsdHRbnEPJw8d9mXHjufPn1f/\n3IAFC5WzyUs4hUNY7oR/X8jvEnPXuW9fzDIvwQqHIMBkgXbt2lW3qdyEnTHMmmUxffK56KsHTxxP\ne/Xqs1atJg8cZH/w6E2JxelsUzfCt1CZfafaopqE3ZlncfDgQdWd2IYABCCgFQEk5VphNOxK6KLm\nf/65k76Pc9V3K2hL8obJX1jyP2GYfPLkybVq1aLn+voGctm52TybzbG1Wmdrl/qYO3IUyXZkZZwm\nhVdjPEsk9zisDh696TB5ixP8148fX5CanxLyLvxAq8riAAAvl0lEQVSwtlHt3PVPJJLCpxQJTRPr\nBWL2bGKWKDQbGB039Kuv6Jz1a9euFVlYdWfuoitxcaqzyVWPFtqmc29e8DhTvvnGxITOzcEXBCBg\nkAK/7dyxcsP6e48ftf/sM8+p01o2aXLv+7UhLwUPBPwgOb/h+2kqJbRtTLZ0z9ZtJRTAIQhAAAJl\nE0BSXjY3IzyrUaNGfH7ueilSqVwmF2ULwxSK0hcekcoe5AgPLVniS0+ks0eSklIszQqyeTabX6XS\nNrsqT+6a9u1PMruzM+k7OPLtgs3IXF+f7/jhnmTQ2gWBCwUsun6wI+GcEHB3Z5AdPovpAuf0PUQl\nP2RJh8nvxMYqV3qhj5YuUPCTROafnbvSq0uXUQMHxcXF5V/u442AhaUPk+efdY/IDslFvv7++Xuw\nAQEIGJzAoEGDqleyqVOt+nqPb0dfvpUitdot5PYhXDpXTZ220Pl4toQVeekiZrCow4UyEIDAJwkg\nKf8krgpReOjQwQcO/G5bfXO6oEOOMLzkNgskq11dJ9K33NFidJicx57LYhUsY6I8l822qWKzz84u\n8apJl64kw5mddYtIaXZ+hSXt4jngAolzPGvz4N69KaTgxN7E9LLAdNXzzGVTpnVo3ryEyeLKYXJz\nlR+odM56gJyfKLawP376s7ZtJ40dl5yc/HErlGuTqzlMTk8P4smmTJ1Ss2bNj6vCHghAwIAEFi9e\n7CYil7NzV1OxIer+EEwmskC2sKGZaL4df/GiRZaWeli81YCQESoEIFAGAXXvR2WoGqcYroCzs/Pd\nuzd++XWJiZnfW8GXQvHZItsilT3MFuxfutSPHqU57t27SZZm3xRZku7kmNSoWvloFdtbZ01aO5K3\nA1lZM2bP/t7iMB0mX+ezfJ6IReeHqJ5LFzgfTXh3BDz3u08mDR3Wu0uXK1euqBag26rD5IUO2RH2\n91LeXYml+cG/WzVt+u20ac+ePVMtg2FyVQ1sQ6DiCIyd5BbBUmtqHDUREEUYEfU2l7TgZMcP6rM5\n/GDyyxeBK1bQZawqjhhaCgEIlI8AkvLycTa8q7DZbFdX10eP7i0LHC+QuWQIh4olsYWaIRCvGe/i\nUq9ePbqfDpPzTbxZNLUu8YvDaWBX+ay11fY0E5MuXgPpMPnnl2vEXL/ukfemoY9PpQ9Z0tEsOlm8\n1+Wbfbp2pTNS7t27l1/s42Hy/EPKjVqE/auERx/eygrd09Teft0PPyj3Y5i8EBS+hUDFEaDT1l4q\n5DEqU+mKbPslIpmet0LLOoe6I3/47mnqyz/CD/Xt25feG4ssj50QgAAENBTAzUVDQCM/nb6b2tvb\nKyXlwcxZn6XnfJUpnCyVJSvbLJU9zhbuWbZsEf2WjljfvhVvYTZVTQ4Zax/9ubipxikvMmi97wpP\nCcfyw2HyQvXQ2SmL82akWByL/MzRUXm0hGHyQqc3ICY7RFxnYpqYvyw6ZpMXMsK3EKgwAubm5vQR\nmj+KWhWKGjwn8jUk90VCQ6yJ5fTJF65FX4m7M2PGDBsbmwojhIZCAAL6EUBSrh93w7qqlZVVcHBQ\n8sOEkWPsXmU4ZQrmy+SpAvH3Y8aMtbe3p23x9Q3gcbzYLHN12iWSXBVLL3zlO5wOk/e6aX/+wvnZ\ncq46J9IZKWwTk/FjxyoLlzpMrlpnEpEdUUgWLllCd2KYXFUG2xCogAKuU6fsZIlVGy4hioNENMhM\n0oCT+V+frvRFQimvX/+4fn2rVq1Ui2EbAhCAgO4EkJTrztbYaq5evXpIyC934mK/7P06NaNVlnBX\nQOBi2ki6LviNGzctzdzVbLBItmL+fO8frf7OHSb3C54pM1XzWasEIturEC0KCKAXUn+YXBnVCq7U\nze3dA6llnk2elJS0efNmoVCoZktRDAIQYKbA3Llz3xC5cjEoOo/FiyOqxctZal+lx3L/qzdudO3z\n1XJfv6ioKGYGj6ggAAFjFUBSbqw9q6t20V/7Hjr058WLp0NDtzds2JBexs8vkM+Zw2ZZqHNJkSRK\nIrvSY8EQOkzeLbr20eMnvGVqDZPTygN5sm++maxc6eWThskf5GbzYt+lS2klmgyTz5s5c9ms2U3q\n1AkJCZFKC9Z2VKfhKAMBCDBHgE7Ma9rMYQErp4OFpIeZROw25q/Tp9b88suFf/91atfuzLIV9e8m\nbfvtN+YEjEggAIGKIICkvCL0svbb6OjoOGbMGFqvSCS6fTueRcwVaixqnlteFuTjM+8Hy8N0mPxH\n/5/EUskClvAJKX1B9HgiC1eI/JYto5V86jB5MFfqOmFC3bp16bllHia/ceNGZOSpO1LLTa/Fv3nP\na17ffvfu3XK5nNaJLwhAwOAEFgUse2lfd/6WTf9FX7WtWnXkgAGeo8Z0PH42UWr1l8B0qYIfHh6O\n/3sbXLciYAgYtACScoPuPv0Hz+Pxduz4tXLV7ekCpxxRRMkBCcX/yRQx3ecN/I/E94trevLkyUqV\n9h8wqdmIpHuzcl6TkhJcOkzuPn26cpnwTxomf0hkfypEvss0HSYP9PWlc99tCXsA4V7L4QY/fbt8\nqnvbxk0iIkppdckmOAoBCOhFgA4rnL0cFfLzWqc2bR7+/EtYmvReDtdPYUaXbKLxtCMcOwWL/mJN\nL7HhohCAQMUUQFJeMftdm62mb8i7n3Bzw0Y/E77vW0F3ofh0cbWL5csXLVqwxjyCDpOvWLDSgu9u\nznO2tb1VqVL4/7Er1SVvAlmCLKL4+HT6sqEjROyT95jmJw+Tm0pdxrvUr1+fVqvhMPlc2buViZVr\nqN8W8OY+eOE5bnynVq3pfzA+Dht7IAABJgv4+Pi8unqNvtQzVMTtQUzp51o12lFCxd7QUNU92IYA\nBCCgUwEk5TrlrSiV04V73dzc6KLmgUETRHK3DOEAkSS6UOPpG4gUdFVyz6/pMHmv2Ab/+9//zHlz\nlWXMeH1t7ZLMrLb8wDatTd6sIwLxh6l5AF8+49tvq1WrRst/0jD5IyLbTUR+AbmTXjSZTZ4/TK7a\nKBPCmkz49wR819tJroMG9+zU6eLFi6oFsA0BCDBZYOrUqXcVUvpBLjLIUXIOZrAUKYOdEICAjgSQ\nlOsItiJWS5+d8vT0fJKS5Ondg+blGcLxEundfAixPGjxkoWrzcK9yeAV81da8GaasO3yj9INSzMX\nW7tnbIvgJWxFXZK+gwjleak5XRvhf0SycNG7BdHvxMbOLOZNQ6q1KbdXmkrHjX23bqO2hskLXYVL\nWLOIGX29Ub8rtwf26Dmo91cxMTGFyuBbCECAgQJdunSxMTcPL2bBcsxgYWCXISQIGLcAknLj7l89\ntM7S0jIgYOnjx0lukxqmZXXNFHpIZU+E4kjCTug0u99FEt/9ep0zZ89a8OYUGZy1hZdtldcCc89v\nWeImrAz683IpXz7b09POLjeD/6Rh8sdERt+PvSgwkJ6oyTB5gI/PbJkpnU1eZMDKnfT1Rr4KfpLY\nvP7pi52cnOjzryUUxiEIQIAhAl379dvBlhQXzEChHDNYisPBfghAQOsCJeUZWr8YKqw4AjSHXrvu\nx4TE+MHDuKkZ7TIE05ct813NP+RFBgd4rzDnzmazK5egUdkyuLJd6guey3iSc0KSM8/Hhxb+1Nnk\nq0ylY0aPbtCgAT1Xk2HyU6dOz1Xv9UaVCNuUzR45ZAh9/rWE1uEQBCDAEAF/f/8zchFds1w1nkyi\n+J0Ie5lLNrElNWrUUD2EbQhAAAK6E0BSrjtb1EzomuK//77l1q3r8xdM7jdjWBS51zmqetSlyxa8\nWaXqsNlcO+vfuFa9p3jMrFw5N4P/pGHyFCLbqY1h8iJnkxcXPH1B9/+xRP7BwcUVwH4IQIBRAu3b\nt7eztDqYN4OFzpc7QcQTeOKanMwtbRqP+WnN09SXwT/8wKiAEQwEIGDEAhwjbhuaxhCBpk2bBgUF\n0WCSSMgPR35msVqTD5/jLC5OseSaWHppxYo9tIBymPyIwry4woX202HyUSNG0lcd0f00m18oYpsV\n8zhXoRPvEdkhuei+vz/dr1ybfKtMrfci0fKrOZIhgwc3a9asUJ34FgIQYKxAz4EDftl74C6L7DKV\n8ypZu06bdsPNrXHjxowNGIFBAALGKoCRcmPtWSa2y5qYu7mN/fwLq1dZrTJzNioU4pKjFMqC5871\ntLGxocU+aZj8KZH/TkSLg5bTE3Nnk8fFeSjUnU8SxJNNmTpFuSA6hslL7iAchYARCCxduvS+CevN\n+BF/nji+OzycxWbXqlXLCNqFJkAAAgYngKTc4LrMsAOmQ9cnT/59/PjBOvb70nLaZQv3KhRFLExO\nG0mHyUWSC/Pn5y6b+Kmzyb8zlYwYNkw51lWGYXJflWHy/LXJS3XHMHmpRCgAAQYKNG/ePFMsatzc\nYeq48UN69Nz63Zq9e/cyME6EBAEIGL0AknKj72ImNrB79+6xNy9v376GbxX0Vti1yPcNlXmY/BmR\nb8sdJs+dMINhciZ2P2KCAMMEjh8/Tlds/THlbYrIfInYZOdvvzEsQIQDAQhUCAEk5RWim5nZyFGj\nRj14ELcieLJQPjFDOEQsvZkfpybD5HTEetiQIXQiO60Nw+T5pNiAAASKE/jyyy9lCkVNwuYQ1mjC\nvXTt2uPHj4srjP0QgAAEdCSApFxHsKhWLQFTU9PZs2c/eZI0c1anN9m9M4XT6KLm9EzVYfIZU2fY\niKX3iUydGpXrnyxZsYIWxjC5OmIoAwEI8Pn8xs2a/sHKfcqlMmE7c8zCdu4ECwQgAIFyFkBSXs7g\nuFwRAlZWVsHBQQ8e3Bsy3PxVZvv07On5s8npr5WTn7x+wnHqSN6OYWc/KC01X0PXPxk0SLn+CYbJ\ni7DGLghAoCiBye7udBFVRd7CUBOFJDQkpKhS2AcBCEBAhwJIynWIi6o/SYAueLJ9e0hMzNVuPXKC\nggLpoiv0GVBvLz8+Z1GVypF2tpf/Yds3I+nfsnJSP3zTR/5VXhL5FpYIw+T5INiAAATUFJgzZ04W\nIZeIlJb/mpimvnh59epVNc9FMQhAAAJaEUBSrhVGVKI1AQcHh7//3j9vnhet8eLFi4mJDyzMJtBt\nLqeVre01m0r/7DSpXI+8CWAJsj5a7JwOkw8cMICupUDLY5hca12CiiBQAQQ4HI5Dyxa7WRLaVlPC\nGivj7Ny6tQK0G02EAAQYJICknEGdgVAKCbRq1aphQ/t0Qads4QHlyolmvB62tgkWVtt+ZJvWJm82\nEIHkfWpOh88363WYPDo62sbC0nf+/PT09EINwbcQgADzBabNnLlbITpGxG488Q6WKC01lfkxI0II\nQMCYBJCUG1NvGltbrK2t78RdCwkJ4loszRB2E4pPKVtoYTbW1u4Z22LFIra8Pkn/I28m6PcmYuf+\nX7ds2ZKW0csweYCPz0iBLO6XkEZ16ny/erVQKDS2/kB7IGDUAtOnTxeasKZX4debNzsqJmbuokWz\np0+vX6NGXFycUbcbjYMABJgigKScKT2BOIoUYLFY48aNe/jwblDwJJFiUoZwoFhyXVnS2sLbxvZV\nptnMqSxhC1bGryyR/8pgekgvi67QYfJz5879oDCLEHL/yjY5HBjcpE6drVu3ymRqLRpTZNuxEwIQ\nKE8BNpv9+OXLq3du21WtOnbgoD5dPifbdrd7nbEND32WZzfgWhCowAJIyitw5xtO0/NXTpzj1T1d\n0D9T6CqRJtLw6Q9RW6s1tnYvE00aNm7Wgk53oTv1NUzuKTW1IbkfqM+J6TkB97fX4rWzvVo3ahQe\nHm440ogUAhVagP52rnPzFkf8li5JevFMbLFBylsgNd29Ywf+d12h/1mg8RAoLwEk5eUljetoLGBp\naRkYuOzRo8SxLrVeZ3XKFHjJ5C9orXLFaxY75c/9e+i2HofJveVc1SYOJNwYAdfn4SvPcS6ft213\n9uxZ1aPYhgAEGChAH/dMz8qaIlSMIjwuYdEIvyAcnlBMbywMjBYhQQACRiaApNzIOtT4m1O1atVf\nf11/9+7tr/pnp2a0zhQEZouWjh41mi7bQhuv92Fy1Q5gE5Yb4d8T8kfdTBjet+/AXr1v3ix4a6lq\nSWxDAAIMEfjS+esQdu4aLMovFmG5ChU7MYPlPQj+hgAEdCeApFx3tqhZhwL29vZ794ZduXKhbYcY\ngfhQ8MoAejHmDJOrtpxHWN4KfpLIvN3Zy190cJo4egyWZ1H1wTYEGCUQFBR0Ti56ofIyBFcFN/yv\nv7Ky6Drm+IIABCCgQwEk5TrERdW6FmjTps2pU0dfvXpZr149eq0lc+ctELLN8n7pXOql7xHZIbnI\n19+flrxx40Zk5Km5MtNSz1IWWE3fGzp48Lv3hvr45M8mL+F0a8JeIeNdkZjv2rc3OTm5hJI4BAEI\n6FGAruBU085uNxHlx9CYmLQx5R84cCB/DzYgAAEI6EIASbkuVFFnuQpYWVkpryfLyVnHEu55/67s\nkoMI4smmTJ1CXyNKiwX6+s6Wc23zHtMs+Sx69DmR/x9d6SU4d6UX5aIrhWaTl1DDXrak35dftmvX\nroQyOAQBCOhXYISr6ya2OD8GMVG0yhbt3rIlfw82IAABCOhCAEm5LlRRp34E/ou7M3DG9Gkmwuas\njCOk4Gfqx9GU8zC5MoAMIv+ZI122evXH8WAPBCDAHIFly5Yly6W3iPQ6kc7hiGrxsi81rD3B3Z05\nESISCEDAKAWQlBtlt1bQRtGVE3755ZeXWZmfTRg3mp3tyM44TQoe2FJF0csw+Tq2uFPnTvRLNRJs\nQwACTBOwsbFp3LjxF+zM3uYS+eRxxy9ciE1MdJ04kWlxIh4IQMDIBDj6aI80MeqfI2di3opIteY9\nhg/tVlUvUeij5bhmOQjw+fydO3du3LjRbeLErw8fdmKZ/ijnf0YK5osrh8nvq8wm3yqzUDOwMswm\nV9asHCb/B8PkakKjWPkJ4IZchPW2sLDTp097eXnxeLwiDmMXBCAAAR0IlH86/HSza22PsIKmeBD3\n6IzNju9mBRfsxxYENBGgLwE5FB7+/PlzV5cJ3SIje7G538v5rUjuP/jcYfJvyj6b/KrKbPJQubrZ\nvHKYvHPnzpo0CudCQNsCuCEXLZr7Ky38UqtoG+yFAAR0JVDe01cS9wfSjHzd0diU5Lh9qyfkNSuk\nw9LDUl01EPVWaIEaNWr879+T8UmJGU5tO5C3Y9jZR4m4PBddUerTYfK1mE1eof8lMrTxuCEztGMQ\nFgQgUCEFyjkpT909KsQrInlO/9a16juMXBgauz3v0ZmYZ5kVUh+NLh+BBg0aXIiKir55836LxgNJ\nZt/+/cp50RU6TP5Z504YJi+f7sZV1BbADVltKhSEAAQgoHuB8p2+InycNmHdosH189vVvOcAQkKI\nyoqw+YewAQHtCrRq1eoafaPmzZvKRc2Va5OXz2xyOkx+BLPJtdudqE1zAdyQNTdEDRCAAAS0J1C+\nSTnf8edQR9XgpYK03G/b2mNKuSoLtnUn0Lp1a2XlN65f50ql54h0COGWejm6NvkWlii6TLPJ12OY\nvFRfFNCLAG7IemHHRSEAAQgUI1C+SflHQSRfu0j3+fVzKi6OuXPnWlpa0jL0V/8eHh4fVYAdECij\nwHgXl7Pnzrns2NmQCH+S878qMTWni64M/cRXeCrDUi66gmHyMnaSsZx28eLFzZs3K1sjEhW8KpJp\n7Sv5hiyXy/ft2xcfH0/Drlat2po1a5gWP+KBAAQgUKrAixcvfHx8lMUePnyofDl3qWeVT4HikuGy\nXV0qzBQUvS50bn2mZlb8D673JsrXJYRM2OXXv1Zx13NwcKhcuTI9Wrdu3eLKYD8EyiDA5XK3bdu2\nfv36yZMnDz5wsC1LRFPzLiorJ+bXqXyFZ9kWXcEweT5jRd6g617n/4pmz5495UWh5Rsyi8WiT04r\nG1KpUqXyagWuAwEIQECbAnSd0/wb8smTJ7VZtcZ1sRQKhcaVvK8gM6qXdedT77/76O82ZzNiuhXM\nUxHudjVzCfOKE/zswP+oLCFCodDMzIz+J0Y5A7iIEtgFAS0JvHr1ytXF5dSJ/3XLWzmxXd7Kifl1\ne3NEqUP6he3fT/cM+uorx1OXAuVF/ZPNP+H9Bh0mb8DNOXLmNB7xfE+CvwldrHPWrFkrV67UuYVW\nb8g0WnNz86VLl/r6+uo8clwAAhCAQLkIdOjQgb7b5MKFC+VytdIv8sHIdenFSy5hVmOin1dbYmZW\nRDGBgHR0KMjIybn1zi5hE6LTis7Ii6gAuyCgM4EqVaocPX780aNHE8aN6/TffwPY/FVyfjNiQi+o\nrWFyH2/vOg0aTPfwoCP0OmsHKoaAigBuyCoY2IQABCDAfAGtJuWc+pNW/qxOm6/97trd0/rsy1DH\n3JkpuV+pD59Wrl9Lq9EoK8afEFBXgP5O5uyFC3FxcRPHjW8dc2Ms2yxIz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"prompt_number": 59, "text": [ "<IPython.core.display.Image at 0x10ab33d50>" ] } ], "prompt_number": 59 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Easily generalisable to D dimensions (see book for details...)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>4.1.7 The Perceptron algorithm</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another example of a linear discriminant model (and has historical importance, apparently). Used for two-class models where a set of feature vectors, $\\phi(x)$ is used to construct a generalised linear model of the form" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$y(x) = f(w^T\\phi(x))$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the nonlinear activation function, $f(.)$ is a step function. Find parameters, w, of the discriminant function by minimising the error function. Could choose to minimise the number of misclassified points, but this leads to discontinuities where a change in w causes the decision boundary to move across one of the data points and therefore you can't calc gradient." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The alternative: the perceptron criterion." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A correctly classified data point will contribute zero error, whereas for a misclassified point the algorithm will try to minimise the quantity, $-w^T\\phi(x_n)t_n$. So the perceptron criterion is" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$E_p(w) = - \\sum_{n\\in\\mathcal{M}} wT \\phi_n t_n$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\mathcal{M}$ is the set of misclassified data. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the contribution to the error associated with a particular misclassified point is a linear function of w in regions of w space where the pattern is misclassifiesd and zero in regions where is is correctly classified. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use stochastic gradient descent to find the weights. This algorithm will find an exact solution in a finite number of steps!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d.Image(filename=\"fig4.7.png\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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REscl86WKpUtkLsqekUuWD1OwV1RRIipNKd+CmWCmxqj2Ur1YI0xTTQtoPdbO3uGhI6bz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+PvlQ65sqUV2rq2qMcgYx7btCIsdzRw4ED8YmzatMlyIlDNgiJgFbOzbh42Ww47XzyT8uqZPH9h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\nrYMDezVysGQaLedgo5BIZiLwZPAopdlW1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"prompt_number": 72, "text": [ "<IPython.core.display.Image at 0x108154350>" ] } ], "prompt_number": 72 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>4.2 Probabilistic Generative Models</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Models with linear decision boundaries arise from simple assumptions about the data. In this section the class-conditional densities, $p(x\|C_k)$ are used, with some priors to compute posteriors." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider 2 classes. The posterior for class 1 can be written" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$p(C_1\|x) = \\frac{p(x\|C_1)(p(C1)}{p(x\|C_1)p(C1) + p(x\|C_2)p(C2)}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$= \\frac{1}{1+\\exp(-a)} = \\sigma(a)$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$a = ln\\frac{p(x\|C_1)p(C_1)}{p(x\|C_2)p(C_2)} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and $\\sigma(a)$ is the logistic sigmoid function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d.Image(filename=\"fig4.9.png\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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REscl86WKpUtkLsqekUuWD1OwV1RRIipNKd+CmWCmxqj2Ur1YI0xTTQtoPdbO3uGhI6bz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\nTq1OP3Iqc+d+xZ6dVF7qo3ndfN169av63gj/po1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"prompt_number": 73, "text": [ "<IPython.core.display.Image at 0x1097b6ad0>" ] } ], "prompt_number": 73 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generalising to K > 2 classes:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$p(C_k\|x) = \\frac{p(x\|C_k)p(C_k)}{\\sum_jp(x\|C_j)p(C_j)}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$= \\frac{\\exp(a_k)}{\\sum_j \\exp(a_j)},$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $a_k = \\ln p(x\|C_k)p(C_k).$ The above is a 'normalised exponential', a multiclass generalisation of the logistic sigmoid, also known as the 'softmax function'." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If $a_k > a_j$ for all $j \\neq k$, then $p(C_k\|x) \\sim 1$ and $p(C_j\|x) \\sim 0$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now explore different forms fot the class conditional densities: continuous and discrete." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>4.2.1 Continuous inputs</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assume class-conditional densities are Gaussian and share the same covariance matrix:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$p(x\|C_k) = \\frac{1}{(2\\pi)^{D/2}}\\frac{1}{\|\\Sigma\|^{1/2}} \\exp \\left\\{-\\frac{1}{2}(x - \\mu_k)^T\\Sigma^{-1}(x - \\mu_k)\\right\\}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider 2 classes and plug the above into " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$p(C_1\|x) = \\sigma(w^Tx + w_0)$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The quadratic terms in x in the exponents cancel because the covariances are the same, leading to a linear function of x." ] }, { "cell_type": "code", "collapsed": false, "input": [ "d.Image(filename=\"fig4.10.png\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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REscl86WKpUtkLsqekUuWD1OwV1RRIipNKd+CmWCmxqj2Ur1YI0xTTQtoPdbO3uGhI6bz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MGuxRBoe7MNYtmxoPVOEuDEeCWg6YlxGY4wx+DCKwZLhDH81elAio6ADgJZDfQkLkecrz0j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mb9CgVsUV02YkCmtETL5rEDt899JY5CC9ns5+QHlkuPuxx1/88ldvvfcHz6xaffqxx/UZ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\nly2Le3QA/ExTQPtcbp2xlkAtgVoCcyKBRGlIJWaiFVvJTgNSrVEHxY8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"prompt_number": 74, "text": [ "<IPython.core.display.Image at 0x10c64ce10>" ] } ], "prompt_number": 74 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Decision boundaries correspind to surfaces along which the posterior probabilities $p(C_k\|x)$ are constant and so will be given by linear functions of x, and therefore the decision boundaries are linear in input space. The prior probabilities, $p(C_k)$, enter oly through the bias parameter $w_o$ so that changes in the priors have the effect of making parallel shifts of the decision boundary and more generally of the parallel contours of constant posterior probability." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is trivially generalisable to more than 2 classes by using the normalised exponential instead of the logistic sigmoid." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you relax the assumption of shared covariances you get quadratic functions of x, giving rise to a quadratic discriminant." ] }, { "cell_type": "code", "collapsed": false, "input": [ "d.Image(filename=\"fig4.11.png\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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REscl86WKpUtkLsqekUuWD1OwV1RRIipNKd+CmWCmxqj2Ur1YI0xTTQtoPdbO3uGhI6bz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/3PthZGb0ZlV1W7PeATVO51AXaI5mFCenpxKFOu0wVqBGSgaiE3NEKzCRUv02BYhk1FoSBV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\nqNbj8znxLIRCFImtoapHH4k9yoQjAdE7LlqJnEysdVSGMIN40lELG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0WABmUa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\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K9tTl8xRlh6wQnyFAlEKwFBQcpAv3q6qlKzdE1zAUwHfUSDEw/MBc0wvTLkJCOcIcORRp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\n0JZZ+hsbTWA9PbiPUifU1w4ODnd1d+J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"prompt_number": 75, "text": [ "<IPython.core.display.Image at 0x10c97a610>" ] } ], "prompt_number": 75 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>4.2.2 Maximum likelihood solution</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have a parametric functional form for the class-conditional densities, you can find the parameter values using max likelihood. The likelihood is given by" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$p(t\|\\pi, \\mu_1,\\mu_2,\\Sigma) = \\prod_{n=1}^{N}[/pi\\mathcal{N}(x_n\|\\mu_1,\\Sigma)]^{t_n}[(1-\\pi)\\mathcal{N}(x_n\|\\mu_2,\\Sigma)]^{1-t_n}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>4.2.3. Discrete features</h3>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Feature values treated as independent. Class-" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": {} } ] }	mit
araichev/budgeting	notebooks/prototyping.ipynb	1	83594	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "import sys\n", "\n", "sys.path.append('../')\n", "import mustaching as ms\n", "\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load transactions" ] }, { "cell_type": "code", "execution_count": 26, "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>date</th>\n", " <th>amount</th>\n", " <th>description</th>\n", " <th>comment</th>\n", " <th>category</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2020-01-01 00:00:00</td>\n", " <td>61</td>\n", " <td>0x66736</td>\n", " <td>0xfa3d4004d8</td>\n", " <td>programming</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2020-01-01 12:00:00</td>\n", " <td>79</td>\n", " <td>0x55ce2</td>\n", " <td>0xf853bc66f1</td>\n", " <td>programming</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2020-01-02 00:00:00</td>\n", " <td>14</td>\n", " <td>0xee9a3</td>\n", " <td>0x38bc13c668</td>\n", " <td>reiki</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2020-01-02 12:00:00</td>\n", " <td>76</td>\n", " <td>0x346de</td>\n", " <td>0x6f1942aa09</td>\n", " <td>programming</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2020-01-03 00:00:00</td>\n", " <td>36</td>\n", " <td>0xb3df1</td>\n", " <td>0x8a936a1843</td>\n", " <td>programming</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2020-01-03 12:00:00</td>\n", " <td>42</td>\n", " <td>0x2b6bd</td>\n", " <td>0x545c0e9945</td>\n", " <td>reiki</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2020-01-04 00:00:00</td>\n", " <td>-65</td>\n", " <td>0x5bb</td>\n", " <td>0x8aa462342b</td>\n", " <td>shelter</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2020-01-04 12:00:00</td>\n", " <td>62</td>\n", " <td>0xc7581</td>\n", " <td>0x99146ccd75</td>\n", " <td>investing</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2020-01-05 00:00:00</td>\n", " <td>85</td>\n", " <td>0xf3334</td>\n", " <td>0x9f85d84001</td>\n", " <td>programming</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2020-01-05 12:00:00</td>\n", " <td>84</td>\n", " <td>0x7b335</td>\n", " <td>0x346ec6bc74</td>\n", " <td>investing</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date amount description comment category\n", "0 2020-01-01 00:00:00 61 0x66736 0xfa3d4004d8 programming\n", "1 2020-01-01 12:00:00 79 0x55ce2 0xf853bc66f1 programming\n", "2 2020-01-02 00:00:00 14 0xee9a3 0x38bc13c668 reiki\n", "3 2020-01-02 12:00:00 76 0x346de 0x6f1942aa09 programming\n", "4 2020-01-03 00:00:00 36 0xb3df1 0x8a936a1843 programming\n", "5 2020-01-03 12:00:00 42 0x2b6bd 0x545c0e9945 reiki\n", "6 2020-01-04 00:00:00 -65 0x5bb 0x8aa462342b shelter\n", "7 2020-01-04 12:00:00 62 0xc7581 0x99146ccd75 investing\n", "8 2020-01-05 00:00:00 85 0xf3334 0x9f85d84001 programming\n", "9 2020-01-05 12:00:00 84 0x7b335 0x346ec6bc74 investing" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Invent some sample transactions.\n", "transactions = ms.create_transactions('2020-01-01', '2020-12-31')\n", "\n", "# Alternatively, upload your own transactions as say 'my_transactions.csv' \n", "#transactions = ms.read_transactions('my_transactions.csv')\n", "\n", "transactions.head(10)" ] }, { "cell_type": "code", "execution_count": 27, "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>date</th>\n", " <th>amount</th>\n", " <th>description</th>\n", " <th>category</th>\n", " <th>comment</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2020-01-01 00:00:00</td>\n", " <td>61.0</td>\n", " <td>0x66736</td>\n", " <td>programming</td>\n", " <td>0xfa3d4004d8</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2020-01-01 12:00:00</td>\n", " <td>79.0</td>\n", " <td>0x55ce2</td>\n", " <td>programming</td>\n", " <td>0xf853bc66f1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2020-01-02 00:00:00</td>\n", " <td>14.0</td>\n", " <td>0xee9a3</td>\n", " <td>reiki</td>\n", " <td>0x38bc13c668</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2020-01-02 12:00:00</td>\n", " <td>76.0</td>\n", " <td>0x346de</td>\n", " <td>programming</td>\n", " <td>0x6f1942aa09</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2020-01-03 00:00:00</td>\n", " <td>36.0</td>\n", " <td>0xb3df1</td>\n", " <td>programming</td>\n", " <td>0x8a936a1843</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>726</th>\n", " <td>2020-12-29 00:00:00</td>\n", " <td>-23.0</td>\n", " <td>0xdf05d</td>\n", " <td>transport</td>\n", " <td>0xb482ea9eaf</td>\n", " </tr>\n", " <tr>\n", " <th>727</th>\n", " <td>2020-12-29 12:00:00</td>\n", " <td>32.0</td>\n", " <td>0x12d0e</td>\n", " <td>investing</td>\n", " <td>0x440177a330</td>\n", " </tr>\n", " <tr>\n", " <th>728</th>\n", " <td>2020-12-30 00:00:00</td>\n", " <td>86.0</td>\n", " <td>0x33cfd</td>\n", " <td>programming</td>\n", " <td>0xa72ad6bcd5</td>\n", " </tr>\n", " <tr>\n", " <th>729</th>\n", " <td>2020-12-30 12:00:00</td>\n", " <td>-29.0</td>\n", " <td>0x5003a</td>\n", " <td>shelter</td>\n", " <td>0x4c4e261e73</td>\n", " </tr>\n", " <tr>\n", " <th>730</th>\n", " <td>2020-12-31 00:00:00</td>\n", " <td>0.0</td>\n", " <td>0x4686d</td>\n", " <td>soil testing</td>\n", " <td>0x2473651aa2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>731 rows × 5 columns</p>\n", "</div>" ], "text/plain": [ " date amount description category comment\n", "0 2020-01-01 00:00:00 61.0 0x66736 programming 0xfa3d4004d8\n", "1 2020-01-01 12:00:00 79.0 0x55ce2 programming 0xf853bc66f1\n", "2 2020-01-02 00:00:00 14.0 0xee9a3 reiki 0x38bc13c668\n", "3 2020-01-02 12:00:00 76.0 0x346de programming 0x6f1942aa09\n", "4 2020-01-03 00:00:00 36.0 0xb3df1 programming 0x8a936a1843\n", ".. ... ... ... ... ...\n", "726 2020-12-29 00:00:00 -23.0 0xdf05d transport 0xb482ea9eaf\n", "727 2020-12-29 12:00:00 32.0 0x12d0e investing 0x440177a330\n", "728 2020-12-30 00:00:00 86.0 0x33cfd programming 0xa72ad6bcd5\n", "729 2020-12-30 12:00:00 -29.0 0x5003a shelter 0x4c4e261e73\n", "730 2020-12-31 00:00:00 0.0 0x4686d soil testing 0x2473651aa2\n", "\n", "[731 rows x 5 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import io\n", "\n", "ms.read_transactions(io.StringIO(transactions.to_csv(index=False)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Summarize transactions and plot" ] }, { "cell_type": "code", "execution_count": 28, "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>date</th>\n", " <th>category</th>\n", " <th>income</th>\n", " <th>expense</th>\n", " <th>balance</th>\n", " <th>savings_pc_for_period</th>\n", " <th>spending_pc_for_period</th>\n", " <th>spending_pc_for_period_and_category</th>\n", " <th>income_pc_for_period_and_category</th>\n", " <th>expense_pc_for_period_and_category</th>\n", " <th>daily_avg</th>\n", " <th>weekly_avg</th>\n", " <th>monthly_avg</th>\n", " <th>yearly_avg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2020-01-01</td>\n", " <td>food</td>\n", " <td>0</td>\n", " <td>1595</td>\n", " <td>9010</td>\n", " <td>44.03</td>\n", " <td>55.97</td>\n", " <td>7.79</td>\n", " <td>0.00</td>\n", " <td>13.93</td>\n", " <td>-4.36</td>\n", " <td>-30.51</td>\n", " <td>-132.55</td>\n", " <td>-1590.64</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2020-01-01</td>\n", " <td>healthcare</td>\n", " <td>0</td>\n", " <td>2203</td>\n", " <td>9010</td>\n", " <td>44.03</td>\n", " <td>55.97</td>\n", " <td>10.77</td>\n", " <td>0.00</td>\n", " <td>19.24</td>\n", " <td>-6.02</td>\n", " <td>-42.13</td>\n", " <td>-183.08</td>\n", " <td>-2196.98</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2020-01-01</td>\n", " <td>investing</td>\n", " <td>4721</td>\n", " <td>0</td>\n", " <td>9010</td>\n", " <td>44.03</td>\n", " <td>55.97</td>\n", " <td>0.00</td>\n", " <td>23.07</td>\n", " <td>0.00</td>\n", " <td>12.90</td>\n", " <td>90.29</td>\n", " <td>392.34</td>\n", " <td>4708.10</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2020-01-01</td>\n", " <td>programming</td>\n", " <td>11017</td>\n", " <td>0</td>\n", " <td>9010</td>\n", " <td>44.03</td>\n", " <td>55.97</td>\n", " <td>0.00</td>\n", " <td>53.84</td>\n", " <td>0.00</td>\n", " <td>30.10</td>\n", " <td>210.71</td>\n", " <td>915.57</td>\n", " <td>10986.90</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2020-01-01</td>\n", " <td>reiki</td>\n", " <td>4725</td>\n", " <td>0</td>\n", " <td>9010</td>\n", " <td>44.03</td>\n", " <td>55.97</td>\n", " <td>0.00</td>\n", " <td>23.09</td>\n", " <td>0.00</td>\n", " <td>12.91</td>\n", " <td>90.37</td>\n", " <td>392.67</td>\n", " <td>4712.09</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2020-01-01</td>\n", " <td>shelter</td>\n", " <td>0</td>\n", " <td>3724</td>\n", " <td>9010</td>\n", " <td>44.03</td>\n", " <td>55.97</td>\n", " <td>18.20</td>\n", " <td>0.00</td>\n", " <td>32.52</td>\n", " <td>-10.17</td>\n", " <td>-71.22</td>\n", " <td>-309.49</td>\n", " <td>-3713.83</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2020-01-01</td>\n", " <td>soil testing</td>\n", " <td>0</td>\n", " <td>2164</td>\n", " <td>9010</td>\n", " <td>44.03</td>\n", " <td>55.97</td>\n", " <td>10.58</td>\n", " <td>0.00</td>\n", " <td>18.89</td>\n", " <td>-5.91</td>\n", " <td>-41.39</td>\n", " <td>-179.84</td>\n", " <td>-2158.09</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2020-01-01</td>\n", " <td>transport</td>\n", " <td>0</td>\n", " <td>1767</td>\n", " <td>9010</td>\n", " <td>44.03</td>\n", " <td>55.97</td>\n", " <td>8.64</td>\n", " <td>0.00</td>\n", " <td>15.43</td>\n", " <td>-4.83</td>\n", " <td>-33.80</td>\n", " <td>-146.85</td>\n", " <td>-1762.17</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date category income expense balance savings_pc_for_period \\\n", "0 2020-01-01 food 0 1595 9010 44.03 \n", "1 2020-01-01 healthcare 0 2203 9010 44.03 \n", "2 2020-01-01 investing 4721 0 9010 44.03 \n", "3 2020-01-01 programming 11017 0 9010 44.03 \n", "4 2020-01-01 reiki 4725 0 9010 44.03 \n", "5 2020-01-01 shelter 0 3724 9010 44.03 \n", "6 2020-01-01 soil testing 0 2164 9010 44.03 \n", "7 2020-01-01 transport 0 1767 9010 44.03 \n", "\n", " spending_pc_for_period spending_pc_for_period_and_category \\\n", "0 55.97 7.79 \n", "1 55.97 10.77 \n", "2 55.97 0.00 \n", "3 55.97 0.00 \n", "4 55.97 0.00 \n", "5 55.97 18.20 \n", "6 55.97 10.58 \n", "7 55.97 8.64 \n", "\n", " income_pc_for_period_and_category expense_pc_for_period_and_category \\\n", "0 0.00 13.93 \n", "1 0.00 19.24 \n", "2 23.07 0.00 \n", "3 53.84 0.00 \n", "4 23.09 0.00 \n", "5 0.00 32.52 \n", "6 0.00 18.89 \n", "7 0.00 15.43 \n", "\n", " daily_avg weekly_avg monthly_avg yearly_avg \n", "0 -4.36 -30.51 -132.55 -1590.64 \n", "1 -6.02 -42.13 -183.08 -2196.98 \n", "2 12.90 90.29 392.34 4708.10 \n", "3 30.10 210.71 915.57 10986.90 \n", "4 12.91 90.37 392.67 4712.09 \n", "5 -10.17 -71.22 -309.49 -3713.83 \n", "6 -5.91 -41.39 -179.84 -2158.09 \n", "7 -4.83 -33.80 -146.85 -1762.17 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Summarize over entire period by category\n", "s1 = ms.summarize(transactions, by_category=True)\n", "s1" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<iframe style=\"border:0;outline:none;overflow:hidden\" srcdoc=\"<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8" /> <link href="https://www.highcharts.com/highslide/highslide.css" rel="stylesheet" /> <script type="text/javascript" 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{}, "subtitle": {}, "title": {"text": "Account Summary"}, "tooltip": {"headerFormat": "<b>{point.key}</b><table>", "useHTML": true, "pointFormat": "\\n <tr>\\n <td style=\\"padding-right:1em\\">{series.name}\\n ({point.percentage:.0f}%)</td>\\n <td style=\\"text-align:right\\">{point.y:,.0f} \\n </td>\\n </tr>\\n ", "footerFormat": "\\n <tr>\\n <td style=\\"padding-right:1em\\">Stack total</td>\\n <td style=\\"text-align:right\\">{point.total:,.0f} \\n </td>\\n </tr></table>\\n ", "shared": false}, "xAxis": {"type": "category", "categories": ["2020-01-01"]}, "yAxis": {"title": {"text": "Money"}, "reversedStacks": false}}; var chart = new Highcharts.Chart(option); var data = [{"data": [11017], "type": "column", "color": "rgb(67,162,202)", "stack": "income", "name": "Income programming", "borderColor": "white"}, {"data": [4725], "type": "column", "color": "rgb(168,221,181)", "stack": "income", "name": "Income reiki", "borderColor": "white"}, {"data": [4721], "type": "column", "color": 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width=820></iframe>" ], "text/plain": [ "<highcharts.highcharts.highcharts.Highchart at 0x7fd175789eb0>" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms.plot(s1)" ] }, { "cell_type": "code", "execution_count": 30, "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>date</th>\n", " <th>category</th>\n", " <th>income</th>\n", " <th>expense</th>\n", " <th>balance</th>\n", " <th>savings_pc_for_period</th>\n", " <th>spending_pc_for_period</th>\n", " <th>spending_pc_for_period_and_category</th>\n", " <th>income_pc_for_period_and_category</th>\n", " <th>expense_pc_for_period_and_category</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2020-01-01</td>\n", " <td>food</td>\n", " <td>0</td>\n", " <td>333</td>\n", " <td>2759</td>\n", " <td>51.00</td>\n", " <td>49.00</td>\n", " <td>6.16</td>\n", " <td>0.00</td>\n", " <td>12.56</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2020-01-01</td>\n", " <td>healthcare</td>\n", " <td>0</td>\n", " <td>434</td>\n", " <td>2759</td>\n", " <td>51.00</td>\n", " <td>49.00</td>\n", " <td>8.02</td>\n", " <td>0.00</td>\n", " <td>16.37</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2020-01-01</td>\n", " <td>investing</td>\n", " <td>1481</td>\n", " <td>0</td>\n", " <td>2759</td>\n", " <td>51.00</td>\n", " <td>49.00</td>\n", " <td>0.00</td>\n", " <td>27.38</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2020-01-01</td>\n", " <td>programming</td>\n", " <td>2953</td>\n", " <td>0</td>\n", " <td>2759</td>\n", " <td>51.00</td>\n", " 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<td>49.00</td>\n", " <td>4.10</td>\n", " <td>0.00</td>\n", " <td>8.37</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2020-04-01</td>\n", " <td>food</td>\n", " <td>0</td>\n", " <td>568</td>\n", " <td>4928</td>\n", " <td>42.16</td>\n", " <td>57.84</td>\n", " <td>11.04</td>\n", " <td>0.00</td>\n", " <td>19.09</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2020-04-01</td>\n", " <td>healthcare</td>\n", " <td>0</td>\n", " <td>727</td>\n", " <td>4928</td>\n", " <td>42.16</td>\n", " <td>57.84</td>\n", " <td>14.13</td>\n", " <td>0.00</td>\n", " <td>24.43</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2020-04-01</td>\n", " <td>investing</td>\n", " <td>1317</td>\n", " <td>0</td>\n", " <td>4928</td>\n", " <td>42.16</td>\n", " <td>57.84</td>\n", " <td>0.00</td>\n", " <td>25.60</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2020-04-01</td>\n", " <td>programming</td>\n", " <td>2573</td>\n", " <td>0</td>\n", " <td>4928</td>\n", " <td>42.16</td>\n", " <td>57.84</td>\n", " <td>0.00</td>\n", " <td>50.01</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2020-04-01</td>\n", " <td>reiki</td>\n", " <td>1255</td>\n", " <td>0</td>\n", " <td>4928</td>\n", " <td>42.16</td>\n", " <td>57.84</td>\n", " <td>0.00</td>\n", " <td>24.39</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2020-04-01</td>\n", " <td>shelter</td>\n", " <td>0</td>\n", " <td>864</td>\n", " <td>4928</td>\n", " <td>42.16</td>\n", " <td>57.84</td>\n", " <td>16.79</td>\n", " <td>0.00</td>\n", " <td>29.03</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2020-04-01</td>\n", " <td>soil testing</td>\n", " <td>0</td>\n", " <td>435</td>\n", " <td>4928</td>\n", " <td>42.16</td>\n", " <td>57.84</td>\n", " <td>8.45</td>\n", " <td>0.00</td>\n", " <td>14.62</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2020-04-01</td>\n", " <td>transport</td>\n", " <td>0</td>\n", " <td>382</td>\n", " <td>4928</td>\n", " <td>42.16</td>\n", " <td>57.84</td>\n", " <td>7.42</td>\n", " <td>0.00</td>\n", " <td>12.84</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2020-07-01</td>\n", " <td>food</td>\n", " <td>0</td>\n", " <td>325</td>\n", " <td>6714</td>\n", " <td>37.59</td>\n", " <td>62.41</td>\n", " <td>6.84</td>\n", " <td>0.00</td>\n", " <td>10.96</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2020-07-01</td>\n", " <td>healthcare</td>\n", " <td>0</td>\n", " <td>540</td>\n", " <td>6714</td>\n", " <td>37.59</td>\n", " <td>62.41</td>\n", " <td>11.37</td>\n", " <td>0.00</td>\n", " <td>18.21</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2020-07-01</td>\n", " <td>investing</td>\n", " <td>797</td>\n", " <td>0</td>\n", " <td>6714</td>\n", " <td>37.59</td>\n", " <td>62.41</td>\n", " <td>0.00</td>\n", " <td>16.78</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2020-07-01</td>\n", " <td>programming</td>\n", " <td>2537</td>\n", " <td>0</td>\n", " <td>6714</td>\n", " <td>37.59</td>\n", " <td>62.41</td>\n", " <td>0.00</td>\n", " <td>53.40</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2020-07-01</td>\n", " <td>reiki</td>\n", " <td>1417</td>\n", " <td>0</td>\n", " <td>6714</td>\n", " <td>37.59</td>\n", " <td>62.41</td>\n", " <td>0.00</td>\n", " <td>29.83</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>2020-07-01</td>\n", " <td>shelter</td>\n", " <td>0</td>\n", " <td>829</td>\n", " <td>6714</td>\n", " <td>37.59</td>\n", " <td>62.41</td>\n", " <td>17.45</td>\n", " <td>0.00</td>\n", " <td>27.96</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2020-07-01</td>\n", " <td>soil testing</td>\n", " <td>0</td>\n", " <td>484</td>\n", " <td>6714</td>\n", " <td>37.59</td>\n", " <td>62.41</td>\n", " <td>10.19</td>\n", " <td>0.00</td>\n", " <td>16.32</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2020-07-01</td>\n", " <td>transport</td>\n", " <td>0</td>\n", " <td>787</td>\n", " <td>6714</td>\n", " <td>37.59</td>\n", " <td>62.41</td>\n", " <td>16.56</td>\n", " <td>0.00</td>\n", " <td>26.54</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2020-10-01</td>\n", " <td>food</td>\n", " <td>0</td>\n", " <td>369</td>\n", " <td>9010</td>\n", " <td>44.52</td>\n", " <td>55.48</td>\n", " <td>7.16</td>\n", " <td>0.00</td>\n", " <td>12.90</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2020-10-01</td>\n", " <td>healthcare</td>\n", " <td>0</td>\n", " <td>502</td>\n", " <td>9010</td>\n", " <td>44.52</td>\n", " <td>55.48</td>\n", " <td>9.73</td>\n", " <td>0.00</td>\n", " <td>17.55</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2020-10-01</td>\n", " <td>investing</td>\n", " <td>1126</td>\n", " <td>0</td>\n", " <td>9010</td>\n", " <td>44.52</td>\n", " <td>55.48</td>\n", " <td>0.00</td>\n", " <td>21.83</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2020-10-01</td>\n", " <td>programming</td>\n", " <td>2954</td>\n", " <td>0</td>\n", " <td>9010</td>\n", " <td>44.52</td>\n", " <td>55.48</td>\n", " <td>0.00</td>\n", " <td>57.28</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2020-10-01</td>\n", " <td>reiki</td>\n", " <td>1077</td>\n", " <td>0</td>\n", " <td>9010</td>\n", " <td>44.52</td>\n", " <td>55.48</td>\n", " <td>0.00</td>\n", " <td>20.88</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2020-10-01</td>\n", " <td>shelter</td>\n", " <td>0</td>\n", " <td>988</td>\n", " <td>9010</td>\n", " <td>44.52</td>\n", " <td>55.48</td>\n", " <td>19.16</td>\n", " <td>0.00</td>\n", " <td>34.53</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2020-10-01</td>\n", " <td>soil testing</td>\n", " <td>0</td>\n", " <td>626</td>\n", " <td>9010</td>\n", " <td>44.52</td>\n", " <td>55.48</td>\n", " <td>12.14</td>\n", " <td>0.00</td>\n", " <td>21.88</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>2020-10-01</td>\n", " <td>transport</td>\n", " <td>0</td>\n", " <td>376</td>\n", " <td>9010</td>\n", " <td>44.52</td>\n", " <td>55.48</td>\n", " <td>7.29</td>\n", " <td>0.00</td>\n", " <td>13.14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date category income expense balance savings_pc_for_period \\\n", "0 2020-01-01 food 0 333 2759 51.00 \n", "1 2020-01-01 healthcare 0 434 2759 51.00 \n", "2 2020-01-01 investing 1481 0 2759 51.00 \n", "3 2020-01-01 programming 2953 0 2759 51.00 \n", "4 2020-01-01 reiki 976 0 2759 51.00 \n", "5 2020-01-01 shelter 0 1043 2759 51.00 \n", "6 2020-01-01 soil testing 0 619 2759 51.00 \n", "7 2020-01-01 transport 0 222 2759 51.00 \n", "8 2020-04-01 food 0 568 4928 42.16 \n", "9 2020-04-01 healthcare 0 727 4928 42.16 \n", "10 2020-04-01 investing 1317 0 4928 42.16 \n", "11 2020-04-01 programming 2573 0 4928 42.16 \n", "12 2020-04-01 reiki 1255 0 4928 42.16 \n", "13 2020-04-01 shelter 0 864 4928 42.16 \n", "14 2020-04-01 soil testing 0 435 4928 42.16 \n", "15 2020-04-01 transport 0 382 4928 42.16 \n", "16 2020-07-01 food 0 325 6714 37.59 \n", "17 2020-07-01 healthcare 0 540 6714 37.59 \n", "18 2020-07-01 investing 797 0 6714 37.59 \n", "19 2020-07-01 programming 2537 0 6714 37.59 \n", "20 2020-07-01 reiki 1417 0 6714 37.59 \n", "21 2020-07-01 shelter 0 829 6714 37.59 \n", "22 2020-07-01 soil testing 0 484 6714 37.59 \n", "23 2020-07-01 transport 0 787 6714 37.59 \n", "24 2020-10-01 food 0 369 9010 44.52 \n", "25 2020-10-01 healthcare 0 502 9010 44.52 \n", "26 2020-10-01 investing 1126 0 9010 44.52 \n", "27 2020-10-01 programming 2954 0 9010 44.52 \n", "28 2020-10-01 reiki 1077 0 9010 44.52 \n", "29 2020-10-01 shelter 0 988 9010 44.52 \n", "30 2020-10-01 soil testing 0 626 9010 44.52 \n", "31 2020-10-01 transport 0 376 9010 44.52 \n", "\n", " spending_pc_for_period spending_pc_for_period_and_category \\\n", "0 49.00 6.16 \n", "1 49.00 8.02 \n", "2 49.00 0.00 \n", "3 49.00 0.00 \n", "4 49.00 0.00 \n", "5 49.00 19.28 \n", "6 49.00 11.44 \n", "7 49.00 4.10 \n", "8 57.84 11.04 \n", "9 57.84 14.13 \n", "10 57.84 0.00 \n", "11 57.84 0.00 \n", "12 57.84 0.00 \n", "13 57.84 16.79 \n", "14 57.84 8.45 \n", "15 57.84 7.42 \n", "16 62.41 6.84 \n", "17 62.41 11.37 \n", "18 62.41 0.00 \n", "19 62.41 0.00 \n", "20 62.41 0.00 \n", "21 62.41 17.45 \n", "22 62.41 10.19 \n", "23 62.41 16.56 \n", "24 55.48 7.16 \n", "25 55.48 9.73 \n", "26 55.48 0.00 \n", "27 55.48 0.00 \n", "28 55.48 0.00 \n", "29 55.48 19.16 \n", "30 55.48 12.14 \n", "31 55.48 7.29 \n", "\n", " income_pc_for_period_and_category expense_pc_for_period_and_category \n", "0 0.00 12.56 \n", "1 0.00 16.37 \n", "2 27.38 0.00 \n", "3 54.58 0.00 \n", "4 18.04 0.00 \n", "5 0.00 39.34 \n", "6 0.00 23.35 \n", "7 0.00 8.37 \n", "8 0.00 19.09 \n", "9 0.00 24.43 \n", "10 25.60 0.00 \n", "11 50.01 0.00 \n", "12 24.39 0.00 \n", "13 0.00 29.03 \n", "14 0.00 14.62 \n", "15 0.00 12.84 \n", "16 0.00 10.96 \n", "17 0.00 18.21 \n", "18 16.78 0.00 \n", "19 53.40 0.00 \n", "20 29.83 0.00 \n", "21 0.00 27.96 \n", "22 0.00 16.32 \n", "23 0.00 26.54 \n", "24 0.00 12.90 \n", "25 0.00 17.55 \n", "26 21.83 0.00 \n", "27 57.28 0.00 \n", "28 20.88 0.00 \n", "29 0.00 34.53 \n", "30 0.00 21.88 \n", "31 0.00 13.14 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Summarize by month start\n", "s2 = ms.summarize(transactions, freq='QS', by_category=True)\n", "s2" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<iframe style=\"border:0;outline:none;overflow:hidden\" srcdoc=\"<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8" /> <link href="https://www.highcharts.com/highslide/highslide.css" rel="stylesheet" /> <script type="text/javascript" src="https://ajax.googleapis.com/ajax/libs/jquery/1.9.1/jquery.min.js"></script> <script type="text/javascript" src="https://code.highcharts.com/6/highcharts.js"></script> <script type="text/javascript" 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height=520 width=820></iframe>" ], "text/plain": [ "<highcharts.highcharts.highcharts.Highchart at 0x7fd175598820>" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms.plot(s2)" ] }, { "cell_type": "code", "execution_count": 16, "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>date</th>\n", " <th>category</th>\n", " <th>income</th>\n", " <th>expense</th>\n", " <th>balance</th>\n", " <th>savings_pc_for_period</th>\n", " <th>spending_pc_for_period</th>\n", " <th>spending_pc_for_period_and_category</th>\n", " <th>income_pc_for_period_and_category</th>\n", " 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<td>63.26</td>\n", " <td>0.00</td>\n", " <td>45.67</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2017-01-01</td>\n", " <td>reiki</td>\n", " <td>1391</td>\n", " <td>0</td>\n", " <td>1844</td>\n", " <td>36.74</td>\n", " <td>63.26</td>\n", " <td>0.00</td>\n", " <td>27.71</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2017-01-01</td>\n", " <td>shelter</td>\n", " <td>0</td>\n", " <td>1236</td>\n", " <td>1844</td>\n", " <td>36.74</td>\n", " <td>63.26</td>\n", " <td>24.63</td>\n", " <td>0.00</td>\n", " <td>38.93</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2017-01-01</td>\n", " <td>soil testing</td>\n", " <td>0</td>\n", " <td>498</td>\n", " <td>1844</td>\n", " <td>36.74</td>\n", " <td>63.26</td>\n", " <td>9.92</td>\n", " <td>0.00</td>\n", " <td>15.69</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>2017-01-01</td>\n", " <td>transport</td>\n", " <td>0</td>\n", " <td>535</td>\n", " <td>1844</td>\n", " <td>36.74</td>\n", " <td>63.26</td>\n", " <td>10.66</td>\n", " <td>0.00</td>\n", " <td>16.85</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>2017-04-01</td>\n", " <td>food</td>\n", " <td>0</td>\n", " <td>476</td>\n", " <td>4331</td>\n", " <td>48.47</td>\n", " <td>51.53</td>\n", " <td>9.28</td>\n", " <td>0.00</td>\n", " <td>18.00</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>2017-04-01</td>\n", " <td>healthcare</td>\n", " <td>0</td>\n", " <td>513</td>\n", " <td>4331</td>\n", " <td>48.47</td>\n", " <td>51.53</td>\n", " <td>10.00</td>\n", " <td>0.00</td>\n", " <td>19.40</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2017-04-01</td>\n", " <td>investing</td>\n", " <td>1269</td>\n", " <td>0</td>\n", " <td>4331</td>\n", " <td>48.47</td>\n", " <td>51.53</td>\n", " <td>0.00</td>\n", " <td>24.73</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2017-04-01</td>\n", " <td>programming</td>\n", " <td>2698</td>\n", " <td>0</td>\n", " <td>4331</td>\n", " <td>48.47</td>\n", " <td>51.53</td>\n", " <td>0.00</td>\n", " <td>52.58</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2017-04-01</td>\n", " <td>reiki</td>\n", " <td>1164</td>\n", " <td>0</td>\n", " <td>4331</td>\n", " <td>48.47</td>\n", " <td>51.53</td>\n", " <td>0.00</td>\n", " <td>22.69</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>2017-04-01</td>\n", " <td>shelter</td>\n", " <td>0</td>\n", " <td>852</td>\n", " <td>4331</td>\n", " <td>48.47</td>\n", " <td>51.53</td>\n", " <td>16.60</td>\n", " <td>0.00</td>\n", " <td>32.22</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>2017-04-01</td>\n", " <td>soil testing</td>\n", " <td>0</td>\n", " <td>419</td>\n", " <td>4331</td>\n", " <td>48.47</td>\n", " <td>51.53</td>\n", " <td>8.17</td>\n", " <td>0.00</td>\n", " <td>15.85</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>2017-04-01</td>\n", " <td>transport</td>\n", " <td>0</td>\n", " <td>384</td>\n", " <td>4331</td>\n", " <td>48.47</td>\n", " <td>51.53</td>\n", " <td>7.48</td>\n", " <td>0.00</td>\n", " <td>14.52</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>2017-07-01</td>\n", " <td>food</td>\n", " <td>0</td>\n", " <td>553</td>\n", " <td>7577</td>\n", " <td>54.69</td>\n", " <td>45.31</td>\n", " <td>9.32</td>\n", " <td>0.00</td>\n", " <td>20.57</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>2017-07-01</td>\n", " <td>healthcare</td>\n", " <td>0</td>\n", " <td>301</td>\n", " <td>7577</td>\n", " <td>54.69</td>\n", " <td>45.31</td>\n", " <td>5.07</td>\n", " <td>0.00</td>\n", " <td>11.19</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>2017-07-01</td>\n", " <td>investing</td>\n", " <td>1263</td>\n", " <td>0</td>\n", " <td>7577</td>\n", " <td>54.69</td>\n", " <td>45.31</td>\n", " <td>0.00</td>\n", " <td>21.28</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>2017-07-01</td>\n", " <td>programming</td>\n", " <td>2856</td>\n", " <td>0</td>\n", " <td>7577</td>\n", " <td>54.69</td>\n", " <td>45.31</td>\n", " <td>0.00</td>\n", " <td>48.12</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>2017-07-01</td>\n", " <td>reiki</td>\n", " <td>1816</td>\n", " <td>0</td>\n", " <td>7577</td>\n", " <td>54.69</td>\n", " <td>45.31</td>\n", " <td>0.00</td>\n", " <td>30.60</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>2017-07-01</td>\n", " <td>shelter</td>\n", " <td>0</td>\n", " <td>1033</td>\n", " <td>7577</td>\n", " <td>54.69</td>\n", " <td>45.31</td>\n", " <td>17.41</td>\n", " <td>0.00</td>\n", " <td>38.42</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>2017-07-01</td>\n", " <td>soil testing</td>\n", " <td>0</td>\n", " <td>367</td>\n", " <td>7577</td>\n", " <td>54.69</td>\n", " <td>45.31</td>\n", " <td>6.18</td>\n", " <td>0.00</td>\n", " <td>13.65</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>2017-07-01</td>\n", " <td>transport</td>\n", " <td>0</td>\n", " <td>435</td>\n", " <td>7577</td>\n", " <td>54.69</td>\n", " <td>45.31</td>\n", " <td>7.33</td>\n", " <td>0.00</td>\n", " <td>16.18</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>2017-10-01</td>\n", " <td>food</td>\n", " <td>0</td>\n", " <td>308</td>\n", " <td>10216</td>\n", " <td>52.52</td>\n", " <td>47.48</td>\n", " <td>6.13</td>\n", " <td>0.00</td>\n", " <td>12.91</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>2017-10-01</td>\n", " <td>healthcare</td>\n", " <td>0</td>\n", " <td>397</td>\n", " <td>10216</td>\n", " <td>52.52</td>\n", " <td>47.48</td>\n", " <td>7.90</td>\n", " <td>0.00</td>\n", " <td>16.64</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>2017-10-01</td>\n", " <td>investing</td>\n", " <td>1259</td>\n", " <td>0</td>\n", " <td>10216</td>\n", " <td>52.52</td>\n", " <td>47.48</td>\n", " <td>0.00</td>\n", " <td>25.05</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>2017-10-01</td>\n", " <td>programming</td>\n", " <td>2832</td>\n", " <td>0</td>\n", " <td>10216</td>\n", " <td>52.52</td>\n", " <td>47.48</td>\n", " <td>0.00</td>\n", " <td>56.36</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>2017-10-01</td>\n", " <td>reiki</td>\n", " <td>934</td>\n", " <td>0</td>\n", " <td>10216</td>\n", " <td>52.52</td>\n", " <td>47.48</td>\n", " <td>0.00</td>\n", " <td>18.59</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>2017-10-01</td>\n", " <td>shelter</td>\n", " <td>0</td>\n", " <td>751</td>\n", " <td>10216</td>\n", " <td>52.52</td>\n", " <td>47.48</td>\n", " <td>14.95</td>\n", " <td>0.00</td>\n", " <td>31.48</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>2017-10-01</td>\n", " <td>soil testing</td>\n", " <td>0</td>\n", " <td>379</td>\n", " <td>10216</td>\n", " <td>52.52</td>\n", " <td>47.48</td>\n", " <td>7.54</td>\n", " <td>0.00</td>\n", " <td>15.88</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>2017-10-01</td>\n", " <td>transport</td>\n", " <td>0</td>\n", " <td>551</td>\n", " <td>10216</td>\n", " <td>52.52</td>\n", " <td>47.48</td>\n", " <td>10.97</td>\n", " <td>0.00</td>\n", " <td>23.09</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " date category income expense balance savings_pc_for_period \\\n", "0 2017-01-01 food 0 496 1844 36.74 \n", "1 2017-01-01 healthcare 0 410 1844 36.74 \n", "2 2017-01-01 investing 1336 0 1844 36.74 \n", "3 2017-01-01 programming 2292 0 1844 36.74 \n", "4 2017-01-01 reiki 1391 0 1844 36.74 \n", "5 2017-01-01 shelter 0 1236 1844 36.74 \n", "6 2017-01-01 soil testing 0 498 1844 36.74 \n", "7 2017-01-01 transport 0 535 1844 36.74 \n", "8 2017-04-01 food 0 476 4331 48.47 \n", "9 2017-04-01 healthcare 0 513 4331 48.47 \n", "10 2017-04-01 investing 1269 0 4331 48.47 \n", "11 2017-04-01 programming 2698 0 4331 48.47 \n", "12 2017-04-01 reiki 1164 0 4331 48.47 \n", "13 2017-04-01 shelter 0 852 4331 48.47 \n", "14 2017-04-01 soil testing 0 419 4331 48.47 \n", "15 2017-04-01 transport 0 384 4331 48.47 \n", "16 2017-07-01 food 0 553 7577 54.69 \n", "17 2017-07-01 healthcare 0 301 7577 54.69 \n", "18 2017-07-01 investing 1263 0 7577 54.69 \n", "19 2017-07-01 programming 2856 0 7577 54.69 \n", "20 2017-07-01 reiki 1816 0 7577 54.69 \n", "21 2017-07-01 shelter 0 1033 7577 54.69 \n", "22 2017-07-01 soil testing 0 367 7577 54.69 \n", "23 2017-07-01 transport 0 435 7577 54.69 \n", "24 2017-10-01 food 0 308 10216 52.52 \n", "25 2017-10-01 healthcare 0 397 10216 52.52 \n", "26 2017-10-01 investing 1259 0 10216 52.52 \n", "27 2017-10-01 programming 2832 0 10216 52.52 \n", "28 2017-10-01 reiki 934 0 10216 52.52 \n", "29 2017-10-01 shelter 0 751 10216 52.52 \n", "30 2017-10-01 soil testing 0 379 10216 52.52 \n", "31 2017-10-01 transport 0 551 10216 52.52 \n", "\n", " spending_pc_for_period spending_pc_for_period_and_category \\\n", "0 63.26 9.88 \n", "1 63.26 8.17 \n", "2 63.26 0.00 \n", "3 63.26 0.00 \n", "4 63.26 0.00 \n", "5 63.26 24.63 \n", "6 63.26 9.92 \n", "7 63.26 10.66 \n", "8 51.53 9.28 \n", "9 51.53 10.00 \n", "10 51.53 0.00 \n", "11 51.53 0.00 \n", "12 51.53 0.00 \n", "13 51.53 16.60 \n", "14 51.53 8.17 \n", "15 51.53 7.48 \n", "16 45.31 9.32 \n", "17 45.31 5.07 \n", "18 45.31 0.00 \n", "19 45.31 0.00 \n", "20 45.31 0.00 \n", "21 45.31 17.41 \n", "22 45.31 6.18 \n", "23 45.31 7.33 \n", "24 47.48 6.13 \n", "25 47.48 7.90 \n", "26 47.48 0.00 \n", "27 47.48 0.00 \n", "28 47.48 0.00 \n", "29 47.48 14.95 \n", "30 47.48 7.54 \n", "31 47.48 10.97 \n", "\n", " income_pc_for_period_and_category expense_pc_for_period_and_category \n", "0 0.00 15.62 \n", "1 0.00 12.91 \n", "2 26.62 0.00 \n", "3 45.67 0.00 \n", "4 27.71 0.00 \n", "5 0.00 38.93 \n", "6 0.00 15.69 \n", "7 0.00 16.85 \n", "8 0.00 18.00 \n", "9 0.00 19.40 \n", "10 24.73 0.00 \n", "11 52.58 0.00 \n", "12 22.69 0.00 \n", "13 0.00 32.22 \n", "14 0.00 15.85 \n", "15 0.00 14.52 \n", "16 0.00 20.57 \n", "17 0.00 11.19 \n", "18 21.28 0.00 \n", "19 48.12 0.00 \n", "20 30.60 0.00 \n", "21 0.00 38.42 \n", "22 0.00 13.65 \n", "23 0.00 16.18 \n", "24 0.00 12.91 \n", "25 0.00 16.64 \n", "26 25.05 0.00 \n", "27 56.36 0.00 \n", "28 18.59 0.00 \n", "29 0.00 31.48 \n", "30 0.00 15.88 \n", "31 0.00 23.09 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Summarize by quarter start and by category\n", "s3 = ms.summarize(transactions, freq='QS', by_category=True)\n", "s3" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<iframe style=\"border:0;outline:none;overflow:hidden\" srcdoc=\"<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8" /> <link href="https://www.highcharts.com/highslide/highslide.css" rel="stylesheet" /> <script type="text/javascript" src="https://ajax.googleapis.com/ajax/libs/jquery/1.9.1/jquery.min.js"></script> <script type="text/javascript" src="https://code.highcharts.com/6/highcharts.js"></script> <script 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height=520 width=820></iframe>" ], "text/plain": [ "<highcharts.highcharts.highcharts.Highchart at 0x7fd1756038e0>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ms.plot(s3)" ] }, { "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
griffinfoster/fundamentals_of_interferometry	0_Introduction/editing_guide.ipynb	1	21864	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Editing Guide\n", "\n", "This is a general guide for section editors to help create a unified editing process.\n", "\n", "The editing guise is divided into two categories." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Format Editing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When format editing a section add comments by starting with your initials, the formatting tag abbreviation and wrap the comment in a css span tag to color the text so that it sticks out, an example is:\n", " \n", "```\n", "<span style=\"background-color:cyan\">GSF:LF:this is the comment text</span>\n", "```\n", "\n", "renders as:\n", "\n", "<span style=\"background-color:cyan\">GSF:LF: this is the comment text</span>\n", "\n", "We use cyan for format editing comments.\n", "\n", "In the chapter introduction place the following list to indicate which of the formatting \n", "errors were checked by the editors in each chapter. This is not to indicate if the author corrected the suggestions. Only to indicate whether the content was checked by an editor. If they are red they were not checked. If they are green they were. The aim is to as far as possible correct formating errors without involving the author. Except for big changes like \n", "a conclusion section. So there ought to be very few cyan editor commments. \n", "\n", "#### Format status:\n", "\n", "* <span style=\"background-color:red\">    </span> : LF: Date\n", "* <span style=\"background-color:red\">    </span> : NC: Date\n", "* <span style=\"background-color:red\">    </span> : RF: Date\n", "* <span style=\"background-color:red\">    </span> : HF: Date\n", "* <span style=\"background-color:red\">    </span> : GM: Date\n", "* <span style=\"background-color:red\">    </span> : CC: Date\n", "* <span style=\"background-color:red\">    </span> : CL: Date\n", "* <span style=\"background-color:red\">    </span> : ST: Date\n", "* <span style=\"background-color:red\">    </span> : FN: Date\n", "* <span style=\"background-color:red\">    </span> : TC: Date\n", "* <span style=\"background-color:red\">    </span> : XX: Date\n", "\n", "The following formatting errors are to be checked for. The formatting guide for the book is discussed in more detail in the [outline ➞](0_introduction.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Links functioning (LF)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check that all the links are functioning. Check that they point to the correct destination. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Numbering and naming convention (NC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check that the sections, figures, equations etc... are in the correct numerical order. Including table of contents. Also check numbering convention. \n", "* Sections: For Chapter 3: 3.1, 3.1.1, ..., 3.2, etc...\n", "* Figures, Tables etc...: For each main section you order the figures per section. In other words the first three figures in Section 3.1 would be Figure 3.1.1, Figure 3.1.3, Figure 3.1.2. We do not abbreviate Figure, Table, Equation etc... in the caption.\n", "* Number all sections, figures and tables." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Referencing (RF)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "We reference internal, external sections, figures, tables as follow:\n", "\n", "* `[Fig. xxx ➞](#destination)`: [Fig. 3.1.1 ➞](../3_Positional_Astronomy/3_1_Equatorial_Coordinates.ipynb#pos:fig:geo). Note we used the external symbol here ➞ as we are pointing to another notebook. If we were pointing within the current notebook we would use ⤵, i.e. `⤵`. \n", "* We use the abbreviated version of Figure when we reference (Fig. xx). We do not when we are refferring to a Chapter or Table.\n", "* `[$\\S$ xxx ➞](#destination)`: [$\\S$ 3.1. ➞](../3_Positional_Astronomy/3_1_Equatorial_Coordinates.ipynb). To refer to sections we use the $\\S$ (`$\\S$`) symbol.\n", "* All links within chapters using ➞ or ⤵ must point to a numbered Figure, Section etc... \n", "* `[<cite data-cite='Duffett2011'>Practical Astronomy with your calculator or spreadsheet </cite> ⤴](https://books.google.co.za/books?id=MTGYxQyW998C&dq=astronomy+with+your+calculator+or+spreadsheet)` : [<cite data-cite='Duffett2011'>Practical Astronomy with your calculator or spreadsheet </cite> ⤴](https://books.google.co.za/books?id=MTGYxQyW998C&dq=astronomy+with+your+calculator+or+spreadsheet). Make sure that citations are as follows and that the text that is displayed is the name of book article. Make sure reference is in chapters .bib file. \n", "* An equation is only numbered if it is referenced. Use same standard as Figure. Also use abbreviated Eq. when reffering to it.\n", "* Check that the reference label is correct, is it the same as the actual Figure etc.. caption." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Header and Footer (HF)\n", "\n", "The header and footer should look exactly like this. If someone is including some code in the standard modules cell that should be in section specific modules correct it. Are the names of links in headers and footers correct." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**\n", "\n", " [Outline](0_introduction.ipynb)\n", "* [Glossary](1_glossary.ipynb)\n", "* [3. Positional Astronomy](../3_Positional_Astronomy/3_0_Introduction.ipynb)\n", " * Previous: [3.1 Equatorial Coordinates (RA,DEC)](../3_Positional_Astronomy/3_1_Equatorial_Coordinates.ipynb)\n", " * Next: [3.3 Horizontal Coordinates (ALT,AZ)](../3_Positional_Astronomy/3_3_Horizontal_Coordinates.ipynb) \n", "\n", "*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import standard modules:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from IPython.display import HTML \n", "HTML('../style/course.css') #apply general CSS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import section specific modules:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from IPython.display import HTML \n", "HTML('../style/course.css') #apply general CSS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Next: [3.3 Horizontal Coordinates (ALT,AZ)](../3_Positional_Astronomy/3_3_Horizontal_Coordinates.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Glossary and Nomenclature (GM)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are all the new definition in italics* when first used in a chapter and described in the glossary. Are the mathematical symbols in nomenclature. Is the basic mathematic style guide followed: \n", "\n", "1. Vector, scalar and matrix:\n", " * $a, A$ - Denotes a scalar quantity\n", " * $\\mathbf{A}$, $\\boldsymbol{\\mathcal{A}}$ - Denotes a matrix\n", " * $\\mathbf{a}$ - Denotes a vector\n", "\n", "2. $2\\times2$-Polarized vs. $N\\times N$-Unpolarized matrices: \n", " * $\\mathbf{A}$ - Denotes a $2\\times2$ polarized Jones matrix. Number a Jones matrix\n", " with any other subscript than $N$.\n", " * $\\boldsymbol{\\mathcal{A}}$ - Denotes a $N\\times N$ unpolarzed matrix (contain all the unpolarized quantities associated with an array in one matrix).\n", "\n", "3. Jones versus Jacobian:\n", " * Please use $\\mathbf{J}$ to denote a Jones matrix and $\\mathbb{J}$ to denote a\n", " Jacobian matrix.\n", "\n", "4. Fourier transform:\n", " * Please use $\\mathscr{F}\\{\\cdot\\}$ to denote the Fourier transform. \n", " \n", "5. Subscript to avoid ambiguity:\n", " * If one symbol is used to denote two quantities use a subscript to remove ambiguity. For instance $\\lambda$ can mean wavelength or the LM-damping factor. Add a subscript, for\n", "instance $\\lambda_{\\textrm{LM}}$ now refers to to the LM-damping factor, while $\\lambda$ still refers to wavelength. Please add any new subscripted symbol to the glossary. \n", "\n", "The general list of symbols can be found in the [glossary ➞](1_glossary.ipynb#preface:sec:glossary)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Chapter Introduction and Conclusion (CC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Chapter Introduction:\n", "* Provide an overview of the topics in the chapter.\n", "* Outline of the notebooks in the chapter.\n", "* Include a list of editors and contributors of the chapter.\n", "See [$\\S$ 5.0. ➞](../5_Imaging/5_0_introduction.ipynb) for exact format. Make sure editors are filled in. Add year to editor name.\n", "\n", "Chapter Conclusion:\n", "\n", "Should have following sections:\n", "* Summary: Summarizes chapter\n", "* References: All external references\n", "* Further reading: Material not reference but is for enrichment\n", "\n", "No good example as everyone does it different." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correct labelling (CL)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure that all figure, section, equation and citation strings contain the correct label string format, i.e. `chapterStr:type:uniqueID`. Do they use the correct label string (see [outline ➞](0_introduction.ipynb)).\n", "\n", "\| Chapter \| chapterStr \|\n", "\|------------\|-------------:\|\n", "\| Preface \| `preface` \|\n", "\| 1. Radio Science using Interferometers \| `science` \|\n", "\| 2. Mathematical Groundwork \| `math` \|\n", "\| 3. Positional Astronomy \| `pos` \|\n", "\| 4. Visibility Space \| `vis` \|\n", "\| 5. Imaging \| `imaging` \|\n", "\| 6. Deconvolution in Imaging \| `deconv` \|\n", "\| 7. Observing Systems \| `instrum` \|\n", "\| 8. Calibration \| `cal` \|\n", "\| 9. Putting it all together \| `pract` \|\n", "\n", "\n", "\| type \| value \|\n", "\|----------\|--------:\|\n", "\| code \| `code` \|\n", "\| equation \| `eq` \|\n", "\| figure \| `fig` \|\n", "\| section \| `sec` \|\n", "\| table \| `tbl` \|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Styling (ST)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is styling used at all if not it should. Is it used correctly. Where can it be applied." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To write a \"warning\" text box, one can use in a markdown:\n", "\n", "```\n", "<div class=warn>\n", "<b>Warning:</b> This relation assumes this particular hypothesis \n", "</div>\n", "```\n", "\n", "To write a note \"note\" or a piece of advice, use:\n", "\n", "```\n", "<div class=advice>\n", "<b>Advice:</b> Check the homogeneity of your equations !!!\n", "</div>\n", "```\n", "\n", "To create a green summary block:\n", "\n", "\n", "```\n", "<p class=conclusion>\n", " <font size=4> <b>Take-away message</b></font>\n", " <br>\n", " <br>\n", "• <b>Conclusion 1</b>: Important item to remember with a specific <em>emphasized</em> word <br><br>\n", "• <b>Conclusion 2</b>: A second important item to remember with a specific <em>emphasized</em> word.\n", "</p>\n", "```\n", "\n", "To create a \"Prerequisites\"/\"To read\" header block:\n", "\n", "```\n", "<p class=prerequisites>\n", " <font size=4> <b>Prerequisites</b></font>\n", " <br>\n", " <br>\n", "• <b>Definition of ($u$,$v$,$w$):</b> [Go to 4.1](4_1_The_Baseline.ipynb) <br><br>\n", "• <b>The visibility function:</b> [Go to 4.3](4_3_The_Visibility_Function.ipynb)\n", "</p>\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### File Names (FN)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are the file names in the correct format. Example: `6_1_sky_models.ipynb`. All small letters. Name of section. Single digits if possible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Table of Contents (TC)\n", "\n", "Check that the table of contents accurately reflects the contents of the chapter you are editing. Also check that the table of content links are working. Please create github issues for TC format errors and assign to Trienko." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spelling (SP)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check spelling. Copy text to some spell-checker." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### General Formatting Comment (XX)\n", "\n", "If you have other formatting comments that are not summurazide above. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Content Formatting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The main goal of the editors is to consider the quality of the content, each editor should consider these questions and hopefully answer them by the end of editing a section.\n", "\n", "* What is the point of the section?\n", "* Is that point clear or could it be presented in a different way?\n", "* Are the examples useful?\n", "* Are there holes or assumptions in the section content which need to be improved?\n", "* What could be added or removed to improve the quality of the section?\n", "\n", "When content editing a section add comments by starting with your initials, the formatting tag abbreviation and wrap the comment in a css span tag to color the text so that it sticks out, an example is:\n", " \n", "```\n", "<span style=\"background-color:red\">GSF:MC:this is the comment text</span>\n", "```\n", "\n", "renders as:\n", "\n", "<span style=\"background-color:red\">GSF:MC: this is the comment text</span>\n", "\n", "We use red and yellow for content comments. Red means that the comment should definitely be implemented by the original author. Yellow means that the comment should be seen as a suggestion only. The different content tags are listed below. Please make github issues for the different editor comments." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Move Content (MC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Move content somewhere else. Be specific as to why and to where it should be moved. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Re-Write Content (RC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Content needs to be rewritten as it is written in incorrect language. Or the sentences are not clear. These are the comments that the editors may be able to adress themselves.\n", "You may want to postpone correcting until you finish the chapter." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Improve Content (IC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The content is logically flawed and needs to be improved. It is not clear or simple enough for a student to grasp.\n", "Be specifc in your comments." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add Content (AC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add content. State the reason why." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remove Content (EC)\n", "\n", "Completely remove the content." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### General Comment (GC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add other general comments." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Additional Information\n", "\n", "\n", "### Spelling Style\n", "\n", "Since we all come from different backgrounds on our spelling stylea we need to formalize the text on one style, we will use the [MNRAS style](http://www.oxfordjournals.org/our_journals/mnras/for_authors/#6.2 Spelling, grammar, punctuation and mathematics) which is essentially British English spelling, e.g. colour instead of color, but uses 'z' in most '-ize' ending words. The specific direction taken from the MNRAS style guide:\n", "\n", "> ##### Punctuation \n", "> Hyphens (one dash in LaTeX) should be used for compound adjectives (e.g. low-density gas, least-squares fit, two-component model). This also applies to simple adjectival units (e.g. 1.5-m telescope, 284.5-nm line), but not to complex units or ranges, which could become cumbersome (e.g. 15 km s–1 feature, 100–200 µm observations). Some words (e.g. time-scale) are always hyphenated as part of journal style (see below). \n", "> \n", "> N-rules (two dashes in LaTeX): these are longer than hyphens and are used (i) to separate key words, (ii) as parentheses (e.g. the results – assuming no temperature gradient – are indicative of …), (iii) to denote a range (e.g. 1.6–2.2 µm), and (iv) to denote the joining of two words (e.g. Kolmogorov–Smirnov test, Herbig–Haro object). \n", "> \n", "> M-rules (three dashes in TeX/LaTeX) are not used in MNRAS.\n", "> \n", "> ##### Spelling and grammar \n", "> Please use British English spellings – e.g. centre not center, labelled not labeled. For words ending in -ise/yse or -ize follow this style: use -ise/yse for devise, surprise, comprise, revise, exercise, analyse; use -ize for recognize, criticize, minimize, emphasize, organize, ionize, polarize, parametrize (note the spelling of this word in particular). \n", "> \n", "> ‘None’ is a singular word (none of the stars is a white dwarf), whilst ‘data’ is a plural word (these data show…). \n", "> \n", "> Miscellaneous journal spellings: acknowledgements, artefact, best-fitting (not best-fit), disc (except computer disk), haloes (not halos), hotspot, none the less, non-linear, on to, time-scale. \n", "> \n", "> For any other spellings, use whichever version is listed first in the Oxford English Dictionary.\n", "\n", "### Editor/Author Collaboration\n", "\n", "#### 2016\n", "\n", "We leave it to the editors and authors on how best to collaborate on incorporating edits and comments into the text. A suggestion is for the editor to send a pull request to the author's github fork. Once the edits are worked out, the author will then do a pull request to the main github repository.\n", "\n", "#### 2017\n", "\n", "We will be using an edit branch to keep a stable version which can be released to the students. I have created a branch called edit in the ratt-ru repository. So the steps \n", "to work on this branch is as follow:\n", "\n", "* `git fetch upstream`.\n", "* `git checkout edit`.\n", "* `git merge upstream\\edit`.\n", "* Make the edits you wish to `edit` branch.\n", "* `git add, commit and push origin edit`.\n", "* Go to local fork edit branch and create a pull request to ratt-ru edit branch.\n", "\n", "Only after the authors have corrected the edits will I merge it into the main branch of ratt-ru.\n", "\n", "### Editing Attribution\n", "\n", "At the introduction of each chapter there is a list of chapter writers and editors. Please add your name to the editor's list, include the particular section number which you edited. We need this so we can keep track of who knows about each section. And, we want to give you recognition for your work." ] } ], "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-2.0
openearth/notebooks	analysis-brouwershavensegat.ipynb	1	117568	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "url = 'http://opendap.deltares.nl/thredds/dodsC/opendap/rijkswaterstaat/waterbase/27_Waterhoogte_in_cm_t.o.v._normaal_amsterdams_peil_in_oppervlaktewater/nc/id1-BROUWHVSGT08.nc'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import datetime\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "import xarray\n", "import matplotlib.pyplot as plt\n", "\n", "import statsmodels.regression.linear_model \n", "import statsmodels.api as sm\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# open dataset\n", "ds = xarray.open_dataset(url)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# get first location (only 1 location in file)\n", "ssh = ds.sea_surface_height[0]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# convert to dataframe\n", "df = pd.DataFrame(ssh.to_pandas(), columns=['ssh'])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ssh\n", "time \n", "1978-12-31 2\n", "1979-12-31 8784\n", "1980-12-31 8760\n", " ssh\n", "time \n", "2015-12-31 52704\n", "2016-12-31 52560\n", "2017-12-31 35943\n" ] } ], "source": [ "# check number of measurements per year\n", "# this data is up to date until 2018-09\n", "# after that waterbase was is no longer available.\n", "# the updates based on DDL are not yet available\n", "counts = df.resample('A', label='left').count()\n", "print(counts.head(n=3))\n", "print(counts.tail(n=3))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# drop incomplete years\n", "df = df[df.index > datetime.datetime(1980, 1, 1)]\n", "df = df[df.index < datetime.datetime(2018, 1, 1)]\n" ] }, { "cell_type": "code", "execution_count": 8, "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>ssh</th>\n", " </tr>\n", " <tr>\n", " <th>time</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2017-12-31 23:19:59.999998976</th>\n", " <td>1.80</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-31 23:30:00.000003328</th>\n", " <td>1.80</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-31 23:39:59.999997696</th>\n", " <td>1.87</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-31 23:50:00.000002304</th>\n", " <td>1.89</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-31 23:59:59.999996672</th>\n", " <td>1.88</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ssh\n", "time \n", "2017-12-31 23:19:59.999998976 1.80\n", "2017-12-31 23:30:00.000003328 1.80\n", "2017-12-31 23:39:59.999997696 1.87\n", "2017-12-31 23:50:00.000002304 1.89\n", "2017-12-31 23:59:59.999996672 1.88" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64Index([2015], dtype='int64', name='time')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# double check date calculation (different timezones)\n", "# calculation is done while in UK, double check if back in NL\n", "test_df = df[\n", " np.logical_and(\n", " df.index >= datetime.datetime(2015, 1, 1),\n", " df.index < datetime.datetime(2016, 1, 1)\n", " )\n", "]\n", "# this should only show 2015\n", "test_df.resample('A').mean().index.year" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# define some extra variables\n", "annual_mean_df = df.resample('A', label='right').mean()\n", "annual_max_df = df.resample('A', label='right').max()\n", "year = [x.year for x in annual_mean_df.index]\n", "annual_mean_df['year'] = year\n", "year = [x.year for x in annual_max_df.index]\n", "annual_max_df['year'] = year\n", "# m to mm, sealevelmonitor is in mm\n", "annual_mean_df['height'] = annual_mean_df['ssh'] * 1000\n", "annual_max_df['height'] = annual_max_df['ssh'] * 1000\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot measured data (labels are a bit off)\n", "fig, ax = plt.subplots(figsize=(8, 5))\n", "ax.plot(annual_mean_df.year, annual_mean_df.height, 'k.')\n", "ax.set_ylabel(\"{} [{}]\".format(ssh.standard_name, 'mm'))\n", "ax.set_title('Annual mean');" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot annual maxima\n", "fig, ax = plt.subplots(figsize=(8, 5))\n", "ax.plot(annual_max_df['year'], annual_max_df['height'], 'k.')\n", "ax.set_ylabel(\"{} [{}]\".format(ssh.standard_name, 'mm'))\n", "ax.set_title('Annual max (values at end of period)');" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# define the statistical model\n", "def linear_model(df, with_ar=True):\n", " y = df['height']\n", " X = np.c_[\n", " df['year']-1970, \n", " np.cos(2np.pi(df['year']-1970)/18.613),\n", " np.sin(2np.pi(df['year']-1970)/18.613)\n", " ]\n", " month = np.mod(df['year'], 1) * 12.0\n", " names = ['Constant', 'Trend', 'Nodal U', 'Nodal V']\n", " X = sm.add_constant(X)\n", " if with_ar:\n", " model = sm.GLSAR(y, X, missing='drop', rho=1)\n", " else:\n", " model = sm.OLS(y, X, missing='drop')\n", " fit = model.fit(cov_type='HC0')\n", " return fit, names" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>GLSAR Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>height</td> <th> R-squared: </th> <td> 0.317</td>\n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>GLSAR</td> <th> Adj. R-squared: </th> <td> 0.255</td>\n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 5.949</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Fri, 10 May 2019</td> <th> Prob (F-statistic):</th> <td>0.00233</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>15:58:08</td> <th> Log-Likelihood: </th> <td> -179.32</td>\n", "</tr>\n", "<tr>\n", " <th>No. Observations:</th> <td> 37</td> <th> AIC: </th> <td> 366.6</td>\n", "</tr>\n", "<tr>\n", " <th>Df Residuals:</th> <td> 33</td> <th> BIC: </th> <td> 373.1</td>\n", "</tr>\n", "<tr>\n", " <th>Df Model:</th> <td> 3</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>HC0</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\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>Constant</th> <td> -54.8593</td> <td> 17.873</td> <td> -3.069</td> <td> 0.002</td> <td> -89.889</td> <td> -19.829</td>\n", "</tr>\n", "<tr>\n", " <th>Trend</th> <td> 2.0208</td> <td> 0.522</td> <td> 3.874</td> <td> 0.000</td> <td> 0.998</td> <td> 3.043</td>\n", "</tr>\n", "<tr>\n", " <th>Nodal U</th> <td> -1.5794</td> <td> 8.248</td> <td> -0.191</td> <td> 0.848</td> <td> -17.745</td> <td> 14.586</td>\n", "</tr>\n", "<tr>\n", " <th>Nodal V</th> <td> -19.1110</td> <td> 6.761</td> <td> -2.827</td> <td> 0.005</td> <td> -32.361</td> <td> -5.861</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <th>Omnibus:</th> <td> 1.283</td> <th> Durbin-Watson: </th> <td> 1.341</td>\n", "</tr>\n", "<tr>\n", " <th>Prob(Omnibus):</th> <td> 0.526</td> <th> Jarque-Bera (JB): </th> <td> 0.876</td>\n", "</tr>\n", "<tr>\n", " <th>Skew:</th> <td>-0.377</td> <th> Prob(JB): </th> <td> 0.645</td>\n", "</tr>\n", "<tr>\n", " <th>Kurtosis:</th> <td> 2.967</td> <th> Cond. No. </th> <td> 98.6</td>\n", "</tr>\n", "</table><br/><br/>Warnings:<br/>[1] Standard Errors are heteroscedasticity robust (HC0)" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " GLSAR Regression Results \n", "==============================================================================\n", "Dep. Variable: height R-squared: 0.317\n", "Model: GLSAR Adj. R-squared: 0.255\n", "Method: Least Squares F-statistic: 5.949\n", "Date: Fri, 10 May 2019 Prob (F-statistic): 0.00233\n", "Time: 15:58:08 Log-Likelihood: -179.32\n", "No. Observations: 37 AIC: 366.6\n", "Df Residuals: 33 BIC: 373.1\n", "Df Model: 3 \n", "Covariance Type: HC0 \n", "==============================================================================\n", " coef std err z P>\|z\| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Constant -54.8593 17.873 -3.069 0.002 -89.889 -19.829\n", "Trend 2.0208 0.522 3.874 0.000 0.998 3.043\n", "Nodal U -1.5794 8.248 -0.191 0.848 -17.745 14.586\n", "Nodal V -19.1110 6.761 -2.827 0.005 -32.361 -5.861\n", "==============================================================================\n", "Omnibus: 1.283 Durbin-Watson: 1.341\n", "Prob(Omnibus): 0.526 Jarque-Bera (JB): 0.876\n", "Skew: -0.377 Prob(JB): 0.645\n", "Kurtosis: 2.967 Cond. No. 98.6\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors are heteroscedasticity robust (HC0)\n", "\"\"\"" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# print the fit properties\n", "fit, names = linear_model(annual_mean_df)\n", "prediction = fit.get_prediction()\n", "fit.summary(xname=names)\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10, 5))\n", "ax.plot(annual_mean_df.year, annual_mean_df.height, 'k.')\n", "ax.set_ylabel(\"{} [{}]\".format(ssh.standard_name, 'mm'))\n", "ax.set_title('Annual mean')\n", "conf_int = prediction.conf_int()\n", "pred_int = prediction.conf_int(obs=True)\n", "ax.fill_between(annual_mean_df.year, pred_int[:, 0], pred_int[:, 1], alpha=0.1, color='blue', label='prediction interval')\n", "ax.fill_between(annual_mean_df.year, conf_int[:, 0], conf_int[:, 1], alpha=0.3, color='blue', label='confidence interval')\n", "ax.plot(annual_mean_df.year, fit.predict(), color='blue', label='fitted trend')\n", "ax.legend()\n", "ax.grid(True)\n", "fig.savefig('nodal.pdf')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x360 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "exog = fit.model.exog.copy()\n", "exog[:, 2:4] = 0 \n", "prediction = fit.get_prediction(exog=exog)\n", "\n", "fig, ax = plt.subplots(figsize=(10, 5))\n", "ax.plot(annual_mean_df.year, annual_mean_df.height, 'k.')\n", "ax.set_ylabel(\"{} [{}]\".format(ssh.standard_name, 'mm'))\n", "ax.set_title('Annual mean')\n", "conf_int = prediction.conf_int()\n", "pred_int = prediction.conf_int(obs=True)\n", "ax.fill_between(annual_mean_df.year, pred_int[:, 0], pred_int[:, 1], alpha=0.1, color='blue', label='prediction interval')\n", "ax.fill_between(annual_mean_df.year, conf_int[:, 0], conf_int[:, 1], alpha=0.3, color='blue', label='confidence interval')\n", "ax.plot(annual_mean_df.year, fit.predict(exog=exog), color='blue', label='fitted trend')\n", "ax.legend()\n", "ax.grid(True)\n", "fig.savefig('linear.pdf')" ] } ], "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": 2 }	gpl-3.0
DANA-Laboratory/CoolProp	doc/notebooks/Check critical point derivative matrices.ipynb	5	5796	{ "metadata": { "name": "", "signature": "sha256:a09a62c5ca301d3f63e20ae1ddea45013e338af6b73280fca44334e814ae310d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import division\n", "from sympy import \n", "from IPython.display import display, Math, Latex\n", "from IPython.core.display import display_html\n", "init_session(quiet=True, use_latex='mathjax')\n", "init_printing()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "IPython console for SymPy 0.7.6 (Python 2.7.6-32-bit) (ground types: python)\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "x1,x2,tau,delta = symbols('x1,x2,tau,delta')\n", "L11 = symbols('L11', cls=Function)(x1,x2,tau,delta)\n", "L12 = symbols('L12', cls=Function)(x1,x2,tau,delta)\n", "L21 = symbols('L21', cls=Function)(x1,x2,tau,delta)\n", "L22 = symbols('L22', cls=Function)(x1,x2,tau,delta)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "Lstar = Matrix([[L11,L12],[L21,L22]])\n", "L1star = Lstar.det()\n", "deriv1 = L1star.diff(t)\n", "deriv2 = (Lstar.adjugate()Lstar.diff(t)).trace()\n", "\n", "Mstar = Matrix([[L11,L12],[Lstar.det().diff(x1),Lstar.det().diff(x2)]])\n", "\n", "deriv1 = Mstar.det().diff(tau)\n", "deriv2 = (Mstar.adjugate()Mstar.diff(tau)).trace()\n", "simplify(deriv1-deriv2)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$0$$" ], "metadata": {}, "output_type": "pyout", "png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOBAMAAADkjZCYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJdjLNVN0iZu+7\nq0QgoRR7AAAAVklEQVQIHWNgEDJRZWBgSGeQmMDAtYGBOYGB5wID+0cG/gsMfN8Z5BUY+L4wzDdg\nYP0MJeUNQCL8Cgzs3xk4DjBwfWRg2cDAlMDA0M4gHcDAIOxylQEA9FISlFfRJtkAAAAASUVORK5C\nYII=\n", "prompt_number": 3, "text": [ "0" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "Mstar = Matrix([[0.00112865, 8.76232e-006],[9.57021e-007, 7.42578e-009]])\n", "dMstar_dTau = Matrix([[-0.000245724,-0.00118232],[3.20921e-006, -7.171e-008]])\n", "adjM = Matrix([[7.42578e-009,-9.57021e-007],[-8.76232e-006, 0.00112865]])\n", "(adjMdMstar_dTau).trace()\n", "print Mstar.adjugate()\n", "print adjM" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Matrix([[7.42578000000000e-9, -8.76232000000000e-6], [-9.57021000000000e-7, 0.00112865000000000]])\n", "Matrix([[7.42578000000000e-9, -9.57021000000000e-7], [-8.76232000000000e-6, 0.00112865000000000]])\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "a,b,c,d = symbols('a,b,c,d')\n", "M = Matrix([[a,b],[c,d]])\n", "display(M)\n", "M.adjugate()" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\left[\\begin{matrix}a & b\\\\c & d\\end{matrix}\\right]$$" ], "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAADcAAAAyBAMAAAAKOF7GAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhAiq3bdRLtm\nmc0lg57xAAABSUlEQVQ4Ee3UPUsDMRwG8OdSr9DT1oIuouANDgoO9w0qWPdzFrlu7rp0cOhQEARB\nEHHVT2D9AIqjo4Pgpi7iVBGlDlI4E/LSZ7hmdjDLJfnxz11yD8Fi/omiJvK8jpnmRpEhbK7XMVtI\nanKiGLfmPIg3H355MBp4UBwTlne7p2poW619lLivvWhNflhQz+wV3xbDBVR7jI0U8tj0PqsDVDqM\nTwgdBj1ka4x9RHIzujKL0UBKOoRczGKCm1KL8AebtxZrnakzQYZ9XMuhXjZsL+0cMm4fyEKDPE99\nXUkT3P1HeRpjDyG6uh+PmE48eJl6cMXzTvRHKJa7/LNkyF3AUD7BO6MMuQsYghh7hCrkL27ZZ84l\nVC4rscN5KpPdQIb83KLKPjcTcnN8Q6BEmumQG3xE+EBoQm5QrN6RwYTcIMuo/xfRe6X6LuNfjWlM\nFpMM9N8AAAAASUVORK5CYII=\n", "text": [ "\u23a1a b\u23a4\n", "\u23a2 \u23a5\n", "\u23a3c d\u23a6" ] }, { "latex": [ "$$\\left[\\begin{matrix}d & - b\\\\- c & a\\end{matrix}\\right]$$" ], "metadata": {}, "output_type": "pyout", "png": "iVBORw0KGgoAAAANSUhEUgAAAFMAAAAyBAMAAADSNPrMAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhBEu5ndzSKr\ndmb+gm2XAAABbElEQVRIDWOQ//+JgTBg+v9fgEHYxZWwSgZWF2cBBhEiFIKUsCArTZfArou1cAK6\nUobP2JUycB1AV8r2FYdSfgN0pUwge7CB/ACgKIpbuRqwqQOKaYLEkZTu0fUH2YMNrLnbjKyUTZqh\nHmQPFsD6K4B/A5KpgQ8YurAoAwkxf2XgV0BS2h/AMAlNKevKmUAwy4FjAUM8slJxBtZvaEphXKB3\n8w0QprJ+YmD+wAqTRKX5HzAA7YSHAOtnBvYF1qhKYDz+BFY55BBYy/DygAJMEpXmecDegKw05pL3\n2QRUJTAea+85IBPuAJgwbnpU6WgIDLoQULrrgJZi9+h6QooHNLfuNOBagKoUVJJARNCUCjHwoBVG\nwJKkHVkpvGzALDWBuXouslKYnYwFMBacBpYkX7ApBRY3aACpJEF1K7AYYUB1K1JJgqqU4wBDhAOq\nuYiSBFUpg9K7C6gqGRAlCZpSNHUo3OGrlIRKnvimAwCJ/VUvfMvpJAAAAABJRU5ErkJggg==\n", "prompt_number": 11, "text": [ "\u23a1d -b\u23a4\n", "\u23a2 \u23a5\n", "\u23a3-c a \u23a6" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
christiano/exemplos_mongodb	exemplo_modelagem01.ipynb	1	1132	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exemplo de modelagem\n", "\n", "Esse exemplo simula uma aplicação de colégio com duas coleções:\n", "\n", "1. Coleção de Alunos (cadastro básico)\n", "2. Coleção de Livros (alguns títulos de exemplo)\n", "\n", "O objetivo é:\n", "\n", " * Analisar melhor modelagem para alunos que querem alugar livros\n", " * Verificar quais livros foram alugados\n", " * Gerar histórico de alugueis de livros\n", " " ] }, { "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 }	gpl-2.0
google/hypernerf	notebooks/figures/hypernerf_optim_latent.ipynb	1	5245	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "hypernerf_optim_latent", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "code", "metadata": { "id": "-QWb-snnOf1I" }, "source": [ "def apply_model(rng, state, batch):\n", " fine_key, coarse_key = random.split(rng, 2)\n", " model_out = model.apply(\n", " {'params': state.optimizer.target['model']}, \n", " batch,\n", " extra_params=state.extra_params,\n", " metadata_encoded=True,\n", " rngs={'fine': fine_key, 'coarse': coarse_key})\n", " return model_out\n", "\n", "\n", "def loss_fn(rng, state, target, batch):\n", " batch['metadata'] = jax.tree_map(lambda x: x.reshape((1, -1)), \n", " target['metadata'])\n", " model_out = apply_model(rng, state, batch)['fine']\n", " # loss = ((model_out['rgb'] - batch['rgb']) ** 2).mean(axis=-1)\n", " loss = jnp.abs(model_out['rgb'] - batch['rgb']).mean(axis=-1)\n", " return loss.mean()\n", "\n", "\n", "def optim_step(rng, state, optimizer, batch):\n", " rng, key = random.split(rng, 2)\n", " grad_fn = jax.value_and_grad(loss_fn, argnums=2)\n", " loss, grad = grad_fn(key, state, optimizer.target, batch)\n", " grad = jax.lax.pmean(grad, axis_name='batch')\n", " loss = jax.lax.pmean(loss, axis_name='batch')\n", "\n", " optimizer = optimizer.apply_gradient(grad)\n", "\n", " return rng, loss, optimizer\n", "\n", "\n", "p_optim_step = jax.pmap(optim_step, axis_name='batch')\n", "\n", "key = random.PRNGKey(0)\n", "key = key + jax.process_index()\n", "keys = random.split(key, jax.local_device_count())\n", "\n", "optimizer_def = optim.Adam(0.1)\n", "init_metadata = evaluation.encode_metadata(\n", " model, \n", " jax_utils.unreplicate(state.optimizer.target['model']), \n", " jax.tree_map(lambda x: x[0, 0], data['metadata']))\n", "# init_metadata = jax.tree_map(lambda x: x[0], init_metadata)\n", "# Initialize to zero.\n", "init_metadata = jax.tree_map(lambda x: jnp.zeros_like(x), init_metadata)\n", "optimizer = optimizer_def.create({'metadata': init_metadata})\n", "optimizer = jax_utils.replicate(optimizer, jax.local_devices())\n", "devices = jax.local_devices()\n", "batch_size = 1024\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "gzqX4gTWOf3b" }, "source": [ "metadata_progression = []\n", "\n", "for i in range(25):\n", " batch_inds = random.choice(keys[0], np.arange(train_data['rgb'].shape[0]), replace=False, shape=(batch_size,))\n", " batch = jax.tree_map(lambda x: x[batch_inds, ...], train_data)\n", " batch = datasets.prepare_data(batch)\n", " keys, loss, optimizer = p_optim_step(keys, state, optimizer, batch)\n", " loss = jax_utils.unreplicate(loss)\n", " metadata_progression.append(jax.tree_map(lambda x: np.array(x), jax_utils.unreplicate(optimizer.target['metadata'])))\n", " print(f'train_loss = {loss.item():.04f}')\n", " del batch" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "-s9pCeiqZhKi" }, "source": [ "frames = []\n", "for metadata in metadata_progression:\n", "# metadata = jax_utils.unreplicate(optimizer.target['metadata'])\n", " camera = datasource.load_camera(target_id).scale(1.0)\n", " batch = make_batch(camera, None, metadata['encoded_warp'], metadata['encoded_hyper'])\n", " render = render_fn(state, batch, rng=rng)\n", " pred_rgb = np.array(render['rgb'])\n", " pred_depth_med = np.array(render['med_depth'])\n", " pred_depth_viz = viz.colorize(1.0 / pred_depth_med.squeeze())\n", " media.show_images([pred_rgb, pred_depth_viz])\n", " frames.append({ \n", " 'rgb': pred_rgb,\n", " 'depth': pred_depth_med,\n", " })\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "tu8cC9gKe2BS" }, "source": [ "media.show_image(data['rgb'])\n", "media.show_videos([\n", " [d['rgb'] for d in frames],\n", " [viz.colorize(1/d['depth'].squeeze(), cmin=1.5, cmax=2.9) for d in frames],\n", "], fps=10)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "EUpAEa4boPbw" }, "source": [ "" ], "execution_count": null, "outputs": [] } ] }	apache-2.0
pablocastelo/mexprimero	Untitled.ipynb	1	53929	{ "cells": [ { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from numpy import nan\n", "plt.style.use('ggplot')\n", "import itertools" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>body {\n", " margin: 0;\n", " font-family: Helvetica;\n", "}\n", "table.dataframe {\n", " border-collapse: collapse;\n", " border: none;\n", "}\n", "table.dataframe tr {\n", " border: none;\n", "}\n", "table.dataframe td, table.dataframe th {\n", " margin: 0;\n", " border: 1px solid white;\n", " padding-left: 0.25em;\n", " padding-right: 0.25em;\n", "}\n", "table.dataframe th:not(:empty) {\n", " background-color: #fec;\n", " text-align: left;\n", " font-weight: normal;\n", "}\n", "table.dataframe tr:nth-child(2) th:empty {\n", " border-left: none;\n", " border-right: 1px dashed #888;\n", "}\n", "table.dataframe td {\n", " border: 2px solid #ccf;\n", " background-color: #f4f4ff;\n", "}\n", "h3 {\n", " color: white;\n", " background-color: black;\n", " padding: 0.5em;\n", "}\n", "</style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "css = open('style-table.css').read() + open('style-notebook.css').read()\n", "HTML('<style>{}</style>'.format(css))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "infr = pd.read_csv(\"infraestructura.csv\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "secciones = [['P3'], ['P11', 'P12', 'P13A', 'P14', 'P15', 'P16'], ['P17A', 'P18A', 'P19', 'P20', 'P21', 'P22'], ['P23', 'P24'], ['P25', 'P26', 'P27', 'P28', 'P29', 'P30', 'P31', 'P32', 'P33', 'P34', 'P35', 'P36', 'P37', 'P38', 'P39', 'P40', 'P41'], ['P42', 'P44', 'P46', 'P47', 'P48', 'P49', 'P52', 'P62', 'P72', 'P82', 'P92', 'P102', 'P103', 'P112', 'P113', 'P117', 'P122', 'P123', 'P125'], ['P126'], ['P133', 'P134', 'P135', 'P136', 'P137', 'P138', 'P139', 'P140', 'P141', 'P142', 'P143'], ['P144', 'P145']]\n", "max_cal = [11, 13, 13, 11, 14, 17, 2, 11, 8]\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "i=1\n", "for seccion in secciones:\n", " infr['seccion_'+str(i)]=infr[seccion].sum(axis=1)\n", " i+=1" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sec_lst = []\n", "for i in range(1,10):\n", " sec_lst.append('seccion_'+str(i)) \n", "#dict(zip([1,2,3,4], [a,b,c,d]))" ] }, { "cell_type": "code", "execution_count": 32, "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>seccion_1</th>\n", " <th>seccion_2</th>\n", " <th>seccion_3</th>\n", " <th>seccion_4</th>\n", " <th>seccion_5</th>\n", " <th>seccion_6</th>\n", " <th>seccion_7</th>\n", " <th>seccion_8</th>\n", " <th>seccion_9</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>8</td>\n", " <td>8.0</td>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>11.75</td>\n", " <td>4.80</td>\n", " <td>0</td>\n", " <td>8.8</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>8</td>\n", " <td>8.5</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>11.50</td>\n", " <td>2.75</td>\n", " <td>0</td>\n", " <td>4.8</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>8</td>\n", " <td>9.0</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>11.75</td>\n", " <td>2.75</td>\n", " <td>0</td>\n", " <td>4.8</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>8</td>\n", " <td>8.0</td>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>11.75</td>\n", " <td>2.65</td>\n", " <td>0</td>\n", " <td>10.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>8</td>\n", " <td>8.0</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>10.70</td>\n", " <td>5.35</td>\n", " <td>0</td>\n", " <td>8.4</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>8</td>\n", " <td>11.5</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>11.45</td>\n", " <td>2.75</td>\n", " <td>0</td>\n", " <td>6.0</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>8</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>8.00</td>\n", " <td>4.85</td>\n", " <td>0</td>\n", " <td>8.2</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>8</td>\n", " <td>6.0</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>11.75</td>\n", " <td>3.80</td>\n", " <td>0</td>\n", " <td>7.2</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>11</td>\n", " <td>11.5</td>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>11.50</td>\n", " <td>1.40</td>\n", " <td>0</td>\n", " <td>1.2</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>11</td>\n", " <td>11.5</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>10.50</td>\n", " <td>4.05</td>\n", " <td>0</td>\n", " <td>6.0</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>12</td>\n", " <td>0.0</td>\n", " <td>12.75</td>\n", " <td>3.80</td>\n", " <td>0</td>\n", " <td>4.8</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>11.45</td>\n", " <td>3.50</td>\n", " <td>0</td>\n", " <td>8.4</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>11</td>\n", " <td>9.0</td>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>9.50</td>\n", " <td>3.75</td>\n", " <td>2</td>\n", " <td>8.4</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>9.20</td>\n", " <td>3.50</td>\n", " <td>0</td>\n", " <td>3.6</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>8</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>11.75</td>\n", " <td>3.00</td>\n", " <td>0</td>\n", " <td>9.6</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>8.95</td>\n", " <td>4.60</td>\n", " <td>0</td>\n", " <td>4.8</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>11.75</td>\n", " <td>4.30</td>\n", " <td>0</td>\n", " <td>9.6</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>11</td>\n", " <td>10.0</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>11.75</td>\n", " <td>5.35</td>\n", " <td>0</td>\n", " <td>9.6</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>11</td>\n", " <td>9.0</td>\n", " <td>12</td>\n", " <td>0.0</td>\n", " <td>11.45</td>\n", " <td>5.35</td>\n", " <td>0</td>\n", " <td>9.6</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>11.75</td>\n", " <td>5.10</td>\n", " <td>0</td>\n", " <td>8.4</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>12</td>\n", " <td>5.5</td>\n", " <td>11.75</td>\n", " <td>4.80</td>\n", " <td>0</td>\n", " <td>0.4</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>8</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>11.75</td>\n", " <td>4.05</td>\n", " <td>0</td>\n", " <td>9.6</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>11</td>\n", " <td>13.0</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>9.50</td>\n", " <td>2.95</td>\n", " <td>0</td>\n", " <td>9.6</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>12</td>\n", " <td>5.5</td>\n", " <td>11.75</td>\n", " <td>3.75</td>\n", " <td>0</td>\n", " <td>7.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>11.75</td>\n", " <td>4.30</td>\n", " <td>0</td>\n", " <td>7.2</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>8</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>9.50</td>\n", " <td>3.25</td>\n", " <td>0</td>\n", " <td>8.4</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>11</td>\n", " <td>13.0</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>9.00</td>\n", " <td>1.85</td>\n", " <td>0</td>\n", " <td>9.6</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>8</td>\n", " <td>12.0</td>\n", " <td>12</td>\n", " <td>5.5</td>\n", " <td>11.75</td>\n", " <td>4.05</td>\n", " <td>0</td>\n", " <td>8.4</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>11.75</td>\n", " <td>4.05</td>\n", " <td>0</td>\n", " <td>7.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>8</td>\n", " <td>11.5</td>\n", " <td>12</td>\n", " <td>5.5</td>\n", " <td>9.00</td>\n", " <td>0.80</td>\n", " <td>0</td>\n", " <td>0.0</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", " </tr>\n", " <tr>\n", " <th>151066</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>8</td>\n", " <td>0.0</td>\n", " <td>9.50</td>\n", " <td>2.20</td>\n", " <td>0</td>\n", " <td>1.7</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>151067</th>\n", " <td>11</td>\n", " <td>13.0</td>\n", " <td>8</td>\n", " <td>0.0</td>\n", " <td>9.25</td>\n", " <td>3.80</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151068</th>\n", " <td>11</td>\n", " <td>13.0</td>\n", " <td>8</td>\n", " <td>5.5</td>\n", " <td>8.00</td>\n", " <td>3.80</td>\n", " <td>0</td>\n", " <td>1.7</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151069</th>\n", " <td>11</td>\n", " <td>13.0</td>\n", " <td>9</td>\n", " <td>11.0</td>\n", " <td>11.75</td>\n", " <td>2.45</td>\n", " <td>2</td>\n", " <td>2.2</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>151070</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>9</td>\n", " <td>11.0</td>\n", " <td>11.75</td>\n", " <td>2.20</td>\n", " <td>0</td>\n", " <td>3.4</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151071</th>\n", " <td>11</td>\n", " <td>12.5</td>\n", " <td>7</td>\n", " <td>5.5</td>\n", " <td>11.75</td>\n", " <td>2.45</td>\n", " <td>0</td>\n", " <td>2.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>151072</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>12</td>\n", " <td>0.0</td>\n", " <td>11.75</td>\n", " <td>0.25</td>\n", " <td>0</td>\n", " <td>1.2</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151073</th>\n", " <td>11</td>\n", " <td>13.0</td>\n", " <td>9</td>\n", " <td>5.5</td>\n", " <td>9.50</td>\n", " <td>0.80</td>\n", " <td>0</td>\n", " <td>8.4</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151074</th>\n", " <td>11</td>\n", " <td>12.5</td>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>10.20</td>\n", " <td>3.00</td>\n", " <td>0</td>\n", " <td>2.9</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>151075</th>\n", " <td>11</td>\n", " <td>13.0</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>10.65</td>\n", " <td>2.75</td>\n", " <td>0</td>\n", " <td>3.6</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>151076</th>\n", " <td>11</td>\n", " <td>12.5</td>\n", " <td>12</td>\n", " <td>11.0</td>\n", " <td>10.50</td>\n", " <td>2.70</td>\n", " <td>0</td>\n", " <td>7.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>151077</th>\n", " <td>11</td>\n", " <td>12.5</td>\n", " <td>12</td>\n", " <td>0.0</td>\n", " <td>9.50</td>\n", " <td>0.90</td>\n", " <td>0</td>\n", " <td>4.6</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151078</th>\n", " <td>11</td>\n", " <td>13.0</td>\n", " <td>9</td>\n", " <td>5.5</td>\n", " <td>7.70</td>\n", " <td>2.45</td>\n", " <td>0</td>\n", " <td>10.6</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>151079</th>\n", " <td>11</td>\n", " <td>9.0</td>\n", " <td>10</td>\n", " <td>0.0</td>\n", " <td>10.20</td>\n", " <td>3.00</td>\n", " <td>0</td>\n", " <td>7.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>151080</th>\n", " <td>11</td>\n", " <td>8.5</td>\n", " <td>8</td>\n", " <td>0.0</td>\n", " <td>6.95</td>\n", " <td>0.25</td>\n", " <td>0</td>\n", " <td>3.6</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151081</th>\n", " <td>11</td>\n", " <td>11.5</td>\n", " <td>9</td>\n", " <td>0.0</td>\n", " <td>7.70</td>\n", " <td>0.25</td>\n", " <td>0</td>\n", " <td>7.7</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151082</th>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>10</td>\n", " <td>0.0</td>\n", " <td>7.35</td>\n", " <td>0.25</td>\n", " <td>0</td>\n", " <td>5.3</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>151083</th>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>7</td>\n", " <td>0.0</td>\n", " <td>8.35</td>\n", " <td>1.05</td>\n", " <td>0</td>\n", " <td>3.6</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>151084</th>\n", " <td>11</td>\n", " <td>10.0</td>\n", " <td>6</td>\n", " <td>0.0</td>\n", " <td>5.85</td>\n", " <td>0.00</td>\n", " <td>0</td>\n", " <td>6.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>151085</th>\n", " <td>11</td>\n", " <td>11.0</td>\n", " <td>7</td>\n", " <td>0.0</td>\n", " <td>3.00</td>\n", " <td>1.15</td>\n", " <td>0</td>\n", " <td>1.7</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151086</th>\n", " <td>11</td>\n", " <td>7.0</td>\n", " <td>6</td>\n", " <td>0.0</td>\n", " <td>4.60</td>\n", " <td>1.15</td>\n", " <td>0</td>\n", " <td>1.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>151087</th>\n", " <td>11</td>\n", " <td>11.5</td>\n", " <td>6</td>\n", " <td>0.0</td>\n", " <td>6.35</td>\n", " <td>0.25</td>\n", " <td>0</td>\n", " <td>6.0</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151088</th>\n", " <td>11</td>\n", " <td>11.5</td>\n", " <td>8</td>\n", " <td>0.0</td>\n", " <td>9.50</td>\n", " <td>0.00</td>\n", " <td>0</td>\n", " <td>3.6</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151089</th>\n", " <td>11</td>\n", " <td>8.5</td>\n", " <td>5</td>\n", " <td>0.0</td>\n", " <td>4.85</td>\n", " <td>0.25</td>\n", " <td>0</td>\n", " <td>3.6</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>151090</th>\n", " <td>11</td>\n", " <td>13.0</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>10.60</td>\n", " <td>2.20</td>\n", " <td>0</td>\n", " <td>8.9</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>151091</th>\n", " <td>8</td>\n", " <td>11.5</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>4.10</td>\n", " <td>1.95</td>\n", " <td>0</td>\n", " <td>6.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>151092</th>\n", " <td>8</td>\n", " <td>11.5</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>8.35</td>\n", " <td>1.95</td>\n", " <td>0</td>\n", " <td>7.6</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151093</th>\n", " <td>8</td>\n", " <td>12.0</td>\n", " <td>11</td>\n", " <td>5.5</td>\n", " <td>11.45</td>\n", " <td>2.75</td>\n", " <td>0</td>\n", " <td>7.2</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>151094</th>\n", " <td>11</td>\n", " <td>12.5</td>\n", " <td>11</td>\n", " <td>0.0</td>\n", " <td>11.25</td>\n", " <td>2.45</td>\n", " <td>0</td>\n", " <td>6.0</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>151095</th>\n", " <td>11</td>\n", " <td>12.0</td>\n", " <td>12</td>\n", " <td>5.5</td>\n", " <td>11.20</td>\n", " <td>2.75</td>\n", " <td>0</td>\n", " <td>5.3</td>\n", " <td>8</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>151096 rows × 9 columns</p>\n", "</div>" ], "text/plain": [ " seccion_1 seccion_2 seccion_3 seccion_4 seccion_5 seccion_6 \\\n", "0 8 8.0 11 11.0 11.75 4.80 \n", "1 8 8.5 11 5.5 11.50 2.75 \n", "2 8 9.0 11 5.5 11.75 2.75 \n", "3 8 8.0 11 11.0 11.75 2.65 \n", "4 8 8.0 11 5.5 10.70 5.35 \n", "5 8 11.5 11 0.0 11.45 2.75 \n", "6 8 12.0 11 0.0 8.00 4.85 \n", "7 8 6.0 11 0.0 11.75 3.80 \n", "8 11 11.5 11 11.0 11.50 1.40 \n", "9 11 11.5 11 5.5 10.50 4.05 \n", "10 11 12.0 12 0.0 12.75 3.80 \n", "11 11 12.0 11 0.0 11.45 3.50 \n", "12 11 9.0 11 11.0 9.50 3.75 \n", "13 11 12.0 11 11.0 9.20 3.50 \n", "14 8 12.0 11 0.0 11.75 3.00 \n", "15 11 11.0 11 5.5 8.95 4.60 \n", "16 11 12.0 11 11.0 11.75 4.30 \n", "17 11 10.0 11 5.5 11.75 5.35 \n", "18 11 9.0 12 0.0 11.45 5.35 \n", "19 11 12.0 11 5.5 11.75 5.10 \n", "20 11 12.0 12 5.5 11.75 4.80 \n", "21 8 12.0 11 0.0 11.75 4.05 \n", "22 11 13.0 11 0.0 9.50 2.95 \n", "23 11 12.0 12 5.5 11.75 3.75 \n", "24 11 12.0 11 5.5 11.75 4.30 \n", "25 8 12.0 11 5.5 9.50 3.25 \n", "26 11 13.0 11 0.0 9.00 1.85 \n", "27 8 12.0 12 5.5 11.75 4.05 \n", "28 11 12.0 11 5.5 11.75 4.05 \n", "29 8 11.5 12 5.5 9.00 0.80 \n", "... ... ... ... ... ... ... \n", "151066 11 12.0 8 0.0 9.50 2.20 \n", "151067 11 13.0 8 0.0 9.25 3.80 \n", "151068 11 13.0 8 5.5 8.00 3.80 \n", "151069 11 13.0 9 11.0 11.75 2.45 \n", "151070 11 12.0 9 11.0 11.75 2.20 \n", "151071 11 12.5 7 5.5 11.75 2.45 \n", "151072 11 12.0 12 0.0 11.75 0.25 \n", "151073 11 13.0 9 5.5 9.50 0.80 \n", "151074 11 12.5 11 11.0 10.20 3.00 \n", "151075 11 13.0 11 5.5 10.65 2.75 \n", "151076 11 12.5 12 11.0 10.50 2.70 \n", "151077 11 12.5 12 0.0 9.50 0.90 \n", "151078 11 13.0 9 5.5 7.70 2.45 \n", "151079 11 9.0 10 0.0 10.20 3.00 \n", "151080 11 8.5 8 0.0 6.95 0.25 \n", "151081 11 11.5 9 0.0 7.70 0.25 \n", "151082 11 11.0 10 0.0 7.35 0.25 \n", "151083 11 11.0 7 0.0 8.35 1.05 \n", "151084 11 10.0 6 0.0 5.85 0.00 \n", "151085 11 11.0 7 0.0 3.00 1.15 \n", "151086 11 7.0 6 0.0 4.60 1.15 \n", "151087 11 11.5 6 0.0 6.35 0.25 \n", "151088 11 11.5 8 0.0 9.50 0.00 \n", "151089 11 8.5 5 0.0 4.85 0.25 \n", "151090 11 13.0 11 0.0 10.60 2.20 \n", "151091 8 11.5 11 0.0 4.10 1.95 \n", "151092 8 11.5 11 0.0 8.35 1.95 \n", "151093 8 12.0 11 5.5 11.45 2.75 \n", "151094 11 12.5 11 0.0 11.25 2.45 \n", "151095 11 12.0 12 5.5 11.20 2.75 \n", "\n", " seccion_7 seccion_8 seccion_9 \n", "0 0 8.8 4 \n", "1 0 4.8 8 \n", "2 0 4.8 4 \n", "3 0 10.0 0 \n", "4 0 8.4 8 \n", "5 0 6.0 8 \n", "6 0 8.2 8 \n", "7 0 7.2 8 \n", "8 0 1.2 4 \n", "9 0 6.0 8 \n", "10 0 4.8 4 \n", "11 0 8.4 8 \n", "12 2 8.4 8 \n", "13 0 3.6 8 \n", "14 0 9.6 4 \n", "15 0 4.8 8 \n", "16 0 9.6 8 \n", "17 0 9.6 8 \n", "18 0 9.6 8 \n", "19 0 8.4 4 \n", "20 0 0.4 4 \n", "21 0 9.6 4 \n", "22 0 9.6 0 \n", "23 0 7.2 0 \n", "24 0 7.2 4 \n", "25 0 8.4 4 \n", "26 0 9.6 0 \n", "27 0 8.4 8 \n", "28 0 7.2 0 \n", "29 0 0.0 0 \n", "... ... ... ... \n", "151066 0 1.7 0 \n", "151067 0 1.0 8 \n", "151068 0 1.7 8 \n", "151069 2 2.2 4 \n", "151070 0 3.4 8 \n", "151071 0 2.2 0 \n", "151072 0 1.2 8 \n", "151073 0 8.4 8 \n", "151074 0 2.9 0 \n", "151075 0 3.6 4 \n", "151076 0 7.2 0 \n", "151077 0 4.6 8 \n", "151078 0 10.6 4 \n", "151079 0 7.0 0 \n", "151080 0 3.6 8 \n", "151081 0 7.7 8 \n", "151082 0 5.3 4 \n", "151083 0 3.6 4 \n", "151084 0 6.0 0 \n", "151085 0 1.7 8 \n", "151086 0 1.2 0 \n", "151087 0 6.0 8 \n", "151088 0 3.6 8 \n", "151089 0 3.6 4 \n", "151090 0 8.9 4 \n", "151091 0 6.0 0 \n", "151092 0 7.6 8 \n", "151093 0 7.2 8 \n", "151094 0 6.0 4 \n", "151095 0 5.3 8 \n", "\n", "[151096 rows x 9 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "infr[sec_lst]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "infr[infr['P12']==2]['P12'].count()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_columns = ['ENT','MUN','LOC','AGEB','MZA','ID_INM']\n", "new_columns.extend(sec_lst)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "infr_secciones = infr[new_columns]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "infr.to_csv('infraestuctura.csv')\n", "infr_secciones.to_csv('infr_secciones.csv')" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def densidad_inversa(j):\n", " #-column+max_cal\n", " #infr_secciones[sec_lst[j]] = max_cal[j]-infr_secciones[sec_lst[j]]\n", " return max_cal[j]-infr_secciones[sec_lst[j]]\n", "\n", " #infr_secciones[col]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'infr_secciones' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-45-400b7f55768b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlst2\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m17\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Sección \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\":\\t\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfr_secciones\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msec_lst\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m<=\u001b[0m\u001b[0mlst2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'infr_secciones' is not defined" ] } ], "source": [ "lst2= [11, 13, 13, 11, 14, 17, 2, 11, 8]\n", "for i in range(9):\n", " print(\"Sección \", i+1, \":\\t\", max(infr_secciones[sec_lst[i]])<=lst2[i])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "###### 2,3,5 11, 13, 13, 11, 14, 17, 2, 11, 8" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "seccion_1 5.0\n", "seccion_2 13.5\n", "seccion_3 16.0\n", "seccion_4 11.0\n", "seccion_5 14.0\n", "seccion_6 10.0\n", "seccion_7 2.0\n", "seccion_8 11.0\n", "seccion_9 8.0\n", "dtype: float64" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#j=1\n", "#print(min(-infrtmp.ix[:,sec_lst[j]]+max_cal[j]))\n", "#print(max(-infrtmp.ix[:,sec_lst[j]]+max_cal[j]))\n", "infr[sec_lst].max()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "61 13.5\n", "64 13.5\n", "72 13.5\n", "239 13.5\n", "335 13.5\n", "492 13.5\n", "540 13.5\n", "544 13.5\n", "603 13.5\n", "613 13.5\n", "621 13.5\n", "650 13.5\n", "679 13.5\n", "681 13.5\n", "700 13.5\n", "738 13.5\n", "756 13.5\n", "814 13.5\n", "819 13.5\n", "987 13.5\n", "1029 13.5\n", "1104 13.5\n", "1213 13.5\n", "1214 13.5\n", "1359 13.5\n", "1428 13.5\n", "1716 13.5\n", "1773 13.5\n", "1774 13.5\n", "1775 13.5\n", " ... \n", "149785 13.5\n", "149806 13.5\n", "149860 13.5\n", "149861 13.5\n", "149978 13.5\n", "150084 13.5\n", "150085 13.5\n", "150088 13.5\n", "150201 13.5\n", "150246 13.5\n", "150268 13.5\n", "150296 13.5\n", "150337 13.5\n", "150405 13.5\n", "150406 13.5\n", "150408 13.5\n", "150410 13.5\n", "150439 13.5\n", "150544 13.5\n", "150564 13.5\n", "150567 13.5\n", "150593 13.5\n", "150626 13.5\n", "150711 13.5\n", "150735 13.5\n", "150774 13.5\n", "150815 13.5\n", "151044 13.5\n", "151077 13.5\n", "151094 13.5\n", "Name: seccion_2, dtype: float64" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "infr[infr[sec_lst[1]]>13][sec_lst[1]]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nan:\t 217\n", "0:\t 11799\n", "1:\t 110188\n", "2:\t 28892\n", "total:\t 151096\n" ] } ], "source": [ "print(\"nan:\\t\",len(infr[infr['P12'].isnull()]))\n", "print(\"0:\\t\",len(infr[infr['P12']==0]))\n", "print(\"1:\\t\",len(infr[infr['P12']==1]))\n", "print(\"2:\\t\",len(infr[infr['P12']==2]))\n", "print(\"total:\\t\", len(infr['P12']))" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1., 2., 0., nan])" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.Series(infr['P12'].values.ravel()).unique()" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-740" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "11059-11799" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "217" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#nan:\t 217 -> nan:\t 217 0\n", "#1:\t 110794 -> 1:\t 110188 606\n", "#2:\t 29026 -> 2:\t 28892 134\n", "#3:\t 11059 -> 0:\t 11799 -740\n", "#total:\t 151096 -> total:\t 151096 0\n", "len(infr[infr['P12'].isnull()])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "infr['P3'].replace({1:11, 2:8, 3:3, 4:0, 5:0},inplace=True)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "infr.ix[(infr.P22==3)& (infr.P20==3),'P20']=0" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "infr.to_csv(\"infra_corregido.csv\")" ] }, { "cell_type": "code", "execution_count": 44, "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-44-cd9dfe73e5d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'INMUEBLES.xlsx'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/luis/anaconda/lib/python3.4/site-packages/pandas/io/excel.py\u001b[0m in \u001b[0;36mread_excel\u001b[0;34m(io, sheetname, header, skiprows, skip_footer, index_col, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, has_index_names, converters, engine, kwds)\u001b[0m\n\u001b[1;32m 161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m 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206\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 207\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'xlrd'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/luis/anaconda/lib/python3.4/site-packages/xlrd/__init__.py\u001b[0m in \u001b[0;36mopen_workbook\u001b[0;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[0mformatting_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mformatting_info\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 421\u001b[0m \u001b[0mon_demand\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mon_demand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m \u001b[0mragged_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mragged_rows\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 423\u001b[0m )\n\u001b[1;32m 424\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mbk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/luis/anaconda/lib/python3.4/site-packages/xlrd/xlsx.py\u001b[0m in \u001b[0;36mopen_workbook_2007_xml\u001b[0;34m(zf, component_names, logfile, verbosity, use_mmap, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[1;32m 792\u001b[0m \u001b[0mx12sheet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX12Sheet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msheet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbosity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 793\u001b[0m \u001b[0mheading\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Sheet %r (sheetx=%d) from %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msheetx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 794\u001b[0;31m \u001b[0mx12sheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_stream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzflo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheading\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 795\u001b[0m \u001b[0;32mdel\u001b[0m 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\u001b[0mrow_tag\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m \u001b[0mself_do_row\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 532\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# destroy all child elements (cells)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 533\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtag\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mU_SSML12\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"dimension\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/luis/anaconda/lib/python3.4/site-packages/xlrd/xlsx.py\u001b[0m in \u001b[0;36mdo_row\u001b[0;34m(self, row_elem)\u001b[0m\n\u001b[1;32m 641\u001b[0m \u001b[0mchild_tag\u001b[0m \u001b[0;34m=\u001b[0m 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] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = df[df['P3']<6]\n", "df.ix[df.P12>2, 'P12'] = 3\n", "df['P12'].replace({1: 1.5, 2: 1, 3: 0},inplace=True)\n", "len(df['P12'])" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16.0" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "max(infr[sec_lst[2]])" ] } ], "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
PhilHarnish/forge	src/puzzle/examples/panda/mag_20_05_cousin_oliver.ipynb	1	13887	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import forge\n", "from data import warehouse\n", "from puzzle.puzzlepedia import prod_config\n", "prod_config.init()\n", "\n", "top_words = warehouse.get('/words/top')" ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [], "source": [ "original = \"\"\"\n", "ACE AGE AMA ARB ART CLE CTI EGO EHI ERA ERB ERS ESS ETE ETE ETY FRO HOW IAL IDE IGH IMP KIC KNU MBE\n", "MMI NAN NDE NDI NGG NGV NIO NTC NTD NTH OMO OUS OVE OWI OWS PRO REA RFL RRA RSE RSH RSU RTO SAL\n", "SFO SUR TOL TON TPA TPR TSH TTE TUR VEO VER VID\n", "\"\"\".strip().lower().split()\n", "\n", "remaining = \"\"\"\n", "ACE CTI EGO ERA ERB ESS ETE ETY FRO HOW IDE IGH KIC KNU MBE\n", "MMI NAN NGG NIO NTC NTD NTH OMO OUS OWS REA RRA RSE RSH RSU RTO\n", "SFO SUR TOL TON TPA TPR TSH TTE TUR VEO VER\n", "\"\"\".replace('.', '').strip().lower().split()\n", "\n", "given = original\n", "\n", "# reactive ('rea', 'cti', 'veo') \n", "# tolerant ('tol', 'era', 'ntc') \n", "# tolerant ('tol', 'era', 'ntd') \n", "\n", "# .\|...\|...\|..\n", "# creatures ('ntc', 'rea', 'tur', 'ess') \n", "# detective ('ntd', 'ete', 'cti', 'veo') \n", "# detective ('ntd', 'ete', 'cti', 'ver') \n", "# overtones ('veo', 'ver', 'ton', 'ess') \n", "# overtures ('veo', 'ver', 'tur', 'ess') \n", "\n", "# ---\n", "\n", "# .\|...\|...\|.\n", "# covenant ('kic', 'ove', 'nan', 'tpr')\n", "# covenant ('ntc', 'ove', 'nan', 'tpr')\n", "\n", "# ..\|...\|.\n", "# tenant ('ete', 'nan', 'tpr')\n", "# tenant ('ete', 'nan', 'tpr')\n", "# alight ('ial', 'igh', 'tpr')\n", "# desalt ('ide', 'sal', 'tpr')\n", "# desalt ('nde', 'sal', 'tpr')\n", "# divert ('ndi', 'ver', 'tpr')\n", "# threat ('nth', 'rea', 'tpr')\n", "# flight ('rfl', 'igh', 'tpr')\n", "# summit ('rsu', 'mmi', 'tpr')\n", "# alight ('sal', 'igh', 'tpr')\n", "# tenant ('tte', 'nan', 'tpr')\n", "\n", "# ...\|...\|...\| ...\|...\|...\|. ..\|.. .\|...\|...\|.. .\|. ..\|. ..\| ...\|...\|...\|.. .\|...\|...\|\n", "# imp\|art\|ial\| sal\|ama\|nde\|r ..\|.. .\|...\|...\|.. .\|. ..\|. ..\| ove\|rfl\|owi\|ng g\|arb\|age\|\n", "# ...\|...\|.. .\|...\|...\|.. .\|...\|\n", "# ...\|...\|.. .\|...\|...\|.. .\|...\|\n", "# ...\|...\|. ..\|.. .\|...\|...\|. ..\|...\|. ..\|...\|. ..\|...\|. ..\|.. .\|. ..\|.. .\|...\|. ..\|...\|...\|.. .\|...\|...\|. ..\|. ..\|...\|.. .\|...\|...\|...\|\n", "# ...\|...\|. ..\|.. .\|...\|...\|. ..\|...\|. ..\|...\|. ..\|...\|. ..\|.. .\|. ..\|.. .\|...\|t pr\|ete\|ndi\|ng v\|ehi\|cle\|s ..\|. ..\|...\|.. .\|...\|...\|...\|\n", "# ...\|...\| ...\|...\|...\| \n", "# ...\|...\| pro\|vid\|ers\|\n", "\n", "\n", "enums = list(map(int, '9 10 4 9 2 3 2 11 7 8 9 4 7 4 8 6 6 6 4 2 4 5 10 8 3 7 10 6 9'.split()))\n", "\n" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2, 2 (3 blocks)\n", "aceaceace 1 -1 \"ceaceac\" ('ace', 'ace', 'ace')\n", "genders ('age', 'nde', 'rse')\n", "genders ('age', 'nde', 'rsh')\n", "genders ('age', 'nde', 'rsu')\n", "marshes ('ama', 'rsh', 'ess')\n", "matters ('ama', 'tte', 'rse')\n", "matters ('ama', 'tte', 'rsh')\n", "matters ('ama', 'tte', 'rsu')\n", "maturer ('ama', 'tur', 'era')\n", "maturer ('ama', 'tur', 'erb')\n", "maturer ('ama', 'tur', 'ers')\n", "matures ('ama', 'tur', 'ess')\n", "lemming ('cle', 'mmi', 'ngg')\n", "lemming ('cle', 'mmi', 'ngv')\n", "lenders ('cle', 'nde', 'rse')\n", "lenders ('cle', 'nde', 'rsh')\n", "lenders ('cle', 'nde', 'rsu')\n", "lending ('cle', 'ndi', 'ngg')\n", "lending ('cle', 'ndi', 'ngv')\n", "letters ('cle', 'tte', 'rse')\n", "letters ('cle', 'tte', 'rsh')\n", "letters ('cle', 'tte', 'rsu')\n", "timbers ('cti', 'mbe', 'rse')\n", "timbers ('cti', 'mbe', 'rsh')\n", "timbers ('cti', 'mbe', 'rsu')\n", "titters ('cti', 'tte', 'rse')\n", "titters ('cti', 'tte', 'rsh')\n", "titters ('cti', 'tte', 'rsu')\n", "hinders ('ehi', 'nde', 'rse')\n", "hinders ('ehi', 'nde', 'rsh')\n", "hinders ('ehi', 'nde', 'rsu')\n", "hitters ('ehi', 'tte', 'rse')\n", "hitters ('ehi', 'tte', 'rsh')\n", "hitters ('ehi', 'tte', 'rsu')\n", "raiders ('era', 'ide', 'rse')\n", "raiders ('era', 'ide', 'rsh')\n", "raiders ('era', 'ide', 'rsu')\n", "teeters ('ete', 'ete', 'rse')\n", "teeters ('ete', 'ete', 'rsh')\n", "teeters ('ete', 'ete', 'rsu')\n", "teeters ('ete', 'ete', 'rse')\n", "teeters ('ete', 'ete', 'rsh')\n", "teeters ('ete', 'ete', 'rsu')\n", "tenants ('ete', 'nan', 'tsh')\n", "tenders ('ete', 'nde', 'rse')\n", "tenders ('ete', 'nde', 'rsh')\n", "tenders ('ete', 'nde', 'rsu')\n", "tending ('ete', 'ndi', 'ngg')\n", "tending ('ete', 'ndi', 'ngv')\n", "teeters ('ete', 'ete', 'rse')\n", "teeters ('ete', 'ete', 'rsh')\n", "teeters ('ete', 'ete', 'rsu')\n", "teeters ('ete', 'ete', 'rse')\n", "teeters ('ete', 'ete', 'rsh')\n", "teeters ('ete', 'ete', 'rsu')\n", "tenants ('ete', 'nan', 'tsh')\n", "tenders ('ete', 'nde', 'rse')\n", "tenders ('ete', 'nde', 'rsh')\n", "tenders ('ete', 'nde', 'rsu')\n", "tending ('ete', 'ndi', 'ngg')\n", "tending ('ete', 'ndi', 'ngv')\n", "rotters ('fro', 'tte', 'rse')\n", "rotters ('fro', 'tte', 'rsh')\n", "rotters ('fro', 'tte', 'rsu')\n", "alights ('ial', 'igh', 'tsh')\n", "nuclear ('knu', 'cle', 'arb')\n", "nuclear ('knu', 'cle', 'art')\n", "numbers ('knu', 'mbe', 'rse')\n", "numbers ('knu', 'mbe', 'rsh')\n", "numbers ('knu', 'mbe', 'rsu')\n", "beehive ('mbe', 'ehi', 'veo')\n", "beehive ('mbe', 'ehi', 'ver')\n", "benders ('mbe', 'nde', 'rse')\n", "benders ('mbe', 'nde', 'rsh')\n", "benders ('mbe', 'nde', 'rsu')\n", "bending ('mbe', 'ndi', 'ngg')\n", "bending ('mbe', 'ndi', 'ngv')\n", "bereave ('mbe', 'rea', 'veo')\n", "bereave ('mbe', 'rea', 'ver')\n", "betters ('mbe', 'tte', 'rse')\n", "betters ('mbe', 'tte', 'rsh')\n", "betters ('mbe', 'tte', 'rsu')\n", "dieters ('ndi', 'ete', 'rse')\n", "dieters ('ndi', 'ete', 'rsh')\n", "dieters ('ndi', 'ete', 'rsu')\n", "dieters ('ndi', 'ete', 'rse')\n", "dieters ('ndi', 'ete', 'rsh')\n", "dieters ('ndi', 'ete', 'rsu')\n", "diverts ('ndi', 'ver', 'tsh')\n", "divider ('ndi', 'vid', 'era')\n", "divider ('ndi', 'vid', 'erb')\n", "divider ('ndi', 'vid', 'ers')\n", "divides ('ndi', 'vid', 'ess')\n", "threats ('nth', 'rea', 'tsh')\n", "winding ('owi', 'ndi', 'ngg')\n", "winding ('owi', 'ndi', 'ngv')\n", "rotters ('pro', 'tte', 'rse')\n", "rotters ('pro', 'tte', 'rsh')\n", "rotters ('pro', 'tte', 'rsu')\n", "flights ('rfl', 'igh', 'tsh')\n", "flowing ('rfl', 'owi', 'ngg')\n", "flowing ('rfl', 'owi', 'ngv')\n", "raiders ('rra', 'ide', 'rse')\n", "raiders ('rra', 'ide', 'rsh')\n", "raiders ('rra', 'ide', 'rsu')\n", "senders ('rse', 'nde', 'rse')\n", "senders ('rse', 'nde', 'rsh')\n", "senders ('rse', 'nde', 'rsu')\n", "sending ('rse', 'ndi', 'ngg')\n", "sending ('rse', 'ndi', 'ngv')\n", "seniors ('rse', 'nio', 'rse')\n", "seniors ('rse', 'nio', 'rsh')\n", "seniors ('rse', 'nio', 'rsu')\n", "setters ('rse', 'tte', 'rse')\n", "setters ('rse', 'tte', 'rsh')\n", "setters ('rse', 'tte', 'rsu')\n", "severer ('rse', 'ver', 'era')\n", "severer ('rse', 'ver', 'erb')\n", "severer ('rse', 'ver', 'ers')\n", "sherbet ('rsh', 'erb', 'ete')\n", "sherbet ('rsh', 'erb', 'ete')\n", "sherbet ('rsh', 'erb', 'ety')\n", "showing ('rsh', 'owi', 'ngg')\n", "showing ('rsh', 'owi', 'ngv')\n", "summing ('rsu', 'mmi', 'ngg')\n", "summing ('rsu', 'mmi', 'ngv')\n", "summits ('rsu', 'mmi', 'tsh')\n", "sutures ('rsu', 'tur', 'ess')\n", "totters ('rto', 'tte', 'rse')\n", "totters ('rto', 'tte', 'rsh')\n", "totters ('rto', 'tte', 'rsu')\n", "alights ('sal', 'igh', 'tsh')\n", "onagers ('ton', 'age', 'rse')\n", "onagers ('ton', 'age', 'rsh')\n", "onagers ('ton', 'age', 'rsu')\n", "panders ('tpa', 'nde', 'rse')\n", "panders ('tpa', 'nde', 'rsh')\n", "panders ('tpa', 'nde', 'rsu')\n", "panther ('tpa', 'nth', 'era')\n", "panther ('tpa', 'nth', 'erb')\n", "panther ('tpa', 'nth', 'ers')\n", "patters ('tpa', 'tte', 'rse')\n", "patters ('tpa', 'tte', 'rsh')\n", "patters ('tpa', 'tte', 'rsu')\n", "presser ('tpr', 'ess', 'era')\n", "presser ('tpr', 'ess', 'erb')\n", "presser ('tpr', 'ess', 'ers')\n", "presses ('tpr', 'ess', 'ess')\n", "pretend ('tpr', 'ete', 'nde')\n", "pretend ('tpr', 'ete', 'ndi')\n", "pretend ('tpr', 'ete', 'nde')\n", "pretend ('tpr', 'ete', 'ndi')\n", "sherbet ('tsh', 'erb', 'ete')\n", "sherbet ('tsh', 'erb', 'ete')\n", "sherbet ('tsh', 'erb', 'ety')\n", "showing ('tsh', 'owi', 'ngg')\n", "showing ('tsh', 'owi', 'ngv')\n", "teeters ('tte', 'ete', 'rse')\n", "teeters ('tte', 'ete', 'rsh')\n", "teeters ('tte', 'ete', 'rsu')\n", "teeters ('tte', 'ete', 'rse')\n", "teeters ('tte', 'ete', 'rsh')\n", "teeters ('tte', 'ete', 'rsu')\n", "tenants ('tte', 'nan', 'tsh')\n", "tenders ('tte', 'nde', 'rse')\n", "tenders ('tte', 'nde', 'rsh')\n", "tenders ('tte', 'nde', 'rsu')\n", "tending ('tte', 'ndi', 'ngg')\n", "tending ('tte', 'ndi', 'ngv')\n" ] } ], "source": [ "def find(pattern):\n", " pattern = pattern.strip('\|')\n", " blocks = pattern.count('\|') + 1\n", " parts = pattern.split('\|')\n", " len_front = len(parts[0])\n", " len_end = len(parts[-1])\n", " print('%d, %d (%d blocks)' % (len_front, len_end, blocks))\n", " sample = True\n", " for inner_set in itertools.product(given, repeat=blocks):\n", " #if inner_set[-1] != 'tpr':\n", " # continue\n", " if len_end == 3:\n", " w = ''.join(inner_set)[(3 - len_front):]\n", " else:\n", " w = ''.join(inner_set)[(3 - len_front):-(3 - len_end)]\n", " if sample:\n", " print(''.join(inner_set), (3 - len_front), -(3 - len_end), '\"%s\"' % w, inner_set)\n", " sample = False\n", " #if inner_set[0][0] != 'r':\n", " # continue\n", " #if inner_set[-1][-1] not in 'tocd':\n", " # continue\n", " if w in all_words_set:\n", " print(w, inner_set)\n", "\n", "\n", "find('..\|...\|..')\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'banana' in top_words" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "...\|...\|...\| ...\|...\|...\|. ..\|.. .\|...\|...\|.. .\|. ..\|. ..\| ...\|...\|...\|.. .\|...\|...\| ...\|...\|.. .\|...\|...\|.. .\|...\| ...\|...\|. ..\|.. .\|...\|...\|. ..\|...\|. ..\|...\|. ..\|...\|. ..\|.. .\|. ..\|.. .\|...\|. ..\|...\|...\|.. .\|...\|...\|. ..\|. ..\|...\|.. .\|...\|...\|...\| ...\|...\| ...\|...\|...\| \n" ] } ], "source": [ "result = []\n", "i = 0\n", "for n in enums:\n", " for x in range(n):\n", " result.append('.')\n", " i += 1\n", " if i % 3 == 0:\n", " result.append('\|')\n", " result.append(' ')\n", "\n", "print(''.join(result))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'salamander' in all_words_set" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "all_words = list(prod_config._get_words())" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "all_words_set = {k.lower() for k, v in all_words}" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(44638, 4332)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(all_words_set), len(top_words)" ] }, { "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
tschinz/iPython_Workspace	00_Admin/Plots/Pandas.ipynb	1	76274	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')\n", "plt.plot([1,2,3,4])\n", "plt.ylabel('some numbers')\n", "plt.title(\"Title\")\n", "plt.show() \n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "x = np.linspace(0, 3np.pi, 500)\n", "plt.plot(x, np.sin(x*2))\n", "plt.title('A simple chirp');\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureCanvasNbAgg()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fa33b64e630>]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib widget\n", "import matplotlib.pyplot as plt\n", "plt.figure()\n", "x = [1,2,3]\n", "y = [4,5,6]\n", "plt.plot(x,y)" ] }, { "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 }	gpl-2.0
GoogleCloudPlatform/training-data-analyst	courses/machine_learning/deepdive2/recommendation_systems/solutions/content_based_preproc.ipynb	2	25280	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Create Datasets for the Content-based Filter\n", "\n", "This notebook builds the data you will use for creating our content based model. You'll collect the data via a collection of SQL queries from the publicly available Kurier.at dataset in BigQuery.\n", "Kurier.at is an Austrian newsite. The goal of these labs is to recommend an article for a visitor to the site. In this notebook, you collect the data for training, in the subsequent notebook you train the recommender model. \n", "\n", "This notebook illustrates:\n", "* How to pull data from BigQuery table and write to local files.\n", "* How to make reproducible train and test splits." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import tensorflow as tf\n", "import numpy as np\n", "from google.cloud import bigquery \n", "\n", "PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID\n", "BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME\n", "REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1\n", "\n", "# do not change these\n", "os.environ['PROJECT'] = PROJECT\n", "os.environ['BUCKET'] = BUCKET\n", "os.environ['REGION'] = REGION\n", "os.environ['TFVERSION'] = '2.1'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Updated property [core/project].\n", "Updated property [compute/region].\n" ] } ], "source": [ "%%bash\n", "gcloud config set project $PROJECT\n", "gcloud config set compute/region $REGION" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will use this helper function to write lists containing article ids, categories, and authors for each article in our database to local file. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def write_list_to_disk(my_list, filename):\n", " with open(filename, 'w') as f:\n", " for item in my_list:\n", " line = \"%s\\n\" % item\n", " f.write(line)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pull data from BigQuery\n", "\n", "The cell below creates a local text file containing all the article ids (i.e. 'content ids') in the dataset. \n", "\n", "Have a look at the original dataset in [BigQuery](https://console.cloud.google.com/bigquery?p=cloud-training-demos&d=GA360_test&t=ga_sessions_sample). Then read through the query below and make sure you understand what it is doing. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Some sample content IDs ['299922662', '299826775', '299437612']\n", "The total number of articles is 15634\n" ] } ], "source": [ "sql=\"\"\"\n", "#standardSQL\n", "\n", "SELECT \n", " (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(hits.customDimensions)) AS content_id \n", "FROM `cloud-training-demos.GA360_test.ga_sessions_sample`, \n", " UNNEST(hits) AS hits\n", "WHERE \n", " # only include hits on pages\n", " hits.type = \"PAGE\"\n", " AND (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(hits.customDimensions)) IS NOT NULL\n", "GROUP BY\n", " content_id\n", " \n", "\"\"\"\n", "\n", "content_ids_list = bigquery.Client().query(sql).to_dataframe()['content_id'].tolist()\n", "write_list_to_disk(content_ids_list, \"content_ids.txt\")\n", "print(\"Some sample content IDs {}\".format(content_ids_list[:3]))\n", "print(\"The total number of articles is {}\".format(len(content_ids_list)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There should be 15,634 articles in the database. \n", "Next, you'll create a local file which contains a list of article categories and a list of article authors.\n", "\n", "Note the change in the index when pulling the article category or author information. Also, you are using the first author of the article to create our author list. \n", "Refer back to the original dataset, use the `hits.customDimensions.index` field to verify the correct index.\t " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['News', 'Stars & Kultur', 'Lifestyle']\n" ] } ], "source": [ "sql=\"\"\"\n", "#standardSQL\n", "SELECT \n", " (SELECT MAX(IF(index=7, value, NULL)) FROM UNNEST(hits.customDimensions)) AS category \n", "FROM `cloud-training-demos.GA360_test.ga_sessions_sample`, \n", " UNNEST(hits) AS hits\n", "WHERE \n", " # only include hits on pages\n", " hits.type = \"PAGE\"\n", " AND (SELECT MAX(IF(index=7, value, NULL)) FROM UNNEST(hits.customDimensions)) IS NOT NULL\n", "GROUP BY \n", " category\n", "\"\"\"\n", "categories_list = bigquery.Client().query(sql).to_dataframe()['category'].tolist()\n", "write_list_to_disk(categories_list, \"categories.txt\")\n", "print(categories_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The categories are 'News', 'Stars & Kultur', and 'Lifestyle'. \n", "When creating the author list, you'll only use the first author information for each article. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Some sample authors ['Marlene Patsalidis', 'Yvonne Widler', 'Thomas Trescher', 'Johanna Hager', 'Elisabeth Spitzer', 'Daniela Wahl', 'Stefan Berndl', 'Heinz Wagner', 'Brigitte Schokarth', 'Mathias Kainz']\n", "The total number of authors is 385\n" ] } ], "source": [ "sql=\"\"\"\n", "#standardSQL\n", "SELECT\n", " REGEXP_EXTRACT((SELECT MAX(IF(index=2, value, NULL)) FROM UNNEST(hits.customDimensions)), r\"^[^,]+\") AS first_author \n", "FROM `cloud-training-demos.GA360_test.ga_sessions_sample`, \n", " UNNEST(hits) AS hits\n", "WHERE \n", " # only include hits on pages\n", " hits.type = \"PAGE\"\n", " AND (SELECT MAX(IF(index=2, value, NULL)) FROM UNNEST(hits.customDimensions)) IS NOT NULL\n", "GROUP BY \n", " first_author\n", "\"\"\"\n", "authors_list = bigquery.Client().query(sql).to_dataframe()['first_author'].tolist()\n", "write_list_to_disk(authors_list, \"authors.txt\")\n", "print(\"Some sample authors {}\".format(authors_list[:10]))\n", "print(\"The total number of authors is {}\".format(len(authors_list)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There should be 385 authors in the database. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create train and test sets\n", "\n", "In this section, you will create the train/test split of our data for training our model. You use the concatenated values for visitor id and content id to create a farm fingerprint, taking approximately 90% of the data for the training set and 10% for the test set." ] }, { "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>visitor_id</th>\n", " <th>content_id</th>\n", " <th>category</th>\n", " <th>title</th>\n", " <th>author</th>\n", " <th>months_since_epoch</th>\n", " <th>next_content_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1013445690169368902</td>\n", " <td>299827911</td>\n", " <td>News</td>\n", " <td>\"Vulkanausbrüche sind normal\"</td>\n", " <td>Michaela Reibenwein</td>\n", " <td>574</td>\n", " <td>299779564</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1013445690169368902</td>\n", " <td>299779564</td>\n", " <td>Stars & Kultur</td>\n", " <td>Geschenk: Nicole Kidman bekommt Traumhaus um 4...</td>\n", " <td>Elisabeth Spitzer</td>\n", " <td>574</td>\n", " <td>299777664</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1022059616427871901</td>\n", " <td>299798467</td>\n", " <td>Lifestyle</td>\n", " <td>Frau täuscht Tod vor um Fake-Liebhaber zu entk...</td>\n", " <td>Elisabeth Mittendorfer</td>\n", " <td>574</td>\n", " <td>299777082</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1022059616427871901</td>\n", " <td>299777082</td>\n", " <td>Lifestyle</td>\n", " <td>Die simple Strategie für strahlende Model-Haut</td>\n", " <td>Maria Zelenko</td>\n", " <td>574</td>\n", " <td>299814775</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1029992987987017563</td>\n", " <td>299779564</td>\n", " <td>Stars & Kultur</td>\n", " <td>Geschenk: Nicole Kidman bekommt Traumhaus um 4...</td>\n", " <td>Elisabeth Spitzer</td>\n", " <td>574</td>\n", " <td>299826775</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " visitor_id content_id category \\\n", "0 1013445690169368902 299827911 News \n", "1 1013445690169368902 299779564 Stars & Kultur \n", "2 1022059616427871901 299798467 Lifestyle \n", "3 1022059616427871901 299777082 Lifestyle \n", "4 1029992987987017563 299779564 Stars & Kultur \n", "\n", " title author \\\n", "0 \"Vulkanausbrüche sind normal\" Michaela Reibenwein \n", "1 Geschenk: Nicole Kidman bekommt Traumhaus um 4... Elisabeth Spitzer \n", "2 Frau täuscht Tod vor um Fake-Liebhaber zu entk... Elisabeth Mittendorfer \n", "3 Die simple Strategie für strahlende Model-Haut Maria Zelenko \n", "4 Geschenk: Nicole Kidman bekommt Traumhaus um 4... Elisabeth Spitzer \n", "\n", " months_since_epoch next_content_id \n", "0 574 299779564 \n", "1 574 299777664 \n", "2 574 299777082 \n", "3 574 299814775 \n", "4 574 299826775 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sql=\"\"\"\n", "WITH site_history as (\n", " SELECT\n", " fullVisitorId as visitor_id,\n", " (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(hits.customDimensions)) AS content_id,\n", " (SELECT MAX(IF(index=7, value, NULL)) FROM UNNEST(hits.customDimensions)) AS category, \n", " (SELECT MAX(IF(index=6, value, NULL)) FROM UNNEST(hits.customDimensions)) AS title,\n", " (SELECT MAX(IF(index=2, value, NULL)) FROM UNNEST(hits.customDimensions)) AS author_list,\n", " SPLIT(RPAD((SELECT MAX(IF(index=4, value, NULL)) FROM UNNEST(hits.customDimensions)), 7), '.') as year_month_array,\n", " LEAD(hits.customDimensions, 1) OVER (PARTITION BY fullVisitorId ORDER BY hits.time ASC) as nextCustomDimensions\n", " FROM \n", " `cloud-training-demos.GA360_test.ga_sessions_sample`, \n", " UNNEST(hits) AS hits\n", " WHERE \n", " # only include hits on pages\n", " hits.type = \"PAGE\"\n", " AND\n", " fullVisitorId IS NOT NULL\n", " AND\n", " hits.time != 0\n", " AND\n", " hits.time IS NOT NULL\n", " AND\n", " (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(hits.customDimensions)) IS NOT NULL\n", ")\n", "SELECT\n", " visitor_id,\n", " content_id,\n", " category,\n", " REGEXP_REPLACE(title, r\",\", \"\") as title,\n", " REGEXP_EXTRACT(author_list, r\"^[^,]+\") as author,\n", " DATE_DIFF(DATE(CAST(year_month_array[OFFSET(0)] AS INT64), CAST(year_month_array[OFFSET(1)] AS INT64), 1), DATE(1970,1,1), MONTH) as months_since_epoch,\n", " (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(nextCustomDimensions)) as next_content_id\n", "FROM\n", " site_history\n", "WHERE (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(nextCustomDimensions)) IS NOT NULL\n", " AND ABS(MOD(FARM_FINGERPRINT(CONCAT(visitor_id, content_id)), 10)) < 9\n", "\"\"\"\n", "training_set_df = bigquery.Client().query(sql).to_dataframe()\n", "training_set_df.to_csv('training_set.csv', header=False, index=False, encoding='utf-8')\n", "training_set_df.head()" ] }, { "cell_type": "code", "execution_count": 8, "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>visitor_id</th>\n", " <th>content_id</th>\n", " <th>category</th>\n", " <th>title</th>\n", " <th>author</th>\n", " <th>months_since_epoch</th>\n", " <th>next_content_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1017181701779883231</td>\n", " <td>299813480</td>\n", " <td>Lifestyle</td>\n", " <td>Alice Schwarzer: Periode des Rückschlags für F...</td>\n", " <td>Elisabeth Mittendorfer</td>\n", " <td>574</td>\n", " <td>299800661</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1044735394411897753</td>\n", " <td>299584180</td>\n", " <td>News</td>\n", " <td>Paaradox: Berlin bei Nacht</td>\n", " <td>Gabriele Kuhn</td>\n", " <td>574</td>\n", " <td>299821998</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1044735394411897753</td>\n", " <td>299821998</td>\n", " <td>News</td>\n", " <td>Dieses Wort</td>\n", " <td>Peter Pisa</td>\n", " <td>574</td>\n", " <td>299787238</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1044735394411897753</td>\n", " <td>299584180</td>\n", " <td>News</td>\n", " <td>Paaradox: Berlin bei Nacht</td>\n", " <td>Gabriele Kuhn</td>\n", " <td>574</td>\n", " <td>299575760</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1125519382829212847</td>\n", " <td>296772080</td>\n", " <td>News</td>\n", " <td>5 vor 12 am Semmering: Neues Tragseil lässt ho...</td>\n", " <td>Patrick Wammerl</td>\n", " <td>574</td>\n", " <td>296772080</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " visitor_id content_id category \\\n", "0 1017181701779883231 299813480 Lifestyle \n", "1 1044735394411897753 299584180 News \n", "2 1044735394411897753 299821998 News \n", "3 1044735394411897753 299584180 News \n", "4 1125519382829212847 296772080 News \n", "\n", " title author \\\n", "0 Alice Schwarzer: Periode des Rückschlags für F... Elisabeth Mittendorfer \n", "1 Paaradox: Berlin bei Nacht Gabriele Kuhn \n", "2 Dieses Wort Peter Pisa \n", "3 Paaradox: Berlin bei Nacht Gabriele Kuhn \n", "4 5 vor 12 am Semmering: Neues Tragseil lässt ho... Patrick Wammerl \n", "\n", " months_since_epoch next_content_id \n", "0 574 299800661 \n", "1 574 299821998 \n", "2 574 299787238 \n", "3 574 299575760 \n", "4 574 296772080 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sql=\"\"\"\n", "WITH site_history as (\n", " SELECT\n", " fullVisitorId as visitor_id,\n", " (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(hits.customDimensions)) AS content_id,\n", " (SELECT MAX(IF(index=7, value, NULL)) FROM UNNEST(hits.customDimensions)) AS category, \n", " (SELECT MAX(IF(index=6, value, NULL)) FROM UNNEST(hits.customDimensions)) AS title,\n", " (SELECT MAX(IF(index=2, value, NULL)) FROM UNNEST(hits.customDimensions)) AS author_list,\n", " SPLIT(RPAD((SELECT MAX(IF(index=4, value, NULL)) FROM UNNEST(hits.customDimensions)), 7), '.') as year_month_array,\n", " LEAD(hits.customDimensions, 1) OVER (PARTITION BY fullVisitorId ORDER BY hits.time ASC) as nextCustomDimensions\n", " FROM \n", " `cloud-training-demos.GA360_test.ga_sessions_sample`, \n", " UNNEST(hits) AS hits\n", " WHERE \n", " # only include hits on pages\n", " hits.type = \"PAGE\"\n", " AND\n", " fullVisitorId IS NOT NULL\n", " AND\n", " hits.time != 0\n", " AND\n", " hits.time IS NOT NULL\n", " AND\n", " (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(hits.customDimensions)) IS NOT NULL\n", ")\n", "SELECT\n", " visitor_id,\n", " content_id,\n", " category,\n", " REGEXP_REPLACE(title, r\",\", \"\") as title,\n", " REGEXP_EXTRACT(author_list, r\"^[^,]+\") as author,\n", " DATE_DIFF(DATE(CAST(year_month_array[OFFSET(0)] AS INT64), CAST(year_month_array[OFFSET(1)] AS INT64), 1), DATE(1970,1,1), MONTH) as months_since_epoch,\n", " (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(nextCustomDimensions)) as next_content_id\n", "FROM\n", " site_history\n", "WHERE (SELECT MAX(IF(index=10, value, NULL)) FROM UNNEST(nextCustomDimensions)) IS NOT NULL\n", " AND ABS(MOD(FARM_FINGERPRINT(CONCAT(visitor_id, content_id)), 10)) >= 9\n", "\"\"\"\n", "test_set_df = bigquery.Client().query(sql).to_dataframe()\n", "test_set_df.to_csv('test_set.csv', header=False, index=False, encoding='utf-8')\n", "test_set_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's have a look at the two csv files you just created containing the training and test set. You'll also do a line count of both files to confirm that you have achieved an approximate 90/10 train/test split. \n", "In the next notebook, Content Based Filtering you will build a model to recommend an article given information about the current article being read, such as the category, title, author, and publish date. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 25599 test_set.csv\n", " 232308 training_set.csv\n", " 257907 total\n" ] } ], "source": [ "%%bash\n", "wc -l _set.csv" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==> test_set.csv <==\n", "1017181701779883231,299813480,Lifestyle,Alice Schwarzer: Periode des Rückschlags für Frauen,Elisabeth Mittendorfer,574,299800661\n", "1044735394411897753,299584180,News,Paaradox: Berlin bei Nacht,Gabriele Kuhn,574,299821998\n", "1044735394411897753,299821998,News,Dieses Wort,Peter Pisa,574,299787238\n", "1044735394411897753,299584180,News,Paaradox: Berlin bei Nacht,Gabriele Kuhn,574,299575760\n", "1125519382829212847,296772080,News,5 vor 12 am Semmering: Neues Tragseil lässt hoffen,Patrick Wammerl,574,296772080\n", "1908943635382619435,299912041,News,Deutscher Bürgermeister bei Messerangriff schwer verletzt,,574,299791583\n", "2023876788459819379,299898438,News,USA: Oberstes Bundesgericht nimmt Klagen zu Waffengesetzen nicht an,,574,299853016\n", "2041575691631100228,299964154,News,Neue Seidenstraße: Ein chinesischer Keil in Europa,Hermann Sileitsch-Parzer,574,299826775\n", "2090601016705774782,299954138,News,\"Augsburg-Boss: \"\"Leipzig darf keine Lizenz haben\"\"\",Mathias Kainz,574,299903877\n", "2347336822873602099,299768605,Lifestyle,Benetton holt sich wieder Schock-Fotograf Oliviero Toscani ins Boot,Maria Zelenko,574,298324599\n", "\n", "==> training_set.csv <==\n", "1013445690169368902,299827911,News,\"\"\"Vulkanausbrüche sind normal\"\"\",Michaela Reibenwein,574,299779564\n", "1013445690169368902,299779564,Stars & Kultur,Geschenk: Nicole Kidman bekommt Traumhaus um 40 Mio. Dollar ,Elisabeth Spitzer,574,299777664\n", "1022059616427871901,299798467,Lifestyle,Frau täuscht Tod vor um Fake-Liebhaber zu entkommen,Elisabeth Mittendorfer,574,299777082\n", "1022059616427871901,299777082,Lifestyle,Die simple Strategie für strahlende Model-Haut,Maria Zelenko,574,299814775\n", "1029992987987017563,299779564,Stars & Kultur,Geschenk: Nicole Kidman bekommt Traumhaus um 40 Mio. Dollar ,Elisabeth Spitzer,574,299826775\n", "1029992987987017563,299777722,Stars & Kultur,\"Willow Smith: \"\"Es ist schrecklich so aufzuwachsen\"\"\",Elisabeth Spitzer,574,299772450\n", "1031539128969021923,299918278,News,Skipässe in Wintersport-Hochburgen massiv teurer,Stefan Hofer,574,299928807\n", "1031539128969021923,299928807,News,Missbrauchsvorwürfe: Tiroler Skiverband durchsuchte Heim-Protokolle,Mirad Odobasic,574,299816215\n", "1031539128969021923,299925086,News,Marihuana-Adventkalender findet in Kanada reißenden Absatz,,574,299814194\n", "1031539128969021923,299814194,News,WM-Qualifikation: Härtetest auf Mallorca für Österreichs Damen,Günther Pavlovics,574,299913368\n" ] } ], "source": [ "!head _set.csv" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "environment": { "kernel": "python3", "name": "tf-gpu.1-15.m91", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf-gpu.1-15:m91" }, "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": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 4 }	apache-2.0
ignaciochr/purchase_optimizer	YaEsta.com+optimizer (notebook).ipynb	1	58418	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## YaEsta.com purchase optimizer" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "import bs4\n", "Soup = bs4.BeautifulSoup\n", "import csv\n", "import re\n", "import pandas as pd\n", "import numpy as np\n", "import math\n", "from itertools import combinations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Importing website HTML data to be parsed" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#To parse local HTML files\n", "with open(r'C:/Users/ignacio.chavarria/Desktop/Scraping/aaa.html', \"r\") as f:\n", " content = f.read()\n", "\n", "#To parse from web\n", "#response = requests.get(\"http://dataquestio.github.io/web-scraping-pages/2014_super_bowl.html\")\n", "#content = response.content" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(r\"C:\\Users\\ignacio.chavarria\\Desktop\\Scraping\\YaEsta\\drinks.html\", encoding=\"utf8\") as f:\n", " content_d = f.read()\n", " \n", "with open(r\"C:\\Users\\ignacio.chavarria\\Desktop\\Scraping\\YaEsta\\snacks.html\", \"r\", encoding=\"utf8\") as f:\n", " content_s = f.read()\n", "\n", "with open(r\"C:\\Users\\ignacio.chavarria\\Desktop\\Scraping\\YaEsta\\chocolates.html\", \"r\", encoding=\"utf8\") as f:\n", " content_c = f.read()" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p_d = Soup(content_d, 'html.parser')\n", "p_s = Soup(content_s, 'html.parser')\n", "p_c = Soup(content_c, 'html.parser')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parsing titles and prices:" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": true }, "outputs": [], "source": [ "parsers = [p_d, p_s, p_c]" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def cat_name(n):\n", " if n == 0:\n", " return \"drink\"\n", " elif n == 1:\n", " return \"snack\"\n", " elif n == 2:\n", " return \"chocolate\"\n", "\n", "names = []\n", "prices = []\n", "categories = []\n", "ct = 0\n", "\n", "for parser in parsers:\n", " #Get product names\n", " names_raw = parser.select(\".productName\")\n", " for i in names_raw:\n", " names.append(i.text)\n", "\n", " #Get product prices\n", " prices_raw = parser.select(\".prices\")\n", " for i in prices_raw:\n", " if len(i) == 3:\n", " prices.append(float((i.text)[3:]))\n", " elif len(i) == 5:\n", " prices.append(float((i.find_all(\"span\")[1].text)[1:]))\n", "\n", " #Get product categories\n", " cats = [ cat_name(ct) for i in range((len(categories)), len(prices))]\n", " for i in cats:\n", " categories.append(i)\n", " ct += 1" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Create dataframe\n", "df = pd.DataFrame(\n", " {'name': names[:-9],\n", " 'category': categories,\n", " 'price': prices\n", " })" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def amount(i):\n", " if re.findall('[ ]([0-9\\,\\.]+)(?=\\s[mMgG]([ lLrR\\.]\|\\Z)([ \\.]\|\\Z))', i):\n", " test2 = re.findall('[ ]([0-9\\,\\.]+)(?=\\s[mMgG]([ lLrR\\.]\|\\Z)([ \\.]\|\\Z))', i)\n", " else:\n", " test2 = re.findall('[ ]([0-9\\,\\.]+)(?=\\s[mMgG]([ lLrR\\.]\|\\Z))', i)\n", "\n", " if test2:\n", " return float(test2[0][0].replace(\",\", \".\"))\n", " else:\n", " return math.nan\n", "\n", "df['amount'] = df['name'].apply(lambda x: amount(x))" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = df.dropna(subset=['amount']).reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#df['category'].value_counts()" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(245, 4)" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['amount_per_dollar'] = df['amount'] / df['price']" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['idx'] = df.index" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gc = 5" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#df.shape" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(175, 6)" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df[df['price'] <= gc]\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#df['category'].value_counts()" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_c = df.loc[df['category'] == 'chocolate', :].sort_values('amount_per_dollar', ascending=[False])\n", "df_d = df.loc[df['category'] == 'drink', :].sort_values('amount_per_dollar', ascending=[False])\n", "df_s = df.loc[df['category'] == 'snack', :].sort_values('amount_per_dollar', ascending=[False])\n", "#df_d = df.loc[lambda df: df.category == 'drink', :].sort_values('amount_per_dollar', ascending=[False])\n", "#df_s = df[df['category'] == 'snack'].sort_values('amount_per_dollar', ascending=[False])" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_s1 = df_s.iloc[:10].sort_values('price', ascending=True)\n", "df_d1 = df_d.iloc[:10].sort_values('price', ascending=True)\n", "df_c1 = df_c.iloc[:10].sort_values('price', ascending=True)" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dfs = [df_s1, df_d1, df_c1]\n", "\n", "#Create dictionary with K values for computing combinations per df\n", "d = {}\n", "for n in range(len(dfs)):\n", " products = 0\n", " total = 0\n", " for i in range(dfs[n].shape[0]):\n", " if dfs[n].price.iloc[i] <= gc:\n", " total += dfs[n].price.iloc[i]\n", " if total >= gc:\n", " break\n", " products += 1\n", " else:\n", " break\n", " d[n] = int(products)" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Find top combinations per segment\n", "top = {}\n", "\n", "for y in range(len(dfs)): \n", " combination_id = {}\n", " ti, tp, ta = [],[],[]\n", "\n", " for k in range(1, d[y] + 1):\n", " ti += list(combinations(dfs[y].idx, k))\n", " tp += list(combinations(dfs[y].price, k))\n", " ta += list(combinations(dfs[y].amount, k))\n", "\n", " for i in range(len(ti)):\n", " if sum(tp[i]) <= gc:\n", " combination_id[i] = []\n", " combination_id[i].append(list(ti[i]))\n", " combination_id[i].append(sum(tp[i]))\n", " combination_id[i].append(sum(ta[i]))\n", "\n", " top[y] = sorted(combination_id.items(), key=lambda x: x[1][2], reverse=True)[0][1:]" ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{0: ([[71, 81], 4.8799999999999999, 500.0],),\n", " 1: ([[42, 15, 14, 16, 2], 4.7200000000000006, 2737.0],),\n", " 2: ([[190], 4.5800000000000001, 250.0],)}" ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Basket: 'snack'\n", " CRIS MANI FAMILIAR 100 G\n", " CRIS MANI CLÁSICO TARRO 400 G\n", "Total cost: $ 4.88\n", "Total grams: 500.0\n", "\n", "\n", "Basket: 'drink'\n", " Sunny Jugo 237 ml. Guayaba\n", " TONI Té Adelgazante Toronja 500ml.\n", " TONI Té Energizante Mora 500ml.\n", " TONI Té Adelgazante Limón 500ml.\n", " Sunny Jugo 1000 ml. Durazno\n", "Total cost: $ 4.72\n", "Total milliliters: 2737.0\n", "\n", "\n", "Basket: 'candy'\n", " Hoja Verde Choco Avellana 250gr\n", "Total cost: $ 4.58\n", "Total grams: 250.0\n", "\n", "\n" ] } ], "source": [ "labels = [\"'snack'\", \"'drink'\", \"'candy'\"]\n", "\n", "def gr_or_ml(category):\n", " if category == \"'drink'\":\n", " return \"Total milliliters:\"\n", " else:\n", " return \"Total grams:\"\n", "\n", "for i in top:\n", " print('Basket:', labels[i])\n", " for x in top[i][0][0]:\n", " print(\" \", df.loc[lambda df: df.idx == x, 'name'].item())\n", " print('Total cost: $', top[i][0][1])\n", " print(gr_or_ml(labels[i]), top[i][0][2])\n", " print(\"\\n\")" ] }, { "cell_type": "code", "execution_count": 226, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.utils import shuffle\n", "\n", "original_dfs = [df_s, df_d, df_c]\n", "results_dict = {}\n", "random_baskets = 100000\n", "\n", "for df_no in range(len(original_dfs)):\n", " random_results = []\n", " min_price = original_dfs[df_no].sort_values('price', ascending=True)['price'].iloc[0]\n", " for i in range(random_baskets):\n", " random_df = shuffle(original_dfs[df_no])#.sample(frac=1)\n", " random_basket = []\n", " balance = gc\n", " amount = 0\n", "\n", " for n in range(random_df.shape[0]):\n", " if balance - random_df.price.iloc[n] >= 0:\n", " balance -= random_df.price.iloc[n]\n", " amount += random_df.amount.iloc[n]\n", " random_basket.append(random_df.name.iloc[n])\n", " elif balance < min_price:\n", " break\n", " else:\n", " pass\n", "\n", " random_results.append(amount)\n", " results_dict[df_no] = random_results" ] }, { "cell_type": "code", "execution_count": 230, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2800.0\n", "2737.0\n", "20\n" ] } ], "source": [ "print(max(results_dict[1]))\n", "print(top[1][0][2])\n", "print(len([x for x in results_dict[1] if x >= top[1][0][2]]))" ] }, { "cell_type": "code", "execution_count": 231, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Random 'snack' baskets tie or beat the optimized basket 0.19% of the time.\n", "Random 'drink' baskets tie or beat the optimized basket 0.02% of the time.\n", "Random 'candy' baskets tie or beat the optimized basket 3.07% of the time.\n" ] } ], "source": [ "for i in range(len(labels)):\n", " print(\"Random\", labels[i], \"baskets tie or beat the optimized basket\", \"%.2f%%\" % (100 \n", " len([x for x in results_dict[i] if x >= top[i][0][2]]) / random_baskets), \"of the time.\")" ] }, { "cell_type": "code", "execution_count": 232, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The optimized 'snack' basket is over 3 standard deviations away from the random basket mean\n", "The optimized 'drink' basket is over 2 standard deviations away from the random basket mean\n", "The optimized 'candy' basket is over 1 standard deviations away from the random basket mean\n" ] } ], "source": [ "optimized_amount_snack = top[0][0][2]\n", "optimized_amount_drink = top[1][0][2]\n", "optimized_amount_candy = top[2][0][2]\n", "\n", "opt_baskets = [optimized_amount_snack, optimized_amount_drink, optimized_amount_candy]\n", "\n", "for i in range(len(opt_baskets)):\n", " print(\"The optimized\", labels[i], \"basket is over\", \n", " math.floor((opt_baskets[i] - np.mean(results_dict[i])) / np.std(results_dict[i])), \n", " \"standard deviations away from the random basket mean\")\n", " #print(np.mean(results_dict[i]) + (np.std(results_dict[i]) * 3))" ] }, { "cell_type": "code", "execution_count": 221, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "500.0 232.7705 81.0998213916\n" ] } ], "source": [ "print(opt_baskets[0], np.mean(results_dict[0]), np.std(results_dict[0]))" ] }, { "cell_type": "code", "execution_count": 237, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DQUEBfHx8xHK1Wo2GhgYUFxejuLgYTU1NUKvVYrm3tzfy8/Pb230iIiIik9bu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\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb401150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams.update({'font.size': 8})\n", "\n", "fig, ax = plt.subplots(figsize=(6.2,5))\n", "sns.set_style(\"white\")\n", "\n", "plt.subplot(2,2,1)\n", "plt.title('Snacks')\n", "plt.hist(results_dict[0], color='#F04824')\n", "plt.axvline(np.mean(results_dict[0]), color='y', linewidth=2)\n", "plt.axvline(optimized_amount_snack-4, color='y', linestyle='dashed', linewidth=2)\n", "sns.despine(right=True)\n", "\n", "plt.subplot(2,2,2)\n", "plt.title('Drinks')\n", "plt.hist(results_dict[1], color='#F04824')\n", "plt.axvline(np.mean(results_dict[1]), color='y', linewidth=2)\n", "plt.axvline(optimized_amount_drink, color='y', linestyle='dashed', linewidth=2)\n", "sns.despine(right=True)\n", "\n", "plt.subplot(2,2,3)\n", "plt.title('Candy')\n", "plt.hist(results_dict[2], color='#F04824')\n", "plt.axvline(np.mean(results_dict[2]), color='y', linewidth=2)\n", "plt.axvline(optimized_amount_candy-2, color='y', linestyle='dashed', linewidth=2)\n", "sns.despine(right=True)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 239, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 43 total items in the 'drink' category with 6096454 million combinations at k = 6 .\n" ] } ], "source": [ "k = 6\n", "drink_items = df.loc[df['category'] == 'drink', 'name']\n", "print(\"There are\", drink_items.shape[0], \"total items in the 'drink' category with\", \n", " (len(list(combinations(drink_items, k)))), \"million combinations at k =\", k, \".\")" ] } ], "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": 1 }	mit
stechma2/PECAN-MP-Analysis	utilities/PrintMeltLayerInfo.ipynb	1	8603	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-12-10T17:22:48.109278", "start_time": "2017-12-10T17:22:46.946631" }, "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import numpy as np\n", "import datetime as dt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-12-10T17:22:48.185777", "start_time": "2017-12-10T17:22:48.110986" }, "collapsed": true }, "outputs": [], "source": [ "flight = '20150709'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-12-10T17:22:48.569981", "start_time": "2017-12-10T17:22:48.197565" }, "collapsed": true }, "outputs": [], "source": [ "if flight == '20150617':\n", "\n", " # Time/[temp at same time] when last evidence of ice was observed\n", " mlBotTemp = np.array([np.nan, 2.3943, np.nan, 3.3489, 4.3656, 2.5243, 5.2405],dtype=float)\n", " mlBotTime = np.array([np.nan, 17018, np.nan, 20530, 22632, 23810, 25750],dtype=float)\n", " # S1 - No particles warmer than -5\n", " # S2 - No particles between +2 and +8, so ML bottom actually warmer than +2\n", " # S3 - No particles warmer than -4\n", "\n", " # Time/[temp at same time] when first evidence of liquid water was observed\n", " mlTopTemp = np.array([np.nan, 0.3578, np.nan, 0.9657, 0.4826, 0.1111, 0.3433],dtype=float)\n", " mlTopTime = np.array([np.nan, 17074, np.nan, 20580, 22512, 23857, 25635],dtype=float)\n", " # S1 - No particles warmer than -5\n", " # S3 - No particles warmer than -4\n", "\n", "elif flight == '20150620':\n", "\n", " mlBotTemp = np.array([np.nan, 6.8400, np.nan, 5.9069, 5.7851, 4.6184, 4.7246],dtype=float)\n", " mlBotTime = np.array([np.nan, 18647, np.nan, 21219, 22080, 24352, 28124],dtype=float)\n", " # S1 - No particles warmer than -9\n", " # S2 - Very little melting all the way through +7 (nothing recorded warmer)\n", " # S3 - No particles warmer than -6\n", "\n", " mlTopTemp = np.array([np.nan, 0.0282, np.nan, 0.1974, 0.1935, 2.2460, 0.0326],dtype=float)\n", " mlTopTime = np.array([np.nan, 18582, np.nan, 21122, 22210, 24276, 28268],dtype=float)\n", " # S1 - No particles warmer than -9\n", " # S3 - No particles warmer than -6\n", "\n", "elif flight == '20150701':\n", "\n", " mlBotTemp = np.array([3.4335],dtype=float)\n", " mlBotTime = np.array([22482],dtype=float)\n", "\n", " mlTopTemp = np.array([0.7108],dtype=float)\n", " mlTopTime = np.array([22375],dtype=float)\n", "\n", "\n", "elif flight == '20150702':\n", "\n", " mlBotTemp = np.array([np.nan, 1.5034, np.nan],dtype=float)\n", " mlBotTime = np.array([np.nan, 15826, np.nan],dtype=float)\n", " # S1 - No particles warmer than -13\n", " # S3 - No particles between ~+1 and +9.59 - ML bottom somewhere in here\n", "\n", " mlTopTemp = np.array([np.nan, 0.7152, np.nan],dtype=float)\n", " mlTopTime = np.array([np.nan, 15852, np.nan],dtype=float)\n", " # S1 - No particles warmer than -13\n", " # S3 - Melting not observed at T<0.79; no particles warmer than this before +9.59\n", "\n", "\n", "elif flight == '20150706':\n", "\n", " mlBotTemp = np.array([np.nan, 1.9535, 3.7707, 4.9644, 1.9219, 3.5011, 3.0759, 3.6202],dtype=float) \n", " mlBotTime = np.array([np.nan, 13099, 16228, 17252, 20851, 21872, 23420, 24383],dtype=float) \n", "\n", " mlTopTemp = np.array([np.nan, 0.8741, 0.0271, 0.2957, 0.0111, 0.3134, 0.1221, 0.0554],dtype=float) \n", " mlTopTime = np.array([np.nan, 13054, 16336, 17156, 20917, 21792, 23492, 24281],dtype=float)\n", "\n", "\n", "elif flight == '20150709':\n", "\n", "\n", " mlBotTemp = np.array([0.827, 2.76, 1.584, 2.2527, 2.8054, 4.7628, 3.823, 5.7988, 3.7277, \n", " 5.6487, 2.3316, 4.7067, 3.6857, 4.9586, 2.5909, 5.7844],dtype=float)\n", " mlBotTime = np.array([9166, 10102, 10997, 11769, 13072, 14202, 14867, 15861, 16956, 17932, \n", " 19429, 20445, 21312, 22169, 23075, 23942],dtype=float)\n", "\n", " mlTopTemp = np.array([0.0104, 0.0034, 0.0423, 0.1119, 0.4757, 3.4426, 1.3057, 2.5534, 0.4398, \n", " 2.0643, 0.0656, 1.5315, 0.6095, 2.740, 0.6454, 2.5692],dtype=float)\n", " mlTopTime = np.array([9185, 10028, 11036, 11711, 13146, 14163, 14903, 15820, 17038, \n", " 17866, 19494, 20391, 21391, 22136, 23121, 23882],dtype=float)\n", " \n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-12-10T17:22:48.657741", "start_time": "2017-12-10T17:22:48.583085" }, "collapsed": false }, "outputs": [], "source": [ "mlBtime_hhmmss = np.floor(mlBotTime/3600)10000 + np.floor(np.mod(mlBotTime,3600)/60)100 + np.floor(np.mod(mlBotTime,60))\n", "mlTtime_hhmmss = np.floor(mlTopTime/3600)10000 + np.floor(np.mod(mlTopTime,3600)/60)100 + np.floor(np.mod(mlTopTime,60))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-12-10T17:22:48.909958", "start_time": "2017-12-10T17:22:48.693174" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Melting layer top times (hhmmss)\n", "\t23246\n", "\t24822\n", "\t30317\n", "\t31609\n", "\t33752\n", "\t35642\n", "\t40747\n", "\t42421\n", "\t44236\n", "\t45852\n", "\t52349\n", "\t54045\n", "\t55512\n", "\t60929\n", "\t62435\n", "\t63902\n", "\n", "Melting layer bottom times (hhmmss)\n", "\t23305\n", "\t24708\n", "\t30356\n", "\t31511\n", "\t33906\n", "\t35603\n", "\t40823\n", "\t42340\n", "\t44358\n", "\t45746\n", "\t52454\n", "\t53951\n", "\t55631\n", "\t60856\n", "\t62521\n", "\t63802\n", "\n", "Melting layer top temp (deg C)\n", "\t0.01\n", "\t0.00\n", "\t0.04\n", "\t0.11\n", "\t0.48\n", "\t3.44\n", "\t1.31\n", "\t2.55\n", "\t0.44\n", "\t2.06\n", "\t0.07\n", "\t1.53\n", "\t0.61\n", "\t2.74\n", "\t0.65\n", "\t2.57\n", "\n", "Melting layer bottom temp (deg C)\n", "\t0.83\n", "\t2.76\n", "\t1.58\n", "\t2.25\n", "\t2.81\n", "\t4.76\n", "\t3.82\n", "\t5.80\n", "\t3.73\n", "\t5.65\n", "\t2.33\n", "\t4.71\n", "\t3.69\n", "\t4.96\n", "\t2.59\n", "\t5.78\n" ] } ], "source": [ "print('Melting layer top times (hhmmss)')\n", "for t in mlBtime_hhmmss:\n", " if np.isnan(t):\n", " print('\\tN/A')\n", " else:\n", " print('\\t{:.0f}'.format(t))\n", "\n", "print('\\nMelting layer bottom times (hhmmss)')\n", "for t in mlTtime_hhmmss:\n", " if np.isnan(t):\n", " print('\\tN/A')\n", " else:\n", " print('\\t{:.0f}'.format(t))\n", " \n", "print('\\nMelting layer top temp (deg C)')\n", "for t in mlTopTemp:\n", " if np.isnan(t):\n", " print('\\tN/A')\n", " else:\n", " print('\\t{:.2f}'.format(t))\n", " \n", "print('\\nMelting layer bottom temp (deg C)')\n", "for t in mlBotTemp:\n", " if np.isnan(t):\n", " print('\\tN/A')\n", " else:\n", " print('\\t{:.2f}'.format(t))" ] } ], "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": 1 }	gpl-3.0
OeslleLucena/IA369Z	dev/2017-05-02-OASL-diary.ipynb	1	1284	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Jupyter Notebook\n", "\n", "## Instalação\n", " - Fácil instalção no linux sudo pip install\n", "\n", "## Pontos Positivos\n", " - Várias linguagens\n", " - Interatividade no notebook\n", "\n", "## Positivos Negativos\n", " - Restart and Clear outputs sempre que muda algo na biblioteca" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Docker" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Keras" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# AWS" ] }, { "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }	mit
sequana/sequana	notebooks/viz/imshow.ipynb	1	25224	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# imshow demonstration\n", "- This notebook is part of biokit \n", " - https://github.com/biokit/biokit\n", " - https://pypi.python.org/pypi/biokit\n", "\n", "- imshow with pandas dataframe : imshow" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "from sequana.viz import Imshow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Imshow\n", "- we overwrite the pylab.imshow to change the default behaviour (no interpolation, hot cmap)\n", "- input can be a dataframe, in which case, X and Y ticks are filled automatically" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = numpy.random.randn(10,10) " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f586cdcf438>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "imshow(data) # this is the default behaviour in matplotlib" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "im = Imshow(data)\n", "im.plot(cmap='jet')" ] }, { "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>A</th>\n", " <th>B</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>X</th>\n", " <td>1</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>Y</th>\n", " <td>2</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " A B\n", "X 1 3\n", "Y 2 4" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.DataFrame({'A':[1,2], 'B':[3,4]}, index=['X','Y'])\n", "df" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# by default xticks are rotated by 90 degrees since names could be long \n", "# but in this example names are short, so we reset the rotation.\n", "Imshow(df).plot(rotation_x=0)" ] } ], "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 }	bsd-3-clause
SiggyF/notebooks	process_paintings.ipynb	1	108262	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/fedor/.virtualenvs/py3/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] } ], "source": [ "\n", "import mako.template\n", "import glob\n", "import IPython.display\n", "import matplotlib.pyplot as plt\n", "import PIL.Image\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "\n", "uris = []\n", "for path in glob.glob('paintings/.jpg'):\n", " uris.append(path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "template = \"\"\"\n", "<style>\n", "\n", ".paintings {\n", " display: flex;\n", " flex-flow: column wrap;\n", " max-height: 800px;\n", " overflow: hidden;\n", "\n", "\n", "}\n", ".painting img {\n", " width: 200px;\n", " height: 200px;\n", "}\n", "</style>\n", "<div class=\"paintings\">\n", "% for uri in uris:\n", " <div class=\"painting\">\n", " <img src=\"${uri}\" /> \n", " </div>\n", "% endfor\n", "</div>\n", "\"\"\"\n", "T = mako.template.Template(template)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "<style>\n", "\n", ".paintings {\n", " display: flex;\n", " flex-flow: column wrap;\n", " max-height: 800px;\n", " overflow: hidden;\n", "\n", "\n", "}\n", ".painting img {\n", " width: 200px;\n", " height: 200px;\n", "}\n", "</style>\n", "<div class=\"paintings\">\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q1000128.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q1016012.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q1022046.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q1025704.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q1029173.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q1029574.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10301958.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10303119.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10317575.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10322900.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10326430.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10335781.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10339123.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10357231.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10366854.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10387302.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10396023.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10430515.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q1043744.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10467459.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10468599.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10487617.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10493941.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10499488.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10524271.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10540242.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10543734.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10585075.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10585101.jpg\" /> \n", " </div>\n", " <div class=\"painting\">\n", " <img src=\"paintings/Q10605028.jpg\" /> \n", " </div>\n", "</div>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IPython.display.HTML(T.render(uris=uris[:30]))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "path = 'paintings/Q1634134.jpg'\n", "path = 'paintings/Q1170315.jpg'\n", "path = 'paintings/Q1131476.jpg'\n", "path = 'paintings/Q11824931.jpg'\n", "path = 'paintings/Q11824917.jpg'\n", "path = 'paintings/Q1000128.jpg'\n", "path = 'paintings/Q11824961.jpg'\n", "#path = 'paintings/Q1132486.jpg'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import cv2\n", "import numpy as np\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def imread(path):\n", " img = cv2.imread(path)\n", " img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", " return img_rgb\n", "\n", "def fill_corners(img):\n", " img_filled = img.copy()\n", " h, w = img.shape[:2]\n", " mask = np.zeros((h+2, w+2), np.uint8)\n", " lo_diff = (10, ) 3\n", " up_diff = (10, ) * 3\n", "\n", " fill_mask = 255\n", " # pff\n", " flags = 4 \| cv2.FLOODFILL_MASK_ONLY \| (fill_mask << 8)\n", "\n", " # fill all corners\n", " for pt in [(0, 0), (w-1, 0), (0, h-1), (w-1, h-1)]:\n", " l = cv2.cvtColor(img_filled, cv2.COLOR_RGB2HLS)[pt[1], pt[0]][1]\n", " # not a very bright corner\n", " if l < 200:\n", " # skip it\n", " continue\n", " else:\n", " ret = cv2.floodFill(img_filled, mask, pt, 255, lo_diff, up_diff, flags)\n", " # final mask\n", " n, img_filled, mask, box = ret\n", " # mask is in values 1,255, rescale to 255, 0\n", " mask_bool = mask[1:-1,1:-1] == 255\n", " # convert to alpha channel (0, 1) => 255,0\n", " alpha = ((1-mask_bool.astype('uint8'))255)[:,:,np.newaxis]\n", "\n", " if mask_bool.sum() > 100:\n", " # if we filled in several pixels, return the filled\n", " img_rgba = np.dstack([img_filled, alpha])\n", " else:\n", " # else return the original\n", " img_rgba = cv2.cvtColor(img, cv2.COLOR_RGB2RGBA)\n", " return img_rgba" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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MgTxFZD6b8Mjtm3zj7/ldfO3XPc2NW6eUE8tsXnHt2inVZDI0lcHkAJnGp5CI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"text/plain": [ "<PIL.Image.Image image mode=RGBA size=199x256 at 0x10C88CB00>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "PIL.Image.fromarray(fill_corners(imread(path)))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(40407, 3)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def flat_img(img):\n", " # compute mask from alpha channel\n", " if img.shape[-1] == 4:\n", " mask = (img[...,3] == 0).flatten()\n", " else:\n", " mask = np.ones_like(img[...,0].flatten(), dtype='bool')\n", " img_flat = img.reshape((np.prod(img.shape[:2]),) + img.shape[2:])\n", " # return and drop 4th channel\n", " return img_flat[~mask, :3]\n", "img = fill_corners(imread(path))\n", "flat_img(img).shape\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "values, bins = np.histogramdd(flat_img(img))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.mlab" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import vispy.gloo" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "R, G, B = np.meshgrid([x[:-1] for x in bins])\n", "df = pandas.DataFrame(\n", " data={\n", " 'R': R.flatten(),\n", " 'G': G.flatten(),\n", " 'B': B.flatten(),\n", " 'value': values.flatten()\n", " }\n", ")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import vispy.mpl_plot\n", "import vispy.plot" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vispy.use('pyqt5')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<LinePlot at 0x11ebe7748>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig = vispy.plot.Fig(bgcolor='w', size=(800, 600), show=True)\n", "ax = fig[0, 0]\n", "ax.plot(R.flatten())" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fig.show()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "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
wuafeing/Python3-Tutorial	01 data structures and algorithms/01.07 keep dict in order.ipynb	2	3231	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Previous](01.06 map keys to multiple values in dict.ipynb)\n", "\n", "\n", "## 1.7 字典排序\n", "\n", "\n", "问题\n", "\n", "你想创建一个字典，并且在迭代或序列化这个字典的时候能够控制元素的顺序。\n", "\n", "\n", "解决方案\n", "\n", "为了能控制一个字典中元素的顺序，你可以使用 `collections` 模块中的 `OrderedDict` 类。在迭代操作的时候它会保持元素被插入时的顺序，示例如下：" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "foo 1\n", "bar 2\n", "spam 3\n", "grok 4\n" ] } ], "source": [ "from collections import OrderedDict\n", "\n", "d = OrderedDict()\n", "d[\"foo\"] = 1\n", "d[\"bar\"] = 2\n", "d[\"spam\"] = 3\n", "d[\"grok\"] = 4\n", "# Outputs \"foo 1\", \"bar 2\", \"spam 3\", \"grok 4\"\n", "for key in d:\n", " print(key, d[key])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "当你想要构建一个将来需要序列化或编码成其他格式的映射的时候， `OrderedDict` 是非常有用的。比如，你想精确控制以 `JSON` 编码后字段的顺序，你可以先使用 `OrderedDict` 来构建这样的数据：" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'{\"foo\": 1, \"bar\": 2, \"spam\": 3, \"grok\": 4}'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import json\n", "json.dumps(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "讨论\n", "\n", "`OrderedDict` 内部维护着一个根据键插入顺序排序的双向链表。每次当一个新的元素插入进来的时候，它会被放到链表的尾部。对于一个已经存在的键的重复赋值不会改变键的顺序。\n", "\n", "需要注意的是，一个 `OrderedDict` 的大小是一个普通字典的两倍，因为它内部维护着另外一个链表。所以如果你要构建一个需要大量 `OrderedDict` 实例的数据结构的时候(比如读取 `100,000` 行 `CSV` 数据到一个 `OrderedDict` 列表中去)，那么你就得仔细权衡一下是否使用 `OrderedDict` 带来的好处要大过额外内存消耗的影响。\n", "\n", "[Next](01.08 calculating with dict.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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }	gpl-3.0
diegocavalca/Studies	phd-thesis/Benchmarking 2 - Identificação de Cargas através de Representação Visual de Séries Temporais-Copy1.ipynb	1	339722	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Identificação de Cargas através de Representação Visual de Séries Temporais" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `Artigo:` Imaging NILM Time-series\n", "* `URL`: https://link.springer.com/chapter/10.1007/978-3-030-20257-6_16\n", "* `Source-code`: https://github.com/LampriniKyrk/Imaging-NILM-time-series\n", "* `Estratégia proposta`: converter série-temporal em imagens, extrair características com DNN (VG16) e classificação supervisionada." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Carregando ambiente e parâmetros" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:23:56.599072Z", "start_time": "2019-10-01T15:23:52.608174Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('ggplot')\n", "plt.rc('text', usetex=False)\n", "from matplotlib.image import imsave\n", "import pandas as pd\n", "import pickle as cPickle\n", "import os, sys\n", "from math import \n", "from pprint import pprint\n", "from tqdm import tqdm_notebook\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "from PIL import Image\n", "from glob import glob\n", "from IPython.display import display\n", "\n", "from tensorflow.keras.applications.vgg16 import VGG16\n", "from tensorflow.keras.preprocessing import image as keras_image\n", "from tensorflow.keras.applications.vgg16 import preprocess_input\n", "\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n", "\n", "REDD_RESOURCES_PATH = 'datasets/REDD'\n", "BENCHMARKING_RESOURCES_PATH = 'benchmarkings/Imaging-NILM-time-series/'\n", "\n", "sys.path.append(os.path.join(BENCHMARKING_RESOURCES_PATH, ''))\n", "\n", "from serie2QMlib import " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pré-processamento dos dados " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:45:12.696304Z", "start_time": "2019-10-01T14:45:12.658419Z" } }, "outputs": [], "source": [ "# Define sliding window\n", "def window_time_series(series, n, step=1):\n", " # print \"in window_time_series\",series\n", " if step < 1.0:\n", " step = max(int(step * n), 1)\n", " return [series[i:i + n] for i in range(0, len(series) - n + 1, step)]\n", "\n", "# PAA function\n", "def paa(series, now, opw):\n", " if now == None:\n", " now = len(series) / opw\n", " if opw == None:\n", " opw = ceil(len(series) / now)\n", " return [sum(series[i * opw: (i + 1) * opw]) / float(opw) for i in range(now)]\n", "\n", "\n", "def standardize(serie):\n", " dev = np.sqrt(np.var(serie))\n", " mean = np.mean(serie)\n", " return [(each - mean) / dev for each in serie]\n", "\n", "# Rescale data into [0,1]\n", "def rescale(serie):\n", " maxval = max(serie)\n", " minval = min(serie)\n", " gap = float(maxval - minval)\n", " return [(each - minval) / gap for each in serie]\n", "\n", "# Rescale data into [-1,1]\n", "def rescaleminus(serie):\n", " maxval = max(serie)\n", " minval = min(serie)\n", " gap = float(maxval - minval)\n", " return [(each - minval) / gap * 2 - 1 for each in serie]\n", "\n", "\n", "# Generate quantile bins\n", "def QMeq(series, Q):\n", " q = pd.qcut(list(set(series)), Q)\n", " dic = dict(zip(set(series), q.labels))\n", " MSM = np.zeros([Q, Q])\n", " label = []\n", " for each in series:\n", " label.append(dic[each])\n", " for i in range(0, len(label) - 1):\n", " MSM[label[i]][label[i + 1]] += 1\n", " for i in xrange(Q):\n", " if sum(MSM[i][:]) == 0:\n", " continue\n", " MSM[i][:] = MSM[i][:] / sum(MSM[i][:])\n", " return np.array(MSM), label, q.levels\n", "\n", "\n", "# Generate quantile bins when equal values exist in the array (slower than QMeq)\n", "def QVeq(series, Q):\n", " q = pd.qcut(list(set(series)), Q)\n", " dic = dict(zip(set(series), q.labels))\n", " qv = np.zeros([1, Q])\n", " label = []\n", " for each in series:\n", " label.append(dic[each])\n", " for i in range(0, len(label)):\n", " qv[0][label[i]] += 1.0\n", " return np.array(qv[0][:] / sum(qv[0][:])), label\n", "\n", "\n", "# Generate Markov Matrix given a spesicif number of quantile bins\n", "def paaMarkovMatrix(paalist, level):\n", " paaindex = []\n", " for each in paalist:\n", " for k in range(len(level)):\n", " lower = float(level[k][1:-1].split(',')[0])\n", " upper = float(level[k][1:-1].split(',')[-1])\n", " if each >= lower and each <= upper:\n", " paaindex.append(k)\n", " return paaindex\n", "\n", "\n", "# Generate Image (.png) files of generated images\n", "def gengramImgs(image, paaimages, label, name, path):\n", " import operator\n", " index = zip(range(len(label)), label)\n", " index.sort(key=operator.itemgetter(1))\n", " count = 0\n", " for p, q in index:\n", " count += 1\n", " #print 'generate fig of pdfs:', p\n", " plt.ioff();\n", " fig = plt.figure();\n", " fig.set_size_inches((1,1))\n", " ax = plt.Axes(fig, [0., 0., 1., 1.])\n", " ax.set_axis_off()\n", " fig.add_axes(ax)\n", " plt.imshow(paaimages[p], aspect='equal');\n", " plt.savefig(path+\"/fig-\"+name+\".png\")\n", " plt.close(fig)\n", " if count > 30:\n", " break\n", "\n", "# Generate pdf files of trainsisted array in porlar coordinates\n", "def genpolarpdfs(raw, label, name):\n", " import matplotlib.backends.backend_pdf as bpdf\n", " import operator\n", " index = zip(range(len(label)), label)\n", " index.sort(key=operator.itemgetter(1))\n", " with bpdf.PdfPages(name) as pdf:\n", " for p, q in index:\n", " #print 'generate fig of pdfs:', p\n", " plt.ioff();\n", " r = np.array(range(1, length + 1));\n", " r = r / 100.0;\n", " theta = np.arccos(np.array(rescaleminus(standardize(raw[p][1:])))) * 2;\n", " fig = plt.figure();\n", " plt.suptitle(datafile + '_' + str(label[p]));\n", " ax = plt.subplot(111, polar=True);\n", " ax.plot(theta, r, color='r', linewidth=3);\n", " pdf.savefig(fig)\n", " plt.close(fig)\n", " pdf.close\n", "\n", "\n", "# return the max value instead of mean value in PAAs\n", "def maxsample(mat, s):\n", " retval = []\n", " x, y, z = mat.shape\n", " l = np.int(np.floor(y / float(s)))\n", " for each in mat:\n", " block = []\n", " for i in range(s):\n", " block.append([np.max(each[i * l:(i + 1) * l, j * l:(j + 1) * l]) for j in xrange(s)])\n", " retval.append(np.asarray(block))\n", " return np.asarray(retval)\n", "\n", "\n", "# Pickle the data and save in the pkl file\n", "def pickledata(mat, label, train, name):\n", " #print '..pickling data:', name\n", " traintp = (mat[:train], label[:train])\n", " testtp = (mat[train:], label[train:])\n", " f = file('fridge/' + name + '.pkl', 'wb')\n", " pickletp = [traintp, testtp]\n", " cPickle.dump(pickletp, f, protocol=cPickle.HIGHEST_PROTOCOL)\n", "\n", "\n", "def pickle3data(mat, label, train, name):\n", " #print '..pickling data:', name\n", " traintp = (mat[:train], label[:train])\n", " validtp = (mat[:train], label[:train])\n", " testtp = (mat[train:], label[train:])\n", " f = file(name + '.pkl', 'wb')\n", " pickletp = [traintp, validtp, testtp]\n", " cPickle.dump(pickletp, f, protocol=cPickle.HIGHEST_PROTOCOL)\n", " \n", "\n" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-09-10T14:33:17.434313Z", "start_time": "2019-09-10T14:33:17.430325Z" } }, "source": [ "## Parâmetros gerais dos dados utilizados na modelagem (treino e teste)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:45:12.711246Z", "start_time": "2019-10-01T14:45:12.699276Z" } }, "outputs": [], "source": [ "#################################\n", "###Define the parameters here####\n", "#################################\n", "\n", "datafiles = ['dish washer1-1'] # Data file name (TODO: alterar aqui)\n", "trains = [250] # Number of training instances (because we assume training and test data are mixed in one file)\n", "size = [32] # PAA size\n", "GAF_type = 'GADF' # GAF type: GASF, GADF\n", "save_PAA = True # Save the GAF with or without dimension reduction by PAA: True, False\n", "rescale_type = 'Zero' # Rescale the data into [0,1] or [-1,1]: Zero, Minusone\n", "\n", "directory = os.path.join(BENCHMARKING_RESOURCES_PATH, 'GeneratedImages') #the directory will be created if it does not already exist. Here the images will be stored\n", "if not os.path.exists(directory):\n", " os.makedirs(directory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gerando dados" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A fim de normalizar os benchmarkings, serão utilizados os dados das séries do `bechmarking 1` para o processo de Extração de Características (conversão `serie2image` - benchmarking 2)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extração de Características" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:45:12.728250Z", "start_time": "2019-10-01T14:45:12.714235Z" } }, "outputs": [], "source": [ "def serie2image(serie, GAF_type = 'GADF', scaling = False, s = 32):\n", " \"\"\"\n", " Customized function to perform Series to Image conversion.\n", " \n", " Args:\n", " serie : original input data (time-serie chunk of appliance/main data - REDD - benchmarking 1)\n", " GAF_type : GADF / GASF (Benchmarking 2 process)\n", " s : Size of output paaimage originated from serie [ INFO: PAA = (32, 32) / noPAA = (50, 50) ]\n", " \"\"\"\n", " image = None\n", " paaimage = None\n", " patchimage = None\n", " matmatrix = None\n", " fullmatrix = None\n", "\n", " std_data = serie\n", " if scaling:\n", " std_data = rescale(std_data)\n", " paalistcos = paa(std_data, s, None)\n", " # paalistcos = rescale(paa(each[1:],s,None))\n", " \n", " # paalistcos = rescaleminus(paa(each[1:],s,None))\n", "\n", " ################raw###################\n", " datacos = np.array(std_data)\n", " #print(datacos)\n", " datasin = np.sqrt(1 - np.array(std_data) 2)\n", " #print(datasin)\n", "\n", " paalistcos = np.array(paalistcos)\n", " paalistsin = np.sqrt(1 - paalistcos 2)\n", "\n", " datacos = np.matrix(datacos)\n", " datasin = np.matrix(datasin)\n", "\n", " paalistcos = np.matrix(paalistcos)\n", " paalistsin = np.matrix(paalistsin)\n", " if GAF_type == 'GASF':\n", " paamatrix = paalistcos.T * paalistcos - paalistsin.T * paalistsin\n", " matrix = np.array(datacos.T * datacos - datasin.T * datasin)\n", " elif GAF_type == 'GADF':\n", " paamatrix = paalistsin.T * paalistcos - paalistcos.T * paalistsin\n", " matrix = np.array(datasin.T * datacos - datacos.T * datasin)\n", " else:\n", " sys.exit('Unknown GAF type!')\n", " \n", " #label = np.asarray(label)\n", " image = matrix\n", " paaimage = np.array(paamatrix)\n", " matmatrix = np.asarray(matmatrix)\n", " fullmatrix = np.asarray(fullmatrix)\n", " #\n", " # maximage = maxsample(image, s)\n", " # maxmatrix = np.asarray(np.asarray([each.flatten() for each in maximage]))\n", " \n", " if save_PAA == False:\n", " finalmatrix = matmatrix\n", " else:\n", " finalmatrix = fullmatrix\n", "\n", " # uncomment below if needed data in pickled form\n", " # pickledata(finalmatrix, label, train, datafilename)\n", "\n", " #gengramImgs(image, paaimage, label, directory)\n", " \n", " return image, paaimage, matmatrix, fullmatrix, finalmatrix" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:23:57.742712Z", "start_time": "2019-10-01T15:23:57.737744Z" } }, "outputs": [], "source": [ "# Reading power dataset (benchmark 1)\n", "BENCHMARKING1_RESOURCES_PATH = \"benchmarkings/cs446 project-electric-load-identification-using-machine-learning/\"\n", "\n", "size_paa = 32\n", "size_without_paa = 30\n", "\n", "# devices to be used in training and testing\n", "use_idx = np.array([3,4,6,7,10,11,13,17,19])\n", "\n", "label_columns_idx = [\"APLIANCE_{}\".format(i) for i in use_idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conjunto de Treino" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:48:50.872764Z", "start_time": "2019-10-01T14:45:12.743156Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing train dataset (Series to Images)...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9ab5c63eacda44b0b40b1a5a04db9637", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=4000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Saving processed data...\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"Processing train dataset (Series to Images)...\")\n", "\n", "# Train...\n", "train_power_chunks = np.load( os.path.join(BENCHMARKING1_RESOURCES_PATH, 'datasets/train_power_chunks.npy') )\n", "train_labels_binary = np.load( os.path.join(BENCHMARKING1_RESOURCES_PATH, 'datasets/train_labels_binary.npy') )\n", "\n", "data_paa_train = []\n", "data_without_paa_train = []\n", "\n", "#for idx, row in tqdm_notebook(df_power_chunks.iterrows(), total = df_power_chunks.shape[0]):\n", "for idx, power_chunk in tqdm_notebook(enumerate(train_power_chunks), total = train_power_chunks.shape[0]):\n", "\n", " #serie = row[attr_columns_idx].tolist() \n", " #print(serie)\n", " #labels = row[label_columns_idx].astype('int').astype('str').tolist()\n", " serie = power_chunk\n", " labels = train_labels_binary[idx, :].astype('str').tolist()\n", " labels_str = ''.join(labels)\n", " \n", " for g_Type in ['GASF', 'GADF']:\n", "\n", " #image, paaimage, matmatrix, fullmatrix, finalmatrix = serie2image(serie, g_Type)\n", " image, paaimage, _, _, _ = serie2image(serie, g_Type, scaling=True)\n", " \n", " # Persist image data files (PAA - noPAA)\n", " np.save(\n", " os.path.join( \n", " BENCHMARKING_RESOURCES_PATH, \n", " \"GeneratedMatrixImages\", \n", " \"{}_WITHOUTPAA_{}_train_{}.npy\".format(idx, g_Type, labels_str) \n", " ), \n", " image\n", " )\n", " # x is the array you want to save \n", " imsave(\n", " os.path.join( \n", " BENCHMARKING_RESOURCES_PATH, \n", " \"GeneratedImages\", \n", " \"{}_WITHOUTPAA_{}_train_{}.png\".format(idx, g_Type, labels_str) \n", " ), \n", " image\n", " )\n", " data_without_paa_train.append( list([idx, g_Type]) + list(image.flatten()) + list(labels) )\n", " \n", " np.save(\n", " os.path.join( \n", " BENCHMARKING_RESOURCES_PATH, \n", " \"GeneratedMatrixImages\", \n", " \"{}_PAA_{}_train_{}.npy\".format(idx, g_Type, labels_str) \n", " ), \n", " paaimage\n", " )\n", " imsave(\n", " os.path.join( \n", " BENCHMARKING_RESOURCES_PATH, \n", " \"GeneratedImages\", \n", " \"{}_PAA_{}_train_{}.png\".format(idx, g_Type, labels_str) \n", " ),\n", " paaimage\n", " )\n", " data_paa_train.append( list([idx, g_Type]) + list(paaimage.flatten()) + list(labels) )\n", "\n", "# VIsualizgin some results...\n", "plt.figure(figsize=(8,6));\n", "\n", "plt.suptitle(g_Type + ' series');\n", "ax1 = plt.subplot(121);\n", "plt.title(g_Type + ' without PAA');\n", "plt.imshow(image);\n", "divider = make_axes_locatable(ax1);\n", "cax = divider.append_axes(\"right\", size=\"2.5%\", pad=0.2);\n", "plt.colorbar(cax=cax);\n", "\n", "ax2 = plt.subplot(122);\n", "plt.title(g_Type + ' with PAA');\n", "plt.imshow(paaimage);\n", "\n", "print('Saving processed data...')\n", "df_without_paa_train = pd.DataFrame(\n", " data = data_without_paa_train,\n", " columns = list([\"IDX\", \"TYPE\"]) + [\"DIMESION_{}\".format(d) for d in range(size_without_paasize_without_paa)] + list(label_columns_idx)\n", ")\n", "df_without_paa_train.to_csv(os.path.join( BENCHMARKING_RESOURCES_PATH, \"datasets\", \"df_without_paa_train.csv\"))\n", "\n", "df_paa_train = pd.DataFrame(\n", " data = data_paa_train,\n", " columns = list([\"IDX\", \"TYPE\"]) + [\"DIMESION_{}\".format(d) for d in range(size_paasize_paa)] + list(label_columns_idx)\n", ")\n", "df_paa_train.to_csv(os.path.join( BENCHMARKING_RESOURCES_PATH, \"datasets\", \"df_paa_train.csv\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conjunto de teste" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:52:18.857838Z", "start_time": "2019-10-01T14:48:50.874719Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing test dataset (Series to Images)...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cc94d091cb09440b84704f1f1cda20c6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=4000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Saving processed data...\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(\"Processing test dataset (Series to Images)...\")\n", "\n", "# Test...\n", "test_power_chunks = np.load( os.path.join(BENCHMARKING1_RESOURCES_PATH, 'datasets/test_power_chunks.npy') )\n", "test_labels_binary = np.load( os.path.join(BENCHMARKING1_RESOURCES_PATH, 'datasets/test_labels_binary.npy') )\n", "\n", "data_paa_test = []\n", "data_without_paa_test = []\n", "\n", "#for idx, row in tqdm_notebook(df_power_chunks.iterrows(), total = df_power_chunks.shape[0]):\n", "for idx, power_chunk in tqdm_notebook(enumerate(test_power_chunks), total = test_power_chunks.shape[0]):\n", "\n", " #serie = row[attr_columns_idx].tolist() \n", " #print(serie)\n", " #labels = row[label_columns_idx].astype('int').astype('str').tolist()\n", " serie = power_chunk\n", " labels = test_labels_binary[idx, :].astype('str').tolist()\n", " labels_str = ''.join(labels)\n", " \n", " for g_Type in ['GASF', 'GADF']:\n", "\n", " #image, paaimage, matmatrix, fullmatrix, finalmatrix = serie2image(serie, g_Type)\n", " image, paaimage, _, _, _ = serie2image(serie, g_Type, scaling=True)\n", " \n", " # Persist image data files (PAA - noPAA)\n", " np.save(\n", " os.path.join( \n", " BENCHMARKING_RESOURCES_PATH, \n", " \"GeneratedMatrixImages\", \n", " \"{}_WITHOUTPAA_{}_test_{}.npy\".format(idx, g_Type, labels_str) \n", " ), \n", " image\n", " )\n", " # x is the array you want to save \n", " imsave(\n", " os.path.join( \n", " BENCHMARKING_RESOURCES_PATH, \n", " \"GeneratedImages\", \n", " \"{}_WITHOUTPAA_{}_test_{}.png\".format(idx, g_Type, labels_str) \n", " ), \n", " image\n", " )\n", " data_without_paa_test.append( list([idx, g_Type]) + list(image.flatten()) + list(labels) )\n", " \n", " np.save(\n", " os.path.join( \n", " BENCHMARKING_RESOURCES_PATH, \n", " \"GeneratedMatrixImages\", \n", " \"{}_PAA_{}_test_{}.npy\".format(idx, g_Type, labels_str) \n", " ), \n", " paaimage\n", " )\n", " imsave(\n", " os.path.join( \n", " BENCHMARKING_RESOURCES_PATH, \n", " \"GeneratedImages\", \n", " \"{}_PAA_{}_test_{}.png\".format(idx, g_Type, labels_str) \n", " ),\n", " paaimage\n", " )\n", " data_paa_test.append( list([idx, g_Type]) + list(paaimage.flatten()) + list(labels) )\n", "\n", "# VIsualizgin some results...\n", "plt.figure(figsize=(8,6));\n", "\n", "plt.suptitle(g_Type + ' series');\n", "ax1 = plt.subplot(121);\n", "plt.title(g_Type + ' without PAA');\n", "plt.imshow(image);\n", "divider = make_axes_locatable(ax1);\n", "cax = divider.append_axes(\"right\", size=\"2.5%\", pad=0.2);\n", "plt.colorbar(cax=cax);\n", "\n", "ax2 = plt.subplot(122);\n", "plt.title(g_Type + ' with PAA');\n", "plt.imshow(paaimage);\n", "\n", "print('Saving processed data...')\n", "df_without_paa_test = pd.DataFrame(\n", " data = data_without_paa_test,\n", " columns = list([\"IDX\", \"TYPE\"]) + [\"DIMESION_{}\".format(d) for d in range(size_without_paasize_without_paa)] + list(label_columns_idx)\n", ")\n", "df_without_paa_test.to_csv(os.path.join( BENCHMARKING_RESOURCES_PATH, \"datasets\", \"df_without_paa_test.csv\"))\n", "\n", "df_paa_test = pd.DataFrame(\n", " data = data_paa_test,\n", " columns = list([\"IDX\", \"TYPE\"]) + [\"DIMESION_{}\".format(d) for d in range(size_paasize_paa)] + list(label_columns_idx)\n", ")\n", "df_paa_test.to_csv(os.path.join( BENCHMARKING_RESOURCES_PATH, \"datasets\", \"df_paa_test.csv\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modelagem" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:24:04.139925Z", "start_time": "2019-10-01T15:24:04.129950Z" } }, "outputs": [], "source": [ "def metrics(test, predicted):\n", " ##CLASSIFICATION METRICS\n", "\n", " acc = accuracy_score(test, predicted)\n", " prec = precision_score(test, predicted)\n", " rec = recall_score(test, predicted) \n", " f1 = f1_score(test, predicted)\n", " f1m = f1_score(test, predicted, average='macro')\n", " \n", "\n", " # print('f1:',f1)\n", " # print('acc: ',acc)\n", " # print('recall: ',rec)\n", " # print('precision: ',prec)\n", "\n", " # # to copy paste print\n", " #print(\"{:.4}\\t{:.4}\\t{:.4}\\t{:.4}\\t{:.4}\".format(acc, prec, rec, f1, f1m))\n", "\n", " # ##REGRESSION METRICS\n", " # mae = mean_absolute_error(test_Y,pred)\n", " # print('mae: ',mae)\n", " # E_pred = sum(pred)\n", " # E_ground = sum(test_Y)\n", " # rete = abs(E_pred-E_ground)/float(max(E_ground,E_pred))\n", " # print('relative error total energy: ',rete)\n", " return acc, prec, rec, f1, f1m\n", "\n", "\n", "def plot_predicted_and_ground_truth(test, predicted):\n", " #import matplotlib.pyplot as plt\n", " plt.plot(predicted.flatten(), label = 'pred')\n", " plt.plot(test.flatten(), label= 'Y')\n", " plt.show()\n", " return\n", "\n", "def embedding_images(images, model):\n", " \n", " # Feature extraction process with VGG16\n", " vgg16_feature_list = [] # Attributes array (vgg16 embedding)\n", " y = [] # Extract labels from name of image path[]\n", "\n", " for path in tqdm_notebook(images):\n", "\n", " img = keras_image.load_img(path, target_size=(100, 100))\n", " x = keras_image.img_to_array(img)\n", " x = np.expand_dims(x, axis=0)\n", " x = preprocess_input(x)\n", "\n", " # \"Extracting\" features...\n", " vgg16_feature = vgg16_model.predict(x)\n", " vgg16_feature_np = np.array(vgg16_feature)\n", " vgg16_feature_list.append(vgg16_feature_np.flatten())\n", "\n", " # Image (chuncked serie) \n", " file_name = path.split(\"\\\\\")[-1].split(\".\")[0]\n", " image_labels = [int(l) for l in list(file_name.split(\"_\")[-1])]\n", " y.append(image_labels)\n", "\n", " X = np.array(vgg16_feature_list)\n", " \n", " return X, y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Benchmarking (replicando estudo)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:24:08.433486Z", "start_time": "2019-10-01T15:24:05.881668Z" } }, "outputs": [], "source": [ "# Building dnn model (feature extraction)\n", "vgg16_model = VGG16(\n", " include_top=False, \n", " weights='imagenet', \n", " input_tensor=None, \n", " input_shape=(100, 100, 3), \n", " pooling='avg',\n", " classes=1000\n", ")" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2019-09-17T15:48:46.528682Z", "start_time": "2019-09-17T15:48:46.448861Z" } }, "source": [ "### Embedding das imagens de Treino" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:24:56.733090Z", "start_time": "2019-10-01T15:24:08.536071Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fe6324917fcd4d658e39f15775dd6646", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=4000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# GAFD Images with PAA (Train)\n", "images = sorted(glob( \n", " os.path.join(\n", " BENCHMARKING_RESOURCES_PATH, \n", " \"GeneratedImages\",\n", " \"_PAA_GADF_train_.png\"\n", " ) \n", "))\n", "X_train, y_train = embedding_images(images, vgg16_model)\n", "\n", "# Data persistence\n", "np.save( os.path.join(BENCHMARKING_RESOURCES_PATH, 'datasets/X_train.npy'), X_train)\n", "np.save( os.path.join(BENCHMARKING_RESOURCES_PATH, 'datasets/y_train.npy'), y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Embedding das imagens de Teste" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T15:25:43.302959Z", "start_time": "2019-10-01T15:24:56.737085Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4d366790dea348cf9cc5cbf2bdea2da1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=4000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# GAFD Images with PAA (Train)\n", "images = sorted(glob( \n", " os.path.join(\n", " BENCHMARKING_RESOURCES_PATH, \n", " \"GeneratedImages\",\n", " \"_PAA_GADF_test_.png\"\n", " ) \n", "))\n", "X_test, y_test = embedding_images(images, vgg16_model)\n", "\n", "# Data persistence\n", "np.save( os.path.join(BENCHMARKING_RESOURCES_PATH, 'datasets/X_test.npy'), X_test)\n", "np.save( os.path.join(BENCHMARKING_RESOURCES_PATH, 'datasets/y_test.npy'), y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Treinando Classificador Supervisionado" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:53:37.081711Z", "start_time": "2019-10-01T14:53:35.929829Z" } }, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=15,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_decrease=0.0, min_impurity_split=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, presort=False,\n", " random_state=None, splitter='best')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Training supervised classifier\n", "clf = DecisionTreeClassifier(max_depth=15)\n", "\n", "# Train classifier\n", "clf.fit(X_train, y_train)\n", "\n", "# Save classifier for future use\n", "#joblib.dump(clf, 'Tree'+'-'+device+'-redd-all.joblib')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Avaliando Classificador" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-10-01T14:53:42.795535Z", "start_time": "2019-10-01T14:53:37.083712Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RESULT ANALYSIS\n", "\n", "\n", "ON/OFF State Charts\n", "-------------------------------------------------------------------------------------------------------------------\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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QId2MHtzOwIEDaW1tLQrvOA6dnR19dGlra2PTdUbQ3esxbMhA2lujT4q1tbXR2lqIL0q/nl6Pzce0RsYVFb6trY0xo4YxqLObkZ3tdAYuDwyHF1uN6COjvzgAuru7Wb58OYMGRb8Xh16AoiiKoiiKoihKEb29vbS16bpPWrS1tdHb21vxezqYU7JL0fJD3Jxf8fP83LKUFz1LkV4a8pOvaVG+PdRyiqKkgVbTzUel2/2U2qnG5jrcVhRFURRFURQlUyxYsIDDDjsMgLlz5+I5LYxYayTtrQ733nMPAwYM6EdC/8yZM4eDDjqIv/71r3R1yXdHf/7Dc/jwlh/gpJNOqll+GuhgTskHMVN+4ae5uWWpCWYw00xCE5grUfQ2y3zheZ7OcNcRXRHKCrlpgZWcMHLkSB544AEALr74YpZ5bbhfPo4NRxTOzHmet/rykGpYe+21OfHEEzn//PO55JJLePXll3jt5Ze4/Jc/Sywd9Ua3WSqKoiiKoiiKkgumTZvKXnvtxZlnnsm+++7LzJkz2WKLLVb//e677+a0004DYMH8eXz31K+x//77c8ABB/Dcc8/1kfflL3+ZN954gyeffJJLL/ghp5x9bq7OCupgTskHcStzXlnBlDqQpq01X4up7DbLSuSqoeuBWrW+qH2zQdbzIev65YWs2PH111/n8MMPZ+LEiay77rqx4X71k/M4/Ojjue+++7jqqqs4/fTT+4RpaWnhwgsv5Nhjj2WT8R9gmw/vUE/VEyc/w05FURRFURRFUVJn5F+uZ8DMN/HaW+gJPF93ldy+2NPe0ucZQHubQ09gm/mwHo/OXo8BrQ7dG28G7rFV6bPhhhuy3Xbb9Rvuuaee4O1pU7nsQtFh0aJFLF+2jPCW4A996ENsttlmHHzYEVXp00h0MKdkl6ZeJWjmtCnVUo9SUekqnp54SR5PDVtntD7NAllvssvVT3co5IPOzs7VP7e0tBTl2/Lly1f/7HkeV93yJ7ZYt/Bh8p5eDwJhgnKcKs/eNRIdzCmKoiiKoiiKEsuCA48GYJORHbQEVtremb8MgM1GDerzDGBs6KPh85esYvEK+Wh455AOuru7a9atpaWF4cOH87///Y/NNtuM+++/n1GjRgGww867cudtN7HFKScD8PLLL7PFllvVHGeWyN/wU1kzqeA2y1yQG0Xj0dssk6fsmeNKZNYhfmXNIC8rFDlRU2kwXpm1oRanfHL22WfzpS99ic997nOMGTNm9fNTzz6Xl194jk9+8pPsueee3HLLLQ3Usj7oypyiKIqiKIqiKJnl9NNPZ/LcJXT3emy00carP1ngM2HCBCZMmEBbW1vRat+IkaM475eXF60c9vRGD9nvuusuJgdWFfOCrswpOUFvs8waeptl8pSbzDXFHs1CXrMrL3rnRc9mJ4l8yEJeav2q5A0dzCmKoiiKoiiKouQQHcw1AeXuA28kVWkYnB6LEdA37dm3hZAXPeNJt9zl317lUe6ZjvLtUcm5pzXFymmT15n+vOqtNIYkyks9i5yWZ6VZ0cGcoihKRtDOhqIoiqIolaCDuSageft/wZW5JruFKjeKZoM1xVx1+c5cJWHXFEPXk0gbqmHriZbbbJDIbo06ZmbZZ5LrpoGi1AcdzCmKoiiKoiiKouQQHcw1A1VOI6V64qnmyPQ2y6yht1nWRlSSyv7OXN3s0YSGTpnIfE1di2TIi9550bNamj19Qeqa1obXr01CyvZZf/312Weffdh999357re+zvJl1X864IknnuCoo44C4PF/PshN114VG3bRokVcf/31Fcfx85//nNuuv7bo2cMPP8yBBx64+gx7T08Px37+QJ555pmK5UehgzlFUZSMoH0IRVEURSnQ0dHBAw88wCOPPEJbezt3/6H4o9+e59Hb21ux3I994pN86biTYv++ePFibrjhhorlRrHHHnswbtw4br31VgDuuOUGNt9qG3bcccdE5OtHw5uAajuA6d5FWEVsXszP/T/OAfnV3Cf/KWgsMWvNNbwbE7aCwJqntVPLimvWyKvezUZesiHzt1lmQov800jrbLv9jrwx6VXefvttvvLlo9h111157rnnuO6665gyZQoXXXQRq1atYoMNNuDiiy9m8ODBPP3Yw1z2sx+zzuhRbLPNNqtl3Xf3n5j035e57Bc/Ye7cuZx11lm8+eabrOzx+NZ3z+Nvf7yJN998k3322Yc999yTE089g1t/dw2PPngfvd2r2G+//TjttNMAuPHqK/jHvXcxduxYurq6GLPJB/vofu655/LZz36WHXbYgTtvu5GrbrkjMbvoypyiKEpG0M6zoiiKovSlu7ubpx57mE3Gbw7AlClTOPTQQ5k4cSKdnZ1ceuml3H777Tz44INsu+22XH311Sxfvpyf//AcLvz11dx5553MmTMnUvb3vvc9dt55Zx588EGuuf1uNtp0PGeffTYbbrghDzzwAD/4wQ944tFHmP7WNG69489MnDiRl156iaeeeopJr7zM3+//KxMnTuTaa6/lxRdfjIxjnXXW4bjjjuOggw7iyBNOZtjwEYnZJvcrc5V8Q6lZqXplLkXbVRdT5UtzWhpSJM0zc+lFlRpR/ld2Ouu02qbVaQI0lQ0LifE8D8dxGqhL5TRLec5LPycRLeuY1D5fpY0p0/mwdvrc9eoCZi5eSUd7C0GrLVslWxwHtbf0eQYwoK2F1sALK3s8eno92lsdNh89mGO2H10y3uXLl7PPPvvgOA4f3HZ7Djjk87B8IePGjWOHHXYA4LnnnuP1119nwoQJOI7DypUr2WGHHZg8eTJj1lufcRtuhOM4fO5zn+Omm27qk8ePP/44l156KQCtra0MGToUlq4oCvPk44/y7JOP8fmDPk2rA0uXLmXq1KlMm/0uH9/7UwwaNAiAfffdNzYtRx99NBdccAH7T/hcyTRXSu4Hc4qiKIqiKIqiNB/+mbm2tjYmz11Cd68Hy6Gzs3N1GM/z2H333bniiitoa2uju7sbgJdffpmk5qA8z+OIY0/i6C8fxchBheHThZdcQblRtLS01GVSLPeDuazMoHhlZ2VdIs8+1ehYtDBX3gmjnExgZibPapnxzddtqPmg7Nss66uGUgNR54Pzml/hvRH5WpfLr91zS5Jn5ury0c1ioXFlek1pbyrl4C1GArDxWh20thQsN3m+3C652ahBfZ4BjB06gM4Brat/n7NkFYtXdDN6cDujhnSsHnjVwg477MA555zD1KlTGT9+PMuWLWPmzJlsttlmzJoxnRlvv8lmoz7IXXfdFfn+brvtxg033MDxxx9PT08Py5ctZeTQwSxZsmR1mF13251f/fIXuId+jpGDhjNr1iza29vZdocdufB7Z7Ls9FPo6elh4sSJ7PfZw2pOUyXkfjCnKIrSLGgfQlEURVEqY9SoUVx88cWcfPLJrFy5EoAzzjiDTTfdlNO+fz5nnXw864wexU477cRrr73W5/3zzjuPM844g9tuu41uz+HU7/6Qbff6GDvuuCN77bUXe++9NyeeegavvPY6R3z+s7S1OHR2dvLrX/+aD2y5NXvtewCf+tSnGDduHB/96EfTTn7+B3NZmUFppBpV3RRJ2rdZ1kdAn5W53HSHs6FnLVpk/jbUjFNL3VXZOTi9wy1NmvU2Sy+FpbnE7ZRTu4fJSzIq1TMqv73Q/0kSuZMnpeXmPJ45zQpvvPFGn2frr78+//jHP4qe7bbbbtx7771F2ywBPrrbHnx0tz2KVg67ez32n/A5Pn3woQCMHj2a3/3ud0DxquLll18OQFtbG+8sXsahXzqaE044vmib5eT5yzjyhK/xw+98e3XY12a/VzI9wTiSQG+zVBRFURRFURRFySG5H8xlZsaqgTMu1d9mmd579bvNMjMlIJfUZL41wPb1TGJNouu02rYGZGndiV5tUMOWQ9J2aha758UvK16ZizpfahNbjzSXKzKNVUFFSZLcD+YURVEURVEURVHWRPI/mEtpyqq/aBoy62IjTXvWrqKZ/mpe8l/Jwm2WdbJtXmZaIepcYvTzuupQIjKP+umSlNzoc1Tp3iTaN7qoWxdzVDBroYmSmdbqcWQ05cSdVJgqSMx/81RhRxCnf3L2Cf9Qe1x1sbhX8tfC83qsCqZUhMqpw8vY81T4e87Lfn2or02qsXn+B3OKoiiKoiiKoiRKS0tLIp8OUMqju7ublpbKh2Zl32bpuu7hwIvGmFdd190cuAboBr5mjOl7z2dKZGXOoKHfmauSNCdcap/xL3NlrsZYqiH7d4nGk5/bLJUgeg4uu0SemctpHvS5zTLF+LIkL8VLD6PjrzEdaRW/JG+zrIfW5e7kWWN2KJRBR0cHy5cvZ8WKFUyatRiArgFDGdBaGHBMmrUIgLGDvD7PADq8TuhsX/37jPnLmPneSryuDoa0O1Z2IXxrC4zpGN5Hl4EDBzJ59kJWdHuMaBtCZ3trnzB+uBUrVpTUb/mqHibNWkJbi8OYjmFF70eFHzhwILPmL+atRSvZeK2BdKzVERt+4MCBTJq1sI+M/uLwPI+WlhY6Ojoi3ylFJZ8mOB/Y1f58EfAvYAlwBbBXxTEriqIoiqIoipJJHMdh0CC50v/qf78NwMfHr03nwMJA6up/vwXAAVuP6fMM4IejhjGus3P170++s5i/TV7ESTt2sMvW6zBv3ryi8ANaHfbbsiDLp6uri1v/PJV5S7vZfsNRdHYOiNS5q6uLefPmldRv2dJVXP3vGQxqb2XfLdctej8qfFdXFze/NJ8/vbKIL23bxZbrdcaG7+rq4urb3ugjo784aqGSwdxoY8xs13U7gN2AQ4FVwLzSr9WXvM54JknVt1KWuEkq6fhqXr2K3dyeSEQ1keYsXtL712u7zTIxNbIVVzDazN5mWZ94tD6tncjzkalrkQzFdVv916eyaqc0v0cWGX+NlknvvFbt4X1d07nNMr2MzWrZrgptKDJHJRsz57quuxmwP/CMMWYF0EFjdx8oiqIoiqIoiqKskVSyMvcj4DmgBzjMPtsb+HfSSlVCVuYHvBx+Z65aWVXFV/ORueyemav5ps76RlU3iWvAwlxd461lcjPrt3cqIZrAsHlMQh51rgep2SHBiOqhc3hnSzPeZpkGWUhKXs6RpkXZK3PGmOuBMcA4Y8wD9vHTwBfqoJeiKIqiKIqiKIpSgkpus2wBlgd+hgaflxOyMb7Oy22WRScgqrx1rZr9+7Wv5mV3ZS7N2ywTv+2tQe9mOa7ieOsXc1q2r6jMZKM6zTVR51rrsspQB5l94tDbLEVOMmKqj79OO1uSJpHbLFM0dvxtlnWJjWY5ldRof4C6bfbKLZVss+wm3n7R94MqiqIoiqIoiqIodaGSwdzGod/HAGcBf0lOncppttF1NVRyw2EwbNW3rqV2Rqz/2yz7pL0BBSLPK1Q1nduq4eWK322Uo9fzNsuU7FfJ6qJWp7WT1m2WSd9sGxlH3WOod3zJSGx0P6PmVYhEtEg+nmhfkafp3GZZa8A6xJ0HMpAY/RZgMWUP5owxb4Yevem67peBZ4DfJqqVoiiKoiiKoiiKUpJKPk0QxTBgdBKKVEtWxuaNPDOXqA3K+c5cNWKreCd0wq+O8dRIVQap9jrLrJT42khiBjcNmsHalRSZZkhvJsmr3/a/OSLh6JKNpbqqOa211fRI7TtzFUZUKr/rs5pdXhxpxJ0miX+fNlFpVVLrbZYZ9YlqqeQClBspNl8nsDtwU9JKKYqiKIqiKIqiKKWp5Mzc5NDv7wNXGWMeTFCfisnrhOeaQi3rlZm49ykTSmSLJEyStN9mPZui9Eu77ur7KcwIrdaUCjXrBSYjJFIayrG15kdDSMrsjtN/1VFuXGtKFZQm4Xs0435OmwZ+nrmmXQBOBiusSgZz9xtjng4/dF13J2PMvxLUKZd42cvbSPr9NEGFMupKf8rSV5f8fJogG3HppwmyGW9/ZFUvJZq8fpqgKL509lkmK66ay7rKfJYmTfuB5EZfLJPqpwmah+QvYqtcYl7yKK0PUlRyZu6BmOf3J6FItaR1o01eCo6iKPkgi3VKFnXKG820stBESVFyQun7tpVqaKY6SYmm35U5+4FwB3Bc13UoHmRuinx/TsngsmsURR+B7efv8UKqibc+U4ZxFSkAACAASURBVIt9xDag0krrgKvElR15qTYQOVqaS+XKeL3UJFc0w4UKaUycZqGsNvpD1lHUHn02J70bvbocW6brkOFZKNtJkYULVfJiTy+lpblytlkGPxYeHrj1Aj9OVKNKSe1GmnTiqYYs66bEsWZn2pqdeiGL38nJnkb5o6lsqI2L0iC06Cn1pNnKVzmDuY2RceXDyO2VPh4w1xizrB6K5Y08louozmS9zsxVZx8v5uek48kPyZ+Zy4fFcrQwlzmLNlujlQaJ20zzoCzU7NHUWk+ndw17heHro0aJ+BrXj9B6OJ6qbKP2LKLfwVzgY+Eb1lmXqkgrP7Pc6c2uZkosa3imacNGNstAFnVSGoYWByVt/LZBy151VHux3ZpGs9mkktsscV33IGAPoIvALlBjzFEJ65U7GvnR8ErwYn8p+bA4RGrTfEWRxugS/0papBtndqqgNAdkDVuZq+aWrRSU1cFwfcnDClGjzxvVJ5KExVVz5rXMZ6lSnyPnjafRZxHjjszVI65GJzZBspCSLOhQDmnpWfZtlq7r/gD4jX3n88B8YF9gYX1UK4+8ZKiiKEHUc7NogSzqlDfSvBCp3jRPSpS8oGWuNiInI9SoTU8lnyY4BtjHGHMqsNL+fyCwUT0Uyxu5+c5cwKnTdPqa5cZ+Z84LBUu/1kp1hSoDM9er302x2c1TBzmdBYzyY2mmGeG80gxncHK4MFeVxGa8zVLPzNn4yt3Jo1VmSRLvh1SlQ/YyKUqntNSsZDA3whjzsv15peu67fZj4XvUQa+yyWollSZZLNRKf6zZebZmp17IottmUKXc0VQ2bKrEKHnAn3zKYv2YX9SYYZqtfFUymJviuu5W9ueXga+6rnsk8G7yauWRfCzNBWfpqz0bkNrFQ83mbQmQh7M8zUQzlNuMqZMLkl8Br8eaU/MtzSX+/arqHDjiSWOdqOaVudS+M1dZPGlPRPeJLc0zc1oPx9IstonbP5YGlVyA8l1glP35LOAWYAhwctJKVUZalVR2ybJuSgxreKYlWXnn1ZSN7iBGoav8tdNMFmymtCg5wSv6T6kQvc2yPJrNJmUP5owx9wZ+/hewWV00yim5LBhVng1oSH9Pb7OUuDK0NJfm7XaN8q+kbsNLGh1z1ZcsDrQbTaUWyYIN09pFUvdJkNqX5pQI4sxSl5W5OshsFMkX9yrOtiatQgJ4Hn026aWlZyW3WS6IeT4nOXUqRzs12SzUipIaeXWAvOqtlKaJ8rWJkqLkBC1ztRE1iaL95L5kYbIpSSo5M9cefuC6bjvQmpw6+SU335kLlN/oM3PVFfD+ZiZrn7ksbzdyI1au0v3eWsJnShr0bqVx5KkxSuU7c3UKq1gycFtbvzIzfmQuC6va1a2ylfes1ngqIS8Lc81ym2VdVlqbqCLOQj8kL32CtPTsd5ul67qPIrbucF33kdCfxwFP1EOxcslqJZUqWdZNiWHNzrQkU59XS2ZR7yzqlDeayYZ6hlJJGy1xNaIGLI8ms1M5Z+autf/vCPw28NwDZgP/SFqpXJKPhbmi8hvZUJezEhXzrJQJqvKb/pYRU6CcaFNdrs/BikGSNHorRFpnbuqJ9scrJ/EVomaY6K8wEZmYbU9IXn9i6n5krsYIUpv0rjR8o1eXU1RAq+F4mqWNamQyytlm+TLwHPBhY8zvgfuAfYFvA58FBtZPvf5J7TtzGXbF7GqmxLKGZ1qyt1nm05hZbMCyqFPeUBMqSg2oA9VE3GS7Ukyz2aScwdwlwLrGmNfs71cD4+3/WwM/q5NuuSIvZ+b6o6yVqCrOFFRHcGUu5sxceA98k69cJX+JVPUSUzkb5qUXV6n4K3wraTX6xpC1gtlkqHn7UrFNMmDExFbWM5CWWtD6QghP/MWematL3M1DFspTBlToQyM/C1HOYG4L4FEA13VHAAcARxhjLgcOBw6sn3rlkNrSXGbJsGqKEkmSq2lZaFiqIa8riko/9HcZVEpqJEGedFWaAy1ztRG5MqdG7UOzmaScwVwbsNL+vDMwyxjzOoAx5m1gRJ10yxV5WZnr/zbLMmRU802Qmj0nZmWuz+/pL82le5tlduSlWRk2auDTmLJeRhwVhW22ZisFkq5G6nFmrtHnjRIOX+07ietQxex6vbOidvnpFJas32YZjjB2Za4uS3PJC23UBUVJ1wWZOF+bCH2VSkvPcgZz/wU+b3/+AvCg/wfXddcDFtVBr7JJ78xchslmqVZKonm2ppPFEqBVSe00+rKMJMmTrkpzoDeo1kjkZITatA9NZpJybrM8E/iL67pXAT3AboG/HQY8Xg/FlPoQdOokz77VfeYyRkC9K6ly5Ke6QpWhM4FpNBB5rG/T0LkS22vfqH/CHcjkV4jqMCuftneksOqS/G2WVaysR82uJx9NRdQqP6uT3g1fXc7xrpp6yawH/X+HuAqZGUx99G63/tLu4Ti17+zrd2XOGPMYsAGwD7CJMWZS4M/3AKfWrEUNpJWd2Ss2BbKsW8NQo2SaZG+zzCkZVDyLDWTeaPQHphVFWXPR+qU8ms1O5azMYYx5D/k8Qfj5pIjgayReAiPrVOi3BJexEpXgil75EcUuzdVVj+w5fHoJTKCo1Eweb7NMRdcK4sheGc4edT97Ww8avaqR+AvJ2z0xaf06dbbLS3qT3pXFlLaf9fXzmHD1OOOavMhctYsl5WXcf8qluhXGZD5TXc6ZuUyTWhHIcFnTbVQ5ZA3PsySTn9fyn1O1lZrJT87n1beU/KJFrjYitwmrUfvQbGczcz+Yywp5KRb9rXWVU76jg5R+Wo3f9BYJKi/WxPOhHHukue8+AyuPXuj/fsMnoHM95sXLqcyrs0/lZ24qj6PywE3WdtVGPyv6iftZTmb6k4xvtR+U82Kd7J3Uub1Gr8vVKj+u/CWlt1dGo5CJ6ifs57HBmsBhS1DGnqfy3y+nHY0rfzXYpJHtWZJn1pNKR+4Hc2mNrjPkh0pZZD3Hsq5fnVnDkw/ZHFxlUae80UwmbJbtT0p+0DqoNhr54WqlceR+MJcd8nFmrnhGpfTfq5Ub/fcaq5M6zyxWGG0oTHpVZZorPEld5FCLfepp2brJTqE4VBKFNuSNp796t1aZaVDpxGlSq2LVUNGqYJ93y3tW9Pc6Z0a9vtOaNBWv3qZciPvs5ElRgbqcmauDzPLi9QI/lxO+n78n5KeNppq6Iyl0MFcmWZ6hzK5mSp6pZ5lPUnJey38W65TsaZQ/dGVBUZRMoXVSH5rNJDqYS4jcFAwv+GPE2Z4qz4hVsy+431mxvNxmmWLmJ3+WJ15gv1GVqUstKvv61eXMUZVlvd93Kn+lrpHkpm5qIH1m7FOMq/GC6kQDfaeMI1yVKdFgW9c68ZPZ78zVRYsS8ZVpiLyccW3YxFGwH5mADlmvysol0hYJ7XDqj9wP5lKrpDJc2rKsmxJDHvIs6Z5VHURDultlEiWnaitrDnl1LSW/aJGrjQzORWSSZqvbcj+Yywp5+c5c/+W3nBDJeEFFKz9Zvs0y6TgzElv/5yCTkZOWjOrirTzmNHStKI4ma7TqQZq3Web2zFydwydK9UfmqjszV0U8aZKaflk3RIg4deuzitY8p+YSPxtZzSp+BstaNTdZ622Wlgzmp6KUQfZLbh0X5rJZE6dMORZIe9VRs6V2sngWslqaJyVKXtA6qDb0Nss1k9wP5pTKKLqFqIqzb1ZIOY/6/XtlFUzMylxI4aQ7UuXIS7OiTPVMYL9HGut/aK7wvajkrVyv85BFqzCVv15mHOVLbqbBRf2or43CtVQyMrM90K9qVSzpVcsqBFa1MlfnrKhVflolJfEVm4Tps5Mn51VjBo7MlTcp2U+o6lbQ85F5afUgcz+Y0+/M5adQK/kiue20lW89qEh+Tot/lfMmdSWnpswUeS2PipIFtD9TB7RS6kOzWST3g7ms4OXlO3P9rBpU28Gs6iv3lRyaS/OAVqXiUqwVko6qVL7VdZtlpe/WwcZl3WZZneRaBSRKBlTIPH1m7BOXX3pHRFUys56x1axqJ3weuyppEYbtb9K43gOQWqWnNuld8bcIs7m6rLdZ9hdxZTpUs3OrZqENILqfm/yqZBQ6mCuTLDecWdZNiSYXeVbH0VyiK3MJykqTcjo+9S0nUSumebVmdmgmC+ainlIUZTW1H2lZM2i2uk0HcwnRLOWi6pW5KixQ0RsNus2yfqs31ZGluNJcLK1Puuuzmz2VM3N1C7xm0vc2y/ot8ae+dTahtFR+Zq6qpblk8KoXp+6SIg02dlyZbqpVtDrglfgNIuqcfjsTde4/pkQ1A2m9zdKSxQxNG7VB/shDntV1O1geDNDkaL4o/aHFQUmbZhr0NIKoyRs1aV+abRdK7gdzmSGH35mLvs2yPktRVV2C4cX+Unha4QRQPUizUkh6xaCWM3MpXGYZuM0yecqSWePiQr06JpXIba4mK594MT8nJTOJcP3LqfQ8VDVxJMNqXRNaHExrdr1e8tMaIFUaTeqr1H1+j9vxk49as2FH5gIFqhx/qceZuUzmUJ2PlJQi94O5rFZSilKSNXz6MdEzczk1ZaO38OrCXH3Ia3mMIi+dWqV50BJXB9SoTU/uB3NZIS++UnyepzqtIzuB/Yiq5pafqg6aJH3UpZwOd14yP4JSZSC5szbVy6llhr1f2XUaTKVxZq4SsqBD1ukzk1y/I3Op32bZqPqpmlX1pFctq5IXuWOl4lcSpdZBdXX1WBXnmCp8JfWVuQb1IyqKm/Jtn4W+RznfK67HylxVq+51Nlik9JTqjtwP5tIqy2ld7VsNGVZNiSEPWVbP7WCJrszlwpp9KUfrevq2rszVh6ayYVMlRlGan8jBVfpqZJ5m6zfnfjCXFfLynbkiqpiBlNf6Bqr/nue4ve2161F5rI0jSxVQGqrUNMOeSPzZ3M1fUQwZKjNZpe5nb73YX3JDtXVPQ8qqvypYzex9DfHVjRrlV7XKVluUGYokGF1xhHGrSvVQq5K2pPwFxMbUJRXHWoeluSy2zNVMkOptlpYsr5ilhVogf+Qhz+q5HSxJv82DLaMozwb1S53eZlkvmseIzZMSJS9ol642tFovj2azSe4Hc9kh2ytzfsex6OxGVLiyZFX+YuQrlczWxH1nLjyj3oCWoFSMid8+mai00nmQ1IxSTTqvnmFPPl/rtc2x6MxcncpjZbdZNluzlTz1tlEZVVlNMpMIl7Sc1emsIMFJ65rU7H3jz8ylTxqrmo2umeK6Mg3Xq9EK9EN/58L77pgqnaDq/LSK1ea6O2rlu9aSant0MFcm2XauTCvXGLKdYZplSZJTWzZ2XS5aek5NmSmyXvVUQhMlRckJOvmUPLqDrfnRwVxCeNlemCvMVPb3fZAqnb4++4L7mf4hhRn1MhSvZXWrUtJc6esvrnJtX9ttlvWjbt+Ai/k52TjSX+1Yk0j8Nst+6t1aZZYOl1CEFVLN6lhS9XkhzdXM3kdNcpS/stCoXQT1fL/seCqMKO2BW15us2ykzCR0SOM2yyykPUySfepKyf1gTr8zl81CrZQmD1lWz+1ZSaY/D7aslrreZhnZCDezNdOhqSzYVIlRcoGWuZrQKrw8ms1OuR/MZYWs32aZ5GUHSYkqY82r4hgSXwlLWJ5SGbWcfelfdmUlsHy52aKa732tafSdSa6ftdLvRCS92lW/95I+T5iareuw8losv8bXUzj/Ju9U9lbavtDnLFfMhFZ92pvkwzaqTi/umZXRI+xH0bQmEet+ZK6adxJSKveDOe2gqA3ySB7yrL63WSYjG/JhyygaPTOYV7tlniYybBMlRckJWuZqI6lPRzU7zXY2M/eDOaUyKr2FqD8ZpR8G5UZUMJVspC77Nst+ZNaB0ufO0ourKnklBPabPWUqU5PO/qpSo879VDOjHZylr5PejR4INht9ZuzrKj/dlbJ6bpcuGT4DhTSplfVKbrOsy9moBryfgeyrO3F9oEaX3Xqd7avnBG3R3/v5PREdqnmnAUtzlXRzayH3g7m0RteZrtiyrJui1Juclv96bfMsO/4qGh6lf5rJho3u1CprHlriaqOayYg1kiazSVtaEbmuux9wKdAKXGuM+UlacSeJk+LRuLKjSkmnatJejWpO6P+k5Cb5fr/yG3CEMq04+4smNT0C8WT7xGp9y3K1NKKMpkW/aXOK/isnaPlxVxi+Gag5zXVuW9bEPCmHpOziOP0PGArtet9Ys5A/5eqQBV3jqCYNDo1rC7LQBlWig2MDR5XhRpPKypzruq3A5cD+wJbA4a7rbpmI8JRG1/1fTdzAzC3DBlEXSVS19bFKFdK4Br4u8suxbYlAcX+p+hMQKW7bTOpDubWcmStv9SobtoyMo8b448JUdJg+Zqtqvc8y5ooE9wX1NzOe+kfDE4uvMkFRbU6579RKTZ9DaeBWqXj5tcVQnT0qf6fyTxMkEm358YXrwBSXZyqzTXmBK7VfYp/+CMgp69MEce2Y3zZVo0NVSfEifkqOqlZFE1IkrW2WOwGTjTH/M8asBG4DJqQUt6IoVVCPil9pHJoLiqIoitJ8OGnsiXdd91BgP2PMcfb3I4GPGmO+XuI17+M/uS/2j8tbBwIwoGcVLfQWPQvS0bOiar2D9DotrGxpj/17UI+kWNnSTq9TGG+H0xKV3mC44N/9Z8F0DOhdRYtXrLOHw4rWASXjC77nPxvYsxInorvo/72tt5s2rydWr6h3WrweBvR0F/7g9A0zoGclK62+/u8teLG2CVJO2VjltNHT0loyTJQdg+nubonezVxN2Swnf8qJo1S+RekelBO0fUuJPPeJkhNMR5Suvoz23lW0er304qzO53D5bu9dxaqAb1ZiV19G3Dv9+X0UQZv2OC2rdQvH0Z/sKD/2ibN9FN1OK90tbTheL55Tev6uHLmlfCup+rZW/DRD6Tom+PfgM7/cxYUNPu/oWdFvfRO0a3/1X7mUKj9xdUOl8QX9LqpsrGhpX12mwrL9PGjt7aHd66YUUW1Lf5Rq/4J+F3xejpyg/5bbvg3sWRlbLydBOK8rzd9wfVuqbomLoxT9tQnBMMG+QDBdUX2UanSJi7ejZ0VReQ7rG9Svxevt096E5bV4PQzoLV2ug+HjylCcruWGC+dhuD0Mxh2sF8shtg8YyucovePq0+Df/LLQX38gUocq6oqgzKiyVyp8OE1Raa2m7ii3bD961v5QYidtWmfmohTokzrXdU8ATgAwxvjKK4qiKIqiKIqiKCHS2mY5HVg/8Ps4YGY4kDHmamPMR4wxH3Fd9zns2cxS/8oNV0nYPMStMrMvs9nSozK1fKhMLR8qMxsymy09KlPLh8rs918saa3MPQOMd113Y2AG8AXgiynFrSiKoiiKoiiK0nSksjJnjOkGvg78DXhVHpn/phG3oiiKoiiKoihKM5Lad+aMMfcC91bwytUJh6uHzEbGrTKzL7PZ0qMymydulZl9mc2WHpXZPHGrzOzLbLb0rOkyS5LKbZaKoiiKoiiKoihKsqR1AYqiKIqiKIqiKIqSIKltsywX13X3Ay4FWoFrjTE/Cf19GvAe0AN025svRwL/AdYBlgPrG2Petc9vBzYCpgGufX4dcglLKzAJOBo4CDgemGujOtvKvAHYChgOLASOQi50Cco9Ffg1sC6wNtALzAKeBD4TkvkP4BHkds+RNi1HAK8jH1MfCTwPHAf8HegANrTpfQN4G9gBWGRlHm2MedF13VYb52Bgio3rewF5RxpjVrquOxC4Eflo+1Lgw8C5wB4RMqcBQ4ChNv7dbHqLbAq8YNOxrg37ekzanwCuBXYHRlh9vw2cGJFHI6ydN7T2PBHYOJRHlwMn25/XtvHPA74Wlmn/fjuwHjAMKfsXA0vom++bW/uPsvacBZxv4wrb8zRr5w5rg48B3wnb0/5+PDCGQvn4BnBOSM+jIsL5tuywus8wxmwE4LruecBpNj0vA3uXKOPTrA22sXJmIRcS+b//x+o7Cdga2AL4JnLetRV41z5rt7K+Yd871z5/C+i24Ta2tnqJAh3ASmC+zY+BwAokj4MyPwkcCAwARtu8mR8hs8e+1wssQ8pUu5W5cSjtn0XK/HCgC5iN3LIbTvsdxpjzbL7+HPGltZGJrxU2bUsRH3k+EO5/Vo+lVuYKG/cd9t3jkbppPWCBtbtftpfYtPwL2Mumd6i1z/KAzOVW5lPGmJNc1/0/xH/Wtemfae0VlPkN4AM2Dzus3AVRMoG1kLI/yNrPAyZHyHzH2hGknA63toySeZX9txZS580HFluZfplaClwAnGJ1HG7/tiLGnndbmetaObNtmsL2vBj4MlKHvY/4cztyK9j6VuZ84K+I399u07+WzYOHrB22ReqBeUi5nEaxH82x9u9EbmqeZcNG+dFAq9tQituUsB9hw3nAJjZv59nnQT8aa9OyHFiFlLUFiI9sY8O9h5ThuLTPRXxjoJX3rpXjv+fXQ08DH7W/t1qbODbsuIBOVxtjzgNwXfcapC7164aVIZlRftQSJZO+frTQ5utKpF7w0x7lR91RaY/wo+VI+XkvJDPKjxbH2DPKj+ZGyIzyo3diZIb9aKG1YTiPovyoJ8aeYT+aZ8OG7RnlRy02fetaPRcBdwE/RfoxY5AyPR8pJ3Mo7sd8G/iFjWeZDTM2YKN51hb/Rcrcptb2p1Aoc0NtHAuB/YFd7d83RcqRZ8P12nyYG7BvB+IvM5D2tw1pbzpCMr8NfMTqN9TKiJLZY2U41hbrWNsPsO8NsmF3Q/oLH7H5NtTmWw9Sf4wNpP0yY8y1ruv+Gin3bwfSPjIo0xgzDcB13SeAnZF6uzdC5vv290VIWYJCvRG25zeQfkuLzWc/neG034H42Oet/QYiZe9dm34HaR+OBj5nww2x9lpsdVwHKXeLgCuAfaw+Q5Fys9zKW4CUY19mN4VFKb8f8U6MzMeROmY4Uj79vHyX4rJ5KdKnakH6FA5SxocifrLKyvwuUs4usnnptwV+3RnU83KkTzrA2ncdq/Mfkf7qVPv7HYG6s+RYKEymVubsgORypCBtCRzuuu6WEUE/YYzZzhjzEfv7WcBfgJ2QiviswPO/G2PGIwMj//lryADkDeS7dlfa5xdbudvZM37dwGWIs41FHOEapLMelHsS4vTfBp5DCscFwAERMlcgmTcHccjpwG+Bn9mw4+37X0IapKuQjsMbNo07AKcHZL5odb/K6vtPm6bfh+Qda8MdixSyPyEF6Kf2eZTMQcCLiIPuZe0UZ9MfA8/ad06ISfulwL+tPUciHaNrgH9EyPsj4sADkc79/0Xk0TXGmO2Qm1HnWpseEiXTGDMJ6bA9izj6QmDvsEyr3zes/Wcjl/ZcjVQGlwTt6bruelZfY4wZaPPohrA9bb4cj1Tiz9g4zgNuCdnyophwByAN6meRhm0JgPWNk6ztN0cqpO/Y+KPK+FnIgHopUqn9BdgM6US+H9D3XGvHR4AzKfjj2tbOk4H9rE1eAQ5FyvU3bbi1kIpuZUDmGcDWxpgPWV2fB8Zb2ywMyfw78CGkMb4ZKatRMj9hjPmQ/XmsTfN4pPKbGkr7z4E9kQr7R0hHvU/a7UBufaQx6QYOAw62OnZY+ScAV9pw+9tw+yL1z3rIIGaKL8/mxyX2/y2RzsNaNt+GI5NF+9l8vgTxcZBJjy2RRuLtgMyTXNf9BNIBnwt8wtr0PaSsBmVeZ8P55XDfOJnGmMOsLRch/vfjGJnjrI47IP55ZZxMpF77IYXGb4qV+UekYfPteZUNt5e181sl7Hkt4su9iH/cFmPPS2y47ZAB0f3Wpj0UBulLgV9h6zWkI3Aj8AebT68ig5gu+9719PWjiUhDf7L9eSlSJ51LXz/anUInOdimhP3I1/MkpFz7MsN+9B+kg/iyDbMQ6fR+DPGhD9u8+XuJtP/cyltkdXkIeBOpn4Lt2YdtuL2QztDDNk2dwIMBmX8EcF13Q2SiciYyWdpj8yQoM8qPYmVS7Ec7WZlnIHW/n/YoP4pMe4Qf7WbTfklIZpQfRcqM8KNLY2RG+VFcHoX9aFpMHkX5UZw9w350d4w9o/xoC6vLFMTv30R846dIW/UO0he5CJlYeITifswtNtwExA8uQQY4IGXwTaTsX4S0/28DpyPt3442PS8ig4J5iM8+jgwW/PzZEelMn2J19mV+B/ggMkmzK3CTTU834u9BmTcDuyCDggdtHkXJ/IQxZlvE37ZBBrZbIL7+NytzmbX5qcDh1sa/RQZDA4AfIHXtm8BJdiDnD/oGBNI+zL4TlInrup+36Vpq099HprX/6Uj56LH6xtkTpCz1IP6+XUza30YmGLZEBjqfseGXIfWaP0D6gw23C9K32y2QRw9SKEcTjTEfp+C/DyL9v4VI/+CZgMxV1tf2QgZyp8fJtHl3pJV5IVJ/LbR5FSybv0HqrS8iA86HrT3b7N98ma8g/ewTkfJxldXJQdoiX89fIz78BcS/t7LPdkImSZ4K9j9sXpY7FlpNpgZzSOImG2P+Z4xZiTTSE8p4bwJS6BYgmXNw4Pnv7c+/Dzzf1MrGGPMUMvoeEhZqjJmFzMbdZoyZjzScc5AVlKDcfYwxz9v4fodUCPMpzOAFZXpIR/E2pLPaiwzo9qHQYP0eONgYs8TKvAUpqP9AnKQI13XHAZ9GGhyQzsdw4LGItLtW1rXIoGpv4ukUlY0XsNMhRNt0f+CGQNiitLuuOwwpyN2IPZcYY15CKpz/BOXZsDsBF1h5j1lZffLIMgEZpE2xYfvIDIS7wab5NZu+KJltyMDJ2HT8x8p8O0LmYOAPruu2IeXvgxHytkA6pPvZdx9GGsUu4L6AzP1iwnUAQ40xj9g4gul2gOuMMVORRvMw+7eoMn4I0sg+gFQ8+yOV1yZBZY0xr9rB7zDg7YA/vop0Djwb32QbZgRScc+w4X6LNJRBmRONMd2u6/orji3WF15BOlJBmYuQinwy0hCOjZG5GMDKHAS8Y2VORzo3BZjQ2wAAGi1JREFUwbQPRirkByjMtPVJu+VipIHvBt40xvzD6rgq5AtXIo3EcmChMeZdpJM/LELmBhTXbb+1+bHIGLPCpn2BDReuB2+36QvyVaSRHmqMedIYMwdpSA4IyWxFOmnbWZnPlJDp23IY8HHg1hiZk62OOyGd0CtKyPRsmvyV4JlW5j7WZr492224fZHZ+LdK2HNza/PJVsfPxtizzYbbCSm7extj3kI6FUONMe/Z5+shvnSfjetHiH9fg0wA3GDDvoAMGsN+tAdwD4XBxavAejF+9BayYjct1KaE/egtpC3ZB/EXX2bYj1bYtmeGlfOKtdF/EB/aCen4D41LuzFmcSDf5yIDzMuQDmewPRtqw+2LdOKXG2NeQ3YOjA7ZE/vuixRWQH4HHBOSGeVHpWQG/ci30clIG+mnPcqP4vI97Ecv27QfE5IZ5UdxMsN+dE2MzCg/ipMZ9qNpMXkU5Udx9gz70T4x9ozyo5XAn5F2ud3+85BO9WNWz58hfvQ7+vZjxiH9nQk2vZ8xcsP5bGQVqB1pE14wsuo0CHjYGLPA1gsLkDbc34kxBPHJLSmujydavZ2AzHttO7ETUi47bHrm2N+DMl+g0G48hkxCR8lcbG21E7LaNN/KfN/q2o5MYn3E/j8BaZ8H2L+/gbRFLb5M26H/OTK50hNI+zKb/6tl2rA/QeojbP3XRyYF9gUe6Meeg5A2NxiuT9oRHzoPGUT5vvEuUpfMsuFeRtrxK4FPWZmv23DPICtewXLk23IqMoj9E1J+jkHKqi9zhPW1CfbZzSVkehRWLbuRfsItyCArWDYHIeV3go1jurXnO8iKmi+zx9pqM6R8/AXpU01EJih9PV+y9nrP2v0RYD+r57NWzzAVj4WyNphbj0KHGcTY64XCeMBE13Wfc133BPtsHTvwAsmktcPP7f/+8/WQQhaMZxjwddd1X3Jd9zrXddcKhH3bdd2NkFmMV4CuEnJX2XBPI059fITMcciMxhykEEwGlhn5hMPqdFsH3R3pKDxgjHkCmQH4iZV5sd02eQmy3Dvbvj/Khls3wo4fBr6PdAQ8pOM8EPhxSCZIA/btgK2nx9jUQyrtHwTypCjtSKUwF9lSdKLrute6rjsYaSg6QvI2QRzlSNd1X3Bd91qkE1gqjzZDnIcYmavzEnHeW4nId2RW6yJk9v1Ma5/nkAaiyJ7GmBnI5MGdSHlaiMxqFdkT6bztjsz0z0EG3utTqBB9PYfEhFttS6SB9P12PaAzUPb/h+S9/7dwGV8HGdS8HbDLdPtOp+u6/3Zd9z7Xdbey7wykUKZAyoMTkrme/bci9Hw0MMDm38Ou6348oNdACoPYAfZflMy3kcr7vjiZruv+GGlwO5Fyjf25I0LmsUjeH2HDRqX9ZKRBX4bUJT7+dhGf5QS2eweez0XK76YhWx4G7Bgot9MR+68KvLvYhrsN2ChQvt9BOr2bBmz5AWTGeox9tqOV2RWS2Y50KG8APmTDxcnE2mkJMNsY80aMTN+enwSW2HBxMk9BJto2QvzqO/b9ESGZL9lwVyINmb/CHGXPl5H65m1ky876Mfacb8P5ZW79gJ6jXNf9L9K4tlBopKdH+Eaw/p9CvB+NRlbE9gdO78ePWkJtSpwfrWvTvz9Sv5byoyEU2p6gH40MhItKO8g28o3o6xurQjLPR/JoIwr+NhfY3HXdV5CO2iLXdQ+iMDMfTM/aIZml/KhIpn0e5UdjbVzBdjfOj8Jpj/OjtUMyS/lRlD2j/Cgss5QfhWXG+VE4j0r5UdiecX4UtmecH81GfOE9pI4YjbSB61o5fvpmWpn3IP2d15H8HmH//iqF/s7GNq5xiA/5cbUi9TKB379AcR/KL+/BcjQD2Qm1v5V5bsAvN7B6329/H2DDRcmcgazq3B8n03Xd3yF+1oasvIAMDNyAzPlInh2F9DE+aMPOQPqEJ/hpR1Zg/4zUNb2BNA2gsLvLl3mmtXPQ36JkdiKrRr8Atg719cL27ETqgwmBfmGftCM+dBgyAbql67rjbV5ug0x8PWCMedra5UikD/MJ13XH27jnIf3D95AyscQ+X8++83c7WJ6BTKZcGZDpl7FPAW8EBtVRMo9D+sq7WT1+YmVuQnEfezaSj+fYNPjbG99FyuZ71jYzkXrho4gPHYr4xgwbx5XWlv9Cyvsnra6jKfiQ75fhvlc5Y6EisjaYi/rCefi6zY8ZY7ZHCtPJruvunlA8f0BWM7ZDOsG/CIQdiMwMnEJxZyFMG9LYnRIofP7WjKBMkIIyDqlwR0TI8owxPUgHfUdgJ9d1t0ZmUPzl9pHIjPQcpAELpy9oO8913c8gheo/FPMjpFLxZZ5pnz+BdID3R2brovQE6cA8huwz9vMknPbTgO2RBuS7yIyVv6UynMdtyMriXcaYD9uwGxGfR61W9h+C6Y3Q07GyDwqEDcv8tdX7RmRpf7BNf1imZzsJQ5FtA2Nt2CH0tefByPaTna3O/6bQ4ARl9saEC9pyDoXV2ahyTD9/i3o+E3jdyDaRXyPbDsrFi5G5FJhk8+9bwC12xfUYCtsnfX3CeeXL9Lc0+WH7yETsdSDSAfp6P3r+DZnpvDkQNpj2qxG7fz8iTat/d123EymPV0WE88vQFAq2vBIpS3fQtx4I8iriZ6fbtPrhFiKz2FMC6R6ATEQ8Y8ObGJkOUi7PRhoV47quEyXT5o+DdMZujRa3Gg9psKYEnkXp+U3gl0j9cCpSX0Uxzqb3x8gnbPxwUfY8Blm9Phjxv5UxMh9FOsM/RRpdP9xCpOOxHKmLrrXP43xmEIX6v6dE2NnIjPkXkE5DKT9qpbhNifOjFUjn9wuILeP8qA3pCJ8S6NB4yLazYLg+abfyrkLyKOgbDsXtGcg2px8jdZMfbhbSQVuG5OEtSPsWVSZbfJmIb29EvB8FZd5MvB+1WNv4epbyo3C+x/mRE5JZyo+i7BnlR2GZEO9HYT3j/CicR6X8KGzPOD8K2zPOj961uo1Cyv5vrT7BvPR3Hy3F9mOQnSr+31b3VWx/x/fv/1LYxhbH7RT6UIOJ9iHP2m2clflnCn55PLKb41H7u781L0rmgcAjNmykTGPMV5Atdwsp7JBxkBVdX+YA+/4/EP951Yb1kD7SulbmNAp3MUTVC1cHZHYgfZpwvy4s8xWkn/JBZNtfJ4W+XpQ9L0Payysp9Auj0j4MKatnIRPX19m8fB27Y8H2Xf0dMb9CVqT8nWQzkXI+Chko3Ruw3aYUfMhDyvEhAZn+8w9TfD4/SuapyFbkW5HV4l9SWNAI9rGHI3ni7476pZX5FjK4HWV1+yNSLx+EDJrfQ+o0D+m3H2Jt6U9Afgs5wrOSQv9vFnB+RN+rnLFQEVkbzE2nMGIFKTAzgwGMMTPt/3OQFZGdgNl2qRWkUZtjf1793P7vP59O8XbFccDLxpgeY0wvsi1iJ/u3mUghvtkYc4cNOy8s13XddiTTnrHhwM7YRsicjlzSshBZQh8PDHJlq1443dORwvUQslIzBFhgjFmBFMidkcK0OzKI2AvpSAygMBvsy/sY4sDPILOWe2Fnxux2J1+mr+cUq6dv6/WjbGrzZDrSIPh5Ek775jbM81bOH5EBWDd2NjqQR9ORAdx7Vo8/IrMZcXk0GKmU/fRGyfRt+RngeRs2Kt93R5b2JyMV3x3IvvqOCHt+Eqm4BxtjViGOOCzKnsaY31qb/5TCtgrP6hq0ZVS4oC1vo7CVbTqwNFD2N0Fm6fy/hcv4bGSGav2AXcYhM0C9sPp7kO2u63ZZG64TkOFfyBCU6ef9wNBz/1A3xpjnkLJ0KpJn79ptLtg4VoXenYnMiG0GHGHDxsn8gI0fpOMK0nFYHqPn+khn83PhtCN53o40ir9CytXzruv6h/x9nTe1f7sJOV8yPBButK+Pb0ur81s2br/cjkPs3x7Qs8u++zYyOeOX73Xt+8F0L0Q6nuOMMf+yadgCmZEMyuxFGtTpSBnutfFEyfyAtfG6SMPu2y4s0y9LO4bsHCXzS8jEyDhk4sRP+8KQzLHWntORxtVPe5Q95yGN53NIwzwlxp5DkS1AX0LKhN9hHovUhTcbYy4K2HMlMC7kG/ORlZBg/R/lRxsg2z1vNMbc0Y8f9SKzxkGZUX60AYVtnnfE+ZFte3YC5gbannE2jq2RGWu/7Eal3fehcRR8Y0NsWxGS6es5l4K/rYN0XG42xnwXqZ82QWa0j7DvPY9sgRsekFnKj8Iy4/xoA6TcvRLQM86P4vI97EdbIxOXQZlxfhRnz7AfbRghM86PomRG+VFUHsX5UZQ9o/woyp5xfrQu8D/bj/kr4hOjbNrHUSgvvr/7/ZhdkcmMRVbPLSj0d0Yj2/0eQtp//9xUD8UrEz3IANDvQ/mXBE2n+GK/ccBUu4vmIaS8tbuu6w9Mg6t9K5Ct9GGZOyF5/a1+ZHYh5XMJxW3RCivzEWRQtICCv91OoS163fa1HrIyhiFt0hnIjpTJVuZK5PI/X+YIZFLkcGRA0GnDhmW+B2xj64I3Ef/wy0eUPV+isFLr9wuj0t6DDE6n258/FLDnUhtuP/u3W2y49wLhRiPbzhci/a2h1paLkXJ7T8Dus5Gt7r7MccgExXo2LuJkIgO+h5D643akHPp1vF82D0Dqrwetnm/ZcL7Mt6zMW4EPGWOeRCau7rF58QaFch/UcxjSfz8T6du+EZA5Dfr0vfodC4XJ1Hfm7GDmdeRM0wxk0PFFI3upsdvyWowx79mfH0D26u6NZMhtyJmjG4wxZ7iu+3Nk7/JPXNc9Cxhpnx+AzMJ1IUuvvwIm+NvVXNc9FVn9OhzpoPuZPhY5SH4HMC8oF6nYhiGN8aft+1faVYSgzP9DGrHfIAOHB5AK9FHgT8aY21zXvQoZ2V+NNIbbIbMpTwBbGGMOsrOCFyPnFs6yafo+0siej8xEfC0g7yVjzBV2G9k21la/RCqLbxpjZgVlIgPD/ZGVuUORPfcDkZmYoE3XsfHujszmDUEaxy9HpH09pIG/wMpZB9mC8FNjzIWhPHoZqfT2QBruA4Dtw3lkjPmC67r3Ig3weJtHL8TIPMDKOgeZEYvK9/2tnkchM0cvWRudDhxvjLnVtyfSCN5pbX084rhDgQMi7PlLpNE+DRlk/R+yZefSUDm6KCLczwK2PA/4hjFmhF2S/ydyBukW7GyXMeb0mDL+MFLhHWXjHoFMBBxn9djadd2dkMHzhjY9myLbi2bYNLfb9HwG8YXxFPaDH4ZsQ3nGlp9zrcxNkNnseVbWvTZdTyMV4Ib2fV/m160dOpFLCWbEyHwC2NIYs8B13TeRTsbeNuwAZLLKT/sRSB3xHDLT+DGkMYlLeyvSSHwUmVWbjHRUhthnvzLG7OTKjbmzEf9cgKxYHEhh5vuPNvxcpG67GZnY2BxpeH9t82EshUbUsTKfQBr65ync/nYQUlf8wr63PzLj/kur52PIjKQv818ULkXwz9BtFCNzG6TBNsi2ladtXoVl+nl0JtLg+PV1lMwlyGz1z5GZzq8gg5/XrO2HWfv806blJWTGd7bN+yh7fsTa+nXkTNZ9Vp+wPf+JlF//wpEfIRNds5GbDI+05ehRpE6fYe39L6TB3QbxwRHIqtdHifejY6yMo+0Aq5QfzaVwqYBvzyg/egfpVBxujOkp4Udfs3mwAukYj0XKzrvIhN0TgTyKSvsEY8yzrus+Q8Efd0E6MgsptGe/McZsa8v8G7ZcHI10Cm80xnw1wodeR3xxR5uvLyCdUV9mnB9FyYzyo09aPTZEym4pP4pKe5QftSDlsysgM86PomRG+dH0CJlxfhQlM8qP/MmTYB7F+VGUPaP86KcR9ozyo5uQsnkYUjc+Ym3+KNK3+LaN8ynkLN4bSNvh92O+iLRP/0H86RdIh/gvyKD/fvv8k8aYnV3XfQuZTNsOqYcft+8eiPRNOo0xHwiVI8faeF8Kl2DcgdRlk2zevETBd56wOn/Kl2nLxAnIhOqHbdxhmXci7fV6tlzMs3l/LtIW+X7yvP3bV5C+1C3IIGgU0n/6mLXvxFDa/ds9R9l/TyP9p6/5Mo0xH7f9gVtsXu2BrPyHZe5vjNnBypyK9HEuj7KnlbECaTPvt/bcP8Ke5yITTDcg5XEBUuaeRgb2vUg/+tvW9nchZXMW0v/7JzLxtMzmwXpI3/CrSHn8kNXjb8jOgIOtfnch5e86q+vOyALBqBiZLUi9eQ/S//44Us7vtvH5ZfMUxIfbbbqfRfrUdyLtwABrkxYK90A8Z9N+DbIqfgbSJvh6HmGM2d5OVE21ej+KtG/b2T5MVN0ZORaKIlODOQDXdT+NNLitSMf0x4G/bYIYFGT25RZjzI9d1x2FFI4uxMCzkAHGXYhTbYAY+vPWaLcinY1OJCMuRDJpO6TCmIZUnJsiBvdnAzwko64Iyb0IaVj/Q+FK3llWpw1CMkcjlfEYK/M9ZP/uJAqfJngBGfRca9O5gdVziv1/KOJYLyK3Hi2xA4c7kIrof4jTnBOQ9yVjzArXdTuQGb5dbfp3sPGMDspEKq87KVzlvwDpbE8Npf005BYrkAbJQ2YQ3opI+zo2rnE2DTOtjidG5NF2SGUxHBk8HIw4RziPFiEV8OVIh70buQ0ySmanTccspHH8CjIADcs8CXG2tayNZiFl5KsR9jwfWTofaPNyN6R8hO15H1LJrE3heuhTkG07q/VEKpZwON+W6yOzwq1ImfwBUm6/hZSTV4C9SpTxS23ebW3t/w7Ssd7C5vEqa8trkE7GaKTTOsCGXYI02EMo3PT6CNKBXsemdyXScG9A4VyUfwi6x6ZnkJX9LoVObVDmgdaeKymcI5oWIdNfte5FOjRrB+JZL5T2rZDO33Cbh3OsDcNpP9LIvnlc151t89RfVelEysk7yAVFz9pw85BZxF6bD/5e/5VIJ2V9pIwNoXAF+CKrr3/mdB5SX/i/L6VwMLo1IHMV0qk8DWnEdrZ/e9fmz9CQzOORsrwdhWujV0bJNMZ8w3Xd6yncPjiIwjX6QZlfsTKfQsqmX19H6WmQcjeUwizospDMd5Cy/DX7rn+pwooYe85GttF12n9+2sP2vAsZzID44XYUrkD3V1o9pMP2a6vreKR8zEU6SodYnYfa/D0TGUgE/ehdK3OmfRcKt/uF/WihDRtuU16m2I+6bV69SaH8zbDpDfrRGKvDfGtzX/eWQLihSJ3iX0seTvs2iG8MtHG9jwwUwu2Zf16w1/4bbvNpvYDMXuD7xphfwOr2/G6r05gImYdG+NGAKJk2/4J+9F5Az9GBtE+irx91xKT9Aor9yL96Pyzzq/T1o5YomcaYc0N+tJYNF5bp+2bQj+LK54MU+9F7No1he/6Ivn7kxNizm2I/Whaj51/p60eDrK5+nfgu0nb+l+JPEyy29nqH4n7M6cjA1P80wQgb53IbfiEy4fo4MhBa16a5Bak7PQqfC1iITPbuinSix9i/L0Pa+zabpwuRduQjiF+9R+ETIcso/pSAL/MpG7YdKfvLbPxhmZ0UtpjORer8gTbcIAqfEdgDGfQMs/nn90lW2WfDAmn/qpHLa3BddznS3vnny4cFZO5ujPmfDXcOUgbesrYOy/TzzZ848tvMKHv+zObJWhQ+o+K3ccG0fx/pR21g/zYM6Yu/S6H8zUAmnk634QYF0jKEQjmaBxxmjHnCdd2HkIHzkTa8P1BeLyDzK0j/+ydI2T2bwvnhIpk2/86z6VzX6g9SRwbL5nWIX/Rae7RaOcOtHv52z08jix2fQcpvG1I+/K2bQT1fQCa0WpB2xT9z+xQySdBt3/1WoP8ROxaKInODOUVRFEVRFEVRFKV/snZmTlEURVEURVEURSkDHcwpiqIoiqIoiqLkEB3MKYqiKIqiKIqi5BAdzCmKoiiKoiiKouQQHcwpiqIoiqIoiqLkEB3MKYqiKEoCuK57ruu6NzVaD0VRFGXNoa3RCiiKoihKrbiuuxvybaStkG+svQqcYox5pqGKKYqiKEod0cGcoiiKkmtc1x2GfNz4q8gHvQcAH0c+lKwoiqIoTYsO5hRFUZS88wEAY8yt9vdlwEQA13U3Ba4BtgU84G/AycaYhfbv04DLgSOBTYHbgLOB64HdgKeBzxtj3nVddyNgKnAicC7gABf9f3t3E2JjFMdx/DuL2ZGIEg1ZKBQLG7byFpoo+ZeyYDFbs2BiI02pWZmEPZPZ+BdWM8pLyIYoC8qk2UwzqTHJW7KhsTh3cT2Nmsyt8Uzfz+p57j33nnNWT797zv/czLw406AiYjvQD2wCxoDuzHzcqklLkmTNnCSp7t4BvyJiICL2RcTSpvfagD5gFbAR6KAEsWaHgd2UUNgJ3KUEuuWU5+TJSvsdwHpgD3A2InZVBxQRq4Eh4AKwDDgN3IqIFf8+TUmS/uTKnCSp1jLza6Nm7gxlFW5lRAwDXZk5Cow2mk5FRD9wvvIVVzJzEiAingIfMvNV4/4OsLPSvjczvwOvI+IacBR4UGlzDBjOzOHG/f2IeAnsBwbmOGVJkgDDnCRpAcjMt8BxgIjYAAwClyKiG7hMqaFbTFlp+1T5+GTT9Y8Z7hdV2o83XY8Bm2cY0lrgSER0Nr3WDjyaxXQkSZoVw5wkaUHJzJGIuE6pbeuj1MptycyPEXEIuDrHLjqAkcb1GuD9DG3GgRuZ2TXHviRJ+ivDnCSp1horcQeAm5k5EREdlK2Pz4AlwBfgc6OOracFXZ6LiC5gHXCCsqWyahB4ERF7KVsw24HtwGhmTrRgDJIkeQCKJKn2vgHbgOcR8Z0S4t4Ap4BeYCsl0A0Bt1vQ3xNKHd5DymmW96oNMnMcOEg5SGWKslLXg89dSVILtU1PT8/3GCRJ+u81/TVBe2b+nOfhSJLkL4SSJEmSVEeGOUmSJEmqIbdZSpIkSVINuTInSZIkSTVkmJMkSZKkGjLMSZIkSVINGeYkSZIkqYYMc5IkSZJUQ4Y5SZIkSaqh3z44VpTup7YNAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x144 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-------------------------------------------------------------------------------------------------------------------\n", "\n", "FINAL PERFORMANCE BY APPLIANCE (LABEL):\n" ] }, { "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>Appliance</th>\n", " <th>Accuracy</th>\n", " <th>Precision</th>\n", " <th>Recall</th>\n", " <th>F1-score</th>\n", " <th>F1-macro</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>APLIANCE_3</td>\n", " <td>52.98</td>\n", " <td>53.04</td>\n", " <td>59.63</td>\n", " <td>56.14</td>\n", " <td>52.73</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>APLIANCE_4</td>\n", " <td>59.72</td>\n", " <td>52.20</td>\n", " <td>41.86</td>\n", " <td>46.46</td>\n", " <td>57.09</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>APLIANCE_6</td>\n", " <td>97.98</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>0.00</td>\n", " <td>49.49</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>APLIANCE_7</td>\n", " <td>94.88</td>\n", " <td>3.57</td>\n", " <td>30.43</td>\n", " <td>6.39</td>\n", " <td>51.88</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>APLIANCE_10</td>\n", " <td>98.12</td>\n", " <td>52.87</td>\n", " <td>57.50</td>\n", " <td>55.09</td>\n", " <td>77.07</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>APLIANCE_11</td>\n", " <td>95.62</td>\n", " <td>34.03</td>\n", " <td>37.98</td>\n", " <td>35.90</td>\n", " <td>66.82</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>APLIANCE_13</td>\n", " <td>99.00</td>\n", " <td>8.00</td>\n", " <td>10.53</td>\n", " <td>9.09</td>\n", " <td>54.29</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>APLIANCE_17</td>\n", " <td>96.32</td>\n", " <td>1.79</td>\n", " <td>5.13</td>\n", " <td>2.65</td>\n", " <td>50.39</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>APLIANCE_19</td>\n", " <td>88.55</td>\n", " <td>30.65</td>\n", " <td>4.38</td>\n", " <td>7.66</td>\n", " <td>50.78</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Appliance Accuracy Precision Recall F1-score F1-macro\n", "0 APLIANCE_3 52.98 53.04 59.63 56.14 52.73\n", "1 APLIANCE_4 59.72 52.20 41.86 46.46 57.09\n", "2 APLIANCE_6 97.98 0.00 0.00 0.00 49.49\n", "3 APLIANCE_7 94.88 3.57 30.43 6.39 51.88\n", "4 APLIANCE_10 98.12 52.87 57.50 55.09 77.07\n", "5 APLIANCE_11 95.62 34.03 37.98 35.90 66.82\n", "6 APLIANCE_13 99.00 8.00 10.53 9.09 54.29\n", "7 APLIANCE_17 96.32 1.79 5.13 2.65 50.39\n", "8 APLIANCE_19 88.55 30.65 4.38 7.66 50.78" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "OVERALL AVERAGE PERFORMANCE:\n" ] }, { "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>Metric</th>\n", " <th>Result (%)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Accuracy</td>\n", " <td>87.02</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Precision</td>\n", " <td>26.24</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Recall</td>\n", " <td>27.49</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>F1-score</td>\n", " <td>24.38</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>F1-macro</td>\n", " <td>56.73</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Metric Result (%)\n", "0 Accuracy 87.02\n", "1 Precision 26.24\n", "2 Recall 27.49\n", "3 F1-score 24.38\n", "4 F1-macro 56.73" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Predict test data\n", "y_pred = clf.predict(X_test)\n", "\n", "# Print metrics\n", "final_performance = []\n", "y_test = np.array(y_test)\n", "y_pred = np.array(y_pred)\n", "\n", "print(\"\")\n", "print(\"RESULT ANALYSIS\\n\\n\")\n", "print(\"ON/OFF State Charts\")\n", "print(\"-\" * 115)\n", "for i in range(y_test.shape[1]):\n", " \n", " fig = plt.figure(figsize=(15, 2))\n", " plt.title(\"Appliance #{}\".format( label_columns_idx[i]))\n", " plt.plot(y_test[:, i].flatten(), label = \"True Y\")\n", " plt.plot( y_pred[:, i].flatten(), label = \"Predicted Y\")\n", " plt.xlabel('Sample')\n", " plt.xticks(range(0, y_test.shape[0], 50))\n", " plt.xlim(0, y_test.shape[0])\n", " plt.ylabel('Status')\n", " plt.yticks([0, 1])\n", " plt.ylim(0,1)\n", " plt.legend()\n", " plt.show()\n", " \n", " acc, prec, rec, f1, f1m = metrics(y_test[:, i], y_pred[:, i])\n", " final_performance.append([\n", " label_columns_idx[i], \n", " round(acc100, 2), \n", " round(prec100, 2), \n", " round(rec100, 2), \n", " round(f1100, 2), \n", " round(f1m100, 2)\n", " ])\n", "\n", "print(\"-\" 115)\n", "print(\"\")\n", "print(\"FINAL PERFORMANCE BY APPLIANCE (LABEL):\")\n", "df_metrics = pd.DataFrame(\n", " data = final_performance,\n", " columns = [\"Appliance\", \"Accuracy\", \"Precision\", \"Recall\", \"F1-score\", \"F1-macro\"]\n", ")\n", "display(df_metrics)\n", "\n", "print(\"\")\n", "print(\"OVERALL AVERAGE PERFORMANCE:\")\n", "final_performance = np.mean(np.array(final_performance)[:, 1:].astype(float), axis = 0)\n", "display(pd.DataFrame(\n", " data = {\n", " \"Metric\": [\"Accuracy\", \"Precision\", \"Recall\", \"F1-score\", \"F1-macro\"],\n", " \"Result (%)\": [round(p, 2) for p in final_performance]\n", " }\n", "))\n", "# print(\"-----------------\")\n", "# print(\"Accuracy : {0:.2f}%\".format( final_performance[0] ))\n", "# print(\"Precision : {0:.2f}%\".format( final_performance[1] ))\n", "# print(\"Recall : {0:.2f}%\".format( final_performance[2] ))\n", "# print(\"F1-score : {0:.2f}%\".format( final_performance[3] ))\n", "# print(\"F1-macro : {0:.2f}%\".format( final_performance[4] ))\n", "# print(\"-----------------\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusões" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assim como no benchmarking 1, foi possível reproduzir a abordagem proposta no trabalho `Imaging NILM Time-Series`. Todavia, como esperado, alguns dados utilizados no estudo não foram disponibilizados, estando acessível apenas o código-fonte da abordagem, o que tomamos como base para realizar esse experimento. Com os códigos em mãos, implementei as mesmas rotinas dentro de um pipeline similar ao benchmarking 1, a fim de que o processos de Geração de dados e Extração de Características fosse <u>exatamente</u> os mesmos propostos nos trabalhos originais.\n", "\n", "A partir desta perspectiva, visando inclusive potencializar um estudo comparativo entre Benchmarkinks e a Proposta do doutorado, utilizei os mesmos dados do Benchmarking anterior para a Geração de Imagens GAF e Extração de características com a rede VGG16 (abordagem proposta pelos autores). Por fim, a partir do pipeline implementado, foi treinado um classificador baseado em Árvore de Decisão (multilabel) e avaliado a performance do mesmo, considerando métricas tradicionais de abordagens supervisionadas de ML.\n", "\n", "Portanto, a partir deste ponto, temos implementado e avaliado duas abordagens de referências, os quais permitiram exportar os dados, realizar o pré-processamento dos mesmos e extrair as características - cada um dentro de sua estratégia - para os dados da base REDD, permitindo estabelecer um cenário razoável de comparação para a classificação de séries temporais no contexto de NILM. Como próximos passos, está a definição dos cenários de testes (janela temporal, SEED, algoritmos de classificação, métricas de desempenho, etc.) e o consequente desenvolvimento da abordagem baseada em Gráfico de Recorrência para o problema de TSC, considerando os dados da residência 1 da base REDD." ] }, { "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": "3.6.7" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	cc0-1.0
wtsi-medical-genomics/team-code	python-club/notebooks/vector-class-exercise.ipynb	1	4737	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##Making a Vector class\n", "In this exercise we will construct a class to represent a mathematical object called the vector. A vector is a list of elements, exactly like a list in Python:\n", "\n", "```\n", "v = [1, 2, 3]\n", "w = [3, 2, 5]\n", "```\n", "\n", "\n", "We want to implement the following methods:\n", "\n", "###1. `__init__(self, elements)`\n", "Create the `__init__` method in your class `Vector` and use `self.elements` to store the provided `elements` parameter as a list.\n", "\n", "###2. `__mul__(self, other)`\n", "These objects can be multiplied together in different ways but we will consider only the dot-product:\n", "\n", "```\n", "v * w = 13 + 22 + 35\n", " = 22\n", "```\n", "We will do this in our method with a list comprehension. Use the following guidelines to implement `__mul__` in your class:\n", "\n", "1. A list comprehension looks like:\n", "```\n", "list_comp = [some_operation(element) for element in list]\n", "```\n", "Try the following to get a feel for listcomps:\n", "```\n", "squares = [element * 2 for element in [1,2,3]]\n", "```\n", "\n", "2. `zip(A, B)` will return an element each from `A`, `B` as a tuple so in our listcomp:\n", "```\n", "list_comp = [some_operation(a, b) for a, b in zip(A, B)]\n", "```\n", "3. `a * b` is the operation we want to perform \n", "4. `sum(List)` will add all of the values of the list together\n", "\n", "###3. `rss(self)`\n", "This stands for Root Sum Square and is essentially the square root of the dot product of self with its self.\n", "\n", "Hints: \n", " * Use the `math` module's `sqrt`\n", " * Reuse `__mul__`\n", "\n", "###4. `__cmp__(self, other)`\n", "This is the compartor operator where self and other are both Vector instances. We will say that for vectors `U`, `V`,\n", "\n", "```U > V if rss(U) > rss(V)\n", "U < V if rss(U) < rss(V)\n", "U == V if rss(U) == rss(V)```\n", "\n", "###5. `__str__(self)`\n", "All we want to do here is return a string representation of a list object.\n", "\n", "Hint: Apply the function `str` to something.\n", "\n", "##Testing the Vector class\n", "Once you have made your Vector class run the `test` function and make sure all tests pass. The `test` function has stored values for what it expects to be the result for each of these methods." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import math\n", "\n", "# make your class here ..." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def test():\n", " p = Vector([1, 2, 3.14])\n", " q = Vector([1, 2, 3.14])\n", " r = Vector(range(3))\n", "\n", " tests = ['str(p)', 'str(q)', 'str(r)', 'p * q', 'q * r',\\\n", " 'p.rss()', 'q.rss()', 'r.rss()', 'p > q', 'p < q', 'p == q', 'q > r', 'q < r', 'q == r']\n", " truths = ['[1, 2, 3.14]', '[1, 2, 3.14]', '[0, 1, 2]',\\\n", " 14.8596, 8.280000000000001, 3.85481517067, 3.85481517067, 2.2360679775,\\\n", " False, False, True, True, False, False]\n", "\n", " for truth, test in zip(truths, tests):\n", " eval_result = eval(test)\n", " if type(eval_result) == type(truth):\n", " if (isinstance(truth, float) and eval_result - truth < 1e-8) or (eval_result == truth):\n", " test_result = 'PASSED'\n", " else:\n", " test_result = 'FAILED expected: {}'.format(truth)\n", " else:\n", " test_result = 'FAILED expected type: {} but got {}'.format(type(truth), type(eval_result))\n", " print '{:10} = {:>15}\\t{}'.format(test, eval_result, test_result)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test()" ] } ], "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
benbovy/tp_geomod_nb	0_Notes.ipynb	1	8431	{ "metadata": { "name": "", "signature": "sha256:0be4e6b0b7b05a146da16a433ddaf8c5ee6a615816776212965c8714644250d8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "TP Mod\u00e9lisation : infos utiles" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Outils utilis\u00e9s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le support des travaux pratiques est donn\u00e9 sous forme de [notebooks IPython](http://ipython.org/notebook.html). Les notebooks permettent \u00e0 la fois l'\u00e9dition et l'ex\u00e9cution de code Python (ou autre language) par cellules et une mise en page riche de texte, d'\u00e9quations math\u00e9matiques, d'images, de liens ou de contenu web, tout cela avec l'aide d'un simple navigateur web.\n", "\n", "Les exercices de TP reposent sur l'usage intensif de Python et de ses principaux packages scientifiques ([Scipy stack](http://www.scipy.org/index.html)):\n", "\n", "- [Numpy](http://docs.scipy.org/doc/numpy/reference/) (package de base pour la manipulation de tableaux, similaire \u00e0 Matlab)\n", "- [Scipy](http://docs.scipy.org/doc/scipy/reference/) (collection d'algorithmes num\u00e9riques de base pour l'int\u00e9gration, l'interpolation, les syst\u00e8mes lin\u00e9aires, l'optimisation, etc...)\n", "- [Matplotlib](http://matplotlib.org/) (package de base pour la visualisation, similaire \u00e0 Matlab)\n", "- [Sympy](http://sympy.org/fr/index.html) (math\u00e9matique symbolique, \u00e0 la Mathematica)\n", "\n", "\n", "Quelques packages suppl\u00e9mentaires utiles et/ou utilis\u00e9s dans le cadre de ces TPs :\n", "- [Seaborn](http://web.stanford.edu/~mwaskom/software/seaborn/) (package de visualisation bas\u00e9 sur Matplotlib)\n", "- [JSAnimation](https://github.com/jakevdp/JSAnimation) (animation de figures Matplotlib)\n", "- [LandLab](http://landlab.readthedocs.org/en/latest/) (package d\u00e9di\u00e9 sp\u00e9cialement \u00e0 la mod\u00e9lisation en g\u00e9omorphologie, en d\u00e9veloppement)" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Installation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python ainsi que tous les outils list\u00e9s ci-dessus sont open-source et disponibles pour la plupart des plateformes communes (Linux, OS-X, Windows) !\n", "\n", "Il existe plusieurs mani\u00e8res d'installer ces libraires, le plus simple \u00e9tant d'installer une distribution scientifique Python, comme par exemple Anaconda ou Miniconda (\\) :\n", "\n", "- [Anaconda](http://continuum.io/downloads) reprend l'interpr\u00e9teur Python, le gestionnaire de packages conda* ainsi que +195 packages scientifiques.\n", "- [Miniconda](http://conda.pydata.org/miniconda.html) est une version all\u00e9g\u00e9e qui ne reprend que Python + conda (les autres packages peuvent toutefois \u00eatre install\u00e9s par la suite avec conda).\n", "\n", "Pour installer l'une de ces deux distributions, suivre un des liens ci-dessus et choisir l'installeur correspondant \u00e0 votre plateforme (Linux / OS-X / Windows ; 32/64 bits). Vous avez aussi le choix entre Python2 ou Python3, le mieux \u00e9tant de choisir la derni\u00e8re version (Python3), bien que cela n'a pas beaucoup d'importance (on pourra installer des packages Python2 dans le cadre de ces TPs). Une fois l'installeur t\u00e9l\u00e9charg\u00e9, l'ex\u00e9cuter et suivre les instructions.\n", "\n", "Une fois la distribution correctement install\u00e9e, la prochaine \u00e9tape est de cr\u00e9er un environnement virtuel. Les environnements virtuels permettent d'installer plusieurs versions de libraries sur le m\u00eame syst\u00e8me et de facilement changer de version. Pour cr\u00e9er l'environnement, ouvrir d'abord un terminal (Linux / OS-X) ou une invite de commande `cmd` (Windows). V\u00e9rifier ensuite que la distribution est bien install\u00e9e en tapant:\n", "\n", " conda --help\n", "\n", "Vous devriez voir apparaitre un message d'aide.\n", "\n", "La commande suivante va cr\u00e9er un nouvel environnement virtuel, nomm\u00e9 `tp_geomod_py27_0`, et y installer tous les packages dont nous avons besoin:\n", "\n", " conda create -n tp_geomod_py27_0 python=2.7 numpy scipy matplotlib sympy ipython ipython-notebook pip\n", "\n", "Un r\u00e9sum\u00e9 est affich\u00e9 avec tous les packages (d\u00e9pendences) qui vont \u00eatre t\u00e9l\u00e9charg\u00e9s et install\u00e9s. Tapez `'y'` pour proc\u00e9der \u00e0 l'installation.\n", "\n", "Les commandes suivantes permettent d'activer l'environnement qui vient d'\u00eatre cr\u00e9\u00e9. Sous Linux ou OS-X, tapez :\n", "\n", " source activate tp_geomod_py27_0\n", "\n", "ou plus simplement sous Windows :\n", "\n", " activate tp_geomod_py27_0\n", "\n", "Il reste quelques packages \u00e0 installer pour ces TPs. Lancer les commandes suivantes (avec l'environnement `tp_geomod_py27_0` activ\u00e9) :\n", "\n", " conda install -c https://conda.binstar.org/benbovy jsanimation\n", " conda install -c https://conda.binstar.org/benbovy seaborn\n", "\n", "---\n", "\n", "(\\) Des distributions alternatives existent: [Enthought Canopy](https://www.enthought.com/products/canopy/) (Linux, OS-X, Windows), [Python XY](https://code.google.com/p/pythonxy/) (Windows), [WinPython](http://winpython.sourceforge.net/) (Windows)... La proc\u00e9dure d'installation peut changer selon la distribution." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "T\u00e9l\u00e9charger et ouvrir les notebooks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le support des TPs (notebooks) est disponible sous forme de d\u00e9p\u00f4t Git*, h\u00e9berg\u00e9 sur GitHub:\n", "\n", "https://github.com/benbovy/tp_geomod_nb\n", "\n", "[Git](http://git-scm.com/) est un logiciel de contr\u00f4le de version, tr\u00e8s utile pour g\u00e9rer le d\u00e9veloppement de code ou de texte. Si Git est install\u00e9 sur votre machine (Linux, OS-X avec Xcode, peu probable sous Windows), la commande ci-dessous permet de t\u00e9l\u00e9charger le contenu du d\u00e9p\u00f4t dans le r\u00e9pertoire courant.\n", "\n", " git clone https://github.com/benbovy/tp_geomod_nb\n", "\n", "Sinon, il est possible de t\u00e9l\u00e9charger le contenu sous forme d'archive \u00e0 l'adresse suivante :\n", "\n", "https://api.github.com/repos/benbovy/tp_geomod_nb/zipball\n", "\n", "D\u00e9compressez l'archive au besoin, ouvrez un terminal ou une invite de commande, et allez dans le r\u00e9pertoire qui contient les notebooks (fichiers `.ipynb`). Par exemple, si vous avez clon\u00e9 le d\u00e9p\u00f4t ou t\u00e9l\u00e9charg\u00e9 / d\u00e9compress\u00e9\n", "son contenu dans `C:\\Users\\me\\tp_geomod_nb` (Windows), tapez:\n", "\n", " cd C:\\Users\\me\\tp_geomod_nb\n", "\n", "Sous OS-X, ce sera plut\u00f4t\n", "\n", " cd /Users/me/tp_geomod_nb\n", "\n", "et sous Linux\n", "\n", " cd /home/me/tp_geomod_nb\n", "\n", "Assurez-vous ensuite que l'environnement virtuel cr\u00e9\u00e9 \u00e0 l'\u00e9tape pr\u00e9c\u00e9dente est bien activ\u00e9. Lancez ensuite le serveur de notebooks avec la commande :\n", "\n", " ipython notebook\n", "\n", "Un nouvel onglet devrait s'ouvrir dans votre navigateur web par d\u00e9faut, avec la liste des notebooks. Cliquez sur le nom d'un notebook pour l'ouvrir. A noter qu'un naviguateur r\u00e9cent est pr\u00e9f\u00e9r\u00e9 (Chrome, Firefox, Safari...).\n", "\n", "Pour en savoir plus sur l'utilisation des notebooks IPython, n'h\u00e9sitez pas \u00e0 aller voir dans l'aide (menu `Help`, et notamment le `User Interface Tour`)." ] } ], "metadata": {} } ] }	gpl-3.0
rdhyee/working-open-data-2014	notebooks/Day_15_A_Midterm_Prep_Outline.ipynb	1	30497	{ "metadata": { "name": "", "signature": "sha256:8c3e297b67faee0c836fd936fa3e79c0602db1f885e2bf9096b5212e1ef967e5" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Midterm\n", "\n", "The upcoming midterm: (Day 17, 2014-03-18). It will probably consist of mostly multiple choice questions.\n", "\n", "The goal of this notebook is to help students to prepare for the midterm through providing highlights of what we've covered so far." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Suggestions about How to Prepare\n", "\n", "* read through all the materials from the course so far and outline what you understand and don't.\n", "* focus on key concepts and those programming constructs that are repeated often." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Open Data\n", "\n", "[Working definition of open data](http://rdhyee.github.io/wwod14/day01.html#(19)\n", "\n", "\n", "From <http://en.wikipedia.org/w/index.php?title=Special:Cite&page=Open_data&id=532390265>:\n", "\n", "> Open data is the idea that certain data should be freely available to everyone\n", "to use and republish as they wish, without restrictions from copyright, patents\n", "or other mechanisms of control.\n", "\n", "<http://opendefinition.org/>:\n", "\n", "> A piece of content or data is open if anyone is free to use, reuse, and\n", "redistribute it \u2014 subject only, at most, to the requirement to attribute and/or\n", "share-alike." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples of Open Data\n", "\n", "[Day 1: OKFestival /OKCon as indicator of vibrancy of the international open data community](http://rdhyee.github.io/wwod14/day01.html#(20%29)\n", "\n", "[Day 1: Examples of Open Data](http://rdhyee.github.io/wwod14/day01.html#(31%29)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Readings from Day 1](http://rdhyee.github.io/wwod14/day01.html#%2825%29)\n", "\n", " * read [Python for Data Analysis Chap 1. Preliminaries : Safari Books Online](http://my.safaribooksonline.com/book/programming/python/9781449323592/1dot-preliminaries/id2664030) The instructions for using\n", " Enthought Python Distribution are out of date. If you are\n", " looking for a distribution, follow [the installation\n", " instructions for Anaconda](https://github.com/rdhyee/working-open-data-2014/wiki/IPython-Installation-Options#2-anaconda) for your computer platform.\n", " * read `PfDA`, Chap 3 [Python for Data Analysis > 3. IPython: An Interactive Computing and Development Environment](http://my.safaribooksonline.com/book/programming/python/9781449323592/3dot-ipython-an-interactive-computing-and-development-environment/id2545624)\n", " * skim `PfDA`, [Appendix: Python Language Essentials](http://my.safaribooksonline.com/book/programming/python/9781449323592/adot-python-language-essentials/id2819503) -- to help remind yourself of key elements of standard Python \n", " * skim `PfDA`, Chap 2 Introductory Examples\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# World Populations\n", "\n", "[Day_01_B_World_Population.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_01_B_World_Population.ipynb)\n", "\n", "* How was the JSON data from the Wikipeida and the CIA Factbook produced?\n", "* Why do the totals from the two sources differ?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Racial Dot Map (as a framing example)\n", "\n", "[The Racial Dot Map: One Dot Per Person \| Weldon Cooper Center for Public Service](http://www.coopercenter.org/demographics/Racial-Dot-Map)\n", "\n", "\n", "* What is the Racial Dot Map displaying?\n", "* How would you get data relevant to the Racial Dot Map from the Census API?" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Census API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Day_02_A_US_Census_API.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_02_A_US_Census_API.ipynb)\n", "\n", "* What's the purpose of an API key?\n", "* What is `pip` and how to use it?\n", "* Remember the issues of sometimes having to filter to Puerto Rico" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# set up your census object\n", "# example from https://github.com/sunlightlabs/census\n", "\n", "from census import Census\n", "from us import states\n", "\n", "import settings\n", "\n", "c = Census(settings.CENSUS_KEY)\n", "for (i, state) in enumerate(states.STATES):\n", " print i, state.name, state.fips" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0 Alabama 01\n", "1 Alaska 02\n", "2 Arizona 04\n", "3 Arkansas 05\n", "4 California 06\n", "5 Colorado 08\n", "6 Connecticut 09\n", "7 Delaware 10\n", "8 District of Columbia 11\n", "9 Florida 12\n", "10 Georgia 13\n", "11 Hawaii 15\n", "12 Idaho 16\n", "13 Illinois 17\n", "14 Indiana 18\n", "15 Iowa 19\n", "16 Kansas 20\n", "17 Kentucky 21\n", "18 Louisiana 22\n", "19 Maine 23\n", "20 Maryland 24\n", "21 Massachusetts 25\n", "22 Michigan 26\n", "23 Minnesota 27\n", "24 Mississippi 28\n", "25 Missouri 29\n", "26 Montana 30\n", "27 Nebraska 31\n", "28 Nevada 32\n", "29 New Hampshire 33\n", "30 New Jersey 34\n", "31 New Mexico 35\n", "32 New York 36\n", "33 North Carolina 37\n", "34 North Dakota 38\n", "35 Ohio 39\n", "36 Oklahoma 40\n", "37 Oregon 41\n", "38 Pennsylvania 42\n", "39 Rhode Island 44\n", "40 South Carolina 45\n", "41 South Dakota 46\n", "42 Tennessee 47\n", "43 Texas 48\n", "44 Utah 49\n", "45 Vermont 50\n", "46 Virginia 51\n", "47 Washington 53\n", "48 West Virginia 54\n", "49 Wisconsin 55\n", "50 Wyoming 56\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Formulating-URL-requests-to-the-API-explicitly](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_02_A_US_Census_API.ipynb#Formulating-URL-requests-to-the-API-explicitly)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import requests\n", "# get the total population of all states\n", "url = \"http://api.census.gov/data/2010/sf1?key={key}&get=P0010001,NAME&for=state:\".format(key=settings.CENSUS_KEY)\n", "r = requests.get(url)\n", "\n", "r.json()[:5]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "[[u'P0010001', u'NAME', u'state'],\n", " [u'4779736', u'Alabama', u'01'],\n", " [u'710231', u'Alaska', u'02'],\n", " [u'6392017', u'Arizona', u'04'],\n", " [u'2915918', u'Arkansas', u'05']]" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "[#Total-Population-of-California](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_02_A_US_Census_API.ipynb#Total-Population-of-California)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "c.sf1.get(('NAME', 'P0010001'), {'for': 'state:%s' % states.CA.fips})" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "[{u'NAME': u'California', u'P0010001': u'37253956', u'state': u'06'}]" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Execution environments for programs\n", "\n", "[Day 3: Key Concept for Today: Execution Environment of Python](http://rdhyee.github.io/wwod14/day03.html#(13%29)\n", "\n", "[Day 3: How I use conda](http://rdhyee.github.io/wwod14/day03.html#(17%29)" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Learning the Basics of NumPy and Pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " read [Python for Data Analysis Chap 1. Preliminaries : Safari Books Online](http://my.safaribooksonline.com/book/programming/python/9781449323592/1dot-preliminaries/id2664030) The instructions for using\n", " Enthought Python Distribution are out of date. If you are\n", " looking for a distribution, follow [the installation\n", " instructions for Anaconda](https://github.com/rdhyee/working-open-data-2014/wiki/IPython-Installation-Options#2-anaconda) for your computer platform.\n", " * skim `PfDA`, [Appendix: Python Language Essentials](http://my.safaribooksonline.com/book/programming/python/9781449323592/adot-python-language-essentials/id2819503) -- to help remind yourself of key elements of standard Python \n", " * read `PfDA`, Chap 3 [Python for Data Analysis > 3. IPython: An Interactive Computing and Development Environment](http://my.safaribooksonline.com/book/programming/python/9781449323592/3dot-ipython-an-interactive-computing-and-development-environment/id2545624)\n", " * read `PfDA`, Chap 2 Introductory Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " \n", "[Day 4](http://rdhyee.github.io/wwod14/day04.html#(7%29): Work through [Day_04_B_numpy_and_pandas_series.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_04_B_numpy_and_pandas_series.ipynb) (everything before [Advanced: Operator Overloading](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_04_B_numpy_and_pandas_series.ipynb#Advanced:-Operator-Overloading))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Census API skills\n", "\n", "You should be able to [calculate the total population of the US](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_04_C_Census.ipynb#Total-Population) in the fill-in section of [Day_04_C_Census.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_04_C_Census.ipynb).\n", "\n", "You should be able to [calculate the population of California by totaling the county populations](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_04_C_Census.ipynb#Total-Population-of-California). \n", "\n", "For [Day 5: Geographical Hierarchies in the Census](http://rdhyee.github.io/wwod14/day05.html#(1%29), study:\n", "\n", "* [Day_05_A_Geographical_Hierarchies.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_05_A_Geographical_Hierarchies.ipynb) \n", "* and answers ([Day_05_B_Geographical_Hierarchies.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_05_B_Geographical_Hierarchies.ipynb) \n", "\n", "\n", "[Day_06_C_Calculating_Diversity_Preview.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_06_C_Calculating_Diversity_Preview.ipynb) and \n", "[Day_06_D_Assignment](https://bcourses.berkeley.edu/courses/1189091/assignments/4634716)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generators \n", "\n", "generators [Day 6: Generators for Geographic Entities](http://rdhyee.github.io/wwod14/day06.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Day_06_D_Assignment.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_06_D_Assignment.ipynb): exercise to write a generator for Census Places (answer: [Day_06_E_Assignment_Answers.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_06_E_Assignment_Answers.ipynb))" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# You should understand how this works.\n", "\n", "import pandas as pd\n", "from pandas import DataFrame\n", "\n", "import census\n", "import settings\n", "import us\n", "\n", "from itertools import islice\n", "\n", "c=census.Census(settings.CENSUS_KEY)\n", "\n", "def places(variables=\"NAME\"):\n", " \n", " for state in us.states.STATES:\n", " print state\n", " geo = {'for':'place:', 'in':'state:{s_fips}'.format(s_fips=state.fips)}\n", " for place in c.sf1.get(variables, geo=geo):\n", " yield place\n", "\n", "r = list(islice(places(\"NAME,P0010001\"), None))\n", "places_df = DataFrame(r)\n", "places_df.P0010001 = places_df.P0010001.astype('int')\n", "\n", "places_df['FIPS'] = places_df.apply(lambda s: s['state']+s['place'], axis=1)\n", "\n", "print \"number of places\", len(places_df)\n", "print \"total pop\", places_df.P0010001.sum()\n", "places_df.head()\n", "\n", "assert places_df.P0010001.sum() == 228457238\n", "# number of places in 2010 Census\n", "assert len(places_df) == 29261" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Alabama\n", "Alaska" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Arizona" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Arkansas" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "California" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Colorado" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Connecticut" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Delaware" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "District of Columbia" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Florida" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Georgia" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Hawaii" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Idaho" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Illinois" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Indiana" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Iowa" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Kansas" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Kentucky" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Louisiana" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Maine" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Maryland" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Massachusetts" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Michigan" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Minnesota" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Mississippi" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Missouri" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Montana" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Nebraska" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Nevada" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "New Hampshire" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "New Jersey" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "New Mexico" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "New York" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "North Carolina" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "North Dakota" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Ohio" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Oklahoma" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Oregon" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Pennsylvania" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Rhode Island" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "South Carolina" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "South Dakota" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Tennessee" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Texas" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Utah" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Vermont" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Virginia" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Washington" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "West Virginia" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Wisconsin" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Wyoming" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "number of places" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 29261\n", "total pop 228457238\n" ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Apply and lambda functions\n", "\n", "apply + lambda functions: [Day_06_A_Apply_Lambda.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_06_A_Apply_Lambda.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# P005 variables in the census\n", "\n", "http://www.census.gov/developers/data/sf1.xml\n", "\n", "compare to http://www.census.gov/prod/cen2010/briefs/c2010br-02.pdf \n", "\n", "I think the P0050001 might be the key category\n", "\n", "* P0010001 = P0050001\n", "* P0050001 = P0050002 + P0050010\n", "\n", "P0050002 Not Hispanic or Latino (total) = \n", "\n", "* P0050003 Not Hispanic White only \n", "* P0050004 Not Hispanic Black only\n", "* P0050006 Not Hispanic Asian only\n", "* Not Hispanic Other (should also be P0050002 - (P0050003 + P0050004 + P0050006)\n", " * P0050005 Not Hispanic: American Indian/ American Indian and Alaska Native alone\n", " * P0050007 Not Hispanic: Native Hawaiian and Other Pacific Islander alone\n", " * P0050008 Not Hispanic: Some Other Race alone\n", " * P0050009 Not Hispanic: Two or More Races\n", "\n", "* P0050010 Hispanic or Latino\n", " \n", "P0050010 = P0050011...P0050017\n", "\n", "\"Whites are coded as blue; African-Americans, green; Asians, red; Hispanics, orange; and all other racial categories are coded as brown.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Some graphics demonstrations you should try\n", "\n", "[Day 7: Preview of Plotting Graphs and Maps](http://rdhyee.github.io/wwod14/day07.html#(3%29)\n", "\n", "Do the following notebooks work for you to show basic graphics.\n", "\n", "* [Day_07_A_D3_Choropleth.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_07_A_D3_Choropleth.ipynb)\n", "\n", "* [Day_07_B_Google_Chart_Example.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_07_B_Google_Chart_Example.ipynb)\n", "\n", "* [Day_07_C_Google_Map_API.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_07_C_Google_Map_API.ipynb)\n", "\n", "* [Day_07_D_Matplotlib.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_07_D_Matplotlib.ipynb)\n", "\n", "* [Day_13_D_Vincent_Examples.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_13_D_Vincent_Examples.ipynb)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Census has lots of interesting data (optional)\n", "\n", "[Day_07_E_Census_fields.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_07_E_Census_fields.ipynb)\n", "is an exploration of the concepts and variables in the 2010 Census.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Groupby\n", "\n", "[Day_07_F_Groupby.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_07_F_Groupby.ipynb): gives you background on how to understand and use `groupby` in Pandas. Don't miss AJ's [Day_10_Groupby_Examples.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_10_Groupby_Examples.ipynb), which should be helpful, especially if you found [Day_10_Groupby_Examples.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_10_Groupby_Examples.ipynb) obscure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Census Metro Diversity Exercise\n", "\n", "[Day_07_G_Calculating_Diversity.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_07_G_Calculating_Diversity.ipynb): a prelude to the big diversity-calculation assignment [Day_08_A_Metro_Diversity.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_08_A_Metro_Diversity.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Projects\n", "\n", " not a focal point for the midterm (though, of course, it's good for projects to be in the background of your thinking)\n", "\n", "Relevant references:\n", "\n", "* [Day 9: Creating Projects](http://rdhyee.github.io/wwod14/day09.html)\n", "* [Day 9: Creating Projects: Project Topic Ideas](http://rdhyee.github.io/wwod14/day09.html#(5%29)\n", "* [Project-Starter_OpenContext.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Project-Starter_OpenContext.ipynb)\n", "* [Day 11: Project Brainstorming](http://rdhyee.github.io/wwod14/day11.html#(6%29)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting and Mapping preparation\n", "\n", "I will assume that you've read Chapter 8 of `PfDA` and can run [Day_11_B_Setting_Up_for_PfDA.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_11_B_Setting_Up_for_PfDA.ipynb).\n", "\n", "study overview slide: [Day 12: Overview of Plotting Options](http://rdhyee.github.io/wwod14/day12.html#(3%29).\n", "\n", "Note some fundamental conceptual aspects to `matplotlib` (as I outline in [Day_12_A_Matplotlib_Intro.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_12_A_Matplotlib_Intro.ipynb)\n", "and try to make basic plots on your own (line plots, scatter plots, bar plots).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Baby Names\n", "\n", "[Day_12_B_Baby_Names_Starter.ipynb#Names-that-are-both-M-and-F](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_12_B_Baby_Names_Starter.ipynb#Names-that-are-both-M-and-F)\n", "\n", "Before you use [Day_13_C_Baby_Names_MF_Completed.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_13_C_Baby_Names_MF_Completed.ipynb),\n", "try the approach in [Day_13_B_Baby_Names_MF_Starter.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_13_B_Baby_Names_MF_Starter.ipynb)\n", "\n", "Assignment in [nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_13_B_Baby_Names_MF_Starter.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_13_B_Baby_Names_MF_Starter.ipynb):\n", "\n", "> Submit a notebook that describes what you've learned about the nature of\n", "ambigendered names in the baby names database. (Due date: <s>Monday, March 10</s> Wed, March 12 at\n", "11:5pm --> bCourses assignment) I'm interested in seeing what you do\n", "with the data set in this regard. At the minimum, show that you are able to run\n", "Day_13_C_Baby_Names_MF_Completed. Be creative and have fun.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# mpld3\n", "\n", "[Day 13: mpld3 references](http://rdhyee.github.io/wwod14/day13.html#(5%29)\n", "\n", "[Day_13_A_mpl3d.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_13_A_mpl3d.ipynb)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# pivot_table\n", "\n", "[Day_14_A_Pivot_table_example.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_14_A_Pivot_table_example.ipynb)" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 } ], "metadata": {} } ] }	apache-2.0
tensorflow/text	docs/tutorials/text_similarity.ipynb	1	7945	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "tuOe1ymfHZPu" }, "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": "qFdPvlXBOdUN" }, "source": [ "# TF.Text Metrics" ] }, { "cell_type": "markdown", "metadata": { "id": "MfBg1C5NB3X0" }, "source": [ "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/text/tutorials/text_similarity\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/text/blob/master/docs/tutorials/text_similarity.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/text/blob/master/docs/tutorials/text_similarity.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView on GitHub\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/text/docs/tutorials/text_similarity.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n", " \u003c/td\u003e\n", "\u003c/table\u003e" ] }, { "cell_type": "markdown", "metadata": { "id": "xHxb-dlhMIzW" }, "source": [ "## Overview\n", "\n", "TensorFlow Text provides a collection of text-metrics-related classes and ops ready to use with TensorFlow 2.0. The library contains implementations of text-similarity metrics such as ROUGE-L, required for automatic evaluation of text generation models.\n", "\n", "The benefit of using these ops in evaluating your models is that they are compatible with TPU evaluation and work nicely with TF streaming metric APIs." ] }, { "cell_type": "markdown", "metadata": { "id": "MUXex9ctTuDB" }, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "K_8D_DtQJ0kC" }, "outputs": [], "source": [ "!pip install -q \"tensorflow-text==2.8.*\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IqR2PQG4ZaZ0" }, "outputs": [], "source": [ "import tensorflow as tf\n", "import tensorflow_text as text" ] }, { "cell_type": "markdown", "metadata": { "id": "QKp40qS-DGEZ" }, "source": [ "### ROUGE-L\n", "\n", "The Rouge-L metric is a score from 0 to 1 indicating how similar two sequences are, based on the length of the longest common subsequence (LCS). In particular, Rouge-L is the weighted harmonic mean (or f-measure) combining the LCS precision (the percentage of the hypothesis sequence covered by the LCS) and the LCS recall (the percentage of the reference sequence covered by the LCS).\n", "\n", "Source: https://www.microsoft.com/en-us/research/publication/rouge-a-package-for-automatic-evaluation-of-summaries/\n", "\n", "The TF.Text implementation returns the F-measure, Precision, and Recall for each (hypothesis, reference) pair.\n", "\n", "Consider the following hypothesis/reference pair:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WUgEkGHRKafG" }, "outputs": [], "source": [ "hypotheses = tf.ragged.constant([['captain', 'of', 'the', 'delta', 'flight'],\n", " ['the', '1990', 'transcript']])\n", "references = tf.ragged.constant([['delta', 'air', 'lines', 'flight'],\n", " ['this', 'concludes', 'the', 'transcript']])" ] }, { "cell_type": "markdown", "metadata": { "id": "qeiXnY-_Khp1" }, "source": [ "The hypotheses and references are expected to be tf.RaggedTensors of tokens. Tokens are required instead of raw sentences because no single tokenization strategy fits all tasks.\n", "\n", "Now we can call text.metrics.rouge_l and get our result back:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LS_NigzqKgtT" }, "outputs": [], "source": [ "result = text.metrics.rouge_l(hypotheses, references)\n", "print('F-Measure: %s' % result.f_measure)\n", "print('P-Measure: %s' % result.p_measure)\n", "print('R-Measure: %s' % result.r_measure)" ] }, { "cell_type": "markdown", "metadata": { "id": "FQoprhImKoD0" }, "source": [ "ROUGE-L has an additional hyperparameter, alpha, which determines the weight of the harmonic mean used for computing the F-Measure. Values closer to 0 treat Recall as more important and values closer to 1 treat Precision as more important. alpha defaults to .5, which corresponds to equal weight for Precision and Recall." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Q2ZnjOIgKnnS" }, "outputs": [], "source": [ "# Compute ROUGE-L with alpha=0\n", "result = text.metrics.rouge_l(hypotheses, references, alpha=0)\n", "print('F-Measure (alpha=0): %s' % result.f_measure)\n", "print('P-Measure (alpha=0): %s' % result.p_measure)\n", "print('R-Measure (alpha=0): %s' % result.r_measure)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iYUYiLJhKseb" }, "outputs": [], "source": [ "# Compute ROUGE-L with alpha=1\n", "result = text.metrics.rouge_l(hypotheses, references, alpha=1)\n", "print('F-Measure (alpha=1): %s' % result.f_measure)\n", "print('P-Measure (alpha=1): %s' % result.p_measure)\n", "print('R-Measure (alpha=1): %s' % result.r_measure)" ] } ], "metadata": { "colab": { "collapsed_sections": [ "Tce3stUlHN0L" ], "name": "text_similarity.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
gaargly/gaargly.github.io	2021_06_26_Lira_Samanta_Assignment2_Dr_Kasaran_class.ipynb	1	5957	{ "nbformat": 4, "nbformat_minor": 5, "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": "3.8.0" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "colab": { "name": "2021_06_26_Lira_Samanta_Assignment2_Dr_Kasaran_class.ipynb", "provenance": [], "include_colab_link": true } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/gaargly/gaargly.github.io/blob/master/2021_06_26_Lira_Samanta_Assignment2_Dr_Kasaran_class.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "absent-christianity" }, "source": [ "# Assignment" ], "id": "absent-christianity" }, { "cell_type": "markdown", "metadata": { "id": "temporal-deputy" }, "source": [ "1. If a list of people has 24 women and 21 men, then the probability of choosing a man from the list is 21/45.\n", "\n", "What is the probability of not choosing a man?\n", "\n", "a) 24/45\n", "\n", "b) 1-(21/24)\n", "\n", "c) 21/24\n", "\n", "d) 24/21" ], "id": "temporal-deputy" }, { "cell_type": "markdown", "metadata": { "id": "y1q4kc0azc3u" }, "source": [ "# The answer is a) 24/45" ], "id": "y1q4kc0azc3u" }, { "cell_type": "markdown", "metadata": { "id": "infinite-decimal" }, "source": [ "2. The probability that Bernice will travel by plane sometime in the next year is 10%. The probability of a plane crash at any time is 0.005%.\n", "\n", "What is the probability that Bernice will be in a plane crash sometime in the next year?\n", "\n", "\n", "a) 50%\n", "\n", "b) 0.0010%\n", "\n", "c) 10%\n", "\n", "d) 0.0005%" ], "id": "infinite-decimal" }, { "cell_type": "markdown", "metadata": { "id": "BEV3iwEszqpf" }, "source": [ "# The answer is d) (0.0005%)\n", "# P(Travel ∩ Crash)= 0.10.00005 = 0.000005 (0.0005%)\n", "with Travel and Crash being independent events" ], "id": "BEV3iwEszqpf" }, { "cell_type": "markdown", "metadata": { "id": "normal-theta" }, "source": [ "3. A data scientist wants to study the behavior of users on the company website. Each time a user clicks on a link on the website, there is a 5% chance that the user will be asked to complete a short survey about their behavior on the website.\n", "\n", "The data scientist uses the survey data to conclude that, on average, users spend 15 minutes surfing the company website before moving on to other things. What is wrong with this conclusion?\n", "\n", "\n", "a) Customers should be asked to complete surveys 25% of the time.\n", "\n", "b) People who surf longer are likely to click more links, increasing the odds of getting a survey.\n", "\n", "c) The average internet user only surfs a site for 12 minutes, on average.\n", "\n", "d) The data scientist is not considering mobile applications." ], "id": "normal-theta" }, { "cell_type": "markdown", "metadata": { "id": "8G2w3HTB0BBj" }, "source": [ "# The data scientist is only considering users who finished the survey, missing the 95% of the visitors who never get the survey. Their conclusion is only representative of 5% of the users, and thus does not reflect the population average." ], "id": "8G2w3HTB0BBj" }, { "cell_type": "markdown", "metadata": { "id": "palestinian-flower" }, "source": [ "4. A diagnostic test has a 98% probability of giving a positive result when applied to a person suffering from Thripshaw's disease and a 10% probability of giving a (false) positive when applied to a nonsufferer. It is estimated that 0.5% of the population has the disease.\n", "\n", "Suppose that the test is now administered to a person whose disease status is unknown. Calculate the probability that the test will be positive.\n", "\n", "\n", "a) 0.0049\n", "\n", "b) 0.0995\n", "\n", "c) 0.1044\n", "\n", "d) 0.995" ], "id": "palestinian-flower" }, { "cell_type": "markdown", "metadata": { "id": "U-fFG9G90cWI" }, "source": [ " P(Positive \| Disease) = 0.98\n", "* P(Positive \| Not Disease) = 0.10\n", "* P(Disease) = 0.05\n", "\n", "* What is P(Positive)?\n", "\n", "# Answer: P(Positive) = (0.1 x 0.995) + (0.98 x 0.005) = 0.1044 i.e. a 10.44% chance of the result being positive" ], "id": "U-fFG9G90cWI" } ] }	mit
jasemi/Computerphysik-ss17-Uebungen	Uebung-4/Aufgabe2-Merlin.ipynb	1	54858	{ "cells": [ { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "using PyPlot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Teilaufgabe a" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Method definition f(Any) in module Main at In[29]:2 overwritten at In[30]:2.\n", "WARNING: Method definition simpson(Any, Any, Any, Any) in module Main at In[29]:5 overwritten at In[30]:5.\n" ] }, { "data": { "text/plain": [ "simpson (generic function with 1 method)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function f(x)\n", " return 5x^4\n", "end\n", "function simpson(func, start, stop, N)\n", " if N%2 != 1\n", " throw(ArgumentError(\"N is not uneven\"))\n", " end\n", " I = 0\n", " h = (stop-start)/(N-1)\n", " x = linspace(start, stop, N)\n", " for i in 1:N\n", " f_i = func(x[i])\n", " if i in [1, N]\n", " I += f_i\n", " elseif i%2 == 0\n", " I += f_i4\n", " else\n", " I += f_i2\n", " end\n", " end\n", " return Ih/3\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Teilaufgabe b" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f48e8f7b910>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a = 1\n", "b = 2\n", "N_step = 10\n", "I_ana = 31\n", "x = Array(Float64, Int(round((10000-10)/N_step, RoundDown)+1))\n", "y = Array(Float64, Int(round((10000-10)/N_step, RoundDown)+1))\n", "i = 1\n", "for N in 11:N_step:10001\n", " x[i] = log((b-a)/(N-1))\n", " d_I = abs(simpson(f, a, b, N)-I_ana)\n", " y[i] = log(d_I)\n", " i += 1\n", "end\n", "p = plot(x, y, \"b-\")\n", "xlabel(\"\\$ln(h)\\$\")\n", "ylabel(\"\\$ln(\\\\Delta I)\\$\")\n", "title(\"Fehlerabschätzung der Simpsonregel\")\n", "show()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f48e9032290>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "forward_diff_x = Array(Float64, length(x)-1)\n", "forward_diff = Array(Float64, length(x)-1)\n", "for i = 1:length(x)-1\n", " forward_diff_x[i] = x[i]\n", " forward_diff[i] = (y[i+1]-y[i])/(x[i+1]-x[i])\n", "end\n", "p = plot(forward_diff_x, forward_diff, \"b,\")\n", "xlabel(\"\\$ln(h)\\$\")\n", "ylabel(\"\\$\\\\alpha = \\\\frac{d\\\\ ln(\\\\Delta I)}{d\\\\ ln(h)}\\$\")\n", "title(\"Berechnung des Koeffizienten \\$\\\\alpha\\$\")\n", "#ax = gca()\n", "#ax[:set_ylim]((-10,10))\n", "show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.5.1", "language": "julia", "name": "julia-0.5" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.5.1" } }, "nbformat": 4, "nbformat_minor": 1 }	unlicense
JohnGriffiths/LabNotebook	resources/notebooks/about_the_notebook__html_nb__2015-03-28.ipynb	1	13119	{ "metadata": { "celltoolbar": "ipynb-workdocs-0.2 tags", "css": [ "" ], "name": "", "signature": "sha256:ae2a58cd41ce6b2cf3c03085ccd29c6600857595a40c927c7015d09ee2798487" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": { "cell_tags": { "html": true } }, "source": [ "About the Notebook" ] }, { "cell_type": "heading", "level": 2, "metadata": { "cell_tags": { "html": true, "pdf": true, "slides": true } }, "source": [ "Welcome" ] }, { "cell_type": "markdown", "metadata": { "cell_tags": { "html": true, "pdf": true, "slides": true } }, "source": [ "...to my open, digital lab notebook. \n", "\n", "I have been inspired to adopt this new format for scientific record-keeping primarily by Carl Boettiger's [impressive efforts](http://www.carlboettiger.info/lab-notebook.html). The benefits of [Open Notebook Science](http://en.wikipedia.org/wiki/Open_notebook_science) for individual researchers, collaboration networks, and to the scientific community in general are, I think, pretty compelling. Most of my daily work as a computational research scientist involves small-scale problem solving, which in my case is centred around coding for computer simulations and statistical data analysis. Occasionally, (I like to think) I have interesting ideas also. In general, the best thing that could possibly happen to the fruits of this labour is for other people to see them and find them useful. This isn't some kind of wishful head-in-the-clouds altruism: maximizing the transparency of my work to my close collaborators, and to a lesser extent to other peers, reviewers, and to myself in the future, has clear practical benefits, and I stand to be the primary beneficiary. However I would be lying if I said that this is a completely ideology-free exercise. " ] }, { "cell_type": "heading", "level": 2, "metadata": { "cell_tags": { "html": true, "pdf": true, "slides": true } }, "source": [ "Boettiger's Disclaimer" ] }, { "cell_type": "markdown", "metadata": { "cell_tags": { "html": true, "pdf": true, "slides": true } }, "source": [ "In the introductory description of his own pioneering Lab Notebook, Carl Boettiger is careful to emphasize the following:" ] }, { "cell_type": "markdown", "metadata": { "cell_tags": { "html": true, "pdf": true, "slides": true } }, "source": [ "> *\"Disclaimer: this is not a blog.* \n", "> ...This is the active, permanent record of my scientific research, standing in place of the traditional paper bound lab notebook. The notebook is primarily a tool for me to do science, not communicate it. I write my entries with the hope that they are intelligible to my future self; and maybe my collaborators and experts in my field. Only the occasional entry will be written for a more general audience. [\u2026] In these pages you will find not only thoughts and ideas, but references to the literature I read, the codes or manuscripts I write, derivations I scribble and graphs I create and mistakes I make.\"\n", "\n", "...all of which applies in equal measure to the following pages. " ] }, { "cell_type": "heading", "level": 2, "metadata": { "cell_tags": { "html": true, "pdf": true, "slides": true } }, "source": [ "Well, not completely open..." ] }, { "cell_type": "markdown", "metadata": { "cell_tags": { "html": true, "pdf": true, "slides": true } }, "source": [ "Clearly one of the trickiest parts of maintaining an on online digital notebook is choosing what not to make publicly available immediately <a href=\"#fn:availableimmediately\" rel=\"footnote\">[1]</a>, so that any novel ideas and/or discoveries don't get 'scooped'. In my fields of computational and cognitive neuroscience, where the heat isn't turned up quite as high as in more commercialization-driven fields such as genetics or quantum computing, such concerns seem to me a little OTT. They are nevertheless perfectly valid concerns, particularly for close collaborators - and to my mind these are precisely the people who stand to benefit most from the OpenNotebook approach. " ] }, { "cell_type": "markdown", "metadata": { "cell_tags": { "html": true, "pdf": true, "slides": true } }, "source": [ "Fortunately, I have found a handy solution to this problem in the [pelican encrypt-content plugin](https://github.com/mindcruzer/pelican-encrypt-content). This little gem adds password-protection on a per-entry basis using [AES](http://en.wikipedia.org/wiki/Advanced_Encryption_Standard) 256-bit encryption, which is decoded at the browser level with the Crypto-JS javascript library. From a security point of view, the end-result is entirely analogous to a password-protected dropbox folder, or indeed sending attachments via e-mail <a href=\"#fn:security\" rel=\"footnote\">[2]</a>, both of which are commonplace media for collarborative work. " ] }, { "cell_type": "markdown", "metadata": { "cell_tags": { "html": true, "pdf": true, "slides": true } }, "source": [ "At a practical level, this probably means that most people will find that they are unable to view some non-negligible proportion of the posts on this site. If you are frustrated by this, I apologize. My personal feeling is that it's a reasonable compromise. " ] }, { "cell_type": "heading", "level": 2, "metadata": { "cell_tags": { "html": true } }, "source": [ "Tell me more!" ] }, { "cell_type": "markdown", "metadata": { "cell_tags": { "html": true } }, "source": [ "I will add a more thorough and technical post soon on how the digital notebook is set up and managed. Please also feel free contact me or leave comments if you have some constructive feedback. This is still in experimental stages and will likely evolve considerably over the months and years. \n", "\n", "Happy browsing :)\n" ] }, { "cell_type": "markdown", "metadata": { "cell_tags": { "html": true, "pdf": true, "slides": true } }, "source": [ "----" ] }, { "cell_type": "markdown", "metadata": { "cell_tags": { "html": true } }, "source": [ "<script type=\"text/javascript\" src=\"http://themodernscientist.com/static/js/jquery-2.1.3.min.js\"></script>\n", "<script type=\"text/javascript\" src=\"http://themodernscientist.com/static/js/bigfoot.js\"></script>\n", "<script type=\"text/javascript\">\n", " var bigfoot = $.bigfoot(\n", " {\n", " activateOnHover: true,\n", " deleteOnUnhover: true,\n", " preventPageScroll: false,\n", " hoverDelay: 500\n", " }\n", " );\n", "</script>\n", "\n", "\n", "<link href=\"http://themodernscientist.com/static/css/bigfoot-number.css\" rel=\"stylesheet\" type=\"text/css\">\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "cell_tags": { "html": true, "pdf": true } }, "source": [ "<li id=\"fn:availableimmediately\">[1] obviously, eventually (probably after acceptance or submission for publication), everything should be expected to be made publicly available. This is Open Science, after all. </li>\n", "\n", "<li id=\"fn:security\">[2] In fact, browser-level decryption is almost certainly more secure than these and most other ways of storing information in, or pushing it through, the cloud. The password-protected LabNotebook pages are only decrypted when you read them; until that point the information in them is, for all intents and purposes, gobbledeygook. </li>\n" ] }, { "cell_type": "heading", "level": 2, "metadata": { "cell_tags": { "html": true, "slides": true } }, "source": [ "Notebook styling" ] }, { "cell_type": "code", "collapsed": false, "input": [ "workdocs_dir = '/media/sf_SharedFolder/Code/git_repos_of_mine/bitbucket/Work/workdocs/masters'\n", "css_file = workdocs_dir + '/styles/CFDPython_css_modified_2.css'\n", "#HTML(open('styles/ketch_tnwn_example_modified.css', 'r').read())\n", "d(HTML(open(css_file, 'r').read()))" ], "language": "python", "metadata": { "cell_tags": { "html": true, "slides": true }, "run_control": { "state": "n" } }, "outputs": [ { "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:800px;\n", " margin-left:16% !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', serif;\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", "\th4{\n", "\t\tfont-family: 'Fenix', serif;\n", " }\n", " h6 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\t \n", " div.text_cell_render{\n", " font-family: 'Alegreya Sans',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 135%;\n", " font-size: 120%;\n", " width:700px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\";\n", "\t\t\tfont-size: 90%;\n", " }\n", "/* .prompt{\n", " display: None;\n", " }*/\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", " font-size: 50pt;\n", "\t\tline-height: 100%;\n", " color:#CD2305;\n", " margin-bottom: 0.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\t\n", " .text_cell_render h6 {\n", " font-weight: 300;\n", " font-size: 10pt;\n", " color: #191919;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\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" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0xa9286ac>" ] } ], "prompt_number": 8 } ], "metadata": {} } ] }	gpl-2.0
mne-tools/mne-tools.github.io	0.15/_downloads/plot_mne_point_spread_function.ipynb	1	4230	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n==========================================================\nCompute point-spread functions (PSFs) for MNE/dSPM/sLORETA\n==========================================================\n\nPSFs are computed for four labels in the MNE sample data set\nfor linear inverse operators (MNE, dSPM, sLORETA).\nPSFs describe the spread of activation from one label\nacross the cortical surface.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Authors: Olaf Hauk <[email protected]>\n# Alexandre Gramfort <[email protected]>\n#\n# License: BSD (3-clause)\n\nfrom mayavi import mlab\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import read_inverse_operator, point_spread_function\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nsubjects_dir = data_path + '/subjects/'\nfname_fwd = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\nfname_inv_eegmeg = (data_path +\n '/MEG/sample/sample_audvis-meg-eeg-oct-6-meg-eeg-inv.fif')\nfname_inv_meg = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\nfname_label = [data_path + '/MEG/sample/labels/Aud-rh.label',\n data_path + '/MEG/sample/labels/Aud-lh.label',\n data_path + '/MEG/sample/labels/Vis-rh.label',\n data_path + '/MEG/sample/labels/Vis-lh.label']\n\n\n# read forward solution\nforward = mne.read_forward_solution(fname_fwd)\n\n# read inverse operators\ninverse_operator_eegmeg = read_inverse_operator(fname_inv_eegmeg)\ninverse_operator_meg = read_inverse_operator(fname_inv_meg)\n\n# read label(s)\nlabels = [mne.read_label(ss) for ss in fname_label]\n\n# regularisation parameter\nsnr = 3.0\nlambda2 = 1.0 / snr ** 2\nmethod = 'MNE' # can be 'MNE' or 'sLORETA'\nmode = 'svd'\nn_svd_comp = 1\n\nstc_psf_eegmeg, _ = point_spread_function(\n inverse_operator_eegmeg, forward, method=method, labels=labels,\n lambda2=lambda2, pick_ori='normal', mode=mode, n_svd_comp=n_svd_comp)\n\nstc_psf_meg, _ = point_spread_function(\n inverse_operator_meg, forward, method=method, labels=labels,\n lambda2=lambda2, pick_ori='normal', mode=mode, n_svd_comp=n_svd_comp)\n\n# save for viewing in mne_analyze in order of labels in 'labels'\n# last sample is average across PSFs\n# stc_psf_eegmeg.save('psf_eegmeg')\n# stc_psf_meg.save('psf_meg')\n\ntime_label = \"EEGMEG %d\"\nbrain_eegmeg = stc_psf_eegmeg.plot(hemi='rh', subjects_dir=subjects_dir,\n time_label=time_label,\n figure=mlab.figure(size=(500, 500)))\n\ntime_label = \"MEG %d\"\nbrain_meg = stc_psf_meg.plot(hemi='rh', subjects_dir=subjects_dir,\n time_label=time_label,\n figure=mlab.figure(size=(500, 500)))\n\n# The PSF is centred around the right auditory cortex label,\n# but clearly extends beyond it.\n# It also contains \"sidelobes\" or \"ghost sources\"\n# in middle/superior temporal lobe.\n# For the Aud-RH example, MEG and EEGMEG do not seem to differ a lot,\n# but the addition of EEG still decreases point-spread to distant areas\n# (e.g. to ATL and IFG).\n# The chosen labels are quite far apart from each other, so their PSFs\n# do not overlap (check in mne_analyze)" ], "outputs": [], "metadata": { "collapsed": false } } ], "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": "2.7.14", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }	bsd-3-clause
Weenkus/Machine-Learning-University-of-Washington	Regression/examples/week-2-multiple-regression-assignment-1-blank.ipynb	1	12739	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Regression Week 2: Multiple Regression (Interpretation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The goal of this first notebook is to explore multiple regression and feature engineering with existing graphlab functions.\n", "\n", "In this notebook you will use data on house sales in King County to predict prices using multiple regression. You will:\n", "* Use SFrames to do some feature engineering\n", "* Use built-in graphlab functions to compute the regression weights (coefficients/parameters)\n", "* Given the regression weights, predictors and outcome write a function to compute the Residual Sum of Squares\n", "* Look at coefficients and interpret their meanings\n", "* Evaluate multiple models via RSS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fire up graphlab create" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import graphlab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load in house sales data\n", "\n", "Dataset is from house sales in King County, the region where the city of Seattle, WA is located." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "sales = graphlab.SFrame('kc_house_data.gl/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Split data into training and testing.\n", "We use seed=0 so that everyone running this notebook gets the same results. In practice, you may set a random seed (or let GraphLab Create pick a random seed for you). " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_data,test_data = sales.random_split(.8,seed=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning a multiple regression model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall we can use the following code to learn a multiple regression model predicting 'price' based on the following features:\n", "example_features = ['sqft_living', 'bedrooms', 'bathrooms'] on training data with the following code:\n", "\n", "(Aside: We set validation_set = None to ensure that the results are always the same)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_features = ['sqft_living', 'bedrooms', 'bathrooms']\n", "example_model = graphlab.linear_regression.create(train_data, target = 'price', features = example_features, \n", " validation_set = None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have fitted the model we can extract the regression weights (coefficients) as an SFrame as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_weight_summary = example_model.get(\"coefficients\")\n", "print example_weight_summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Making Predictions\n", "\n", "In the gradient descent notebook we use numpy to do our regression. In this book we will use existing graphlab create functions to analyze multiple regressions. \n", "\n", "Recall that once a model is built we can use the .predict() function to find the predicted values for data we pass. For example using the example model above:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "example_predictions = example_model.predict(train_data)\n", "print example_predictions[0] # should be 271789.505878" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Compute RSS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we can make predictions given the model, let's write a function to compute the RSS of the model. Complete the function below to calculate RSS given the model, data, and the outcome." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_residual_sum_of_squares(model, data, outcome):\n", " # First get the predictions\n", "\n", " # Then compute the residuals/errors\n", "\n", " # Then square and add them up\n", "\n", " return(RSS) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test your function by computing the RSS on TEST data for the example model:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rss_example_train = get_residual_sum_of_squares(example_model, test_data, test_data['price'])\n", "print rss_example_train # should be 2.7376153833e+14" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create some new features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although we often think of multiple regression as including multiple different features (e.g. # of bedrooms, squarefeet, and # of bathrooms) but we can also consider transformations of existing features e.g. the log of the squarefeet or even \"interaction\" features such as the product of bedrooms and bathrooms." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will use the logarithm function to create a new feature. so first you should import it from the math library." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from math import log" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next create the following 4 new features as column in both TEST and TRAIN data:\n", "* bedrooms_squared = bedrooms\\bedrooms\n", " bed_bath_rooms = bedrooms\\bathrooms\n", " log_sqft_living = log(sqft_living)\n", "* lat_plus_long = lat + long \n", "As an example here's the first one:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_data['bedrooms_squared'] = train_data['bedrooms'].apply(lambda x: x2)\n", "test_data['bedrooms_squared'] = test_data['bedrooms'].apply(lambda x: x2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# create the remaining 3 features in both TEST and TRAIN data\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Squaring bedrooms will increase the separation between not many bedrooms (e.g. 1) and lots of bedrooms (e.g. 4) since 1^2 = 1 but 4^2 = 16. Consequently this feature will mostly affect houses with many bedrooms.\n", "* bedrooms times bathrooms gives what's called an \"interaction\" feature. It is large when both of them are large.\n", "* Taking the log of squarefeet has the effect of bringing large values closer together and spreading out small values.\n", "* Adding latitude to longitude is totally non-sensical but we will do it anyway (you'll see why)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: What is the mean (arithmetic average) value of your 4 new features on TEST data? (round to 2 digits)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning Multiple Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will learn the weights for three (nested) models for predicting house prices. The first model will have the fewest features the second model will add one more feature and the third will add a few more:\n", "* Model 1: squarefeet, # bedrooms, # bathrooms, latitude & longitude\n", "* Model 2: add bedrooms\\bathrooms\n", " Model 3: Add log squarefeet, bedrooms squared, and the (nonsensical) latitude + longitude" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_1_features = ['sqft_living', 'bedrooms', 'bathrooms', 'lat', 'long']\n", "model_2_features = model_1_features + ['bed_bath_rooms']\n", "model_3_features = model_2_features + ['bedrooms_squared', 'log_sqft_living', 'lat_plus_long']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that you have the features, learn the weights for the three different models for predicting target = 'price' using graphlab.linear_regression.create() and look at the value of the weights/coefficients:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Learn the three models: (don't forget to set validation_set = None)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Examine/extract each model's coefficients:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: What is the sign (positive or negative) for the coefficient/weight for 'bathrooms' in model 1?\n", "\n", "Quiz Question: What is the sign (positive or negative) for the coefficient/weight for 'bathrooms' in model 2?\n", "\n", "Think about what this means." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing multiple models\n", "\n", "Now that you've learned three models and extracted the model weights we want to evaluate which model is best." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First use your functions from earlier to compute the RSS on TRAINING Data for each of the three models." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Compute the RSS on TRAINING data for each of the three models and record the values:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: Which model (1, 2 or 3) has lowest RSS on TRAINING Data? Is this what you expected?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now compute the RSS on on TEST data for each of the three models." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Compute the RSS on TESTING data for each of the three models and record the values:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quiz Question: Which model (1, 2 or 3) has lowest RSS on TESTING Data? Is this what you expected?Think about the features that were added to each model from the previous." ] } ], "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
valter-lisboa/ufo-notebooks	r/.ipynb_checkpoints/ufo-sample-r-checkpoint.ipynb	1	1335729	null	gpl-3.0
cassiogreco/udacity-data-analyst-nanodegree	P1/P1_Cassio.ipynb	1	36194	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. What is our independent variable? What is our dependent variable?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The independent and dependent variables of the experiment are: \n", "\n", "- Independent\n", " - Word/Color congruency\n", " \n", "- Dependent\n", " - Time to name ink" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. What is an appropriate set of hypotheses for this task? What kind of statistical test do you expect to perform? Justify your choices." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have as starting data two samples gathered from the same test (time taken to say the name of the color a given word is printed in) applied in different conditions: one for _Congruent_ word/colors (the word and color are the same. I.e. the word \"blue\" printed in blue) and one for _Incongruent_ word/colors (the word is a different color than the printed color. I.e. The word \"blue\" is printed in red).\n", "\n", "From the sampled data, we want to infer whether or not the time taken to say a _Congruent_ word/color is less than the time taken to say an _Incongruent_ word/color.\n", "\n", "Having Con be the symbol of the _Congruent_ words and Incon be the symbol of the _Incongruent_ words, and Diff be the symbol of the _difference_ between Con and Incon (Con - Incon), we have:\n", "\n", "H0 (HNULL): muCon = muIncon <=> muDiff = 0\n", "\n", "Ha (HALTERNATIVE): muCon != muIncon <=> muDiff != 0\n", "\n", "HNULL hypothesis: The population mean time it takes to say the correct ink color in the _Congruent_ condition is equal to the population mean time it takes to say the correct ink color in the _Incongruent_ condition, based on the sample means.\n", "\n", "HALTERNATIVE hypothesis: The population mean time it takes to say the correct ink color in the _Congruent_ is different than the population mean time it takes to say the correct ink color in the _Incongruent_ condition, based on the sample means.\n", "\n", "I will be performing a two-tailed _Dependent T-Test_ because:\n", "- The sample size is smaller than 30\n", "- The standard deviation of the entire population is unknown\n", "- I am measuring the results between the same test based on two different conditions on the same subject group.\n", "\n", "I will evaluate the results based on a confidence level of 99% (T-Critical value of 2.807, for 23 degrees of freedom).\n", "\n", "I expect to reject the _HNULL_ hypothesis that states that the mean time it takes to say the name of the ink colors in the _Congruent_ group will be equal to the mean time it takes to say the name of the ink colors in the _Incongruent_ group\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Report some descriptive statistics regarding this dataset. Include at least one measure of central tendency and at least one measure of variability." ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import pandas as pd\n", "import math\n", "%pylab inline\n", "import matplotlib.pyplot as plt\n", "CONGRUENT = 'Congruent'\n", "INCONGRUENT = 'Incongruent'\n", "TCRITICAL = 2.807 # two-tailed difference with 99% Confidence and Degree of Freedom of 23" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": false }, "outputs": [], "source": [ "path = r'~/udacity-data-analyst-nanodegree/P1/stroopdata.csv'\n", "\n", "initialData = pd.read_csv(path)\n", "\n", "dataDifference = [initialData[CONGRUENT][i] - initialData[INCONGRUENT][i] for i in range(0, len(initialData[CONGRUENT]))]\n", "\n", "congruentMean = mean(initialData[CONGRUENT])\n", "incongruentMean = mean(initialData[INCONGRUENT])\n", "differenceMean = mean(dataDifference)\n", "\n", "def mean(data):\n", " return sum(data) / len(data)\n", "\n", "def valuesMinusMean(data):\n", " meanOfData = mean(data)\n", " return [value - meanOfData for value in data]\n", "\n", "def valuesToPower(data, power):\n", " return [value ** power for value in data]\n", "\n", "def variance(data):\n", " return sum(data) / (len(data) - 1)\n", "\n", "def standardDeviation(variance):\n", " return math.sqrt(variance)" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean of Congruent values: 14.051125\n", "Mean of Incongruent values: 22.0159166667\n", "Mean of Difference values: -7.96479166667\n", "\n", "Range of Congruent values: 13.698\n", "Range of Incongruent values: 19.568\n", "Range of Difference values: 19.969\n", "\n", "Standard Deviation of Congruent values: 3.5593579576451955\n", "Standard Deviation of Incongruent values: 4.797057122469138\n", "Standard Deviation of Difference values: 4.864826910359056\n" ] } ], "source": [ "print('Mean of Congruent values:', congruentMean)\n", "print('Mean of Incongruent values:', incongruentMean)\n", "print('Mean of Difference values:', differenceMean)\n", "print()\n", "print('Range of Congruent values:', max(initialData[CONGRUENT] - min(initialData[CONGRUENT])))\n", "print('Range of Incongruent values:', max(initialData[INCONGRUENT] - min(initialData[INCONGRUENT])))\n", "print('Range of Difference values:', max(dataDifference - min(dataDifference)))\n", "print()\n", "print('Standard Deviation of Congruent values:', standardDeviation(variance(valuesToPower(valuesMinusMean(initialData[CONGRUENT]), 2))))\n", "print('Standard Deviation of Incongruent values:', standardDeviation(variance(valuesToPower(valuesMinusMean(initialData[INCONGRUENT]), 2))))\n", "print('Standard Deviation of Difference values:', standardDeviation(variance(valuesToPower(valuesMinusMean(dataDifference), 2))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Provide one or two visualizations that show the distribution of the sample data. Write one or two sentences noting what you observe about the plot or plots." ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([array([ 5., 6., 7., 4., 1., 1., 0., 0., 0., 0.]),\n", " array([ 0., 0., 1., 7., 6., 6., 2., 0., 0., 2.])],\n", " array([ 8.63 , 11.2925, 13.955 , 16.6175, 19.28 , 21.9425,\n", " 24.605 , 27.2675, 29.93 , 32.5925, 35.255 ]),\n", " <a list of 2 Lists of Patches objects>)" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10d85cb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(\n", " x=[initialData[CONGRUENT], initialData[INCONGRUENT]], \n", " normed=False, \n", " range=(min(initialData[CONGRUENT]), max(initialData[INCONGRUENT])),\n", " bins=10,\n", " label='Time to name'\n", ")" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 4., 2., 4., 1., 6., 2., 2., 1., 1., 1.]),\n", " array([ 8.63 , 9.9998, 11.3696, 12.7394, 14.1092, 15.479 ,\n", " 16.8488, 18.2186, 19.5884, 20.9582, 22.328 ]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10d989e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(\n", " x=initialData[CONGRUENT],\n", " normed=False, \n", " range=(min(initialData[CONGRUENT]), max(initialData[CONGRUENT])),\n", " bins=10,\n", " label='Time to name'\n", ")" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 4., 4., 6., 3., 4., 1., 0., 0., 0., 2.]),\n", " array([ 15.687 , 17.6438, 19.6006, 21.5574, 23.5142, 25.471 ,\n", " 27.4278, 29.3846, 31.3414, 33.2982, 35.255 ]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10dabdc88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(\n", " x=initialData[INCONGRUENT], \n", " normed=False, \n", " range=(min(initialData[INCONGRUENT]), max(initialData[INCONGRUENT])),\n", " bins=10,\n", " label='Time to name',\n", " color='Green'\n", ")" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 1., 0., 1., 0., 0., 5., 5., 4., 1., 7.]),\n", " array([-21.919 , -19.9221, -17.9252, -15.9283, -13.9314, -11.9345,\n", " -9.9376, -7.9407, -5.9438, -3.9469, -1.95 ]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10dbdb780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(\n", " x=dataDifference, \n", " normed=False, \n", " range=(min(dataDifference), max(dataDifference)),\n", " bins=10,\n", " label='Time to name',\n", " color='Red'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From analyzing the histograms of both the _Congruent_ and _Incongruent_ datasets we can visualy see that the _Incongruent_ dataset contains a greater number of higher _time-to-name_ values than the _Congruent_ datasets.\n", "\n", "This is evident from looking at the values of the mean values of both datasets, previously calculated (14.051125 and 22.0159166667 for _Congruent_ and _Incongruent_ datasets, respectively)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Now, perform the statistical test and report your results. What is your confidence level and your critical statistic value? Do you reject the null hypothesis or fail to reject it? Come to a conclusion in terms of the experiment task. Did the results match up with your expectations?" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Degrees of Freedom: 23\n", "Standard Error: 0.9930286347783406\n", "T Value: -8.02070694411\n", "T Critical Regions: Less than -2.807 and Greater than 2.807\n", "Is the T Value inside of the critical region? True\n", "Is p < 0.005? True\n", "Confidence Interval: (-10.752223044489469, -5.1773602888438637)\n" ] } ], "source": [ "degreesOfFreedom = len(initialData[CONGRUENT]) - 1\n", "\n", "def standardError(standardDeviation, sampleSize):\n", " return standardDeviation / math.sqrt(sampleSize)\n", "\n", "def getTValue(mean, se):\n", " return mean / se\n", "\n", "se = standardError(standardDeviation(variance(valuesToPower(valuesMinusMean(dataDifference), 2))), len(dataDifference))\n", "tValue = getTValue(differenceMean, se)\n", "\n", "def marginOfError(t, standardError):\n", " return t * standardError\n", "\n", "def getConfidenceInterval(mean, t, standardError):\n", " return (mean - marginOfError(t, standardError), mean + marginOfError(t, standardError))\n", "\n", "print('Degrees of Freedom:', degreesOfFreedom)\n", "print('Standard Error:', se)\n", "print('T Value:', tValue)\n", "print('T Critical Regions: Less than', -TCRITICAL, 'and Greater than', TCRITICAL)\n", "print('Is the T Value inside of the critical region?', tValue >= TCRITICAL or tValue < TCRITICAL)\n", "print('Is p < 0.005?', tValue >= TCRITICAL or tValue < TCRITICAL)\n", "print('Confidence Interval:', getConfidenceInterval(differenceMean, TCRITICAL, se))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on the data calculated above, we have that the T Value of the difference of the two conditions (_Congruent_ and _Incongruent_) is inside of the critical region of 99% Confidence.\n", "\n", "With this, I reject the HNULL Hypothesis (H0). Since the T Value falls inside of the critical region, it is statistically significant to say that _muCon != muIncon_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6. Optional: What do you think is responsible for the effects observed? Can you think of an alternative or similar task that would result in a similar effect? Some research about the problem will be helpful for thinking about these two questions!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I think that the reason behind this effect is that the brain already has associated the name of the color with it's visual representation (the actual color). When we are shown the name of a color, but it is in a different color our brain can't process the two at the same time (as the logical and the creative side of our brain are each giving a different response as to what we are seeing).\n", "\n", "Similar tasks that will have similar results could be a _Spatial_ Stroop Effect (as described in the wikipedia article referenced at the bottom) where show words like _Big_, _Small_, _Up_, _Down_ in different sizes and positions can also trigger this effect." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Sources" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Matplot documentation: http://matplotlib.org/api/pyplot_api.html\n", "- T-Table: https://drive.google.com/file/d/0B8LCYo988pznaUs4dDE5dkJrOEk/view?usp=sharing\n", "- More information on the study: https://en.wikipedia.org/wiki/Stroop_effect\n", "- When to use T Score vs Z Score: http://www.statisticshowto.com/when-to-use-a-t-score-vs-z-score/\n", "- Types of T Tests: http://support.minitab.com/en-us/minitab/17/topic-library/basic-statistics-and-graphs/hypothesis-tests/tests-of-means/types-of-t-tests/\n", "- About Null and Alternative Hypothesis: http://support.minitab.com/en-us/minitab/17/topic-library/basic-statistics-and-graphs/hypothesis-tests/basics/null-and-alternative-hypotheses/\n", "- What is a hypothesis test?: http://support.minitab.com/en-us/minitab/17/topic-library/basic-statistics-and-graphs/hypothesis-tests/basics/what-is-a-hypothesis-test/\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": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
diging/tethne-notebooks	2. Working with data from JSTOR Data-for-Research.ipynb	2	185160	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pprint import pprint\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Tethne: Working with data from the Web of Science" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the basics down, in this notebook we'll begin working with data from the [JSTOR Data-for-Research (DfR) portal](http://dfr.jstor.org/?&helpview=about_dfr).\n", "\n", "The JSTOR DfR portal gives researchers access to\n", "bibliographic data and N-grams for the entire JSTOR database.\n", "\n", "Tethne can use DfR data to generate coauthorship networks, and to improve\n", "metadata for Web of Science records. Tethne is also able to use\n", "N-gram counts to add information to networks, and can interface with MALLET to perform LDA topic modeling.\n", "\n", "## Methods in Digital & Computational Humanities\n", "\n", "This notebook is part of a cluster of learning resources developed by the [Laubichler Lab](http://devo-evo.lab.asu.edu) and the [Digital Innovation Group](http://diging.asu.edu) at Arizona State University as part of an initiative for digital and computational humanities (d+cH). For more information, see our evolving online methods course at [https://diging.atlassian.net/wiki/display/DCH](https://diging.atlassian.net/wiki/display/DCH).\n", "\n", "### Getting Help\n", "\n", "Development of the Tethne project is led by [Erick Peirson](http://asu.academia.edu/erickpeirson). To get help, first check our [issue tracking system on GitHub](http://github.com/diging/tethne/issues). There, you can search for questions and problems reported by other users, or ask a question of your own. You can also reach Erick via e-mail at [email protected]." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting bibliographic data from JSTOR Data-for-Research\n", "\n", "For the purpose of this tutorial, you can use the sample dataset from https://www.dropbox.com/s/q2jy87pmy9r6bsa/tethne_workshop_data.zip?dl=0.\n", "\n", "Access the DfR portal at [http://dfr.jstor.org/](http://dfr.jstor.org/) If you don't already have an\n", "account, you will need to [create a new account](http://dfr.jstor.org/accounts/register/).\n", "\n", "After you've logged in, perform a search using whatever criteria you please.\n", "When you have achieved the result that you desire, create a new dataset request.\n", "Under the \"Dataset Request\" menu in the upper-right corner of the page, click\n", "\"Submit new request\".\n", "\n", "![](./images/wos/getting.5.png)\n", "\n", "On the Download Options page, select your desired Data Type. If you do\n", "not intend to make use of the contents of the papers themselves, then \"Citations\n", "Only\" is sufficient. Otherwise, choose word counts, bigrams, etc.\n", "\n", "Output Format should be set to XML.\n", "\n", "Give your request a title, and set the maximum number of articles. Note that\n", "the maximum documents allowed per request is 1,000. Setting Maximum Articles\n", "to a value less than the number of search results will yield a random sample of\n", "your results.\n", "\n", "![](./images/wos/getting.6.png)\n", "\n", "Your request should now appear in your list of Data Requests. When your\n", "request is ready (hours to days later), you will receive an e-mail with a\n", "download link. When downloading from the Data Requests list, be sure to use\n", "the link in the full dataset column.\n", "\n", "![](./images/wos/getting.7.png)\n", "\n", "When your dataset download is complete, unzip it. The contents should look\n", "something like those shown below.\n", "\n", "![](./images/wos/getting.8.png)\n", "\n", "``citations.XML`` contains bibliographic data in XML format. The ``bigrams``,\n", "``trigrams``, ``wordcounts`` folders contain N-gram counts for each document.\n", "\n", "If you were to open one of the XML files in the ``wordcounts`` folder, say, you would see some XML that looks like this:\n", "\n", "```\n", "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<article id=\"10.2307/4330482\" >\n", " <wordcount weight=\"21\" > of </wordcount>\n", " <wordcount weight=\"16\" > the </wordcount>\n", " <wordcount weight=\"10\" > university </wordcount>\n", " <wordcount weight=\"10\" > a </wordcount>\n", " <wordcount weight=\"9\" > s </wordcount>\n", " <wordcount weight=\"9\" > d </wordcount>\n", " <wordcount weight=\"9\" > harvard </wordcount>\n", " <wordcount weight=\"8\" > m </wordcount>\n", " <wordcount weight=\"7\" > and </wordcount>\n", " <wordcount weight=\"6\" > u </wordcount>\n", " <wordcount weight=\"6\" > press </wordcount>\n", " <wordcount weight=\"5\" > cambridge </wordcount>\n", " <wordcount weight=\"5\" > massachusetts </wordcount>\n", " <wordcount weight=\"5\" > journal </wordcount>\n", " <wordcount weight=\"4\" > by </wordcount>\n", " ...\n", " <wordcount weight=\"1\" > stephen </wordcount>\n", " <wordcount weight=\"1\" > liver </wordcount>\n", " <wordcount weight=\"1\" > committee </wordcount>\n", " <wordcount weight=\"1\" > school </wordcount>\n", " <wordcount weight=\"1\" > lewontin </wordcount>\n", " <wordcount weight=\"1\" > canguilhem </wordcount>\n", " <wordcount weight=\"1\" > assistant </wordcount>\n", " <wordcount weight=\"1\" > jay </wordcount>\n", " <wordcount weight=\"1\" > state </wordcount>\n", " <wordcount weight=\"1\" > morgan </wordcount>\n", " <wordcount weight=\"1\" > advertising </wordcount>\n", " <wordcount weight=\"1\" > animal </wordcount>\n", " <wordcount weight=\"1\" > is </wordcount>\n", " <wordcount weight=\"1\" > species </wordcount>\n", " <wordcount weight=\"1\" > claude </wordcount>\n", " <wordcount weight=\"1\" > review </wordcount>\n", " <wordcount weight=\"1\" > hunt </wordcount>\n", " <wordcount weight=\"1\" > founder </wordcount>\n", "</article>\n", "```\n", "\n", "Each word is represented by a ``<wordcount></wordcount>`` tag. The ``\"weight\"`` attribute gives the number of times that the word occurs in the document, and the word itself is between the tags. We'll come back to this in just a moment." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parsing DfR datasets\n", "\n", "Just as for WoS data, there is a module in ``tethne.readers`` for working with DfR data. We can import it with:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tethne.readers import dfr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, ``read()`` accepts a string containing a path to either a single DfR dataset, or a directory containing several. Here, \"DfR dataset\" refers to the folder containing the file \"citations.xml\", and the contents of that folder.\n", "\n", "This will take considerably more time than loading a WoS dataset. The reason is that Tethne automatically detects and parses all of the wordcount data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dfr_corpus = dfr.read('/Users/erickpeirson/Dropbox/HSS ThatCamp Workshop/sample_data/DfR')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combining DfR and WoS data\n", "\n", "We can combine our datasets using the ``merge()`` function. First, we load our WoS data in a separate ``Corpus``:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tethne.readers import wos\n", "wos_corpus = wos.read('/Users/erickpeirson/Dropbox/HSS ThatCamp Workshop/sample_data/wos')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both of these datasets are for the Journal of the History of Biology. But note that the WoS and DfR corpora have different numbers of ``Paper``s:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1917, 1168)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(dfr_corpus), len(wos_corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then import ``merge()`` from ``tethne.readers``:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tethne.readers import merge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then create a new ``Corpus`` by passing both ``Corpus`` objects to ``merge()``. If there is conflicting information in the two corpora, the first ``Corpus`` gets priority." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "corpus = merge(dfr_corpus, wos_corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``merge()`` has combined data where possible, and discarded any duplicates in the original datasets." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1627" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(corpus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## FeatureSets\n", "\n", "Our wordcount data are represented by a ``FeatureSet``. A ``FeatureSet`` is a description of how certain sets of elements are distributed across a ``Corpus``. This is kind of like an inversion of an index. For example, we might be interested in which words (elements) are found in which ``Paper``s. We can think of authors as a ``FeatureSet``, too.\n", "\n", "All of the available ``FeatureSet``s are available in the ``features`` attribute (a dictionary) of our ``Corpus``. We can see the available ``FeatureSet``s by inspecting its:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'authors': <tethne.classes.feature.FeatureSet at 0x124347810>,\n", " 'citations': <tethne.classes.feature.FeatureSet at 0x124347890>,\n", " 'wordcounts': <tethne.classes.feature.FeatureSet at 0x10611a3d0>}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corpus.features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that citations and authors are also ``FeatureSet``s. In fact, the majority of network-building functions in Tethne operate on ``FeatureSet``s -- including the ``coauthors()`` and ``bibliographic_coupling()`` functions that we used in the WoS notebook.\n", "\n", "Each ``FeatureSet`` has several attributes. The ``features`` attribute contains the distribution data itself. These data themselves are ``(element, value)`` tuples. In this case, the elements are words, and the values are wordcounts." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(u'PORTER_CM_2005_JOURNAL_OF_THE_HISTORY_OF_BIOLOGY',\n", " [('essay', 1),\n", " ('all', 2),\n", " ('curate', 1),\n", " ('dynamic', 1),\n", " ('resistance', 1),\n", " ('alien', 1),\n", " ('contributions', 1),\n", " ('certainly', 1),\n", " ('william', 1),\n", " ('to', 12),\n", " ('excluded', 1),\n", " ('devotes', 1),\n", " ('summarizes', 1),\n", " ('garden', 1),\n", " ('sloppy', 1),\n", " ('waldo', 1),\n", " ('stiff', 1),\n", " ('did', 2),\n", " ('notes', 1),\n", " ('amerindian', 2),\n", " ('biological', 1),\n", " ('obsolete', 1),\n", " ('second', 1),\n", " ('theory', 1),\n", " ('even', 2),\n", " ('what', 1),\n", " ('burch', 1),\n", " ('contributed', 1),\n", " ('remain', 1),\n", " ('rafinesque', 15),\n", " ('suspect', 1),\n", " ('conduct', 1),\n", " ('publications', 1),\n", " ('loose', 1),\n", " ('superstition', 1),\n", " ('met', 1),\n", " ('separation', 1),\n", " ('alone', 1),\n", " ('consideration', 1),\n", " ('change', 1),\n", " ('extreme', 1),\n", " ('technical', 1),\n", " ('study', 1),\n", " ('moneygrabber', 1),\n", " ('suggestion', 1),\n", " ('opinion', 1),\n", " ('involves', 1),\n", " ('criticism', 1),\n", " ('family', 1),\n", " ('sicily', 1),\n", " ('biota', 1),\n", " ('from', 2),\n", " ('would', 2),\n", " ('doubt', 2),\n", " ('rosetta', 1),\n", " ('memory', 1),\n", " ('today', 1),\n", " ('sort', 1),\n", " ('stone', 1),\n", " ('leonard', 2),\n", " ('hold', 1),\n", " ('corpulent', 1),\n", " ('permits', 1),\n", " ('this', 4),\n", " ('science', 5),\n", " ('work', 1),\n", " ('reviews', 2),\n", " ('emerson', 1),\n", " ('can', 1),\n", " ('believed', 1),\n", " ('meet', 1),\n", " ('history', 4),\n", " ('states', 1),\n", " ('numbers', 1),\n", " ('sense', 1),\n", " ('dress', 1),\n", " ('species', 3),\n", " ('biography', 2),\n", " ('native', 1),\n", " ('united', 1),\n", " ('rather', 1),\n", " ('discussion', 1),\n", " ('acts', 1),\n", " ('nuts', 1),\n", " ('dismissed', 1),\n", " ('rude', 1),\n", " ('critics', 1),\n", " ('such', 1),\n", " ('man', 1),\n", " ('a', 19),\n", " ('natural', 3),\n", " ('nascent', 1),\n", " ('so', 2),\n", " ('dread', 1),\n", " ('archaeology', 1),\n", " ('interpretation', 1),\n", " ('welcomed', 1),\n", " ('indeed', 1),\n", " ('soon', 1),\n", " ('years', 1),\n", " ('including', 1),\n", " ('still', 1),\n", " ('warren', 9),\n", " ('linguistics', 1),\n", " ('fit', 1),\n", " ('offers', 1),\n", " ('good', 1),\n", " ('somebody', 1),\n", " ('unveiling', 1),\n", " ('fixation', 1),\n", " ('questions', 1),\n", " ('nation', 1),\n", " ('qualified', 1),\n", " ('they', 2),\n", " ('half', 1),\n", " ('communities', 1),\n", " ('now', 2),\n", " ('university', 2),\n", " ('naturalist', 2),\n", " ('habits', 1),\n", " ('found', 1),\n", " ('quarter', 1),\n", " ('mental', 1),\n", " ('fish', 1),\n", " ('engaging', 1),\n", " ('zoology', 1),\n", " ('large', 2),\n", " ('funding', 1),\n", " ('god', 1),\n", " ('furthermore', 2),\n", " ('health', 1),\n", " ('medicine', 1),\n", " ('issue', 2),\n", " ('mercantile', 1),\n", " ('belief', 1),\n", " ('associate', 1),\n", " ('million', 1),\n", " ('immigrated', 1),\n", " ('patronage', 1),\n", " ('language', 1),\n", " ('created', 1),\n", " ('could', 1),\n", " ('david', 1),\n", " ('turn', 1),\n", " ('american', 3),\n", " ('first', 1),\n", " ('origin', 1),\n", " ('botany', 1),\n", " ('researches', 1),\n", " ('number', 1),\n", " ('one', 1),\n", " ('another', 2),\n", " ('threatening', 1),\n", " ('system', 1),\n", " ('tradition', 1),\n", " ('their', 1),\n", " ('zealot', 1),\n", " ('too', 1),\n", " ('xiv', 1),\n", " ('lingering', 1),\n", " ('interests', 1),\n", " ('that', 4),\n", " ('serve', 1),\n", " ('prodigious', 1),\n", " ('heretical', 1),\n", " ('than', 1),\n", " ('colleagues', 1),\n", " ('were', 2),\n", " ('sublime', 1),\n", " ('and', 21),\n", " ('charlotte', 1),\n", " ('portraits', 1),\n", " ('mind', 2),\n", " ('have', 2),\n", " ('need', 1),\n", " ('thought', 1),\n", " ('documents', 1),\n", " ('speculative', 1),\n", " ('able', 1),\n", " ('ideas', 1),\n", " ('also', 6),\n", " ('subjects', 1),\n", " ('which', 2),\n", " ('divert', 1),\n", " ('lecturer', 1),\n", " ('who', 2),\n", " ('procure', 1),\n", " ('most', 1),\n", " ('enduring', 1),\n", " ('awe', 1),\n", " ('knowingly', 1),\n", " ('why', 1),\n", " ('achievement', 1),\n", " ('professor', 1),\n", " ('cover', 1),\n", " ('fact', 1),\n", " ('laws', 1),\n", " ('particularly', 1),\n", " ('show', 1),\n", " ('chapters', 1),\n", " ('dupe', 1),\n", " ('gratified', 1),\n", " ('fine', 1),\n", " ('decade', 1),\n", " ('craziness', 1),\n", " ('with', 4),\n", " ('only', 1),\n", " ('enthusiasm', 1),\n", " ('his', 7),\n", " ('get', 1),\n", " ('cannon', 1),\n", " ('samuel', 2),\n", " ('stripes', 1),\n", " ('parlance', 1),\n", " ('him', 5),\n", " ('societies', 1),\n", " ('artist', 1),\n", " ('lexington', 2),\n", " ('oestreicher', 1),\n", " ('banking', 1),\n", " ('bad', 1),\n", " ('automatic', 1),\n", " ('human', 1),\n", " ('observes', 1),\n", " ('elected', 1),\n", " ('officials', 1),\n", " ('college', 1),\n", " ('are', 1),\n", " ('said', 1),\n", " ('ways', 1),\n", " ('ralph', 1),\n", " ('state', 1),\n", " ('ends', 1),\n", " ('vindicated', 1),\n", " ('catalogs', 1),\n", " ('nature', 2),\n", " ('key', 2),\n", " ('flowering', 1),\n", " ('pagan', 1),\n", " ('many', 7),\n", " ('against', 1),\n", " ('s', 15),\n", " ('constantine', 2),\n", " ('context', 1),\n", " ('faces', 1),\n", " ('exemplary', 1),\n", " ('pp', 1),\n", " ('outsider', 1),\n", " ('transylvania', 1),\n", " ('capable', 1),\n", " ('olum', 4),\n", " ('much', 1),\n", " ('parents', 1),\n", " ('perplexed', 1),\n", " ('worked', 1),\n", " ('presented', 2),\n", " ('plants', 1),\n", " ('commerce', 1),\n", " ('these', 1),\n", " ('kentucky', 2),\n", " ('will', 1),\n", " ('ongoing', 1),\n", " ('error', 1),\n", " ('voice', 1),\n", " ('huckster', 1),\n", " ('costly', 1),\n", " ('almost', 2),\n", " ('is', 2),\n", " ('hoax', 2),\n", " ('minds', 1),\n", " ('in', 17),\n", " ('if', 2),\n", " ('harsh', 1),\n", " ('things', 1),\n", " ('benign', 1),\n", " ('arguments', 1),\n", " ('peoples', 1),\n", " ('european', 1),\n", " ('primary', 1),\n", " ('student', 1),\n", " ('anticipated', 1),\n", " ('scholars', 1),\n", " ('i', 1),\n", " ('perpetrate', 1),\n", " ('academic', 1),\n", " ('the', 20),\n", " ('spend', 1),\n", " ('valuable', 1),\n", " ('not', 3),\n", " ('aspect', 1),\n", " ('walum', 4),\n", " ('yes', 1),\n", " ('book', 5),\n", " ('collections', 1),\n", " ('botanists', 1),\n", " ('has', 1),\n", " ('around', 2),\n", " ('early', 1),\n", " ('game', 1),\n", " ('press', 1),\n", " ('like', 1),\n", " ('collective', 1),\n", " ('informative', 1),\n", " ('raffish', 1),\n", " ('output', 1),\n", " ('competition', 1),\n", " ('born', 1),\n", " ('describing', 1),\n", " ('for', 7),\n", " ('novelty', 1),\n", " ('boggled', 1),\n", " ('creative', 1),\n", " ('porter', 1),\n", " ('be', 1),\n", " ('scientific', 3),\n", " ('power', 1),\n", " ('vexing', 1),\n", " ('by', 1),\n", " ('on', 4),\n", " ('about', 3),\n", " ('of', 22),\n", " ('purported', 1),\n", " ('wilderness', 1),\n", " ('communication', 1),\n", " ('intellectual', 1),\n", " ('esteem', 1),\n", " ('balanced', 1),\n", " ('lastly', 1),\n", " ('way', 1),\n", " ('was', 6),\n", " ('war', 1),\n", " ('regard', 1),\n", " ('but', 3),\n", " ('administrators', 1),\n", " ('illus', 1),\n", " ('comprehending', 1),\n", " ('he', 4),\n", " ('systematics', 1),\n", " ('problem', 1),\n", " ('recognize', 1),\n", " ('sales', 1),\n", " ('an', 6),\n", " ('as', 8),\n", " ('diverse', 1),\n", " ('at', 3),\n", " ('no', 2),\n", " ('when', 1),\n", " ('other', 1),\n", " ('role', 2),\n", " ('reckless', 1),\n", " ('contemporaries', 1),\n", " ('permitting', 1),\n", " ('detractors', 1),\n", " ('m', 2),\n", " ('energy', 1),\n", " ('suppression', 1),\n", " ('jurisprudence', 1),\n", " ('time', 1)])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corpus.features['wordcounts'].features.items()[0] # Just show data for the first Paper." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``index`` contains our \"vocabulary\":" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 134540 words in the wordcounts featureset\n" ] } ], "source": [ "print 'There are %i words in the wordcounts featureset' % len(corpus.features['wordcounts'].index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the ``feature_distribution()`` method of our ``Corpus`` to look at the distribution of words over time. In the example below I used [MatPlotLib](http://matplotlib.org/) to visualize the distribution." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x121678ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 5))\n", "\n", "plt.bar(corpus.feature_distribution('wordcounts', 'evolutionary')) # <-- The action.\n", "\n", "plt.ylabel('Frequency of the word ``evolutionary`` in this Corpus')\n", "plt.xlabel('Publication Date')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we add the argument ``mode='documentCounts'``, we get the number of documents in which ``'evolutionary'`` occurs." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1272a0b90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 5))\n", "\n", "plt.bar(corpus.feature_distribution('wordcounts', 'evolutionary', mode='documentCounts')) # <-- The action.\n", "\n", "plt.ylabel('Documents containing ``evolutionary``')\n", "plt.xlabel('Publication Date')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we can look how documents themselves are distributed using the ``distribution()`` method." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x12165e450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 5))\n", "\n", "plt.bar(corpus.distribution()) # <-- The action.\n", "\n", "plt.ylabel('Number of Documents')\n", "plt.xlabel('Publication Date')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, putting these together, we can normalize our ``feature_distribution()`` data to get a sense of the relative use of the word ``'evolution'``." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x12745bfd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dates, N_evolution = corpus.feature_distribution('wordcounts', 'evolutionary', mode='documentCounts')\n", "dates, N = corpus.distribution()\n", "normalized_frequency = [f/N[i] for i, f in enumerate(N_evolution)]\n", "plt.figure(figsize=(10, 5))\n", "\n", "plt.bar(dates, normalized_frequency) # <-- The action.\n", "\n", "plt.ylabel('Proportion of documents containing ``evolutionary``')\n", "plt.xlabel('Publication Date')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Topic Modeling with DfR wordcounts\n", "\n", "[Latent Dirichlet Allocation](http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) is a popular approach to discovering latent \"topics\" in large corpora. Many digital humanists use a software package called [MALLET](http://mallet.cs.umass.edu/) to fit LDA to text data. Tethne uses MALLET to fit LDA topic models.\n", "\n", "Before we use LDA, however, we need to do some preprocessing. \"Preprocessing\" refers to anything that we do to filter or transform our ``FeatureSet`` prior to analysis. \n", "\n", "### Pre-processing\n", "\n", "Two important preprocessing steps are:\n", "\n", " 1. Removing \"stopwords\" -- common words like \"the\", \"and\", \"but\", \"for\", that don't yield much insight into the contents of documents.\n", " 2. Removing words that are too common or too rare. These include typos or OCR artifacts.\n", " \n", "We can do both of these by using the ``transform()`` method on our ``FeatureSet``.\n", "\n", "First, we need a stoplist. [NLTK](http://www.nltk.org/) provides a great stoplist." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from nltk.corpus import stopwords\n", "stoplist = stopwords.words()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then need to define what elements to keep, and what elements to discard. We will use a function that will evaluate whether or not a word is in our stoplist. The function should take three arguments:\n", "\n", " ``f`` -- the feature itself (the word)\n", "* ``v`` -- the number of instances of that feature in a specific document\n", "* ``c`` -- the number of instances of that feature in the whole FeatureSet\n", "* ``dc`` -- the number of documents that contain that feature\n", "\n", "This function will be applied to each word in each document. If it returns ``0`` or ``None``, the word will be excluded. Otherwise, it should return a numeric value (in this case, the count for that document).\n", "\n", "In addition to applying the stoplist, we'll also exclude any word that occurs in more than 500 of the documents and less than 3 documents, and is less than 4 characters in length." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def apply_stoplist(f, v, c, dc):\n", " if f in stoplist or dc > 500 or dc < 3 or len(f) < 4:\n", " return None # Discard the element.\n", " return v" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We apply the stoplist using the ``transform()`` method. ``FeatureSet``s are not modified in place; instead, a new ``FeatureSet`` is generated that reflects the specified changes. We'll call the new ``FeatureSet`` ``'wordcounts_filtered'``." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "corpus.features['wordcounts_filtered'] = corpus.features['wordcounts'].transform(apply_stoplist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There should be significantly fewer words in our new ``\"wordcounts_filtered\"`` ``FeatureSet``." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 134540 words in the wordcounts featureset\n", "There are 41035 words in the wordcounts_filtered featureset\n" ] } ], "source": [ "print 'There are %i words in the wordcounts featureset' % len(corpus.features['wordcounts'].index)\n", "print 'There are %i words in the wordcounts_filtered featureset' % len(corpus.features['wordcounts_filtered'].index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The LDA topic model\n", "\n", "Tethne provides a class called ``LDAModel``. You should be able to import it directly from the ``tethne`` package:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tethne import LDAModel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we'll create a new ``LDAModel`` for our ``Corpus``. The ``featureset_name`` parameter tells the ``LDAModel`` which ``FeatureSet`` we want to use. We'll use our filtered wordcounts." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = LDAModel(corpus, featureset_name='wordcounts_filtered')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we'll fit the model. We need to tell MALLET how many topics to fit (the hyperparameter ``Z``), and how many iterations (``max_iter``) to perform. This step may take a little while, depending on the size of your corpus." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "model.fit(Z=50, max_iter=500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can inspect the inferred topics using the model's ``print_topics()`` method. By default, this will print the top ten words for each topic." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Topic\tTop 10 words\n", "0 \tweismann heredity variation inheritance characters vries hereditary johannsen variations transmission\n", "1 \thenderson blood oxygen chemistry vinogradskii chemical hemoglobin carbon liebig barcroft\n", "2 \tfamily father language romanes white letters autobiography children carpenter mental\n", "3 \tfemale women sexual male behavior rats hormones woman control birth\n", "4 \tcells immunology cell antibodies jerne immune antibody metchnikoff ehrlich immunity\n", "5 \tpractices discipline cultural broader debates emerged fields diverse disciplines project\n", "6 \tecology environmental ecological fish conservation management ecologists salmon wildlife fisheries\n", "7 \tdisease cancer health rous diseases clinical illness patients symptoms beaumont\n", "8 \tharvey blood civet renaissance fernel fish spirits medieval circulation seventeenth\n", "9 \tbehavior lorenz agassiz bees instincts girard instinct psychology learning thorpe\n", "10 \tzoology museum brooks hopkins morphology johns marine alexander teaching museums\n", "11 \thuxley darwinism spencer darwinian wilson ruse revolution bastian sociobiology kingsley\n", "12 \tibid notion principle types instance distinct organic proposed phenomena occur\n", "13 \tcell cells division organism haeckel wilson embryology nucleus protozoa morphological\n", "14 \tmayr synthesis simpson population ernst ecology speciation anderson systematics isolation\n", "15 \tmcclintock agricultural corn mexico blakeslee station chase reighard barbara college\n", "16 \tletter report committee meeting institute program january march june december\n", "17 \tmolecular proteins pauling sequences protein sequence zuckerkandl goodman acid amino\n", "18 \tcited referred apparently statement felt edition observed version previously subsequent\n", "19 \tleeuwenhoek needham oyster bonnet ibid hamburger royal spemann embryo eggs\n", "20 \tmitchell membrane cell synthesis protein boyer williams electron energy acid\n", "21 \tclassification genera linnaeus genus candolle characters botany plant temminck botanical\n", "22 \tbirds specimens distribution geographical island naturalists islands regions museum bird\n", "23 \tlamarck cuvier paris buffon french histoire naturelle france daubenton geoffroy\n", "24 \tvirus viruses bacterial phage bacteria bacteriophage delbruck gene cells genetic\n", "25 \tmendel traits maupertuis unger kluwer hybrids publishers hybrid dordrecht editor\n", "26 \tmolecular biochemistry biochemical sanger institute monod chemistry watson rashevsky protein\n", "27 \thooker royal britain victorian joseph college barry professional botany edinburgh\n", "28 \tcreation religious theology design religion laws earth belief organic doctrine\n", "29 \tbateson galton fisher pearson statistical heredity variation hogben mendelian weldon\n", "30 \tmorgan breeding chromosomes drosophila chromosome heredity mendelian breeders inheritance genes\n", "31 \tfigure experiment experimentation regeneration techniques images objects visual technique tissue\n", "32 \tgoldschmidt physiological chemical cowdry buchner internal cells aging riddle extracts\n", "33 \torganism physics systems phenomena laws properties mathematical mechanism conceptual mechanistic\n", "34 \twallace lyell letter letters geology geological beagle voyage hooker gray\n", "35 \tkammerer wells freud morris humanity politics socialism acquired ethics socialist\n", "36 \tbiography essays chapters readers reader historian narrative scholars illus bookshelf\n", "37 \tcreighton azara atomic japanese japan radiation spanish spain energy abcc\n", "38 \tgerman haeckel germany berlin kuhn wilhelm vogt uber geschichte naef\n", "39 \tfossil paleontology gould record agassiz morphology gregory fossils bronn morphological\n", "40 \tgenetic muller gene population genes wright dobzhansky populations mutations mutation\n", "41 \towen anatomy goethe knox baer grant comparative lectures morphology carpenter\n", "42 \tquoted truth thing hope controversy matters criticism attack famous prove\n", "43 \teugenics race racial eugenic heredity movement nazi dunn races anthropology\n", "44 \tvariation notebooks varieties notebook malthus divergence economy lawrence principle transmutation\n", "45 \tnervous nerve wolff haller brain force spinal nerves muscle physiological\n", "46 \teconomic local government institutions industry agricultural resources education private practical\n", "47 \tplant south africa trees african herbis roux theophrastus dioscorides leaves\n", "48 \taristotle heart galen blood male movement semen female pneuma soul\n", "49 \tsoviet lysenko haldane russian darlington party union moscow academy geneticists\n" ] } ], "source": [ "model.print_topics()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at the representation of a topic over time using the ``topic_over_time()`` method. In the example below we'll print the first five of the topics on the same plot." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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PPvggb7zxBkuWLIn6ewuXMnIiIiJxSuMHRKTfRJhF62tVVVXk5eUF7xtjyM3NpbKyMvhY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\nJf4Sbp9ya7+/R18RNsGYn4zBjKgwQuE8tqdDSkljvJEdzTvY0byD7U3b2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"text/plain": [ "<matplotlib.figure.Figure at 0x1277b2bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15, 5))\n", "for k in xrange(5): # Generates numbers k in [0, 4].\n", " x, y = model.topic_over_time(k) # Gets topic number k.\n", " plt.plot(x, y, label='topic {0}'.format(k), lw=2, alpha=0.7)\n", "plt.legend(loc='best')\n", "plt.show() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating networks from topic models\n", "\n", "The ``features`` module in the ``tethne.networks`` subpackage contains some useful methods for visualizing topic models as networks. You can import it just like the ``authors`` or ``papers`` modules." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tethne.networks import topics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``terms`` function generates a network of words connected on the basis of shared affinity with a topic. If two words i and j are both associated with a topic z with $\\Phi(i\|z) >= 0.01$ and $\\Phi(j\|z) >= 0.01$, then an edge is drawn between them. " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "termGraph = topics.terms(model, threshold=0.01)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(280, 1105)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "termGraph.order(), termGraph.size()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "termGraph.name = ''" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tethne.writers.graph import to_graphml\n", "to_graphml(termGraph, '/Users/erickpeirson/Desktop/topic_terms.graphml')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](images/lda2.png)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "topicCoupling = topics.topic_coupling(model, threshold=0.2)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1059 nodes and 17707 edges\n" ] } ], "source": [ "print '%i nodes and %i edges' % (topicCoupling.order(), topicCoupling.size())" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "to_graphml(topicCoupling, '/Users/erickpeirson/Desktop/lda_topicCoupling.graphml')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](images/lda_topicCoupling.png)" ] }, { "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-3.0
milroy/Spark-Meetup	exercises/03_aggregation.ipynb	1	7267	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simple Aggregation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "\n", "data = np.arange(1000).reshape(100,10)\n", "print data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pandas" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "pand_tmp = pd.DataFrame(data, \n", " columns=['x{0}'.format(i) for i in range(data.shape[1])])\n", "pand_tmp.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the row sum?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pand_tmp.sum(axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Column sum?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pand_tmp.sum(axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pand_tmp.to_csv('numbers.csv', index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spark" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import findspark\n", "import os\n", "findspark.init() # you need that before import pyspark.\n", "\n", "import pyspark\n", "sc = pyspark.SparkContext('local[4]', 'pyspark')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lines = sc.textFile('numbers.csv', 18)\n", "for l in lines.take(3):\n", " print l\n", "\n", "lines.take(3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type(lines.take(1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do we skip the header? How about using find()? What is Boolean value for true with find()?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lines = lines.filter(lambda x: x.find('x') != 0)\n", "for l in lines.take(2):\n", " print l" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = lines.map(lambda x: x.split(','))\n", "data.take(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Row Sum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cast to integer and sum!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def row_sum(x):\n", " int_x = map(lambda x: int(x), x)\n", " return sum(int_x)\n", "\n", "data_row_sum = data.map(row_sum)\n", "\n", "print data_row_sum.collect()\n", "\n", "print data_row_sum.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Column Sum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### This one's a bit trickier, and portends ill for large, complex data sets (like example 5)...\n", "Let's enumerate the list comprising each RDD \"line\" such that each value is indexed by the corresponding column number." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def col_key(x):\n", " for i, value in enumerate(x):\n", " yield (i, int(value))\n", "\n", "tmp = data.flatMap(col_key)\n", "tmp.take(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Notice how flatMap works here: the generator is returned per partition, meaning that the first element value of each tuple cycles." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tmp.take(3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tmp = tmp.groupByKey()\n", "for i in tmp.take(2):\n", " print i, type(i)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_col_sum = tmp.map(lambda x: sum(x[1]))\n", "for i in data_col_sum.take(2):\n", " print i" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print data_col_sum.collect()\n", "print data_col_sum.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Column sum with Spark.sql.dataframe" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.sql import SQLContext\n", "sqlContext = SQLContext(sc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sc" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pyspark_df = sqlContext.createDataFrame(pand_tmp)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pyspark_df.take(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "groupBy() without arguments groups by all columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in pyspark_df.columns:\n", " print pyspark_df.groupBy().sum(i).collect()" ] }, { "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 }	mit
pauliacomi/pyGAPS	docs/examples/parsing.ipynb	1	62348	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parsing examples\n", "\n", "Some examples on parsing to and from supported formats. More info about all\n", "parsing methods can be found in the [manual section](../manual/parsing.rst).\n", "\n", "## Declare paths\n", "\n", "First, let's do all the necessary imports and generate the paths that we'll use\n", "for file import and export." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import pygaps.parsing as pgp\n", "\n", "# Get directory paths\n", "base_path = Path.cwd() / 'data' / 'parsing'\n", "\n", "# Find files\n", "aif_file_paths = list((base_path / 'aif').rglob('.aif'))\n", "json_file_paths = list((base_path / 'json').rglob('.json'))\n", "xl_file_paths = list((base_path / 'excel').rglob('.xls'))\n", "csv_file_paths = list((base_path / 'csv').rglob('.csv'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Manufacturer import\n", "\n", "Many report files from various adsorption device manufacturers can be imported\n", "directly using pyGAPS. Here are some examples." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: 'temperature_unit' was not specified , assumed as 'K'\n", "WARNING: 'temperature_unit' was not specified , assumed as 'K'\n" ] } ], "source": [ "cfld = base_path / \"commercial\"\n", "micromeritics = pgp.isotherm_from_commercial(cfld / \"mic\" / \"Sample_A.xls\", 'mic', 'xl')\n", "belsorp_dat = pgp.isotherm_from_commercial(cfld / \"bel\" / \"BF010_DUT-13_CH4_111K_run2.DAT\", 'bel', 'dat')\n", "belsorp_xl = pgp.isotherm_from_commercial(cfld / \"bel\" / \"Sample_C.xls\", 'bel', 'xl')\n", "threeP_xl = pgp.isotherm_from_commercial(cfld / \"3p\" / \"AC_ref_filter_Ar_87K_run 3_rep.xlsx\", '3p', 'xl')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## AIF Parsing\n", "### AIF Import\n", "\n", "Adsorption information files are fully supported in pyGAPS, both for import and\n", "exports. Isotherms can be imported from an `.aif` as:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Material: DMOF\n", "Adsorbate: ethane\n", "Temperature: 298.15K\n", "Units: \n", "\tUptake in: cm3(STP)/g\n", "\tPressure in: kPa\n", "Other properties: \n", "\tuser: single gas\n", "\tdate: 2019-08-19T00:00:00\n", "\tinstrument: BEL VC-05\n", "\tmaterial_mass: 0.817\n", "\tmaterial_batch: [Zn2(tm-bdc)2(dabco)]\n", "\t_units_mass: g\n", "\n" ] } ], "source": [ "# Import all\n", "isotherms = [pgp.isotherm_from_aif(path) for path in aif_file_paths]\n", "\n", "# Display an example file\n", "print(isotherms[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### AIF Export\n", "\n", "Similarly, an isotherm can be exported as an AIF file or a string, depending on\n", "whether a path is passed. For this purpose use either the module\n", "`pygaps.isotherm_to_aif()` function or the convenience class function\n", "`to_aif()`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# module function\n", "for isotherm in isotherms:\n", " filename = f'{isotherm.material} {isotherm.adsorbate} {isotherm.temperature}.aif'\n", " pgp.isotherm_to_aif(isotherm, base_path / 'aif' / filename)\n", "\n", "# save to file with convenience function\n", "isotherms[0].to_aif('isotherm.aif')\n", "\n", "# string\n", "isotherm_string = isotherms[0].to_aif()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## JSON Parsing\n", "### JSON Import\n", "\n", "Isotherms can be imported either from a json file or from a json string. The\n", "same function is used in both cases." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: 'temperature_unit' was not specified , assumed as 'K'\n", "WARNING: 'temperature_unit' was not specified , assumed as 'K'\n", "Material: HKUST-1(Cu)\n", "Adsorbate: carbon dioxide\n", "Temperature: 303.0K\n", "Units: \n", "\tUptake in: mmol/g\n", "\tPressure in: bar\n", "Other properties: \n", "\tcomment: None\n", "\tdate: 2010-05-21 00:00:00\n", "\tiso_type: Calorimetrie\n", "\tlab: MADIREL\n", "\tinstrument: CV\n", "\tmaterial_batch: Test\n", "\tproject: None\n", "\tactivation_temperature: 150.0\n", "\tuser: ADW\n", "\n" ] } ], "source": [ "# Import them\n", "isotherms = [pgp.isotherm_from_json(path) for path in json_file_paths]\n", "\n", "# Display an example file\n", "print(isotherms[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### JSON Export\n", "\n", "Exporting to JSON can be done to a file or a string, depending on whether a path\n", "is passed. For this purpose use either the module `pygaps.isotherm_to_json()`\n", "function or the convenience class function `to_json()`." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# module function\n", "for isotherm in isotherms:\n", " filename = f'{isotherm.material} {isotherm.adsorbate} {isotherm.temperature}.json'\n", " pgp.isotherm_to_json(isotherm, base_path / 'json' / filename)\n", "\n", "# save to file with convenience function\n", "isotherms[0].to_json('isotherm.json')\n", "\n", "# string\n", "isotherm_string = isotherms[0].to_json()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Excel Parsing\n", "\n", "Excel does not have to be installed on the system in use.\n", "\n", "### Excel Import" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The file version is None while the parser uses version 3.0. Strange things might happen, so double check your data.\n", "WARNING: 'temperature_unit' was not specified , assumed as 'K'\n", "The file version is None while the parser uses version 3.0. Strange things might happen, so double check your data.\n", "WARNING: 'temperature_unit' was not specified , assumed as 'K'\n", "Material: HKUST-1(Cu)\n", "Adsorbate: carbon dioxide\n", "Temperature: 303.0K\n", "Units: \n", "\tUptake in: mmol/g\n", "\tPressure in: bar\n", "Other properties: \n", "\tdate: 2010-05-21 00:00:00\n", "\tlab: MADIREL\n", "\tinstrument: CV\n", "\tproject: None\n", "\tactivation_temperature: 150.0\n", "\tuser: ADW\n", "\tcomment: None\n", "\tiso_type: Calorimetrie\n", "\tmaterial_batch: Test\n", "\n" ] } ], "source": [ "# Import them\n", "isotherms = [pgp.isotherm_from_xl(path) for path in xl_file_paths]\n", "\n", "# Display an example file\n", "print(isotherms[1])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n", "<span style=\"font-weight: bold\">(</span>\n", " <span style=\"font-weight: bold\"><</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">AxesSubplot:</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">xlabel</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Pressure [$bar$]'</span><span style=\"color: #000000; text-decoration-color: #000000\">, </span><span style=\"color: #808000; text-decoration-color: #808000\">ylabel</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'Loading [$mmol\\\\/g^{-1}$]'</span><span style=\"font-weight: bold\">></span>,\n", " <span style=\"font-weight: bold\"><</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">AxesSubplot:</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">ylabel</span><span style=\"color: #000000; text-decoration-color: #000000\">=</span><span style=\"color: #008000; text-decoration-color: #008000\">'enthalpy(kJ/mol)'</span><span style=\"font-weight: bold\">></span>\n", "<span style=\"font-weight: bold\">)</span>\n", "</pre>\n" ], "text/plain": [ "\n", "\u001b[1m(\u001b[0m\n", " \u001b[1m<\u001b[0m\u001b[1;95mAxesSubplot:\u001b[0m\u001b[1;33mxlabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Pressure \u001b[0m\u001b[32m[\u001b[0m\u001b[32m$bar$\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[39m, \u001b[0m\u001b[33mylabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'Loading \u001b[0m\u001b[32m[\u001b[0m\u001b[32m$mmol\\\\/g^\u001b[0m\u001b[32m{\u001b[0m\u001b[32m-1\u001b[0m\u001b[32m}\u001b[0m\u001b[32m$\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m\u001b[1m>\u001b[0m,\n", " \u001b[1m<\u001b[0m\u001b[1;95mAxesSubplot:\u001b[0m\u001b[1;33mylabel\u001b[0m\u001b[39m=\u001b[0m\u001b[32m'enthalpy\u001b[0m\u001b[32m(\u001b[0m\u001b[32mkJ/mol\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[1m>\u001b[0m\n", "\u001b[1m)\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "isotherms[1].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Excel Export" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Export each isotherm in turn\n", "for isotherm in isotherms:\n", " filename = ' '.join([str(isotherm.material), str(isotherm.adsorbate), str(isotherm.temperature)]) + '.xls'\n", " pgp.isotherm_to_xl(isotherm, base_path / 'excel' / filename)\n", "\n", "# save to file with convenience function\n", "isotherms[0].to_xl('isotherm.xls')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CSV Parsing\n", "### CSV Import\n", "\n", "\n", "Like JSON, isotherms can be imported either from a CSV file or from a CSV string. The same function is used in both cases." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING: 'temperature_unit' was not specified , assumed as 'K'\n", "WARNING: 'temperature_unit' was not specified , assumed as 'K'\n", "Material: HKUST-1(Cu)\n", "Adsorbate: carbon dioxide\n", "Temperature: 303.0K\n", "Units: \n", "\tUptake in: mmol/g\n", "\tPressure in: bar\n", "Other properties: \n", "\tmaterial_batch: Test\n", "\tiso_type: Calorimetrie\n", "\tuser: ADW\n", "\tmachine: CV\n", "\tdate: 21/05/2010 00:00\n", "\tactivation_temperature: 150.0\n", "\tlab: MADIREL\n", "\n" ] } ], "source": [ "# Import them\n", "isotherms = [pgp.isotherm_from_csv(path) for path in csv_file_paths]\n", "\n", "# Display an example file\n", "print(isotherms[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CSV Export" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Export each isotherm in turn\n", "for isotherm in isotherms:\n", " filename = ' '.join([str(isotherm.material), str(isotherm.adsorbate), str(isotherm.temperature)]) + '.csv'\n", " pgp.isotherm_to_csv(isotherm, base_path / 'csv' / filename)\n", "\n", "# save to file with convenience function\n", "isotherms[0].to_csv('isotherm.csv')\n", "\n", "# string representation\n", "isotherm_string = isotherms[0].to_csv()" ] } ], "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
csampez/analisis-numerico-computo-cientifico	MNO/proyecto_final/MNO_2017/proyectos/equipos/equipo_6/avance_22_05_2017/code/Clase_SVD_Imagen.ipynb	1	757327	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SVD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SVD a una imagen para validar resultados con CUDA __cusolverDnDgesvd__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Equipo_6 \n", "\n", "Integrantes:\n", "\n", "* Ricardo Lastra\n", "\n", "* Adrián Vázquez" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Antecedentes:\n", "\n", "La factorizacion $SVD$ es uno de los modelos de factorizaciones de matrices mas usados hoy en dia por muchas paqueterias computacionales, esta nos ayuda a hacer aproximaciones a matrices de una forma muy eficiente.\n", "\n", "Usando el metodo thin $SVD$ visto en clase, el cual dice que necesitamos encontrar la matriz $Vi$ ortogonal de $nxn$ y una matriz $Ui$ con columnas ortonormales de $mxn$ tales que $Ui^T A-V=B$ sea Bidiagonal.\n", "\n", "Posteriormente en una necesitamos multiplicar $U=U_1U_2$, $V=V_1V_2$ para obtener $A=U\\Sigma V^T$ y asi obtener valores singulares en la diagonal de $\\Sigma$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Desarrollo del programa:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#SE IMPORTAN LIBRERIAS PARA GRAFICAR, PARA COMPUTO DE MATRICES Y PARA LEER IMAGENES\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from PIL import Image" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x8100ba8>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Jvtsct9udw27rtV2zna199oV5INs9fGRXFDVzXVfqyNfrdZk4nkhEiY2JLKx9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o0NQKpr77//fqRSKTQ1NWF1dRXRaFQSdUCxmtLpdGJqago9PT1wu904deoUIpEIOjs7JTm3a9cu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"text/plain": [ "<matplotlib.figure.Figure at 0x4dbec18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#SE LEE UNA IMAGEN, SE LEE Y SE GUARDA COMO BLANCO Y NEGRO \n", "img = Image.open('GTR.jpg')\n", "#img.rotate(45).show() #Para rotar la imagen cierto no. de grados, eje 45grados\n", "imggray = img.convert('LA')\n", "plt.figure(figsize=(6, 6))\n", "plt.imshow(imggray)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x8601358>" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WT382M+12+t67eGQP9mAP9uAXHHaFRE/opXaxoBltWOT+KysrOHv2LNrtNhYXF8UcQM/0\n+vo6AOAnP/kJfvrTnyIajeKDH/wgxsfHuzz/1BDq9fpNdkKbnfQ/GmzjME0aNMnQT2FGHWjfBKVw\nwk7sx+a7elwsDsfiYbq4WDwelyJr6XS6K758fX0djuNIkTedMKX7Zz86JNMsk7Ad6CWp97Me+m+b\nuWa7piTb2Gxz2omJiiY+r/PGz7w0JNt8E4kEHMeRwn/ZbBaBQEB8a6VSSaJltNZM6V6bcrzm1e/5\n63c9evlfNG7xf6/17wdME5DXPOmvMEPKzfFtp29glxN6vQjlcrkrY1JvgN/vx9jYGE6ePIlKpYJS\nqYTFxUVks1mJ6Dh8+DAmJyexuLiIRCIh0TkMh9MhimYSk81+txvARtxMtdKmZjJscrsqpG6DfZoJ\nPa7rypozdJOfx2IxFItF+Hw+iXoKBoPCtB3HEV+K3+8X5/lWY7M5rPV4d2LGMd/rV10337HZ+/sB\n/a7NDt2LoXntqRkT7mWPNz/zOgf6+aWlJUxMTGBychLAZvBDqVSSMVOI0iG8uuaN67pbRt7YbPzm\nvPuFXs/2MoHuhGnbCLStDYZ8swrn7TJD7SpC3wtob6d3vlaryXckFNevX0c0GkU4HBY7PonOsWPH\ncPLkSYTDYXEUsZRuo9HoKs9qOyT/WcR9K2nRJCQ6iUXPw8ymBG6uq7+TMen2CKurqxgYGOgKb6Wj\nnMyVETnNZlMiNSKRiMQg53I58a/Y+jDBi6n1CyYx3ul+2xyc5nf9jsdrTl42W5PA69+m3dvmzzH/\n9pqb+b/jOBgbGxP/GLBZmpeZsa7rIp1Oi1au29IaaD+wleB1K2e1H+a3k7682rHtI0NQNS7dSvY2\nsIsIfSgU6koYIRApWOOc9ckpCQQCAWQyGQwODqJWq+Hq1asSwlWtVnHw4EFpi8hUqVS6GIaueWP2\n67quJO5wsSmRMNuV0nGtVpPYb2Bzc5koYSaAEJgQoU0qTOjS9bG12ttqtdBoNCQ+nWPmuDl2rRaz\nrVgsJjXqdYINo5l0fXKOJRgMSrloJmUBkLrZfIbJU5w/9ykYDHbVNNeXSPB5riUT4xg5RGceCb/r\nupIQR82M9wDow8911VFIXF+9N8xgNhmp3nMyID7XbDYlqcuM2LIRVZocCTphhuuk95tz4P4Sx7jn\nWgNlTXZdJ56SIfGLEUxayzIJpXaos20m7tikS64PxxWLxaTGfq1WQ6VSkTsf6IDn/RFsKxgMyt4x\nCYpz0VVog8Gg4Bz3k/hjS1jkXhKndNi0TjzT91Hos8LnqeHTmqC1EeIP6YBZ4oJCjqlJ9TKbAZ06\n+roGlxn9o/OI+oVdQ+htEqoG2q1o5yWEw2GkUikkEgmsr6+LxM6NHBoaArAZh6+lXrN/cyyAPYrh\nVlSq7b5H5CGyaGQk4zKR4HYkcfQLei14ALdK/99KCtqOuYUHmmvhpQGZ66KJnFcEgzk3/fmtgJf2\nsBO8utOapzbd2IDfmWdTMx1q2F4MwzaP/wgwTZdec7RpVl6ms37Ono3BAjdH/HmZ5XYCu4LQawJv\n2t/5m9yXag2lu0AggMnJSUxMTODQoUNdtnxmVwKbiGde32YbixfHNQ+7+Yxpy9Sf9bsOfMfsrxd4\nEdFejHOnYB4IrhcJ/FbhqVuth7n3ei3Mtd7K3GKuCyUvm+nJa61uxRzkBTbJ0wufbOAlXd8O6Ne2\nbzJKajbU4NrtNkqlEiqVivhdKKjczvF6gZeQob8zLzm6VbDRB/5tCmW3YhrciY9gVxB6wE5gtTRh\nMgGqpY7jYHR0VOK1NROgnRDYJPRst5+xeIGNEXglV3CM5mG2SXNefZltaFXVtHf2GrMGr4O7XdAm\nLnMPzSxM9qXrxmjwkmDMNTIjiXSfZj/8TmcCm1oRzUP9EPmtJD8bozXXwDY/LwbXqy9zjDvZw15S\no4m/XozU5/OJCYqgpVRtbuwVSXI7ib/X+bXto85Y3QoHbWuxXQ3a3FsbDeiFb1vhhg12FaHnb9ti\n835LXf4VgNTPoI2vV/u6ng3B3LR+pTbbAbNJDrbPbO9sdah6gW6DEpbXePQ7O5FWTVXe3C9NXPVY\ntOlpq7A1L0bYa/w2TUr/rc19/RJHL7zoR0u6naYeLQl7CSs7lfRsfZkMsx/Qew5sBk9EIhFEo1FE\no1GJourV9+2EXlp7v/MyyxZonPbqqx9B0vabYNvrW12bvYSpPdiDPdiDX3DYNRI94G171JIgnyMw\nGoMp1vyeMbt8Vkcu9LKh8/9e4UymScKUcE07nKme95q72dZ2nbF6XLb5mc/sRFLwUuP7lY57gZeU\no9eVEVPa9mk6Y/V3juNItAyjKHR0hU0zMCVb25hsn5vP6+gQ2xp4mXd6radp3rwV6FcztfWjzXU6\nQgZA12ftdhvValXOVS8N7nZBP1I1n9kqCsb27lbjNfdI46824bL/fmE749Swawh9Lzugz9e5lqzd\nbottXpsnotEo6vU6SqWS2Ar1YQYgyTm22s8a+TQymmMxia7XgehlGtjKBnurCO9FtMw+dkogvJBc\nr4m+ZlGbyTTxNccK3Fxdsdd4bWMwmaQemxmSZmPCvYgsn9nKRt9rbHo8NtPVdhhvL1vydnFoK5Me\ni2zZMqlJ0KPRaNf3FKyYz8KwRE3wdDu3w+xkm5cXg+LYvZgPwfQBcazbNXua+61/bDhrmm1s728H\ndmy6cRxnxnGc7zqOc9ZxnLccx/lf3v38/3AcZ95xnDPv/nz8FvqQH2aLMSaWh5eFrxhvymf4PReF\n8dZb9WX+7XWgesFOua6tfV2NUidDmZ/ZGEyvg+/FgG5lzNo+S+2DDjjGTpMRmH17HUjNFGzPea2L\n/t88QLYf21x2Sji3AhO3tkPsbdLw7YZ+JXpgUxoulUqoVqvyU6vVRJvhOTSjd/i+V793Emz7b8MH\nLY1rvLJJ6f32p5+/E/tng1uR6JsA/jfXdX/qOE4SwGnHcV5497v/x3Xd/7Pfhlx38xYg8/YcLiSR\nhbezMHkiEolIogzThilNMDkDQFc1xEAg0HULjVYzmRijuS25vqkh8PYfbhYjOHREEP/nM2ZmoE29\n0xxcaxHtdlsSmhqNBqLRKGq1miRm6Oc4NkpTGqE4JmpJTFjRSSTAZm1sJrPpNsxbwBhix9u4qtUq\n4vF41x5TM6O2ZdNq9LprR5i+HEZH0tAUQ21PEw5GXXFfmNRCgYB9m4xVa496n6rVqiSbMSiAc9Y4\nq3GA/WtcdpzupCDXdeXCer3vmtFQGiZu6MACLexwr3Q9c5qtdLIUCVcgEBD80cEMOknIthY6lJYR\nbVpjDoVCaDabkvyjb37SBI/h0gBEGCPeaCbPsTNxknvNM8G2ODaWwdaamHlGiD86yY1rwPPB9dNj\n0HPnXjJ5jiHgOnNf4xmT+DgPrSlxj5hYpvFdC1FkoNtJmtoxoXdddwHAwrt/FxzHOQdgaqftvdvO\nrbzeBTbpwFSDbmUMNiK1lTd+J+D1vu7fJpGYkr5WEW3j34nmYhtjP5JNv9Dvs16Skhl+qiNDTAHC\nJr2bWp7pA7A9Y+6FTu+/XXM3NTezPhM/325fNs3mdu5nP+/1M26+a2prWnDrpRnpvTMZqs2ktxVs\npfn10h77XV/+1oLYduC2RN04jnMAwHsB/Pjdj/5nx3FedxznbxzHGfB453OO47zmOM5rrHhnW4Tt\nIpLX81sdHC9i4TUGL1NPP+r+dg/8VmMwEclG5HVbBJtZxNaPqar2+tG3dnmZmLYbd9xrbcz/bf2y\ndIQmvqZZx9w/s+ib/sy2B+a+m2vaa317zc8cXy+GbDKpfta7F155MY5+idVWYbR8xqvNrfDRfE7j\nvo2A2+bQ67z2yyxtWuFWe97rs144Qg3OvIFqK7hlQu84TgLA1wH8r67r5gH8vwAOATiBjsT/f9ne\nc133L13XfZ/ruu/T0TLvtrkje50p6XhJ9frvfhlDPwfC7M8LGXcC5rv9MBTb+7Z2gP7v2zQJvilB\n3wno96AAN+97P4TWJGZ6bbXJzibRm2CL/tkJXm8lmNichLZ9MdfBqz0bQeZvU4L00n7MaDSbTX4r\nHOvlk9kOTfAKmDDbNumBju7bLnjhnleftj0xx2N+5tXXVnBLUTeO4wTRIfJfcV33G+8Oakl9/1cA\nnuujHQDoCkUjuO7tC73SbfVL4DVodf92jOV2ge3A9VLjvSTF7TC+ncB22/A6BPpzrwgpG/G2haP2\nOjD6OxvRsh1sbW/1enc7DNVr72hLNiNdtA3ei2iYY7ExJe0rshEoPUcvwugVKOC1rzbfFUHP08u/\nwz3WJjpzzKb0b9NWbet3O8BL6OhHkLkVIQ+4BULvdHr5awDnXNf9v9XnE27Hfg8AvwHgzX7asxEf\n7bzqtw0+32shtpJ2enFc23u9iOR2pNFe4HXg+2WEJtJvd1zm2prZt/qQ2frod023AzycGjShMImt\nF6Mnsep3LW3EEsBNpRRMDWE7895qL/TZ0M+aBNrWVi8Gqome+YwmyOb8eo2ftvN+iL159ncKtjnY\nxuX1zk5w1DyP/TD3rST423VWbkWifz+A/wHAG47jnHn3sz8B8F8cxzkBwAUwC+B/vKUR7sEe7MEe\n7MEtwa1E3bwMwCb+fOsW2rzt6pKtfdMubZNItpIoTInJ62/z+X6lRoKWDrw0EbNvM0TTdbvD4Wz2\nXd1mrznbzEKmTft22uq3kkpNldtr/F62YlMD6WVmML83tQMdpruddfUCm5TrlVDW632NF73Mjl7x\n4cQlL+2pnzn0O/9+zGh6Hub+mLHuXmMyNTqvPvsZt3mubWfEpv33g29e67Bdc/auyIy9XTb4XnCr\nDKQXodlK3byVvvs9xPpvGwOwvd8Psm93jHdiL/sxY5hzNRmrjSDY2jaf83KuaTBt/vpZ9t3Llr2T\nOev+NCHeLiGwzd+0lTOe3obz2w0b3cqc0u+YbYKKbe8JXiY77Tw38Xg7a9hrDts129na51gYP+84\njuRq9AO7oqhZu92WOvK81Nt1XbmRiBIbLy5g7Wt9UwzbcRxHbk/S0h7b5XtMMum1STptmzV1QqGQ\n3GDFGjt0grEd8zPtxLU5vvg/E1va7bbcsKQlLErUfB7oJHaEw2GEw+GuW3F0eKDpIORnNhtyNBqV\n/4PBoCSCuK5rRSw9Nsdxuu681I51JimZmgVDHxnyyL017ZxmWJnrbjokdaKO3jtdLkPjgFkfh+vF\nseskJ42jTPohjnFNiGM2O7Qm+DrRh4lvOuHHRlD138SjUCiEeDyOeDwuiWrm/H0+n+wf58L1121p\nPOHz/FyX+WbyE2/24uXvPAvcDzOZiXfBsk2upY0Qc3zlcrlr/NSU9A1i3A8mM+nzwN/6zOg90gmG\nWhvUkjdpCdvn3ya+EB+Y4MSkLuKIplP6ljzitKYn/Izj4Fh1qHCpVJLEsK3u19WwKyR6AHLVmM6M\n1QfEBJP73qpq6PXMrWoCNqLaCzT37lei0M/rDEotdZn1fMx3df82yW07YCNU+vNe4zdVdPN7/tZj\n22qPbJK5jq4gc9UMyKtdm2NVS1skItq8wGsY+Y5m/CbjJjHcykSma7xzPmaBNp29SUbJzG/OV79j\n7rWWdnWtIF0YTke56Hlste9eeLeVdGyuxVaf6/HrfvkcBQB+pgvQmdE7WhvZKv7fBHNdbBFJ/Ftn\nzZr9MbOcmla/sCsIPRGxVCpZywzYatRoIuCllnn1BWyWK+iFoASTUGyH+NlUQls/5oZvB/p5vteh\nsIXSadBEp5++SEhs0q1tXF6MTb9jxmh7EUCveZrPmn3osXG+pkbCchOO40g5BUqzbKtWq8maUuIy\nibAmPl5hg3oNefiphWqia2OCNkbptW+mxqnb4HeUZG2ho/o98/xxHWzf69wEcy/0//q3Zjw6Ac4k\nnrax2HBRM0DNxPiO1gjMNTXb1t/bcF336XUueJc1y03wjo1gMIhoNAoAWFpakuzY7ZgCdwWh5wL4\nfJ3bobhoVOV0PRO9YPp9/rYhm4btEGlbe9sFjfBbSaFeyLAVeDkUTRNILyJojoO/vQjoVuPxsqHa\nmKZX2zaGo9/RhMRrPTURs0mNOh69l+TpOB3VnbVkdB0YjouXvnNsmpjp9rk3ur6SXh8Ackua6Txl\nbRothXsaSYI6AAAgAElEQVQxSJMJsC1t49VzNgmHHre5jxqfvfbPixD1i3P6eVtWsna87gRMAcd2\nPjSh1+ugtTjiEMdCnNBnvRfx5+c0B/ESehJzvU+6kuhW13Zq2BWE3rTxlkol+VxfLAxsLn4/dm8b\naCLaS8o0JQ3z+37APEQ2aWUrZOgHbM5AE2xSrCaYmsH0kv76AXNuuk/gZuLVS5L36n+7zMerfY1P\n/PHyEfA77VvQd9DafAWcr02r0+YTElS+r6V3AF12eK1tmBKsJjrmumkCofFO2+u5Nr0imfQ69DJh\n2KRXL+jF/DWuUjviumrHsfmuDT80zrP4HPdHm3Ho8+qHiWgmQU3DK7rJNj6uSzgcRrPZRKFQQDKZ\nRDQaFdwgTdRrsR1asSsIPZG8VCpJDWsAUuWtXC53Pd8PUpmSqEnENOG1HUKvvrYDJpL0g/Q2yWIr\n0M97XaRhqrWmdtFLOuulhfQak27PZGRa6rGtiylJaaJiEhdTgga67/D1snfr5+j00lKs+ZxJoPW8\nAIg5R8/JJjBo04s2R9AZrOeoCZm5z7p/07zmuu5NwpG5/jQL6LnYpGbTgcu/yexM7UqD7ncrm7K5\nP+xP+xJMIUCblPTfpkNd/zZNX7aoG3MN9G+9v+Z9C15WB3Oeps8AAIrFIuLxOCKRiDhpC4UCUqmU\njJdBJtulEbsi6mYP9mAP9mAP7hzsCome0gzVVV3n2iY9aY7qZR4wv9PQS7K2JfyYHHQ7phtTItUS\nlW5Lj327WoRtzl72QABdTmj9vE2bMdvrZ+5awvZSfb3mq9dKS60aD8xbxraScLxMAfyOkrQO02Wf\n7KPd7oS8UsvUNmObpGdK/9o5yzBBbS4wpdVoNCrj1KYFhqnqCCHdp7apm9qIzVyj19FcI9Pc6GVy\nsK29Kd1SMzGDIPQ+2zQuPX62yxBbHUJp9kkcMvdI336mtSfiAX0jer21dqEjjfQaEMw1M9fJNkf+\nppOVvqBKpSIXuPCZfD4vTtvtSPW7gtBXKhWsra2h0Wggl8vh6tWrADoLEIlEMDIyIpcRcJN6Eahe\n4EVEe9n1dgo2AmZ+14u49gs2xmRDLnM8pkrfS33dzti02cYkHvpv05HK9rXPxhaxQVOFuZe2sZk2\nXNs7juN0xYNrQsD+a7Ua8vm8XFrDSyhopwc2L6ggITDt7FpgCYVCXRda0DxB4lMsFmXsJq4Hg0HE\nYjFpUxMqvV76chWaPrSZyLxDuReebMUsga19Lxq89s52Ds3IHX2RjhYMGcPOtdQ+DdMkqBkFmbHO\n/XAcRy7d0eYxrps255jmm16Cmkno9TwHBgbgui42NjYQCASQTCYBdPBqaalTKzKXy6FcLovQ0S/s\nCkKfzWbxxhtvoFarodFo4Ny5cwCA+fl5PP7445iamsKDDz4oC2omSdH+pzk0N1NLPnrDg8GgLFit\nVpNbgszQJX2Tj5aIiUi0c2r7qD74RCZyajMhyHU7IXnxeLwrPrZSqXRFZnBM7XZbbu/RN96wLdNG\nyN+BQACVSgUA5H5P9kVptVar3eQ4tIWg2iQT09YZDAZRLBZFQkokEqhUKrI3+lYe7qWZIMNnKUVr\n6ZprTFyIRCLI5/MyxkgkgkqlIg4ursXGxgZisRiKxaK0Va1WEQwGUSgUZG/y+TzC4bDgVKVSkVDf\neDyOcrmMWCyGQqHQJZE1Gg0MDAyg2WyiVquhXC4jmUwim81iZGRE1iyZTKJWq8F1XeRyOQAdZpJK\npWTdOW465Yh34XC4a4+IW9pWrYkA+zcjhFqtFsrlcteNTrVaDRsbG7jrrruEaPIGJj6XzWaRy+WQ\nSCSQyWRQq9UQCoW6bkVi9Bz3LRaL3aThAt1RT3reDCfUz+irRDWD08+SIBOXucc674BnJhgMYmNj\nQ9ZQj4vnrFQq4cqVKzhw4ABc18XY2BiKxaK0rcNsNfPg+IiLPH+u68pe6Qgdjr1erwv+rK2tAegQ\nd97Zsbq6CsdxumhIP7ArCH2z2cRPf/pTHD9+HCMjI6hWq/L5j3/8Yzz88MN48MEHre9q1cyL6PA5\nUx0k0mipC7CbdvS7ptpvLrgm4lp11RKs2Z5NWvbaSFNqMKVeU6Ln+tgcfHp8XpECtr7NvkzTUCwW\n63L01et1UUN1QoiWQvP5PPL5PAYGBrB//34UCgVks1lhUJRiaTohM9HMgg7IfD6PlZUV+P1+kZA2\nNjYQiUSwuroqBHV5eRnRaBTZbBbZbBYDAwOSmbm8vCxSVaFQQCwWQz6fRyQSEYahcYe/q9WqZDD6\nfD4kk8mupL9HHnkEpVIJqVQK+Xwe3/ve95DP5+E4jgQeNJtNDAwMIBQKiXQfi8UQCoXkOjoCAxhI\nCHUYKNAhkIlEAul0GqlUCrVaDYVCAaFQCCMjIxgcHAQAYV4TExPI5/O4ceMGpqamEI1Gkc/n8eKL\nLwIA3nnnHSwvL+N3fud3RMip1WoSRAFACG0mk4HjbGaOkkBx/Awj1EKTqV3yPHuZAU1N2XVdYWSm\npqS12Hw+j1QqhWg0io2NDRQKBczPzwMAZmdncfHiRVy9ehV33XUX/uAP/kDGk0wmZZ8ikYjgvdYA\nTVrD6zqbzabcqes4DsLhcBeT02eUghwtG0BHSGOsfT/nlbArCH00GsXc3JxIP6OjowCAkydP4vTp\n07jrrrvkWW03A7qJqpZmTSlU2wS1ZK/fJZhRBjYg8pFQ6Th/G6Hvpc7pcfT7rH6nn+90u2YkhZba\n++nPFsFjI/4mIkYika7PSDD53szMDFqtFlZWVnD9+nUkk0n4/X4kk0kUCgXk83kAwPr6uhCEbDaL\nQqEA13VRLpdFUi8Wi8jlcqJV3bhxA8PDw7hx4wYSiYQc1GKxiEAggFQqhVwuh9nZWZG2w+EwVlZW\nZM0KhQIikQgWFhYQiUTQarWwtraGoaEhAMDa2pqUxaAW1Wq1sL6+jo2NDWnrW9/6FuLxuBA2jqFe\nr4t06vf7sbi4KBJoIBCQqDTTn0AJldI0NSMSt6GhIfzwhz9EMplEJpNBJpPBE088gWQyifn5ecHz\nSqWCjY0NLCwsYP/+/Ziensb6+joWFxcxOTmJxx57DADwxBNP4PLly5iYmBCCVS6XkUqlRIMrl8uo\nVqsytlarheHhYZTLZZTL5S6NzCSQ1WpVtD8AXeVGNH5p4YXv8nOztAAZiza3cR0XFhbwyiuvYGFh\nATdu3JB1rVarSKVSGBkZQTqdxurqKsrlMtLptKw173clvusoKtN0xH2kgBMIBBCNRmXNmINBcxQF\nGq1Z8n5eU/PeCnYFoadkcf36dTQaDdx9990AgIMHD+Lo0aOYmJjoIjA2SZ0ES6tEGhHMGhL6XdP2\naJo+CDabpLYX26R/E8ggbLZnG5jf2543w940k7oTvoet2opGo6LKalWeZqJ6vY5UKoXZ2dkuifLy\n5cuoVCpIp9MolUp444034DgOYrEY1tfXpf2NjQ3U63UhhJSQqtVql9MM6BCS69evY9++ffjRj36E\nw4cP49KlS8I0pqenhSmUy2VMTU1hbm4OBw4cwPz8vJhbaF7hoa5UKvI37aeVSqVL4lpeXhZJ3Ofz\nSZ80G+RyOfj9fqlZUygUhNC32225YJraRz6flwvA+RnVff5NW7M21a2srGBiYgILCwsYGhrCyZMn\nkc1mUavVEI1Gsbi4KGs1MTGBUqmECxcuIJlMYn19Hfv27cPCwoKsaSgUwuHDh+E4DkKhkGhqs7Oz\nWFjoXEVRr9e7/C1cH2rB+vJ0zpdCWKlUQjwel/d4kbrOQtbzJWhTDc1G2n9B3KVQpstBzM/PC04B\nHVPj0NAQAoEAxsbGUK/XJaenWq12rbkGMlxNV4jzNDNRozElekrt2izDBDldw0dnXPcLu4LQt9tt\n/NIv/RLOnj2LQqEgE3j99dcxPT3d9axN4tWSqv7f9gxBc19NeG1ec7ONfqVfzUhMk5K28en2TQnF\na96aGfUzPlML6lctts3J/G2bI81vlFboU9COtEOHDqFarYoUVavVUCwWcfHiRSwvLyMej2Nubg6F\nQgHLy8syhkajIfbVRCKBQCCAarUqhBfYJCDlchmZTAYbGxvw+/1C0MfHxwF07M20QY+Pj4vf5s03\n38TAwIAwmGg0inq9juXlZUxMTODGjRtotVqYnJwU4jY8PIx6vY5qtYpQKIRsNivjoYmCfVKKDwQC\nmJ2dFedaoVCQ9eLace40v9CHAHTXs+Hea1MC23rrrbfQbrdx5MgRKYR348YNDA0NgVd50qw2PDyM\nXC6HdruNmZkZzM3NIRqNCnPJ5XLI5XLw+XxIJBKo1WrSDtf19ddfxw9/+EO8+eabaDQaiMfjYr7R\n0q4umEczKk1IWkijyUo/y7kR2DYFDNvZ0OD3+5HP5xGPxzE8PIzp6WkROprNJvL5PGq1GkZGRrr8\nDKurq/Kc3+8XX4s2F9uEIa11cZ/4LnGMe0DmxLNJzYTPbOfMAruE0LMS3pEjRxCPx+Xgv/zyy4jH\n4zh37hwee+wx4fqawGkCTS7JBdIEnL+1A5P1QzSykNNr6GUG0pKzPmy6T4J+Rjt5tws2HwLb99J4\nvPwVXkjZC7Z6vlwuw3VdJBIJcXKTCDOKYXFxEfF4HGtra7h8+TIAYHFxEbOzs1heXhZHbS6XQyQS\nEemSbQWDQVFj2+22OEsJrVZLshtLpRI2NjYwPj6OQqGAZrPZ5YSjs5O28Bs3bmBsbKxLIyGxJREc\nHBxEpVJBoVAQUyOrrdLByc9rtZpI50CH+SUSCWGGAwMDIsnSD0FHIM05iUQC7XYb169fRz6fx9jY\nmMyT8wC6pdzh4WEAHU2JUikAlEolvPXWW0gmk7h48aIQrWPHjmFtbQ1LS0uIRqOIRCK4ceMGRkdH\nkclkpN1oNCpmJ0Yira6uIp1OY2pqCgAwOTmJD37wg3jnnXfw7W9/Gy+99BIOHjyIQCDQVeZEh8xy\nz6vVKpLJpOA2TTem057fEXR1VF2FFOhmCFwvCiQ08SwuLnaF7larVQwMDODYsWOIRCJiIvT5fEin\n0124biPwWqvUwSF0cnPOmgaQvmmmoQVAanw6xLYf2EuY2oM92IM9+AWHXSHR12o1vPjiixgdHcXU\n1JRINalUCuvr65idnRVHEHBzYopXRIztO80dtR3fVO1MqdXmbDRjaM3kCtNR6eVo9ZKQdVu9wOuZ\nXmrkTsHL8ao/owRm1gpJJBKo1+tYWFhAKBTCF77wBVQqFRw5cgQAcPbsWeRyOUxPT4s5pFwuiy2Z\nZo2HHnoIx48fx8TEhNg/NzY2kE6nxc7ZbrcxODgoY8jn8/jyl7+MP/mTP0EkEpFQwcHBQbz88sto\nNpuIx+NYXV3FXXfdhXa7jXg8jldffRVARwp+3/veh3vvvRfXr1/HzMwMstksVldXRbqen5/HzMwM\nFhYWkE6npQ0ddQF0olb279+PhYUF+P1+TE5Ootls4tKlS6IFnDlzBqdOncKZM2dQLBa7qlcODw+L\nhqA1Sm3nrlarYt4JhUK4dOkS4vE4Tp06hW9961uIxWISrUMT0YMPPohjx47hzJkzeOONNzA7O4uV\nlRV8+MMfxoc+9CEcPXoUQOe8zs/PY319HaFQCKOjozJPjouOzCeeeALvfe978ZnPfAaXLl1COp3G\nxMSE+D7i8bg4Pmu1moRzUmIGIFFGTDTTIaZ8h/Okpq5t8l5nuVwuS9jvuXPn8PTTT4uWRchkMhLu\nzNDZeDwue1ksFm861zqqDUBXUTLtQ9L3W5jfEW+pLVCD0T6Z/+6csfF4XBxAp0+fxvHjxwFAEqXM\n+i3adONFcDQx1t9TbeTi0dyjzTX9OkipJpsmml5j6tXmVkTdqw1zjgTtCzDn1a+fwWucNmZG22E4\nHBaVlA5Bx3GQSCSwvr6Ot956C08//bQg+U9/+lMAHcZ+6NAhYRQk1vl8Hul0WojgU089hUwmg7m5\nORSLRYnI0XOam5sTJ1YoFMLp06cRDAbxwgsvCMMAOip9IpFALpdDIBDAysoKXnvtNeRyuS7n3Pj4\nuIxzYmICq6urWFlZwfj4OGZnZwF0TCXJZBJra2syV4Y0AsD169cBdBjhyMgIwuGw2K5d1+1yxl66\ndAkAukrWksjR18H15+emkEPz1NTUFEKhkJjReA7K5TIajQZ++Zd/GQDw8Y9/HIFAACMjIxgaGkI+\nn0e1WsXs7CyazSYOHjwIoOPcvffee8XGfObMGTz99NNIJpNiupmenu6KLJqcnMTIyAjq9ToajYbE\nhZdKJTmD+jxp4mgKdhoXdUiimYFMxmiGV2qTLU21jMQi04jH4wiFQjh+/DiSyWRXxE+z2ZQiY8lk\nEhsbG11lk3VUG0EHbpAR0OHOM8KQUKA7V0T7d/i8Nln1A7dE6B3HmQVQANAC0HRd932O4wwC+CcA\nB9C5HPx3XNfd6NVOOBzGxz72MbzyyitoNptyID760Y9iYWEBn/jEJ7ps59o+RZs7N9R1XbHdaiLc\nbDYl5pXhV/ybBwmAJOxoT722mQEQ+5q+ScZ0HLFP2nkZK0ypg1yZ3+nY8nq9LqFfnDP/5gGlT4LP\nvrsfXY4bM4FMj0unglNa4vg5T86PEqIZCQFsxgjn83k0Gg1MTExgeXm5a47c43feeQf//M//jG98\n4xsYGBhAJBLB8PBw114y7JFSVDKZhOM4WF5elv344he/KISvUqkIsRwcHJTwSs6b+8eY6vn5+Zuc\n72YC28WLF2XP2ef8/LzYV8+fP3+T7R6AJIi1222srq5KpA2TajT+zs3NdREhjod+iFgsJsRIx02T\nUCYSCQDoWmM6cJlglslkAEASmsrlskR+VKtVRCIRpNNpHDhwAEBHIo7FYuIk//GPf4xcLocHHngA\nP/rRjySR8bHHHkMmkxEh7LHHHsPJkyfx8ssv4/Tp0wA6ESR///d/j2g0iqeeegpHjhyROZXL5a6o\nFeI28Zlnj8wgmUwiFotJ6CsJJCViEkqeS332+BnxqlarIRaLiQOeEUNDQ0NypoAOoT948CCefPJJ\nEWyCwaDcmWGWluZY6DDWhJ6JcKVSqYuQMzmTuMc5EV8pqHCNeI4GBwclP6BfuB0S/Ydd111V//8R\ngBdd1/1Tx3H+6N3///deDTiOg7W1NTz66KMoFos4f/48gE6RfTreTAnSlF62I5Fr6PUsGYUpGfM7\nHZNPjq7L1nJ8Zn+9nKnmczYTlXbSmKDHqiNqzLb0OpqmmF5AAk9CUywWsbq6iomJCQSDQVy8eBFT\nU1NYX1+XzEkAOHXqFP7qr/4KV69exYkTJxAOh1EsFiX6hFAsFlGr1SR+nqaFSCTSVcGPjk/+brVa\nXeF3OkWcErAtX0A70kgY+Jxm9maElnZ8U1Km0zQSiUicve5HS5psh8lNnBcPNBkAJVAdxRQOh0Va\nJwOnE9JxHBQKBSwsLHQRI22eJOHy+/1YWlrCPffcI2vGqKNIJIJcLoePfOQj+PznP4+FhQWcOXNG\n9vLs2bN45JFHcO+998pVlk8++SSefPJJAB2TWyAQwKlTp/Dss8+iWCzigx/8ID75yU/ehGfNZhNT\nU1O4du2a1LfKZrNdiUSLi4si7OnzD9hNGPpdL6AwxcgaAF0Oco5VR4xxrUmPbHWPbJp3rVbrcgjr\nsg1aSKTgxQgfhnKyPVukYD9wJ0w3vw7gQ+/+/fcAvoctCH2hUMDly5eRyWQwPj4uyD47O4tyuYzv\nfve7+OQnPynSu0ZabYfeykTSLwPQRJRIpTm0JpK6bzO+1cZxzTGabZmmFiKEmQmp+9G2PW1W0SYv\nrz5ta2Yj+nwmk8ng+vXrgtiUuJllOD09jaWlJSQSCTSbTTz77LMAgH/6p39CsVjEwYMH0Wq1xCyg\no54opYyPj2N4eBgDAwNot9uS/UqCSuKWSqVQLpfF7FcsFuWgUlpiDLbWujRhIC5p7YVrbWPSeo9I\nWDmueDyOwcFBsccziieZTHbdnUqTVb1eFyLOvWDbJPKO4wijc92OfbZer0sYI7UMMsd4PC7JVbrM\nA39rDbTVamFwcFBMSxqvlpaW4PP5sH//foTDYczMzIjkX6vVcOnSJbz99ts4e/YsBgcHce+992L/\n/v2Cr/l8HlNTU4jH4xgaGkKz2cTXvvY1fPe738VTTz2FD3zgA9JWrVbDlStXxERHiVubmlh6wsRH\nTQv0ZwTzXOlollwuhwMHDmBhYQGlUgnHjx8Xa8Jv/dZvYXp6WkpHmDWFNNPkb31HAfeGY2BeiZfJ\nGYCYj3gmWKKC3+n5bNfvdquE3gXwHcdxWgC+5LruXwIYc1134d3vFwGM2V50HOdzAD4HbEopL7zw\nAjKZDB544AEAEM72/e9/H7/2a7/W5Tg17dlUdbQ5xnPQPaRq8xkePjNulQijkyNMQqI3xEtitmko\num+bFG46fbUZRRN6aiS9JCATcXqti+u6uHr1KkZHRwWJ6dBqt9vI5XKIxWJIpVKo1+v4xje+gW9+\n85sAOns8NjYmEjgJcCAQEILk9/uRyWQwOjqKaDQqpQhI6Cj5kghyfjrbUkv9TEZhjRGv+ep901e5\naeZtK1zF9+iADAQCWFtbk0sj0um0mGdarZY8VygUpFQBQztJKLQJkQee5j0+py8hoWTOapcMNaU5\nwxyzxql2u41MJiOEfmlpCWNjY7h69aqUf7j//vu7NA6gw9BOnDiBI0eOYH5+XjJLmQAHdLKE33zz\nTVy4cAFLS0sYHBzE2NgYVldX8bd/+7f4yle+AqDjb3nyySdRq9WQyWRw48YNTE5Oypg4TjpnmTBm\nm4+Jt9TObMEW3DuaPgOBAKampvC7v/u7spcUGrRmrNsHNjUH7p3eGx06ymeJdzQLal8Es3/JzM0a\nP+xbO937hVsl9B9wXXfecZxRAC84jnNef+m6rus4jlWMfpcp/CUAjI2NuUeOHMHs7CwuXLgg6n4i\nkcDExESXjVNz5Hfb6XKE8DN9sFWf5hhuIsD9Sv1s2xwPcHP8fC/G4oWkBC0VmPbk7djpvMZoI3x6\nDCZBjEajiMVikimay+XQarUkcanRaGB5eRlf/epX8corr4gkwphuzoMEqd1ui+Q2MjKCkZERiWnn\nQaAPgYjPiItCoYCNjQ3Mzc0hl8shGo2KM5NF4bTD3WaG06q2JuCa8fI3HcxastOOOWbGFgoFJBIJ\nqfdD7ULXgjElUrantQWOvdFoSB/xeBzpdLorJp44wtIBXFcTz7R0SyZZqVSkrVqthtXVVQQCAVy4\ncAH33HMPJicnkc/nhSEBEAdrPB7HkSNHxEn+zDPPCINZWFhApVLB2NgYVlZWxBaeSqXESQsA3/jG\nN3DhwgX85m/+Jlqtzr3RN27ckGgcQjweF3OO1uq1Oc7cQ0rXlI653/xJJBIoFApyF/Cv//qvi19g\ndHQUy8vL4vPg2mmzHtCttds0YA0mw+Ce83/a/1m0UGvdFIZsZ7MfuCVC77ru/Lu/lx3H+WcADwFY\nchxnwnXdBcdxJgAs92zkXVhbW8PHPvYxXL16Fe+88w6ATrTA5OQkPv7xj99kmzK5qkmQbItgSra9\nwDTLmAdHIw6Jl94YtmGTOmyqlyklmokeRGr9v3Yemetg/m/r02uNvJDJdV0MDQ1heXlZxsHkGSLt\n5cuX8aUvfQmXL19GKpWS0EMepmg0ioWFBTm0AwMDmJmZAQDJ0HRdF8lkUpx9lIa1nZrRU6VSCbVa\nTaQyHZ1AxzYPazAYlOJfJjMuFotdhdf4LJkQJS9KWtTkSFyBjnTIBK3V1VVhVKFQCMlkUqRSSt9A\nt9lN7xftsjo5qNlsSukFwsrKijA1/ub66CQtEw/ZXywWw7e//W0Am/V1BgcHUS6X8dnPfhaJRKLL\n/sy1qNfruHbtGl555RW8+uqrmJubw9LSkhCk4eFhqfJ4//3349q1a0gkEmLSYpuJRAKnT5/G6uoq\nfv/3fx/RaBRLS0sYGhoSgY+mj0qlgpGRkZvOimm6tDFukwjT3s9bndjv5OQkAODChQs4fPiwmG7Y\nD4VOTeg1A9UmNw1m1A4d0KZ1wnVd8RtwrW1ZsNs13+w4YcpxnLjjOEn+DeCXAbwJ4L8B+K/vPvZf\nAXxzp33swR7swR7swa3DrUj0YwD++V2uEgDwVdd1/z/HcV4F8DXHcX4fwFUAv7NVQ7VaDWfPnsWH\nPvQhHDp0CFeuXAEACWVjbK6WgrS6Zv7vBabErT+3PQfcnGTFz7Qkrx1cpnbB32abNonejBBgu+b3\ndAbRXq+f9+LyNnu/13p5fUYbfSKREOmbUnCtVsPrr7+OZ555BufPn8eJEye6knZGR0dx/fp1XL16\nFYcPH0Y6nUY6ncbY2JhIbq1WC4VCQapJstY7Ix1ovmA9pPX1dVHNadMm0LRAR6XjbBaQor1bA80y\nOm09FAp1pZxTc9HhjqbttlKpdJVnoEOtXC5LTH6r1UIymRSHMyV8XW1ROyJpz6/X61hfX0exWJS1\nYDIR/VPAZs0g7bwENqN9tKmIuQNAx3Rw4sQJvPLKK/jDP/xDDA8Py5j8fr/E9v/gBz/A22+/LXea\nzs/Po1AoYHBwEKurq4JjmUwGKysrOHTokEjplUoFd999t+BFsVjE5OQkstksfv7zn+Po0aMol8td\nJaKDwaDUD2Lb2g9lSrs8h/wbuPkWLdft3E0wNjaGZrMpJapZjmN6ehqlUkn6ovnHbLder3f5xfT3\neoy6X0r0+v4FoBNSy6qf2izr8/luqrlP01S/sGNC77ruZQD3Wz5fA/DR7bRFO9U3v/lNHD16VOy5\n4+PjOHfuHM6ePYupqamug2Y6RrST1jRheIy/r3Fpm7JpxrC1oYntdh0mbFe/o5GY7RGZbIxLj0uH\nf+rDoRkU39GMy3Q6azV4ZmYGlUpFDjTtiS+++CL+7d/+DcvLy1IzRZs+3n77bQSDQRw9ehTZbBaT\nk5MYGhpCKpXqilbKZDJig/f5fMjlchKGqSsjMnKFIY2sX6PxwnVdqZPOpBOuB+3SjLemI4w/juPI\nIZBYrQcAACAASURBVOY6mJUDTSHDcTqVNiORCNrtTlljXori8/m6ClPR9HHt2jUEAgHE43EkEgk5\n0LycotlsYn19HWtra1Knh2GZes9pl4/H4wgGg10OaDIcEx98Ph8efPBBYdpAJyN3ZWUFzz77LObm\n5jAyMoIzZ87g7NmzmJubk7VoNpsoFouIRCIYHx+XEEia4a5fv45UKoVAICDVQ7PZrIRREo4dO4ZS\nqYTz58/jueeek0s3crmcmMRIH3gfABmATUDTuGwTbAj6jObzeanLxKx8FhujOVDTHpoTCaYfwEzc\n0qGy3As+o88aTZT0H2jfFMevI6a2A7smM5aE/PTp0yLBZzIZHD16FK+99hre//73I5FIyIHREhnL\n02pHFqUtnV7MSxC4wEy20DZTSmjcTNo8mZHItnT2nu6b0SQcF8vWkmiYCNdqtSR+mePVlf4IrttJ\nytClVc0wS9PurG2L+mIIRm5QmmDdcCKnvm2LdvWlpSXE43FJx9ehji+88AK+/vWvw3VdjI+Py52W\nLF8AbDKEarWKqakpjI2NCcHluEhIOXcm1szNzYljDOgkFaVSKaTTaZTLZVmXZDIpkqLrdlLcWRaW\na8AQXRMY901JnePQ4Y60t3NvyRA049WaF8sMkIkwVE4nORFn8vl8V8YuHcvNZhOFQkGiiMLhsBAD\nYJNgkfDTj0NNQLe3traGffv2SRJXJBLB9PQ0fvVXfxVAx97/1ltvYXV1Fa+++irefPNNYbrMHCZe\n+P1+CXnkpS0A5FYkRmL5/X5JtiuVSqhUKkgkEviVX/kVAJ2yC3/zN3+DiYkJ5HI5/Mu//As+9alP\nYWlpSaLvlpaWpJZ+pVJBs9mU+fCSFwBSdoLEmiGNOg6d+6QdnrxxTMe6F4vFrrrzOneDe0a8pkYb\ni8W67rnWzIQ3cDGBi1UvuTfsgxe2MPyWe87gh1gshjfffBNPPfWUFH/sB3YFoW80GkilUlheXsZj\njz0mk3rjjTfw6U9/GsvLy7h69SrGx8cxODjYVfGNi8iEGiI4OajODjU5sZeJgpvABAmzmqUt0qVX\nCCYlKFPa59w18zEdZhqxeBmGVtU1MTIdyGyDQMRitmU4HBbJmIkyDI8DOuV0l5aW5PN8Pi/lYxn+\n9pWvfAXPPvss0um0IDnnV61WhbgBkEzYmZkZYRaO43SFMBLJNzY2EAqFUCgUJFZeX0tYr9exsrKC\nWCwmB5oSGICu2HkdteXllOY4tJSu8UObf/SzNu2SZhGG2pmEppf5kGPju9pEp6V5s1qllt45HyZV\n8VapiYkJXLt2DZlMBtVqFbFYTJyhQMckc+nSJSQSCXF2z83NSSVJnYDVaDQQi8XQarWwtLTUFQ6p\ngUSPEVvvf//78aEPfagrvZ8RW81mEysrK/jOd76DJ554Am+88QaAjmM3GAzi7NmzcjdFNptFuVyW\n7GlgUwMnzi0vLyOVSonAQryYm5uTOxFYX4ihnXQmj46OIpFIIJ/Pi5Cgib3GWS150/zIstnAppDA\npD6tHWjHa6lUkqxnMvlisSimLKBTiXR9fR1nz54VptoP7ApC7zgOTpw4gQsXLmB1dVWSKc6dO4dz\n587h7rvvFkJu1pnmoaJ6RwnLywbuZeYwP7N5xk3vuJbQaeLQKhuAriQKhrURcYDNEqZkTqbUz3mY\nWZmcp2ZeXqGWPp8PQ0NDgnjavpjP5+HzdeqKF4tFMSUAHQIxPj4ukhPL/AaDQTzzzDMAOuFxtBNH\no1GR2kztIJlMilQWjUblGc2M9bpynjTHBAIBMemFw2EJ39OEXJs0eAcu2zKlbhuT1+YNzaS5Zjqy\nihKbuVfaV8OxkSnoUFmOxcQ9gsYRSoDM/NWXVei+TIGCdzmsrKwgHo9jaWlJ4uaZnHP58mWRiF99\n9VXJxOStUrwXN5lMirmOqfq8O5eRObY11eMcHR3FRz7yEQwMDIgZ6IUXXsDy8jIikQgGBwcRCoVw\n5swZPPLIIyJ0UAOhv4bRRywtTILHayhpyqO5cH19Xc7G1NSUJPe99tprqNfrcptWoVAQIfPatWtS\nIprlOFhcT5dgoeZaLBaF4bBmDjW3QCDQpWWQWbC2vL7tjJoWNVFg85xyPfP5PM6ePSuf9QO7gtDH\nYjE4joMHH3wQFy9elKsDH374YZw/fx6pVErqYWsbHLBZK6ZcLkttCH2Q9EHV6pT5vf6M4XPsS9/d\nyGe8pEITdMKGPuxaotGHk6omMx2JLOTyNAPxe5YS4By9/AOXL18WRKS67ff7RarL5/OSlUrJeWFh\nAQMDA0gmk1hZWYHP50OxWMSpU6fw9NNPy/iPHz8ul2lwzlxr9jk9PS2SITNBuWd0xtJBxjotLBzG\nPdaJJ1r6J7HTl2br+kI6/4J7qvdbaxU61t4UGMhQuN8mLrAdhmCSyWvbLLB5cTbfN/GP49DJNPxM\nr68eh/mbOMDnx8fHkclkcPHiRdnrP/7jP5ba9ADkusVMJtN12TgvVOE+DQwM4MaNG7LuJL7ahMnx\nakJfqVTw1a9+FY8//ji+/vWvyz6trKygXC5jcHAQjuNgeHgYf/d3f4fPfOYzADrmoPn5eXz2s5+V\nQmxXrlzBpUuXkM1mpZ5+tVrFtWvX0G53LrsvFotIpVJwXVeY2dWrVyVfgJrpjRs3cPXq1S5fBTVI\n4jQlcdbf10BiTVyggMi563Bwx3G6NDJgM6FqZGSkC5+J80xC5BxZKZTMox/YFYTe5/PhwIEDKJVK\nOHToUJez5dixY2ITzOVyUtmQi6PrmZDImyYTwC5F2WzmJPTsg1EHutaKrlOh56AJBHBzqQLdB4EE\nRldspDSgVUQyH37PcdH0YwP9+bVr1/Cd73wHAOSGpYGBAczPz0s2aiqVwsTEhByccDiMRCKBUqmE\nZDKJXC6Hl19+GX/9138tiHfXXXdJqj8vxOZcGD8OQDJbyax18Seul74lSms+rB9DjSSbzYoZyXRK\nacbOfdHE3aadca30WEhMiWfcS41XXtoBJXjOz8ZgtgKaGomP2uZOYgBsSvvaac59J+5kMhmcOHEC\n3/ve90RCHRoawoc//GF8+ctflqsQU6mU2OO5zqlUCtlsFtVqFSdOnAAAHDlyRBzD4XAYi4uLkhhH\nMDXvVqtzd2673caf//mfd2Xb6uQyzrFYLOIf//EfAQDvec974LounnnmGXz6059Gq9XCwYMHMTY2\nBsfZvG2LtYIGBwexsrIi0jbv5QUgl4lUq1UUCgUMDw+jUqnI/cIECpck4HofNBNsNpuiDdMvwWAB\nrWmzECElee6jz+frKsmscYVanA4K4IUw7XZ3Ds1WsCsIPdONm83ONWZ07DQaDczMzIhXnIgVjUa7\nwg6J5ExY0R5qbUvT4VF81zykVLe1Gcbn84mTxGyLfZP46rAoPQ6bFEbQ6j6RnRIBDwTXg5IrHZgs\n+qbHT2me/bRaLVy5ckWiVh555BH83u/9HsLhMP7sz/4MP/7xjxEIBCRRhpddf+Yzn8GBAwfEMfX9\n738fzz33nDjxgM3r2Gj+WV1dheu6GBgYwODgoIT4MTyQDItVJ1lJkXNhTZdarSbVFindUrvJ5/Ni\n8tGZijoawfzcjDCymce0VkUJle2ZNwGZNnH+rTMdKZVRA9FSnWmTN8eiNQFt4jL9ATZzCdthJczB\nwUHs378f6+vr8Pl8WF9fx3333SfSJ4mg43SyjVkhcXFxUZyMx48fFzw7e/asMHQ6Sr3mo9d8cnIS\n165dw/T0tBQunJ6eRiwWEw1Ta+b79+8HADz++ON4/vnn5XrGw4cPy17wblegI0wsLy+LiYe1kEZH\nRyWQgreXXbx4Ec1mE7Ozs4jFYhgYGOjKMg4Gg1haWhJfj3ZEa3OjtqNrOmAL96UwShrFtriuPMta\nyNB1jNgfTYS2oAIv2BWEvtVqIZ1OI5/PY25uTqrpBYNBXLp0Cffee6+oWvqAA5s2ejqruJg8pGbq\nsyaAXqYbv98voXGxWEy4s1bvKXHTKUqV3evgcQyM+dYxsiQqnJuWhvibcyKC2TQUMzyQfdLUQ+Jc\nqVTwF3/xFzLPhx56CMViEdlsVsrZAsBLL70kxP8HP/gB/uEf/gHRaBTHjx8XIsLIFqq6rFczPDws\n4XUcPwm93jtd30VrN9VqVeK7yXj5HPusVqsIh8NdRFnvt5aq2R8ZqiZMprlD+wi0bV//tuGQ9p1o\nBqClb/MdDXosNDtpCZLvaPzRIaM6nM/n80mW5+XLl/HFL34R4XBYJNqNjQ187WtfAwC8973vBQCp\n/b+2toZAIIB0Oo3BwUFUq1XcfffduHDhAoBO1ujMzAyq1SpyuRyGh4e7ggI4F/oOuCcLCwsSa/+e\n97xH9pJXJi4uLiKTyaDRaOCuu+7C4cOHAXRMu8ywZlXOwcFBua+WYZhkFqlUSvwYPKvEnXg8LoX4\n6vU63nzzTaytrUl9GfqUKE1rxzuwmbGs7edaA9WmNw28IIV0QjN7XVacpmJGo3H/KTCxTpIWaPuB\nvasE92AP9mAPfsFhV0j0VO0YGcKEivHxcaRSKVGhmImnJV7GKevQSjMiAdhUo22OVBOi0SjW19dR\nrVaRTqcluoCSCe3FlOi1FAjgJqlb20z5DtW1er3eZXOndEBzBqW0VCol89ThhtqMxXZ0jD1Vv9df\nf11uQkqn06jVakgmk6hUKtjY2JA6H3R+Ap2op3vuuQfZbBZPP/00pqenMTk5iYWFBTEd0dHlui5W\nV1cxODiIe+65R+KnaRagj4Nrw7onvACCY2U8OaMPRkZGxEzAeUajUcTjcal5ryOdtJSjbda2CBn+\npuRJicwMYdQ4aravP9NmFuKaNst5+Ww4Do2X4XBY1pWZwaxlQ6mT72n7MT/z+/0SJTMwMIBgMCh7\nu2/fPjzyyCN46aWXxJ5MvHUcR/INWq0WLly4gMHBQbzxxhsiVSaTSTGT7t+/H5VKxeoM1msLdHwF\ntKdzbNrhOzIygunpaTzwwAN4++23xbzz7//+72g0Gjh//jwWFxfx6KOPYnR0VLJa6SSm6Y/mlFgs\nhuHh4S5nLE0oPIOPPvooLl++jMuXL4sEDaDL6aqtBpy31shte2hq2jTRaP8bx0qNhNo8cYrauLbR\nM9+Gdvp+YVcQeqpJXFwiHgthmbZN7Ziibd5xHEE4xtb7fD5R/yuVCg4cOADXdZFIJCQ9n0RR27yp\nZjmOI/G6XHSgo2qtra1hcnKyy266srKCUqkkYWGRSESImTanaBNDMBjE9evXkc1mkclkJClkbW1N\n0umBDtOjL6NWq4kfY3BwsCu6gqYRrTIDnUM2MTEh/+dyObkc45133kEkEsEbb7zRFenz2GOP4aWX\nXsLp06exb98+pFIpSfDSN1Kx0BYPaq1WQy6X60ow4RoC3TdfkREBmwkjOoac0Ss6p4A3XXE8TFhh\n8TKNF8ye5eHRORZ8jrHQ2uxlhvJq05EZCWMSeq4LD74m+sR303xjmoRYVZLx1O12W4Qd7i/XkA5z\nEhM6qfmM3++XTOVGo4Hh4WF84QtfkFugCAyRpS8oEAhgdXUVhUIBqVRKCBL3mzcq8VkSQp4RmikC\ngQAGBgawvr6OyclJLC4u4tChQwA6TOPkyZOC99lsFs8995zEogObTuL77rsP6+vreOmllxCPx3Hv\nvfd2MeP9+/eLmZURWDR9kKbQdh4Oh5HNZsXflEgkcOXKFaEX8XhcrqfkvIhDxFHuPwU4fZcrv+f6\nc6/YHgk8iTawWbqC7ZKu0TzJ8QObtvp+YVcQ+mq1iosXLwqC0SvNSoNAZ/GuXbuGWq0msbIABPk3\nNjbg8/mQTCZRq9VQKpXQbDYloiCXy+Gdd95BJpNBJBKRuiGNRgPJZLJLwmZFQl0VUdtAiSgLCwtC\nyAOBgJQ81SV5NdHSNltN6GkTZ+o37X6UZIFOdqCuBx4Oh0Ua17ZfM5yTMDo6imPHjsla3HfffRKz\nzAiclZUVNBoNse22222cPn1aboqitKeJM+2YvFyjVCpJ+rqOCNKhjswS1HHmbEv/D0CeZ6Yqx0VC\nHIlEMDo6ilqtho2NDZEYmVGo7eWUqrV0T0me0T7cN9Mm3+u3l4PUdNR62eX19/yftXK4VsQJagcs\nDTw/Py/ZwcxPYHIX8YL/x2IxrK+viyP87rvvxkMPPSR34bJMLvMT0uk0ZmZmxG5MXEwmk2Ifpx+L\nN4Vx72hHDwaDyOVymJ+fl0u1jx07hpMnTwKACF2vv/46fvu3fxvxeBwTExP46le/2pUNOj09jU99\n6lMolUp48cUXcf78ebz66qs4cuSIRIkx54ahyNxX9gN0CGgul0O5XJZy2wwCqVQq4nvSiVhm5JQm\nsHS02/ZUg7bdezmuddKZxiEb7uh2+oFdQehLpRJee+21mwo66Xhj3kvqOI4QbwIXe3FxEWNjY/D5\nfBJvSmTn1VwLCwuiHpFL04ELdA4+39ORPfoQknvn83lJyWbSiBklo1V701zAsfPddrst6e5mxI1G\nNh1uZ15ZZ8ZeE9773vfKutEhValUsG/fPnF0HT9+XJIxAOD8+fPI5/M4evSoSEVra2tdDlRK+KwV\nwhormrjyOU0EKKWa68OICwBCfE11WDOyjY0NJJNJyfTVe8Q94P/MWQA2TWI6QkbvjS2KRzMJ20Ej\nYTY/M81GZIBmeK6eY7FYlHf5PDWBYDAoCUcsbcHIsFwuh4GBASmjAXRwn1oPpcNYLIb5+Xncfffd\nUhY8Ho8jk8lI4g5jtalJmk5DrTUMDw93MWOfzyfCFqV2mn42NjbEPPuJT3wCDzzwAF5++WXMzc1J\n4bdHHnkEr732GgBIDsdzzz2HRqOB559/HmNjYwiHwzhz5gx+4zd+AwBw//33I5vNIhgMipOWTk3u\nezQale/IJAcGBmTvGOmTy+W6pGptgjLxRQsmJrPXOLUVaOsFf5tMRPf/353phlI8pWoz0YDJFroO\nuZYoWQJhenoa6XS6K6qDi8E0f4ZLOY7ThQDsk5XytHqmE7XYrrbH63ICWqXie+Zh123p2tyU1nQ5\nB41Q9AeYcdVaMjB9EPz75z//ucTrjo6OolQqSc31t99+G8ePHxdthneDDgwMYP/+/RgcHMT6+jrm\n5+dFG+JcSOQpQdG2bmNuBBIEEnqNsHr8nC8Jna7gR8m/3d68iFvXfOd+c538fr/Y3TWBonlFm1m0\nFqeZTK+wTIJW2U0pUD9nahY6Dh7oEF2G9PJGJQoVsVgMR48eBdBJJmJ1zFgshpGREYl9J9BUQKYQ\nCASwtLSEixcv4sCBA2Lj1uYHmlKKxSLW19cxODjYlaBD86jf3yn4tby83KVp+3yduwYGBgZkzUdH\nR/G+970Phw8flvGfPXsWzz//vPhzaFY7cuSISNenT5/G0tIS2u02Tp48iY9//ON4+eWXJdOa0UBA\nJ1yTl68cPnxYtD1mz169elXCGFm4zGRKQMdGn8/nEY/HZZ+0uVgzZi1cmbisgQKEBk0LNO6Y75mw\nHSIP7BJCr+3fejF0uBqJByUrHmht+2y1WnK49c0wwKadTFeD4wLqBBQ64nSWrQ51I5BQcQyMwW21\nWuL4sUnXpiqmQ620441aCsdIRxy/CwQCqNVqUpKAoAmWRqrp6WkpM9tqtaSg2759+xAIBPCzn/0M\nExMTuHLlSpeUpv0mVLN1XHE8HhdbLZkvTQ2a6WlJWu+3LXlJz1FLSzaET6fTclcsM24J2vluMkOT\nYHNtuW46+Y59mxUhNYNgf3oPtI1cP6elNM1wTBu/dvITZ6empvCBD3ygKzCg3W5jcXERs7Ozsse6\nvguJPjXZxx9/HKdOnZLnqIXQiVmpVLpqFx08eFBKSgAd4YR+DWp6vCiEeEFzZjKZxAMPPICTJ0/i\n6NGjqNVqOHXqFP71X/8VAPC9730P8XgcH/jAB8QWz9BaJmhFo1F85zvfQT6fx+LiIh5++GH4/X78\n5Cc/wfT0tODS888/j8997nOYnJxEJpPBxsYGzpw5g0KhINoE58qaMix1TR+Dzl6mYGHGrJt+GZOA\n6z0lcD974aJNONJ4pJ/ZLuwKQg90q+WmROo4jhA0Otl0IhQzWelsosSun+NvSvRA9wHUi0uTAQ++\nGSOrpUt9tyclZFNL0PMzf7PCYbvdFvWannXt1OHB4rxIrDVx0ESJxJ5/F4tFsYteuXJFHL31eh3p\ndBpTU1N4/vnnEY1Gcf/9nerTjLcOh8M4d+4cRkdHhRGazi3WI2HkgK4zA6ArSoc+DpuUrAmojmyi\n05VrQWLMaohkQroUMCOUdBQW++BnXCNdjZMMWpubNMPpBSZOaWJPRsAEGD1/03TDWvyUmOmMPXjw\nIJ588skuG/3169fFfMVicI7jiPO00WggnU5LZdTPf/7z+NM//VNcvnwZ169fFzNQIpFAOp0Wgk0f\nVaFQkEQ4oCPtDw0NiZaUyWSQzWYRi8Wwb98+AB0z4MmTJ+USc9d18f3vfx+nTp3Cz3/+c0nec10X\ni4uLOHPmDB566CE88sgjADpBBpSup6amcN999+HcuXO4cOECxsbG8OCDD2J5eRm5XA533303gI5g\n8rOf/QwzMzNw3U5VSu3zAyCJmTRHsagf58o+x8bGsLS0JH5Dk0hrLVyDNrGZph6TLpi0TpdW4Nkx\n/T87hV1B6F3X7br2jKAJJe3RJgGn+hWJRITY8HktOXHRGP1B25op+WoJU0t0elNMyZJtaPMNgK4r\n38xkCi3d6WgUSpc8RJRWmAjiOJ1IIMdxRK1kf6Ykr8fJ8Dp+Pjk5KYWYlpeX8eqrr8Ln82F4eFhU\n9OvXr4vTrVqtIpFIIJvNimmDc9T7QdB1aTRwTW37bZqdvECbWAqFgqSFA90XpXNP9B7wAOnSBjRD\n6IPJZ7W2SPMUP9fMgvD/s/emwXFe17Xo+hpDz91o9IDGDGIGSHAWRWqWrmLJkiwpUflat2LLdpKX\nVOrl2VUZ6t4Mlfx4z/FN8pJU2S47cWLZiWNFlmPHtqiZlEVREklxFCcQJAACaIw9AOgJQwPofj9a\na2P3R1CWbNcrOcVTxSLQ6OHr852zz95rr722PqQ32uwAJElMqIvXpA8GJtwNw5AIcW1tDVNTUzh1\n6pR46/X19ejp6YHNZsPp06cxMDAg2DTngnr8i4uLaG5uRi6Xw7lz5zA+Pl5yfXRW2PeWf2PhGw2m\n3++HzWaTKLasrAwf/ehHcfvtt0shFA+mfD6Po0eP4tVXX8W5c+ewulqUXebhQjGzbDaLM2fOIJFI\noKGhAXfffbesEbfbjTvvvBPT09MlDtFnPvMZvPXWW/jud78LYN2I33fffdi5c6cY83w+f8395mN6\nfWvqKpVTHQ7HNWtbG2DzejV74xuNjSA//q5thP7fPD4odHOjYOrGuDFujBvjv/j4UHj0hlHKVtEn\nHr0lhtPZbFaoX3w8nU5LqMvH6HXp5s5sBKJDZnNGXZ/+xIrNOC+LLvRz+T7klQMoCRnNUQLfi01H\nWH5Nto9OGAOQJthWq1Vw8YqKCvE6zEOzIjQ0ABQ5+Sw+KxQKOH36NDKZDOx2ewlO6ff7kUwmkU6n\nEQqFRJSKLA9g3aPXXv5GkZKGQsxJ5o0SVJx/crEJGem1sLa2hvr6eikq0Z41oS4KU+naBUJy/HzN\nZNLUTcJGfD8thWHO2fC62RxHe45cM5rCy++mcwL6HhGe431kYdjo6Cj279+PTZs2ASg2YYlEIlhb\nW8PQ0JBIEmttJtJ2ifF/6UtfwuTkJBoaGlBVVSXrh0WCXL9UsvT7/SUsnlQqJcVGe/fuxdatW+Fy\nuTA1NSW66bW1tZidncUPfvADvPzyy5ifn4fVakUsFkMulxN5A9J1t27diunpaaHnvvbaawI1VldX\nI5fLYfPmzQCA6elpNDc3I5vNYnh4WBq2EGq8dOkSNm3aJDCMhkD1PS4UCiKQFw6HhZ4KFD36xsZG\nSeLqhOz7jTqvl6S/3jBj9NdL3vJvv3SsG42H6y+nDSxDxKqqqhKoh8ZcJy/5T3eOYiipDa35Z309\nHJxMJkmB0gOIQ0M8TPxo2OC9hsPhKDHWGx16PHAI11Cutbq6WgwYKXHURtFVsqSKAcXNRSGoF198\nEQMDAwiFQmhsbLymKjefz5cYWM6Dxq7NoS0NP//OwcdoJHndug6ARpSGkpXPemFreGZhYaGk4tac\njCUswyYUNpsNs7OzMt9seEPohrCOhsT4XmRtcd3oxJ6+X1rPR8NEusiGn2XuBaq1gdhUhVWUzD8t\nLCygv78fQJE7TgYJ9Wmmp6dLepGSQlhRUQG32y2aM21tbQgGgyU0YoqAdXV1IRwOI5VKIRKJwGaz\niXH2+/2orKyEz+dDKBTC2toaYrEY/H6/7IOjR4/i0qVLJZRQMr3YlQkoSpHX1dXhe9/7ngivAUXx\nMeL4mzZtEpnyL3/5yygvL4ff70dfXx8CgYDM2eLiImw2GyYmJpBIJODz+aRanPdb521YvZ3P56V4\nkmvf7/djfn5e4DWuK0235D2lM2ZOoHPfaMdQJ1/N+UHaKK2fZYYQtY0026D3Gj+zoTcMowvAd9VD\nrQD+HEAVgP8DQOzdx/+kUCg8/17vRVbNRicYJ5X/9EQCECybk2k+KHQyg5PH34Fr+7AC6xrx+j3M\n7IqNGCVmz/l6HFjz9/tpc6OvSdMa9fXy8/Si4JwRq+ZrrVYrotEoTp48ifn5edTW1pbQFfXGt1gs\nwpEn9VR7EpqZxAOZCWYmzs3flzkVfTBz0LPWHpT5OXwfs1HfaG5pkFlNy2vWBpVtDc0ekvbIdVKZ\nZexmBpXGVXVORhfNAShpKqFpljpHwcSq2+2Gx+ORJGswGEQymZTrYku9UCiEXC6HRCIhbQjpnfJQ\nZ7FhIpGA1WqVxuws2Nu6dSv6+vrg9/vF8yZmv7q6Ks+bmJhARUUFtm3bhra2NukSNTs7K9z32dlZ\nfP/730d5eTnuv/9+vPHGGygrK5M8B5lAAwMD8Pl8eOKJJ/DUU0+J6qnX68XExAQA4Jvf/CYeqhfP\n9wAAIABJREFUeOABjIyMwO/3Ix6Pi1yI0+mU75dIJOSgjcfjaGlpwfT0NNra2oSfz4rX9vZ2VFRU\nYG5uTuil/L5AMRlus9mE1cW1aF4PLCQzR4nA+v7XxAiuK+1g6rXAtUNCCAkeOjrTn/F+x8/THHwA\nwPZ3P7QMwASA/wTwWQB/XygU/t/3+16a9fDue5f8jY8RKjF7yjRwmucKlCYsOKEsnDKzVbS3bg7J\n32tSN0p8mhfDe43rhWb6uwHrWjH0DOi1E6IAIOwBMpDoxRMGYCgbCARQW1srOiCLi4sYHR0tgRSA\n9eSc1+sVVgcZNWY2ilkx1Mwi0fOhYTKzZ6LnbqODgOO9oj/OKz+bnh6ZQE6nU+aV88Xr5yZkxKGN\nN/sL68pgc7GaPgB4XeYD2HxI8FDQkcDCwgIaGhpkw1dVVYl3qa8/k8mgvb1dPF6v14vLly+LgeBc\neL1eFAoFKTrcu3cvWltbYbVaRRqjqqpKuPDNzc1IJpO4evUqMplMSbPumpoabN++HTfffDMcDgey\n2SxGR0dx8OBBvPrqqzLn6XQa9fX1mJqaQjgcRldXF7q6ujA8PCzPm5qaQn9/Pz7zmc/gT//0T/GD\nH/wAsVgMJ06ckHVht9slcpmYmCiBxex2uzB9JiYmpCDs1KlT2L59O7q7uzE3NycHy4ULF3DXXXfh\n5ptvhs1mw8GDB1EoFOD3+xEKhcQBmJubQyAQkMhZq0kycubgXtTrWx/sej28F1WS/H46r1xT5nWm\n1/v7Hb8o6Oa/ARgqFAqjH/Sk4TAvfGB9s3LTc9OYT0dzeKaNhTkLrpv3mk9c/sySce1p6WHWMXk/\n3uVP++4c2nDp70KjqPFkGiRilHa7XQSniNU2Njaivr4ea2tr4rlks1lks1mEQiEkk0kMDAxIxaD+\nbEYtuVyuRGDLTIkkW4TzQAOjN4OGyjRGav7OZq96IyOvPSIefObIjNdE6EZXPvPA4vvr5u9cX7we\nvh8jF3r0fI3e4ISZzJ4a19pGm1V/J23obTabSBYsLS2hurpatFCobQOs1zHYbDYRAGSeQOumsMKV\nkWFPTw/8fj/q6+sFPqIHqa+tq6sLFy5cwJkzZ4S1FQ6H0dLSArvdjlwuh5mZGezfvx8nT56UNRSL\nxeB2u5HJZHDgwAF86lOfEhpsfX09du/eDaBYB5HNZvGd73wHXV1dyOVy2L17N7Zv3y6Ncqanp3H8\n+HF87GMfEyon59Nms6G+vl7mmetnbW0NqVQKx48fRyKRENtw++23Y8+ePbDb7bh69SrOnz+P1dVV\n1NfXS3TH9UM6KmUSNP1RO3hmJ/F6jBuuJ7NDofebnnu933WUvpFz+dPGL8rQPw7g39Xv/5dhGE8A\nOAHgDwqFwpz5BYZh/DaA3wbWRYvMhv7d54mBYVcY7WnpknmzNwVcyy0ndqkXBVA6uWZBK7Px0TfR\nfKP1e2l65c86+J5LS0tigBhGMxJiCzTyyP1+P7Zv3y5eHhU/GXqPj49jaGhI5pr4JJUMiT1TJZLY\ndj6fRzAYvAbq0rCD/hzzAWm+r/q15ufrjXC9pBP/pu+lPrD5N2LYrDvQ3HryxelM8DXmzWSOOrTD\nobF8RkVcb+ZEr54H/Tx6cpwDyk3Y7XZ4vV4kk0k4HA74/X6k02kxSOl0Gvfddx+effZZWK1W1NXV\nIZFIoLGxUSI49mj1+/1wu91CUWxtbS3xHnUlN41SRUUFtmzZAo/HI/g5D/u5uTnY7XbU1NQgk8kg\nl8uJ09HU1ASr1YpcLifyB/v27cPa2hq2bdsmUfk//MM/yHravXs3Pv/5zyMWi8EwDGlxyEjiyJEj\nomVEp69QKIjWjYYzc7kcXn/9dZw7dw41NTX4vd/7PQDAo48+isrKSnznO9/Bd7/7XYyPjyMQCGBi\nYkIcQaCYTJ6fn5ecGA9iGmrzXud91b/rdW1GLMywMq+fcCchT1Yy64JPrkfz/nqv8XMbesMwKgE8\nDOCP333oawD+bwCFd///WwC/YX5doVD4OoCvA0BtbW3het6xxucZjuqJ1t69HuZNxcHXm6Gbjbxq\n/bt5Us3c9Y3G+8mKm71e8980FkgckJ9XVlZWUgBWW1uLcDgsG2x6elpgG3YXAiCHw/LyMmpra2G1\nWjEyMoLa2loYxjoDiv1k7Xa7eKqJROKaSImYNefC6XRew/YxJ6F0RbD2dPnddJ5go6iKXhE3hzk6\n0M8j9up0OmXONAuDAmzauPEfN6jVasXOnTtlbrPZrIT0+mCjMdDwj8Z29fPM12nG+71er8y12+2W\nw8hutwt+XVZWhjNnzmB+fl4EyOrr66XeASgmNlkBSxkFevdUiOVn8vP1gWm1WkWyAChW2s7MzGB6\nehpra2uYn5/Hnj170NvbK0JkxOErKirw4IMPwul0YmpqCpOTk0in0xKRTExMIJfLIRQKYWhoCOl0\nGqOjo+KIAMW9Nj4+jvHxcdHi4bXqZCirYcvKypDJZHD8+HFUV1dj9+7d+MhHPgKgyBgaGBjAt7/9\nbZw7d07IHZlMBlNTUyLhQPFDvQ65ZnSOECitgN/Ik9cRrx6aYMD35eM6T6hhP0bZ2hl5P+MX4dF/\nFMCpQqEwAwD8HwAMw/gnAPt/2hvQQGwUyuskhGEY0hiXQ8MwLper5BTdyNDSu+K43iGhMbCNDA3/\nrr1Z83PMAlcbDf05Gx1yHPowYS9WbmSq3hmGIVLJmh1gGIbgjXwvNmTX7czq6uqExsl5SCaTiMVi\nKBQKYiT1vHKxak+DoaZmnPB+8LDi+5kNPeeDHpReE3pu6H1TK8jsYekQurKyEg6H4xqjDBQPUOLf\nfA2NvL5+Hqgs3NNGnpHb9Q587d3zfmmqqdkrBIrRmd/vl94DTqdTepsGAgGpBh0bG0M0GpUoj2J1\nVqtVkpSxWAx1dXWyJnw+H2pqaqRNID3mXC4Hj8cDi6Wo8Ej9KTPJoaqqSooDDaPYszWTyUjxE1BM\nxhqGgeHhYRw9elSowZlMRuiMAHDrrbcKfEK5jb6+Ply6dEnuFWmy1NRhno3rl/coEAggHo9LcSEh\n2DvuuENgoGg0ihdffBFDQ0MIh8Ni5Mneo4SEz+fDli1bsLa2Jo4L7YzuVUD6r77XZrVbVnXrSNic\nr+J70Xvn+gMgBWJAafXsBxm/iIKp/wEF2xiGUav+9qsAzv8CPuPGuDFujBvjxvgZx8/l0RuG4QTw\nKwB+Rz3814ZhbEcRuhkx/W3DsZFXp3/W3hpDbHUNEuowHNQesnmY6UkbhVSEkHiibkSTNDN/NsLs\n3k9opbnVG72O3iITZjz1XS6XhN5kFLCIyul0wuv1SkRBuhafd/bsWUQiESnY6e3txUc+8hGkUiks\nLS2JJ2W1Wkv47PRqdccbUhUZThKPJFVTfxeNp/Oz9fzS62VYrmUSNH2N64WaORoq0binhroKhQIu\nXryI4eHhEvlqu92O7u5uNDc3C8WU3piuEcjn81JvQNnfhYWFksIkwjo6GavhEM4FZTh0XoOeHJ+T\nTCYxOjoKj8eDiooKTE9Pw2KxSHJwbq6Y9nI4HIhGo+jr64PD4cDMzAxqamrgcrlKCvcymQzOnTuH\nT3/60wLDsNhQC37xNWTpZLNZaUaiIR42N6moqIDf75fogx7xiy++iFOnTuHKlSvo6+vDvffei0wm\ng3w+j9raWtmrXHP0YBsaGuDz+dDX14fnny+ysolTz8/Po66uTr477wuH1+uV34mvP/TQQ9izZw/+\n/d+LvuhTTz2FwcFBNDU1CczGebBYLLIfx8fHpSiN64BUSu3Rk4FmGIbsBcKKugmLLvzknjIzyrhf\nucfMRXsASn7+IOPnMvSFQiELwG967FM/04VsUGCjNxyxM7Px1Ak5c+J1o0HowZx8NXOgNUXKzOjQ\noThhBrI/9GfT4HFDEA8mOwNY16Dm+ywsLAit0eFwyI2lFofX60Vtba1UKpaXlwt0Qx1xVnBSU9tm\ns2FwcFDkh8+cOYO1taJs7IMPPohHHnkEq6urwp3mxie1j9xjjTVq7F0fvmtra8I71pRXLmoaXZfL\nJdfLe0vqJrF3zb7aiG1QKBREUprJeh7ALpdLDDGNxMsvv1yCoQPFLlr8nbRJ3nt9GPH5c3NzEuqT\nJMDrpJF0uVxSjVpWVlRaNQxDWDBmDSOgyITKZDIlITqhiGw2i3A4LNgzNYc43729vQJ7uFwuoZJq\n3nt/fz9+93d/Fzt27IBhFJllqVRK3p/vxarpfD6PqqoqqZTt7+8XA7u4uAifz4e6ujqMjo6is7MT\nJ06cwOHDh6VpzZtvvomZmRlZo7w+JoL5vdvb23Hx4kWUlZVhamoK7e3t0oGKzexnZmbEgSFUx4PB\narXi5MmTAIrFY5lMBnV1dbKubrnlFuzfvx9PPvkkgCI81dzcLPuXe5f1IXSszp07h7a2NmH06Byi\nJoCY2Te0HWVlZSWNSwjRbVRRzUFHiuuO76lt2886PhSVsTRG9Ar10PjvRlgsX8/JvZ43rxM7+veN\nnqex042wfnqbNFxm5oS+dhosStyyyQi/Jwsz+B3IcMlms4jFYiL1EAwG4fV65WBIJBJi2Eh7c7vd\nWFtbQzweh9frRTgcxtLSEkZHR5FKpYQ1Qanbjo4OWCwWnD17FrFYTA4WXhsFnZjI1YeZeWiseSNJ\nBjMzycz31/eETCFNVTQf3nx8bW0Nfr9fCl947fF4HM3Nzcjn87hw4YLMUSgUwrZt26SVXXd3NwKB\nAMbHxwWDpXdmZjbwAATWq271xuV6iMfjyGQyUvzDtpA0XKlUCrlcTg4GrhUAJclfHpiBQAA2mw1z\nc3PSwYxUWbfbjUKhIF2eKioqpB/ra6+9BgAYGhrCl770Jdx8882ydoLBoETBnJvBwUF84QtfgN1u\nx2c/+1k4nU7MzMwgGo3izTffxMDAAACUJDDdbjceeeQRVFZWoqenRzD6/fv3Y2pqSoqyOE+ZTAaT\nk5NiUBOJhDDh4vE4otEo2tvbRYIAKHLtdTMeevi5XA4jIyM4ePCgzFlTUxOi0SgsFgt6enqwurqK\nv/qrv5IDlAc6q40XFhako1ZZ2XobzVwuh8nJSVRXV0s+STt+msa5ETvMbFcYtVDtVct468ppM5qh\nnR29Tj7o+FAYen3Kb5Qo1QbebGQ0u4MQwkZDn6h6bPR8Jnv0DTVfDzc1X89FoK+VfXDJRCkrKxOD\nTyiGxS+sBeDCaWlpwfbt2+Xz2fqQTBpeDwt4gKJXaLPZEA6HRc2TTIdCoYCbbroJANDY2Air1Ypk\nMikwBtuqhcNh8TxJV7VardJ8hBIA5gNVJ0E38lj0Acv35KLn5zEa4WGm6xW0V6MNP1vVBQIBTE1N\nCdV069ateOONN3D27FmUlZWhs7MTn/zkJ9He3i7QC1A0NKlUSg5afg5ZD5q9pe+RXqs83MmtNwwD\n4XAYTqcTiURCmCFkyvDAZfGfjly1ASEdMpVKYW5uDi6XC263G8lkUhKtqVQKNpsNMzMzos1SUVGB\nqakp+ZzPf/7zuOmmm5BMJrG8vIyamhqMj49jcnISX/nKV+RgmJ2dxT333IPdu3djfHwcqVQK77zz\njnSg4n1yOp1Ip9OSgB0YGMC2bdtKGnzY7XYEg0Hk80Wt/FAoJE0/dOTCtcC9Mj8/L4aThVBnzpyR\nxt9sjlNeXmxVODw8LGuMdQROpxMulwtbtmzBt771rZK2gGyosry8LD2F19bW+1hwLC8v49KlS/D7\n/bLmdaS/kYN4PVIFDwq9T/S+0Gq31ysE5PhZPfsPhaHXoZj2GLmZKPTF5+rxXvRE4FrPfSNmhNlb\nNAsg8Zq00ddtv/gcM4+WLAQyCgzDgMvlEo0Nvo/P5xNMnRi2xVJsh8hwOZvNyiYi64FeAeeAhwqr\nFc+fP49YLAabzYYdO3bIe2n5AcMo6uYQAuJG4pzxOaQTbnQYX4+RZL4/mqEUj8dF90Rj3Bu1qeO1\n6HupWRf0MAOBgBilF198UfTRW1tbBb9dWFgQAwVAOjNpbrx5o3KQmaEjjbW1dW2gTCaDLVu2wOl0\n4urVq7hy5Yp4cewFDKxHSnRwCAv4/X6JhtgP2Gq1wuFwSA2JWbq4oqKiBLtmQVFNTY1o09x///0A\nikwSrtNXXnkFzz77LADIuujs7ERDQwMqKiowODiIY8eOoby8XA4Wzj89+127dmHXrl2ora0tgXqA\nUv2fiYkJMfCUQDA30s7n88KO4r3god3Q0CD3NZfLiW5+MpkU+i/vZW1tLW699VbkcjlEo1H85Cc/\nKZmvdDoNr9crXbkIvVH4TmvisNFJOByWyJpeuMbczevFbIwNw5BDSvdLIDKgOfNEBcz2RttEvucH\nGR8KQw+UGlUzB14n9cyhi/nk5GPaw9T/v5/XaoOtPSw9SEvUeLUZo19ZWRG1TS5ih8MBp9Mphsbh\ncCCdTmNiYgKZTAbV1dWoq6vD2toaZmZmxAukl0J8X0sN0LsLhUKC46bTafj9fgSDQdhsNmnFCEAo\neOyduby8DLfbDYfDgVgsdk35Nb2tmpoaLCwsbAih6EGhMfNiZeRlGIaU92sKZaFQuIYWuhEUpzH2\nYDCIxcVFEfLid2xsbITf75eKUob87F5kToLxkNTKmOY1w+sh3Y2HKo0D4a0XXnhBGuUsLCzA4/Gg\nurpaevZms9mS5B0jCL122F2Lf9MGJpfL4c477wQAnDp1ChZLsVn4448/Lj1/33zzTTz66KMAiklK\nHTH85V/+Jd5++23MzMygtbUVd999NwDgjjvuEArjO++8g/7+foHWZmZm5NrC4TA8Hg+6u7tF/yga\njYraKdcAczw8QLl2CdVyX/G6XC5XSWUqobK2tjYMDw/LnNDRYREY57+8vByPPPKISCb09/fLOmNO\no6mpCVNTU/D7/airq5PuaIlEQjqVAesQGg9RKrvyPpjrIPTaNEe53Ku6fodRAem6XIea3KAdI21j\nrgdPv9f4UBh6JvSA0mSsZmWYG0hoXJcb0KxBwefqz9HjehOlM9tm/Fg/bj4czMVbmUwGlZWVqKqq\ngsfjkQXJRB1Q7GE5MTGBfD6P1tZW6ecajUaRyWQQDAYBQBqasAE0PUEmqHjdDEOB9fJ4HjT0aqLR\nKJaXl6Xy1WKxSGLU4XBImDs0NCTsBUYUZpbQRnNIr0RDadrLr6iokM/TC117KzqK0wlx/s6NMj09\nDafTie7ubgwNDckcb926VdQH2TeUWLTNZhOMO5FIyOGmr1eXnHPovA250RprT6fTOHr0KGw2G/x+\nv2jIRCIRDA0NlUhOO51OacZtsVikFSPvpcfjQTAYFLivrKwMgUAAFRUVmJmZwaVLlwAUE78NDQ24\n4447sLy8jEgkgmeffRajo6PCGhkZGcGdd96JhYUFfOtb38Kbb74puH53dzd27NgBoJjXmJ+fx9LS\nEmZnZ0WZkjkTDrZtnJycxOzsLGw2G+bn5zExMSFrjFALI3JGSlarVaQ6gKJBjUQimJycxOLiouwr\njV0Hg0FZ/4yGcrmcqGtSTqGvrw+FQkGKrmKxGKanp0v2Hu8be0yTQeZyuTA5OSlNy5mgpdwCH6PB\n1pi/2RboRC+wznqjIed78x7wQGNxoxZMNEM3evzSGXqe8Pn8ur47HwfWdUCAazVgtFetDYB+PYc5\n/OdzzFECDaV5aM+TCoTEVzm0rrjH40FbWxtsNhsikQiy2SwcDgcikYgwYJxOJyoqKtDW1obGxkaU\nlZXJhvF6vbLBtOQwC24Ip+gDxGKxoKqq6pqFQukIoGhQm5qasLS0hOHhYWlRNzs7C6fTKZri58+f\nx9TUlBQIxePxa9hPZuhMs23MFaGcH97jcDiM9vZ2eU/mG2j0KFnA72GmTgJFrfKKigqMjIxIAQxQ\nLCSioiMrKklVjEajAoNZLEV1Tl30wkNF52B4z0l7I81xbGxMoq50Oo2mpiYEAgHcdtttcLvdOHr0\nqPRBoFfJXqgaArDZbJLHAYoHO4uOamtrsba2JlWtzLFwPqkgmUwmcejQIZw9exbz8/P40pe+BKCY\ncH766aeFPcPPe+KJJ9De3i6Rxvj4OJ577jl0dHSgrq4OJ0+eFIIA6ZO8toWFBUQiEdTV1YmEhq44\nXlhYEOqlzWbD5OQkAoGANPTmQWu32xEIBBCNRqUZucfjwerqqkAyHo8H9fX1ortfVVUlXcVmZ2dx\n2223AQA6Ojpw4MABWCwWbN68WaCXeDyOjo4OAMDly5fR3d0tzCXCJZRw1npZqVQKDodDErapVErw\ndq4XTank0Owb7ksztm9mcwHruUG9n8xr/5cao6dHz8ngYBjEjbdRok+fajR0Gj/daJiN1Eb4u55Q\nbWA4iFUzKctqNi2gVVtbi8HBQSwtLSEQCCCXy+HYsWMSDgJFvjRZIAsLC4hGo1KNR2MHrNNCdS6A\nXii9C4bZbK+oE4plZWWCec7MzGB8fBwejwdNTU0oKytDJBKB3+/H6OioHEKsnEwmk3C73QgGgwJZ\n6aHpppwvHnjaq9FztX37dtTX16O6ulrCZWrymBu7m5Nf5tA1nU7D5/OV6OAHg0GkUilpXG0YRol2\nid5ozFHwOumF6oOG+Qk24M5mswI7BQIBAMUoghHV22+/DcMwMDY2hnw+j4aGBmH6kGVCo8F1rZOx\nmUxGnABdS2EYhigpckxNTaGyshLPP/88XnvtNYyPj6O7u1uSrKyWnp6eRlVVFWpra7Fv3z74fD6M\njIzg7NmzAICf/OQnWFhYQE9PDyKRiOgekWtPQ+/3+5HJZDA8PAyv1yuHVCwWE8ckEAhIcra5uRm7\nd+8WSWwNfehaBMIoZWVlko/g925tbcXExIRIQJSXlyMSieDWW2+V+aeuzYkTJ3Du3DkMDg4CAJqb\nmwXjb2pqkjzUzp07Rcxsenq65GCPRqOYmZlBVVWVqMLy2sz0aHO1OIcmTXi9XvHYbTYbfD6fHBB8\nHh1Vsx3TdvFnZd3caCV4Y9wYN8aN8V98fCg8eg6dZDM/rjvm6GQpYRt9yuqhvXcNLdAzJVzAE5Ne\nhcauqXao35+hPL0tau3YbDYJOfv7+5FOp5FMJhGPx3Hu3Dm4XC6Ew2GBR1pbW1FZWYl4PC6hOfHz\n8vJywQzD4bBgs729vbjlllvw3e9+F9PT0+IdMWE6NzcHr9eL3t5eAEBLS4t4SXoumFxiLmB0dBRv\nvfWWXBvb1xnGerEPsX5+pqZJ0sOtrKwUj1RHVeXl5QiHw7j33nulUQUT1kCRXbGwsIB4PI5AICCt\nC8mU0Zos9HKsVis8Ho/AKcxpABAdFN5f5iso4wusa+6TEUUWDaM6MkzeeecdLC4uSiK9pqYGDoej\nhDWUzWaFRx+NRlFeXo7W1lapZ9Be+PLyMtbW1nDlyhW5F42NjXL95HmziUZDQ4NgvaFQSNZ1V1cX\nstksxsbG8Mwzz6BQKGrXx+NxWa/T09Oora1FfX09nE4n7r77boEfIpEIzp07J9fk9Xpx5coVSfyX\nlZXJvWJESFXNlZUVLCwsSA0MmTd8DpP81dXVQg0l7Zd7JJ8v9lag1j8F5mZnZ9Hc3AwAuHr1KkKh\nkOxN1pPcdNNN6O7uFuhpbm4OS0tLUhvAQr+5uTnh93PtNzY2wmaz4ZVXXsHS0hIaGhqwZcsWgZ4m\nJiZgt9sxNzeHRCIh2v3Mmehksq6Gpc3ROQZGa4zQCEWTJqrXBfNpXFMkBejCOj5vI4LJ9caHwtBf\nj11hhmX0BOrEnd60+nUa6tEUJg3ZENs3tyaMxWICofh8vmvEi8rLyzEyMoKdO3dicHAQHR0dqKmp\nwbFjx+SGu91uTE5O4vLly2hvb0dbWxtGRkZEKRIoGkqtIe5yuUSKlhg8AHz9619HKBSC3+/H888/\nj8OHD8NmswnDBiiGuMPDw1LOf+jQIezevRtzc3PYtGmTHEpUAKTYldVqxenTp/H2229LogooTSJZ\nLBak02lZ5FzElEwg5ZDl2+T0857YbDbccccdaG1txfLyMlZWVgS3JoyVzWYRj8dRW1uLQ4cOYXh4\nWCik5mQsmS+rq6sIBAKS4Ga4X1VVVdL2sLu7G729vXC73UKl43Xp0vmlpSXE43EpTuL7bd++Xa4j\nk8kgm81icnJSKjW5nsrKiu0uw+Ew6uvrJfeUSCRKyv4JK/HQ6ujowL59+yRJH4lEUCgUJQj6+voQ\niUQkkZlKpYRp5XA4EA6HcfvttwsjhbAVD+f6+nokk0kEg0F0dXXJ/eN31gacydiOjg4Eg0HY7XaM\njY2VcN+515ikZVOccDgs+YpsNguLxYLa2lopzNNyErw2wooAZM0/9dRTCAaDcmhUVlYiEAhgaWkJ\nvb292LlzJzo7O9HS0oJMJiNQI/fPlStXBELzeDwlhpNzFAgE8MYbb0ghFpuskLufSCRw9uxZ5PN5\nZLNZybNUVlaWUEcJvdEOabE6DQ/SZnGN6ASutkdcY3RkWKmtsX3+/kuXjCXP1Hzx+neegpqLCpS2\nbTPTJXUxgnlhak0YRgzAOj5PxgDLkqkICaxPdldXFy5evIhbb70Vhw4dQkVFBbq7u8UIsjhjZGQE\np06dEqMaCoVKeLlsGpHL5TA3NyfJVpfLhdOnTwOAFCwtLCzIhtq8eTPi8bhsVG44Kg/29fVh69at\nCAQCGBsbk89k4oxG7+zZszh79qyUjdO4ZTIZmWu+t1YOBNZzB5xn8qRZP8BEK3uDMsk8NTUFj8eD\n5uZmRCIRAMWDsaamBv/xH/+BmZkZlJeXC/1TyxHwsCc2zO5HbrdbPo/UzZqaGtx2220lm9ThcMhh\ntrKygrm5uRJaLdsOJpNJORC4fgYGBsTwkLJKo+vxeGC1WhEIBMSw0nCGQiFJxpIGCwDHjx9Hc3Mz\n7rzzTszOzopWTHV1NVZWVhAOhzE4OCjMjLq6OszPz0uV7VtvvYVnn31WGDmzs7MYGBjA1q1bMT4+\nDmCdIMD5YSJ1YGBAGFUAhCFWXl4u1azZbBZutxvz8/PyPHrzjPR8Ph/C4TBqamqkzytmXPHcAAAg\nAElEQVT58FVVVSgrK8P09LRUbuuWfToPk8/ncfDgQdTU1IgcNt8rk8ngM5/5DKqrq3HTTTfB7/cL\nA4kePSNLagktLi7K53CPUPojFovh8uXLwo5aWys2KqHT0d7ejnPnzmF1dRXpdBpLS0twu92wWq0l\nvSHMzDtqydMz53VpjF0z+TSZgwcF95d+Lv/XyMYHGR8KQ8/wELi+R08Dr0uWgWu7SPF/TYMD1kMg\np9MpIZi52QMA+Qy32y2aJNSY0XRP3sTNmzfjyJEjcDgceOyxxzA+Pi4Qw8jICM6fPy83nQUwPp9P\nelgyDF1cXBR5hLKyMszMzGBwcFAaLbC3Z1VVFdLpNAKBABKJBKqrq8WAaHigs7MT+/btEw50OBwW\nj4wQw9LSEo4ePYrz588LzzyTycj7kVHAA9Pj8chi5uLUSWgmuSip293djZ07dwKAMCkor9zb24t4\nPF7inZ4+fRpXr14toXTa7Xb4fL4Sj5CwBznvlLxlKAwUIbGOjg6RQaAs8+LiIpaWlkoapadSKUlI\n8nuT/sj1Mz8/D7vdjlAohHQ6jStXrsDpdKKjowOhUAhAEepib9e77roLTU1NmJiYgMvlwuXLl6Wk\nf22tqOF+5swZbNu2DR0dHbh69SqsVqvsA855NBoVvvf8/DwikQgeeOABgT4OHDiAM2fOyHp0Op1o\naWnB8PCw6LSMjY1h586d2LJlC/r7+zE/Pw+Xy4V9+/bh3Llzsi5YZUu6J+eYByqZSowGmFQlE4jf\ni/uVRV4DAwOyhyhtQX2mxcVFoXA6nU6srq5ibm4OnZ2dkvzlIR0IBKShjsvlwpkzZ3D58uWS4rdE\nIiGsIB7Y1InievX7/Thy5AjW1op9GlpbW1EoFOT+AcV2iT6fTyIZTfXVjqHW7aEDQodBw9B8XNOT\nGQHTlukDgWJmfG9tezYq+vxp40Nh6IHrV1hy6BaANNTAeocpshG0sdeHAH/WehPAeiMSwho8HDSP\n2263ywYEigsql8uho6MDR44ckaYUXPx8r/Pnz2NxcRG1tbUSVlJbm3CLx+NBbW2t0B81tqe99Xg8\njs7OTkQiEenUc/DgQeFiA5DirJtvvhk9PT1yIHIxchHb7XaUlZXh3Llzwr4hTxlYL5RyuVyi7MfG\nFblcThQjgXXPmc/N5XJwu91obGzE5s2bhRHBa7NarchmsyJy5fF45GAcGxuT1nkaSiGPnEPzjPP5\nPNLpNAzDQF1dneQlOjs7RcuE7BlGaORaA8CVK1dw5cqVEmovWUtmiYvx8XE0NTXBZrOhrq4Od911\nF5aXl/H2228DKLJR+vr6sGPHDtnsiURCmnFTkmB4eBjDw8PYvn27QGo8ZOlRkp/Ooisekqw7uHr1\nKgDghRdekHwGNZKcTieqq6tLjFZHR4d4/fTI3377bdHV5+Dcs2KYz6U2PbDOJwcgMF0+n8fMzIw4\nJpSVcLvdMAwDLS0tElFRTRMo5g9Iz6T4nMvlktaHQNEbDwaD2Lx5M9xutxT6DQ0NIRqNlqhvFgrF\nvriLi4uiwa+9YK6lZDIpbBpGX3pd+Hw+tLW14cyZMyUIAosUeW2Li4vicBhGsfcBI2LtqJI/T3ah\nmRYOlFInzXUq5vqODzo+FIaeFZHvNaxWq5Sf64nmZuQJaqYgmnnzrOykh6+1TfTziI3xlNUVow6H\nAzU1NcIzf+yxx6Sj05UrV8Tzefvtt0W5b3FxEZ2dnXA4HBgYGJDkUHt7u3R70t4yP4/eit/vF8w4\nHA7j0qVLUhCjD8Suri60tbXBbrfLgdTe3o6RkRHxolZWVnDlyhVMTEyUYO1UTOQg1sgog4lMblIA\nQo9MJpOSAG5ubsaOHTukohaA9KTNZDKCNTscDjz33HNS/MPOQX6/H4lEQj6Lm1P3MyVcxJ6h27dv\nx5YtW+S60um0JO5YnKLxd82D3rJli1BHuaaSySSi0agc2uSrOxwOtLW1YX5+HkePHkU+nxdc9557\n7kFdXR3a29vx5ptv4tSpU5iYmJA5HRkZAVA0jg8//HAJHdVqtcLpdIp2i81mw6ZNm0Rzpr6+HoZh\noK+vD3/1V3+FQ4cOAShiycyF5HI5ifxWV1cFqnviiSdQXl6OyclJXLhwAdPT06irqxPaJdcAaZO6\nYDCZTMo6JMecXjv3bTweF0VUGtTKykqEQiF0d3fDYrHA5/NJMjufzwuuPjw8LOJti4uLAnEFg0FZ\n3zt27JDohBXfx44dQyKRKBGyIw+fa4ytDZuammRfl5eXi0orI73p6Wk0NzdjcXFRkuGrq6uoq6vD\nxYsXJTqhYSbODqCE/sq1YxY31IV2XLdmSrJ5vwEba33x8V9KjF5XmpkTs/yZWjFMZGjuKSeQ+hE0\n9JpNQ8OvRaS0gpzWt+ENoJ4Nb4ZO9F24cAEVFRX4xCc+IVWB+XxRrZK9LlnksbS0hNraWrjdbkSj\nUczOzkqHoHw+j6mpKWF6cHMSq6W30tLSgpGREfzGb/wGnn76afT39wvXntfX0NCAvr4+ABDDm0ql\nkEgkpLQcKB5AR44ckTmkR5nNZkUPBYB0KuLhSmOte64Suy8vL0cwGERPTw8aGxvloOKBpkvQp6en\nUV1djS984QsliTJuPB5AxM6dTmeJh8dwv6ysDDfffDMaGxtRW1sr+iVA0WiVlZVhfHwcLS0tOHv2\nLE6cOIGpqSkRcQPWvUDmb7LZrPDH9fPsdju2bt2K6elpXLp0CVNTU7Db7XjkkUcEkmF3rkOHDqGj\nowMjIyPYsmULNm/ejK985Styb++66y64XC5EIhGMj48LTDg6OiqFPUtLS3jrrbewZ88eNDU1IZFI\nIBwO43d+53eE1QMUI8Lx8XEYhoGamhoMDQ2hpqYGgUAA+/btA1A0IMzR1NfXIxqNyn4hywtYl5j2\n+/3SbUqzwHj9lA1m1LO8vIyBgQFZb5zXYDAoTtnc3JzAEYuLixKRkItvt9uleOq+++7Dzp07Rebh\nxIkTePLJJ/Fbv/VbaGhoQDweR39//zW5OYvFguHhYVmrXAv6AOrq6kIkEpGaC5fLJXLSLKrjWvT7\n/aLrn06nZT2QT8/1v9HQ0A0ZTppIogsBOTZqTq9x+p9nfCgMPVAqZaATEHzMLDalJ4iLSXvnGrYB\n1g39yspKCUZPQ6UNvdbN5vO1qBMFuXbu3ImXXnoJFy5cQFtbG3p6ejA+Pi4efSgUknJ4oFiVV1tb\ni82bN0uIzgVZX1+PhYUFzM/PS6/Wj3/84/iVX/kVAEVP5IUXXsDf/d3fYXl5WRak0+mUQpxbbrlF\n5oPeTFdXF0ZGRrC2toa33noLAGQzkD5HwSWPx1NSgMWELdkfnMNQKCRzQTx9165d6O7uFl0Vwgk0\n8BRaGxsbw/z8PF577TVpOE0PMhQKYWZmBg0NDRI1cF3QS+V79fT0oL29Hc3NzZK/0fRXrTT5T//0\nT5ifn5eIgXpAwDobi9EBde0tFgvq6+sFfyfLyuVywel04oEHHsD999+PRCKBl156CUARLvJ4PHA6\nnbh8+bJgy6+++iocDofcp3Q6LUaysbFRaKbE/4Gik3PLLbcgHo+LaNuf//mfiwIk7z+pihT5CgQC\nyOfzEl0A6/rxIyMjOHbsGOrq6rB9+3akUinpzwpAVDYpUqYZT6Sx8p5oBglhJRpBAFIBS9E5GtuN\nyBQ2mw0ulws+nw/33HMP9u7di7a2Nnz/+98HABw9ehQPPvggGhoaMDs7i3feeQcrKyvweDy4cuWK\nMNiCwSBisZjkn0jntVgsIn8RCARw+vTpErri4uIicrkcWltbS6I6t9sNt9stDgDJCDzMuEdIJGGE\nQ9VX7fXTdvEfjbiueiXSoPOKzJXoPICOLN7vuFEwdWPcGDfGjfFffHwoPHrDMOBwOFBWVlZyeulM\nNemOZlxLn2r0MvgcLeSvPRDDMKTQB7g2wUHIh94NMURGCBcvXsTevXvxb//2b8JUcLlcmJ6els7z\nHNu2bUMoFMLq6moJVZG4re6OBKzTE++55x7Mzc3h8OHDAIrh8uTkJHbv3l2SsF1bWxPPjdAUucmG\nYSCRSIi3x+dR7IzJJ4aw+Xwe0WhUkruk1xGHJOVLc4l9Ph88Hg9aW1sRDAaFEsnEFMNntp7r7OzE\n008/jampKfGitR632+2WlnO8XxTRYmL69ttvR09Pj+Q1mD+gxwUUoy6WwjMhRy/QbrdLYlTnXYAi\n5NDR0YHa2tprGlTfdtttUuw1Pj4uDUaYqJ+fnxdWD4uDXn75ZaRSKTzwwAOyzsjJZq0BoUTeewAS\n7ezatQtHjhzBF7/4RfFSySDiunY6nUIbbWxsxLZt20qawXOtXL58WYTALl26BIfDAYvFIp66y+VC\nKBQSuG5ubg61tbVYWVlBY2OjCJFFIhHMzc0JqaGyslIojoQak8kkamtr4XK5pOahuroaqVQKFRUV\n4hWT079jxw7s3LkTt912GwqFAp555hlZ+w899JA0LZ+fn5doKRKJiPAaAPHmtTQJIUze58XFRSQS\nCRFHW1pagtfrxYkTJ1BdXS1zZhjFngLNzc04fvy45OpYoKjhFY2lM/ekZQvYfpCPkWuvqZQcmuhA\n2Q1zcZSGq9/v+FAYeovFUpKpNjNl+DP/bjb0moGhMXk9Efydr3+vjDcpgAsLC7h48SJ6e3uxvLws\nHXtCoZBQJMvLyzE1NYVYLIaenh7k83lhO0xOTkoIS1qlzWZDY2NjCT3RPBd6sACFrJLW1lZ5DQ0g\nB7P/+rvzuwaDQTmAyMZh2EwDDhTZHtzQZCORmXS9qmUmwTWrgDr4PDQAiC7/tm3bsHfv3pL7xWvl\n/4VCAZFIBF6vF263W94XgFRaEkfXmu4sOIrFYggEArj//vslIZfL5TAzMyOJOqBYTelwODA1NYWe\nnh44nU5JorJoCCjWAezYsQOrq6u4cOECxsfHcebMGUxOTgp2zUYV4XAYi4uLiEQi6OnpwQMPPACr\n1SraMxUVFRgfHxfqonY4eI9I3Tx27Bi+/e1vo7KyUpgimh+u81AdHR244447EAwGxZgBxYQtZYMr\nKyulOI+HNw8qQiBk3lRWVqK6ulpgO641FsfxvqdSKaEmcl1XV1dLxTJzV6RWrq2tyWfNzMzgV3/1\nV9Ha2oq9e/fCYrHgD//wDzE6OooHH3wQAIThdvbsWQwMDFxDZ+S6pJIo60i4PrWQHqEQAFLZzL2+\nvLwsMCspwzxgSLPk99F5De4P7hEzbExYmeub18x7ybXIdamdU841B4kJH5RL/1MNvWEYTwJ4CEC0\nUChsefexagDfBdCCYgPw/14oFObe/dsfA/hNAGsAPlcoFF76aZ/BSkddPACsM2pId9Rjo+pW3biD\nRl0bJzMLRx8aGvufmJiA1+vF8vIyWltbUVVVJRrjQHFxer1eOJ1O+P1+7Nq1C+l0GufOnRNhMg5W\nbHJDLy4uoq2tTW6U7mGrs/lA0dDSiLBKjjIJXAQsruJc0NATf+d3mp+fL5lDUjn5fEYWmkrKyIeU\nUc6xrtQjtZXzTgor+eQ0NlQzZP9TMgy0Z8Lr5z1paWmRdWFuvcdojdc1NjaG8+fPS36EUcjMzAw8\nHg9OnDghaoV8HQDxZm+66SZMTU3hypUrciD39PTIob28vIwf/OAHuHTpEg4cOICqqiqEQqES5lI2\nm5Xq3qmpKdx1112499575UCmt7uwsIDNmzfj6tWrSCaT8Pl8SCaTaGxsLOk8dvjwYfzrv/4rZmZm\nEI/H4Xa7pZKT10+vtFAoYHh4GJcvX5YDnPPF2g5Wq+o173A4Svrskt5aVVWFiooKxGIxuN3uEsNS\nU1ODeDxeIjvCClKuxerqalH7ZLRBQ0quOwB89rOfxY4dOxAOh6UZysMPP4znnnsO58+fBwA8+uij\nIkVCj5v9bnmtvIbq6moRKWO+jXko3keuW1a/0wlraWkRps/CwgKSyaREOCsrKzKvvAf8TO4xJvW1\nwec8M2mt8XuK9/E9SHWlw6T3sLYJ3Lu/aNbNtwB8BcC/qsf+F4CDhULhfxuG8b/e/f1/GobRC+Bx\nAJsB1AE4YBhGZ6FQeE/yJydC60ZwcFGav5x+Dh9nSMQEktlwmrPYZoYPn0MohlzkkydP4siRIyIv\n+9hjj+HSpUvYsWMHhoeHceDAAfGY6+rqZIOtrKxIYwRSCqknorU+aHB5Y/ldgHVtCy4crY650WIj\n20jPTeHdQjH9uE5Cc7HzfegVa6aS3W6H1WoVVUFCN8lkEoZhCHOEzbgp/auTXqTWpVIpMS76PjKZ\nzu8Sj8exvLyMiooKSVAD62Xn9ORPnDiBY8eOCZuEg/d/eHhYWEfLy8tYWlqS9+rq6hIuPCtzJyYm\nYLPZ8Pd///fCy//xj38sFZU8qCYnJzE/Py+HcV9fH86ePYtUKiXetcfjQSqVgsfjEePscrkwMDCA\nYDCIubk5FAoFtLa2oq6uTiKIS5cu4cknn5SKbBp5q9UqvHPOGbnqCwsLOHDgAKanp3HnnXeWcNrp\nObKq0+PxiNwvYSwyv7TEBdcltZ+A4iHU398vz2dhkr7fPp/vGtYa60dyuZx0vbr11luxurqKr371\nq6Lv1NPTgx/96Ed44oknABSLEqenp+XA0o4dixqBYgQ9PDyMubk5mRPOkTb0NNihUEj0lJaWljAy\nMlKiXcXaF34/zo024oxqOPR9MXv13I96bZojEkYSjNRoyziHOpr6hRr6QqHwumEYLaaHHwFw17s/\n/wuA1wD8z3cff7pQKCwDuGoYxiCAPQCO/JTPuMY4AaVQjHni9ETTOLKKk6fmRoaeB4rGOPVn0suZ\nn5/HwsKCiIRZrVbccccdAIAjR45gZGQEHR0d+M3f/E388Ic/lG48Z8+eLWn8kc/nS6QE2G2Ioffs\n7GzJtZqpV5rHzb8TouFCMEc7nAt622aqqX4954LwmcvlKtEkASCFJdoj5GeSwcTrZuTh8XgQj8fl\neeRhl5WViddNI2CG6ng4uVwuKcjSPT0pS5FIJHDgwAEMDw9LA3ONb7I4zev1YmZmRg7KvXv3Cjx1\n8uRJBAIB1NXVoaysDIODgwgEAqisrERTU5OwNZaXl5FOp7F7926pEu3t7cXQ0JB4nouLi9i5cydi\nsRgeeeQROJ1OaZA9PT0thxv1glZWVtDc3Cy1A01NTfje974HAHjyySeFdkvJjJtuugmRSKSkV6ph\nFGUILl++LFHZ+Pg4jh49KjkZwl40xoZhIJlMCo2UkJLH40FdXV2JhlBdXZ0wZxidsTI1Ho9fY2zo\nwFCGl2uDxrRQKKCzsxMf/ehHARQ7ZL3xxhuwWq3SNPyll17CAw88IFXVrJgeHx9HfX29wC9sYKIl\npnmIcZ0bhiEHJZ/D/BwlpBlpXr58uUQPSmvtcJ9oLRuuWXr72lk0Q8fcpzyAuCc1PbqyslJ6EjDH\nqB1XAFJoyHv/fsfPitHXFAqFqXd/ngZAknY9gKPqeePvPnbNMAzjtwH8NrCuCWM2zPoLmmEWbQx1\nqKShBHN4o40Kk7N8P32TMpmMhObPPPMMYrEYHA4HfvjDHwKA6HpQofHxxx/H97//fczMzKC6ulre\nt7q6GqFQSBQryRMuKysTfWw+35xIpvFlApI9N7lZdIk0PTJeP3VIdOELsL4JCQHpeaNKIvuTAkWD\nSmofMXgKvvE+kd+u2+MtLS2J168jMqoe6lyCzrfwevj9o9EoLBYLHA6HQAkARMjq1KlT6O/vF6xb\nJ8aY2CQX3+/3o6qqCnfffTf6+/tx/PhxAMDevXvx6quvIhQKYXFxUTb3rl27cPr0aVF2bGxsxL33\n3ovl5WU0NTXh05/+NMLhMN555x189rOfBVAsiKLODAucDKPYHzedTgv+TgkNVmMSB//CF76A/fv3\nAyji6sFgUCQBGhsbcfz4cRHB46ERi8Xg9XrxwAMPoFAo4OTJkxgYGMDk5CRuv/12WRNMiHJ+WMXq\ncrnE89cQU1dXF1paWpDL5aSNnj5ow+GwRH50YniwAsU6Bu5fHjT01vfu3YunnnoKAPDss89iZmYG\nmzZtwsc+9jFZY5s3b8Yrr7wi6zUYDMLj8SCTyaCmpgbZbFaKu3S+hRElP49rW/eC5dqg9ArXbEVF\nhRS/UYGV0b0ZNTDDjTpfsFGejHCPTtDSRnEvsZBKR7VAqb0j5EWn6v2OnzsZWygUCoZhvH+9zPXX\nfR3A1wGgqampwJBEG2AaBF25CpQqVuqfzRCG5qTqA0R77+bDwGKxoKamBktLSxgcHBRs2OVyiacQ\ni8UQi8XQ19cnsEA8Hsfq6iruueceYSC0trZibW1N2gImk0n09PTA5/OVQDM6McwCLhp9RgdMaJEp\noT1fbnp6AlrKgM/ZsmWLLBJ6IawO5HtVVlZieXkZ77zzDoCiMaC34na7xevXAm9c/DxcGDGxjR8P\nIeqFsImJDknNCXfeQ27ilZUVzM/PlxwaFN7q6+vD1NSUeKgcNGo+nw+pVArt7e1oaGjACy+8AJ/P\nJ8U4AwMDaG1txZYtW+D3+5HP53H69GkcP35cEqL8zBMnTmB1dVWSrtu3b8dPfvITPP/88wCKbJSG\nhgbcdtttiMViwkhZWVlBe3t7CQPJ5/MhnU6jr68Pk5OT+Nu//Vu8+eabJf16qUxaU1ODK1eulJTa\n8x6zmXd/fz+qq6uxc+dOeL1eDA4OSiUuexDTq2Ty0TCMEnkJKpp6vV5s3rwZVVVVGBwchM/nK5FK\noJCbbo1Hw0pIjBz6QqHYLs/r9SIcDmPLli342te+JlHExz72MczMzODAgQNoaWnBzp07cfDgQRw6\ndEiqYc+cOYNYLIbq6moYhoGLFy+K8Q4EAtfI/JK5R/667sLG70/pglwuJ1CYxWKRWgcyhGKxmIjV\n0TbpBKmOknUEbc4Pch9yH/DzGFUB63peOn+mc46c1/8/WTczhmHUFgqFKcMwagEw+zgBoFE9r+Hd\nx95zaOOkvTLtcZq/lD7tOLHmCdYVcRxc1Bt583zf5eVlTE5OYmhoCGVlZfB6vVI8BBS9FXoBFy9e\nFOVEm82GoaEhed5bb70lnkFZWRmy2Sy2bdsmhgtY7xrE6+f10mM3Q1QcfO5GUYoWQuLPMzMzkjuo\nrKyUw2Z+fh5lZUWhp8nJSaEz8trYlpBQw9raGjweT8lC5wHAuaNKocPhEJimqqoKPp9Pkopm7JHf\nUd+LlZUVBAIBGEaxUxPL5lOpFHw+H5qbm2G32+H3+7GysoLR0VH5PKoOTk9P4yMf+QhGR0dx5MgR\n0RWiKigx4rGxMRiGgU996lNoaWnBuXPncOLECfz6r/86AODNN98UvX+LxYKnnnoKk5OTeOedd0Sg\nbteuXdi9ezeqqqqQSCTkuzF/QaNAaeR9+/Zhfn4ef/3Xf40jR47A6XSWVHxHo1E0NzcL/ZDMIXZv\nAooV1ENDQ1LcNz09LTLDNKYtLS0lfXwZtVGumtdJ3ailpSUMDQ2hvr5eDr+ysjJxKBhlMUei7xuf\nQ6aUxbIuVZzNZvEnf/In0nMAKIqy7d69G48//jiam5vx3HPPScu+0dFRuX4WLC0sLGDTpk2SG9L5\nLC1rof/XxXTcTxQ6s1gs0nVsYWEBly9fBrCepCczh0MfIFxnvBcan9csGzpRmgrMf8ybABARPV34\nyUOF70VCyAc19D9rwdSPAXz63Z8/DeBH6vHHDcOwGoaxCUAHgLd/xs+4MW6MG+PGuDF+AeP90Cv/\nHcXEa8AwjHEAfwHgfwN4xjCM3wQwCuC/A0ChULhgGMYzAC4CWAXwf/40xg2w3jBAe7amaxAMdiMv\nlp6sxoSJUZthGRbOEMcjVKJ5/IuLi6KKR5GoaDQqjZW7u7sRCoVECCsajaKvrw8HDhzA2NiYhK+k\nVbI5B5kX+Xy+RKRJwx78n/oYugnIu3Nc4hEA68U+9AboxfP38vJySVZy0POjlzc8PCx8YHKJSePU\nTChGGYzA6H0wQUa6GOeZc1EoFAQCKxQK8Hq9khPQ3asMw5CGJGz4cuHCBbz66qtSqk9qmqbWUW+m\npaUFQNFj27dvH2KxGF577TWUl5dj06ZNIvXLhtK7du3CwYMHsWPHDunru2vXLnR0dMDlconu+9at\nW4W6GIlEYLPZMDMzgzfeeEN0ixobG0Ufn4U4mUxGQnrKKff09AAoFjD9zd/8DQ4fPiyMDkZ6FosF\nDQ0NuOtduWPWAITDYWEw8fq3b9+O2dlZnDlzRuikwLoOSyqVEpltQhts1qJF6iorKxGNRiVnQIhD\nS3RwdHZ24tChQ0ilUgiHw3LdvA8scqSK6cmTJ/Hcc8/B6XRiz549Ag+2t7ejp6cH9913H5599tmS\ndcW9zHXK/UAIkIljMtN0ToCdwOLxOOrr66VA8eLFi9IH2W63S8K8oqJCcim8flKN6dXPzc1Jjo37\ngdG6hjAJY2lSCSmTGt/n33TtD+EwUp41GYVrX9u49zveD+vmf1znT//tOs//AoAvvO8reHdo3Fkn\nRvk7Ez7mL6fhGU6Gfr0Zm9cUM9LItCEjX/fWW29FS0uLaGVEo1EJJScmJhCJRMSQJZNJaZShOwlZ\nLBYpQOKGom4Oseuuri5MTk5KKzVeH2+61j7hHPF/LiaGfjwkNAtHJ9AY2mqIi4tQN2nhgaD1hbR2\nv55TfRDozakTyvpaCM0xoctEKQBpIcfKz2w2i9OnT+PEiRMYGRkR6IkhMIuIiNuycQsAYYzs378f\nhmGgurpa8O5sNisH7bFjx4Sl9fnPfx4HDx7EN77xDTz22GNoamqSw/3s2bNwOByIx+PSEo4Gmriu\n1ughY8jv98NutyOdTgveT4P6+c9/HiMjI9IAZnp6Wg6NQCCAxsZG5PN5vPjii4hEIjLPVKkEisqO\npPW2tLTg+PHjOHPmDNLptByyQ0NDgtOzRoBJaiYK9f1cWloSw0TabSQSEWLA8vIypqamUF1dLXkY\nFmHt3bsXQLEyedu2bZiYmMA3v/lN9Pf3o7e3t4TWChQ58qOjo3j55ZeF/WKz2QwrKTQAACAASURB\nVEpwd+5bQiTxeBzJZFLYa4RKadxZoMR9qA+o1dVVRCIRrK6uIhQKiR4RDz0ewmxryO/LfZDNZhGN\nRksaFWnM3MzI4dA5Qw1Js7k4vydF/sywJgef+0HHh6IyFiidFHOylHxv/m6eQE4Kk1X6PcxYPJOX\nfC7ZPlxY1Pxub2+HYRgYGhpCIpGA2+0WFkNlZSWuXr2KWCyG4eFhLC8vY2JiQvBufYMqKysRi8VE\nA56MFy5OXSShDalmj2w0R3porXxihpQD4MJYWFgoOSxtNltJggkoRhfEafk8naxl1GQu4uC8MnlU\nVlYmkZJOouvCLnbq8Xg8mJycBACRFV5ZWYHP55OmGvTkadz0fFmtVszPz0s0QEN7++2344033hDv\nlQdxJpMRwwhAKIy5XA6HDx9GdXU1Nm3ahFdeeQWTk5MlzJLx8XHs2bMHVqsVCwsLmJ2dRXt7O+69\n9175fh6PB8PDwwgGg4L7hkIhkdIAivmSP/iDP8Dc3Jx8py1btqCnpwebN28GAAwODuL111/HhQsX\nsGXLFinuaW9vx/T0tBi9kydPSgVyR0cHtm3bhmAwiJ/85CfiAOTzeTidTkxPT2N2dhZ1dXUSQbAg\njmvH6/VKgpZMFJbl8/qZozp69ChWVlakoGrr1q3C4Ln33nuRTqfx5S9/GY8//jjKyorqkPv378cn\nP/lJoSqfP38ew8PDWFtbEwfMHLFqxgnJEjSGqVRKDHo8HpfCvJqaGszOziIYDEqSGYDUSZCKTRYb\n54B2gHUDvKcOh0OiKJ1H5FqgDaKzpB1WJs/5HH43Ru7cT6RWbmToORe6neMHGR8aQ68zzRza69Qe\nvx7ayNBQ6iIkMwVJJ27ZTUonkywWC774xS+KrDClgJlk5HOamppw1113obe3F93d3fj2t7+NH/3o\nRygvLxfjSr1sbqrl5WVJFrLxMTVfCH/QCDN5a9Zg58/m7wSgpJjK7XajtrZWWDpUsOTzyZZYWFgQ\nWhh5z+ZikI3oXvox/s5Elea/68NJG3qLxYK6ujpEo9ES+edcLodMJoPDhw/j+PHjAoPoZCaZPYx8\nOGfhcFjYNC+//DKWl5fhdDoRjUbl0Ovt7RVqJd8rn8/j0qVLGBsbQ2trq8gqlJeXS0Q1NzeHmpoa\nUcG8cOEC2tvbcfvtt5dEEUNDQ2IcvV6vtEu0WCz4sz/7MwDFDd3T04NAIICamhq0t7cjEAjA7/eL\nzAY10wOBgKg1VlVV4ezZs6KdBEAiAqfTifHxceRyObS1teHjH/84fvSjYuqM3jAT5kyEMvGqi9/o\nwXu9XvGMWapPj75QKAgbhQVtDz30EG6++WahJ37ta19DdXU14vE49u3bh+eeew4ulwuf+9znEA6H\ncerUKQDFql2r1YpMJiMccxpFvV4JGRK2yefzwuah5+3xeERtNZfLIRgMYmpqCo2NjfKcdDqN22+/\nHa+//rocEHT+0uk0BgcH5R4xQtAyCdxntBdkg2kCiHmYnU5+NxpzvoYHrLZH5tf+Unv05H5vNGiE\nSD/kv43eQ5fIX4+nDUAq+FhxSaMKFDdYeXk5Ll++LNWxxJXp0QSDQWSzWbz00ksYHBxET08PBgcH\nJbtPCQSXyyVsAfbzLBQKqKmpEQOiq+NYqMLrM7OGNH6vPQF+b21Us9ksxsfHpVCFvTs5p7oylU1d\nKEamQ09CNmtra1KZqato6ZnoaElHEfp+6Ptgs9kQiUSkgxEA6Wj0/PPP4+LFi9J1i52tNG2U16ZZ\nWa2trSWFaDfffDM2bdqEkZERyVGMjY0JqwYo6haNjY2hoqIC7e3tePXVV9HQ0IB0Oo3JyUmBIqxW\nK5LJJB588EF8/etfR2NjI2w2m+QEOOfhcFiKxgjZvfbaaxgfHxe47tChQ3jsscfQ2dmJ9vZ2zM3N\nYWZmBpcuXZJ7tHPnTtx777146aWX8I1vfEMMEYt3OKxWKxKJBIaGhrBr1y4sLS1heHgYPp9PZKtf\nf/11TE5Oorm5GeFwGFevXpXiHS2VQKNfUVGBYDAoBriysrKkfWcqlUJVVRXa29sBAL/2a7+GxsZG\ntLS0SP/fbDaLqqoqtLa24tixY+jq6kJnZ6fQlulhk9dut9vFo19dXS2BNMjeWVxcRCwWk54CrNZm\n5MKGMoV3i+1isZhAq6xhsFqt0vSb/xgp0KHTe62+vh6Tk5NCs+Z+oAGmo6ThYr5eO6XaQDMy5udr\n3auN8HftfGl24gcZHwpDr4c25Lr4QBt5vdB1kZH2Ps0QDx/TSVzi87ojzNraGnp7e3H16lWsrq5i\nenoa+XxeKHJAMXSNxWJCzWJLPq/Xi0KhIMkwJgypX7937150dXVhbm5OvMr+/v6SVmrUsqGC4EaG\nnt43DR7pX4SkuHgJF/H9tHom1SQNwxDMmXCWXmysIqQHTd4vr4ubks9nURjVOvk8Yur6UGhsbCxp\ndD0+Pi4aJ9FoVNrGsQqSgzgocxjkb/f29oriodfrxdjYGBYWFpDJZFBbW4tIJCK0QCYPXS4XHn74\nYfT390uSnjDXzp07xdBfunQJs7OzOHDgAO6++25pCq+xZAqH+f1+bNmyBRMTE3juueewf/9+9Pf3\ni0dsGAb+5V/+BY888ggKhYIk7WdnZ+VePvvss7jnnntwyy234D//8z8lomC/A94jKkTG43EcOnQI\nLS0t6OrqwtraGrZt2wag6KkfP35cDupwOCyvY5NzXv/ExAR8Pp/06aUj0NDQUMLLJ+f8nnvuwaZN\nm9Dc3Iwf//jHohUzOzuLcDiMfD6Py5cv4y/+4i9w+PBhjI6OYmxsTKAzh8MhTUm4fnk95roJrsPO\nzk5YLBZUVVVhaWlJIipG51zLHo9H9inhzVgshmQyiU2bNuH8+fOyp3K5HFwul+QOCEdt2rQJo6Oj\nSKVSAufolqXso0z7ode7ueoeWBdZYySpYSMNI2nvn9cIrOcE9Py8n/GhMPT6i2h5AnprOozhRJgP\nA06gTliaDT1fy5tAPNtmswks43a78YlPfAIXL15Ef38/JiYmpGKQC4pSpd3d3di8ebN4i8yUc6TT\naXg8Hvj9fuzZswcPPfQQEokEnnnmGQkR2V6P7BWGrVysG3FlNWauv6f2UvSCocE2F49pKIULyjwI\nyWhITCd7NUwGFA8bsnV0REVNFIbmNpsNNTU12L59u2CfLFA6f/68VGyyCYi5ExUTvzabDXa7HZOT\nkzh+/LgY3nQ6jcceewz19fVyPcz17NmzRyIqNot/+OGHkcvlcP78efzjP/4j8vk8brnlFvHCya4i\nV//3f//3Bd+emioWidfU1KC8vBydnZ3IZDL42te+hqefflrK9HWj90cffRS9vb0iAXz16lXY7XaB\nbrxeL/74j/8YZ86ckeT9xMSEeLr0ECcnJwUGYzKbiqPMfbS2tiKdTuPo0aNYWlpCY2MjqqqqUF1d\nLQemXls1NTWi2uhwOER6mfkENla56667cMstt+CrX/0qLly4gIcfflgqvnt7e1FTU4PPfe5zCIVC\n+PGPf4zZ2Vk4nU5s3bpVPP9EIgGfzye5lurqalmXXDuMlK1WqzSw0Y4X17NWpnS5XBgeHkZVVRVi\nsZjsXUKmfX19uHz5skTcQDFSpG4Raz0ymYwUvpkdL65rHi6EhGmPtJfP6FxX7OsqVwAib6z3JL8P\nv+v1krQ/bXxoDL0W6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zstyWpxcRHZbDbQEGV9fd0gK8oVNV4qlQr6+vqQyWQMo19eXobv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"text/plain": [ "<matplotlib.figure.Figure at 0x4d6d4a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# SE GUARDA EN UNA MATRIZ CON NUMPY Y SE GRAFICA LA IMAGEN\n", "imgmatriz = np.array(list(imggray.getdata(band=0)), float)\n", "imgmatriz.shape = (imggray.size[1], imggray.size[0])\n", "imgmatriz = np.matrix(imgmatriz)\n", "plt.figure(figsize=(6,6))\n", "plt.imshow(imgmatriz, cmap='gray')" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 205. 192. 192. ..., 196. 193. 185.]\n", " [ 205. 192. 191. ..., 197. 195. 187.]\n", " [ 207. 194. 194. ..., 199. 197. 188.]\n", " ..., \n", " [ 160. 160. 159. ..., 164. 166. 167.]\n", " [ 159. 158. 158. ..., 147. 147. 146.]\n", " [ 156. 156. 157. ..., 144. 143. 141.]]\n" ] } ], "source": [ "#VISUALIZAMOS LA IMAGEN ANTERIOR EN FORMA MATRICIAL\n", "print(imgmatriz)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#DIMENSION DEL ARREGLO\n", "imgmatriz.ndim" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(199, 300)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#DIEMNSION DEL ARREGLO \"TUPLA\"\n", "imgmatriz.shape" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "59700" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#NUMERO DE ENTRADAS DEL ARREGLO \n", "imgmatriz.size" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#LONGITUD DE UNA ENTRADA DEL ARREGLO EN bytes\n", "imgmatriz.itemsize" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "477600" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#TOTAL DE bytes DEL ARREGLO\n", "imgmatriz.nbytes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Se computa $SVD$ : " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#SE HACE LA DESCOMPOSICION DE VALORES SINGULARES\n", "U, sigma, Vt = np.linalg.svd(imgmatriz)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Imprimimos resultados de las matrices $U$ $\\Sigma$ $Vt$ : " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "U:\n", "[[-0.09986045 -0.05026461 -0.03315873 ..., -0.02250935 -0.01326875\n", " -0.09977819]\n", " [-0.1000951 -0.05049276 -0.03465434 ..., 0.11629303 -0.04303059\n", " 0.14150533]\n", " [-0.10031486 -0.05080684 -0.0350976 ..., -0.25283985 0.15213553\n", " -0.03689725]\n", " ..., \n", " [-0.08030359 -0.04164378 -0.01553057 ..., -0.02117016 -0.00719392\n", " -0.01020098]\n", " [-0.08043311 -0.0407709 -0.01730467 ..., 0.03684279 0.04020065\n", " 0.02045947]\n", " [-0.08013873 -0.04111574 -0.01785941 ..., -0.03919543 -0.02734283\n", " -0.02599257]]\n", "sigma:\n", "[ 3.39450626e+04 5.20902177e+03 4.47590816e+03 3.13919865e+03\n", " 2.81495761e+03 2.46503752e+03 2.36777518e+03 2.03564403e+03\n", " 1.72070532e+03 1.38711563e+03 1.30083445e+03 1.26088303e+03\n", " 1.23717471e+03 1.07661609e+03 1.05231822e+03 1.03086491e+03\n", " 9.48265831e+02 9.27817334e+02 8.96587430e+02 8.67474840e+02\n", " 7.96122318e+02 7.84705410e+02 7.79125944e+02 7.12892247e+02\n", " 6.83258461e+02 6.57903911e+02 6.37093525e+02 6.21392122e+02\n", " 5.98981823e+02 5.83536783e+02 5.77165317e+02 5.53730993e+02\n", " 5.15869090e+02 4.97537688e+02 4.85260493e+02 4.57597832e+02\n", " 4.50488428e+02 4.41744442e+02 4.23226639e+02 4.18109045e+02\n", " 4.12981511e+02 3.99287096e+02 3.83894020e+02 3.70932492e+02\n", " 3.68844604e+02 3.63957220e+02 3.48382241e+02 3.39928802e+02\n", " 3.31682660e+02 3.30480663e+02 3.22971422e+02 3.16293832e+02\n", " 3.10735716e+02 3.01389278e+02 2.96860820e+02 2.88530663e+02\n", " 2.81727914e+02 2.76780646e+02 2.71481465e+02 2.66200536e+02\n", " 2.61837616e+02 2.60557379e+02 2.50966609e+02 2.48326431e+02\n", " 2.41670708e+02 2.39755184e+02 2.33854775e+02 2.31363649e+02\n", " 2.21345337e+02 2.17211042e+02 2.16233608e+02 2.10551502e+02\n", " 2.07394306e+02 2.03714914e+02 1.98861522e+02 1.96269548e+02\n", " 1.92629281e+02 1.89148512e+02 1.85345530e+02 1.80223190e+02\n", " 1.76096626e+02 1.72396430e+02 1.69983237e+02 1.67073135e+02\n", " 1.63802886e+02 1.58575075e+02 1.55452575e+02 1.52649708e+02\n", " 1.47386155e+02 1.45908009e+02 1.43532346e+02 1.39717179e+02\n", " 1.37930603e+02 1.36685555e+02 1.33747756e+02 1.31014054e+02\n", " 1.28150670e+02 1.25467774e+02 1.24963768e+02 1.22136490e+02\n", " 1.19707891e+02 1.18082014e+02 1.16508533e+02 1.11578708e+02\n", " 1.10394990e+02 1.07379674e+02 1.05391320e+02 1.03170927e+02\n", " 1.02300446e+02 1.00386630e+02 9.73156869e+01 9.48259822e+01\n", " 9.31266299e+01 9.16806898e+01 8.99639931e+01 8.70370421e+01\n", " 8.54008291e+01 8.23344183e+01 8.08408254e+01 7.91823729e+01\n", " 7.75355366e+01 7.60648116e+01 7.49479910e+01 7.17253443e+01\n", " 7.11534491e+01 6.73243625e+01 6.54375192e+01 6.30734614e+01\n", " 6.25089641e+01 6.11750675e+01 5.89022045e+01 5.76997184e+01\n", " 5.61433509e+01 5.56985291e+01 5.39040050e+01 5.14591995e+01\n", " 5.00482166e+01 4.79004243e+01 4.72023828e+01 4.61365482e+01\n", " 4.36097189e+01 4.29822165e+01 4.26550177e+01 4.11204719e+01\n", " 4.00355804e+01 3.88055502e+01 3.79043142e+01 3.66699272e+01\n", " 3.62534593e+01 3.47739099e+01 3.39600712e+01 3.29433571e+01\n", " 3.18843207e+01 3.09285874e+01 2.92583981e+01 2.85680881e+01\n", " 2.74672680e+01 2.65677188e+01 2.50374145e+01 2.49601994e+01\n", " 2.44904829e+01 2.31192166e+01 2.26502255e+01 2.21085854e+01\n", " 2.11357846e+01 2.05604112e+01 1.94947321e+01 1.86924954e+01\n", " 1.71519489e+01 1.53387395e+01 1.50623834e+01 1.42933330e+01\n", " 1.36893746e+01 1.26295236e+01 1.19860057e+01 1.09675652e+01\n", " 1.02371276e+01 9.96453444e+00 9.33797909e+00 8.87823766e+00\n", " 8.59653056e+00 7.54202544e+00 6.83012161e+00 6.34066590e+00\n", " 5.69281523e+00 5.38880830e+00 4.96601127e+00 4.91010792e+00\n", " 4.14034069e+00 3.89442741e+00 3.64830163e+00 3.49637522e+00\n", " 3.21290732e+00 3.08295463e+00 2.76886529e+00 2.60254944e+00\n", " 2.49167428e+00 2.33787384e+00 2.22741604e+00]\n", "Vt:\n", "[[-0.06271599 -0.06268384 -0.06093827 ..., -0.0629332 -0.06430328\n", " -0.06402128]\n", " [ 0.0118114 0.01193346 0.01299712 ..., 0.03147262 0.04580619\n", " 0.04507741]\n", " [ 0.06337646 0.06655198 0.07295977 ..., 0.06488156 0.05777226\n", " 0.06017216]\n", " ..., \n", " [ 0.00315144 0.00821115 0.04503226 ..., 0.48825903 -0.17550617\n", " -0.09710495]\n", " [ 0.08202039 0.00201674 0.00282639 ..., -0.10450923 0.52467418\n", " -0.31077608]\n", " [ 0.01843902 -0.01445306 0.00482328 ..., -0.03902651 -0.25462459\n", " 0.39754666]]\n" ] } ], "source": [ "print(\"U:\")\n", "print(U)\n", "print(\"sigma:\")\n", "print(sigma)\n", "print(\"Vt:\")\n", "print(Vt)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1592" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#TOTAL DE bytes DEL ARREGLO (solo sigma)\n", "sigma.nbytes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualizamos la $/Sigma$ en una matriz diagonal:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 33945.06263786 0. 0. ..., 0. 0.\n", " 0. ]\n", " [ 0. 5209.02177166 0. ..., 0. 0.\n", " 0. ]\n", " [ 0. 0. 4475.90816332 ..., 0. 0.\n", " 0. ]\n", " ..., \n", " [ 0. 0. 0. ..., 0. 0.\n", " 0. ]\n", " [ 0. 0. 0. ..., 0. 0.\n", " 0. ]\n", " [ 0. 0. 0. ..., 0. 0.\n", " 0. ]]\n" ] } ], "source": [ "S = np.zeros(imgmatriz.shape, \"float\")\n", "S[:min(imgmatriz.shape), :min(imgmatriz.shape)] = np.diag(sigma)\n", "print(S)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculo y reconstrucción:\n", "\n", "Se calcula una aproximacion usando la primera columna de U y la primera fila de V reporduciendo la imagen, cada columna de pixeles es una ponderacion de los mismos valores originales $\\vec{u}_1 $ :\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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49OjRnodOj15AL5D2kATLU3oNIppdCOg1IyGQa9QXULp3R5lo1Fc9Eftg5h4k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\nJusfnynqmzOSnkfvuuEDPr/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9J5VDffU+pHwyp0S6Ilm2gF7lCNRUJw8BY5ybA7c7CPTU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"text/plain": [ "<matplotlib.figure.Figure at 0x810e3c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reconstimg = np.matrix(U[:, :1]) * np.diag(sigma[:1]) * np.matrix(V[:1, :])\n", "plt.figure(figsize=(6,6))\n", "plt.imshow(reconstimg, cmap='gray');" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Xd3NzEzs7O59NZD6bzeLly5dxdXUVw+EwdnZ2OgKzs7MTx8fH5f6MCPkO1ZSM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iHMUbLed8dbQVSd9ZkjjOhfC3OUt8dtTMnwnUqW5+FOYSvjqslLGliB7HNPnz4d6lM1QeYEqlnJ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\n2rrO/ThLWLTKoS4tq5L2zJucr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"text/plain": [ "<matplotlib.figure.Figure at 0x853e198>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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oNOoI6vX1dZkAWWjdRwDNExnAMKhcXV3Fzc1NmfRZYeVNTkxw+MeEhC95IZr2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\nZ33f36qq9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"text/plain": [ "<matplotlib.figure.Figure at 0x85bccf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#RECONSTRUIMOS CON 8 Y 9 VECTORES\n", "for i in range(8, 10):\n", " reconstimg = np.matrix(U[:, :i]) * np.diag(sigma[:i]) * np.matrix(V[:i, :])\n", " plt.imshow(reconstimg, cmap='gray')\n", " title = \"n = %s\" % i\n", " plt.title(title)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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N5/Pcb//NWPZ6PS0Wi9w3xtLXSMlaTSmp1+vV1jRzyvXwHe2NwpjySiDQy4Xv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67tKKh/1fS++hwjbdD9QL9xvp0Pr7vD8ILSeRZZKfS/kTnb6vpdApSFiJX5Detdjr20GWnYZ30f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\nCRGaF7glCEg9zvHe9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UhkYcNlRJudm9Iy5h5wLgPnU44c5C57Sg64rE+L8jitkrWemYbV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\nND5W3pOuGXP4Y8/L+p+mPGfGmBvd8t2/t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"text/plain": [ "<matplotlib.figure.Figure at 0x818d2b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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bUNiLQNuydGuEOaexuRklVSclDt51GjB3fRmBFRfzTZF+/Jxjqus0tn9/F6Zx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Nq40mTZ+Bg+84g8j3cwEcw925CYpjBnbsNUvT1TRJU/r8jO6BfDdXJy0vYQ9xGCyJUnMmNFHZ3l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\n4lBGBPdU2qzWhb6thnYS0hwyHK47f0/yV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"text/plain": [ "<matplotlib.figure.Figure at 0x853e358>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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jA1cXZOgYVT0uluWpJaTl2VS9RcGbl4FmrDDQtrDMUJCJzyy7iSPptz0Slv3W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MnJyehu74wIvLLt7W1dX19Ht6Tl5WWdnJzEfsQan52d1cLCgiqVSggvhDzxAElRUG50dNC4o9fr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\nNz+n/Oi1t1fPqXrk72XovfrM/3RVvTkMw5d07KNt1+1yq6r+4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"text/plain": [ "<matplotlib.figure.Figure at 0x82be278>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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9PQB/MwiCzwIIATwA8B+8VAtfkl4WIdr/qdV1e/PPa+LO60OLo/OgyEkL0AZ1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\n7ROFt97jUQjAOPdcz3th29kOWor08TJTCRgf4Uy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8nplANGk164eBGmCcOqhKhOOm7hO2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\npGShFv1CW06ePDkVFL1z505WVlZy7dq1XLx4sSkhK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ATBoHG/XXO3wx4RVAIGBFrFz6M/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"text/plain": [ "<matplotlib.figure.Figure at 0x9b5ef60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#RECONSTRUIMOS DE 10 EN 10 VECTORES PARA VER CUANDO SE REPRODUCE UNA IMAGEN SIMILAR A LA ORIGINAL...\n", "for i in range(10,50, 10):\n", " reconstimg = np.matrix(U[:, :i]) * np.diag(sigma[:i]) * np.matrix(V[:i, :])\n", " plt.imshow(reconstimg, cmap='gray')\n", " title = \"n = %s\" % i\n", " plt.title(title)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reconstruccion de matriz original:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "matrix([[ 205., 192., 192., ..., 196., 193., 185.],\n", " [ 205., 192., 191., ..., 197., 195., 187.],\n", " [ 207., 194., 194., ..., 199., 197., 188.],\n", " ..., \n", " [ 160., 160., 159., ..., 164., 166., 167.],\n", " [ 159., 158., 158., ..., 147., 147., 146.],\n", " [ 156., 156., 157., ..., 144., 143., 141.]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.dot(U, np.dot(S, Vt)) #se usa Vt" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "matrix([[ 205., 192., 192., ..., 196., 193., 185.],\n", " [ 205., 192., 191., ..., 197., 195., 187.],\n", " [ 207., 194., 194., ..., 199., 197., 188.],\n", " ..., \n", " [ 160., 160., 159., ..., 164., 166., 167.],\n", " [ 159., 158., 158., ..., 147., 147., 146.],\n", " [ 156., 156., 157., ..., 144., 143., 141.]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imgmatriz" ] }, { "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 }	apache-2.0
quoniammm/happy-machine-learning	was_ML_coursera/week6/（图像检索）image retrieval.ipynb	1	112561	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import graphlab" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. 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"\timage_array\tarray\n", "\n", "Rows: 10\n", "\n", "Data:\n", "+-----+----------------------+------------+-------------------------------+\n", "\| id \| image \| label \| deep_features \|\n", "+-----+----------------------+------------+-------------------------------+\n", "\| 24 \| Height: 32 Width: 32 \| bird \| [0.242871761322, 1.0954537... \|\n", "\| 33 \| Height: 32 Width: 32 \| cat \| [0.525087952614, 0.0, 0.0,... \|\n", "\| 36 \| Height: 32 Width: 32 \| cat \| [0.566015958786, 0.0, 0.0,... \|\n", "\| 70 \| Height: 32 Width: 32 \| dog \| [1.12979578972, 0.0, 0.0, ... \|\n", "\| 90 \| Height: 32 Width: 32 \| bird \| [1.71786928177, 0.0, 0.0, ... \|\n", "\| 97 \| Height: 32 Width: 32 \| automobile \| [1.57818555832, 0.0, 0.0, ... \|\n", "\| 107 \| Height: 32 Width: 32 \| dog \| [0.0, 0.0, 0.220677852631,... \|\n", "\| 121 \| Height: 32 Width: 32 \| bird \| [0.0, 0.23753464222, 0.0, ... \|\n", "\| 136 \| Height: 32 Width: 32 \| automobile \| [0.0, 0.0, 0.0, 0.0, 0.0, ... \|\n", "\| 138 \| Height: 32 Width: 32 \| bird \| [0.658935725689, 0.0, 0.0,... \|\n", "+-----+----------------------+------------+-------------------------------+\n", "+-------------------------------+\n", "\| image_array \|\n", "+-------------------------------+\n", "\| [73.0, 77.0, 58.0, 71.0, 6... \|\n", "\| [7.0, 5.0, 8.0, 7.0, 5.0, ... \|\n", "\| [169.0, 122.0, 65.0, 131.0... \|\n", "\| [154.0, 179.0, 152.0, 159.... \|\n", "\| [216.0, 195.0, 180.0, 201.... \|\n", "\| [33.0, 44.0, 27.0, 29.0, 4... \|\n", "\| [97.0, 51.0, 31.0, 104.0, ... \|\n", "\| [93.0, 96.0, 88.0, 102.0, ... \|\n", "\| [35.0, 59.0, 53.0, 36.0, 5... \|\n", "\| [205.0, 193.0, 195.0, 200.... \|\n", "+-------------------------------+\n", "[10 rows x 5 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "image_train.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<pre>Starting brute force 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178, 162, 188, 182, 168, 189, 183, 169, 185, 177, 165, 172, 162, 150, 153, 142, 131, 143, 130, 119, 153, 138, 127, 149, 134, 117, 145, 128, 107, 136, 119, 98, 127, 107, 87, 128, 106, 87, 129, 105, 86, 115, 89, 71, 111, 79, 65, 117, 71, 65, 106, 55, 52, 86, 33, 31, 77, 20, 19, 77, 18, 17, 79, 17, 18, 81, 17, 18, 85, 18, 20, 90, 20, 23, 91, 19, 23, 172, 157, 127, 172, 153, 119, 168, 146, 111, 168, 145, 111, 168, 148, 116, 169, 153, 124, 173, 160, 137, 177, 167, 148, 179, 169, 152, 173, 161, 145, 162, 148, 133, 158, 142, 127, 141, 124, 109, 123, 103, 89, 122, 98, 78, 111, 84, 60, 109, 80, 56, 111, 78, 55, 109, 73, 51, 111, 71, 51, 111, 68, 49, 107, 57, 43, 99, 38, 36, 89, 24, 26, 82, 18, 19, 80, 18, 18, 80, 18, 19, 80, 20, 20, 81, 21, 21, 87, 20, 23, 92, 20, 24, 94, 20, 25, 172, 150, 118, 173, 145, 109, 173, 143, 103, 175, 145, 104, 170, 143, 103, 170, 148, 110, 166, 149, 117, 159, 144, 121, 156, 141, 121, 152, 136, 116, 149, 131, 112, 154, 133, 115, 153, 130, 113, 128, 104, 86, 120, 92, 71, 107, 76, 52, 121, 86, 63, 124, 84, 63, 119, 72, 54, 115, 63, 46, 114, 58, 43, 105, 43, 34, 93, 23, 24, 87, 15, 19, 84, 14, 18, 84, 15, 19, 84, 17, 20, 84, 19, 21, 85, 20, 22, 90, 20, 23, 93, 18, 23, 96, 19, 25, 172, 143, 111, 178, 144, 106, 175, 139, 96, 171, 135, 90, 164, 132, 86, 162, 135, 90, 161, 139, 101, 160, 141, 113, 160, 141, 118, 156, 136, 113, 141, 119, 97, 143, 119, 98, 153, 126, 106, 150, 123, 103, 132, 104, 84, 120, 89, 70, 130, 95, 77, 122, 80, 65, 119, 70, 57, 109, 54, 44, 98, 37, 29, 92, 27, 23, 89, 20, 21, 88, 17, 21, 87, 16, 20, 87, 15, 19, 88, 15, 19, 90, 16, 21, 91, 17, 22, 94, 18, 23, 96, 17, 23, 98, 19, 25, 172, 137, 102, 175, 138, 96, 172, 133, 87, 169, 128, 80, 159, 119, 72, 150, 110, 66, 152, 115, 78, 161, 128, 100, 165, 137, 112, 150, 127, 102, 128, 109, 85, 135, 115, 94, 145, 122, 104, 138, 112, 96, 122, 96, 78, 143, 114, 95, 134, 101, 82, 111, 71, 54, 98, 49, 38, 93, 36, 32, 90, 26, 29, 90, 22, 27, 89, 19, 21, 90, 18, 20, 91, 17, 20, 92, 16, 19, 93, 16, 19, 95, 15, 20, 96, 15, 20, 98, 17, 22, 99, 17, 23, 101, 19, 24, 176, 137, 101, 173, 135, 92, 168, 128, 81, 162, 119, 70, 145, 100, 54, 137, 91, 50, 147, 100, 66, 156, 114, 86, 152, 117, 91, 140, 113, 87, 134, 113, 89, 141, 121, 101, 143, 121, 105, 144, 119, 105, 150, 125, 104, 151, 124, 98, 128, 96, 70, 109, 69, 46, 101, 53, 36, 94, 37, 29, 90, 25, 27, 91, 21, 27, 94, 19, 23, 95, 18, 20, 96, 18, 21, 97, 17, 21, 98, 17, 21, 100, 16, 21, 100, 15, 21, 101, 16, 21, 102, 17, 22, 103, 18, 23, 169, 131, 95, 173, 134, 92, 180, 140, 93, 175, 133, 84, 159, 115, 69, 146, 100, 58, 147, 101, 66, 151, 108, 78, 141, 102, 75, 139, 106, 79, 142, 113, 89, 142, 115, 95, 152, 125, 108, 161, 135, 118, 155, 129, 103, 138, 109, 76, 136, 103, 69, 143, 102, 70, 134, 84, 59, 113, 54, 38, 95, 30, 24, 94, 22, 23, 98, 20, 23, 100, 19, 22, 101, 18, 22, 102, 18, 22, 103, 17, 22, 104, 16, 22, 105, 16, 22, 104, 16, 22, 103, 16, 22, 104, 18, 23, 163, 125, 89, 170, 131, 89, 182, 142, 95, 182, 139, 90, 169, 124, 78, 150, 103, 62, 141, 95, 58, 137, 93, 61, 132, 88, 60, 138, 97, 70, 137, 98, 75, 142, 107, 86, 160, 129, 110, 157, 129, 110, 155, 127, 99, 163, 133, 96, 157, 121, 84, 151, 107, 73, 141, 89, 62, 118, 57, 39, 97, 29, 21, 95, 20, 20, 102, 19, 23, 105, 18, 24, 106, 18, 24, 107, 17, 24, 108, 16, 23, 109, 15, 23, 110, 14, 23, 109, 17, 24, 107, 18, 24, 108, 18, 25, 157, 119, 82, 152, 114, 71, 167, 127, 80, 170, 129, 79, 157, 113, 66, 140, 93, 52, 129, 83, 46, 127, 83, 49, 129, 83, 52, 132, 83, 57, 127, 79, 56, 143, 101, 80, 156, 122, 102, 149, 121, 101, 182, 154, 127, 198, 165, 134, 164, 126, 94, 139, 93, 64, 132, 77, 54, 112, 48, 35, 96, 25, 21, 97, 18, 21, 105, 17, 23, 108, 17, 24, 110, 17, 25, 111, 17, 25, 112, 16, 25, 114, 14, 24, 114, 14, 24, 113, 17, 26, 111, 19, 26, 111, 19, 27, 155, 116, 82, 142, 101, 62, 156, 112, 69, 157, 111, 66, 143, 97, 53, 129, 82, 43, 120, 73, 37, 120, 72, 39, 125, 73, 44, 118, 62, 39, 115, 59, 39, 123, 72, 54, 131, 89, 70, 136, 101, 80, 170, 133, 106, 181, 140, 110, 140, 98, 67, 125, 78, 49, 125, 69, 48, 105, 41, 30, 94, 20, 21, 99, 16, 24, 106, 16, 24, 110, 16, 24, 113, 16, 24, 116, 15, 24, 118, 15, 24, 119, 14, 24, 119, 15, 24, 117, 17, 25, 116, 19, 27, 115, 20, 27], \"height\": 32, \"channels\": 3, \"width\": 32, \"type\": \"image\", \"id\": 139701265811728}], \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"Image\", \"view_component\": \"Images\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 5}, \"type\": \"SArray\", \"view_components\": [\"Images\"]}}, e);\n", " });\n", " })();\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cat_neighbors['image'].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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }	mit
elivre/arfe	e2014/050-rede2014_rede_gephi_com_ipca.ipynb	2	7087	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# rede_gephi_com_ipca" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ano_eleicao = '2014'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPCA de outubro de 2014 a outubro de 2018 pela calculadora do Banco Central\n", "\n", "https://www3.bcb.gov.br/CALCIDADAO/publico/corrigirPorIndice.do?method=corrigirPorIndice" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "IPCA_2014_2018 = 1.27872850" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append('../')\n", "import mod_tse as mtse" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import os\n", "home = os.environ[\"HOME\"]\n", "local_dir = f'{home}/temp'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dbschema = f'rede{ano_eleicao}'\n", "\n", "table_gephi_edges = f\"{dbschema}.gephi_edges_{ano_eleicao}\"\n", "table_gephi_edges_com_ipca = f\"{dbschema}.gephi_edges_com_ipca_2018\"\n", "\n", "table_gephi_nodes = f\"{dbschema}.gephi_nodes_{ano_eleicao}\"\n", "table_gephi_nodes_com_ipca = f\"{dbschema}.gephi_nodes_com_ipca_2018\"\n", "\n", "table_receitas = f\"{dbschema}.receitas_{ano_eleicao}\"\n", "table_receitas_com_ipca = f\"{dbschema}.receitas_com_ipca_2018\"\n", "\n", "table_candidaturas = f\"{dbschema}.candidaturas_{ano_eleicao}\"\n", "table_candidaturas_com_ipca = f\"{dbschema}.candidaturas_com_ipca_2018\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ATUALIZA TABELAS PARA REDE COM IPCA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CANDIDATURAS" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "query_crate_table_candidaturas_ipca = f\"\"\"\n", " drop table if exists {table_candidaturas_com_ipca} cascade;\n", " create table {table_candidaturas_com_ipca} as\n", " select * from {table_candidaturas}\n", " ;\n", " \n", " update {table_candidaturas_com_ipca}\n", " set receita_total = receita_total * {IPCA_2014_2018},\n", " despesa_total = despesa_total * {IPCA_2014_2018},\n", " custo_voto = custo_voto * {IPCA_2014_2018}\n", " ;\n", "\"\"\"\n", "\n", "mtse.execute_query(query_crate_table_candidaturas_ipca)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### RECEITAS" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "query_crate_table_receitas_ipca = f\"\"\"\n", " drop table if exists {table_receitas_com_ipca}; \n", " create table {table_receitas_com_ipca} as\n", " select * from {table_receitas};\n", " \n", " update {table_receitas_com_ipca}\n", " set receita_valor = receita_valor * {IPCA_2014_2018}\n", " ;\n", "\"\"\"\n", "\n", "mtse.execute_query(query_crate_table_receitas_ipca)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def query_update_valores(table):\n", " colunas_valor = [\n", " 'valor_doado',\n", " 'valor_recebido',\n", " 'fonte_fundo_part',\n", " 'fonte_fundo_esp',\n", " 'fonte_outros_rec',\n", " 'RP',\n", " 'RPF',\n", " 'RPJ',\n", " 'DPI',\n", " 'RPP',\n", " 'RFC',\n", " 'CBRE',\n", " 'RAF',\n", " 'RONI',\n", " 'ROC',\n", " 'DRC',\n", " 'receita_total',\n", " 'despesa_total',\n", " 'custo_voto' \n", " ] \n", " for cv in colunas_valor:\n", " mtse.execute_query(f\"\"\"\n", " update {table}\n", " set {cv} = {cv} * {IPCA_2014_2018};\n", " \"\"\"\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### NODES" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def query_crate_table_nodes_ipca():\n", " mtse.execute_query(f\"\"\"\n", " drop table if exists {table_gephi_nodes_com_ipca} ; \n", " create table {table_gephi_nodes_com_ipca} as\n", " select * from {table_gephi_nodes};\n", " \"\"\")\n", " \n", " query_update_valores(table_gephi_nodes_com_ipca)\n", " \n", "\n", "query_crate_table_nodes_ipca()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EDGES" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "query_crate_table_edges_ipca = f\"\"\"\n", " drop table if exists {table_gephi_edges_com_ipca} ; \n", " create table {table_gephi_edges_com_ipca} as\n", " select * from {table_gephi_edges};\n", "\"\"\"\n", "\n", "query_crate_table_edges_weight_ipca = f\"\"\"\n", " update {table_gephi_edges_com_ipca}\n", " set \"Weight\" = \"Weight\" * {IPCA_2014_2018}\n", " ;\n", "\"\"\"\n", "\n", " \n", "mtse.execute_query(query_crate_table_edges_ipca)\n", "mtse.execute_query(query_crate_table_edges_weight_ipca)\n", "query_update_valores(table_gephi_edges_com_ipca)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-10-21 12:05:20.159617\n" ] } ], "source": [ "import datetime\n", "print(datetime.datetime.now())\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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }	mit
igmhub/lyaforecast	examples/plot_P1D.ipynb	1	53505	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot analytic P1D" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import analytic_p1d_PD2013 as aP1D" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# s/km units\n", "k=np.logspace(-4,-1,1000)\n", "pk_z2=aP1D.P1D_z_kms_PD2013(2.0,k)\n", "pk_z3=aP1D.P1D_z_kms_PD2013(3.0,k)\n", "pk_z4=aP1D.P1D_z_kms_PD2013(4.0,k)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# something related to fonts in the plot\n", "plt.rc('text', usetex=True)\n", "plt.rcParams['text.latex.preamble'] = [\n", " r'\\usepackage{siunitx}', # i need upright \\micro symbols, but you need\n", " r'\\sisetup{detect-all}', # this to force siunitx to actually use your fonts\n", " r'\\usepackage{helvet}', # set the normal font here\n", " r'\\usepackage{sansmath}', # load up the sansmath so that math -> helvet\n", " r'\\sansmath' # <- tricky! -- gotta actually tell tex to use!\n", "]\n", "plt.rc('xtick', labelsize=15)\n", "plt.rc('ytick', labelsize=15)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+T09Eez6F4D5w5TP6FuE+cq3LmVTWNbJ861HSs/LZnGdF02BqTAS/SxzK5WP6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\nKQrgAS1QfudOVeuqWheRCoAagGk/46XNAsjfQvM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"text/plain": [ "<matplotlib.figure.Figure at 0x111b70ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.xlim(1e-4,0.1)\n", "plt.xlabel(r'k [s $\\rm{km}^{-1}$]',fontsize=15)\n", "plt.ylabel(r'k P(k) / $\\pi$',fontsize=15)\n", "plt.title(r'1D flux power',fontsize=15)\n", "plt.loglog(k,kpk_z2/np.pi,label=r'z=2.0')\n", "plt.loglog(k,kpk_z3/np.pi,label=r'z=3.0')\n", "plt.loglog(k,k*pk_z4/np.pi,label=r'z=4.0')\n", "plt.legend(fontsize=15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4F7n2XP56/l+5evDVZpdUi+lT2yilrEAWxlIFViAOY+BAstbaLReeCR94lp72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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x111c171d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.xlim(1e-4,0.1)\n", "plt.xlabel(r'k [s $\\rm{km}^{-1}$]',fontsize=15)\n", "plt.ylabel(r'k P(k) / $\\pi$',fontsize=15)\n", "plt.title(r'1D flux power',fontsize=15)\n", "plt.loglog(k,pk_z2,label=r'z=2.0')\n", "plt.loglog(k,pk_z3,label=r'z=3.0')\n", "plt.loglog(k,pk_z4,label=r'z=4.0')\n", "plt.legend(fontsize=15)\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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }	gpl-3.0
ogaway/Matching-Market	Nash_Match.ipynb	1	3926	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 純粋戦略ナッシュ均衡\n", "One-to-Oneマッチングにおける純粋戦略ナッシュ均衡を求める。 " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "import matchfuncs as mf" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "prop_prefs = [[0, 1, 2],\n", " [0, 2, 1],\n", " [2, 0, 1]]\n", "resp_prefs = [[2, 0, 1],\n", " [2, 0, 1],\n", " [1, 2, 0]]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([1, 2, 0]), array([2, 0, 1]))" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DAによるマッチング生成\n", "mf.DA(prop_prefs, resp_prefs)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([0, 1, 2]), array([0, 1, 2]))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# BOSによるマッチング生成\n", "mf.BOS(prop_prefs, resp_prefs)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "54" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# DAにおけるNash均衡\n", "nash, behavior = mf.Nash('DA', prop_prefs, resp_prefs)\n", "len(nash)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[0, 1, 2],\n", " [1, 2, 0],\n", " [1, 0, 2]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 54個中0番目のNash均衡における提出リスト\n", "mf.SubmitList(nash[0], behavior)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "36" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# BOSにおけるNash均衡\n", "nash, behavior = mf.Nash('BOS', prop_prefs, resp_prefs)\n", "len(nash)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[0, 1, 2],\n", " [2, 0, 1],\n", " [1, 0, 2]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 36個中0番目のNash均衡における提出リスト\n", "mf.SubmitList(nash[0], behavior)" ] } ], "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
JuliaPackageMirrors/ValidatedNumerics.jl	examples/Range of 2-dimensional functions.ipynb	2	149757	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using ValidatedNumerics" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "split (generic function with 12 methods)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import Base.split\n", "\n", "function split{T<:Real}(x::Interval{T}, n::Integer)\n", " \n", " width = diam(x) / n\n", " \n", " intervals = [Interval(x.lo + widthi, x.lo + width(i+1)) for i in 0:n-1]\n", " \n", "end\n", "\n", "function split{T<:Real}(X::IntervalBox{2,T}, n::Integer, m::Integer=n)\n", " x, y = X\n", " \n", " x_intervals = split(x, m)\n", " y_intervals = split(y, n)\n", " \n", " boxes = IntervalBox{2,T}[]\n", " \n", " for i in x_intervals, j in y_intervals\n", " push!(boxes, IntervalBox(i, j))\n", " end\n", " \n", " boxes\n", " \n", "end\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "9-element Array{ValidatedNumerics.IntervalBox{2,Float64},1}:\n", " IntervalBox([0.0, 0.3333333333333333],[0.0, 0.3333333333333333]) \n", " IntervalBox([0.0, 0.3333333333333333],[0.3333333333333333, 0.6666666666666666]) \n", " IntervalBox([0.0, 0.3333333333333333],[0.6666666666666666, 1.0]) \n", " IntervalBox([0.3333333333333333, 0.6666666666666666],[0.0, 0.3333333333333333]) \n", " IntervalBox([0.3333333333333333, 0.6666666666666666],[0.3333333333333333, 0.6666666666666666])\n", " IntervalBox([0.3333333333333333, 0.6666666666666666],[0.6666666666666666, 1.0]) \n", " IntervalBox([0.6666666666666666, 1.0],[0.0, 0.3333333333333333]) \n", " IntervalBox([0.6666666666666666, 1.0],[0.3333333333333333, 0.6666666666666666]) \n", " IntervalBox([0.6666666666666666, 1.0],[0.6666666666666666, 1.0]) " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = IntervalBox(0..1, 0..1)\n", "\n", "\n", "input = split(X, 3)\n", "input" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "f (generic function with 1 method)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(xx::IntervalBox) = ((x,y)=xx; IntervalBox(2x + y, x + y))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "9-element Array{ValidatedNumerics.IntervalBox{2,Float64},1}:\n", " IntervalBox([0.0, 1.0],[0.0, 0.6666666666666666]) \n", " IntervalBox([0.3333333333333333, 1.3333333333333333],[0.3333333333333333, 1.0]) \n", " IntervalBox([0.6666666666666666, 1.6666666666666667],[0.6666666666666666, 1.3333333333333335])\n", " IntervalBox([0.6666666666666666, 1.6666666666666667],[0.3333333333333333, 1.0]) \n", " IntervalBox([0.9999999999999999, 2.0],[0.6666666666666666, 1.3333333333333333]) \n", " IntervalBox([1.3333333333333333, 2.3333333333333335],[0.9999999999999999, 1.6666666666666667])\n", " IntervalBox([1.3333333333333333, 2.3333333333333335],[0.6666666666666666, 1.3333333333333335])\n", " IntervalBox([1.6666666666666665, 2.666666666666667],[0.9999999999999999, 1.6666666666666667]) \n", " IntervalBox([1.9999999999999998, 3.0],[1.3333333333333333, 2.0]) " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "output = map(f, input)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "draw (generic function with 4 methods)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "include(\"draw_function_image.jl\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31b268d10>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(0.0,3.0,0.0,2.0)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "draw(output, \"cyan\")\n", "draw(input, \"black\", 0.9)\n", "\n", "axis(\"image\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script charset=\"utf-8\">(function ($, undefined) {\n", "\n", " function createElem(tag, attr, content) {\n", "\t// TODO: remove jQuery dependency\n", "\tvar el = $(\"<\" + tag + \"/>\").attr(attr);\n", "\tif (content) {\n", "\t el.append(content);\n", "\t}\n", "\treturn el[0];\n", " }\n", "\n", " // A widget must expose an id field which identifies it to the backend,\n", " // an elem attribute which is will be added to the DOM, and\n", " // a getState() method which returns the value to be sent to the backend\n", " // a sendUpdate() method which sends its current value to the backend\n", " var Widget = {\n", "\tid: undefined,\n", "\telem: undefined,\n", "\tlabel: undefined,\n", "\tgetState: function () {\n", "\t return this.elem.value;\n", "\t},\n", "\tsendUpdate: undefined\n", " };\n", "\n", " var Slider = function (typ, id, init) {\n", "\tvar attr = { type: \"range\",\n", "\t\t value: init.value,\n", "\t\t min: init.min,\n", "\t\t max: init.max,\n", "\t\t step: init.step },\n", "\t elem = createElem(\"input\", attr),\n", "\t self = this;\n", "\n", "\telem.onchange = function () {\n", "\t self.sendUpdate();\n", "\t}\n", "\n", "\tthis.id = id;\n", "\tthis.elem = elem;\n", "\tthis.label = init.label;\n", "\n", "\tInputWidgets.commInitializer(this); // Initialize communication\n", " }\n", " Slider.prototype = Widget;\n", "\n", " var Checkbox = function (typ, id, init) {\n", "\tvar attr = { type: \"checkbox\",\n", "\t\t checked: init.value },\n", "\t elem = createElem(\"input\", attr),\n", "\t self = this;\n", "\n", "\tthis.getState = function () {\n", "\t return elem.checked;\n", "\t}\n", "\telem.onchange = function () {\n", "\t self.sendUpdate();\n", "\t}\n", "\n", "\tthis.id = id;\n", "\tthis.elem = elem;\n", "\tthis.label = init.label;\n", "\n", "\tInputWidgets.commInitializer(this);\n", " }\n", " Checkbox.prototype = Widget;\n", "\n", " var Button = function (typ, id, init) {\n", "\tvar attr = { type: \"button\",\n", "\t\t value: init.label },\n", "\t elem = createElem(\"input\", attr),\n", "\t self = this;\n", "\tthis.getState = function () {\n", "\t return null;\n", "\t}\n", "\telem.onclick = function () {\n", "\t self.sendUpdate();\n", "\t}\n", "\n", "\tthis.id = id;\n", "\tthis.elem = elem;\n", "\tthis.label = init.label;\n", "\n", "\tInputWidgets.commInitializer(this);\n", " }\n", " Button.prototype = Widget;\n", "\n", " var Text = function (typ, id, init) {\n", "\tvar attr = { type: \"text\",\n", "\t\t placeholder: init.label,\n", "\t\t value: init.value },\n", "\t elem = createElem(\"input\", attr),\n", "\t self = this;\n", "\tthis.getState = function () {\n", "\t return elem.value;\n", "\t}\n", "\telem.onkeyup = function () {\n", "\t self.sendUpdate();\n", "\t}\n", "\n", "\tthis.id = id;\n", "\tthis.elem = elem;\n", "\tthis.label = init.label;\n", "\n", "\tInputWidgets.commInitializer(this);\n", " }\n", " Text.prototype = Widget;\n", "\n", " var Textarea = function (typ, id, init) {\n", "\tvar attr = { placeholder: init.label },\n", "\t elem = createElem(\"textarea\", attr, init.value),\n", "\t self = this;\n", "\tthis.getState = function () {\n", "\t return elem.value;\n", "\t}\n", "\telem.onchange = function () {\n", "\t self.sendUpdate();\n", "\t}\n", "\n", "\tthis.id = id;\n", "\tthis.elem = elem;\n", "\tthis.label = init.label;\n", "\n", "\tInputWidgets.commInitializer(this);\n", " }\n", " Textarea.prototype = Widget;\n", "\n", " // RadioButtons\n", " // Dropdown\n", " // HTML\n", " // Latex\n", "\n", " var InputWidgets = {\n", "\tSlider: Slider,\n", "\tCheckbox: Checkbox,\n", "\tButton: Button,\n", "\tText: Text,\n", "\tTextarea: Textarea,\n", "\tdebug: false,\n", "\tlog: function () {\n", "\t if (InputWidgets.debug) {\n", "\t\tconsole.log.apply(console, arguments);\n", "\t }\n", "\t},\n", "\t// a central way to initalize communication\n", "\t// for widgets.\n", "\tcommInitializer: function (widget) {\n", "\t widget.sendUpdate = function () {};\n", "\t}\n", " };\n", "\n", " window.InputWidgets = InputWidgets;\n", "\n", "})(jQuery, undefined);\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div id=\"interact-js-shim\">\n", " <script charset=\"utf-8\">\n", "(function (IPython, $, _, MathJax, Widgets) {\n", " $.event.special.destroyed = {\n", "\tremove: function(o) {\n", "\t if (o.handler) {\n", "\t\to.handler.apply(this, arguments)\n", "\t }\n", "\t}\n", " }\n", "\n", " var OutputArea = IPython.version >= \"4.0.0\" ? require(\"notebook/js/outputarea\").OutputArea : IPython.OutputArea;\n", "\n", " var redrawValue = function (container, type, val) {\n", "\tvar selector = $(\"<div/>\");\n", "\tvar oa = new OutputArea(_.extend(selector, {\n", "\t selector: selector,\n", "\t prompt_area: true,\n", "\t events: IPython.events,\n", "\t keyboard_manager: IPython.keyboard_manager\n", "\t})); // Hack to work with IPython 2.1.0\n", "\n", "\tswitch (type) {\n", "\tcase \"image/png\":\n", " var _src = 'data:' + type + ';base64,' + val;\n", "\t $(container).find(\"img\").attr('src', _src);\n", "\t break;\n", "\tdefault:\n", "\t var toinsert = OutputArea.append_map[type].apply(\n", "\t\toa, [val, {}, selector]\n", "\t );\n", "\t $(container).empty().append(toinsert.contents());\n", "\t selector.remove();\n", "\t}\n", "\tif (type === \"text/latex\" && MathJax) {\n", "\t MathJax.Hub.Queue([\"Typeset\", MathJax.Hub, toinsert.get(0)]);\n", "\t}\n", " }\n", "\n", "\n", " $(document).ready(function() {\n", "\tWidgets.debug = false; // log messages etc in console.\n", "\tfunction initComm(evt, data) {\n", "\t var comm_manager = data.kernel.comm_manager;\n", " //_.extend(comm_manager.targets, require(\"widgets/js/widget\"))\n", "\t comm_manager.register_target(\"Signal\", function (comm) {\n", " comm.on_msg(function (msg) {\n", " //Widgets.log(\"message received\", msg);\n", " var val = msg.content.data.value;\n", " $(\".signal-\" + comm.comm_id).each(function() {\n", " var type = $(this).data(\"type\");\n", " if (val[type]) {\n", " redrawValue(this, type, val[type], type);\n", " }\n", " });\n", " delete val;\n", " delete msg.content.data.value;\n", " });\n", "\t });\n", "\n", "\t // coordingate with Comm and redraw Signals\n", "\t // XXX: Test using Reactive here to improve performance\n", "\t $([IPython.events]).on(\n", "\t\t'output_appended.OutputArea', function (event, type, value, md, toinsert) {\n", "\t\t if (md && md.reactive) {\n", " // console.log(md.comm_id);\n", " toinsert.addClass(\"signal-\" + md.comm_id);\n", " toinsert.data(\"type\", type);\n", " // Signal back indicating the mimetype required\n", " var comm_manager = IPython.notebook.kernel.comm_manager;\n", " var comm = comm_manager.comms[md.comm_id];\n", " comm.then(function (c) {\n", " c.send({action: \"subscribe_mime\",\n", " mime: type});\n", " toinsert.bind(\"destroyed\", function() {\n", " c.send({action: \"unsubscribe_mime\",\n", " mime: type});\n", " });\n", " })\n", "\t\t }\n", "\t });\n", "\t}\n", "\n", "\ttry {\n", "\t // try to initialize right away. otherwise, wait on the status_started event.\n", "\t initComm(undefined, IPython.notebook);\n", "\t} catch (e) {\n", "\t $([IPython.events]).on('kernel_created.Kernel kernel_created.Session', initComm);\n", "\t}\n", " });\n", "})(IPython, jQuery, _, MathJax, InputWidgets);\n", "</script>\n", " <script>\n", " window.interactLoadedFlag = true\n", " $(\"#interact-js-shim\").bind(\"destroyed\", function () {\n", " if (window.interactLoadedFlag) {\n", " console.warn(\"JavaScript required by Interact will be removed if you remove this cell or run using Interact more than once.\")\n", " }\n", " })\n", " $([IPython.events]).on(\"kernel_starting.Kernel kernel_restarting.Kernel\", function () { window.interactLoadedFlag = false })\n", " </script>\n", "</div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "using Interact" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "draw_image (generic function with 2 methods)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function draw_image(f, X, color=\"cyan\")\n", " fig = figure()\n", " @manipulate for n in 1:100\n", " withfig(fig) do\n", " input = split(X, n)\n", " output = map(f, input)\n", "\n", " draw(output, color, 0.1)\n", " draw(input, \"grey\", 0.1)\n", "\n", " axis(\"image\")\n", " end\n", " end\n", " \n", "end" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "Interact.Slider{Int64}(Signal{Int64}(50, nactions=0),\"n\",50,1:100,true)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31b3ae090>)" ] }, "execution_count": 16, "metadata": { "comm_id": "7bd2ea2b-d96c-4149-8c83-1e706c1de67d", "reactive": true }, "output_type": "execute_result" } ], "source": [ "draw_image(f, IntervalBox(0..1, 0..1))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "standard_map (generic function with 2 methods)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function standard_map(X::IntervalBox, k = 1.0)\n", " p, θ = X\n", " \n", " p′ = p + ksin(θ)\n", " θ′ = θ + p′\n", " \n", " IntervalBox(p′, θ′)\n", "end" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "iterate (generic function with 1 method)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function iterate(f, n, x)\n", " for i in 1:n\n", " x = f(x)\n", " end\n", " x\n", "end" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "Interact.Slider{Int64}(Signal{Int64}(50, nactions=0),\"n\",50,1:100,true)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31a0684d0>)" ] }, "execution_count": 16, "metadata": { "comm_id": "8b4e951a-3075-447c-9edf-97e92c97655f", "reactive": true }, "output_type": "execute_result" } ], "source": [ "#@manipulate for i in 1:10, j in 1:10\n", "\n", "i = 9\n", "j = 8\n", "\n", "#X = IntervalBox((i/10)2pi..(i+1)/102pi, (j/10)2pi..(j+1)/102pi)\n", "\n", "X = IntervalBox(0..6, 0..6)\n", "\n", "draw_image(x -> iterate(standard_map, 4, x), X)\n", "\n", "#axis([0, 2pi, 0, 2pi])\n", " #axis(\"image\")\n", "#end" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "mod (generic function with 42 methods)" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import Base.mod \n", "function mod(X::Interval, width::Real)\n", " \n", " \n", " @show X, width\n", "\n", " X /= width\n", " \n", " if diam(X) >= 1.\n", " return [Interval(0, width)]\n", " end\n", " \n", " a = X.lo - floor(X.lo)\n", " b = X.hi - floor(X.hi)\n", " \n", " if a < b\n", " return [Interval(a, b)width]\n", " \n", " end\n", " \n", " return [Interval(0, b)width, Interval(a, 1)width]\n", " \n", "end" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1-element Array{ValidatedNumerics.Interval{Float64},1}:\n", " [0.3, 0.5]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod(Interval(0.3, 0.5), 1)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{ValidatedNumerics.Interval{Float64},1}:\n", " [0.0, 0.19999999999999996]\n", " [0.3, 1.0] " ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod(Interval(0.3, 1.2), 1)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1-element Array{ValidatedNumerics.Interval{Float64},1}:\n", " [0.0, 1.0]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod(Interval(0.3, 1.5), 1)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "mod (generic function with 42 methods)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function mod(X::IntervalBox, width::Real)\n", " x, y = X\n", " \n", " xx = mod(x, width)\n", " yy = mod(y, width)\n", " \n", " vec([IntervalBox(x, y) for x in xx, y in yy])\n", "end" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1-element Array{Any,1}:\n", " IntervalBox([0.0, 0.5],[0.09999999999999999, 0.6000000000000001])" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = IntervalBox(0..0.5, 0.1..0.6)\n", "mod(X, 1)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{Any,1}:\n", " IntervalBox([0.0, 0.5],[0.0, 0.10000000000000009])\n", " IntervalBox([0.0, 0.5],[0.8999999999999999, 1.0]) " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = IntervalBox(0..0.5, 0.9..1.1)\n", "mod(X, 1)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4-element Array{Any,1}:\n", " IntervalBox([0.0, 0.20000000000000018],[0.0, 0.20000000000000018])\n", " IntervalBox([0.7999999999999999, 1.0],[0.0, 0.20000000000000018]) \n", " IntervalBox([0.0, 0.20000000000000018],[0.7999999999999999, 1.0]) \n", " IntervalBox([0.7999999999999999, 1.0],[0.7999999999999999, 1.0]) " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = IntervalBox(0.8..1.2, 0.8..1.2)\n", "mod(X, 1)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{ValidatedNumerics.Interval{Float64},1}:\n", " [0.0, 0.7168146928204147] \n", " [5.999999999999998, 6.283185307179587]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod(6..7, ValidatedNumerics.two_pi(Float64))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{ValidatedNumerics.Interval{Float64},1}:\n", " [0.0, 0.7168146928204147] \n", " [5.999999999999998, 6.283185307179587]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod(6..7, @interval(2π))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[6.283185307179586, 6.283185307179587]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@interval(2π)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6.283185307179586" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2pi" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[6.283185307179586, 6.283185307179587]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2@interval(pi)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6.283185307179586" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2pi" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.7168146928204138" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 - ans" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "standard_map (generic function with 2 methods)" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function standard_map(X::IntervalBox, k = 1.0)\n", " p, θ = X\n", " \n", " p′ = mod2pi( p + ksin(θ) )\n", " θ′ = mod2pi( θ + p′ )\n", " \n", " @show p′, θ′\n", " \n", " IntervalBox(p′, θ′)\n", "end" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ValidatedNumerics.IntervalBox{N,T}" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function IntervalBox{T}(X::Vector{Interval{T}}, Y::Vector{Interval{T}})\n", " vec([IntervalBox(x, y) for x in X, y in Y])\n", "end" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0.0, 1.0] × [0.0, 1.0]" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = IntervalBox(0..1, 0..1)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0.0, 1.8414709848078967]" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p, theta = X\n", "p + sin(theta)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ValidatedNumerics.Interval{Float64}" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "typeof(p + sin(theta))" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(X,width) = ([0.0, 1.8414709848078967],[6.283185307179586, 6.283185307179587])" ] }, { "data": { "text/plain": [ "1-element Array{ValidatedNumerics.Interval{Float64},1}:\n", " [0.0, 1.8414709848078974]" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "mod(p + sin(theta), ValidatedNumerics.two_pi(Float64))" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "mod2pi (generic function with 2 methods)" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod2pi{T}(x::Interval{T}) = mod(x, ValidatedNumerics.two_pi(T))\n", "\n", "mod2pi{T}(X::Vector{Interval{T}}) = map(mod2pi, X)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{ValidatedNumerics.Interval{Float64},1}:\n", " [0.0, 0.7168146928204147] \n", " [5.999999999999998, 6.283185307179587]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mod2pi(6..7)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[6.283185307179586, 6.283185307179587]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ValidatedNumerics.two_pi(Float64)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(X,width) = ([0.0, 1.8414709848078967],[6.283185307179586, 6.283185307179587])" ] }, { "ename": "LoadError", "evalue": "LoadError: MethodError: `convert` has no method matching convert(::Type{ValidatedNumerics.Interval{T}}, ::Array{ValidatedNumerics.Interval{Float64},1})\nThis may have arisen from a call to the constructor ValidatedNumerics.Interval{T}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n call{T}(::Type{T}, ::Any)\n convert{T<:Real}(::Type{T<:Real}, !Matched::Complex{T<:Real})\n convert{T<:Number}(::Type{T<:Number}, !Matched::Char)\n ...\nwhile loading In[84], in expression starting on line 1", "output_type": "error", "traceback": [ "LoadError: MethodError: `convert` has no method matching convert(::Type{ValidatedNumerics.Interval{T}}, ::Array{ValidatedNumerics.Interval{Float64},1})\nThis may have arisen from a call to the constructor ValidatedNumerics.Interval{T}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n call{T}(::Type{T}, ::Any)\n convert{T<:Real}(::Type{T<:Real}, !Matched::Complex{T<:Real})\n convert{T<:Number}(::Type{T<:Number}, !Matched::Char)\n ...\nwhile loading In[84], in expression starting on line 1", "", " in call at /Users/dpsanders/.julia/v0.4/FixedSizeArrays/src/constructors.jl:92", " in standard_map at In[77]:9", " in standard_map at In[77]:2" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "(X,width) = ([0.0, 2.8414709848078976],[6.283185307179586, 6.283185307179587])\n", "(p′,θ′) = (ValidatedNumerics.Interval{Float64}[[0.0, 1.8414709848078974]],[ValidatedNumerics.Interval{Float64}[[0.0, 2.8414709848078985]]])\n" ] } ], "source": [ "standard_map(IntervalBox(0..1, 0..1))" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1-element Array{ValidatedNumerics.Interval{Float64},1}:\n", " [0.0, 1.8414709848078974]" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = [ValidatedNumerics.Interval{Float64}(0.0, 1.8414709848078974)]" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1-element Array{ValidatedNumerics.Interval{Float64},1}:\n", " [0.0, 2.8414709848078985]" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta = [@interval(0.0, 2.8414709848078985)]" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1-element Array{ValidatedNumerics.IntervalBox{2,Float64},1}:\n", " IntervalBox([0.0, 1.8414709848078974],[0.0, 2.8414709848078985])" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IntervalBox(p, theta)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "mod2pi (generic function with 6 methods)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "include(\"standard_map.jl\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "draw_image(standard_map, )" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.2", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }	apache-2.0
AlphaSmartDog/DeepLearningNotes	Torch-2 DQN/Torch-5 DQN/test/main.ipynb	2	30927	{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "%matplotlib inline\n", "from env2mini import env\n", "from DQNCoremini import DQNCore" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "init_state = env.get_initial_state()\n", "action_space = env.get_action_space()\n", "agent = DQNCore(init_state, len(action_space))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n" ] } ], "source": [ "MAX_EPISODE = 10\n", "\n", "global_counter = 0\n", "varepsilon = 0\n", "\n", "for episode in range(MAX_EPISODE):\n", " print (episode)\n", " step_counter = 0\n", " env.reset()\n", " state = env.get_initial_state()\n", " agent.init()\n", " while True:\n", " global_counter += 1\n", " step_counter += 1\n", " if global_counter % 500 == 0:\n", " varepsilon += 5e-5\n", " \n", " action = agent.varepsilon_greedy_policy(state, varepsilon)\n", " reward, next_state, done = env.step(action)\n", " agent.update_cache(state, action, reward, next_state, done)\n", " state = next_state\n", " \n", " if global_counter > 500 and step_counter > 32:\n", " agent.step_learning()\n", " if global_counter % 500 ==0:\n", " agent.update_nextQ_network() \n", " \n", " if done:\n", " break" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reward_list= []\n", "value_list = []\n", "env.reset()\n", "state = env.get_initial_state()\n", "for i in range(600):\n", " action = agent.greedy_policy(state)\n", " reward, next_state, done = env.step(action)\n", " state = next_state\n", " reward_list.append(reward)\n", " value_list.append(env.total_value)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x285ce9a1c50>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ntJ/Y1vNrfecDQ0DHKY7xumG2svL0izRIb4RjShPNMaWJNt3HVCqV5n987Vk2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\nfr+qrDrP7/z64fvot1o+z/J7Xt24i13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"text/plain": [ "<matplotlib.figure.Figure at 0x285ce382198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.Series(value_list).plot(figsize=(16,6))" ] }, { "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
EVS-ATMOS/ACE-ENA-EVA	notebooks/scratch.ipynb	1	2228	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import boto3" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "s3 = boto3.resource('s3')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "aceena\n", "amsworkshop\n", "anlmodal\n", "scollisprivatephotos\n", "testradar\n" ] } ], "source": [ "for bucket in s3.buckets.all():\n", " print(bucket.name)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "s3.Object(bucket_name='aceena', key='test.nc')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = open('/Users/scollis/test.nc', 'rb')\n", "s3.Bucket('aceena').put_object(Key='test.nc', Body=data)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "data.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": 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 }	bsd-3-clause
zlpure/CS231n	assignment1/two_layer_net.ipynb	1	1000939	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing a Neural Network\n", "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# A bit of setup\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from cs231n.classifiers.neural_net import TwoLayerNet\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 external modules\n", "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "def rel_error(x, y):\n", " \"\"\" returns relative error \"\"\"\n", " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use the class `TwoLayerNet` in the file `cs231n/classifiers/neural_net.py` to represent instances of our network. The network parameters are stored in the instance variable `self.params` where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a small net and some toy data to check your implementations.\n", "# Note that we set the random seed for repeatable experiments.\n", "\n", "input_size = 4\n", "hidden_size = 10\n", "num_classes = 3\n", "num_inputs = 5\n", "\n", "def init_toy_model():\n", " np.random.seed(0)\n", " return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n", "\n", "def init_toy_data():\n", " np.random.seed(1)\n", " X = 10 * np.random.randn(num_inputs, input_size)\n", " y = np.array([0, 1, 2, 2, 1])\n", " return X, y\n", "\n", "net = init_toy_model()\n", "X, y = init_toy_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Forward pass: compute scores\n", "Open the file `cs231n/classifiers/neural_net.py` and look at the method `TwoLayerNet.loss`. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters. \n", "\n", "Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Your scores:\n", "[[-0.81233741 -1.27654624 -0.70335995]\n", " [-0.17129677 -1.18803311 -0.47310444]\n", " [-0.51590475 -1.01354314 -0.8504215 ]\n", " [-0.15419291 -0.48629638 -0.52901952]\n", " [-0.00618733 -0.12435261 -0.15226949]]\n", "\n", "correct scores:\n", "[[-0.81233741 -1.27654624 -0.70335995]\n", " [-0.17129677 -1.18803311 -0.47310444]\n", " [-0.51590475 -1.01354314 -0.8504215 ]\n", " [-0.15419291 -0.48629638 -0.52901952]\n", " [-0.00618733 -0.12435261 -0.15226949]]\n", "\n", "Difference between your scores and correct scores:\n", "3.68027209818e-08\n" ] } ], "source": [ "scores = net.loss(X)\n", "print 'Your scores:'\n", "print scores\n", "print\n", "print 'correct scores:'\n", "correct_scores = np.asarray([\n", " [-0.81233741, -1.27654624, -0.70335995],\n", " [-0.17129677, -1.18803311, -0.47310444],\n", " [-0.51590475, -1.01354314, -0.8504215 ],\n", " [-0.15419291, -0.48629638, -0.52901952],\n", " [-0.00618733, -0.12435261, -0.15226949]])\n", "print correct_scores\n", "print\n", "\n", "# The difference should be very small. We get < 1e-7\n", "print 'Difference between your scores and correct scores:'\n", "print np.sum(np.abs(scores - correct_scores))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Forward pass: compute loss\n", "In the same function, implement the second part that computes the data and regularizaion loss." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference between your loss and correct loss:\n", "1.79412040779e-13\n" ] } ], "source": [ "loss, _ = net.loss(X, y, reg=0.1)\n", "correct_loss = 1.30378789133\n", "\n", "# should be very small, we get < 1e-12\n", "print 'Difference between your loss and correct loss:'\n", "print np.sum(np.abs(loss - correct_loss))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Backward pass\n", "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W1 max relative error: 3.669857e-09\n", "W2 max relative error: 3.440708e-09\n", "b2 max relative error: 4.652653e-11\n", "b1 max relative error: 2.738420e-09\n" ] } ], "source": [ "from cs231n.gradient_check import eval_numerical_gradient\n", "\n", "# Use numeric gradient checking to check your implementation of the backward pass.\n", "# If your implementation is correct, the difference between the numeric and\n", "# analytic gradients should be less than 1e-8 for each of W1, W2, b1, and b2.\n", "\n", "loss, grads = net.loss(X, y, reg=0.1)\n", "\n", "# these should all be less than 1e-8 or so\n", "for param_name in grads:\n", " f = lambda W: net.loss(X, y, reg=0.1)[0]\n", " param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n", " print '%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train the network\n", "To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function `TwoLayerNet.train` and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement `TwoLayerNet.predict`, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.\n", "\n", "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.2." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final training loss: 0.000133322687149\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x322e160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "net = init_toy_model()\n", "stats = net.train(X, y, X, y,\n", " learning_rate=1e-1, reg=1e-5,\n", " num_iters=100, verbose=False)\n", "\n", "print 'Final training loss: ', stats['loss_history'][-1]\n", "\n", "# plot the loss history\n", "plt.plot(stats['loss_history'])\n", "plt.xlabel('iteration')\n", "plt.ylabel('training loss')\n", "plt.title('Training Loss history')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load the data\n", "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data shape: (49000L, 3072L)\n", "Train labels shape: (49000L,)\n", "Validation data shape: (1000L, 3072L)\n", "Validation labels shape: (1000L,)\n", "Test data shape: (1000L, 3072L)\n", "Test labels shape: (1000L,)\n" ] } ], "source": [ "from cs231n.data_utils import load_CIFAR10\n", "\n", "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n", " \"\"\"\n", " Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n", " it for the two-layer neural net classifier. These are the same steps as\n", " we used for the SVM, but condensed to a single function. \n", " \"\"\"\n", " # Load the raw CIFAR-10 data\n", " cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n", " X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", " \n", " # Subsample the data\n", " mask = range(num_training, num_training + num_validation)\n", " X_val = X_train[mask]\n", " y_val = y_train[mask]\n", " mask = range(num_training)\n", " X_train = X_train[mask]\n", " y_train = y_train[mask]\n", " mask = range(num_test)\n", " X_test = X_test[mask]\n", " y_test = y_test[mask]\n", "\n", " # Normalize the data: subtract the mean image\n", " mean_image = np.mean(X_train, axis=0)\n", " X_train -= mean_image\n", " X_val -= mean_image\n", " X_test -= mean_image\n", "\n", " # Reshape data to rows\n", " X_train = X_train.reshape(num_training, -1)\n", " X_val = X_val.reshape(num_validation, -1)\n", " X_test = X_test.reshape(num_test, -1)\n", "\n", " return X_train, y_train, X_val, y_val, X_test, y_test\n", "\n", "\n", "# Invoke the above function to get our data.\n", "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n", "print 'Train data shape: ', X_train.shape\n", "print 'Train labels shape: ', y_train.shape\n", "print 'Validation data shape: ', X_val.shape\n", "print 'Validation labels shape: ', y_val.shape\n", "print 'Test data shape: ', X_test.shape\n", "print 'Test labels shape: ', y_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train a network\n", "To train our network we will use SGD with momentum. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "iteration 0 / 10000: loss 2.303173\n", "iteration 100 / 10000: loss 1.986939\n", "iteration 200 / 10000: loss 1.772881\n", "iteration 300 / 10000: loss 1.751775\n", "iteration 400 / 10000: loss 1.751012\n", "iteration 500 / 10000: loss 1.602030\n", "iteration 600 / 10000: loss 1.559349\n", "iteration 700 / 10000: loss 1.494003\n", "iteration 800 / 10000: loss 1.583357\n", "iteration 900 / 10000: loss 1.473938\n", "iteration 1000 / 10000: loss 1.361328\n", "iteration 1100 / 10000: loss 1.417806\n", "iteration 1200 / 10000: loss 1.357796\n", "iteration 1300 / 10000: loss 1.333460\n", "iteration 1400 / 10000: loss 1.459409\n", "iteration 1500 / 10000: loss 1.355586\n", "iteration 1600 / 10000: loss 1.372198\n", "iteration 1700 / 10000: loss 1.373477\n", "iteration 1800 / 10000: loss 1.405657\n", "iteration 1900 / 10000: loss 1.480891\n", "iteration 2000 / 10000: loss 1.453909\n", "iteration 2100 / 10000: loss 1.449138\n", "iteration 2200 / 10000: loss 1.387527\n", "iteration 2300 / 10000: loss 1.425035\n", "iteration 2400 / 10000: loss 1.346936\n", "iteration 2500 / 10000: loss 1.378059\n", "iteration 2600 / 10000: loss 1.381800\n", "iteration 2700 / 10000: loss 1.493109\n", "iteration 2800 / 10000: loss 1.453851\n", "iteration 2900 / 10000: loss 1.398702\n", "iteration 3000 / 10000: loss 1.382979\n", "iteration 3100 / 10000: loss 1.292889\n", "iteration 3200 / 10000: loss 1.349922\n", "iteration 3300 / 10000: loss 1.372948\n", "iteration 3400 / 10000: loss 1.282285\n", "iteration 3500 / 10000: loss 1.289941\n", "iteration 3600 / 10000: loss 1.262948\n", "iteration 3700 / 10000: loss 1.287153\n", "iteration 3800 / 10000: loss 1.117376\n", "iteration 3900 / 10000: loss 1.300954\n", "iteration 4000 / 10000: loss 1.284825\n", "iteration 4100 / 10000: loss 1.345201\n", "iteration 4200 / 10000: loss 1.379239\n", "iteration 4300 / 10000: loss 1.176451\n", "iteration 4400 / 10000: loss 1.368014\n", "iteration 4500 / 10000: loss 1.274182\n", "iteration 4600 / 10000: loss 1.226376\n", "iteration 4700 / 10000: loss 1.248948\n", "iteration 4800 / 10000: loss 1.309601\n", "iteration 4900 / 10000: loss 1.193237\n", "iteration 5000 / 10000: loss 1.295341\n", "iteration 5100 / 10000: loss 1.385274\n", "iteration 5200 / 10000: loss 1.136107\n", "iteration 5300 / 10000: loss 1.317750\n", "iteration 5400 / 10000: loss 1.150766\n", "iteration 5500 / 10000: loss 1.106021\n", "iteration 5600 / 10000: loss 1.255558\n", "iteration 5700 / 10000: loss 1.205317\n", "iteration 5800 / 10000: loss 1.194301\n", "iteration 5900 / 10000: loss 1.193944\n", "iteration 6000 / 10000: loss 1.119043\n", "iteration 6100 / 10000: loss 1.195911\n", "iteration 6200 / 10000: loss 1.333146\n", "iteration 6300 / 10000: loss 1.183365\n", "iteration 6400 / 10000: loss 1.189290\n", "iteration 6500 / 10000: loss 1.235728\n", "iteration 6600 / 10000: loss 1.172337\n", "iteration 6700 / 10000: loss 1.173754\n", "iteration 6800 / 10000: loss 1.128811\n", "iteration 6900 / 10000: loss 1.186782\n", "iteration 7000 / 10000: loss 1.495066\n", "iteration 7100 / 10000: loss 1.183329\n", "iteration 7200 / 10000: loss 1.226022\n", "iteration 7300 / 10000: loss 1.298803\n", "iteration 7400 / 10000: loss 1.134776\n", "iteration 7500 / 10000: loss 1.139218\n", "iteration 7600 / 10000: loss 1.049089\n", "iteration 7700 / 10000: loss 1.146581\n", "iteration 7800 / 10000: loss 1.142495\n", "iteration 7900 / 10000: loss 1.284630\n", "iteration 8000 / 10000: loss 1.172332\n", "iteration 8100 / 10000: loss 1.151727\n", "iteration 8200 / 10000: loss 1.230487\n", "iteration 8300 / 10000: loss 1.204418\n", "iteration 8400 / 10000: loss 1.079166\n", "iteration 8500 / 10000: loss 1.250091\n", "iteration 8600 / 10000: loss 1.115582\n", "iteration 8700 / 10000: loss 1.147554\n", "iteration 8800 / 10000: loss 1.283131\n", "iteration 8900 / 10000: loss 1.182618\n", "iteration 9000 / 10000: loss 1.184478\n", "iteration 9100 / 10000: loss 1.188968\n", "iteration 9200 / 10000: loss 1.169266\n", "iteration 9300 / 10000: loss 1.162916\n", "iteration 9400 / 10000: loss 1.316641\n", "iteration 9500 / 10000: loss 1.155933\n", "iteration 9600 / 10000: loss 1.200453\n", "iteration 9700 / 10000: loss 1.338696\n", "iteration 9800 / 10000: loss 1.230580\n", "iteration 9900 / 10000: loss 1.158417\n", "Validation accuracy: 0.542\n" ] } ], "source": [ "input_size = 32 * 32 * 3\n", "hidden_size = 100\n", "num_classes = 10\n", "net = TwoLayerNet(input_size, hidden_size, num_classes)\n", "\n", "# Train the network\n", "stats = net.train(X_train, y_train, X_val, y_val,\n", " num_iters=10000, batch_size=200,\n", " learning_rate=1e-4, learning_rate_decay=0.95,\n", " reg=0.4, verbose=True)\n", "\n", "# Predict on the validation set\n", "val_acc = (net.predict(X_val) == y_val).mean()\n", "print 'Validation accuracy: ', val_acc\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Debug the training\n", "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n", "\n", "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n", "\n", "Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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cfjrQunX4OXORVLPp77+7tan5/M7PnQtssEFhyuR35JGFHzTklWSA7G+G9+vf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"text/plain": [ "<matplotlib.figure.Figure at 0x6d68da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the loss function and train / validation accuracies\n", "plt.subplot(2, 1, 1)\n", "plt.plot(stats['loss_history'])\n", "plt.title('Loss history')\n", "plt.xlabel('Iteration')\n", "plt.ylabel('Loss')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(stats['train_acc_history'], label='train')\n", "plt.plot(stats['val_acc_history'], label='val')\n", "plt.title('Classification accuracy history')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Clasification accuracy')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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zVZLNNrcTccB39/dpTG9JpsIGpIXH0tfwvdVywCjsVm7VWzw+2mN4WceLyt4l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L8SfbwqsJpNmE2hIKNsi9mA2lYVfyIRXAYx23aSgedg1USNwmy4xA1cus3B2pzngdgHz/Vw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\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Y2yka+yLkp6dMVn2L4+cdOs1NujmA9WqMES0p7VWIyyrE2WiKbps4Q9kiNRaF\nYNp4Tv5QKK6Tt+/oLefkZMjP93wq2RShIpTWuz9ekS3aPNnO8k62a60DmDhrJjfCczxujzh//Ybi\n7iHZggjVzbsqe4c5gpiQoy9e/FO4Q4Cbbzs8+DSNDADw8u0fydllYl4csyounWToYQcqxaRQCo63\n5qrvgS6MztHM4/VpF+o5kpIsxKwUsXfLbMd/Jvbl9oxoOKV+WGYrL/YhZ0d01o8xZG+Tai4wwoBx\nv4Uii9B0dbOAi3CJJ9tRRsMmn5++JFVMsroR831x+TnO5JaEJdZveD2glr3L/Q9WpHriUrrqTHh5\nNaZsirNRLe2wmK1IRmM+viue+YeXN1xHGifSIGh3ehzUDpg0hEz3VhNunBnaOEZctr5ocZXdo63N\nOVsZ0pKVa+ejH3F8HnH1ThiU06sOP3r6c6b6Fd+8FihY23+1z/VsTKr6MQB72ybG1ZK2N8OR3QUT\nx8Pq+TyS3RtlM47izPFlRKVU38Kzt/nyzQnFvFivp/W7WK7GfCjSKLm8xc8+2Oar802u6+SfOGxj\njMYTThdX6JJ5aK67XL9+y0qCaPQyNv1uxFwCegz8cx7XFUp7BX7+L/4cAMM1qZV36cvWnqdWia27\nn9J5+UvyCaH0g9QKK29zWBXnznUDmm97xFIaE4lcYqcaqIZFbDNKzVjCmzYyReLFKtmUAAxahxrz\nhc9CFv4tlwpqQsV0A17+XkD/JmyDda+NrogIX962Gd1+gb56g54QDszg/AWJ9D5FmbZztTWj4TXe\nqMfBh+Ju8FI2irfkVsKUatEAzRszXUp429hmOlH3IW4YJCSQkrXwyVsq9ac1MgWx32fvXqOmMjx9\nLFItE7XDm+OvUMwMekxWx9tJDvUMtwmhF2bJBVXDYjKRsKBnPZykSr1Q5rAmIof+hUvcVPAK4g5S\ntRLdqyZ9b05VpkAW8+9ZbN4XcL0f78f78X68H+/HDz5+MM+4VrrH7p5oEbj1enirIbPgGi8urMXp\ntM3ieIQuC7o+OvpzdMtHn/YpyPCNExhMW2NWMm+LqtI/vSRdEtb4bDEh7E1Z3PS5iIlQw9b9LI3G\nHRaWsNCnmobPmMC/YIkMz759Q/x7+kh///ycD4+Ex7WXsvn2y5d4Mjewc5Bmq+SzlCX5t7M5lj8l\nl9IJZN6sli0QsktGwlpOGLOKR2Dm+eCJaKmIrxzWloeZE1szSRSwy1ucmTP0juyJMxd8+/wPVO4I\nizcRxFATBhllk3RhLi3I0TKLmoBiSVh1W4e7jPpdDA86sse5erdGMJwT08Q6HOw9Qg8LXHWntK+E\nNzqeRFjJBNuHwmMs2Am6c4etT/8lriVCm2P3HXUzQWNfvFP3xKe27mHJorSX375Fze6RjHTMxPdj\nUz+s7nB7fc66uSbSxF7ZZoJ4LIUVk7zOqRjrZYAZmShT4WEd7e1y9uIFaQkU0u4uYT7gUKYKXMsg\nivvMBz1cCegRpDUsFJ4+eSg++33ykUdcU/E98WzfU3B7NziGyPe4yqZ1u2PnSK3H3F5LcolOyFH9\niDulOl0JDBFMI6L1gG8vhaW/iBKcXHmYPeHhtrpDLCNLzq8gme/ove3xq8437H0gvIW/fVile/yG\ntBsnJXtIU9aCuL1kKftHt7cPsBZNprE1Ewl9qBubLTf1nRzffC7W7tWbGacXLyiHadyEsOJ11+Om\necOjp5LA5egBIzfOL15/Sc0W+9ltNmlO4TohQvfj8z6GbbMdX3A9E7J13h3iF5K0ZQHkyvMoM+Gi\nIz5Pz0YkXRWnD23Z7qRlI6ydTW/+1XzOngxfTwsGg9M0mZwM8890lv0xGOs/Af63exP6qzXeSBLd\nuws0NUbYHuNPxJwfbd2jktln2Rde0K5p8BdHDXYl1erff7Fioi4wChEJyT39rFaisnPEN74sTFxd\nYlfTqJ3uxpwfPxOFnWYywbTbRC2U6cvWmxfXbVaGxnwofuf45e/YffiMmGyEL23nuXtQoVGDPz8S\nedrmWZvT0xFaVoRzw1XIl3/4DYN3f2BHep4/+ugR60DjzUJ497kojmqbeDo8+dnfADBaj+h2v+b1\n681IT7YsvL3q1iGx2j5hKDzYMAzoO33Sltj/v/r5X/H5H/+eai3L9EboF9/tMeu3qdwRmBD1+1us\njIDZ9QWVPbFXf/Wjn/D52xaff/UFAOXSIbYVB1+nOxSh/nouz+hWYzgQ0Yd0zMEu6CDxCd58/XZj\n3stJh6nTI70jojuZeIlcTSPhRwQLmS4ya3i4dJpC/pQslMollMSKmPSMvdESLxoRdYU+zCo6nfaE\nKBJndStVofnqgpvRFR/fl5j10yW9gYspCynru2WySshi7EAgdGi18v3tnD/YZTzxTJ6/vADgptXD\nC8dMgy6eLfK95VQDY5xB7cpQzmJMa9DmaVlBX4sDlFLyVKY+Jblx8UyFmJrCMERIddRZY6ePqJtV\n+mMRPu5NIxbxCEteFLGYjzf4mqdPKsxW4mKw1RTdk5ONOZe37jCW6F7NYRc9PuOoLsIR2UyK0WpO\nZU8UFjSMBPMoJGXFOD4VmM2ekqdSe4gmL5eod8ztzTmuVubhE5FfbTbb3Nl+SDoj1uH85Ap1prJ/\n9IywJv4mEbV4O/6SQJcACoU8Nwuwqpt42ilPXEpqqoi7SjBoihBbpZAHZU68UGA8FcrlZnDO0w//\nJd5YhLD6fTD2PiFVnPLmrQg/6Vv3ie/ukX4g1i8RN7j66g8EvkqE+F6jsMticcPrU1F1OOkNMTyV\nt+dCGH/z4oJP/+YeSjyPG2427QPYWon7dzKEQedPIVw9XcCL5chXhEJyOwPCeJrJfMF/fSVIH4zs\niKWW58YVSvzXZ99Sttfsb4nP3sjj61YH76ZJOSXmOzq+Jpk0SBbkhXjZZ6teopQz6HTFZeKNY8Qy\n9ziW+f5W/7uw438fN8sR677F2pV55dic+bhFNh5SkPswmq4wDI31Uiji26lC2tohnxDvqFWLbOsH\ntHoR7RvZG+9bdM6vKfkyH1fPMjsZolbrXKti7xLZJbvVMl92REEKNZ29ehl8g0xGvJc+2SQDSCd0\nLIlo1h71STQKpHcqmBnJyWwWSC4eo8pQp5rN8/UfXnN7O+OgLEK6uwefYroulxIPvBMFmJaBlU3Q\nvRVy051OSCdC9KSYb6qRJ7Vd4OpbUWMxvJ1T0vKkShksGWqv1nUyZm5jzsaehSGrgUPzlo9+ssuB\nzB3+t//wK05/+VuUrEpKpluuuzf8zc//lg8OREiyef4F190x+8V9njWEUfnNxS3NmybKSOx324/x\nKh1nlhZzOTlt8uhhFfI22aLY31w9i21rf8L2Hjgqkd5kGfseXvG0uJTQ4wSxPu+GAf/1d+ICTDeO\n+OBv/y1LySkcY0Vpd4d334h8qz50KT3Mk5y2efuFSK1cTXVenK0IdNlLWyjR6XSxEneIZCGnF0vi\nWhkmY7HmKgrpjM/MVyhXRfovGc1YTJpk7c3ivrTUJ44TkPFdBk0hWwt/xXQWMnGFkWcXkmjOlPmg\nTXsqnan1nAf797Dy4jduLoYQN0jQYNAXBs6KJK9vT7lYCYcmYeqoZpJMIU0okdumqo6SMsnLwr9i\n1kQbX9G+EjU/lr0JdOSEYzTFQ5N92ul4jNZtj/ZoQjwrzmGmWMeNFJpfC/ns0SGxnWE3l2W2lgbF\ncM7J5QXVsnDAymbA+SpgVxa71ZQMamzEwluij8VVupqETNsTeqE4P7UnJdarGeOBiyZBj/r9TXY9\n+AEv43hcY74Uwje9PKGxkyFMLMjIIp/mUCMcR/zVY1E0UEiX8Y0iw8tjLo6F5fy0WqawCtFa4nIz\nzHuoW0c4mti4ldPGTJnsVQ44+3tREOUtb7mzfUggBXTm9knnkuQyFv2X4iAuexMcZbOlYhzA7Tfi\ngpmrQ/7uz3/EHVlA8+bdFYMV2FuyXeHZU6ZLl/5kjRIIRdFurSllfGJCRxAQo7Z3wEqrkbCEJzLo\nXDEa3fLwpyJq0KiHnHXeoCR3WMpepv7FFWq+iCtB9zvTKX0vyXS8WaCTl0xKhEvGE5dgJWHk+j06\nvQ6J+3XuycrE5SJPzDfIpCXYhD+mdPchwxuwLSGA2WoW4j6duPjd7TtbPIjf48vXv8eUFngsDLh/\ndJ+V9LidRUgqkSeQMH13lxp7tXuoapatenFjzgCKuY/nzxi5Y1oDcYkkJjGcaMWsI1uQdIXHP94i\ncIsMZIHe5bCNn7B4IxG39OoO5b0Cx9IiPjl9w539J+w9+yldWfTD9QmZaEkpKfYu0dgllbdYu7d4\nvlAuCmnWK4Wbt+KduotN+Yi8FXbxPk8tYRVbizFxt896PCK2Fkojt4xYXTokjSMAdlIq89mUA4lM\ndRDt8J/e3GCbOtuBsOxz85Db85ecziSR/N1DBhOP9E6FakMyea2OSQdz7m0LQ0VVFqxnDot5wNIR\n65dabAI7nJ+8pCXJJsikKO1t03h0xEFOwg9OhjR2H+BYYv0uR9coFZtScQejJJ49MgIMHQ6eCKVl\nDq4YLl1uYxoJico1xCN3uM88EhGC0EpzCQT7QvZKlkbvjx0SMZtkShqZlTzp5GZOcKW2cb6rct/e\n5+XVKXlFPOdp1uC3589ZhxkcTZKtbNvUzBjJtZCbylad03WXVszAkpCUthUxag2Iz4Vh8u3VlJdv\nPuP+I7FP2xWb589/iVUpsCuRsmbjEat2SK8tzkI8aTNxRqjR5gXReS3WWM2kcf04fhjn4V1BtlJ5\n9Al6wuDCvQBgPB6jBSblAxl9UuKsg4jlyuN3r8QFntp+TNL3sOU+KSEkfYtle8U3kj0rafkUqw+I\nuSKqoRXyFLcyuKM5l5dCH6rOAncVR2GzIDEpZdIuplCMOeddccZS2W0SeR1flsJPZ0O2GgW6by+x\ny8I5iS3rBFGSa8n8NBm1KDcKJA4atIdCDi/6HeKx2J8KU1+/abL2RmznbI6kHEdBjJKVppaXkK3m\nimSosDIldPFisDFvq1yh6wyIFuKHVTUijkYxU2c0Fw7B61dnoMcIs0LXVfYb6IFFTauQljSLlUoe\nPR0gbUzC+Rilfc27C/GdbrDDchGjXn/EeCTmo6xTKO6SrZowvsZzh85ti93qhyRVGcWQ1eP/fLzP\nGb8f78f78X68H+/HDzx+MM/YWzi4kkpqv7LNsHPBzWRIrCKmtLN/n8K2ju4JDyRSVYJFjOU8S64g\nchees6Z4cEBJFZ/7fsAXXz/H2hFW1dKsoW/FOT5/RXMsLOlEKWAeW6PINoz2bIxX3KNSSqPJ8NJs\nNGGy6WQSzkPKBWEVN2Iq/s01b7sijDVN1Snd+RGTpAwL33tE77rPxe01Hzz7OwBGk1dgpJklhJdx\nvCxSqGbRrD0+ey7yTK3zFZmSy3Qq5nK7HNEPF4yuTigrwmL78ZMPSWopmjeS/DtpU9XLzMPN/NpF\nU1izSjUFHtim8NLjiYjkIo0Xarw4FnmXGBHGdEQ6J0k07ATx1JCDhyaFh7JtKpumddMjlJXTv37+\n70hELuXskq9+Icgt1jsHGJV9MrIC+0EmzaR9TBAT+/3goAbhjKWXR7O+p8IX0LJV1FiJUhhndiE2\nIxWLSMVUQtl61e+0uD3+A+V8lqL0EHS1ih+ETKWXnt/ZxyrXiSQ4e3HbJWknePzpY+Yj8U6v/sMZ\nB3adyq6oFp06LtPhJet5xPaOyG+FgxBPjVHZFRGCde97MLXDiFcvXnFHcrja8Szr+Yze1QXLlfBO\nf/TgUy7fnYAqwq+2o/P6m2+wVeGFTFodZidgVixsWf38kwd3sB/9G17JELSrBHzyZ49JWmseVsQ8\nQq3Kf/zDf2Ei+YJH0wV6Lslla82sK7lrvwOc/kejVKrw0Z+J6FOYz7L1sMZ2PcOhLSIW/bNXpIo6\nq4SIwuRPByj7dbxE/U8ed7KgoyUC4nnZV76w8cICr04u8PrCO0llK5w31xT3xPo1dvboXje5cyg8\nz3xmRn4doLoeiio8J2ceoH3X2viPxr1CxI6sjp80T+k9/5JMVYSg9dDnWTHCqyQI68JzV7Ip4qtb\nYiORLsqXd3n26U+46t8wa4t3mCxD7EINxxEyGi8lOT79Ax/WhKdn2xqJdIbexGM6FzopXdjm8g/f\n0LwW+7IO59i1JavvofUb9cQ5jJkVFoqgPLQ04e05ownzZUQgUw6ZbIGGGacn6yCqRx9Q+uA+nWOT\nfFzM99BM409O8WS0w6ht0+vPSIw01hJGNVUvYxRKJCNZYa96nLx9y9r3yGTFfrZ7Ldq3r9H8TYWn\nWmJ+bsJi6LrcrsXvXjs9FEPh2b7Qhem4Tehb2PY2maTYrzeTPq9vOijyO8/ubrFbyjJc+Fim+N3D\n3QZb9z/mYigiFl93fGbeiHnvGldWucc8j2C+Zul/R9c5JJi0iU2FZ99+9+XGvPVUkmnnkkBebzuV\nBInQJezMWThijQ+Se5y1OyxlZKSRzxIxIVtJkJV6NNY6p2KqaEsZRe3MuZd7wrtrMbfuXOfJo2fo\nkxGRKqKW2XIebzjDTorzPBhPUI04EQpXt0LH561NjAL4AS9jPZZlNBDCdrBbZa2PCFY2A4lf3Bu9\n49ndJ4wMGQ70Xb56fsrJ6QkPdgTLkG00uJrFCeIi31GppVgMpwyH4vNIi1PfznDWvyEmidkd95rJ\nsIshScQP9vboLUOef3ZMeinZRDIlEuYm2Hu+ukVFkqWH5yv+4ctfYMpCF+vxfdapJJoEvngzXDNB\n4/G//B+ZTIXi6AUewaiHPhfKUbETXF9dsQpn6EiEsPmSv/hgF0/2mF5fLSlUquTsAH0mDow3mFB6\n9IBAFhE9fFLj9chhoVU2Fzolgh/ebIgfrbAyEqw8aRJYK3rjMVZBhE9W8xFLd4U/laxDcw9DvcFR\nF6T2xAEK1T2Ojoqcvv5a/I7bpHt9zN3KDglHKIZOs8dc+xrLEvO7q5vEFyqLqQjxFos7dFfXFM08\nrZvXm3MG/tPnn2F4I57sJTmoi/Dn2luSyJXoD4SM7CfLJDMey9WQQLJsxS2DebeD3xGpjMN7OdAM\nkDmcuztJIs/n+ovPSadEGLOUS6JmSqS29wBof3vG5cmQvGkSFcUlf3jUIAIW11J5yyKjfzzyxQSD\nhYovOblPrkbo3gRN0ZE6iXedMYM5JHRxwN2FiZqt0ZUsOhdXM3LFI4p5m/1H4oL5+Mkd2p1v2aqI\n+Tav3zEatjBGfX7zv/1fADSeVajnQm4kk9e7dpPSwQPKyg4NWbQ36p1vzDlyFLLWf+fSriRN7m0d\nEEquZ2XSopzLk8gJ4yFZ36c/HjKetjAl4IRFRKUSI1UVcj6YGaz8BMY8x517onBp0hnw9R9PUMtC\n/up375CKigQ9iazU2CEfc9GDCF+SH+iRwXS0mef+u4+fMP1anKGLsyuKWoacTE2N9Dk//5/+FSfd\nJm1fXEIFI08ho6FKDGRnMeTgKEdlr8rzF+JMnZzOyKRN9rfFGl8NXLb6WfJy447/8Edy20/YK+1T\nkTULt3MDzypgpsT6ts+/wjFcLq430wFWXRimHVNj6MxYxRVyUmE/2d+j178hWIu5NKpFFoMmqbkw\ntq3YE9YTGI89tvZke6BV4P6Pn3LcETn33uI1W3mb7WqRW9mvvMivCapz2n2JGnjZpZxIYukBqbzE\nAFCy5KIicWezxXC3IupCJmoWJxZyM70AYLkakc0lGM7Fe09HazKhSl7d5aot+4qnDvmtLVwJhrE0\n83x2fI6W22IoDR7TdMjtFolJXP7+4pRP/+YvUXsX+LK4TUkm0dw4HcmNfnrxloytsyNRF589+gT4\np33dZiIkmUiwWIl98NYayTBiOhqyty3SIlpyj+37Txi7Qs5RFzS7A6ZnEwo5oUf/+NlQ9L1L4zoI\nDcwgS1nWWBALiEKN9XhOJPnUlYTGJz/5kHVM6A3jPEYsf0T3NmImMfVde7PeBH7Ay3gdg+26OByL\n8JKpuiKXaxDLiIXYq5a5l90iMZNUddMJtq9QSGe5c19Y1wX/gEAp0YuJoL7hqzQe3qcfSKXVGnB5\n0SK3kyW9LzxWfTSiFF/gS4tbWQ6oxOK4ywWDlvidSvUQK9jMZfqux7u3ooBiy/E4bodkt4Xgb3s+\nzvSakix6WA8WxP0AZd7n/LXMX8ZjTFtnrMcCRadgRaiDAXmrztiXbFW9W45P58RZGNCkAAAgAElE\nQVRz4h2f3T/i8vw1y96Ee1WhcIz4GmfaJPSFsC28BEY2RX1vs3G/vxAW+W5tD9ZZ4rrMybcuuGkP\nGAzO+Jt/87cAxA2biZqmWhd7sBj0sKsVssqUdSgURc5KkqqBHgmFVLQLGKUE1eQ2QU/Mb+1pVO/k\n6Uqi8ZvTPpNWh+0tuTbJXUwNus0OGVkc8c/HdbdFMRGStHIokXj28Ztj0sU2McnspWgmRmiRUEMi\n6QnPFj6GbaPZ4qIYLV3qyQTqSkYamn3ClUVRc3ENIRMxpYxDFUcWWfkOrGcx1prNZCVh+RYLCHWa\nfRERGEiChn88urM2lfpTVBkBSKxWKL5FpXyXxbei4GQ06+GHt5S3ROX2l7+65uHhIS2J/lW003z4\n5Md8cfyGVk/I8UXTw1BCpk1RbLKf06k2Ukzffo2OeO/5zZyZGbKbF97+xF2TnEI9X6Z7I2Q20DeR\nf6xojeQaYDWasb6GsLDH/FbIVvNiRa875FDud7z2EQe7IZdvzumMJaTiwkFZGyDXL6Eq2DGdQq3B\nj46EHH/1u89ZJK8w8sJ4jSsZjGKCnsz5XXRc8tYWjWKW+UQYOvV0jv5ws2c37frcyKrns2/f4QxC\nyjWJROb1+dWrIWo1xcOfCkNgp2CiTN6yfCcurnKjwLtf/Dsan+xQR8jsh3eyNPJpTn4rGKN++9sv\nuH71hkNDPP/TT/+Ck8EMYzkgL4kDzr9+zmox46AhLhyDkFc3Jzw72OL/+cXxP5nzTUcYkK8WY4Kk\nzZNyjR/dEbKfS+ncHPexJAlN1gzBVLj/UEQs3rSu6TevUZQ1M8n4puomYQShjApm8zZH+RTnr97Q\nXwtD7/wspLqKaA+EwXNyfMGfP7vPnpanO5WUnsRI5jP4w81CyvlE/M5J5w2nyzXruNjfHz37gEwm\nSyMlLrbrb69YRD6GGkOROf5wnmSy8P9UYBVL5LEKMZZBmpikyEwEcy5Pv2GSEDr0cSlgJ+yTKWZ4\nfiZqczprg9HCJyPhTn3fYenMiGTEwtQ3OwSScYVKeYvJQpy5lRcQT+RIlnX0hPieGwSs11OOHgnv\n/uz0FcwUem/nGFviPWvZu/SGbfyY7GxxTBLjORNJW5mvV2ndjmmetmjISNLyZslRSsENhQ5onpyx\nG69iqmmssiQLMTfz8/ADXsYxt0NRbsLJxWv0fBwrVSaU4YlGdpvkbMZ8LkKowWqCYY352YMfk1LE\nheL7U+x8g7giFvhq7hHpI2KmWKxkDlqD10RxjYVsJ9ou5ZndzBlL9p0oMeSgBDlLZSzRWdpjH3W5\nad0msgoFWc7/ML7P1dsL8g+F9VjYqXHd7+NcCEFL7hbZzRW5efE1qzcSKcsuU0hniJeEpeU7XV72\nLynuGtSKQmj3kweEiy7Bmbiw9w4f052OiM8X6BNxYG4GLV799jnxPVERuc5lsQ6qfPnq5cac03Wh\ngI/bfRKqhZ0VCjQezbCMOV11zAphHe7t3qf3Yown4SbLuZDm+CW1TIG1hNR7+dnnpHd0Jp5QSJET\n56cP/pIXn5+hJsWzSqjUjCT7++J3Ph+9QKNAXMLrLZQEqjNCdSC32/ge6QCzWGA8HdNdwZ4tPJyS\nqbBTslHl/mfSKSzyvH79Akti2VqZDL3liocPxGW3TmbpX/fIaGIPdCXBcuSSSS/Rl7JVLKkTjlac\nPhee423zhmwxTyZhMp0IJfBu3kYJItptCWKQLW3MufVZH/2DNZmKiAiUiynsXIZwZZLNi7UZ3bZZ\nL6f4mljPteGRyWZ4+bWAcPXHQx40UlzdxBlIzPBx3iOMutQtcYif3anSnI1ordbsSyjGxWDF5WBE\n4Z64jO/Et9FHJtNRn/NTMed0cbMy+WA7ji/ZtrRQ5eHWFuagQ0LSvPXqVW5uZ9RX4mzMhjPyeQXL\n0qiEEvjFCun2x8QV4R0E4Yp8pFJLFzn+vXivqzc3VDIVDhrCYHzdvsCZrVnMhGFl6Vmy9S0IZzRP\nvwGg8eSAh4ebRvFq+I47h0L+bvbS/PvXb7Hi4lLV6g9oXX5JliJhUswvXbcwEmv+8Gsxl5UWQ9Vh\nfe7gtcR5Pf/6nNTHD+nOhZHlTvscHhQIk2I94ztHhItzLCvDflXoragTsrBLzNcSlW+7xp34LZX0\nZgrjwT1BdUn7FjWdJJ9OMJ7LQqBJj6u5i6EIeZx05gTrGG/PhDE7nvmYoYqSyeCshZzb5QzeMoO2\nEPPbbzRIW8BpSEbiVX9aKLLCwJDyqPvbVPJ5IuLctsWZX00DinmVKKZuzHnuigu7nk8wCV1SMuzf\nqFRJZ3JUZQFpbjdNr31NJZskWgjDuT9b4o11Glnh4DTnPcr1KlpCZdWXOOz1ErN5xOVCor0lFJKz\nDnfr+3h1oSsuxk3m4ZCdgjg/nde3ZDJJdEMWgbU3uaOvX9yiW3mqOSFr3fEFs5hBbG1AJKNh5SLZ\ntEu4lLCVzSG76W3QDd69EZ00W7kkqUKFSUsY8i4ul60mc1dGXGIGrqIyC1RW0iboXo24vP4jvrxL\n1LnHQaZE3swwHIn3tLObaw3vC7jej/fj/Xg/3o/34wcfP5hnnFOnqBIw4+riWx5V/pqCojOUsffe\n+QXD1RB/KSwfdzzk/LZHlNthHgirqJhO0lm8AwmN54VxbEVDl3lmPaGxbq3Qlgr1mjBdRqMuKxeU\nuPDISnYMq5wjYxYIBqIQo9saMxtsFjToyoSUKsOCwZqtB1WWvphvInIpGQa2DBuGgwuq2zEm01My\ni++4LTX29sr0JsIrGsUC/u3//LdsWzWWQxE2v7rq0B8OMKaS/Pu3/y/BzCWX3mIm8YEXMw8zXSQm\nIQo9a4vL7pqvLzYB6nVVbLEaVzh8sEVBl/kabcwyNDDtCmVb2GR3drJ0+3PyVRECjM+HuMMX+GGG\nWCQb6I0ktxcjxnPJPKXEeeGO+KY7IiaZrhKpFL3lnMJEzDedstDV+J/aUVadK0qpFMXyfZzBZn8j\nwBfHX5JSIurZNZWcCOcl4zliXomshLm7U7aw0w3enb1lIPcumYyR0jSCjGz7cmfcXF7ReCzCUWU7\nz3mzi7teU5JYsb47Zz2Z/qmZf9VsYqgx0jsPcWQeL5dL4LgGWQlzF7c2j04ju89Hd36CYoq9NJQJ\nuUSas8El3YUkeajdJZ7eYbKSBXFFm8t5gGMKzy67dUgy3WA8eM58IfJkk4lKbjvP1oEIC5ophaj3\nGcXqFmFcFpPctNCSO6Q04Q2k0yVu36247bdJhsKS345t1kEs11PiiHcsG1n0yYp5uCQueaaTrsGq\n02X4TuT2Y1mTsOUTW7nMHHHORtqKubIk0xH7fXtzScGu068MCXvCM5pcNTl6csS+JeZyPhgxueqS\nVoWXWcvliU87mOaaD7bFGtfTaTRvs+gs0uLUJSBFphRnlQxoSk7hw92H7GaTNB4WmM3Ev70ZzzhK\nupj7QgecKzOquSIvzweokfD2dgs2TrPFTJLEJ7KHpMyItQxJvzjtMpp6PG44tK4F9rO/dpg5W7zq\nC7lO5nTcWRZH32y3CV3xu8p4QBBTCJMJ1jExnzUKtl3kblHoJNVxaA9f8dXnvwTg3oOPuFi4ePM5\nZlWeMWfNtLdGdUXE7ze/+K+48yWV/D7rmfg3ZzwgUyqRtUTUzdM93rx5h6InOCwL+as0MiRtBW+4\niZf8Fx8KIpqzmxFz20bZkdGw2RQ9YTKVKZxcziCvJEkECqYM4d7fvkOUrJGIRNQgX4iRrla5bS4Z\nr8X6fHl8QyxV5+FDUTi5cCckUynGox7JuJCtshLy6uQVtUdCzmslg0whhqR7pz3fLIbyAodG1sZz\nRZQjvlphZ7IkkjkStoguXV28o3GUZiJl1lOL5CtJbvoTAhllidsVuhcnaDJqUCzlsTNpAl+C2yhJ\nsukEO08VIsnEFC/aDLojbPmcSvUBMz9HStVJSp7phPn/s5zxcHxJ/FYsZCZbI+ze4ow9Jm1J9j25\nRWdIrSg2N24X6PsDylqEKRlzLM0ljstUXpw7tW3CpI0uC4figLf3FLOw5EefiL6v67M3fPXZH4k8\nsSBOz6OrazjFDJcyBxa3NDRnUwn8+G4GdSYU9rx1Td5SKMklLKzH6Ez4cF8o+P2yRmrWxnTO+PGR\nePa5dwW+w7YUpNAJGd6+olDxmEm80t7lCfd2CyQkAcU333TZqtWx8nmO34lDfzuzqVYPcONl+Z0h\n48DBTm+GIUuSXenp08eUanu03grjpqLliBQXvVAiJQVnPj9n757B9j3x7MwqS9ovEwsDtJSYtFI+\nRB/0Ua4kGMtyzclkxNwIyMgToqfTGIuAXEKs4TwRZxytSOjfUbAtKRe28NwFYbhZCAUwWAyp7tUJ\nlSXdlphzXjcYO3NcGdCZn41Bn3E7GZOtCUWxVdrCzJUYpMVctlIpzuM6jizgqufrZAsBirPEkLSL\nYRRj3V6QlWHNo0KDMPIw9Aw7NZEKGBoOo7XCPBIXbaa2iRy21CzWjgsxMb+r0ZR8qHI7npFMS0CW\nxDad9hUTGeryklt81XwDEvP67v4OL2/Omcdm7NwXRsg8mnN10eT3V0LOHx8eUDbi6Pk0o74Iofmq\nSsKyqN4R4TzT1FjPTvEXK+7dE8U6aW1TpnUnYq8kLra4A6P2mCiVJiFJU1aLK9LJDCWpvDU1jsqc\n/mRG61Iou66/ZG4ZqKYwHpaTBUbdYDGc8KguZL+zntE5W1GWNKD72X1m0zQ+Eqh/FWO1XDEcDtlK\nSYS1JYTrTcV12gTfFWd14ik8+/hnrGQ4+bTVJuqfUzlUOTwUl/o6NqM5GaJKYprx2qB3O6BqJbiV\n/b+JrIKt5ckY4ixM1RWHqRgxSad3efyCJx9+yNvJGf/7r/8IwE7piNFqTFsWzdV8j/VswVn7YmPO\nL/5BMDL1ZlNqjz5kooEvSeY1FSpmnGpSnLs5U1Yxh0xR6LmL3jEzJYulrXAHwrC/fJlgPAyoSYKU\nfDFDlLGJjByZlHjPzuVbcmmbvilkzVlrnI8dapUUgWS6UwyV9lULO7Ypz8W8WIu37QX5Rh0vKy6u\nWbtN++QdqUh2Zhzss3A0Pv/qJcWU0DfJeJJ0KoG7lLgGLhhRkv7whr4r5PDh3TqVTJ7lUqYq1CSj\ndwNup7csZHFYLFrj9qdcnoq6m59/8DFoS3p9IWsxYzONkbBVfAbEdelQ+WtMQ8EJHCIJVGKkI6bu\nmKkrdEBte5coWuJ4LsmkMGiHw5D+xCEvz43uWhTzOXQJKjVYKVy0m2SrSUKJfhgzAsySTVnS5s4n\nS1o335I1kxQlQp3B5jmEH/AyDiKXkQTcTtslYqxQgyVeV+Qykv4QM2Xh34rDaBoh9zMVtjMZHMn8\nMu3O6bkqZZmUv1MuMM/EWUjAjo8++Qv45lva7RfcXIkNN7wcDTVPcySFutMnDEP6wzhvJUdq2YpR\nyW427o/6Z+ymxYKu9SyBEpAwhECGS5+kBzVNfM/t9/nqZY/J7ZqGRHZS1l0IPPIS5/Bq4XI57jK6\n9dmpCzg1JRVy3rkhFRNK1U8V0ApFFCOGJZF/9hqPSRa3OO+JnNK7lzeYOQ1dMzbmnE5KwPJ4mtfH\nZ3QkHdjDo12c9ZjFMCRTEErATaUobOc56whBL4Qutf09mu0WrqRce/PyOYPRABTpnf7oL7GsJF9+\n9kvsSPz2vfoOkxsf2xD7++G2wTLUuZJrbqpZ9uu7TJYKvdX3M5igp3HjRVTLwpPevJLPkUyniGRB\nRSxncDsf4jQKeL7IQ3mTCVuFLHPJpKPjk7A8zk+FwpxMPWxNhdWIyeg7wIcMCSXGSM4vlYiRsit4\nSpKlJ9a03Rtj50uslkJGbt5sFhYdPbmHXdLpjoUXHCke83Gf6XBJ3BQyOV7MmXgq+1vCU9+zk7yi\ngzMTxpjv3vLVzZil7XLvkVjjnK3yxTeXLGQby3Cc4vj8cyJrzF5Reoi5LAvHpXsjDJejO9s8fLTH\nm8vfsHDFvsRWm1GIAnG2qkK5rOZLnh9fMp55LCWqVKjHURNJAglB25+sSSopJquAruyGWEc2ppql\n3xPPWc4CXDvAtgscvxTnbtqNEV/67Evj2k6nyMSmvL0SnrNmrtC1iChymUkI0sFigfo9DAbjRcCr\n56IYK2tv8cnPfs5vrsTevR59id45p3wa0Jaoe0apwrTVorcQ75+/f8RhPY05V3Fl+1i6HMeIp8go\nYo3X8QH/+i//B35/9t38R9z96AHNeQX1VnhBvSCFqiR4ui+MnQd319i5LU7PQ/7X//ubfzLng7ow\nFkqRQZgNaI36jKVRmbXTOMGaxVQicC3XfHl8hSE9qUZjG3o+vbMeHzwRQCCDyxGd2zXKgZCRx4eH\nZBST12dDXF+cuwdPfko8PiAeiXfQHIsPnn2E6mv0vuPUDecUYkkK6c3aje5afG8WjlA8m7Fs50ul\n6gyHFwQy0jWZX2Al4wy9OPmiMAQe39+n2x0Qk3Ciq/UabzLBUn1SGTHnyTKgkdHpvBV1QZVsiWzR\nxr5/j5tLcYZW/oz9ew+IZBHnWX9Co5QkkiQ5ldwmBDDegtnMoSipDjOmz/nLL7AbO6KQCIipK8yV\njiZrTVBUBq0ZVjwF0tCbtsZYYYTmCIPMXsTQ1yuGYxGFU4r7KCONRUzBkQVw6ZxNxkrSl8BEkR+j\nlErQbXVpHIgzP5f64Z+P9znj9+P9eD/ej/fj/fiBxw/mGReyKpqsHgxjWXLlJGOa3DuS3JtNleF8\nzLAt6d3CKZGdINEaEM+J0Fc2sw1OjMx3lGExj7yaIZEX3uDxZZvR1CFjF5i3hBWvuhGJKENVNl6P\njCneHAJVIZ0SXq8ZesSTmxZX9+oS47sQ2mhGulAlJ6tOZ7dNskaa1qXwVt3QoTkck7YyWN/91ixF\nYGY4laDnX960ma0jqskEwY0IN7bGM4xknI9l24N+06E1WbKVjbh7T+RWRlSZGgbxSMy3gUZl12C9\nSWeM2xMhkUVshd9ViU/Ell8dt+jPA+pHDbKyHWuaSXJyfcNIrlXRga4VkTKTIAFajCjE0NcECRmm\nXrfBB9OYEV8Ji+/bY5XZKIUiAdHrCYOz8x4t2WKhmmmWTpKsnUUzvx+bmjBBgIVqZujMhcd1Pbsi\nWyuRKYn1tPwU7QBmepa45IO+7fW5WV1QqIkwkTZoE9CnviMs4vF0iT9fsWWq+KGwijvdPk6gEujC\nC14FK3rDOYvlkJ78t5EXYzdKMJ+Ld5ysN1ubFvqcVtjnnQR4mI0HZPUYKwcWkigia1hkjTRVmRqI\nJQ26mTgrCXU6Obuit06xe/8xkSdkabVyONjRuWMJmRgM+qzXQ3aqCVLS+i/VG5Q9h6z0pspqmnU2\nolDO0peWfuRudgj4WpXJWnjtCyei1fZ43Wxy/8dCJvbv3+Fde8GV9DydRQxQGEwszJwQOGfWI50s\nkjYk0AEKznqOu/Ypy7BlvV4nr0Vcj4Rs5VWddF4nORHzXfkhZsLAVjKk5DvMgoAotukvHNYPuF1c\nADBPhFzeXjOWWNUx0yFW0EEPmDtif6OujumUCIfCG2y+aaG7OcL2mJ4jPONvP5/x54+yGAnx+W//\n9b9i/9kjXq5EHYYZxNHNLLYbUbSFfokik0IyQTAQc3n18px0yaVQ2YxQ2RnhPStRn9+/uaQ706k1\nhB6rlrPMlh6zsYgCztq3XM50QoRuyZrbJKo6ehCnfiDyuLmlw9adNPOYeNZkAXbWoFYt4csK4bSp\nEMVs4rLvPbVak0zaDCZLQoT+C6wEISvQNiljf/U7EU1Kl0q0b2dMNREdSddSEGYZSV/OjsUxAoVU\npUTlA1nXUK2gLuY4MyHXi8DHbTc5rDdoqGItfve7r/g/fv3fiCSs5gcPc5Rti+m1z1z2n0/HXQ7K\nOxhJMb/2+RVewqGoinfMxjeVnl3V8MMYY1d40/PYCieYYzgzfMlr4I7meG5IUurm0NZhsSbmLfEk\n33DVKjCexhgNZXREmbNGYyAJe1K4WLEkJVunKXu57WSKjJlkKsmB2v0hiUoCJW9zuxZztpXvAQzi\nB7yM01FAUvagOV4EQYKrixbOtQhZtG56+JbPvQOhVINJjOvOAPXK4eOG6F2M60Um3hJV5k38eIHx\nYE1HHvhBFOE7KQIzYDQSi3N7M8QM5pQqQjlPA5NksoimJsjL9iJ1OUbNfw++7O2CQkocxNm0y3p2\njJEVm1uvptnbqhFJ6K7uyOVgexurlmL7sRCYd+GK/mJIKC+yWD2LaRQ4OvopMYnX2lEi0vk0hsQ2\nDfpzrMBh+6BBMiFCkrFhAkVXiG2LUPbSmmNkXJrRprLtj4WQaIGLtojRPpNN7tYKvZYnWEzpXMkc\nhmLTuWjSl4hNZ1OPTz85YqueZSJxfYfn74iUCdt3xFxOz56TcGZktQS+zAWN5i1s9YDeuTjMLy7P\nWS8UdFMc5rWrkHN8MukSkfH9rE1b+3fRsznm8SQriV41HndZZqAnCd/T+YDebMxyNObuoehfzVoJ\n2lOHSHYPNPQCWqRi5YVhMDNX9JwWCS1OLCV5fpMB2hIUmS9s9kOW6xWK4uNIpbVQNCarBTN5oDR9\nU+H+8pf/J6WdBtkdoWx0NUHkRtiqwkq2hqHNKTZqOJq4PJrnTTpnHfiub3ml8fSgQX2vTD4SIfHF\n7Jpe7xKjsAdATE2iJRL8f+y9R4wsW5rf94uIDJuR3lZl+br2vftcm+npHo6hyBGHCxKSCFEAN5QE\nSHttCYiAIGlDyFCQIC0ECQQECVpQIAQJxJDCWE73TE/3c/3MdXXLm/Q+M3yEFue8N93M5lJ4WtRZ\n3apbGXninO9857P/f6xo3C5lwePFJYedDtM78RnL1IgUmKcRBU3MtdTYlOl5oOANxVwWC491pDGZ\neFxdiTC8GgeoS4/eaxEWXiUKmdnijz+8xpFnZGe7RDT0WE1FqmCy8Nh2CjSLOcYz8RxdiVkpGepI\nXAzN0CNn6cxk6Hw8nrN2TexSAaclLnDLcBgPNxG4bpcBelucj599+qecD77ku09EmsexG8xvEip7\nR6xicbkZ+Q7Ndx7zMBPtRS9uBwznS1BCLvtivXqhybsfNNh6Knv5DwtcKT6ljpDPamJy1r1hPPTo\nXYn1Klsx13c9rLK4NHujkOmbU569t9n25si+96J7wPcLe9yM1ziyJzcIIjKrwPOhWKtcCM32Y1TZ\n0qfpJerVEm7JZShJFSajBZVmjfFAzKU3neNvl2jVGkSxuEzejG9x8hXsqght26rH69M7VuMMV5PF\ndtMVxq7DerxZdPZwVxh/aajgK2tqpri4Pv79P2Ydepi7on3wxcUJ7+7uUXPL+HdCBu7mYKcWiUQS\nXEUrVknEbjljKAFljEgh8SKcllgHxbG5HY9I5xMC2Va1Xi7Z369iycvXizzczKSiy6Kq8mZRolaw\n8ZcxvgR9cY4PaTa2uXnVY70SumO7WidVAhZzIV8Vw2LbbvHy/DmpREbTcjo7Ww/pyUK7m0VEIWei\n1IXsGbVD8nGPNApAFiLe9MdQ0YkTCWazV6HRcpjdTlkm4h38xeZawzd4GWuaxmotJnzVnaGsbZ5f\nzJmcyP63uEAamjzY/yq/0IO0SBrlmfSFIHUO9gnVEYmEFjTcbVazJa9eiUsg5ygM7vr4ygxDUhKO\nQ5PIT/EkcUBk77Cy9tneqlGWixX6E8r65tKU6wUMCeWmum32OiX2K+KQfe+dh7TaTdKJuIx/9Ce/\nR7WpE9oxk56YjxIsULWIguxDfOuoztyo0tmpcjESQlx3DMquxotPRJ9xugqo1ne4nWkMXonDGhs7\n+EWFVPaOB94Sy9VZXv4iEg1A9aHIBV3cnBOOrnnwTCibw4cNElPh4vqc6Utx2VW1Et97eMCkI3v8\numPmwZo3t2OsUMzZsFRWvoltiDX/ze+/x/WLN1j+AldWJyfna7R4QZQJofOTNfV6i5LMH5FY4Aec\nTW44PPwl7jxgOHm8LKG/mrElc8b5soOahl8Drad6iBaH1GsFvLlQ6vPVjGmQMJT9jl1dp5RXsWUU\noT8fs0w8bod3uGORr2zaJkW9gP4VEUfq46kJtaaNJguI9HnKPFySK8l+VmOzV7CYm+MtQp5JQ2o9\nSilENnvtB4xWwlDxNAW/nufkVOzV2luzVCx0Sxg3BadCtd1h0L2k2RbKJAh9fD9H2xFKPvYU3jl6\nF7IhV+O/iMQs4hQ1E/s0DxeEacBOx8KMpRdR3Yz2TGeXFKUxu1r10HMjDo7KhLFQ8oO7DEuN8D3x\ns1NvcD0YomkJhqzkdi2FYDIhlD26jWKHYs7g0VaZLz/9oXgHU8csV3nyRER3KuUClxcXOIrYp1xR\nJ/XmGJ5KXmIC5JtbRNEmAterlz8mlsbNdNXjoOxRsMQ7HhzuMXUU7JLGMBBKNHIyfvfL3+dIRkse\nfedbVNcTvvjJjNKuiAoMehNGswFGXux3NJ7w9OH7XHtCH63NNV+c/pjxecBHH4p8sJr4bLczLGnE\nm2oIvoKibuoOVeYmi/U8djXH1sEWqiY8wk9fPSfOEsptMZd81GK/3aRYlhEgXcXMAk7Ob/n8hXBW\ndipbJFGEH4qzsJ72OJucYT/do7YrznwSmVhOiZm8wL0go1SssJj28SSJgqrGTEZrcummZ9yqCi/X\nyFLm4ZSvuKhyZp7IWzMbSCCdScJB2WRLs6lKYJlWpUU+00mWQoZvTz6HUOXkTRcvk2eqXOTbv/Ee\n7Y6Q/ZxSQAkC9ILJm3NxniPDIk517Ip4J72d46p7jl6ScjPadECS1ZjhcM32/oH4jJIQpAaqBU5Z\nGOXFZpXZCtRUzKVS3MZSTBp1l7XMuZdqTRI75KAhdN3V2TkZNrs7sqhz5jOad0mzlKL07qmViTUN\n0xDvXW6UuDh7haEveXAs8CVY/v/MM662O+RkNfD5+ILLXo/YruAcCrai1XCGH2VMVrIScF0lZ5Vw\nag9pd4Tn0ayW6XUHFFWhaJ8dPuLPP/2SQAqWGk+oVGPCREeTDC7tp/ucnKpwQ9QAACAASURBVPuc\n3InqPF81qcQ1hncxJdkqZBwdUMxvoqTksoSctHjyeFhJkbWsFry9HXHZu6Nii8ul1txj7g+4ux1T\n6M/kA3T2Wk2WsRA0N/WZXd+wMCxWfaFUvcktDzo7X9F14rYP2D9+m/lkRZwKJXUXBKSqgpoKodFU\nj4KVR49+STGUIZSZ7gTobRfbEMLXHQ1ZRDHbzQPGS+EdBOsV696ItCgO1PHjR7x58WNeDm9IZe2P\nlS+TaCbLWSL3QKVa2qbvDb8GtmjGa1YTn0AWP7UfV0lmS6pVWflZ2aV7NWWrbWOWN5UAQLmksgz7\nlCo18lK5+fOQaukvDAFTj3FzFuRcurKq+JOX5xSLdZyK2Eun6LDdyhNJ9CC30MK1q0ymcy6ei3By\nbzlnaSWknrhM9EIbu5wjVVcgveW8ZaEVq9RcCd033yyG2uq0GU8zbiRVZLTWMQoHOKUGVkco7EU+\n5cuzK3KSMtFyNRLL5EimJRzdwpsvWAZTzKL0sNMDzKRGEAoZfnNzS6mmkXg+i7mY87Q3YTQYoMki\noGHLQtVTtvabKF95GZs0u7j6mrmEHnTdHHvv75Er7PL5mXiu56/BC1AkTrFaNTBXQw7aMa2mENJ8\nboFSCPnWgVCYhfYxi+U15XTNQUmGAW2D9n6Dzo68IJMpjZJKQ4a2Z9MFsWlSzTv40rPz7q5RlV/W\nQpaSyLbIHUuhWd6l0RCyV2zucd3UWM6WvHkp0htfvv6IQfeSqi4uruGdjuboNFoutiXOx8/enJM3\nnzCfCgPtkzc9wliwgAFkBRVyCc4WPP22iMK8fHWG0YbHjyTEpw1P99+n+XSPf/APfvcX5mxJ5Ds9\nWXA1nDIJNHK2CMd7iU8SJlQqkkNazTOLU5DOillzGUzv8MIhri3eYWurQOf4gFAVRWrRwGc56bHo\npuxvi3M2GnhERYXlQqyVHmh0Cjnqj+ssxhP5uwRXz4iX6411HvfF9zd2WlA0uToXhkB1d49MDcmG\n4kI8chqkY4+T02ssCdpkP0r58OyC4UTI0WR2h7/OGKESp+IdtLLFu+89IZ2I7+7e3VAxbZxq4esL\na7X0WAU5GpmQI9spCnxv6Rwsh5uVyR4WQRQylbCb9WKRNPLY2a6gStrUcepzN17RKAhDwFvD0luR\nN2tMF0IGhrfXDMY3vPtMXL6uliMOfWZDUYE/mi1ZeENq1TKaLivNF1MiXaNUEHvpolOKXHQtI74T\na/Gvqqa+L+C6H/fjftyP+3E/vuHxjXnGqVpmLZlXFOuWimqwv/ddRn3hcag3d2RajlB6tNX9p0SY\nqIVD5pHwPr/XOmCrsIMl2U5qxQKPnz2l8fQZAJP5JYtBj8lwiCebzxPd5J2nz7BHEgzDgl99/wfM\nbidEqrDQYq3IzN+0FD/97EMmV8KjXvV6VLaa1JviHV6dK2CWsMoiFOYqLgUzYxU4eKmwgA09x8XF\nHZO1sEoXq4DeNMTJOqi6sPy2t3bxw4SFZBOJ8x7adM18vETLCS9tMZ0x9cOvC84Krs3N7S3BapMt\nxiqK3+07W6yHKevrcwCm/oJMgVq5iVsWnmZixoynK0xXrJXhetQbRdKSQlAVVmasOLz7aAtHEZ7d\n1c0JqlJELdv85FxYjLu1Q5xEY9YVrphdcskaFUptEaK8u51ykQTsV1p0g5tN4QAOjooMJyOCbIhq\nCa/WyGKCtIciC1tSx8CPVijBhEJRkma8e4yr22SReO+6FaElc7RMWOOBt4JAp6rpRC3h0QzGCaaq\noCTC2tazFDVnsvIDcjK0XiraaMU8a1PYr+vR5lor8TbNvIMlYSK7wZS7KMa4XEBRyPXBgz3eP3jK\nyULg5p73Lph3e5xIYJiD7Q7lrQad996l1xdh/qGvEDgGX8qw+idn1zyz9imioEteV1sDQxmi22Kt\nyq4L6ox8LkDivpAtN3NVdm7CUvb+eoGKkk8I1ynFvDhTnS2LdKmw9CVBfdlnp1imN1iyJ4khshxU\nCwUIJSmJMqdddoj9AduyH7t+uE19u0Yg3fPUX1PNJwSINe8NPGolE7eiM5MFe0G4ptncbLl5elhm\naQhvPpi1ePTku7z9LZG/nEy7RF5MtjZ4dijO4t6DAre9Bh+8I34eJyb//E9+zNO3v8POocgj/yWz\niVFwub0TMlws7xEpNstEeFKfv7ygWsyx7xToPBY57VU44ePP/zmKJiIs7Wf71FoHmDLM/vPjsxeS\nMapmczdPiMwa2Vc1Hk6esl35uuZCy2Ji1SKVvfFrPyaXK1HJ1ylKcJjAUwm8JVsyxGtmGtfPdQy7\nxNwTnxt7CVk8wJMEMznVgEKdVmuLTlt87u5ihB4GZKvNqNoXzwW8rjO4xbcssrWQEyOz2K016UjP\nfh66eDMfSy8x7IrvmkwuuR4NKEjwDiN0CKM1fhxRlK1NjZ1ddsol7l6eA5D3PWwzQ010yrbkK174\nqJMZuYZovyuHCufDMZWa+O6yudln7MU6KM7XjEx38ZzxKMRLYlbXwjttFnVCT+duLIppl9EV9UIJ\n1SljahKqdu6RT3QuPhX1EjMvxC4oDAayqNjL6Bzs46spimQBXHorbC0iCsRZCEl40CpSKeZZy78Z\nTzfvFgAly/4VfZ7/Hw9FUb6ZL74f9+N+3I/7cT++wZFl2Ubi+D5MfT/ux/24H/fjfnzD4/4yvh/3\n437cj/txP77hcX8Z34/7cT/ux/24H9/w+MYKuP7L/+R/xDZlwRQZ08GEOPEwipIr1MhTydu4iWjL\nuL64IO9Uyesmal4k77u9Ibbuo6SimGadxtSPnnE6kJzCRZtgHZOlKkomk/nzKaXaPpO1+G7NUtmu\nuJg+DK9Fe0y12uR7v/kDfue3fuUX5/zj/4d/+r/8YwD22w3ef/eAu0tBIt6/PiFeBORkUdogCrDN\nPPV6m9QWhULxdEpRC5hJNqMffvIhB+0yu3WDblcU10zvejQMk6pk9qh32oyNBolqUNMlSEXs8LPX\nV1QlItejB02sYpnbW1F49V/8vf/g6zn/2luiVezgg2eYnoJjirV7/9d/lYoyYd6/oDsT3/3m9A1b\nzQO2WgJUxbYSXp3+GS9enuG6ouDjr/z2bxPMhyxuzgFQozHNRh0lK+FKovbBZM3lIqbfEwUfSRaT\nr1axNZkmCddoUcp6NebNa8EG9H/2er+w1v/wf/j38UONQe+GB49F8cZqmZCLE2ot0a+XM5p8eX5N\n5ls82RZrbGkLPvr0jHpVFN6o+oxCpcrSE6I+6a8JUnALGhhCJtJMJ0oMNF0U0NTbLmqQUfRhqyne\nyUumZHaRLBRtYLWdBT/4lf/pF+b8n/2d/xTTzKNJZK+cEtJ52Ga1WqHJFp2FF5LXE4a34r3nscqT\nt9/DlqQBby7HJImOVSxguKIYJk5TJuMZBcRztxoKZX1J4C0wJSlKEOborXV+dif6l0vZgl89foSa\nFbi4EXNWDIv/8L/7z39hzm9++IazHwqUqd5gSalVJZ8POL0Vn/FWXZr1Xcqu6DlVlikD36cf9kGS\nXaz6CX6WkiWiqG92fs7ZzQXf/9d/i9VSvMMgHLBUhpRtUdz2O3/5rzFaLJl7QkYqtooRqfzf/+T/\nYi0LKa3DDmbd5B/9w7//C3P+4O1/k9K+KOLrVIqsRwF5yXfbPtqjWU+5vfmMu6FoW/qt736Ho7JL\nZ0e0m8R6ws18wXJqsFh8RXgS4S+XdCT2vKZoZNGCocR/r1t1Cvk8fnTNQAK0ZKUdtFwNTRbrkCic\nXlzQffUZ/+gP/o9fmPM//u//tpCJQOfq1ZRczqbxWJyp4dqjpE8IZWHP7vYeaqhzIs8Ylsru9mMi\n3+VGtkDG6zW1hs35udBZjdYD6s0KSi7jM8mNXfQi3j98wIVEJzuZLdDdmIPWFo/K4kxtt5v85ItP\niGS/7b/99/7rr+f89/+r/1bsTbNIa6fCqSzQ9LwVBSPm4kIWMWpVFDtAIcYfiyKlum2RWOCWhc6y\nMhWn1OR2PKFaEuuVTxO0MGF0Ld5Jj0K2t2zWBR3DFOfZnwS8fvOGoeSX3zo6pOL6tA3xjPFNzH/0\nH/83v7DWf/dv/xOuzl8wHwqd8uzpIxqNNnaaMvSFjGauCtGSQL53o+CgJAGsPEzZQhoqCfMsgUic\n3bpbIALCWBRJ2qqCEg3Zdl1sXbTojf0FtUqBmiTiuO5eczfuMhh4GBJMpFXbJPSBb5K1aXiDawnl\nYher5EtVbm+u6cmLoV6H2+GMkmTMKJZq2LpF3XUYTsUFXXMc8qUW15LYINF8oiRETeRFG4JBhuOa\nrCQkYGc7j1PMM72VIOhhzHDlUcuZrORlkcUxJ9ebbELxeMBWUTKtZENG5xPsSABxfP9xg/V0zq1U\nYo1KCwUHIw/FLVH9vXtwRDgd8Xkk/ua3/8pfo2475DOfUl6898y6w/RCfEm5d3sTMS8kxHrIMhAH\nr+7kadcdHknUqSz2CQZT9ur7G3O2JFLNcjAlG3ts7QiluroYM5tfMPGGFGpCcA5aT7GNKuW8BA1Q\nPBq1JxgPd3nwRFSrbnceUd2bMN4SAjW8fEWwWLBeQ6JKlhQtD2rMg3dF76yt5zCcHJEv9u3F60/Z\n3qpxXNgh/apZ/l+6jIO4wNnpS4quxcoTf3NxM6c/nNCZi+8uN6uc9GYsZkOqdaFctHTF6UqjKyEA\nTS0iPxvjIf7/NuigOxblYEaWir28G0xwnJSnkuVnmFNpNPa4u7rjZiAUb9GK8CczVEVcHkt7Exmq\n3ihQKTloki4vTUOs3JqF6jGWlZ2h6hBYOSIJPlByixiNPIOu2O/YzlEolNneqeMnotL89K7PXA+J\nY3Fck3VI1ikyjcOvFUWw9lmxpnksqks132WQ05h3e6SSDahU2gT9sEsqpUdiv4f5EWfxnLceb2M3\npBL1O1xeD3k+E9Xf9VyZ2HCYjj2CrgDMz6kWlpun/BV14FHAbnOftFNnW8pSyZsyCV+RRUIGVCUg\nWFyweyCUWOoFlDKVJ89KnA3EWhU7RSqyf/jnRymfJ5DUimkS097bQnI38LPzc/b1MtXmIcNLUcH8\nyYtzzrSIQ9kXW6o4DHIGGg6B7M0fTWZovs8yJ/bl9XDF2dknPDkQMmFbDt3rLvlizFgWS0+HE2zX\nIPOF3mjV6sxNi1lkbsw5yj2R/4jYOn6CZukoUoa8/jne1Cf7yojLGRwc7XPYFMQCN/M7VlaZ44N3\nsNqicjsd3aLEGS+lI7LK1dgtH1Gy85R+/W0AymaO0c2A8UzsU3X/bepbLuOL1/xkInRJub9mslao\nFjbXuX9+DoAR1Zl6XexAXLSGolDWHEYSzyHOaSh6kd7lDelSLE55/wg1yTG8laQLSUh8u2I99Th+\nX+gtO5sTRCGWLjoWzNTGTKoMuwPMhpDr/skNfneKpco+7ckKLVKZeqIjJdU2ZbpaVqGwQNfE+h7q\nffZKTTTd4nIt9sratljOVqxkh8rjo4fgrVEUg0xexpqV0bsbMroVZ36nUyPIVCYLcXcsJzOyKEYv\n5NFlPXLJdigUbKyC0LvqLMW1FZamRqUmwIAaEg3wXx73Yer7cT/ux/24H/fjGx7fHIWibTCVuLqr\neIplmrjFOt5IeKSalWNtmAQSCN0t6azHI2YoxKmYdhzDeByhIqzrkmnjz0Nyivi5VugwGA3w4gS3\nIry/VZAxn09oSKB+NbRw8zZhErNKhTVT03OoyWavoLFKqUokokJTJ1pNKUiEJiWnkCRrirJ30FCK\n9CdDplPja5Qkz3XJp8rXBBkFu8ldf0SOlKXEpk7jFTk1I3YlZ2+ssUgT0iijaEqqQyPh+fkFwalY\nh+lsTi7X5K/+9Xc35hzIUOKR4fLW8RYPDiSv86BPuhiz1Soxl2TZlWID09DxYzGXnSd7tI7KfPHn\nP8WREJ6GsUJ1XRxHhIrLaYztp1xfKWxLQvqkkGd5+ppiW1i8i/41FUxCGZ7aPmxxWNF5sNVmNj7f\nFA7ALTcpOjdkASz6kgzB3MftbEMmvD8/cQkUhyDxedUV0Ybu+SsGM5WCLbyTatHh9HLEzBKesVlt\nY4YJp29ek0g80SRKaDcsujLqMbuZcFW8JJkuqMi0iRdZBKuYguxB7C83+6Pniy6FYhvLEPsShTPm\nM4/ROOByKqzrzN0mnxRREN59xa0z0l1mtvAYn1/fUVQizs7PcLK+fA6MvBjPEt7LyXjN0NEpFesE\na+EhRraC6YJtCTnPRRnjtU9kpBQcsXd3803ax8zIk22Lz5QbDbrXz5naeUJpvcdLn1Wuz2QmUiA5\nR+Hq8oTPP/uEfbnG/87f+hv0pxecPBehd3d3j3quTD/p8vpahM2//+SIHbdOX1LjGbZP3k6Jx2Lf\nlvMRXgS1qkmheACA3Slh5ze9zIOdEjjivb/3cBu1YBNLnu51FlEwXEaDBYbEX84VSnjhikJTyI1i\nmSyXa2pb21h5SVtISjg2iU3x3u/95rehsCIvo2WqoVIF7HqOkkQOnGUuqlFmuyLCzf3ehCBYgrrp\n40Sa0Em7z1qEYcho1iUnI4OD13MW8xkVWzzXiArEUx1k2sRtH+EtVT7r+5xciwjS+PXHVItNIkOs\nz+3NCbkkR8FqUZQ0hnaxjLGTJ/bFO46SBH8JWVbj8mc/Ee/lxDx86+HX4e6fH47EeCjpFeLphLrs\nDx6tlkzCOTm5NYHikaYF9GKFSGKNq2jooYYiCWbUIMLIBdiphT7/Ckq2QNHYplCX/dTDPst5yqS3\npuoIvWXkXB4cPWa0EPNTo5SiUsIwROQmLW9i20/ndzSr0GjJFFN/Qji9IdNsVJmmWy+WGKaPJ/fq\nvHtBNB6jljp4EqOgYCiQZWQSUz/NlSjoOcYyFN/v3mEqGcojF0OiN/Z7A+Z+QrskdF9aKpGGEbPe\ngO5ARI7i0i/H4//GLuOn7x5z+jMRRprN5mRagzBMyBXFS7jbFYrFCsRisbzBAN8PmfZvMGQIwCPD\ncuuUqxILeHDNdLYiccXirSKL6VJHT1UsVwhWHAcsPIXAF5etHyQoqoOes6kXhMJWkpju7dnGnCfT\nEFWGRQ4rW+SLFc6+EEI98aGca/H5z74Uz3AnTBdjGluPmc6EsJ3pfba2t3hyIEBJ5sRkWhXfz2Al\noecKKsPxFW+WMtRpb7H/5Cl+9wWuBDdZKGtW5gKrIQRdI6R784qLq+cbc/7Lv/l9AN7u1Nk18yBz\nJLmcz52fECwy4kyCDTQMZrMFNxIYpJeM2WsX2euUiENxyD794pbOwSE5yW8bqlssgjlGnJCTRN0V\ny+LpQYeLiVD+6azHbKpRfyhCde8/26Jme2ixStHY5KsFaFTKhM0WwShmryXJvEMfO8pxci4Ub5Jb\nU61CvE4JJFdt0dFIpxMOqwLCzsw7rOIQa0eEG8N4wfjuhvl4SqchDkWh4hDHa26uxGFJTJE/N5SM\n7ZJIMSwKDhWnBIoIUdnycv/50fMm2FGJTOKXh7mE2XRAppXZ3xXpAa3QYq6o+Kl4jm9ZnPaXBLGQ\nR6+mUK7D7V2XRiLm02ztU6ya3EgGnBwZ9bqNYYRMlmLvgkylWK0TS/jBer5EpdZhbve4vBX7OZ4O\nN+b8Zx/+lNARe3k77AIKp2cp/al4bv/yCwavLpgOhKw9eechiWOiVgq4D8XazFs6Z4M7FpI0xbIs\n4lyOu9evGcsc52hrQkEvcFyWwCrqjMVgTl8CSRzsORRKOrmbFRKSmTAbcnWzeQ7zeootld3B8SF3\ngz6PO1vyf1e4jQpBc4uKBNJxy4eQzVEKQmHubFXpvnjB5etXsJYgEFrMTvsJOYn/PVotKJVrlGUq\no2CkFGt52g8clop4biVq4xZ32CpJZR1+SLlSpnT8EP74F+f8o58KXedUq6TmjNTVMVzxHL1aQhtc\n4Y+EDFyv3rBlaiwSISM7VonX17csFxespZyrRp3MqZFD/M263+cPv7zALB7Qrol9OD5ssFZSXozE\nvs/NKpXVnI5SJJU80athl2yeMpttOh+He0KvujkTLW2gqCLsm89bTLMIQ5X41kkGVopJyiKQZDHJ\nhLrbxpHyGQVL7FyC7ejEEzHn3jJhNO7T3BZGZne0II40auU9Il9e4mnCMkywyuJibVRdbNVGDaRz\nIHHVf34YJjzYfoIu3/vl9Q2fffhDnEqT6vvvyb0KUMKIVk0YaCfnp+RTj2ZZo58ImUhGIZ2tI8y3\nDwBxphbndxi++P9aXqFZttlvVchkrdDtfMnC66FKaNMws+hPcoS2zkI6nzNtM8UF3+BlPLg6J5MY\nnYcPD8kUHW85ZZmIy8ILhmiBSuzLS2k1J82pNHdquPK0Tr0VuuHiOJIoIouI5lNuxsLL8P0Bmp4j\n830yXwjfQXMb01d5eSZyYIG3xF/Z6FaBkiVpxZIJ9iYpD0sv4KXE7E2GXbbsAa9OBTZsiMaj6gFF\nQyioyt42Nb+P5ueYrsQ7JPk8Y88jrQsBUi2XZBxTVHQCifSk6ys6e23mkqrvcjBnnPaZTG+5mIk8\nye16QRqFPFmKg1kwdYbrBcPzFxtztqviu013gW3Dw7YQ/KuhR6pXeHkWEksPUc3lyKk2k564BJRe\nyFb5bYJpRBiJi2AwGBGvbKy6OCy6aaFGAYvLC27n5wC88+230FyFA108dxpGLIOQvFQAo+s3aCXQ\nEpvE/OX5k6qaELsN1pGGLRHCinmVmmFysfwYgLPeS+JVj1U/5a1f+SsAlJ0682WGo0kaPn9NydSp\nFcUBOH39KeXxHbae40FDHOin+21Wq4DRQqyvr8XsPW1zejckk95BpqaYlk17RxS3zRbXG3P+0Ucv\nGa8jvv2+KJrLGQ6eMibVQiJJUZeyIl8roiliv+Ncxrg3xCxKpLlKkdHkknm6JJQIXL6WZ5gzWBlC\n7h/tNGi2iiyWt5h5SROYlolCg5s3AiEucEpEOxpLL+FKMncNJ5uoYctpD00VazU8u2Uwm6FUqpye\niQiVGc8wTJvQFLL2hx/+Oe3DB1zN3nD3kdiHOL/m7O452Up8ZvDxn7Lzwa/Tn/RwTfGdpdYT8kWI\nJAtT86DBeBJzdSX2oJ+NqDQbJHce15Lpp1pvksWbc27vdihUJKXeespguCaWqGyVgzp3o4Ryq4yJ\nmI8azwiCHitJfRfkl5jjLt+uPWDyFabwLKJ7c0dqibzyZS5mOV8TypqVSg0ubs6ItA5WQ+RyS9tv\n0V8IgwtgrRSY+BGGnWzMeTKTmNzTPO62S84pyhnDdjHg8btb+GfCcMnX65SfvM3lQni0s/Ud6hyq\naoHGtrj4Hdfk2f4BmayP+fLViNx4wPB6Rq0jdFBctRmuY3xVGBieb/L4sMr723u0JBXssn9FapV5\nMdxcZ1OymvW7KxTfJw5FNMhptKi0q8SR2Et/PERbReQrDrm6kKWS5TJfZQw9sZ5OapELSzCdkBji\nvZzOA27Hl7x5I2p+1llAvVLFX3gE8rxEgzXrzGMuERTTdUqroFLMyWiZvZkzfvthi7Tbw5Pv5NoO\nZlUhMkwWa7E3diFPEkcUJOpePQ5p5UKawRWuxBEv7tRQ8ykrVcj+zXCBowYcPjkAIHg5Z7kYMOlf\nc3smiuY+P+1i11XyjpjfJM5wnBLFQkr3VqzffLTamDN8g5fxZDHHlAUlSsnCsC0SK0JbiIkqoU8h\nS8lLRdwfrZkrGqFucC2rJNfrOUZuTdoUlXflyi51u4lEjMMPctxen+DoKkoiFme9SsjimLakL4tI\nMbUY01AgFUKiKzFqbhMgzF+uiKR1cxd5+OEdvi+O1MSL+bg/pCit5unrkE6tRvf6Ei0vNvzw4AC3\nU2UZiLnMExs7V2QxHjCRF0HhyRa/89t/k3cmQql++fyKVycn+K0yuGK7OmqZJF6g5ISwaUkeM68T\nSwacnx83Eu7tYOshvekdHxyI9957WOPT8YDH77/PeCLe+/mbT7kbT5lIXuKiHpJPQS3v058IK7Oe\nVwnGPrmmuEwaRx3KyhZ9Ur74058C8OGLP8OyDY63DwDYqR6ROmUSSeG3nEfolgXqNkZrk5UH4LOP\nfkK+uIdZ3mGSCsvo+uoN3/5Wm3ffEe/w5T/7Kd2LlziBgxGK9QvjhJ3dNoktC4P8JfZySm4p4Qhz\nY44/2CJb5BivJUGGb9AbdLkYip8r2zs8bFfIF5q8lKQPtcoBVtEkzYQCajU3Q027e89w7CKnp0Kp\nHj2u4e7vEqBhukJGF2kOpaazuhYVzJ7tsnZs9t8SRTehNyNSchgrg7Ul9tsoHuPnDDoHkqM5jHk1\nXjMbrwglO5XrFlAChywWnlym2bw8vcVxHaoychREm3N+1HbwZQXxevsBf/yH/xu2W+fZM2FQfPL8\nY+xjm0JOGKH5AlQqHp7vUJNxyk9ff0EuW/GDQ6H0L9QRk/ACt25zWG7JNT6luzbx+uIznvqK+XKF\n2RKRr0kS8KOTL7HihI8/FIqt1a3y6NEHG3P2nRy2jI6d3vbIoWE0xc83yxWdymP2yzXOm8Jg+rJ7\nQ8FYUJR0nRefXbMaayROSt7+CpJX5XQ5JZJcwJQLBOmaQSSKn8a5AlYpxchZ3N3KqEG2Yuar5ORF\n4a0gVCos1pusPP/ev/F3xL4M5vQ/O2ddHLHfFgWXnfoB2fySfFsWnjoZg+lzbnrikm86ef7dv/Fv\n8eMfX/LyThj/re0qaBorX7x3zqmxrSocOA7Hsmr8XNOINKioqXxOnfbDBnfXA7xQvHfj4TbnF3Oq\n9U3Z6EnZWkQJvZPnHNXFZyrFBjeTOUEkdMDac7Fsi3z5ASnCww4WAZ+8uSJZiItNnScQery/8xCn\nIC7sXLmCNrklWAtdq0YhdcMnH6nstYSsH21vc3n1hoUmGdWmc5bBDLclZPbm5hcLPwGOiyFmCr4q\nztx367tc3L5kFHiUjyRdo2Jx8fo5joT+rXl9KgUVfZrw5Ficl1BXWSZL1nN536wiqq1tFjLsn2+X\nyTc0FutTRr6Yh254TIZzuh8JQ9po7fPo+AElq8hOTcj6o87OxpzhLKdgCgAAIABJREFUvoDrftyP\n+3E/7sf9+MbHN+YZx2aCJ4HRjSwlDvqMgxX1LZFbYznm4vSGak1YdScXfSxyJNMZa1WSpVfKKKsR\nU5kT6/amrHyfraawbHw/5fKzF+y0TQoSWFy3fIwwoSLzH5kSYpgWmAnLSFh1s/EI1ptxfbWc8uiR\n8ITrqUpy1yD2hCU9HlxiqyWyWFjFt9Nr8o+PGC4s3nskwkbHh1XCfJlXkhB8YLls7zZYTmc09oUH\ncfxWh2KtwPMXYi761OL9o+8QHOxxd/0SgM8v36BrPkWZ84xXCfmtHIG2Seu3XRW5Tbu4i2cv+WFf\nePbHR084eH+f25M1b+4EP+tgNkZdBXSKIrRgKQ63F0PcvMmrN8I67J+e8fhBm20ZMjVMl1X/mtPh\nCb40rtPM5bo/wrXF/pYb2+w+eMxzTVj6nlIizuVR7Tpdb7YxZwCVCqrb5pOrAQtZKzVbXaGZPrqM\nCNiRzzsH+3iewU9efAhAwzW5jAs8efItAFpbNswNdBn28glxTZtgtuSqKzynXMVl62iPs0wch1ut\nxm5aZL9eYSyJwHWrxZ0fMl6JgqRfe7x5dN7+3nfR4jkFWQgW5nxmsYlVqRLJQr+pF3CzWOJYwmrP\nl2p4qcV1IotNohSj9R6rkcpMkrlHxjFnc4/uifBUto2Uw1oDvW4Sp8IiT0OHy9GM7YrIy1dqFe5e\n9SBTSWUhk5bfzK+d9ueMpVz5dwnmIsfs8oKRLzyGiraiUWxSqIgz16yorAdXNFttNFlI99H5Fe3y\nhO/9pvhu9UcfMTy5wSi3KMmoVZSN6AcWXzwXdQSHszXleoPbO/Hz1uE+ZqGOH/S4nYnIzHzdY//o\naGPOF6dXnEuHqO7mqdTy3M1kRC1RsRo5QiVgHAmvbDof84PfOELXhe8xv56h6mWuhgNOZFh/kc7J\nWXtUGqL4SY/HLLxbMkm3Pa9ZxAsH83Cf6Ea89810we3VkKL02sJoxizQMN2v8td/Mb4vva3f++R/\nZzW7oXqwD4bkAx4HRGlKuSgiC5fTPgv/Ck/26Vtpid8f/QGDaIeCJFtZhCHhWRc3EXJ48uMPmU2m\nbFV1Lj/+MwD6bo1J+Yi2fKfhbEilYjH9ssfwpZDrJ+89YDRNcNxNXvFCW6x9Tu0znxZxW+K9b1ce\nz/u3LBZiPQtKgaJmEHVVtt76jpifck21seLbH4i1ePHxBXd3awoHR6RS9/aX15zfnKDIuqBiXme9\nXmLpBk5J1h+oeSq1JVYmIlSWbaLECfm8iJi+uNisgyhHa9yKiYfMOy97JNefkzctmqlUUsUSJ5NT\nPF0UoppBxlajTHv/gNeSjvVmeE4axeAInV+olkhDByMQ5/t4+wjTSRidfslqLegly60y+UDhxVTS\nNxo6Bdsha9TIx9LDDjfTGPANXsZKBoasUos1kyAaYzo2hiP73aIcq2yCIavfzJ19olnCWjMoSk7e\nupOhpQGDsRTq4SVRrkpR5iFXC5+jwza1soIie+LyaUzbtbmbSyaTgo1bcpmpUKwIgbTrGkq6WdCw\n97CILy9bferh6WUKkjT80WGVwW3AaiEuoAfvfJvDh8c0FyOWK1HUtexfolQdPn0pQl9BDbTaPqW9\nY8qyajyf5fgXf/I5b16I8PL3jt/mcrBgPe6Rycro+WKOrkesM5kvOvsYf6Twq+9t9hnrCKE+7SZU\nyxUmQ3EBrZQeRqNBoK+ouuK5ZqPFkhtiyeLTLO2g5UxeXp/w/ETMR5n0SZ+1mE3FO1T6RZRVRE4t\nMkzFfCLV4MGz76DIQqbbhYcxn1CTPaXN3e9xd3vDaDz9urrxXx5W9YiP33T5yWevaLVFWKdcMskt\nPMK12Lstp4GFhtHcZiCrlZ+WNbxBwO3nItTZ3LOx9AxHysy3qxW8gU/vZkGnLGRNDVJ0BWzZy5gq\nEYpisAwNtJw8vF5AebtNJplX4l+Sy+zOZhTsgMeSm3gyOCUKDCZewCoWzx4thqwNnYuxrNj0THQr\nz+215Faej4mmCsOzCV1ZIeyMNBa5GDMR3135znvM1tC77BNP5XNMg2y5oNUUilcNi6i5MVmcYJpC\nRtu/pM/45PqGN29kiC3n8Pj9D7g87XLTFQZae9shPJ/xav0pAJ1fa3JQTfHNAQMJXvM3f/0HXJ/9\nET/6RKQC7q4ndJoW+989Qgtlv/yoR+w5mIqU/XGKZe6hSbaqYNmiUt4n8M/47tuip12xi6TZJq94\nY/uIk5GQx4LusIhTvFCEa5PQ58NXH9HZ2WE4FcrPSCKimxmhJfRCc/dtwizl6npFoS4umFrzMUFk\nkkh2r/7qjstJj23Zvz7SNA5+5dd4Nctxloh38tcO4/WCVSTkMYzmxJGFaW1MmedfCqNjOg/ZbnYo\n2xYzaYgO5zqW4ZKLxaXeO7ll970GfFX9fTdjss5YJzGqNP7PRjcEF1Nqjuy/ZUyzUqBacfnTT/4Q\ngAtFR233KaRCHoPBgOFoxHH9Ke625KfuX/NWfY/5fHPSyzuxxo4GhqYxkMV2i2mPWsnBzIROzYhp\ndeqougsybJ4EBp2CTVv2Cw86Llno8cXJn3A+Emkcp2TioeDKolLbKeOWbYqGzjQRz1YSn0KnDbLA\nLPBz3PR6eD2xDpH+Veb9L0ZCkfNhF0P2kM/uzpn2+zT2jwkWYs0zJSROAy4vhaXfKbnMyVHUi6QF\nYSzkchqxAmpZ4mHk8xwc7FBria6Vn50M+PQn/wxlOaGx/1D8TbPC1v4eObnfOTVCXU/JqS5rXzyn\nVtg8h/ANXsZ6qpIzxdcrsU9OTUmVDEUqHJ+QSIOZLBLIHxywGvso4ZqDHellnD5HNWM0KUdxvCZn\nN9FM8f+HrTbd64SooHCwJ9F3pjckmFSq4kPeck2xlCNCZSZJrw0zpHu32Qbi+HMs+ewvr64wly6J\nJp47jHt4ZZViW3gflcdvMVktGS1nLAbisC57Zxz+4BmpJKaP0wnzuymdp3vUE+FBvP7wC2qHbb77\nlswgLG7oPz9nNByhFcVh/cG3f4fb+ZDzC0FxNhuv0ZcO8WLzQOmmuExuJzGDdcyuLDCbehmHukHd\nTdjbkzmMssUfTT7h5VLkRC4vCuRrEes0pNgSArT34CmWY0MgD5AeUNgqsb96TF/mz6+nHtVOm+df\nCCPExWJ0p/LOU6FkjUKOyLXoze6445fTiaUVk/GbK/Q8KLKi9bd+9TdYXH+CIZVAo3iImho8ePgI\nS1YEW9MrRvqE83NRvPP6ZY/VeoUjc9w7rRquouLpGXlXPDfJPAZXZ0xlBbtmGVycTNBZk1WFsdB5\nekhZ0/j0c7F34/zmWg/nU6ZhREW2MOhpwE04QbFcnv9MXELT/gVOa5swEYp2mQXM/T5XM5GTjXIF\n9pu7rDPr68jRVtHGj+Y0K8IbiNKML09fkq5iVl2hXOL5lIKakcgL6PH2lFo+I28aTCTKlSpzmz8/\nPvyj30UGhEhNC0+7olBs8PIzcUHfdH2SVy/pu8IDeXvb4PjtDko5T6cmjKsf/+7v8uGXf8L2O0Lp\n397cYk16NB7aKJJ68fMXL4gmPr8mK+MLqy75VcqOJHdfrdbcLfrgVUlVUQn/vfffYny2WZT4ehWR\nVYXyy9yISrOII5G+5vGCLFkwGw5pyariNLCZ+RrI/OVBo0X1uMTQe8WbQEYAjBLPHnW4nQq6vNcn\nK9ZqwngtdNR43SRxH+MvBzT2hPEahiVuB2MS2WlArshwMfmlyb/YFbK/+2jF2cs/Y7wcUX0qvOVy\nyYFBzPRW1BFkvR6n4z5xTuiSt9/5Lj4mgy+veXIkUAFLuRxnSoCRF5vXaOwwGoXsbdWIvyMqhrcK\nBpOhz56shWk/ekz/Zs7N8w85eir+ZttssgrA+yUVvnVTnLNhf4BmmFS3haGX3NywVaoQSXrWRDUh\n9YjDEeWCAHk52umgzTRaHaF/Pnl+jr++pXt7je5Iqs11EW+wxpcXeK3iYpZ1GiWHqS/OUJqvc70K\nWPviu04//pggSajLmo3a8dsb8x5oZaZkJJKR0NjOqJYsbsYr1LWkY9VW5PdqnNwIj9s9fIpTz3OZ\nLig+Evlqq1AjVJaUd4Scm/kCRcukFwodevZmyGAZ4qom1UeixqK38Cnq2+wcyTqXs8+ZXp1RLDmY\nsuanstPamDPc54zvx/24H/fjftyPb3x8cznjwMOSYZlGqcrddEiaqkxlCMOLfSrNEr4i+9TSKTkF\ndvfqmDVh8bh+k6qeUhqJn710yDiKmc9F6KFiQRgFdK+vvm5tSdYLfE3BMYQXpzsG4+UUo1zGkUTn\nJgkLfdNS/PN/8Wc4Euf1z//0c2y1RaKJ0FekzTg+rhDJCESuqKO7Wzi2hSet78uPTtl5oHIkc3ej\nJOVhuUY0WPLF89fyuwMqnQbVorBCg34Pfb1EWa+IYgnnt1PhIL/DKwmPWVFGGGae3sVmyXyvLzwa\n36yxXW+hyLXpnoxo1Ds07SquLd7ppx9/ThgtaB2LcPfvfXhLYamTOg6VRLzY0/d+nXg44tmO8KaX\nfopn2Ohum7p8jhYseP3xay5ficrPD549obzdwJMV7Z989IZ1mvLm6pJAjTeFAzgbj9nZ2+Xps13y\nlrBUv/trH/C//s9fMHwj0xRHjxlPQlafLzisCo8wP1rhzVXaLeFdlY46RFbMPBBW/mgZUtzqkEss\ntmriPT0M1kHAyz/6cwCO3gGzAMF8iIawwO+GV/TWY/yViJj47G7M+bi4YLhcspqJaMli3WeQrLCS\nlMOO8KSXRouLro+miLUaLS4Zrix0KRNWq81wHFIsHKK5wustWFuYWp1dU7z3bmqxKtkkVsibkTzC\nZkrRdWg3xPdcX3xCu1yj9fARX6EzLGWrzM8PMw6oOMJSzzSDT7p3mHt5Gk/E7xarG7J8ib/+V0Xr\nWE674WI8pG5raLLv/ehph9bhr/DgWIT9h6M2N91LbD/DSIUX8de/9Ru4lsnJ74n0gdXeI99s8eIL\nETH4wa/+JQpqi0XV5U6C5Gy7eRJrMycYeBnllogSuGVwdIOTFyL3+847x6BWiOc+tie8qwfbdfJG\ngbUucrIr06X75pofnnX544+EJ/y4Wcb2HhGXxFo5xSLlWsRctiXe3i24en1DMLqlVDoAYLoeoZpr\n/PirjgCVJT7N3GbotCJb0Bw3T1BtE5h5nr33rwEQXfRQ8iPQxf64dDl9+YpyS3j/w36Kn1uiWBV8\nTzznuHZI46nJu09FNOLaX3Jze8YTdYZVEfswny3JUpt0KGRE02Pe2uowTpdM+kK2Vt6a8WSO2Wps\nztkVvzt59RylZqDpQo8t+7fcLucEEpSmWNaI1xr51nukifjug+33UOpFCqpYi7bd5nTxGf5iTjQR\n3nxsjhgOB2xXRJTosL7HW29VsVYjkOfVrO7TPUu47oqf5wvY6rQwJCiIUyhtzHtSaGPoZS5ORKrF\n0lRKZpm04XLTE/KWrHzcik3xSOjZrhowmUx59+136GwLvbBIE85uP2SWFxGWeumIly9uuZycA3Az\n7NF4e5+CpbKqivNr+waFZguZLcKJGpy/+RjF7LF1JPbq4bub6UT4Bi/jdL1kEYqchJHX8YKU4WKM\nK4tzrLyBW1YxC2Ihhv0F46WHV9aIJXqRUa6zmC4IZR63vFPHCGYsJVj+zd0rSqU8qQYXF+K7bMvE\n0GIiCfpRcHTuxrcUlRkVuaCWqjHWN4MGf/TTP2BPFYc1WnZp1kIOt8X8yvUdZlrKWOac1KTL3sPv\n0v1xn2UskajMDr/3o485/o4owsgXGly8vgDNRtoXNIsNwpXDKhSC35pD24mIzBnnFyL0evpmzN/5\nW38XpyQ29SycYTdaBMHmBVEpC2Hd3tshp2pYEm0rTjSu+zH1B2UiR1w4NzOFcuNbqLKFIaupDKcx\nu06b3/yV3wLgwaMPuIj/lFxe/E2tts0oTFl4r+lPxOHMKRqLiz75QBg0ZWPFYTXFk73T61WG6eYh\nSpjONpF/AF6en/Ht4wd8760jzl8KxfGH//SHbO18B1uXONOzC3qjgMeNLZYXEhR+p4RZKLCSjeLq\nXkZPXyFtOsavuqxjA7twwJM9Eap7cfKKiWVRli0Hp28uMKvwoNMhk2hvNWeLwfgWxxb7Eqeb4fVm\nbo7JHGUm5hKsBmhZRhuFZk3Mp/j2t7ioR4xmQr76hspynrIl248GkcXp5Zh2o4Fqi/11JwZVbQuG\nQobVIGVv6wg0n2fvi/yVF/tE6zu+dyRkYNFpMR2NqRcbVGQh3cvxprHmpQl7jrgwzeIez0/+AGPV\n57Ek3jhfzXn0YJe9qhDQ3sxjGhlYl2sOG2LvYmbsH5jkYxHywzA5S0pEtxHH22Kv9tr7ZKFCcCTW\nbbJaMO9PMPJC4b9+8YqWuqbSfu//Ze89uiVJ0jO9x0W4h9b6ap2ZN3XJ7qrumu5poAECAxADnpkF\nueIf4Jb/hcMFz+E5Qw7FSBziQLSY6u6SqTOv1qG19hAeHsGFWRUaiNrXJm13IzM8zM0+++yT7/st\nTrJL1b5NC/3+yAYSzGRQ7/oij3sjQyojZX/kQvWaNNsKYwlYvb4MvciEU2lIRa0pWztL2K4q+zI/\nrfaHfHbSJJwSMpBe2sXkNS55NgZXN/ztkxesL61ipmQP+3xOtzv9NhWk6n5cIRgMF9siT958Jpam\nV6c7nmJGE7Q7Yi1MV4dAVsMxRcjVtCYs6Um2dkQxlDJsojkDIhsP8En98+HqLnZkGWQBV9iM8JOH\nS8xynzG5FgZEZDLg4e4+mi0MK30aIDF34QnHqM2EDq3krxnbLTaTi5exKyiendyOMpm1YCyMm1Ta\nz1Tz0ZZQ5LXcMfe3buN3WfQkOUO+0UMb+FiV+/fTD/8YQzP5P/63f0M7L+S4H7Dp0+Mn98QePLoT\nw+/S8UTibC4JYKTrusPmkp+YLvbltFthLRviqiQKpmqXiwht3UmfkNrBHxJG3WQMnY7G2lKWlVvi\nnF1XLrgu5fFGxFpd5F+xtJnhuF3i5kvxYonNEGfFr9Fkn35q9AAr12bQEU7FTHWIv/NTttfXua7J\nFFB/gmn0uPrNF2Iuz48xplOsxg2hLRHCX80szhm+x8s4GPTRkwDrE2uIL2Bi6CoxCedneky642sM\nyeAy79mkIym0cY/6jVDyxWKdiMvDoxWhkCoti77VRJEQeqP5CK9nTDqQ5vhCKIrB3MGn9nCFhXK8\nOTrFHdTwjKdUKpItxoShtZhfG/R7jGQyP73pYzsVxZHsT1FfhFbPIewXF7pLtRmbDqftAvmmsJwd\nNUxcMdHdwmyah9e496OfU2t26J8JpKFIIM60Oua3X/wGgLV5AXwO02IXoy2szJQ7SPHwNdO5uNw2\nshHazphSq7Qw56G8EG+v7pA7vMaaSojAdIDW1CE3MklmHgLw7r/IcFW8YRQQAhpNh/n7V1/i0vv0\nu2JNPZ4oH/3857RlH3Rn6KddrdMfwlxWg97d2abcGZOTF9ag73B+csrGvqi29Ubi5Cs1goElcH0H\nugpgxCKUhgNy1xecfSG8qe4oRPr2H5KWlvS4cU46EkOnQ+Va5KftzR+xlL2DB+FRuQM93KjY8r1n\n6hTN6jKrKxxWRF90ZzxCu73K3u11AOo3OTIBg+WVdW4mQulbnnW67ipnsj84shRYmLNiqKwuRajK\nnudsyI2uKLybiqHI4sBSb8rD5TitJbFWzaCfgDpnKivsGx0dpTRi2aPy4IGoZg0aJlH/EseSJWfn\n1gaxUJDiyRn3ZG9vu9NADY9J+IUHkfJsco3BZNgkEBDvHl9ZrKZmlKRTkcWMqkW7VmfYrvLxD8Tl\nllwL89NPNplIeNh/++yU/MCNd+cBJzUBnNO3Onz+9JKVhDAevKF1MJJYTpsrWTka1NMEULHGIv+m\n6zqhaJzDc1GkdlPN8dcHX/Jw/ZK9D8R5bhge+o1F2FFzNMTrFsr5bDikbndJBoUcvTw8RDUUqkMX\nUUdcmiuWydr2KsEVcX7UoMa7H7/P+cDL6YX4/efVQzTFxJmIfU1oS+ys6ihTCfoya5C/rJK5t0xO\nVsr2MAmrSYZNsQ6XrTwtS6c3WjR6whIhLPeywq+fv8B9VuZnLmFk7r6zQSQWoVUWRtOS+pidyD26\nFeEpb4W2ibjmrC09JifLyM8nfTzRBGMJ0NOu9nD5NnD0Zbbf+4l47q27zEc+zl4JeSwd5Wi1B/zf\nf/ULlKy4oA2nh+KfcNJYLEi8vBQ1KaZPRWlMsSVK4GW9yjwQw+WT0bvuhEpbJ7gcIroqYVTNOerQ\nTbUi3jE+t2jly3imYHVEjr3eH3L3nQyb68IwCKQDOHONVm9C9UjUCrhCq4SNDumEPIfhAMVqFZ/E\nERj1F3EKRjOYT3oEJfBGvn5D2BvH6rWxZHFqfDWBdymJY4rnllxjei4Da2BhymjdZG4yNVUuc2Id\nwm4H/0ghmxByddCZcPjmb0iEf0AEETl8+voVz/I95mfCAy9/9oKwK8HGgw9IyXz6ZDAFFqMn39tl\nPHdUgiGxCZY1JJ3aQPdr9CWV4Mh2qDeaJGVLRb85wva6CIXD5CWOam3kx0z4mExlSMOZE3MHaUiv\n0owtoeoTBs6EzD2xEAGPn161wtgQB+Z62GE1toLXn6I1FpfbbGrjKItL8/Hjd8iGxSJmo0nsyoCj\nJ8ILv72n8scf/YiCJS4Ka2xz0+qzdf8WYbc4DNUzD7tZL2pGWP7nrRaJ+hvy1QE31+Jyswpz3E2b\n9qVYB93jwqRBwhvCL6H6sqksE6eHLykU/EhxqDz/Gq2/GLKp18U7/e7TpxTOKiRki9d7d+4wdaKU\nT0t49sX3fnF0xrzawJ2RjfGlHgnTpDeyOD8Xlui0usHGziZ1n1DW7csxrfGIVrtJtSWU+os3h2TT\nJv6pUEDhrS3UmEm+IA7OzFGJz7xEEi4qw0WjB8AfCoHhJtfoUmpfAeD1bHD55nckfySUviekcfL1\nCfG4HyUi3uFcTdDv6ZQl61ViYKFZFuWB+O1gKIHR19CGNlZHhm21GenIGoWKOHTpRIr9W3H0QJar\nsnin45sC/cmYmqSxbDn+hTkrgTSdQRNMaQCtbBL2hgk4CtWJMDyHrSqRyJioTxz4UfWY7f09DorC\na/NpAX76KM1+zI0+E8/pj6b0hxc4q+I7d/Y89C7ypJUmnYJ4z5bVZndTISbBL/KXNwSiM3pWg4AE\nyLi1tVg4srF5D70t5NNl2qSiOro6Ye+uOJvJ93ZxzdwEZSX/n33037Ny+y7dfo/zS8naZBVQlFUe\n/vBPATjNt0goYM90FInC9rtXT0jPvVhIxDW7y8gBRxVKTPcZzKJdLkYH3DwTz13XksTdiwAaO9EQ\nDVlRHw55ubpu4goIYzEYMJgoEPDMiWvirMaiQUbtMqGYaGMhYVIs1ZlPTEZ1IQPb2+ukMzEGc2E8\nPDt4RsrvI6mJ5yq9Hu88vE08qvMb6eV2jQzvb9zGJ+kvx/qc/qTF2eHXi7KhC/nrdBp88fR3zEcq\njaaAr/3Z/C8JBwKMcuKiHekTHmZ3qMtoSjiURNXDdLx+9IC43LrzEa1ukUlXnJ9Be8B//MVnZCMz\nMkmxd53DAaYR4NNTsZ5vXr7k9nKW0rjDwRNhVOrGmK3bMVyub/Ci/2FUS2I+XpeO1eijSdQrVTGZ\nTEcYsgNkZ2+fkeKQ649I9sRlt7Ptx9Bd9CVTW7uUwypdkdIMukmxD7PuFWt+H4ZLVmX7vKyklhmU\nahydyt61aRun16Iv9eMoX8YTjDKWAEfz4WLqxc2cVqtBTcJhGpMRS6s+2t0ejkz1LG1ukS+NmMmi\nKtdSlXKuTXdm4pHnd23tFtnUR3gkZOascUrV6nL7h8Jr/2jnMb85aTJoFtmWsrUajNKzBmxEhSG9\n98MAk45CanmbeFroTGv63R0kbwu43o634+14O96Ot+N7Ht+bZ1xtlUkYwtIyTR+vv3rNwLJZSggr\nZDi36PVaxE3hBcUDcVBN4c3MZS+b7aZUtXnSFF5F/uScZCbGjcR4nRk17v74DkRDBL5hW/L6GPQV\nKn3h0SbvPEBzB8hV2kykFRcNqYS8i2HIe8ubuH3CGqzl+1AbkY0Ky38pmiQez2J3hPf6pNYivRJl\nc9fDq6qwlJtOg9YQunURktYjbgZc4XiGBJMiTKQ4DtXDZ3glX+vAFSfXqLH/wEM4JN57GnAT3E7g\nC4qciFWYko7uE5TtO/zid9/OeeYVVvtN5Q1Tw0t0XYQAm9UO7WaFFS2JW8LaXTWGFJ+8ZnlHhAB7\nXRuvS2V/PcV2Uqyf1b3m+WdN3FHZMnXTpFw9RzO69BxhiWpzN16/C/XOuninpRX6wwEu2QtauGoT\njtj01BGu+aLnA5BNbaMYCp2rMp51UYx1d/cRuZxFzyMJMsIq0QSshrxcyMK6ng12q0W+KCzryVaU\n1dQWMVVCS3a9GK0+E49KIC686YNn/xX1+QlJycdcyhfprIYx3DbIz/ZTYQ5yDaKS6GCiLdqxW3du\n0S6f482I9XSP/JiOicsFy7KHORwPcl4u4pYEBWuuKanZOkpaFokoCkG3h6RnxnVR1AjE0jH6zSHe\nmswZdyakEgYpn0M1J6JErojCRb+DUhTP7VtTes6Q/qyEaou5pgOLpObXFy3qsu99Z2+dvfgGd3bC\nfPD+jwH4Ytzm3/xff8OWJHv/1//T/8yw0abfnPDeR/8tAK/PP+Wg8Zr6RISyvatbOM0+dzMrDCXE\n6MVwgPvWLuOKhJO9ekHr+gk/+/jPABh1QxSaVTAmlOV5HoyHhIzFcF7FqdEdiv0eayqxRIbllCQO\nmHqAIJ5gEO9UeGmjQo4pcwyJtTyYzDl6+ZJOfkIiJHvs/QY+Vw1Nwp0q8wIB3x77m+vi360kyqhL\nOBrkdlp49796fUHbCONxxN+TwRQGE9K6n3/Khj6SoBX9XpVU0sNgBl3JPHRwNiaghUhKYgvXtMm4\n1EJyIdByx7noR3CbHv76C5GOibpNfrS9iWGI+a7srXF+XiB72uIWAAAgAElEQVRfO2BkCt1R07p4\nDBfNmAgnR37wMd3yOf/sv/shH/jEbz27ekF/dIPqW/Qw0wnh3fVbdYZOm6zMK7fLNZoXx7hikuBh\nPCa+vkwoGGRnS2C3q1aTUSFH0CWiY47pYWdnhYPnRxQ7si9b8eC4QzRrYm2GzTZDf5TJfEooLsP6\n5SF+xSCxIupsuj0VVY0wmAi50mX71e+P4dRC80ap34jz8u7mCpqqcVUekJQ5d61jMbGn+CTTXDfv\nomBOsb1glUSIPHZio3pNluU71V1d+o05Qwmr+ttnJY6bCg9u3yK9KuaXcM9ZNXNMnoucsVc3uHGm\neF0+zLl472GvBizm6L8/CkXXnJk8ZyN7Sq3e4uikTCUrJry1EiOT2vkWOMJQwszGDkHFRUcitlSa\nHvqhCLkrqYAuxvRHI5BUc5qpYQ1McGs0+7JQaF4iGfATNcVixLMhWvUahmpRaIvLuNwbEY4tLcy5\nflzFnxGhYW2qMbdVfviJUFr9lsWn/98zxorIy503erTLfrLJKPnrpwAEzCnnhyVue9cB+Ms//SlV\nV4mcD1TZvG5WpwT3Qnz2//wSgHFwk8BqnFznBl2VNIHtEiuB+5gSvCqmpkguB2gPFnFaAysinGpM\nTObzJDOPUKpPX7xhPbtBbqCgnIkH3bvzCWE9Qb4uDJXBeEQ6vs1WYpexpEKr2SaOraCXRXi0dtOi\nUqjTVUuYMo/jCwexpxZTRSIeDSzWPG4iMbE2I8NLsXaOyxenXF0MjwFsJLc5zp3S6Dm4A6KqVE9m\ncG6u+Py3AjHKGeZ47A2idUqEJJZ4KhKi3fdyI9fm6qjCwJ9hLSgqGfXOkGJD4fw0z+qmmO80FMYe\nQ3pZhJEU3wpd241uzWkWxMXg8fUYtnJYUyFr9X5rYc5afEJAMXDcIgw167oYdqrsb8cJyUr919dl\nqo0b7kSFUZQ01xgdtFFq0kALGtTtGuvbe2QRiqM5sllb3+RUApsUjp+zdG+XwEoMfSqOsNWvoyXD\nKDInW+yeku/maNh1VjVJ89lfLDp7cnCIOhFnzGP58Lo1dgwPJwdCkbXVGetrWepl8b7/+69+idHT\nybq8HBf+g3hGr4i6HyAqQ53XhQrK1MAf7JN/IoBAEstrzPxettOiSOnW40e8PPp7GqowEJT4Osvv\n/Yxe4Q1rfvHZxcsjAt9RSHlZz9NWxDm03XMC/Skv8+JsvP9oHW02wcrfsLsv5Ob1WY5OV8FtifXM\nXbyiW65jF9zMJJKc1b7Eh04oJgrOlpPr9CpNKprIKW8uRfCi0rq6ZlWeoduJGdFQhHZLzHGiBNja\neERjpFA+/8dzbsgui3kyQnD7Pr3rEhsrYn6jocblzTn3H8v0y9jHdXNCdlXI7MvfPeHz3ICHf/ox\nr8oiXeRXPGANWZIh1aedKvnzCushjUZHyJ+2bhDyx+kVxWGYGG5QvfhjftZ2RSHTsdOmc36N3714\nFSxLRrWc3UALT8kmZBeL7qNWbJGTKRJ3eItYJs56NEqv/Y0j5CM0V2EidNKb8xcUhk3OZ21asn7J\n4wlwfHGN7hYX7+OJQ98aku+o9Bwhs82pzXQ6xZE42aNwika5ztruOgC1+qLOG1hjTFeI93/wcwB8\n7WdUzi7wWNCU4e8iOULJKN6BeM77D5exizb5QhuPTPX88u8/pVit8Ef/6iMAdrwTjECSNw2RGnjx\nuy8pVydsB/8MXOLOUa4mjF+ecP2lIFFpX1SwozvsxhJcPRUX9NCfBH66MO/v7TLG5wEZvx8NG7SH\ndVxeH3ND8u/e3kR3D7iSkHBj1cGceZgPgrw8Forhi/Mha7traLa0dqIm5dmYPVnUMuoWKVUb+F0T\n6m1xwcxsm3kwysqShL2beRk0W8yDMyaSZxjVT9NavCSsVo9QUCgKfRwBl4dSRVih7cIEIxQgkxWW\n1kn9goujK/LHFtOeOEDpaBi9ckxaOt2r3GBObErtDtO2UKK9ehfVHhLOiAhBMrEB6zFObgb4JTTo\nss+Ne2YTUsRcejdFrl78jsjSYpXeN4hRfWtAudlk99GPAAj7P8Qq9ak2LZzRFQBXbZuNlQ2evRRo\nS43WFX/wx/8jWXeQZkkchs8uCiQVlYREtPrVp89Y//BdsrFVsMVeVep55rM2ibAQr1b+iIcPtun0\nhMJMZ5eJpDaYhkKUe4tIZwDXuRpWtwGeCNc5UcTzrjliLe3QlG7HWclLMRolVzhk5hHeqC/fpjrp\nEZOwgflCkcL5mPSuUCzpgAsrOqUc83JRFBpz984W2qTNuCOiE/s7Oxy3O5gbyyBBIE6ujnECc9aT\nImoQDC9SKJ5dn2HXu5hhaej1gswHFpdnl3gkDF9/qBMPZPHJuoZWscCb0wpDifl5OxXCCa0w7T8l\nkhHfuck3cW4ZaDJfPTw9xHZKvDFMdAk3OZ90qJ+WGAyEEdkptpgnfBhhP7ZkTpqpi8e961aJJ8QF\nmbz9M7qFv2JqKIQls9i9ZJD7u5voXqEc/+PrA7yhKJFkkN+cCfrQrYe3MO5sUK8J5Wx65/S6JieX\nVZ68EO1t99M+hr0K9/8bccG4fNu8efmSz38tvHK/3yGjqxSu2qQzYu82ogPikUW4VL/bx01LApmM\nx/zw8S6OJKCYNRQ0OnSLDQ4kq1mnN6Y90/nqM1EUWa8dE7I1glqMiS08wmjAIUmAF4cij+v2rtPv\n98lfC2NiPg2xHjHojh0asmho7mj05x7GHgkkEXOztbXFTBYT/v6onEtiEC1AIvk+frXBksxpn19+\nTuW0znVWXEpLkV161SLzsGyrKhRoN4pc/O0xxa8+BSCUyFAZb1KbC0NgGgygZrwU+zWGZTE/v+ZD\nT5coHIt3qNtuthUV3bA5LQkjQzNMVkMeYpHFqnXLEvuZDkC+WaIvL6FgLMjOew85l9X5kXAIxd2j\nZ9vMGuI70Y0Vor40NXnG4lk/7X4AjztMMCue41gN+tUW2gNhmLaUIM1zC03z0QkI2V9/HKD35jWW\nJfYpvhZHmTnEAzLPPFus3Zi1OphhN34ZzfF4PYSZko776bklhzltPMYMtS2UyVZmi1Eyg9e5oF6S\nSHwRD92Kw+GXVwD0PRprSxG0oHjHzMYSiVCQXmnKf/7iVwDYFyUGlRpDifSlmV1Qb+g1wvQlUqRv\nfzFCBW9zxm/H2/F2vB1vx9vxvY/vzTPWNJ1+V1hWS8kA2maEdsxEi4uKz7bSpVstcnUtwTpGFrub\nu7S7Y9pVESqcWzrlizxLCWHFZ5fXUfp5Ll+9FP/eL5PdSOGJusnI5vNyp06r1WI2Fh5ZZKwwHGuU\nbwaE/cLbmc8sNH3RywytJolnhPU6qBlMPSZvSqLq2W2pLKViNCX831AZ4s9GmAwV9h99AIDCHD1/\nxa9fiGZ0Ixlh7g/Tn0xYluHvWnMK8QDxHWHBla6K9I6KmMEAj1dFGNXRxzw/eoHmk/yslTYxL2zs\nbSzMORES4Sg1onEzOWAal9WXA+jMFGZxg8FY5p5bZapzm8cPRQvSLcdPLOOj2LQIbYgw64vnlxwV\nmvzkHUFt15wOodjg3UefMGsJLyhgt7DLZXSJZetyqTS6DSIbIgdV6akMOwrObEK3893cntVcDWVQ\nZzIssiKjJfpRAevlL1kPiKiGkQph5Vuko7d4/P57AHQHCn1lwDt3RMWjFdtm4kzJBIWlvWlOCQY0\nLqYTrGsxP2fUoXt5SiImPM+T51/zeaVD8eAZm9tiH9YzSXquIEO3kCOrs5irstsjWs0u04qI3PR0\ngxRBrKbC+acibJVZSdGOubmWOWO3GqM1uWDQkfnh+39AYWDxpFwgOJPAKsUq17Vff0vfaNVbBLQe\nnVCQRFx8FvBNGep+3NKbShoJTN2LxzX9Nlc1+Q5qv80Ha8QVCXRh2rT9YcIbQf78L34IwHl9yF//\n7RN2BNofn3wU5fASCl6FXkREALbiGQ5O6rw8EgQFn/zwE5SLKl7D4D0JcBAcFHj6/CUl2WoXTd5n\nmCuSldjppWcvmIZ9KDdtQmmxtuHVNCNtseVGc3RSa+K8WN0qqbiBNRLP0Xxxms0W0YifiCL29/T0\niK42oyNhITW/RrEyZDXQIyBx2d+7dZcJCoeSQzq1ukZ1YuPyizO2u7FCr5HH6/ZQ6givcur2QUBB\nkTIx7DQ5fvmE7nix3eYnPxJhyf/y/z4hnVyjonsI+MVaPN5oM7m+IdQWLVLRtTB79z9g6hPqOfOo\nziNfjHbpNf/lV1cAuKJT5qZCG0kNq6cZOlMcc8i27NFVEnH07Ijb/1LkPAPOGqVPf8fMqVM+FOHa\nnmqT8HjxRhbb3jQZ2fITw+4cEglITmbDi2MkSC7JDpWIQjCWojUZEpeVx1f5Gko8iS8oqUOLc9yO\nwnYmikt63MV8n0AyympYnM0ldcpNq0OlfsbKD0RoOBLQSWyHudSEvrbNMd1Zh2JbyPLq2iKRSNat\nkYobGLZYT2c6oD/q0eq3cMckx3EmhUdzE7AlZGpJ4Z/feoyvWeZvmmJfbopDJsY6ikvow7NCE6ta\nxZRtkl5fkM3oPqPBBFtCzTqmSoUZ05mE313ZYCW7TCK0TCAhzouys74wZ/geL+PteILSlQhR5cpl\nJoMuLnec3Klw5a8KOh6vG20qNrddLDOKhJg5EcZTGdJb2cIadxh3xea60iMMf5+RbGNZWfKhuTUC\nhkEkJWL6hjpj1m3gRnJ15o/o9HK0R0Pwi1BmKm4SSyyS3ndcUwISqWamDqhU7G/RtQKBGNe1EkpK\nGgZru4wUGzsAc5/s/QwFufXOuxRlaLaZ3cEaOoysMf6xZKJaXscsWrhDQtgO+ze4vCaaO0mtJN5r\n9+Ed3N4SXlP87c6YePwrKO7Fnt14TPQQe9czHHZ0WpLv1r8WYTwpUq20KZ6LfGVcHTHxxRlI5dxt\nuNgdu3CbGrosSkpbGbxrYcYSx/v23TVqkxZudYruF4aUM7xCm5iYtji8HVuhobmoVUQ+ycGLrvqo\nFXOcX59+h3SA2bXptXoEgwp+lwjrXB/X2IskOG6Ii6zc6BMPB3lwdwOfJuZTmwyJZTZ4565QSC39\nnGLJJv+lCL2nNn28uCygTFz82Y9/AMDUH+LQhrYjlPfLcpGrehdFN/DL+8vjjDi4uaSOWONYdDGN\nMVQUXKE4CWnoPdoLcn1Y4OSoRF8y1cz6RXRfgKYEsc9mUmw8fExNhhKfPD/AlVjBGQ24roncePH4\nDY/vbNOW5BT9gMKXwxEKTWzZCri2ucTQHNAriRBpoytabwxfBEWyUVUbi5dxcnnA5FoYCkvBIEnX\nhO2Ah96JUNZWz2Ip5OL5M1GEOE2PWF9+h9ftOVPZFvKL3z7FMlWWJFuaXe9jKjajVpuVkJQBq40S\nnvP8Qhhsm7af+6kYcZleME7zGOUroi4fEV2scWY5y8S7GD79za8PMO8K48tvhHjx5QkpySM+y0bo\nT9z0CyW2bgndsZ/NcmWNiMoCQsNwc0OdQr6DT/I0F+Nuotk9NlZFyPSsW2MtGGAzIgw0nz2kXG4w\nbGmosk5yJRMjvZHg6FwYUgGXxfHhK8zRogHRkwVHoV0dZ+qiexHhxbUIiVvXXzFQauxlxV42zn7J\nU98ubbkO2WwMR62BVeMnPxX6Zz2zzGWlS0OeqcurJ3SmDg//5CNuvSfeO5RIYrWG1LrCeJgxY3vL\nzfw0z7IE9Kin9rGUFhfSwfn9EfaLd/eo0HTAkXj0mjFh7neRlUVW9eINiteF4nYTSonvnD25ou/M\nGTtiLW4K17iDBqnlGL1r2caXXMJxVPoSy713WSVlJikP8twcCHnzT7OEo25el4SOGg5ttPEQsyPO\nU+lqMc21mV0hnghwdiwQ9TxmEPfaHZRulal9BUD59IitpQSDpHBwDj4rstLoUjw+IjkWDtWGbXBm\nR4kHbsv1jEL5CLdsp3IKHZKP/KS21mhlhe44Mi5wLJV7jwWGuF4p0agMmfs1WlMx56unT/jkX+4t\nzPt7u4z7pRpJl5DqqjIinI7TU1RCiqR7013Ewik0t7CsQ54ovX4Zv3fIvQ3xvWDCS69n0+l+U4CU\nYm/3FsVDoQAGuQMMdx+fUibilag5hs2UHk5LVqG6IOHqEguFMNxC6Yc1F0p/cZOfHR0SUISwqcMx\nY8vPWkQIZGjqomN6OZG9tCOlRdA/48GdNOWuuHAqvTr12hWurKgO7rrC/Opv/g5l6ib0h8KrDVk9\n9ImLWUMYC/ZE4eH+HXJWh5IqlOk818d2rVCYCMsv4rHpjnK4qotzPryRLEOMiccf0bgRmYl5eIon\nHMUzHBOTSWz7sogedKg3xDvs7O2zEgkzypVoliUQiD/AtDngyReCstBnuNFUlfrzp6zvi/f68NFd\nym8sTg7FvqipJYylbS5vxHyrx8+4vbHP8sYKm9+hBAA69oy2qhFwBhhucaArrg665sKKigNk+pdR\nCWDHQpiI36rW8oy8XtqSSacyGdHullBlAV/1sk2rlidthnFJqrTmoEFnYnFxI5RW5+aStaUUwZUo\naylh2dcqr6nUWhQHQqEnk4uUcxdHN2RWNon7hFFSf/qKRqXHw3fu435HGDj16jWtTpPOWMh5a1Bj\nNA9iyqhM5eIUVWlya2eVnaQwCHe3UozKV+gdocR2trPYIQN13iYelXk8r8Z5vsvWbRGF8adtDq8r\nRAJgSOCKynjxMo7ENBx5Se2kVd5ZuY/XCNLNie8cHF0wiNoMetLb75/x80fv87tPv/wWltblqeKb\nTwnNxFm4yZ/imykMymVCGXFJ+lY97Gd2uCeN64c/+Ij5wOb4r0SUqKtPKRfzpHd3OasKIyTXfM3W\ne4vQge16lT2XUF0+fBQurqkM5FmotvB7DFb1GSeHQhkH4qtcvLmkdSlqN/7k5/+COz9+n//w7/4d\nwbSIsgSCGRKZHSo5Ubg2LJ7hisRZiop93knFqV0Xadht/DLKYmt+Aj6ViGRPm4/63F9NUj7NLczZ\njWTsMT3oQbg+KJC7Er2z98MxzFicTEgipZ2dc/FVmb5H7H/+tc4PfxTnIH/Ayj1ZjzCb41Z8GCOR\nM93cTzGa3LDjaTLI/RqAJWUXr3eF2JrYl2plwtb7d3hWfEW1I/bTHvW5bHTRo4vOR1+uxdQY4p5p\nXEhgmu3lfRSrymwiiyb9CY6fvCSZ2kCVXSAuxUOxUqSeE45S0PSzvrvHYBzk5FDsw9xMkkhkMeX5\nviwMiGYMPOEMoYg4L61yDmPsZm9JzK/X6hAhgiMjkFZ3sZDyrFqn1KkzyMuoZWyOYoxZu5PCVMRz\nnn35OdfVGgm/ON+JRJLzkzxuM807UQkq1SrhnfpYDYkIgG8MwVSGO9Jpq5wNSFTGJOI+UL6pAbni\n3t4KEYl9kavX6XcNeoE+ti6LkY3FehP4PkE/LAMkdVY2tYRqTJiMOmxuitDcWjCAR/OhecXF6719\ni4uzAYpvxrvvCK+nO3QRcAcZ9KXX67ZZijUwb4tLqRRQsMddMss+ZrJtKRnzMPGlmBsSZ3XcRlF1\nVDOIbshQzXhEubPIDXzr3VvspsWFHbImvD7I4RrLtgZbI+D1EpOh42f5EpE0hJIeQj5x0b48O6E+\nsplYkh3qOIc6GrKzeZtaUczZM5uwFs5Sk4woqX2DgT3D9EdoDMQGF5+fY+peUpviudFwlaFtfGv1\n/f6IRcVBbGperF6TuIT8bNSL9Ps3ZGNJ5lIgj+0pNwfH3H4kvI4f/GCd5lGJ1ZUspqwg/fr13zOs\nn7MkrWa/orCUDlEuHHHZF7+f+WifydSgg2TGqvWZ30yYqUKRRKJ9JoqP4UDD5/4OVCjg6KLGzn6U\nTDyM1hRKYPlRBp+mUywKjyGhrHL24pyzSR9VF0aGM9HR531+IYt15kM/ynBOVhXzXd5YJ7O9hncy\nZdARyqbea/PheojtsChk8rz3DuOYzm86x7RHwjCxdR+aahJySx7s+WKYelwcoWcNji7EJe+/bHLY\nbDNP7HBXVn/urC1xfvI1dln8n5FrBCMXXo9Q+sv7t2g6Q/SlDCGZsrkuNqnjZXNLwAZ+uLnC69Ih\nHU8AXZM8yO0pM1x4ZGRGm/bYCgYIhyIMZFV2uzdcmHNfz3Dntghjjo0UXS2AO7jKqCPp/Nom3vUs\nmbDYyz+5dZdRrkUyliYq0yKx1JSZOaIhK5xP8zlml8/YXX5Apy/Wae/jH2EPLEIS6zvg1Tmv12hL\ntCMt5mOgRVh/+Ifkq2Jfzi/eEOsstjatxTSCEhd53FFx46Il+cd3fCobYYW1VIRJV5yh3764oVQr\nshoVytA7nxK3Fd7LbNBGXBa2rePqDwhPxXzubmxy2aqhuYSO6lUKbCzHcehTkuA2lVqFYThHSlY0\nT+hSaV6h6oupl35ZzGU8HHB28wK3z41fUiRu7T8ivZaifCmQ5qLZGEelOpW2hJNN7nLxpkd/4kcd\nCj3RC7lw1ClhGUHLrPvo5Xws6RkmddkyuhtlBsxnsvukUCIwDjF2RzDlZRaIJNHcKe5J2s//8/fm\nPGgL4683m5LyJ4hJXuRUJEjf6zDoCJ3qC4UxA35ylTK+lIiOBDxBCt0almxDtToOqWaTiHvG9m0R\nkToqX3Nnw0tQE1GD83qHTq1FxK/hl8WGMdODX1HRFPGeo0EPTA1TVnqPI4tGhNU+IpbOEJEsU53O\n12wtxVhZS3FzJTzsRCTKq4NnNCTozL1P3uX1pM5xq0xsLttMvS7cc5W59O471WvCZpdQRMjwLJHG\nG4pRP37BdVkYYOOrIoVmk6qcSyTkJZbwY5k+zFVxNie9Rd4DeFvA9Xa8HW/H2/F2vB3f+/j++Iz9\nCaJ+YZWEsxE63SK3QgkSyyIkkIy4mA0tBi1hNaXCM+L3V9DcGnNNWKvN3phsPExfQvUZqobfO8Qt\nk/2JeIpuL4xHU5mUhBWXiQfIN/y4ZQ45OQkSiAVpjadUesIDmyombm0xPLa9vsodWUR1+sUTCoVT\nqpKQoFPz8f6Pg/g9YknXHD/N+hWXxSkP7wrA92m5hW/vFqpLWHOt6yb3Htzi0Z3HaF1h+XkrFsrQ\nYuYRlp81DBAyxijjHpokWt+5tYvTUzBDwtvbu79N4fKXVHKLFnlDQtoNgdXVFYJZidvdUFkOwGos\nQb8hvrf18H0C4Sx7+8JKtmddxgkv/qCfqEdEBG5NkpSnV0RlYYvXdHPr/Q3yxSK/+lLkecrFMIct\nC5dsH1PHba6rN4SjYm8f33pMOBakrxmMq4thJgBnNKJdn3AvnsWWIeeo40Jzh7BlTi5mzNlPGBhu\nL0OJFxyJbWC1BizL1oer6yKuWQBTMulcHZf48N17+EJDWl4hJ665SjiyRM4S/6febmEuKcxPbPKy\nWMMfWSKZGBKSzFlB93cw82SX8UW8dMfCxu0rS2juORdVi6YlLPJlY0SrUieZEKEqV2YDe2IwysnC\noe015t0O5+0cg46Q/clsgivloiPbcEZtF4efXxB/sMqzb/KVIQNHC/Dq6VP52y7G7RF9zYMeE9ER\nVzS+MOeLNzMS27JGoHrG1YmXjz5JE5XgCHi9XFxWCS4Lz3jOPU4rJ3jDUS7fCLlxT2MoK2Fe9YRM\nxNMP2F67gzJWaRyK+TWdFfq5Q45rot4jOG1z/sUxd2VNww8+eY9fjmyMbgujL7zyzWCcyNRZmPOD\njRg1if+d8q/S6c15KGsEfvrDTcLDGSOrysSRBA7DKn/+R3+IOhQpkZ2dW5wd57j74B4jTfyWy/Ti\nGvdZCkhc4v4IS5swkuHciW6izCbsbm5gFIU37a10WbJKTCX73MDTJu+eEo0G4Mt/POf/9J8FbsBV\nP8c04ubO1rvYhlib1yd9asM+H78jiEucu4+wnx4SHAoPKn37DqWTZ4TCd3j3gYhieAM2g/ErXEEJ\nMHM3TV3TGBdmTGWLpmXZ7Ma8HD8RMK+cFmm3VM6++jVRyXqkpDLEvCnuhRZBKJry3DlWl0gySEy2\nog7rR/TGdaaqeEa93YGgh0TGRAmKiI9l6sxSKrpk9qKnUiid0apVsE2hSzZWUqRiblolkTtfWV2n\nYrXJ2TMa1yIitZ+KUq6P0GUGrly2mDh13DJUXFMW62T2Exohj0PCJ+T9pO3i+vVTqo0iUdmG9uEP\n9khE/N+2s15c5jGDSbyhfTol0Y51Sy2zF3UweyLNeDsRw91vM2+Is3pva49ZvUmncUVfYjN8dHcN\nzaXz+vmVmIxvwsqtII1Bm4lkTdPaAH+0MO/v7TI+uLlGXREh6aXgMjMtAo5KQOZJht0qa8kobYlB\n6nGPmDpz4kEPJVm0EJqqRPQo7rl4yc1EAJem4HOLkGqh1kHVJ8SDOo4hnqu7g5wVT3ANhDJdW8ow\n1TR0v4Et847lRpNC9Z9i6EC3VCMngwn5QhdFjZPIiDyprjtgqGTXZI6x1+biyuGBZ5lIQoRuor4C\nPdXhrCmE+vDNFaXSjAAuPtkQIch0Ok3lekReMqR8/vwld7Netm9vEF+S4XiidKcdWpIl6abS5eLs\nJXNrkQEpKsOWTd3FTT2PRxoPM9+IdX+AystXWC1x6ENGhOX4Ek5NhKe04JwH7/2cf/93nzPuCwFN\nKW3GjofDA1Gx/id/8Ye0PBpDr05PXnhn7RpfXZ6wdV9UXE9nDu6Al/5AHMyD8xs2CGJm4nhTi8oW\nIJ3QCJgOxVdvcMs8/UnXQvPUCEaF3ETDcwZDlb/94gVRR7xXam+bar1AZCQU5mP/JsOZjUcVF8eg\nMebqrMw80uWiK5B21ICCVjjGGxHGQrFepdStky/eUJLhsBER3HMvA1lta4/PFubsi/rRJiPcEfEd\nxQixF4J4YImLI3GgfS438aUYfcSad6rnJLPreDYli06jSctq0505eOLCQLNjI0LbSTolIXtXXi/R\n9z7AvZVAlQxlrblC/bpCUxZwBdNRNtf3GM0mlM/FBT1KLlaelg9vaMxFeDlkDYls+lk2deoVIUtb\nm7vUq59SvxF53K+dPu54GMPvRpF946YTJOaL8kDWWla5n8cAACAASURBVF0cviCzv4dvNmU4EUr1\nD+IRqoUA//ZzcUutbW3xXiBBxhbPeP35fyLtHfLupptDt5CTRCzNwFisKbi7fpdTSxhbrjkkoxFi\nXqHKXn35ObdTW4RjaVpSaaZCIdRui3pNrJW19QBXwM1J7uzbVMDt5R1qjWekEuI54+olfm8cRYKq\nRMMxroo5HNUkGl8Xa0yb1vUNGVkla4+GLGWW2Uyv8e/5X/7RnE9leHT3R48pKlNOrw5IRsV7BsdD\njEmJmQxL37v9HtZGlGZXzP/l9ec0OxWiI41v1NJdPUNga4bHJxyIq3GOcdjGGI2Zyj7oYf8SkkFs\nSVyRViz86jX3trwEU2LPK7Ee04MCytZiusgOS3TB7Sim26beFfqmMUNgU2tCzkOhNMO+TcDlwytZ\nrpq9Bm7CqDHZX6+MODm7JhFyocvahfPTPIGhgqqLvyNLDo5rTLlR5aM7ooI5Gkxg9yyslniHdr+L\ngoUZFMaiMV8M7iZCEQJeA69MZfzo3X0uSjqp28uMpBHXHri49/HPOT4V57J3ViEaDHIrlKLUlgQd\nGyGsOqRkt05oPuazv/0tqWVh3AamHlq1LqZbJbMs7pfzWo7+eMTOpgB0efTB+3hXInjaZfKyUDbr\n/25s6u/tMja9EWJx4R3EIlFG/TrFaoeZIzZGnXdweiNaLXEYU2EDw6Wg+FTGA5HPnTghild1pqrw\nlKblG8bjER5Z1WlOwDue4va4McKyZWqkkcpE0TRhUTY6c6aeGbqqMB+KjdXnKrXyzcKcPZ0ZFx1R\nKNLqGqysfkgovQ5A9fKKN89yjM+uADgtDWirKnXLxdcvxLNmjT7F119zKVGdjNaczQeP2d9NwEh4\nsLHtjymMcxSuhJC43V2qxSbpnRUCLuGlXR18SSqYwRuWdH6uJulUkPlQkmjyD6g0F3khALd//AHN\n1pixRJAKzicEvH6a8wptyfHZ7o/o569JL0sEqbSL3FdPcMYeTJmzaXY6vDwakoyKfOGhNSdWtrGH\nYXzbwqCo1c9JmLAi84zX+QF+NYAp6emucjnoT9GvTzk9fPNd4kFzVEWxemwmQwQ1EUFZTW/jKG6u\nr4V35V730K4aeDQvN0fiPVOxCAnFxj8TMjKdNBiNGsykJ2+rKt35nKPjG7oeSY22tk007qcv243m\nMRWXHiDtLDORRWeH9R52fJ2eJWRkeXPRk4ivL1PLF/iGYyTuGlOuFBmNxrhdQmn6kgF6owF6Vlz8\n/VIXtV3EiAn5nHoNpqMwF6c5jJAozomFl1HGGgG3MErO6y6u6zMMV4v1LVH925sptFqHuA1xFua2\ni8OrOnvbt7C94rOD6uXCnG+vxNlbFcplenmKrg5ot/JMJeC/P6Dyz396j68+F/O/qZ6yGVqic11G\nGXblO0yJJ2dkdWn5Nw4IPDkm0Lf5QBUK/Q53aF6eo1yLamorZJDQdHwSzrEeGHN8WCaVv4C5UOCW\nEiF6a3thzm7d5OGm5Owd1An6FVKyKrvbjuPMLdqdJs2mRJJrDBg7NjPJt/2qcoXh9uJfjqLPhFy/\nevkZA71CYFV42MFwlIEyp1IXRojvrp/VsEqxXeeyKi7RsO7i9t4araFkJoqFWPYkMLqLqFD374u9\nC6fcbOxsEhrdYksq5Vm5wMHXp5SvhYF3VTjkol3HL6u2260BjUKV/+Ff/TmGIS4Tb2vAvUd3aHSu\nACi9+pry00/JBNY4PRKFiL944fBXqQe8kqQQhtXl8fuPaNwUyY5FtM5tqjTaZV7njhbmbKRlGycD\nGv0pg55sdUr5qSsKo4F4p9DUIDl1aJ+10VWxLyHHwO0yGcvCJltXCUQjuPUZ9ZZ4B9Xnwo7F+IZw\n0hVLYRUaPNhc4+6G0OFhM0C90SDiFYb+7dub6PMxmVURufzi2T+BOgPKpRtIx0hLKFOrO8SZJynk\nBnz99LcAVCpefvKzGLpPop65vbSu2kzcfZy+2JcLdcpSepnUhoiGNo9OmBthDt+ImoYXh9cYUS+h\nSJyQRI0baAnqox6RubicS0MY5voEg3H8IeHehz3fzVT3Nmf8drwdb8fb8Xa8Hd/z+N4843QgTlWC\nIzz78g1J/5yo0sVuS+9En5HvzDGlV3RdGmBqDh5dZyKBIuaeIFMDFEnmbY9tXp5cMOabirkgWiDM\noG1hesRvNSYzutMZjiMbu0t1zHSUerdHtyZ+WxlOiUUWiSI0t0U0KOYT8YbIleZ0ZOi1NZ6STUfo\n9iW1l66z/3AfTyDOVwdiPndWdtndn+AgKNhmy16272yylMmSktWDX98cEFtNEa/JfuVmB73XpNyt\nE5pL4nO3QyauUZE9krrfTfrONgefXi3M+UK2WTTmXlzpIG0kCL9SZcubJbHiRUV41MNCCXM6Yy5N\n8kq/jTo5IjBbYyrz6Y1Gg73VdUxFElk4KqYWZqwatGzhsS6txwi1DGZdyfWsBZhYFroEo9e9Lmq1\nErsba0R8i1XrAM1+j+evvqK3tcZOSoSs9GSQVhvqLWElKzctbq5rxJPL7EWEB5XJpHBmDRI+Cco+\n0umMLVwj8Z3poI2rd4TL7XB3ReQrrwoV6opGqydsU3/Qy8b+LQxPieJL4RllQxmmcy+aT0RzUqn1\nhTkXRg6n1T7ekIywxHVqVpfzWpNbK8LLaGlNpsaMmexP90cjzAdNDJ/wgsvFMQ1rytyeY8n2unqx\nRK18TSomQovp7AYqCmqrx0j2W7btCavhBClTeHoHJ+c4uougMcbjE5/d3XmXX/yTOecnLaKrQvY0\nd4Jup0NoJ8P2pqgbmNpXlJoNkiHJXdueYT3v0y+NmYXFnK+cKsWjM4IeER6tVZ8Tibu5/uoQX1rW\nXsxjvHx9jscn1mY0mzA03MwkLZ8nEmNqOtjhLMu3xG+/Ojnhl79HevLNCEUjlFpCllwu6PTqzBpC\nzlezSQ5zR4Q9AXxuoSeW95eJLvloTUWUoz4e4NV9RPQZV5dif2e6h7bVYeQIXbK2s8vZm8++9U77\n5XNa+S6m7mdeFSH8+O0tEmkdCmIPHq1FaHbmaCyGIb+pvjcmXfRRE3UyJyK52+1gnM3dOa6xOC/5\n0lPUVgdLVnJHPDonDPhf/+6XLK8Jz/Mv/vUf09OGqBMh18Y8gmXHuRksMZItoyuBCZ3RmO5EnLFo\n2EXfr9JbXcOUuNimbjPpnfLqaLHSfipznEPTT+mm9S3Hu89WWFrL4DOETJwd55iaCp5YjJ6sRp/a\nc4a1Cssb4ryMRlOU0DqgYeuipSuzu4XP52E+FPvkOCNcwwajUpdLTcikPfcy7TS5tSTSf36fRiXX\nYe+eWLt0dpEWNBFPMLdd9BtiLqf5ItVBi9X0Gr6oyLkb9QHF10XWfyi+//EH73J1U6Y+sTAN8dvB\nWYJ2s8/xiZCtxNDFg1sP+G1bpOiMWZ9upcy0PiZwW+Sn7z9+h6Hbja8tUgzTcgkj5KNy0yUp28e8\nziJGPHyfRBGjNpZk7GkNesSXAmiKw1gCgo+nAxr9Pju3hcI0NRfNSpFnhRpzW4biAhaTmcFYcnp6\nVYXOzKDVE4dl3Gsz8bWpNvtEZZ+xEYgwxE9SFnB5HRV6NkGPlxbiwu4MLXzmYoGO5XIxlNybqXSQ\nodukVBG5yYlSJVdoM5K9ZI49JpPykg2EMHcFfFHfASOQZiZZP7SZQ69Q4EbReJMX+Q1r7GLrj2Js\nrYqwx83ZBVpAozdpcZUXYazdsILf7FKtSnzZZJyZS8ET+iZM/Q/5zKTME7anfor5Dnf2ZHuCMeXi\nxUvGtspuYh2AtQd7VCsVaiPJMWxAvfCaxtXnvH9ftCPc/+A+R0+fMxyJtRo3JxxWc2Qi90D2GIbT\naSyXwngolEIisUx3NkULC0V3P52ld9VjUGkQMTPfJR6cV4vYU5tir8W2bMt7efAcDYOExE2e6QkG\n+ohho4HHL5Gcxl3a7QHXHaH0fW43nmwMnyH+dsVN/KE5Q9tkaou9TKhzzlot1rbEJRBMqti+ObXS\n4Nv+S3OiMB1WycTFofOw2J5g2zrtuQvNFAe8O24T2riFM+xhSoabXLtCJu5jKrF2+3OoV0d8KDHN\n1+ITfB4DfTagLg90MJRgJ76GOhdKSwl2GNf6GHqM0xMRMmu1amymk9QlElDMo+EyXehWj60lYcTl\n3YuhdS2tgkeEWWOrOq9fN/ns8hXhtpDjR9kg9WaVQV5cZMt6FI8a5b8ev+DHfykKjubBOQfP/wav\nLc7Ch3fvUPjqDa1umnf++C8BUL06oUyUmOSv3tu5gzF3cXIpFNts2OPjj/dpqVNcESE3nq117PPF\n8Ol6OkJbAll0rAm+qEFEokXdlI44PnjOR/fvoSOUXte+ZtTV0STncbWQYyXuMFFt3CGJEud18Ezc\n6DJP2+0WiKlTNlKyx1QFR9GYjnuse8UlmQlo9EZzoglhcAxHc95cXeK1F9H77LbYu3GpRMdWOPj/\n2XuvWE23NL/r9+b05bhzqFx1Tp3O7unumfEgDzaWkCxuQEICgRFg+QLf4TsuECIJCYGECIOwhHxh\nWZawsMQYxkzyzHi6e3q6T65TVbt2Dl+Ob05crFU13b3PNedmP3c7fO+33met9az1pP//9DXXe2Jh\n9/afs79/yOQjEcK/33+f/dYTBqUsFGsWPPvur/N//fFPefId0Xb48Mk+v/e7H9HRhV0rDYevP/km\nZ6OSSBah/eY3v8FnLy7Y+6uiZe/97z5kZGZcXGec/Uh8l2PMefT0kMS53SIUS3CiIIwZ3oR0ZCGg\n7cM2LudjaX+8ArvXQa95+LL16mvf+VWOPr1hmkmgFS/hzfkpub9kU/Z2T1ZTosylpgvHJ1uuyZdr\nZkpGgrxwa5scuA1GY1lI6W0yVGOWS/Gz7dzu97/38DnpOmW1EnOZFDHnqxuUpUdN5n+7vsmjZweU\nirAB0ckrdtFobtfYkrzxbqRwdPQFO23xcxmPWAQxz58Ke+7pSxRtF8Nq0+0L3VxMp/hlQuqL1F+p\nhHSNXR5sboJcj3339vqAr/AwLswqrarY8FYccTUes9HT0CSKVEVTeDNb8MmFqFjwKEjHU7LBGFX2\nzc2VhL17j1FUsZBenFyyvbtJLL3TsyAjWWac3wypS5D9Xq9FEMYkc5HX6W3vEJYZYRC/K0ioti3K\n4jZAfTSaMF6J/7G3E+49us+xzCnFXoReGiiSjFxP1ly+foG6HIIljHyQQdUzGcpL6JPtA9bzGUfx\nOYnsp3z+4D6LyzHFUHy/nZRERszV+pqJhBLs7zRhlaOpNTmukOvJOSsJ5P7z0pM3ykd7h1yxomKO\n5XM15iuDfn+XQMIkTm/GLKOYuSyAzPMVVhiwWS3oN8Qvv/O1fSZnnzOcic8YWUhkmhwFJ1iSTlLt\n7vCg2WMxEJ779cVL5llGXTKbRDWVpKyynI2I/C/vuTt4dA8rUOjZKpsHYsOdHo8o7QqpzM9M5zd4\nbs7GZod+V3hp1YMu9rzJdCy8NH8Fgzzg/oZ4xsln17jRkiiMMCTIfOEGPPv+r5CYYmNOixlHr45Y\nkLD5jW8JfS1LVMt9dxjP17d1fVBx8R7cJ5JV73VFZTGfEIdzIol8ZrQf82JxScWUTEq1DpNZxv/7\nR6LIynFsiqaB/bSLMhWHUlKUKM0qlgQfyNQSyzLI1QoUwihtdFrU9JzRuTCy3ZqFXyoMJgXBUuhi\npr/tfvwL+es/+A36nhjb6Y9/l3vdPfqOgSHRvv74xz9EqcHDrjA2W7VNSqfHv1bd4OhG7KFsHeJW\nK5yfCw8ic0yWqypJ5wkvA2H8tq8HdFyXz05Fju/3Tm5oHTzjfCguJba2wt2L8ZWUH//hPwXATR28\nL6GqrDQjNh5IWNKRLwp6JCWgOYJdr0XNsHE3xZr96ZsX1GtbPNgSEZanepNNr4mRRDxpCbswng2J\nY4O6I/RZRit2GzEPRbqQMNLY3GrjmjGhRIx6c/0Kc+sBs7nMgeYus6igd7hza8yHEhxocHOD6XXY\ne/xtdFWst+F4zOz8JfbbynFrhxVz3oyFDfhuZxcts9k0bTxJN3jxk08YRzFXciz7dQezb/Deoxrh\nSDynVBsUvYKHT0UtR7Nf5+PPfsTwdMyxnKtOs8nsasLO4e1DbXAhDpQwLmg2HvC2GOLizUe4pUtL\nQlFmVp0wtzACBUsR83L10THDsxVtCXUZRRPqOYxOLoklMl+r4vFoq08UifW3Xoc0vS7NjSarVNiX\nQqtQWi3WqeyQaXdptxN0STfpmLeBbCazGyaLJRuP70t9+qTxFSfTOQ+bwgbVmhrNwy5X8tJezU3C\naES+nrGx25PP9kh8hUdvbcc65OV4yv1H4hLa727T3NR482bIZ5II5LPrGxrdDjtN2W2y0WadLnne\nf8T5pZjPwTzltj9/lzO+kzu5kzu5kzv5yuUr84yPbkJCWcloBhPahk8lq6HJ/s0ZGkl/i7XsZRtM\np+zVm1RwyTLxOW2lkuke93fEPcPVDJZBSCDh/XKrSatVYx1n73CRB6+PCKKcykjcrA7TjKyikxkG\nuis87CLLiOPbcf2uZZMFQmV7/Yc0KhusFwIWMlZyujWbMBWe3pP+DmpNpdZs0ZQ9fJdvLvDnM3Zl\neErXq1iNGi3D5fMzMb6Xnw84eTmhtS1u8WbjAQcPnzL5yQmOJW6QdqPLJz/8Caaskm20O2RFSKty\nuz3h3oHw/g7e65CdzHh1IsKa3b0Nek2DySQGTejr9Rc+ig56S3hk/X6fhhMyfv2as4nwqA8X52xX\ndeKVuNUr3gZb+8/52eAKuyZ0pqsqeahgSxznOExoNXo4cnwvLyZcfnZN1bG4ntxuIQPY39qhaTd5\nuNvBTISH0znscpVWOL8U71AhZsOzsOo1Cjt5913D8SmfHZ0A4ChVvFqDY/k9uRLS3+wzW55zMhBe\nxenlmNHHFh9/IUKxCz2mt9tj88kGx8ciFLfOXVynxJVhwYvl7ciJkiUctGtcSypB//qUZsXGbe9z\nLfVuODX8OKXSFOuk0zPYOuxiSla1emOTL5ZXFIZCZoq9MB+f4zS2KAqx9rJoxsFBH7MoWPgylDgL\nqWoaqoRvTE2NQjUYrjReTMRY9fbt9eGPI8JCzL+t1KHw+PAPfsrTpwcA1OpbKCy4/0TS0TUcXn56\nxeRmyoVsq3r+m495v/s+LyXtXWW7ysHXOrw8jTiRXQmLzEevmLyFfN9sW6yUKT0JAbnz6BmRVWBE\nc+53xHi2ah5ZcDuk5+sxqStz7n2dbLJgtyc8z03tgMJPUCoVxrHQl93bJokWuIpYw++9/z43JyNc\nY8VyLXQzXw3ZOniK1xdekZJU6FqbJKn4+5twyQ/e/wEtp8B5KRH+BgrjZcFaErMbcYzu6KjW2/rg\nvxC3FL/r6imtNhg7D7g6FWtiq2NibprM34i2tFTzwCqZBcLD/dGbK8ZRSa2+jZIJu7U+yQkGPpq0\nCard5OriJenVkmdPRFh6p7PP2TTn5ccC9/zlcUlrew/vWYu9HVFj8dHHp+TjAse5vTbi4ASA/s77\nJIWGH4uQXvveJqt8jh1ISMoSzkcjapUa9w/FBC9Wa+qWzqYrIg9az2a+CFHGY4pC9m6Ha86vrxgO\nRFqtXgmpGwr+4JpQ8lwXdsnlYoAl+8G59Lk+PmIqeYy91u1xn7x6xeuzz2mFYn1+dHrEIvKx7ArJ\nXNhMM9XZmfmMJOGEXm1yeb7i0h+yU4iI3qODLYbHx6wlfaxm9dn6xndZ+hJHYLKkXhZEpocp99aO\nkuE5Hl+TrVklOafHJyTTNZW10Nfxx8d8cGvUX+FhvExWvFXj1naFDa9KnoVosvggSQNiUtJcTMLB\nZp16pmK6TVTJ9FILYTyZMVfkwRklWBWL/XsinBf4Chuui7ez9y6nGGQZpWlSr4lDqu4YpJUmpddi\nKsPbi/XyXej258Vuf4NIgqW/Oh9g3GTMF+IzwTJmlGd0a8JYt4yMw6cfUK05XL8SRp7JOYoxpicb\n9RtWi1WoMr3xGR6LReJ162x1N7gaS4D9wkG7jkgmMaZkLbhQE0KtwVLC1S3GJWpcsrX1tg3kd96N\n+fd/+7cB+F4WcHo041pykG62t8l8lbOzCT0JZILh8snRy3eE7r6W0MhyLsbXKDURnp0NF0TLG1aS\n6H5uJmTaKc+e3Kcpc+7Xn75kOdMxJRxm7DtEWsGyFH830ybPDh7jNDzmH/7slp4BWrpCVKqEqomG\n2ByFkVOv2gxkqFPx6mi2geZoGJ44+NeDG/qWyVjWCBSajel5XF6I8O3j7T2W/pS4puN1RXvWB2mb\n+50m01R8z97OFtVGnTQL2N3QpC5qnE5GaDK/dXBw+5AYTmdUzCWRbMdjFaA7HWyvQilhC3W7SlJt\nI9uimQQRDcdB9cS6qjc9rLWFslzRlsVDwygiJ0WVPbldXadcTFkUGkku5krTXexag0QCjszygHlS\noGsOliV+N53dZhMKghUXmZjLvhFw/eqPuXwzJBqJEOSvffd71N0GsSLhB1OTf/CP/yE3N/DNvyzq\nOb7zsEbFK3BMySFdKnz84px0Mua9nvhcoil8OnrBw18VB0XF9LgcLdmVc2BZda4uTvj445/RkUVf\n+7U21S8h5DifpQSZOPiTzMQ2a3S3RGFOZJxSm00YlwpDaSdCp4EZrriWPL9xfsNivsYsx8RtCaPa\na/JpMuE6koAZkYqVxHAj5jK2m7yaLXAnBYOFbJMzWzh2l7rkyF2eX1I1UubXJ7fGHMn1+N6jb1J2\nNOZGhNcVE6zbBbsHD3koDfrFyQD8gpYpLhhluKaM5qyWBb7M+w+XGYqvcTEVNiAZXTG9voRCp9sR\nF7K4UFFsF6UuxrtOfOxZTtN1WGbCZlZ0nYd/6VfZ6N6uJ/Bk6B8XZrOISl3YAKO1yfD8DZORuJTU\nuybVmk5KiNMR7+kTsIwSTq9FsVZj00AzLXQzR5VsX5V6g/FwRmaIvfEb3/8Bq+vXjIYD6rJfOTQt\nGlUHXZPsZIZJstlhPBJ1QVX1dqGtbW2QaVccnQjdzEdrbiZjHn/QYSyxrDu1DsPRDaEswK1sNDnc\n79NcJySKeC9Xz/AqCpm8PLTaG5ibuyQyL24qDuOT1xiGw55Md/TNksvLS/RcFpCmJlnaZjVxcSUk\nc53bxXLwFR7GOw83wZCVoHpGvAgg16nLDZSoOve2OkShUEQwGKHoNazCoNoSt6aN+7tUzheUEkA/\nK1dYlU0sWVGanQ8YLX3CLMOUqC9qVaO0LWoSQ9rQC0Ji8ihClRWPi+EURbmNPex0d6lJT/3DDz+j\n0HIOnwujVZ00SX0TWSxKFqgk84xVMHjXM7y8OSPJ53zj+6II4/nT9/jki3PevB7xN+TvNisWYXzO\n0VoWdIUJQRzzm7/+TVYSrWp0OaFV6aBVxYUiT3MO21U8bjPcRLKIYXj6ijYe7oa4qJy/OWEyPmVr\nu4Mu8xvf3vs67e0GC3kLVYqA3XqLA/sJSCan69EVLdvgoCO+++/94c9QWmO+vr9DxxS/u4psTKvH\nhiu8qdSJOctTctlR6CoOW902dr/KxvWXg6YfDU5Z5wE7H+xSqYr3Sm8mrMYfUzOF54lmECg29cYO\n+09FX6Kp7OIP57jbkpJyHTObRmSSHvHo5pQ0GHPv+TM2tiXhxOIaU0/ZPhDG5ovliFE0pG54bPSE\nUdjZ6mFeqCxiyRAm0bB+XorSJIhiarJAL3brLDOXGh5dWcBVq9fx2gWvJ8Ljvh5PyamAxFHOsxtq\ntSZ6kJBHYtPacUzp+ygSVCXLPK6nMzSvRrMt5nOZwjQxMSRwSTYf4hgKG3YdV6IgHV3fjvb80Y9/\nyqgnDoF9bpgO/ox79x6SynW8DAKqdovXb8T8a/0GZ2uXhb7mRKIX/dY//gMePGrhSfKMgVJhaql8\n+xvPyHOJpTxeYHXcdxXYlcYOjS2LrZ44wJdHYzx9xTe+9+ucvBRr9vxiDsX01pjrzV0mA/HcXr3F\nKllyOhH6mw2uCRwVq9NlV6LYxUMDL/Rwu+JwiVWN5m6dNA+4kUWHqyTDdhOu34gakH51m/k6JByL\nS9LGjoaihqhrjcmZmJfz7IZWTcNEjLdhh+jVlMnF7ajJxVxcBPSGS4MYqzbBk/SQk+MJ6jRk8EIU\nsxlxySJR+Bc/FiQa1Y7CwZNDJtdD/nQhPrPr2ZyvY/SqeMftSouNp+/TffgUoyNsl1oaVNYZdU8e\nkCuT1SqgXcl4+kDM1Xp6geOEFPmXsDZJr1sLR3ywWef+vthjk2SNptVZlTIi4Bl07U3iyCdXhe0I\nkxX+YsIyFPrNixpZlLEYDamXwhZniYHlh5QSv3oxSliEJrOwSk3iAqz9KyLqHMoiRFUpMfrb7G+I\nNRssb6MOmvU2brOL4clKc7OBmUcY2YJCE7rpdCsoxRRHkl2YyRylCGjbFpE8LD/9+EPUUqflinWT\njIdMyjGVqjgDqhZML14QrwFVbJjW5gYbT55xncoIxqpktlb4889GuBKcqibPol+Wr+wwNmsV1rJ0\nPvHXxJMbql6LqJTctWqOm6WsfbGwp6sIxamiWSZzieDiD6cocYgijXxtc4uloqIbMtStZhSorDIF\ndHELzXOFJDVoaMLA21WNMtW5uhmjyM376PAeumfyR7805sHihnwtNufJxYAwT3j+A4Eyde+bT3nx\n40+YStamlt7n6qefsy6mFLoEkvcSvGqH9VR4Isp8jbYaYcc+uxWxqDRtRl6JactiLcOuUhgNKoVK\nFsniNqOC062RyAIQ2wzIijU3i9vFUPceC2PXb9e4ODtlthLh2mWwIpi+oaiVxIZY+IPZiGA6ZOdA\nLPzzyzmD6RkPahqWI4yA4SQ4eh9DF0b117/zK5wMfS4/fU3rkfDMd+49J00dFEXoXAlVksGYmQSS\nqGkaxXDGXkNns3cbohHAtizSbM5qegKZrDweXXJ19JquDE2lRcFocsMXX4xJTFlBnxVMTq7ZksUl\n4/GEDz/9AksRusnCNaqjMU9H9AwRkmzveETLeicnogAAIABJREFUGZVCzP+GXRIVCelqzVSGgc8+\nWaFYDhXJf0t0O3KC7qCgk+eyxWc5xSi36eoeqeR2LtZrDCOlImnadp0KizBkLVnELs6OaNfbOEqC\nmgr9bbod+mYDU9JshuuEptNlNhwRZGId30wXjA2DjZaYl9XlBVquEzQjTE9civZ3b1euf/N7fw0n\nklGOccbCaWPuP6DmiovKZTalWOqcybB8u+LSf7JPdP6S4UBcKFQz5otXKY5ELropS4bTNb///7zi\nGwciXHfvYYOz0zOOPxXtb0/e+z5Zd5PPPxdtfuospauleIrJE4lGN19rHJ+Nb405cXW81tvwosbu\nwWOGM3GIRkZMs1Olc9jh5OpEzLm9pN32yBWxhlVbI1gvWKszlvKiUto1Dp/dYyUL/4KbhCLJcCQK\n1fHkBN8/p55VKRyh43i45vXZCzY3RJrs88tPQIt5sPfo1phzSc/Z27iPSszF5QU1yf6UaA6vhmOW\nazm+RQgVnb33xXw5toauNcBNsNvSETncZ3j0mlNJ1vDpzOLJwS4fvfqIylocBFv1Xa5mIX/+RoTD\nIz+h7xTE3S5XC/HezbZL/6AO+e2LfFemq5otg5IF7ao4pA43d2jNY84Wcj1qNawoJRtH5Athix/s\n32d+eY1mSWpYp8rJ+RcouUpHetxGETGdj7Cr4jOff35C88EBZdcjlZSj/e4GhdfkfCp049owWvrs\nd8TRZUlgnJ+X04sZUZJRbwpdbR5UOdw2GE/P8SQFrmnr5ElMrS27MFZDiqxAVzIWibi0Dkdrql4N\nX16kxjdHFNUKmSERHo/PsRo6lmVw8EjMudZymRYOCwnPm3sK9Z0WeumSZpIxz7jdqQN3BVx3cid3\ncid3cidfuXxlnnGqVhgvhDew023gmBE1t8b2psgX6emCJFqiSLB302xCljGYzWjqdfk7laAM8SoS\np7RRo+bo7zCcrZbN4OYMp6wRyeb46XSN7VYw6m/zNS6qYVNaKtUt4Q1Efsoiud26shiHNGS+9/Fu\nj1Uc0rSFp+wPbnjU7rLZFZ7oz370Cts2qXkNGofC+5uwZh3675rRP/rpZ5ycn2PnCm9eCpKFqMx5\n+PSQXFI41qouquMRDG7QV8Ib6NSqrOIhUSFuj3azi1nUqfVuez6ZpD68jDQmuUGUi+9ut/pstOHR\n/Qe8Zdbr1gy2LJdAAq18/7sfUAZjrOiSWOZAOoePSOcqP3olMIa1/gYH9+r4gyFThC6W8xVKUXB0\nLnLll1nJukxxa2IspWnR7u8TKjOsypdjU3/7299lNvoEO1y96we2qx0OHiv03mLmHj5gMgzpt1vE\n8taZoaNV6qxSSR5S26W9GdLvyj7jNCEtUzY3euzui/lWy4Klrr2Dq7PGKeky5fp6RGnI3tRhSr1V\np/lIRA3CL6FB26i7GJZNEosbuRup6LrL64sL3EK8g7ftEiUpq2tR2JQaJu3+IXTE81TXpmpXSBZL\nSjkPbs0BPGwZjbC7Fjt727x68RmeDPtajT5eRaNMxDwtAx9ddfBzl1kktrlRvw1G4RU6jUK847Nv\nf51gXSdemmgyCnNxcUrS1Dj81l8WY9ms0d+u0fR2iENJ2HFvi8cPnvHx+YfiM5++5LDewipTmhI+\n9Pn+B/hZQGHKaElti8eH71HpijF98dHnzM8/Qvc1bNkPbOabaNr9W2NudxIaeyLMOvZDCivAl57e\npT/n6eEBc8WnrIn5zMKU49x/B7torkOqnsI8COncE2vAz0zO1tegS7CJts3Z+ZCHuyI0m45SblY5\n5+GE9p7Yh91qjaycMldlcaBnkSc5N5IM4+flgaRe7VVNMtOjNKtImAWCOGYRTmhIrP7QmlKoKY82\nxNhaVZdpkIFrYEtYyNfXlxRliSrt1McfnbG4nFLpNpjL4sqwtWCVuSBDsY/qLRoubNc3mA1FJC5a\nJihLg4rM0f68XE6FR43Xxo98QtkTXlmPmUQDxrItCHsDT++gLjOMltDfOigIlToyS8H5bM4HX38f\nf1WyDkQK7vD+NtZhnVLmUj/59AX52KK/1Sb3hXKCKKe9YTEYioKt8+sb9DRikIi1l+a300V7++/R\nWDlYbdnu5qjYtW2aww6mKdaWoqpMpnPOZdsfKw3HMtGLGYOpiMa0ai32tnewJY98nQDF0olkO+Fo\nNGPvYJPW7hYPHooI0CiYUayzdzSaplMjNBzqlTZ9CUzUVm/3dMNXeBg/+tpTVMmgoek+ZpTi1BpQ\nl3mxEFqtKspQvPh6uMBVdPqtGo2aMBTtXhVaBr4MZVMsCBc+M0OGVFUFy9OoVhvYughtLloJQZYw\nmAljODXXhLFOUurvirOm8yXrILo1Zn0NmsTB3q5XMCu7dDtiLP74kp1qA12GDZe9Kl7PJC1T/IEY\n3067y/Gi5ORKbN5xuOL0eEjVNQlnovJvo9rG4JIryY+p1wKMxhaephENJMOMH7JSrukdiMM3Xo2Y\nhAXP9g5vjfnjV6LYaffhLkkcspSFDzg1Kq7Kxx9fkCrivSd6hqnUSTSx+ObTEwx1jZ7PmYZiY/7o\n84/pVlp8fiT05843WQMPPJeffSQ2TL3bo+n1OX9zIt6zSHn4V37AzgNxUTn56SlTf42m+kyvL26N\nGaCx/Yz9w3vUrIwffShyZz/74qd0vD3StVg305MMLbQpDY8QGbbUC84HMdGFxC/XLdZxhf22CCOV\n8zmeUpJlHqevhAGvtz3CtE4i7wWT8RI1Lul3n+HHYr6tVka11cWxxIWiun07vB5FEXoBLVmZmtYV\nFn6OwprJSuT7rcmCtMyJVfFlNjnzixNKS2zFxkYHs1BI84yKJ/LcyfKG15cXLOWtyfYszNcunm6x\no4sccZnlzMY+qUS0yt0GETYFFdJY7KH54nYuM5yO+dqW+J4WMYXWYl2sOb8RdRh1wyMxK2CL517e\nvKZRM+jtP0GviL2qb3bQIo2dQhweTmkyGsz5YhYza4qD4Xf//FMuwhWVnsTb9lSOvzjjNw5/AMBu\nZ5vp6AVJ1WN2I4xhoSp88/l3+Ie/NOb56DPcvkhDdB2P40//GU3JMZt3TCw3Q6sqOI7Yi5pt0mi3\n6W6J+oSz1y9x0iF6mjBOZ/J/NKJsxVym1Te723z91/dYXYi/VyoqnX6P2SRh9768tFt1Dh9/wOBU\nXDqvJz7ZKse/ut0hUHfEfLddHa/RZKW0UW0x5082G5w2z7iS1dSKvkFeJMRyzaxubqjs7LO1+4hy\nLS5tL6cfYjsaH/zg+wCohs6r449RTMgRc3Wz8EnKkJoj5mBnq03dsmhWeuxvCN0MrnuYpYJr3cZL\nzuTBFRk1FlGMidgvkT9jMLpiHYr59yo2YVmio7CQ6Ihnp2PWyzVZLME7VJNQrfHR2StKTRyAfVXj\nYH+XSFYnjxd1pssbistIQKsB13EEyzMcictf5GPywkeV50SR3i5KtL06AR26PTFPuqajGEu8jS0s\neeSt12vUyoyq+ZaL2qbRdCiTFZ4t5y+JCWcKriz2bVQPMFSF67GwCTvec7pelVZ7j9iXoFKRwr7l\nkEr8ac3w0KwKr08vGcr+78Ip2bg1alDK8nYZ/v8foijKV/PFd3Ind3Ind3InX6GUZXmr6OQuZ3wn\nd3Ind3Ind/IVy91hfCd3cid3cid38hXL3WF8J3dyJ3dyJ3fyFctXVsD1r3+/wde/Joo3yljl6PSC\nX/neX8GVfWDh4id0tzbYevYvAVBtdlFKMF2bjicKlz7655+TEZCOBEVh3S14eXTJxltIQC3hfPSS\npe9zciGS5+9tPGDDMNh9JirbiobB+Ysh2SgilQUVHw+v8dw5/8l/+/d/Ycz/4b/1H1CWoggmmc5p\n1G3c+2IsRwOfpqujRX9RhZ1ka1zHZE++U9Ww+PT1JTsHgthbV3I6fRvDtphKGrEot9GLjOVIVBym\nVNisNIgXC95ItJ2COX3FR1+LtHtmt1hmFitZqPM//u//87sx/Nt/878AYPfgMS0lJZqL8W896fHJ\nyads1XLqsoK0UiRY/jWh7Ad26h3Wjs0fnV4TrEQV7L1Wj/1H7jvSik/++ISdfpdWFRIJ96Y1W5ye\nnaGZopBkkitMc4XDfVEtul+/z6bTZDxZUa2K4qF//+/8tV/Q9X/6H/09VusLonT2rhq0vdMlKUIU\nCUmZxDnL2RlGuaQiUX0sz2Wj0+TiUlRZdpsP0E2dyUCw/1SrNS5GQ8hUdrpv6RBrxPmaYwmocD0M\nyBKTuFBxLFEOGg6ucHKTjZ4oksvx+K/+17/1C2P+O3/9b/Ps/g73u6Jydr6KGIwCnn3zGbEqinGG\nk0tUzeXhnihAOn41w08dHt0XPc+LiY+qbfLmaojEtuFXvnWAtf6cqysJ6XoV0dje5V+8+hy7KSoz\nS9vF0eoYruh5bbZrqGXEyzc/5fJIFMl9+/37/Jt/92/+wpj/87/77zE/kchA7U0K3cZwGswl1GG0\nmqFqBR3ZaTAYj/FaFuvlBEdC12ZpTKu3gy1JUkYXV2TRlMbmNotI6HS9mKAoEKdirfXcNh1HJYzE\nPMWmiq6XXL95wzwSe9XsNBiMr/m//88/+MUx//3/ntMXooBw7s9IMp/CF5VXnd429VqXzU6dVSie\nrdgVTNuhKvvTHUPFLpY0GxscS6KKq6sRWZa/o7LESLEtl0QWN84XU7LSYRWHKBIqVzdNRqsFOZJa\n0HOwGn2CqM7/8bf+3V8Y8//0W78v5/s1q1VCmhQ4rtgf8/WCrbZL1xN7zMhDJoNzDFlImacBDx7f\nI1JMVMkKt1itKdWIMpHV/jc+kaLgmGCHEpTk4gJfh3vvi0pfv0wIwxnbrTbRldjjb04HlGaN3Xui\nuPK/+e/+9rsx/y//2X8s5td2yQpQCqGLLFOxKk1sW4y3xEHJU9IoQZEFhDfDAZPZ+h0Ak2PBVqNB\nxTVYLYT+sjyn0miiaJICN1wzX8UoqoGui/fyoxjPdlBk7/5sNmadKrQkS9e9Rw7/xr/zX/6Crv/J\nf/09vN4eV5JkyFQMsnKNP5tQUUTplJ+1yIsKWirmu9PYZjydcnp6xoN9SXcaXfH5m0s2GkI3SWqS\nRst3jH6REZGmK9ZRSRKI/d2sVWhvVnjcE/vZtl0SK6aIQwJ5TgXREV8mX9lh3D58wFQT1mZd5HyW\nOWyabaqSC7Q0m0wnIbY82/IsJcuXpJHBOpPIXc6SxXhOXxWLL1XhJFxyEUnkJ71ConfpHGzz3nPR\nMqUsVCbzBf1NYVSn/opZPmMYBXQ7wpCVvsrV+HaV3rP37jO6EuACbsOjd9hn5QkVRl6TVquFJjeC\nXhYYZkCpaqSB+J1Xa3CoGtRrEnN4eMFouCaMFXRDGNV+d5dgf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JIH+HHEei2UPhyO2Kip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\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ORMOcHAtwgagJRy/OuD0VHm18MeU8fEE5LazdTGmFdC6PUQihyCKqlY0qU93j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\n2d/fpa059PUjMd75G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"text/plain": [ "<matplotlib.figure.Figure at 0x7bdde80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from cs231n.vis_utils import visualize_grid\n", "\n", "# Visualize the weights of the network\n", "\n", "def show_net_weights(net):\n", " W1 = net.params['W1']\n", " W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n", " plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n", " plt.gca().axis('off')\n", " plt.show()\n", "\n", "show_net_weights(net)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Tune your hyperparameters\n", "\n", "What's wrong?. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n", "\n", "Tuning. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n", "\n", "Approximate results. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n", "\n", "Experiment: You goal in this exercise is to get as good of a result on CIFAR-10 as you can, with a fully-connected Neural Network. For every 1% above 52% on the Test set we will award you with one extra bonus point. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "best_net = None # store the best model into this \n", "\n", "#################################################################################\n", "# TODO: Tune hyperparameters using the validation set. Store your best trained #\n", "# model in best_net. #\n", "# #\n", "# To help debug your network, it may help to use visualizations similar to the #\n", "# ones we used above; these visualizations will have significant qualitative #\n", "# differences from the ones we saw above for the poorly tuned network. #\n", "# #\n", "# Tweaking hyperparameters by hand can be fun, but you might find it useful to #\n", "# write code to sweep through possible combinations of hyperparameters #\n", "# automatically like we did on the previous exercises. #\n", "#################################################################################\n", "best_net=net\n", "#################################################################################\n", "# END OF YOUR CODE #\n", "#################################################################################" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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zVZLNNrcTccB39/dpTG9JpsIGpIXH0tfwvdVywCjsVm7VWzw+2mN4WceLyt4l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L8SfbwqsJpNmE2hIKNsi9mA2lYVfyIRXAYx23aSgedg1USNwmy4xA1cus3B2pzngdgHz/Vw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\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Y2yka+yLkp6dMVn2L4+cdOs1NujmA9WqMES0p7VWIyyrE2WiKbps4Q9kiNRaF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\nXhYYZkCpaqSB+J1Xa3CoGtRrEnN4eMFouCaMFXRDGNV+d5dgf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JIH+HHEei2UPhyO2Kip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\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ORMOcHAtwgagJRy/OuD0VHm18MeU8fEE5LazdTGmFdC6PUQihyCKqlY0qU93j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\n2d/fpa059PUjMd75G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/ieQVYs8aTcrZoFapBxfnrKRHJ7U4fEVdnbF7kWTrUtCF+kx2Kn\n00XLaiTRhscH8oytFqiOgi2T8ap2hcnpGQopiiaeG45u0xYDKEVxu562RIkSJUqUKPH/DqWbukSJ\nEiVKlLhjlMq4RIkSJUqUuGOUyrhEiRIlSpS4Y5TKuESJEiVKlLhjlMq4RIkSJUqUuGOUyrhEiRIl\nSpS4Y5TKuESJEiVKlLhjlMq4RIkSJUqUuGOUyrhEiRIlSpS4Y5TKuESJEiVKlLhjlMq4RIkSJUqU\nuGOUyrhEiRIlSpS4Y5TKuESJEiVKlLhjlMq4RIkSJUqUuGOUyrhEiRIlSpS4Y5TKuESJEiVKlLhj\nlMq4RIkSJUqUuGOUyrhEiRIlSpS4Y5TKuESJEiVKlLhjlMq4RIkSJUqUuGP8X5tUgEbWdpliAAAA\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x6dae748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualize the weights of the best network\n", "show_net_weights(best_net)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Run on the test set\n", "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%.\n", "\n", "We will give you extra bonus point for every 1% of accuracy above 52%." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test accuracy: 0.533\n" ] } ], "source": [ "test_acc = (best_net.predict(X_test) == y_test).mean()\n", "print 'Test accuracy: ', test_acc" ] }, { "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 }	mit
sastels/Onboarding	4 - Sorting.ipynb	1	12408	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Sorting\n", "The easiest way to sort is with the sorted(list) function, which takes a list and returns a new list with those elements in sorted order. The original list is not changed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = [5, 1, 4, 3]\n", "print sorted(a)\n", "print a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's most common to pass a list into the sorted() function, but in fact it can take as input any sort of iterable collection. The older list.sort() method is an alternative detailed below. The sorted() function seems easier to use compared to sort(), so I recommend using sorted().\n", "\n", "The sorted() function can be customized though optional arguments. The sorted() optional argument reverse=True, e.g. sorted(list, reverse=True), makes it sort backwards." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "strs = ['aa', 'BB', 'zz', 'CC']\n", "print sorted(strs)\n", "print sorted(strs, reverse=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Custom Sorting With key\n", "\n", "For more complex custom sorting, sorted() takes an optional \"key=\" specifying a \"key\" function that transforms each element before comparison. The key function takes in 1 value and returns 1 value, and the returned \"proxy\" value is used for the comparisons within the sort.\n", "\n", "For example with a list of strings, specifying key=len (the built in len() function) sorts the strings by length, from shortest to longest. The sort calls len() for each string to get the list of proxy length values, and the sorts with those proxy values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "strs = ['ccc', 'aaaa', 'd', 'bb']\n", "print sorted(strs, key=len)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As another example, specifying \"str.lower\" as the key function is a way to force the sorting to treat uppercase and lowercase the same:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "strs = ['aa', 'BB', 'zz', 'CC']\n", "print sorted(strs, key=str.lower)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also pass in your own MyFn as the key function. Say we have a list of strings we want to sort by the last letter of the string." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "strs = ['xc', 'zb', 'yd' ,'wa']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A little function that takes a string, and returns its last letter.\n", "This will be the key function (takes in 1 value, returns 1 value)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def MyFn(s):\n", " return s[-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now pass key=MyFn to sorted() to sort by the last letter." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print sorted(strs, key=MyFn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use key= custom sorting, remember that you provide a function that takes one value and returns the proxy value to guide the sorting. There is also an optional argument \"cmp=cmpFn\" to sorted() that specifies a traditional two-argument comparison function that takes two values from the list and returns negative/0/positive to indicate their ordering. The built in comparison function for strings, ints, ... is cmp(a, b), so often you want to call cmp() in your custom comparator. The newer one argument key= sorting is generally preferable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## sort() method\n", "\n", "As an alternative to sorted(), the sort() method on a list sorts that list into ascending order, e.g. list.sort(). The sort() method changes the underlying list and returns None, so use it like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "alist = [1,5,9,2,5]\n", "alist.sort()\n", "alist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Incorrect (returns None):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "blist = alist.sort()\n", "blist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above is a very common misunderstanding with sort() -- it does not return the sorted list. The sort() method must be called on a list; it does not work on any enumerable collection (but the sorted() function above works on anything). The sort() method predates the sorted() function, so you will likely see it in older code. The sort() method does not need to create a new list, so it can be a little faster in the case that the elements to sort are already in a list." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuples\n", "\n", "A tuple is a fixed size grouping of elements, such as an (x, y) co-ordinate. Tuples are like lists, except they are immutable and do not change size (tuples are not strictly immutable since one of the contained elements could be mutable). Tuples play a sort of \"struct\" role in Python -- a convenient way to pass around a little logical, fixed size bundle of values. A function that needs to return multiple values can just return a tuple of the values. For example, if I wanted to have a list of 3-d coordinates, the natural python representation would be a list of tuples, where each tuple is size 3 holding one (x, y, z) group.\n", "\n", "To create a tuple, just list the values within parenthesis separated by commas. The \"empty\" tuple is just an empty pair of parenthesis. Accessing the elements in a tuple is just like a list -- len(), [ ], for, in, etc. all work the same." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tuple = (1, 2, 'hi')\n", "print len(tuple)\n", "print tuple[2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tuples are immutable, i.e. they cannot be changed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tuple[2] = 'bye'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to change a tuple variable, you must reassign it to a new tuple:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tuple = (1, 2, 'bye')\n", "tuple" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To create a size-1 tuple, the lone element must be followed by a comma." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tuple = ('hi',)\n", "tuple" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's a funny case in the syntax, but the comma is necessary to distinguish the tuple from the ordinary case of putting an expression in parentheses. In some cases you can omit the parenthesis and Python will see from the commas that you intend a tuple.\n", "\n", "Assigning a tuple to an identically sized tuple of variable names assigns all the corresponding values. If the tuples are not the same size, it throws an error. This feature works for lists too." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(err_string, err_code) = ('uh oh', 666)\n", "print err_code, ':', err_string" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List Comprehensions\n", "\n", "A list comprehension is a compact way to write an expression that expands to a whole list. Suppose we have a list nums [1, 2, 3], here is the list comprehension to compute a list of their squares [1, 4, 9]:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nums = [1, 2, 3, 4]\n", "squares = [ n * n for n in nums ]\n", "squares" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The syntax is [ expr for var in list ] -- the for var in list looks like a regular for-loop, but without the colon (:). The expr to its left is evaluated once for each element to give the values for the new list. Here is an example with strings, where each string is changed to upper case with '!!!' appended:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "strs = ['hello', 'and', 'goodbye']\n", "shouting = [ s.upper() + '!!!' for s in strs ]\n", "shouting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can add an if test to the right of the for-loop to narrow the result. The if test is evaluated for each element, including only the elements where the test is true." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## Select values <= 2\n", "nums = [2, 8, 1, 6]\n", "small = [ n for n in nums if n <= 2 ]\n", "small" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## Select fruits containing 'a', change to upper case\n", "fruits = ['apple', 'cherry', 'bannana', 'lemon']\n", "afruits = [ s.upper() for s in fruits if 'a' in s ]\n", "afruits" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "For practice with sorting, go to the notebook [3.5 - Sorting exercises](4.5 - Sorting exercises.ipynb)" ] }, { "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": { "collapsed": true }, "source": [ "Note: This notebook is based on Google's python tutorial https://developers.google.com/edu/python" ] }, { "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
bioinfo-fr/Jupyter_notebook	R/dyplr_bioinfo-fr.ipynb	1	668637	{ "cells": [ { "cell_type": "markdown", "metadata": { "hide_input": false, "run_control": { "marked": true }, "toc": "true" }, "source": [ "# Table of Contents\n", " <p>" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "frozen": false, "marked": true, "read_only": false } }, "outputs": [], "source": [ "# system(\"wget ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_25/gencode.v25.annotation.gff3.gz\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true }, "scrolled": true }, "outputs": [], "source": [ "library(\"dplyr\") # manipulation de données tabulaires\n", "library(\"readr\") # parseur de fichiers texte\n", "library(\"broom\") # nettoie après que R ait tout salit\n", "library(\"cowplot\") # figures multi-panaux, inclus ggplot2\n", "library(\"svglite\") # pour exporter les figures en svg\n", "library(\"rtracklayer\") # parseur, notamment de fichiers GFF" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "frozen": true, "marked": true, "read_only": true } }, "outputs": [], "source": [ "compiler::enableJIT(3) # Invocation de magie noire pour que R tourne plus vite\n", "compiler::setCompilerOptions(suppressAll = TRUE) # Cachons la magie noir dans le... noir.\n", " \n", "library(dplyr)\n", "library(readr)\n", "library(broom)\n", "library(rtracklayer)\n", "library(cowplot)\n", "library(svglite)\n", " \n", "# l'import peu prendre 5 minutes en fonction de votre configuration\n", "# notez qu'on importe directement la version compressé .gz\n", "gencode <- import(\"gencode.v25.annotation.gff3.gz\", format = \"GFF\") %>%\n", " # import() retoune un objet GRanges, nous le convertissons en data_frame\n", " as_data_frame" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "hide_input": false, "run_control": { "frozen": true, "marked": true, "read_only": true } }, "outputs": [], "source": [ "saveRDS(gencode, \"gencode.v25.RDS\")" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ "1609890024 bytes" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>2577236</li>\n", "\t<li>29</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate}\n", "\\item 2577236\n", "\\item 29\n", "\\end{enumerate}\n" ], "text/markdown": [ "1. 2577236\n", "2. 29\n", "\n", "\n" ], "text/plain": [ "[1] 2577236 29" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gencode <- readRDS(\"gencode.v25.RDS\")\n", "object.size(gencode)\n", "dim(gencode)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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WXZsmVLhw4djIyM6tat6+fnl52dLZPJ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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig1 <- ggplot(gencode, aes(x = factor(type, levels = rev(unique(type))))) +\n", " geom_bar(fill = \"darkorange\") +\n", " # affichons l’effectif sur chaque bar, avec le mot magique de ggplot2 ..count..\n", " geom_text(aes(label = ..count..), y = 10000, hjust = 0, stat = \"count\") +\n", " labs(x = \"\", y = \"\", title = \"Gencode v25\") +\n", " # renversons les axes\n", " coord_flip()\n", "\n", "options(repr.plot.width=8, repr.plot.height=4)\n", "fig1" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ptm.begin <- proc.time()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ " user system elapsed \n", " 0.536 0.004 0.540 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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kVFRUlJSWJiIsMwANTV1YuLi7OysqTmSq08MjISwMaNG9nQE4Cent769etb96a1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\nY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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "fig2 <- filter(gencode, type == \"gene\") %>%\n", " # trions les types de gènes en fonction de leur effectif\n", " ggplot(aes(x = factor(\n", " gene_type,\n", " levels = names(sort(table(gene_type)))\n", " ))) +\n", " geom_bar(stat = \"count\", fill = \"darkorange\") +\n", " geom_text(aes(label = ..count..), y = 10, hjust = 0, stat = \"count\") +\n", " labs(x = \"\", y = \"\", title = \"Le génome humain\") +\n", " coord_flip()\n", "\n", "options(repr.plot.width=8, repr.plot.height=8)\n", "fig2\n", "\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 4.728 0.012 4.743 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "gencode$simple_gene_type <- gencode$gene_type\n", "# si il y a pseudogene dans le nom, c'est un pseudogène\n", "gencode$simple_gene_type[grepl(\"pseudogene\", gencode$simple_gene_type)] <- \"pseudogène\"\n", "# si il y a ARN dans le nom, c'est un gène ARN\n", "gencode$simple_gene_type[grepl(\"RNA\", gencode$simple_gene_type)] <- \"gène ARN\"\n", "# si c'est un autre type de gène, c'est un autre type de gène\n", "gencode$simple_gene_type[!(gencode$simple_gene_type %in% c(\"protein_coding\", \"pseudogène\", \"gène ARN\"))] <- \"autre\"\n", "# en Français, c'est plus jolie\n", "gencode$simple_gene_type[gencode$simple_gene_type == \"protein_coding\"] <- \"gène protéique\"\n", "# trions par effectif, ça simplifiera le code des figures\n", "gencode <- mutate(gencode, simple_gene_type = factor(\n", " simple_gene_type,\n", " levels = names(sort(table(simple_gene_type), decreasing = TRUE))\n", "))\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ " user system elapsed \n", " 0.404 0.000 0.403 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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i1abN261dnZWUSuXr3arVs3XYU7d+4MHz7c0tIyISHB19dXRLRa7fTp0ydPnjx5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\nly9fuHBBt3r69OnMzExvb29lNSEh4ebNm6NGjfrmm28uXLiwefP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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "fig3 <- filter(gencode, type == \"gene\") %>%\n", " ggplot(aes(x = factor(simple_gene_type, levels = rev(levels(simple_gene_type))), fill = simple_gene_type)) +\n", " geom_bar(stat = \"count\") +\n", " geom_text(aes(label = ..count..), y = 100, hjust = 0, stat = \"count\") +\n", " labs(x = \"\", y = \"\", title = \"Le génome humain\") +\n", " coord_flip() +\n", " theme(legend.position=\"none\")\n", "\n", "options(repr.plot.width=8, repr.plot.height=2.5)\n", "fig3\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ " user system elapsed \n", " 0.392 0.000 0.392 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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zt3rkShn5/fjBkzUlNT6aSFhUVxcbFE\nndLS0vbt2yusAqNt27Zt27ZlJi0sLOrdPgCAhuDKSUJp6urqrVu3fv78OZ20sLCgfSBMBYFA\nIBAI2rVrp7AKAACKxImATk1NtbGxoVe5MV69evXixYtu3brRSQ8PD0LIuXPnmAr0db9+/RRW\nAQBAkTgR0P379y8sLAwPD3/x4gUtEQqFCxYsEIvFX331FS0JDAwkhKxfv14kEhFCRCLRhg0b\nCCFBQUEKqwAAoEic6IPW09PbvHnzlClTHB0dR4wYoa2tffHixby8PC8vr3//+9+0jr29vb+/\nP5/PHzBggKenZ1paWmZm5vTp021tbRVWAQBAkdTDw8OV3QZCCOnRo4e7u/ujR48uX7587949\nS0vLxYsXx8bGqqurM3V8fHw0NTXT09OTk5PV1dUXLVoUFRWl4AoypaamZmRkTJ061dLSsq46\nNam/NmC3cITGkOHKbgJAS8QTi8XKboPKW7JkSXR0dFpamqenZ111cKMKAHwoTvRBAwCANAQ0\nAABHIaABADgKAQ0AwFEIaAAAjkJAAwBwFAIaAICjENAAAByFgAYA4CgENAAARyGgAQA4CgEN\nAMBRCGgAAI5CQAMAcBQCGgCAoxDQAAAchYAGAOAoBDQAAEdxMaDFYrGvry+Px5MoFwqFK1eu\ntLa21tbWtrKyioiIEAqFCq4AAKAwXAzo/fv3JyUlSZfPmDEjNDSUEDJ+/HhCSFhYWGBgoIIr\nAAAoDOceGpufn9+1a9fS0lJCCLttubm5Dg4OPXv2TE9P19PTKy8v/+yzz27cuPHgwQNbW1vF\nVKgLHhoLAB8Dt46gxWLx7NmzdXR02rdvL/HWrl27CCEhISF6enqEEH19/ZCQEELI7t27FVYB\nAECRuBXQBw8ePHny5NatWw0NDSXeysjIIIR4e3szJUOGDCGEpKenK6wCAIAiaSi7Af8oKCj4\n5ptvJk6cOGrUqO+//176XUNDQ2NjY6bE2NjYwMCgsLBQYRUYKSkpR48eZSavXLnS4K0GAKgL\nVwJaLBbPmTNHTU0tNlZ2d2dBQYG5ublEoampaUFBgcIqMG7durVjx473bxUAQCNwpYvj0KFD\niYmJP/30U+vWreuqI33hnVgsZl8Gp4AKVFBQ0COWmTNn1tVmAIAG48QRdGFh4bx583x9fSdM\nmFBXHQsLi+LiYonC0tJS5nSiAiowjIyMjIyMmEl2rwgAQFPhxBH0smXLysrKvvvuu9zc3Pv3\n79+/f7+qqooQQl/X1NQQQiwsLMrKygQCATOXQCAQCATt2rWjkwqoAACgSJwI6Pz8fKFQOHjw\nYMf/efLkCSGEvn7z5g0hxMPDgxBy7tw5Zi76ul+/fnRSARUAABSJEwF96tQp8bvs7e0JIfQ1\n7UCgd/StX79eJBIRQkQi0YYNGwghQUFBdCEKqAAAoEic6IOWh729vb+/P5/PHzBggKenZ1pa\nWmZm5vTp05l7/BRQAQBAkVQmoAkhcXFxNjY2CQkJ69at69Chw6pVqxYtWqTgCgAACsO5sThU\nEcbiAICPgRN90AAAIA0BDQDAUQhoAACOQkADAHAUAhoAgKNU6TI7gGag8dfz4KKalgNH0AAA\nHIWABgDgKAQ0QPNXXV3dt29fHo93+vRpZbfl4xKLxb6+vmpqavHx8cpuSxNAQAM0f1paWocO\nHTIxMVm1alXzvnn46NGjp06diouLmz59OlPo4OAg/SwOlYCABmgROnfuvHv37osXL/7+++/K\nbktj8Xg8BwcH6fI3b94sXbpUIp1VGq7iAGgpfH19Q0JC4uPjBw4cqOy2fBSGhoZ5eXnS5Zcu\nXaLP/VA5CGiAFiQyMlLZTVACExMTZTehgdDFAdAcHDp0aNCgQSYmJk5OTsHBwQKBQKIf4NGj\nR19//bWtra2urq6dnd3ixYtLS0uZd2kv7evXr+fPn+/s7Kynp2dnZ7d8+XL2E5PrX4I02oCc\nnJwRI0aYmJg4ODjMmzePPiCJvdKKiooZM2aYmJicOnWKlgsEguDgYGdnZ319fWdn5+Dg4PLy\ncvpWXFwc7U3Ozc3l8XhLliyRf+voa7FYzOfzBw4cSPfVd999V1payt5XMjusP2hnNiEENIDK\nCw4Onjhx4p07d4YPH+7q6rp///6RI0eyK2RkZHTv3v348ePdunULCAjQ19dfu3Ztr169ioqK\n2NVGjBghFou3bNmSmJhobGwcHh6+bNmyD1qChJcvX3p5eTk5Oe3cuXPo0KGbN292d3evqKhg\n15kwYcLdu3fnzZvXrVs3QkhFRYW7u3tMTIyGhsakSZM0NTVjYmKYuQYOHMjn8wkhFhYWfD6f\nPmb6g9o2b968gICAW7duDRo0yNnZ+cCBA0OGDPmgvd2wXdEw6OIAUG2ZmZkxMTEuLi4pKSnm\n5uaEkJKSEnboiESioKAgHR2djIwMR0dHQohYLF65cmVYWFhYWNjWrVuZmr17916/fj19bWVl\nZWdnd+bMmejoaPmXIKG0tDQqKooe544dO7ZNmzahoaGbNm1iPwfD3Nz8xIkTamr/d7AYExNz\n//79wMDAHTt2qKmp1dbWzpw5My4uLjY29vvvv7exsbGxsfH39zcyMpo8efIHbR0h5MKFC1u2\nbHFwcEhNTf3kk08IIYWFhYMHD5Z/bzd4VzQMjqABVNuePXsIIatXr6bpTAgxMzNbtWoVU+HB\ngwc5OTmzZ8+mgUII4fF4S5cuNTIySk5OZi9q5syZzGsbGxtCSFVV1QctQQKPx5szZw4z+c03\n3xBCEhMT2XUWLlzIpDMh5OTJk4SQVatW0UI1NbWIiAhCyIkTJ2Su4oPaRo++16xZQ9OZENK2\nbdsP6pdv8K5oGAQ0gGq7d+8eIcTd3Z1dyJ7MyckhhERGRvJYNDQ0Xr9+XVhYyJ7L2tqaec3u\nh5V/CRLatWvXqlUrZtLIyKhdu3YPHz5k1+nSpQt78uHDh23btm3bti1TYmFh0bp1a4m5Gta2\n+/fvE0L69+/PLuzXr189m9CY1TUeh7o4/v7779DQ0HPnzv3555+ffPLJp59+Gh4e3rlzZ6aC\nUCiMjo5OSEh49uzZJ598Mm3atCVLlmhqaiqyAgDXVFdXSxeqq6szrw0MDAgh4eHhtMe2HnX9\nqcu/BAnsc4xUZWVlbW0tu0RXV/e9y1FTU6usrGx822QuhH38LtPbt28btrrG48oRdGVlZZ8+\nfbZs2dK2bdspU6Z07Nhx9+7drq6uz58/Z+rMmDEjNDSUEDJ+/HhCSFhYWGBgIHshCqgAwDVd\nu3YlhFy7do1dmJWVxby2s7MjhBQUFDiwWFlZXbt2raCgQJ5VNHgJRUVF7K/wo0ePSktL7e3t\n65nFxsamsLDw5cuXTElhYWFhYWFdc31Q2+hCLl26xC68cuWK9GLZ103fuXOnYatrPK4E9JYt\nWx4+fLh8+fK0tLStW7eeO3cuOjq6tLSU6UrLzc3du3dvz549s7Oz+Xx+dna2q6srn89nrktX\nQAUADpo4cSIhJCQkpKSkhJa8evVq6dKlTAVLS0sPD4/4+Hh2EkVHR/v7+9++fVueVTRmCT/8\n8AM9ZBYKhYsXLyaESFxhIsHX15c9V21tLd0WiblEIlED2jZq1ChCyOLFi/Pz82lJcXFxSEgI\nuw49Rr5w4QKdrK6uDg8Pb5Jd0QBcCeiLFy8SQubPn8+U0EPXGzdu0Mldu3YRQkJCQvT09Agh\n+vr6dLfu3r1bYRUAOMjb2zsoKCgrK8vJyWnSpEkBAQFdu3Z1dnYm/+uy4PF4Gzdu1NbW7t+/\nv6+v79y5cwcMGBAWFubh4REUFCTPKhq8BFNT019//dXNzW3atGnOzs7Hjx+3s7NbsGBBPbMs\nWLDA3t4+Li6ud+/eM2fOdHd3j4+Pd3BwCA4OZuro6uo+fvx46dKlqampH9S2MWPGjBw5Micn\np2vXruPGjfPz83NycpIYnIT+QowaNWr+/PlLly51d3fX0tJq/K5oGK70QQ8ePLhHjx7s8wn0\n9DHTP5WRkUEI8fb2ZirQC4nS09MVVqExVvb9qfELUZYIghHiOW379u19+/aNi4tLSkqytbUN\nDg4OCAiIi4tr3749reDm5padnR0SEnLlypXU1NQuXbpERETMnz9fnv7fxiyhdevWSUlJ3333\n3YkTJ1q3bj1nzpyoqCh6AFQXPT29a9euhYaGpqSk7Nu3r0uXLgsWLFi+fDl7RVFRUatWrYqJ\niTE0NPT29pa/bTweLzExMTY29ueff05OTm7fvv3o0aNXrlzZpk0bps4PP/ygpaWVkJCwbds2\nExOTyZMnR0REsBfV+J0pPx7Xhraqra198+ZNXl7ejz/+mJycnJSUNHz4cEKItbV1UVFRWVkZ\nu7KhoWH79u1zc3MVU4GRkpJy9OhRZvLKlSs3b95MS0vz9PSsa7tCE1VyMC0qYjS3/khUmmKe\nqJKVleXm5jZ16tSEhIRGrq7BeDyevb09vXCC4zjbVK4cQTN27Ngxe/ZsQoiamtrhw4dpOhNC\nCgoKmMs8GaampkzHvAIqMG7durVjx44P3TSAj+Hw4cN+fn4rV65k7nsmhOzbt48Q4uXlpbx2\nQRPgSh80Y8KECbdv3z569KiTk5O/vz/73LT0DfJisZh9HY8CKlBBQUGPWNiX9wMo2Oeff25p\naRkZGXnkyJE3b97k5+fHxMRs2rTJxsbGz89P2a2DRuHcEbSJiYmJiUn37t179erVpUuXNWvW\nHD58mBBiYWFRXFwsUbm0tJTpZVNABYaRkZGRkREzaWxs/EHbCNCEWrVqdfbs2ZCQkEmTJtGD\nCX19/aFDh8bGxmpocO4LDh+EE59fZWXlsmXLevTo4e/vzxR27ty5ffv2Dx48oJMWFhaPHz8W\nCAT0IhhCiEAgEAgELi4uCqsA0Hgf45ncnTt3PnDgAJ/Pz8/P19bWNjc358IDRLh2fqsenG0q\nJ7o4tLW19+3bt27dOnahUCgsKioyMzOjkx4eHoSQc+fOMRXoa+Y2TQVUAOAydXX1Dh06tG7d\nmgvpDE2CE0fQPB7Py8vr559/PnLkyLhx42jh2rVrq6ur3dzc6GRgYODatWvXr18/YsQIDQ0N\nkUi0YcMGQghz7aECKgA0XuOv58FFNS0HJwKaEBIdHf3LL7+MHz9+8ODBVlZW9+7dy8jI6NCh\nA3NDlL29vb+/P5/PHzBggKenZ1paWmZm5vTp021tbRVWAQBAkdTZdzEqkZGR0cSJE1++fJmd\nnZ2RkaGjozNx4kT6HGKmjo+Pj6amZnp6enJysrq6+qJFi6KiotiDwiiggkypqakZGRlTp061\ntLSsq85v95c3YLdwxCDHcGU3oflo/F8CPo6Wg3M3qqiiJUuWREdH40YVkIdSujiqq6sHDBhw\n+fLlX375ZcSIEY1sQAMMHDjw999/NzExKSwslB4zT7rTXE1NrUuXLn379o2KiurYsSMtdHBw\nyM3NnTNnzubNm6VXwdmbTRqDEycJAeCj0tLSov+Prlq1SvHHZC9evDh//jwhpLS09OzZszLr\nGBoaTmIZOXKkSCTav39/9+7dX7x4wa65devWzMxMRbSbAxDQAC1C586dd+/effHixd9//13B\nqz569KhYLKYDddLbGqS1b99+H8uJEyfy8vKmTJny+vXr5cvf6RQSi8VBQUHSt481SwhogJbC\n19c3JCQkPj5ewes9dOgQISQ2NpbH4504cULmEwakaWpqrlixghBy+fJldvmsWbPu3Lnzn//8\n52M0lWsQ0AAtSGRk5N69exW5xqdPn168eLFDhw5Dhw7t06dPPb0c0jp27KilpfX06VN2YVRU\nlIWFxYoVK+p6CFZzgoAGaA4OHTo0aNAgExMTJyen4OBggUDA4/EcHByYCo8ePfr6669tbW11\ndXXt7OwWL15cWlrKvOvg4MDj8V6/fj1//nxnZ2c9PT07O7vly5ezexLqX0JdaJ/GqFGjeDwe\nPT9ZVy+HtMrKyurqavbzCQkhxsbGsbGxlZWVs2bNavbXOCCgAVRecHDwxIkT79y5M3z4cFdX\n1/3790s8fyQjI6N79+7Hjx/v1q1bQECAvr7+2rVre/XqVVRUxK42YsQIsVi8ZcuWxMREY2Pj\n8PDwZcuWfdASpNH+DfooEx8fH0KI/L0cKSkphJAxY8ZIlI8dO9bHx+fs2bN00HzY340AACAA\nSURBVL5mjCs3qgBAw2RmZsbExLi4uKSkpNDxcktKSuizJiiRSBQUFKSjo5ORkeHo6EgIEYvF\nK1euDAsLCwsL27p1K1Ozd+/e69evp6+trKzs7OzOnDkTHR0t/xIkPH78+OrVq61ataJXoLq4\nuFhYWBQUFKSmpkpc7VdVVcW+Qq68vPz69evLli0bMGDADz/8ILFYHo+3ZcsWJyenBQsWDB8+\nXHqU4GYDR9AAqm3Pnj2EkNWrVzM5ZWZmxjzMkxDy4MGDnJyc2bNn02wlhPB4vKVLlxoZGSUn\nJ7MXxR4418bGhvzvwUbyL0EC7c3w8fGhT41SU1OjuXzkyBGJmk+ePHFk6dWr18yZMysqKlas\nWCHzSSWdOnWKiIgoLi5euHDh+/eRykJAA6i2e/fuEULc3d3ZhezJnJwcQkhkZCSPRUND4/Xr\n14WFhey5rK2tmdfsm0fkX4IE2r/Ro0eP+/9Dn0GemJgo0cthb28vZhEKhbdv33Z0dPT29r5+\n/brMhX/zzTc9e/bcs2cPe4CzZgZdHACqTWZ/Lnt8Ajp8bnh4+IQJE+pflPQ9fh+6BLYHDx7c\nvHmTELJkyRL2014IIa9fv05JSaFd0jJpaGh07959zZo1Xl5ex44dYwZNk6izc+dOd3f3mTNn\nfownanMBjqABVBs9JmU/e4gQkpWVxbymd4gUFBQ4sFhZWV27dk36cW4yNWwJ9PB55syZ4nfR\nEdCkezmk0SP6/Pz8uir07Nlz/vz5Dx8+ZHfpNCcIaADVNnHiREJISEhISUkJLXn16hUzDCQh\nxNLS0sPDIz4+/sqVK0xhdHS0v7+/nAeeDVsCDeipU6dKlNPncshzLYeamhoh5OXLl/XUWb58\neadOnaKjo9+zDaoJAQ2g2ry9vYOCgrKyspycnCZNmhQQENC1a1dnZ2fyvy4LHo+3ceNGbW3t\n/v37+/r6zp07d8CAAWFhYR4eHnKOdd6AJdy9e/fu3bv29vZ9+vSReMvBwaF37960l6P+9bZq\n1YoQ8uTJk3qudzYwMNiyZYtIJJJnQ1QOAhpA5W3fvn3Xrl3W1tZJSUl3794NDg6OiooihDCP\n03Rzc8vOzh4/fvy9e/cSEhJKSkoiIiLOnDkj8wIJmT50CfT6jalTp8p8vEtAQACR444VAwMD\nGxube/fu7d69u55qPj4+48ePl3NDVAuGG20C8gw3ykvLUGSTmpZ4oIeym9B8KGa40aysLDc3\nt6lTpyYkJDRydaBEOIIGUG2HDx/W0NBYvXo1u5DeYufl5aWkRkHTwGV2AKrt888/t7S0jIyM\ntLa2/vzzzwUCwcGDBzdt2mRjY+Pn56fs1kGjIKABVFurVq3Onj0bEhIyadIkOraRvr7+0KFD\nY2NjNTTwBVdtHPr8nj17tnTp0kuXLj1//tza2nro0KGhoaHGxsZMBaFQGB0dnZCQ8OzZs08+\n+WTatGlLlixhX1qvgAoAjfQxnh/WuXPnAwcO8Pn8/Px8bW1tc3NzmafmQOVwpQ/6+fPn3bp1\n4/P5VlZWAQEBWlpaMTEx3bt3Ly4uZurMmDEjNDSUEELP2IaFhQUGBrIXooAKAJylrq7eoUOH\n1q1bI52bDa5cxREQEMDn83ft2jV9+nRCCL3daPXq1dOmTaMPgMjNzXVwcOjZs2d6erqenl55\neflnn31248aNBw8e2NraKqZCXXAVB8iv8X8J+DhaDq4cQZ89e9bKymratGl0ksfjLV++XEdH\nhxkGZdeuXYSQkJAQPT09Qoi+vn5ISAghhLlAUgEVAAAUiRMB/fbtW01NTW9vb/a/ZlpaWsbG\nxq9evaKTGRkZhBBvb2+mAh3xNj09XWEVAAAUiRMnCfX09J48eSJRePbs2YKCguHDh9PJgoIC\nQ0ND9jlDY2NjAwMDZrRDBVRgJCUl8fl8ZjI7O7shmw3QrPF4PHt7e/Yw/I1RXV09YMCAy5cv\n//LLLxKD/TdjnAhoaadPnx43bpy2tjZ9rC8hpKCgQPq5CaampsxgWgqowMjNzZVnLC4AaCpa\nWlqHDh1ydXVdtWrV8OHDW8iJUE50cbD99ddfkydP9vHx0dHRSUxM7NWrF/OW9EdCB/ZWZAUq\nKCjoEQv7ORQA8JF07tx59+7dFy9e/P3335XdFgXh0BF0bW3t9u3bFy1aVFFRMW3atMjISAsL\nC+ZdCwsL9iV3VGlpKTMcjAIqMIyMjIyMjJhJdq8IAHw8vr6+ISEh8fHxAwcOVHZbFIErR9C1\ntbX+/v5z5szp1q1bdnZ2fHw8O50JIRYWFmVlZQKBgCkRCAQCgaBdu3YKqwAAShcZGbl3715l\nt0JBuBLQq1atOnDgwLfffnv+/HknJyfpCh4eHoQQ9sPH6Ot+/foprAIAN/F4PAcHh5ycnBEj\nRpiYmDg4OMybN+/NmzdMBbFYvH///k8//bR169YGBgbdu3dfu3Ytu+/u0aNHX3/9ta2tra6u\nrp2d3eLFi0tLS5l3HRwcpHv/6EqZ5W/fvt3Dw8PIyMjJyWn27NnSp9YFAkFwcLCzs7O+vr6z\ns3NwcHB5eTm7wqFDhwYNGmRiYuLk5BQcHCwQCNirkLORr1+/nj9/vrOzs56enp2d3fLly+Xf\nTA7iREBXVlZu3LixX79+GzZsoE//lUbv6Fu/fj0dmVskEm3YsIEQwowXroAKAJz18uVLLy8v\nJyennTt3Dh06dPPmze7u7hUVFfTdNWvWTJ48OTc397PPPhs5cmRxcfHixYuZp65kZGR07979\n+PHj3bp1CwgI0NfXX7t2ba9evYqKiuRc++TJk2fNmnXv3r0hQ4Y4OzsfOXJEoguioqLC3d09\nJiZGQ0Nj0qRJmpqaMTEx7BYGBwdPnDjxzp07w4cPd3V13b9//8iRI9lLkLORI0aMEIvFW7Zs\nSUxMNDY2Dg8PX7ZsWVNtpuJxog86KyurpKTk2bNn9LpjCampqYQQe3t7f39/Pp8/YMAAT0/P\ntLS0zMzM6dOnM/f4KaACAGeVlpZGRUXRZ7OOHTu2TZs2oaGhmzZtWrRoESFk06ZNhoaGeXl5\n9HxJWVlZhw4d9u/fv3btWpFIFBQUpKOjk5GR4ejoSAgRi8UrV64MCwsLCwvbunXre1d9+vTp\nAwcOODo6pqam0hM2hYWF7PsJCCExMTH3798PDAzcsWOHmppabW3tzJkz4+LiYmNjv//++8zM\nzJiYGBcXl5SUFHolVUlJCTsN5G9k7969169fT19bWVnZ2dmdOXMmOjq68ZupFJw4gn78+DEh\n5OnTp2dlYarFxcUtX748Pz9/3bp1hYWFq1at2rZtG3s5CqgAwE08Hm/OnDnM5DfffEMISUxM\npJNVVVUCgSArK4sO7dCqVauysrIXL14QQh48eJCTkzN79mwaW3RRS5cuNTIySk5OlmfV9JLT\nNWvWMKfT27ZtGxkZya5z8uRJQsiqVavoYwbV1NQiIiIIISdOnCCE7NmzhxCyevVq5jpXMzMz\n9nNg5W8k+5IqGxsbuu1NsplKwZWxOFQaxuIA+X2MsTh4PF779u2fP3/OLmzfvr1IJKJPXN2z\nZ8/MmTOrqqqcnJwGDRrk6ek5bNgwQ0NDQsixY8fGjh0rc0V0RBpCiIODQ25urkRWMPeh9O3b\n9/Lly8XFxWZmZsy7RUVFbdq0YW5UMTU11dLSkriloE2bNmKxuKioyNPT8/z58yUlJaampsy7\nxcXFrVu3pkuQv5HV1dXs4SeZRsqzBA7iRBcHADSS9NX6lZWVtbW19PWUKVO8vb1Pnjx59uzZ\no0ePbtq0ycTEZO/evV988YWBgQEhJDw8fMKECfKv7u3bt8xrmeeN6JFy/dTU1CorKwkhMh/v\nra6uzryWv5F1DQ7csM1UOk50cQBAIxUVFbGPoB89elRaWmpvb08nMzIyysvL58yZc+zYsefP\nn58+ffrVq1dz584lhNjZ2RFCCgoKHFisrKyuXbsmccBbU1PDvL5z5w7zmi7h0qVL7MpXrlxh\nT9rY2BQWFtLDeaqwsLCwsJC2sGvXroSQa9eusWfJysqSWIU8jaxL45egFAhogGbihx9+oIfM\nQqFw8eLFhBDmQohJkyb5+PjQw141NbXPPvvM0NCQHr1aWlp6eHjEx8ezIzU6Otrf3//27dt0\nkh5+XrhwgU5WV1eHh4czlekx6eLFi/Pz82lJUVERPV3J8PX1ZbewtraWXkNCWzhx4kRCSEhI\nSElJCa3/6tUr5iITORtZv8YvQSnQxQHQHJiamv76669ubm4uLi6ZmZn379+3s7NbsGABfffr\nr79evXq1s7Pz4MGDKyoqzp07V1ZWNnv2bEIIj8fbuHGjl5dX//79R4wY0bFjx+zs7PT0dA8P\nD+YCU19f3+vXr48aNWrq1Kl6enq//PJLly5dmFUPGTKEXv7UtWvXwYMHa2pqpqamduvWjd28\nBQsW7Nu3Ly4u7saNG25ubteuXcvKynJwcAgODiaEeHt7BwUF7dy508nJydvbW11d/ezZsyNG\njLhy5QrtspCnkfVr/BKUAkfQAM1B69atL1y40K5duxMnTtTU1MyZM+fq1at0ZHNCyIoVK9as\nWaOrq3vw4MHExERzc/ONGzcyl0m4ubllZ2ePHz/+3r17CQkJJSUlERERZ86c0dXVpRV++OGH\nqKiotm3bbtu2LSEhYejQoT///DN77Xv27Nm+fbujo+P/+3//Lzk5ecyYMfSyDYaent61a9fm\nz59fWVm5b9++qqqqBQsWXL16lVnF9u3bd+3aZW1tnZSUdPfu3eDg4KioKEIIc2XIexv5Xo1f\nguLhKo4mgKs4QH4f6SqOJhzYkyOysrLc3NymTp2akJCg7LYoDY6gAUDJDh8+rKGhsXr1anbh\nvn37CCFeXl5KahQnoA8aAJTs888/t7S0jIyMtLa2/vzzzwUCwcGDBzdt2mRjY+Pn56fs1ikT\nAhoAlKxVq1Znz54NCQmZNGkSvaBbX19/6NChsbGxGhotOqNa9MYDKN7H6NBvBmeSOnfufODA\nAT6fn5+fr62tbW5u3kKemVI/BDQAcIW6unqHDh2U3QoOQUADKFRmaGOX0DeiKdoBqgBXcQAA\ncBQCGgCAoxDQAAAchYAGAOAoBDQAEPLuQ2CBIxDQAAAcxcWAXrBggcxfcqFQuHLlSmtra21t\nbSsrq4iICImnSCigAgCAwnAuoPPz8/l8vsy3ZsyYERoaSggZP348ISQsLCwwMFDBFQC46fTp\n076+vp06ddLW1jYzM3Nzc4uJiWGegeLg4CB9Yx7TpxEXF0ffzc3N5fF4dKx9OktFRcWMGTNM\nTExOnTpF53r06NHXX39ta2urq6trZ2e3ePHi0tJSxW1nC8OVgBYKhSdOnAgLC3N3dy8uLpau\nkJubu3fv3p49e2ZnZ/P5/OzsbFdXVz6fn5eXp7AKANy0e/duHx+fpKQke3v7GTNmuLu75+Xl\nBQcHs597Uo+BAwfSoyILCws+n89+at+ECRPu3r07b948OgB/RkZG9+7djx8/3q1bt4CAAH19\n/bVr1/bq1auoqOjjbFlLx5WAfv369ejRoyMiIiSeTMzYtWsXISQkJISOQa6vrx8SEkII2b17\nt8IqAHDTunXrCCE//vhjSkrK5s2bz5w5c/nyZUIIc9hbPxsbm8mTJxNCjIyMJk+e7Orqyrxl\nbm6ekZERERFhaWkpEomCgoJ0dHRu3ryZmJi4ffv2rKysFStWPH78OCws7ONsWUvHlVu9zczM\nKioq6GuZDzjIyMgghHh7ezMlQ4YMIYSkp6crrAIANx08eJAQYmlpyZTQJ0Ux36kGW7hwIfN8\n7gcPHuTk5CxdutTR0ZGW8Hi8pUuXrlu3Ljk5uZErApm4EtA8Hk9HR6eeCgUFBYaGhsbGxkyJ\nsbGxgYFBYWGhwiowkpKS2B3l2dnZH7SxAE2rW7duFRUVWVlZ9+7du3v37s2bN+kRdOOxnz2Y\nk5NDCImMjIyMjJSohnPpHwlXAvq9CgoKzM3NJQpNTU2ZR6YroAIjNzf3yJEjDdoOgKZ3/vz5\ncePGvXz50sbGxsvL6+uvv167dm3v3r3rmYU+4fu92P/O0md7h4eHszup4aNSmYAmhEifhhaL\nxeyfbgVUoIKCgsaMGcNMrlmzZvv27XJtA8BHEBQUVFpaeu3aNTc3N1ry5s0b6Wo1NTXq6ur0\n9Z07dz50LXZ2doSQgoIC9lWw1dXVhw8f7tChA25y+Ri4cpLwvSwsLKSv5iktLWUe+quACgwj\nIyMrFnavCIDiFRQUGBkZubi40EmxWExPG9bW1tISevB74cIFOlldXS3zAg+RSFTPWiwtLT08\nPOLj469cucIURkdH+/v73759uwk2A6SoUkCXlZUJBAKmRCAQCASCdu3aKawCADeNHDmyuLjY\nw8Pj+++/X7Jkibu7++7du9u2bZuXl7d48eK3b9/6+voSQkaNGjV//vylS5e6u7traWlJLERX\nV/fx48dLly5NTU2VuRYej7dx40Ztbe3+/fv7+vrOnTt3wIABYWFhHh4eQUFBH30jWySVCWgP\nDw9CyLlz55gS+rpfv34KqwDATdu2bQsODi4sLIyNjT1z5oyXl1d2dvbevXvt7e337t1bVVX1\nww8/REVFtW3bdtu2bQkJCUOHDv35558lFhIVFWVubh4TE3P16tW6VuTm5padnT1+/Ph79+4l\nJCSUlJREREScOXNG5pVX0Hg8Dj7NjMfj2dvb379/n12Ym5vr4OAwcODAlJQUDQ0NkUg0dOjQ\n33777cGDB7a2toqpUJclS5ZER0enpaV5enrWVafxz9FQIjzCownhiSogP5U5SWhvb+/v78/n\n8wcMGODp6ZmWlpaZmTl9+nQmOhVQAQBAkVQmoAkhcXFxNjY2CQkJ69at69Chw6pVqxYtWqTg\nCgAACsPFLg6V02K7OKq+/1axDWlK2tGxSlkvujhAfqp0BA1cs7LvT8puQsNFEOUENOIV5Kcy\nV3EAALQ0CGgAAI5CQAMAcBQCGgCAoxDQAAAchYAGAOAoBDQAAEchoAEAOAoBDQDAUQhoAACO\nQkADAHAUAhoAgKMQ0AAAHIWABgDgKAQ0AABHIaABADgKAQ0AwFEI6HcIhcKVK1daW1tra2tb\nWVlFREQIhUJlNwoAWigE9DtmzJgRGhpKCBk/fjwhJCwsLDAwUNmNAoAWCgH9j9zc3L179/bs\n2TM7O5vP52dnZ7u6uvL5/Ly8PGU3DQBaIgT0P3bt2kUICQkJ0dPTI4To6+uHhIQQQnbv3q3c\nhgFAy4SA/kdGRgYhxNvbmykZMmQIISQ9PV1pbQKAFkxD2Q3gkIKCAkNDQ2NjY6bE2NjYwMCg\nsLBQomZSUhKfz2cms7OzFdREAGhJEND/KCgoMDc3lyg0NTUtKCiQKMzNzT1y5MgHLbxvRKPa\nxk0Ro8XKbgJAc4YujnfweDyJErFYLH2lXVBQ0COWmTNnKqqBANCC4Aj6HxYWFsXFxRKFpaWl\n7du3lyg0MjIyMjJiJtm9IgAATQVH0P+wsLAoKysTCARMiUAgEAgE7dq1U2KrAKDFQkD/w8PD\ngxBy7tw5poS+7tevn9LaBAAtGAL6H/SmwfXr14tEIkKISCTasGEDISQoKEjJLQOAFgl90P+w\nt7f39/fn8/kDBgzw9PRMS0vLzMycPn26ra2tspsGAC0RAvodcXFxNjY2CQkJ69at69Chw6pV\nqxYtWiTnvGlpaS9fvvyozQOF6dSpU58+fZTdCmjpeGIxLmVtrOPHj3///fcPHz5UdkOgyfj5\n+e3fv1/ZrYCWDkfQTWDMmDGampr37t1Tytrz8/M3btzYp0+f0aNHK6UBH0NiYuLly5e//fZb\n6WscFcPJyUkp6wVgwxG0yrt165aLi8vMmTO3bdum7LY0mdmzZ2/btu3GjRsuLi7KbguA0uAq\nDgAAjkJAAwBwFAIaAICjcJJQ5XXo0GH79u2Ojo7KbkhT8vPzc3V17dixo7IbAqBMOEkIAMBR\n6OIAAOAoBDQAAEchoAEAOAoBDQDAUQhoAACOQkADAHAUAlpViUQiXCIJ0LwhoFVSVVXVmDFj\n3Nzc/vvf/yKmAZorBLRKCg4OTkpKunHjxqhRoxDTAM0VAlolDRo0iBBiZmbm6upKY7pnz54n\nT55U0ZiuqqpauXJlZWWlshsCwC0IaJX05ZdfduvWraSkZPXq1Xv27OnYsePNmze//PJLFY3p\nZcuWhYaGfvnll8hogHeIQTUdPnyYEOLu7l5bW/v27duoqKhWrVrRz9TFxSUxMbGmpkbZbZRX\nSUmJq6srIWTYsGEVFRXKbg4AVyCgVVVNTQ19LNOxY8doycuXL+fNm6eh8X8jFPbo0UOFYhoZ\nDSANAa0CKisr169fLxQKJcoPHjxICHFwcGDeEggEffv2dXJyat26NRPTx48fr62tVXir3+/N\nmzcpKSnXr1+vqqoSy53Rb968qaysVGAzAZQGAa0Cvv32W0KIn5+fREaLRCJ7e3tCSHx8vFgs\nFggEXl5evXv3Li0tffv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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "fig4 <- filter(gencode, type == \"gene\") %>%\n", " ggplot(aes(x = source, fill = simple_gene_type)) +\n", " geom_bar(stat = \"count\") +\n", " labs(x = \"\", y = \"\", title = \"Origine des annotations\", fill = \"\") +\n", " theme(axis.text.x = element_text(angle = 45, hjust = 1))\n", "\n", "options(repr.plot.width=4, repr.plot.height=3)\n", "fig4\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 0.112 0.000 0.110 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "# création d'une table contenant le nombre de gène par chromosome, pour chaque type simplifié de gène\n", "genes_by_chr <- filter(gencode, type == \"gene\") %>%\n", " # table() compte les effectifs pour chaque combinaison\n", " with(., table(simple_gene_type, seqnames)) %>%\n", " # tidy() transforme l’affreux objet retourné par table() en un joli tableau\n", " tidy\n", " \n", "fig5A <- ggplot(genes_by_chr, aes(x = seqnames, y = Freq, fill = simple_gene_type)) +\n", " # pour une fois, on ne veut pas stat = \"count\"\n", " geom_bar(stat = \"identity\") +\n", " labs(x = \"\", y = \"\", title = \"Gènes par chromosomes\") +\n", " theme(legend.position = \"none\", axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5))\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Parsed with column specification:\n", "cols(\n", " X1 = col_character(),\n", " X2 = col_integer()\n", ")\n" ] }, { "data": { "text/plain": [ " user system elapsed \n", " 0.208 0.152 0.360 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "# lecture du fichier texte directement depuis le serveur\n", "chr_length <- read_tsv(\"http://hgdownload-test.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes\", col_names = FALSE) %>%\n", " # on nome les colonnes\n", " dplyr::rename(seqnames = X1, length = X2) %>%\n", " # on ne guarde que les chromosomes standards\n", " filter(seqnames %in% c(paste0(\"chr\", 1:22), \"chrX\", \"chrY\", \"chrM\")) %>%\n", " # que l'on ordonne\n", " mutate(seqnames = factor(seqnames, levels = c(paste0(\"chr\", 1:22), \"chrX\", \"chrY\", \"chrM\")))\n", " \n", "fig5B <- ggplot(chr_length, aes(x = seqnames, y = length)) +\n", " geom_bar(stat = \"identity\", fill = \"darkorange\") +\n", " labs(x = \"\", y = \"\", title = \"Taille des chromosomes (pb)\") +\n", " theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5))\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 0.012 0.000 0.012 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "# fusion des deux tableau grâce a dplyr::left_join\n", "genes_by_chr <- left_join(genes_by_chr, chr_length, by = \"seqnames\") %>%\n", " # création d'une nouvelle colonne: le nombre de gènes par mégabase\n", " mutate(perMb = Freq * 1E6 / length)\n", " \n", "fig5C <- ggplot(genes_by_chr, aes(x = seqnames, y = perMb, fill = simple_gene_type)) +\n", " geom_bar(stat = \"identity\") +\n", " coord_cartesian(ylim = c(0, 25)) +\n", " # une facette par type de gènes\n", " facet_wrap(~simple_gene_type, ncol = 1) +\n", " labs(x = \"\", y = \"\", title = \"Gènes par mégabase par chromosomes\") +\n", " theme(legend.position = \"none\", axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5))\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ " user system elapsed \n", " 1.272 0.000 1.273 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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zGLxcXFNTU1dG9N6dmz59OnT58+fcq00ItNPUum3OjgW7dusRsbvboAu8qnr8/b\nitWBelLHAtrFxaW8vHzNmjWlpaV0i1gsDg0NpSiKuWkQfcvAbdu20V9M6+rqtm/fTghZuHCh\nvAIAAAA6iMmTJxNCYmJiqqur6ZZ//vln9+7dTECPHj0GDx58+vRpdqX4zTffRERE5ObmcllF\nW3rYs2cPXYzW1dXt3LmTEOLm5tZM/Lhx49jPamhoiImJkX5WfX19K3IbP348IWTnzp3MRNCq\nqiq6fwZ9lb3ff/+dXhSLxfv375fLrgA1oY5TOAQCwe7du+fNm+fo6Ojp6cnn8zMzM3Nzc93d\n3T/++GM6xsHBwd/fXyQSubm5jRs3Lj09/erVq0FBQcxNBNseAAAA0EGMHDly1qxZJ06c8Pb2\nHjlypJaW1rVr11xdXW/fvk1PyeDxeJ988smiRYsCAwPHjBnTvXv3vLy8GzduDB48eNasWVxW\n0eoeunTpkpmZ6efn5+DgkJOT8+DBg969e7/33nvNPOW9995LTk4+efLk/fv3HR0d79y5c+/e\nvb59+/r5+TExfD6/pKRk9+7dI0aMGDlyJPfc3N3d3dzcLl686OPjQ1/G7rfffpO4rpebm9vd\nu3dDQ0OnT5+up6d3+fJl9hlWbd+ZoHLqOAJNCAkICDhz5syQIUNSUlJOnjxpbm6+a9euCxcu\naGtrMzEHDx5cu3bt48ePt2zZUl5eHhkZuW/fPnYnbQ8AAADoIMLCwiIiInr27Hnx4sWCggI/\nPz/6yhLm5uZ0gKOj43fffefh4VFQUHD69Onq6uolS5bs2rWLy/zjtvTQtWvXgwcPmpmZpaen\nNzQ0eHt7i0QiPT29Zp6ip6cnEonefffdV69eJScnv379+r333ktISGCvaPny5cbGxomJifQw\nMPfceDzeV199FRoa2qtXrytXrty7d8/d3X3Pnj3smAULFixfvtzExOT48eOnT59+8803N27c\n2PZdAeqDR1GUqnPQPFOmTDl//nxVVZVeVDiXeP6mnYpOCQBA012/fl3VKcD/uHfvnp+f3/Tp\n07/44gtV5SBx9z51pkGpQvOGDx/eYoyajkADAACA0ly4cGHkyJHx8fHsRvqCx87OzqrJCUCN\nqeMcaAAAAFAmFxcXS0vLuLi4nj17uri4PH/+/Pz580lJSb169ZoyZYqqswNQOyigAQAAOjp9\nff29e/fGxMSsXr2avjhV586d33zzzZUrV7LPPgIAGgpoAAAAID169IiMjFy/fky40ZUAACAA\nSURBVH1FRYWOjo6xsTH7PoKqkpWVpeoUuNKgVKHtUEADAADA/9HS0urWrZuqswBQdziJEAAA\nAABABiigAQAAAABkgAIaAAAAAEAGKKABAAAAAGSAAhoAALgqLi4OCAiwt7cXCASDBg1asWJF\nVVUV82h1dTWvMZcvX1ZhzgAAcoercAAAACclJSVOTk7V1dWTJk2aMGHCtWvXtm7dmpSUdOPG\nDTMzM0JIfn4+IaR///5WVlbsJxoZGTXf87lz57Zs2fLxxx97enoqLn8A6JjEB3dzjNQJXsYx\nEgU0AABwIhQKq6urY2Njg4KCCCEURYWFhX355ZeffvppXFwcISQvL48QEh0dPW3aNJl6Likp\nSUlJ8fHxUUTaANDBNeTel3ufmMIBAACcpKam2tjYBAYG0os8Hm/t2rV6enppaWl0Cz0CbWdn\np7IUAQCUAgU0AAC07Pnz5zo6OhMnTmTfnU5XV9fY2JiZBp2Xl8fj8aytrVWUIwCAkmAKBwAA\ntEwgEDx48ECiMTU1taysbOrUqfRifn6+oaHh0qVLz5079/TpU3t7+8mTJ4eHh3fp0kXiiVlZ\nWZs3b2YWCwsLFZk7AICcoYAGAIDWSE5O9vb25vP569ato1vy8vJqampqa2sPHjxoZGSUmpoa\nFRV1/Pjx7OxsY2Nj9nNLSkqOHTumiqwB2rlXn4VwCeNv2qnoTNo3FNAAACCbR48ehYWFJSYm\nmpiYHD582NnZmRBCUZRQKDQ1NfX19aWnebi4uPTt2zcgIGD16tUxMTHsHjw8POgJ07SkpCSh\nUKjkrQAAaDUU0AAAwFVDQ8P+/ftXrlz54sWLwMDAqKgoCwsL+iEej7dsmeQVoN59993g4OCU\nlBSJdoFAYGNjwyyam5srNG0AAPlCAQ0AAJw0NDT4+/sfOXJk1KhRcXFxAwYMaPEp2tra5ubm\nJSUlSkgPAEBpcBUOAADgJDIy8siRIyEhIRcvXpSunlNSUuzs7GJjY9mNVVVVpaWlTk5OSkwT\nAEDhUEADAEDLXr58uWPHjtGjR2/fvl1XV1c6wMXFpby8fM2aNaWlpXSLWCwODQ2lKGrOnDnK\nTRYAQLEwhQMAAFqWnZ395MmT4uJiDw8P6UdTUlIEAsHu3bvnzZvn6Ojo6enJ5/MzMzNzc3Pd\n3d0//vhj5ScMAKA4KKABAKBlBQUFhJCioqKioqKmYgICAkxNTTdv3pySkiIWiwcOHBgSErJk\nyRJtbW0lZgoAoHAooAEAoGV+fn5+fn4thnl5eXl5eSkhHwAAFcIcaAAAAAAAGaCABgAAAACQ\nAQpoAAAAAAAZaEABTVHUjBkz6BvDsonF4g0bNtja2vL5fBsbm/Xr14vFYvkGAAAAAABI0IAC\nOjEx8YcffpBuDw4ODg8PJ4T4+PgQQiIiIhYsWCDfAAAAAAAACepeQD9+/DgkJES6/f79+wkJ\nCcOGDcvJyRGJRDk5OUOHDhWJRLm5ufIKAAAACcXFxQEBAfb29gKBYNCgQStWrKiqqmIH4Jc9\nAOgI1LqApihqyZIlenp6lpaWEg/Rd4sVCoUCgYAQoq+vLxQKCSHx8fHyCgAAALaSkhInJyeR\nSGRjYxMQEKCrq7t169ZBgwZVVFQwMfhlDwA6ArUuoI8ePXrq1Km9e/caGhpKPJSRkUEImThx\nItNC3xzr0qVL8goAAAA2oVBYXV0dGxt7/vz5ffv2ZWVlrVq1qri4+NNPP6UD8MseAHQQ6ltA\nl5WVffDBB2+//fbMmTMbfdTQ0NDY2JhpMTY2NjAwKC8vl1cAW2hoqC3LxYsX5bKNAAAaJDU1\n1cbGJjAwkF7k8Xhr167V09NLS0ujW/DLHgB0EGp6J0KKopYuXaqlpbVz585GA8rKyszMzCQa\nTUxMysrK5BXA9uzZs8rKSmaxrq6O86YAALQHz58/19HRmThxIvuaSLq6usbGxsw0aPyyBwAd\nhJoW0N99992JEyeOHj1qbm7eVIz0he0oimKfrdL2AMb+/fv379/PLE6ZMuX8+fMtbYSkV581\ncjakNP6mxr8zAACokEAgePDggURjampqWVnZ1KlT6UXuv+xlZWVt3ryZWSwsLFRI0gAAiqGO\nBXR5efny5ctnzJjh6+vbVIyFhQX7tBVaZWUlc7ph2wMAAKAZycnJ3t7efD5/3bp1dAv3X/ZK\nSkqOHTumjCwBABRAHedAr169uqam5sMPP7x///69e/fu3bv36tUrQgj9d319PSHEwsKipqam\ntraWeVZtbW1tbW2PHj3oxbYHAABAox49euTn5+fl5aWnp3fixAlnZ2fmIY6/7Hl4eOSzbNy4\nUeFJAwDIjzoW0I8fPxaLxRMmTHD8F/27If33P//8QwhxdXUlhDBnrjB/jx49ml5sewAAAEho\naGjYu3fvgAEDjh49GhgYePv2bWb+BiHEwsKCfboIrdFf9gQCgQ1LM7P1AADUkDoW0GfOnKH+\nl4ODAyGE/pueXUdfWHTbtm30+Xx1dXXbt28nhCxcuJDupO0BAADA1tDQ4O/vv3TpUicnp5yc\nnLi4OAsLC3YAftkDgA5CHQtoLhwcHPz9/dPT093c3IRC4dixY3/++eegoCB7e3t5BQAAAFtk\nZOSRI0dCQkIuXrw4YMAA6QD8sgcAHYSmFtCEkIMHD65du/bx48dbtmwpLy+PjIzct2+ffAMA\nAID28uXLHTt2jB49evv27bq6uo3G4Jc9AOgg1PEqHNLu3bsn3airqxsREREREdHUs9oeAAAA\ntOzs7CdPnhQXF9OXdpaQkpJC/v1lTyQSubm5jRs3Lj09/erVq/hlDwDaH80ooAEAQLUKCgoI\nIUVFRUVFRc2EHTx40M7O7tChQ1u2bOnZs2dkZOTKlSuVlSMAgJJo8BQOAABQGj8/P6ppTBj9\ny15hYeHr168LCgrCwsJ0dHRUmDYAgCKggAYAAAAAkAEKaAAAAAAAGaCABgAAAACQAQpoAAAA\nAAAZoIAGAAAAAJABCmgAAJBZaGho//79JRqrq6t5jbl8+bJKkgQAUBBcBxoAAGTz+PFjkUhk\namoq0Z6fn08I6d+/v5WVFbvdyMhIeckBACgeCmgAAOBELBb/+OOP2dnZcXFxFRUV0gV0Xl4e\nISQ6OnratGmqSBAAQElQQAMAACfV1dWzZs1qJoAegbazs1NWRgAAqoECGgAAODE1NX3x4gX9\nd+fOnaUD8vLyeDyetbW1cvMCAFA2FNAAAMAJj8fT09NrJiA/P9/Q0HDp0qXnzp17+vSpvb39\n5MmTw8PDu3TpIhGZlZW1efNmZrGwsFAhGQMAKAYKaAAAkI+8vLyampra2tqDBw8aGRmlpqZG\nRUUdP348Ozvb2NiYHVlSUnLs2DFV5QkA0EYooAEAQA4oihIKhaampr6+vjwejxDi4uLSt2/f\ngICA1atXx8TEsIM9PDzoCdO0pKQkoVCo7IwBAFoLBTQAAMgBj8dbtmyZROO7774bHByckpIi\n0S4QCGxsbJhFc3NzhecHACA/KKABAEBRtLW1zc3NS0pKVJ0IgBp59VkIx0j+pp0KzQRaDXci\nBAAAOUhJSbGzs4uNjWU3VlVVlZaWOjk5qSorAABFQAENAABy4OLiUl5evmbNmtLSUrpFLBaH\nhoZSFDVnzhzV5gYAIF+YwgEAAHIgEAh27949b948R0dHT09PPp+fmZmZm5vr7u7+8ccfqzo7\nAAB5QgENAADyERAQYGpqunnz5pSUFLFYPHDgwJCQkCVLlmhra6s6NQAAeUIBDQAAMqMoqtF2\nLy8vLy8vJScDAKBkmAMNAAAAACADjEBDh8Px+kG4eBAAAAA0CiPQAAAAAAAyQAENAAAAACAD\n9S2gnz59umzZMkdHR4FAYG9vHxgY+PDhQ3aAWCzesGGDra0tn8+3sbFZv369WCyWbwAAAAAA\ngAQ1nQP98uXLUaNG5eXljRs3bvz48ffv34+Pjz916lROTo6VlRUdExwcnJCQYGNj4+Pjk5GR\nERERkZubm5CQwHTS9oAWbXhzF5ew9QSzaQGgXQkNDU1OTr53755Eu1gs3rRp06FDh4qLi62s\nrAIDA1etWqWjo6OSJAEAFERNR6D37NmTl5e3du3a9PT0vXv3pqWlbdq0qbKyMjIykg64f/9+\nQkLCsGHDcnJyRCJRTk7O0KFDRSJRbm6uvAIAAKBRjx8/FolEjT4UHBwcHh5OCPHx8SGERERE\nLFiwQKnJAQAonpoW0JmZmYSQjz76iGmhD8E3btygF2NjYwkhQqFQIBAQQvT19YVCISEkPj5e\nXgEAAMAmFotPnjwZERExYsSIiooK6QAMTABAB6GmUzgmTJgwePDgLl26MC2vXr0ihHTu3Jle\nzMjIIIRMnDiRCfDw8CCEXLp0SV4BAADAVl1dPWvWrGYCGh2Y8PHxiY+PZ34/BABoB9S0gF6y\nZAn9R0NDwz///JObm/vFF19oa2uvXLmSbi8rKzM0NDQ2NmaeYmxsbGBgUF5eLq8Atr/++qu2\ntpZZfPHihXy2EwBAc5iamjJHP2Y4gw0DEwDQQahpAc04cOAAXUxraWklJSVNnTqVbi8rKzMz\nM5MINjExKSsrk1cAW2hoaGJiYts2RQYc7/RBcLMPAFAiHo+np6fXTAD3gYmsrKzNmzczi4WF\nhfJNFQBAodS9gPb19XV1df3zzz/XrFnj7+/fp08fZ2dn+iEejycRTFEU+zp0bQ9gjBw58vXr\n18zipUuXGq2zATQFvqSBInAfmCgpKTl27Jiy8gIAkDN1L6C7du3atWvXQYMGOTs7W1tbb968\nOSkpiRBiYWEhfQpLZWWlpaUl/XfbA9hCQkJCQv5/wTFlyhQU0AAA0jgOTHh4eOTn5zOLSUlJ\n9GncAAAaQR0L6JcvX65evXrw4MH+/v5MY58+fSwtLf/880960cLCoqCgoLa21sDAgG6pra2t\nra0dMmSIvAIACOeRWgzTAhBZBiYEAoGNjQ2zaG5urvDkQBU6yI9dHWQzgU0dC2g+n3/48OGU\nlBR2AS0Wi//++28HBwd60dXVNTMzMy0tbcaMGXRLWloaIWT06NHyCgCAVsBXjo4MAxMA0EGo\n43WgeTyeu7v7zZs32TPkoqOjX79+PXz4cHqRviz0tm3b6urqCCF1dXXbt28nhCxcuFBeAQAA\nIBNXV1fy72AEDQMTANAuqeMINCFk06ZNP/74o4+Pz4QJE2xsbO7cuZORkdGzZ8+wsDA6wMHB\nwd/fXyQSubm5jRs3Lj09/erVq0FBQfb29vIKaJe4jw7KNI6IQUcAIIQsWLAgOjp627Ztnp6e\nnTp1wsAEALRXalpA9+7d++bNm59//vnVq1czMjKsra2XL1++du1a9tWRDh48aGdnd+jQoS1b\ntvTs2TMyMpK5SrS8AgAAgLuOOTABoLkUOnu7fU8NV9MCmhBibW195MiRZgJ0dXUjIiIiIiIU\nFwAAADJp+8BE+/7QBYD2QX0LaAAAUFsURTXajoEJAOgIUEADAACAWsAZNaAp1PEqHAAAAAAA\nagsFNAAAAACADDCFQ3k2vLmLS9h6gl+mAAAAANQXRqABAAAAAGSAAhoAAOSjurqa15jLly+r\nOjUAAHnCFA4AAJCP/Px8Qkj//v2trKzY7UZGRirKCABAIVBAAwCAfOTl5RFCoqOjp02bpupc\n1B3uFwOg0VBAAwCAfNAj0HZ2dqpOpL1Btd12su5D7HNoHgpoAACQj7y8PB6PZ21trepEQAao\nLAFaAQU0AADIR35+vqGh4dKlS8+dO/f06VN7e/vJkyeHh4d36dJFIjIrK2vz5s3MYmFhYevW\niOIPoH3QuP9NFNAaDzc+BQbeDKBaeXl5NTU1tbW1Bw8eNDIySk1NjYqKOn78eHZ2trGxMTuy\npKTk2LFjqsoTAFpB42pchUIBDQAAckBRlFAoNDU19fX15fF4hBAXF5e+ffsGBASsXr06JiaG\nHezh4UFPmKYlJSUJhUJlZyxv+AYL0HGggAYAADng8XjLli2TaHz33XeDg4NTUlIk2gUCgY2N\nDbNobm6u8Pw6EowUAigabqQCAACKoq2tbW5uXlJSoupEAADkCSPQAAAgBykpKYsXLxYKhQsW\nLGAaq6qqSktLR40apcLEWg3juADQFIxAAwCAHLi4uJSXl69Zs6a0tJRuEYvFoaGhFEXNmTNH\ntbkBAMgXRqABoOPCWV9yJBAIdu/ePW/ePEdHR09PTz6fn5mZmZub6+7u/vHHH6s6OwAAeUIB\nDUqFegWgHQsICDA1Nd28eXNKSopYLB44cGBISMiSJUu0tbVVnRqApsJUIvWEAhoAAOTGy8vL\ny8tL1VkAACgWCmgAAADQPBiaBRXCSYQAAAAAADLACDSoL0yYBgAAADWEAhpABfDdAAAAQHOh\ngIb2ADPhAAAAQGkwBxoAAORGLBZv2LDB1taWz+fb2NisX79eLBarOikAADlT3xHo4uLisLCw\nK1eulJSU2NraTpo0KTw83NjYmAkQi8WbNm06dOhQcXGxlZVVYGDgqlWrdHR05BgAoHKY7AGa\nJTg4OCEhwcbGxsfHJyMjIyIiIjc3NyEhQdV5AUC7ovJfntV0BLqkpMTJyUkkEtnY2AQEBOjq\n6m7dunXQoEEVFRVMTHBwcHh4OCHEx8eHEBIREbFgwQJ2J20PAAAA7u7fv5+QkDBs2LCcnByR\nSJSTkzN06FCRSJSbm6vq1AAA5ElNC2ihUFhdXR0bG3v+/Pl9+/ZlZWWtWrWquLj4008/pQNa\nPEy3PQAAAGQSGxtLCBEKhQKBgBCir68vFAoJIfHx8apNDABAvtS0gE5NTbWxsQkMDKQXeTze\n2rVr9fT00tLS6JYWD9NtDwAAAJlkZGQQQiZOnMi0eHh4EEIuXbqkspwAABRAHedAP3/+XEdH\nZ+LEiTwej2nU1dU1NjauqqqiF1s8TLc9AAAAZFJWVmZoaMg+WcXY2NjAwKC8vFwiMisra/Pm\nzcxiYWGhklIEAJAHdSygBQLBgwcPJBpTU1PLysqmTp1KL7Z4mG57AFtaWlpeXh6zWFxc3JYN\nBABol8rKyszMzCQaTUxMysrKJBpLSkqOHTvWaCcynfEj6+lBGtq5+mSiuZ2rTyaa27n6ZKLo\nzrlQxwJaWnJysre3N5/PX7duHd3S4mG67QFscXFxiYmJbdwKAIB2j/3LIY2iKOkr2Xl4eOTn\n5zOLSUlJ9CQ6AACNoO4F9KNHj8LCwhITE01MTA4fPuzs7Mw81OJhuu0BjA8++GDmzJnMYmRk\n5M2bN2XcFLWw4c1dXMLWE1wTDQBkZmFhwb5WEq2ystLS0lKiUSAQ2NjYMIvm5uYKTw4AQH7U\nt4BuaGjYv3//ypUrX7x4ERgYGBUVZWFhwTza4mG67QFso0aNGjVqFLMYGxuroQW0gmhWXa5Z\n2QJoEAsLi4KCgtraWgMDA7qltra2trZ2yJAhqk0MAEC+1LSAbmho8Pf3P3LkyKhRo+Li4gYM\nGCAR0OJhuu0BKsSxwiMo8gBAnbi6umZmZqalpc2YMYNuoS+dNHr0aJXmBQAgZ2paQEdGRh45\nciQkJCQ6OlpXV1c6oMXDdNsDAAiGqwFksWDBgujo6G3btnl6enbq1Kmurm779u2EkIULFzb/\nxJkzZw4ZMqRPnz5KSRMAoK3U8TrQL1++3LFjx+jRo7dv395o9UwIoW8ZuG3btrq6OkKI9GG6\n7QEAACATBwcHf3//9PR0Nzc3oVA4duzYn3/+OSgoyN7evvknmpmZDR8+XPrEbgAA9aSOI9DZ\n2dlPnjwpLi6mL8wsISUlhfx7mBaJRG5ubuPGjUtPT7969Sr7MN32AC42dL3MJWw99x4BADTZ\nwYMH7ezsDh06tGXLlp49e0ZGRq5cuVLVSQEAyJk6FtAFBQWEkKKioqKiombCWjxMtz0AAABk\noqurGxERERERoepEAAAUSB0LaD8/Pz8/vxbDWjxMtz2gI8PcXwAAAIBGqWMBDQCKhiu9AAAA\ntJo6nkQIAAAAAKC2MAKt8TDXAgAAAECZMAINAAAAACADFNAAAAAAADLAFA5QKkw4AQAAAE2H\nAhqg/cD3EwAAACVAAQ0AAGrh+vXrqk4BAIAMHz68xRgU0AAqgKFiAAAAzYUCWnk2dL3MJWy9\novMAAAAAgDZAAQ2g1jBWDQAAoG5wGTsAAAAAABlgBBoAOi4M8AMAQCuggIb2gGMZRFAJAQAA\nQJuhgAb1hdFBTYRXDQAA2j3MgQYAAAAAkAEKaAAAAAAAGWAKBwCoxqvPQriE8TdhsgcAAKgX\nFNAAAACgcGKxeOHChX/88ceOHTtcXV2Vn8D777+fnZ3dpUuXn376qVMnyfrH2dlZokVLS8vS\n0nLQoEHLly/v3r073ThnzpyHDx96e3t/9tln0qtwdnbu06fP8ePHFZE/qBVM4QAAAACF09HR\n2bhxY5cuXWJjYymKUvLa//777xs3bhBCampqfvvtt0ZjBALBVJaxY8fW19efPXvW19f377//\nZkf+97//zcnJUUbeoK4wAg1yoFl3KdesbEFWmBkCoLZ69OixZs2a0NDQ7Ozs4cOHK3PVqamp\nFEX17t370aNHKSkpLi4u0jHm5ubr1//Psb+urm7Dhg1nzpz5+uuvw8LCmHaKojZs2JCYmCg9\nkg0dBF54APlA3QYA0CI3N7fAwMBTp04puYD+6aefCCErV64MCQlJT08XCoU6OjotPqtTp06L\nFy8+c+bMH3/8wW6fM2fO8ePHRSJRYGCgojIG9YYpHAAAAKA8y5YtW7dunTLXWF5efuvWrW7d\nur355ptOTk41NTXXrl3j+Nzu3bvr6OiUlZWxG5cvX25qavr1118XFRUpIF/QACigAQAAQA5+\n+umnxYsXu7u7e3t7b9u27fnz587OznPmzGECiouLw8LCZs2a5eLiMnv27J07d9bU1DCPzpkz\nx9nZuba2dsuWLW+//barq+vs2bMPHDhQV1fHsYemXLhwgRAyfvx4Ho9Hn79It3Dx+vVrsVhs\namrKbjQ0NFy5cuXr1683btyo/PncoA5QQAMAAEBbbdu2LSwsLD8/38XFxcHB4ezZsx9//DE7\n4ObNm76+vj///LOtre20adP09PQSEhL8/f0rKyvZYSEhIRRFrVq16quvvjIwMDhw4MCePXtk\n6kEaPX9j3LhxhJAxY8YQQtLT08ViMZft+vXXXwkh7u7uEu0TJkwYM2bMb7/9dvbsWS79QDuD\nOdAAAADQJjk5OYmJif369duzZ4+xsTEhpLq6eunSpUxAfX39hg0b+Hx+bGystbU1IYSiqNjY\n2H379u3bt08oFDKRTk5OoaGh9N9WVlazZ8++cuVKSEgI9x4klJSU3LlzR19ff9iwYYSQfv36\nmZqaPnny5LfffpO4mp5YLH7w4AGz+OLFi7t37+7du3fYsGELFiyQ6JbH461atcrb23vr1q0u\nLi70VkPHgQIaAFqA8yMBoHlnzpwhhHzwwQdMHWlkZLRs2bKQkP87ejx8+LCwsDAoKIiufQkh\nPB4vKCjo8OHDV65cYXc1e/Zs5u9evXoRQl6/fi1TDxLo2RpjxoyhzxrU0tJydXU9ffp0SkqK\nRAFdWlo6d+5ciacLBILFixfz+Xzpni0sLJYsWbJ169bt27evWbOmmRyg/dGAAjo0NDQ5Ofne\nvXsS7WKxeNOmTYcOHSouLraysgoMDFy1ahX7pNq2B6gKx+usEdkvtYYruAEAgNwVFhYSQgYO\nHMhuHDBgAPM3PbIbFxcXFxcn8Vz2FGdCSM+ePZm/eTxeK3qQQM/f6NevHzO6bGtrSwj5+eef\nw8LC2B/6EvdAqa+vLywsXLdu3ZIlS+Lj4x0dHaU79/X1TU5OPnPmjJeX14gRI5pJA9oZdS+g\nHz9+LBKJJCbv04KDgxMSEmxsbHx8fDIyMiIiInJzcxMSEuQY0JGh1Ka11/2guC9pANABNTqf\nWEvr/59n1blzZ0LI+++/P2nSpOa7aurKytx7YHv06NGff/5JCNm1a9euXbvYD9XW1v7666/0\nlOhGaWtr29nZffjhh4sWLUpLS2u0gNbW1l69enVAQEBUVNS3337LPTHQdGp6EqFYLD558mRE\nRMSIESMqKiqkA+7fv5+QkDBs2LCcnByRSJSTkzN06FCRSJSbmyuvAAAAAODCxsaGEHLnzh12\nI/un4z59+hBCnjx50pfFysrqzp07T5484bKK1vVADz/Pnj07638FBQURQlJSUlpcLz0i3mgp\nQuvfv/8777xTVFQUGxvLZUOgfVDTArq6unrWrFnr168vKSlpNIB+mwqFQoFAQAjR19enTyCI\nj4+XVwAAAABwMXnyZEJITExMdXU13fLPP//s3r2bCejRo8fgwYNPnz59+/ZtpvGbb76hf/vl\nsorW9UAX0NOnT5do9/T0JNyuxUFPI3n69GkzMYsWLbKwsPjmm29a2ghoP9R0CoepqemLFy/o\nv+lfbSRkZGQQQiZOnMi0eHh4EEIuXbokrwAAjdNeJ5wAgJobOXLkrFmzTpw44e3tPXLkSC0t\nrWvXrrm6ut6+fZueksHj8T755JNFixYFBgaOGTOme/fueXl5N27cGDx48KxZs7isohU9FBQU\nFBQU9OnTx8nJSeKhvn37Dhw48Pbt283P4iCEGBgYEEIeP35MURR7TjabQCBYtWrVRx99xGVD\noH1Q0wKax+Pp6ek1E1BWVmZoaMi+aoyxsbGBgUF5ebm8Atg2btyYlpbGLN64caNVmwUA6gVf\nOQDkJSwsbNCgQSdPnrx48WLv3r39/Py8vLxOnjxpbm5OBzg6On733XcxMTF02WplZbVkyZJ3\n3nmn0QtcNErWHujrb0yfPr3RwtfLy+v27dsXLlxovoDu3Llzr169CgoKfvjhhxkzZjQVNmbM\nGA8PD+73ZwFNp6YFdIvKysrMzMwkGk1MTJibbbY9gO327dtcZkqpBCoAAABQOR6PN2PGDHaJ\nSc+BZgpoQkiPHj0iIyOb6oF9BQxGVlYWe7H5HiQsWrRo0aJFTT3q4+PjG2gr3QAAIABJREFU\n4+PT1IoYPB7vxIkTzSdJ27hx48aNGznmBppOUwto8r9Xt6FRFMWezNT2AMb+/fvZZ+/OnTuX\nPSANQAjZ8OauloMIWU92knb9tacdbxoANOXChQuff/750qVL58+fzzQmJycTQpydnVWWFoDC\naGoBbWFhIX1KbGVlpaWlpbwC2PT19fX19ZlFdbhWtIZCdQUA0P64uLhYWlrGxcX17NnTxcXl\n+fPn58+fT0pK6tWr15QpU1SdHYD8aXABXVBQUFtbS8/uJ4TU1tbW1tYOGTJEXgEAAADAhb6+\n/t69e2NiYlavXk3f1qRz585vvvnmypUrtbW1VZ0dgPxpagHt6uqamZmZlpbGTLei51SMHj1a\nXgGgctyHq9vxbUHUYcxeptkpANAx0bOT169fX1FRoaOjY2xs3NQ1KwDaAU0toBcsWBAdHb1t\n2zZPT89OnTrV1dVt376dELJw4UJ5BQCAJkK5D6BCWlpa3bp1U3UWAAqnqQW0g4ODv7+/SCRy\nc3MbN25cenr61atXg4KC7O3t5RUAAAAAACBNUwtoQsjBgwft7OwOHTq0ZcuWnj17RkZGrly5\nUr4BAKA4Mg0Vv/oshEswfxPGlQEAQOE0oICmKKrRdl1d3YiIiIiIiKae2PYAAAAAAAAJWqpO\nAAAAAABAk6CABgAAroqLiwMCAuzt7QUCwaBBg1asWFFVVcU8Wl1dzWvM5ctcr5MDAKARNGAK\nBwAAqIOSkhInJ6fq6upJkyZNmDDh2rVrW7duTUpKunHjhpmZGSEkPz+fENK/f38rKyv2E42M\njJrv+dq1a8ePH589e/bIkSMVlz8AdEw/3V7FMXLSwC85RqKABgAAToRCYXV1dWxsbFBQECGE\noqiwsLAvv/zy008/jYuLI4Tk5eURQqKjo6dNmyZTz7du3dq0aZOtrS0KaACQu0t/buIYyb2A\nxhQOAADgJDU11cbGJjAwkF7k8Xhr167V09Oj70JF/h2BtrOzU1mKAABKgQIaAABa9vz5cx0d\nnYkTJ7JvL6erq2tsbMxMg87Ly+PxeNbW1irKEQBASTCFo02upLpyihuv2DQAABRNIBA8ePBA\nojE1NbWsrGzq1Kn0Yn5+vqGh4dKlS8+dO/f06VN7e/vJkyeHh4d36dJF4om5ubnff/89s3jt\n2jVF5g4AIGcooAEAoDWSk5O9vb35fP66devolry8vJqamtra2oMHDxoZGaWmpkZFRR0/fjw7\nO9vY2Jj93Dt37qxaxfW0HgAAdYMCGqAj4vrjCVGX309kum0hKNqjR4/CwsISExNNTEwOHz7s\n7OxMCKEoSigUmpqa+vr60tM8XFxc+vbtGxAQsHr16piYGHYPI0aMSEpKYhbT0tL27dun5K0A\nAGg1FNAA7QfmFBFCNnTldMnh9TJ2y/Fe4qS93068oaFh//79K1eufPHiRWBgYFRUlIWFBf0Q\nj8dbtmyZRPy7774bHByckpIi0W5paent7c0s1tTUKDRtAAD5QgGtPChu2jcF1W0A6qOhocHf\n3//IkSOjRo2Ki4sbMGBAi0/R1tY2NzcvKSlRQnoAAEqDq3AAAAAnkZGRR44cCQkJuXjxonT1\nnJKSYmdnFxsby26sqqoqLS11cnJSYpoAAAqHEWiA5uB3A4LBdUII50nYpP3Ow3758uWOHTtG\njx69fft29pXsGC4uLuXl5WvWrJk6daqlpSUhRCwWh4aGUhQ1Z84cpecLAKBAKKChw0FNDNAK\n2dnZT548KS4u9vDwkH40JSVFIBDs3r173rx5jo6Onp6efD4/MzMzNzfX3d39448/Vn7CAACK\ngwIaQAXacRHfjjetgysoKCCEFBUVFRUVNRUTEBBgamq6efPmlJQUsVg8cODAkJCQJUuWaGtr\nKzFTAACFQwHdsSiouEHNBK2AmSGaxc/Pz8/Pr8UwLy8vLy8vJeQDAKBCKKAB1Bq+nKgJjuU+\nQcUPANABoIBWRxp3kwsAAACAjgMFNCgVxlMBAABA06GAhvYAY/YAAACgNCigoXEYKga1gjMO\nAQBAfaCA1niodAFAaYqLi8PCwq5cuVJSUmJraztp0qTw8HBjY2MmQCwWb9q06dChQ8XFxVZW\nVoGBgatWrdLR0VFhzgAAcocCGgBagC9pQCspKXFycqqurp40adKECROuXbu2devWpKSkGzdu\nmJmZ0THBwcEJCQk2NjY+Pj4ZGRkRERG5ubkJCQmqzRwAQL5QQIP6Qt0GrYC3jeIIhcLq6urY\n2NigoCBCCEVRYWFhX3755aeffhoXF0cIuX//fkJCwrBhwy5duiQQCJ49ezZ27FiRSBQeHm5v\nb6/q9AEA5EZL1QkAAIBmSE1NtbGxCQwMpBd5PN7atWv19PTS0tLoltjYWEKIUCgUCASEEH19\nfaFQSAiJj49XTcYAAIqBEWgA+cDAp6ywxzTL8+fPdXR0Jk6cyOPxmEZdXV1jY+Oqqip6MSMj\ngxAyceJEJsDDw4MQcunSJeUmCwCgWB26gMbJLgAAHAkEggcPHkg0pqamlpWVTZ06lV4sKysz\nNDRkn1NobGxsYGBQXl4u8cTc3Nzvv/+eWbx27ZpCkgYAUIwOXUDjZBcATaGg4WqMgrdFcnKy\nt7c3n89ft24d3VJWVsacTcgwMTEpKyuTaLxz586qVauUkSUAgAJ03AIaJ7sAALTOo0ePwsLC\nEhMTTUxMDh8+7OzszDzEnuBBoyhKLBZLNI4YMSIpKYlZTEtL27dvn+ISBgCQr45bQDd6souP\nj098fHxkZKSqswMAUEcNDQ379+9fuXLlixcvAgMDo6KiLCwsmEctLCwqKioknlJZWWlpaSnR\naGlp6e3tzSzW1NQoLmcAALnruFfhwMkuAAAyaWho8Pf3X7p0qZOTU05OTlxcHLt6JoRYWFjU\n1NTU1tYyLbW1tbW1tT169FB6sgAACtRxR6C5n+wCACCTV5+FcAnjb9qp6EzkKzIy8siRIyEh\nIdHR0bq6utIBrq6umZmZaWlpM2bMoFvoK9yNHj1aqYkCAChYhy6gOZ7sQgjx8/NLTExUSl4A\noI64nm5ICBlPNry5i0vgeqJJBfTLly937NgxevTo7du3S090pi1YsCA6Onrbtm2enp6dOnWq\nq6vbvn07IWThwoXKTRYAQLF4FEWpOgfV0NfXNzc3l7gqU+/evSsqKp4/fy4RHB4efvbsWWYx\nNze3pqamqqrKyMhICakCAKhcZmamq6trr169+vXrJ/1oSkoK/UdAQIBIJBo9evS4cePS09Ov\nXr0aFBREn3PSjNjY2ODg4AMHDqDUBgC5Cz/R+Hd+aetnca2KO+4INPeTXQgh69evX79+PbM4\nZcqU8+fPKzY/AAB1UlBQQAgpKioqKipqJuzgwYN2dnaHDh3asmVLz549IyMjV65cqawcAQCU\npOOeRIiTXQAAuPPz86OaxoTp6upGREQUFha+fv26oKAgLCwMd6cCgPan4xbQrq6u5N8TXGg4\n2QUAAAAAWtRxC+gFCxYQQrZt21ZXV0cIwckuAAAAAMBFx50D7eDg4O/vLxKJ3Nzc2Ce74DaE\nAAAAANCMjltAE5zsAgAAAACy69AFNH2yS0REhKoTAQAAAACN0XHnQAMAQKuFhob2799forG6\nuprXmMuXL6skSQAABenQI9Bt9ODBA0NDQ1VnAQCazcLCQiAQqDoL2Tx+/FgkEpmamkq05+fn\nE0L69+9vZWXFbsc9pwCgnUEB3RpDhw69fPnykCFDVJ0IAGi8s2fPTpkyRdVZcCIWi3/88cfs\n7Oy4uLiKigrpAjovL48QEh0dPW3aNFUkCACgJCigW2Pjxo16enqlpaXSDx08eNDExGT27Nkt\ndnLnzp3Lly+PHz++0fviSkhKSnr+/Pn8+fNbjCwuLk5OTh4+fPjw4cNbDD537tyjR4/mzZvH\n5/Obj6ypqfn222/t7e3d3d1b7DYzM/OPP/6YNWuWubl5i8EHDhzo3r37zJkzW4zMycm5cuXK\nhAkTbG1tWww+evRoXV2dv79/i5EPHz48f/78iBEjhg4d2mLwjz/+WFJSEhgY2OK9ISorK48d\nO+bg4DBu3LgWu7106dLdu3fnzJkjXZFIqK+vj42N7dGjx/Tp01vs9ubNm7/++quHh8f/Y+++\nw6K42v6BnwVpS2cBqUoVFHtDQEWEaCwxllhiQSn2lmA0QuwiscQSgxqNKIFoIondWCIYE4UY\nsROxAFZqpHdY2Pn9cd53fvsusMzqLuyS7+d6rudizt575t4ZiDfDKfb29s0GHzlyhMfjTZ06\ntdnI58+fX7582d3dvUePHs0Gnz17NicnJzg4WE2tmQFjBQUFx48f79Kly8CBA5vt9o8//njy\n5MmkSZOMjIykR9bW1kZHR9vY2IwcObLZbu/cuXPr1q3333+/Q4cOzQbHxMRoampOmTKl2cj0\n9PQrV654enp27dq14au2trbN9qAkSkpKxo0bJyWAPoF2cnJqqYwAAFqJlJ2l4C1oaGj06dOH\nS+SePXsIId9//z2XYFdXVyMjIy6RFy9eJISsXbuWSzAtKQoKCpqNTEtLI81tRcZavHgxIeTm\nzZvNRopEIkKIh4cHl2537txJCPnxxx+5BNvb27dv355L5JkzZwgh4eHhXIL9/PwIIWVlZc1G\nPnz4kBASGBjIpdu5c+cSQu7du9dsZHV1NSHE29ubS7dbtmwhhBw/fpxLsLW1ta2tLZfIX375\nhRCydetWLsGDBw8mhNTU1DQbeffuXULIvHnzuHQbEBBACElNTW02sqSkhBAybNgwLt1u3LiR\nEHL27FkuwWZmZo6Ojlwijx49SgjZtWsXl2BlJhKJqv4XIcTFxUUiIDAwkMfjVVdXy9rzwYMH\nCSEHDhyQU6YAAP/fqhOE4/+494kn0AAAwAmPx9PW1pYSkJGRoa+vv2DBgosXLxYWFjo7Ow8f\nPnz16tUGBgYSkWlpaSdOnGAPk5OTFZIxAIBioIAGAAD5SE9PLy0tLS8vP3jwoKGhYUJCQkRE\nxPHjx+/cuSMx2CY1NXXlypWtlScAwDtCAQ0AAHLAMExoaKhAIJg8eTKPxyOEeHp62tnZ+fv7\nr1q1KjIyUjy4X79+cXFx7OGVK1e+/fbbls4YAOBtqa9bt661c2hTqqurBw4c6O7u3mxkfX29\nqampj4+PpaVls8E1NTW9evUaMmRIs5EikUhXV9fb25vLTLva2lpXV1dfX99mp8QxDMPj8by9\nvRudBSVBKBR27NjR19e34d9tG6qurh40aFC/fv2ajayrqzM3N/fx8bGwsGg2uKampk+fPnQA\nrnQikUhPT8/Hx4fLTLva2touXbr4+fmpq6tLj2QYRl1d3dvbu0uXLs12KxQK7ezsfH19uSyM\nWFtbO2jQoL59+zYbWVdX1759ex8fH3Nz82aDq6ur+/XrN2jQoGYjRSKRgYGBj49Px44duWTr\n5ubm5+fX7CRCkUikoaHh7e3duXPnZrsVCoUODg6+vr66urpcggcPHty7d+9mI+vq6iwtLYcO\nHWpqatpscHV19YABA7y8vJqNFIlEhoaGPj4+XOYmqor169ebmpouWrSIbeHxeP379+/atSut\nnqmuXbtu3ry5qKhIPJIQoq+v7yYmLy/vzJkzH3zwAZfZzwAAMvn98XqOkUM7r+MYyWMY5i3T\nAQCA/yoej+fi4vL48eNmI21sbEpKSsrKyqTEREVFBQcHHzhwYPbs2fLLEQCAEEJWn+Q1H0QI\nIWTjOK5VMXYiBAAAOYiPj3dycoqKihJvLC4uzs7O5vKXKwAAFYICGgAA5MDT0zMvL2/dunXs\nGvlCoTAkJIRhmAkTJrRubgAA8oVJhAAAIAd8Pn/Pnj0zZ87s3LnzyJEjtbS0kpKS0tLSfHx8\nPv3009bODgBAnlBAAwCAfPj7+wsEgq1bt8bHxwuFQjc3tyVLlsyfP7/ZSbcAAKoFBTSAcqmv\nry8rK2t2h+p3dO/ePYFAoEKbSIOyaWoC+qhRo0aNGtXCyQAAtDCMgYY25enTp2FhYYMHD3Zx\ncTE0NOTz+U5OToMGDVqxYgXdjfwt5OfnBwYG2tnZGRkZjRo16tatWxIBfn5+dN2uzMzMBQsW\neHh4+Pr6XrhwgRBy69atiRMn9uzZ09fXd8WKFcXFxc2eLi0tzdjYmD28fv16SkqKeEBxcfHC\nhQtdXV319fU9PDy2b99eX1/PvlpdXR0dHX3y5Em2JSkpaejQocbGxk5OTnPmzCksLCSE9OrV\ny9HRcfv27XQ39Wbl5eUtWbJkwIAB7733XlJSEiEkKytr4cKFffv2HTZs2Pr16ysqKthghmES\nExNnzpzZp08fKysrTU1NS0vL3r17jxkz5vvvvxePJK19ywAAAN6GArYcB2gFNTU106dPZ7+x\n9fT0OnToYGtrq6enxzYGBQUJhUKZui0sLLSxsSGEaGtrt2/fnhCipaV14cIF8RhfX19CyPPn\nz8UL33bt2v3yyy86Ojo8Hs/a2lpTU5MQYmNjU1BQIP2Mjx49Ev/BJIT4+vqyh8XFxXZ2doQQ\nTU1Na2trWgUOHTq0rq6OYZh///23V69ehJAvvviCxsfHx9MFmGkhSwixtbUtKioihJiammpr\na/v6+r569Up6StnZ2eJrbwsEgps3b1pbW4v/l8TZ2bm4uJhhmPLy8mHDhtFGCwsLPp9PCFFX\nVzczM6PZ6uvr79+/n1GCWyZTt6BQBw8eJIQcOHCgtRMBgDZo1QnC8X/c+8QTaGgjIiIifvjh\nh969e8fFxRUXF5eVlb18+fLVq1dlZWVlZWWnTp3y9vaOioratm2bTN1u2LAhMzMzLCystLQ0\nNzf38uXL6urq06dPz83NlYhct25dUVHRli1b8vPzs7Ky3nvvvUmTJjk5Ob148SIzM7O8vDw8\nPDwzM5NuXcRrGt1GhD1smE9ubm5MTExlZWVmZmZhYeGsWbPYXdy++OKLu3fvLl26dOHChTQ+\nJCTE3d391atXWVlZZWVlYWFhr1+/Xr9+PSGkR48ed+7cKSkp6dSp08qVK+mT6UatXbs2Nzf3\nq6++KiwsTE1NdXZ2HjBggFAoPHnyZElJyfPnzxcuXJiWlrZ27Vp6I3777bcpU6ZkZWXl5OSU\nlZUdP35cV1f3hx9+qK6uPn36tLOz89y5c0+dOtXqtwwAAODtYCMVaCPs7OzU1dVTUlLoI8+G\nhELhoEGD8vPz27XjOvT/8ePHbm5uOjo6ycnJbC3766+/jh49OjAwkF3v1s/PLyEhwcHBwc7O\nLiEhgTbevHnT3d39+PHj48ePpy0Mw3h5eZWWlv7zzz9jxow5e/YsIcTKykpi98GampoXL164\nuLjQwydPnvj6+sbHx9PDrl27fvzxx1988YX45+rcubOFhcX169dtbGycnJyuXr1KXyovL9fX\n1793716PHj3YHPr3719VVfXw4UParVAo3LZt25dffqmurr5kyZKAgICGOzI6OTnZ2tr+/vvv\n9PDevXu9evWKjo6eOXOmeLfl5eWPHj3q379/ZWXlvXv3xK/zrl274uLi6NiP4uLirl272tjY\n5Obmcrxl6enprq6uzdyt/6Wurs7xluG/fsoDG6kAgOJgIxWAJmVnZ3t5eTVVihFCNDQ0fHx8\nXr9+PXLkyPz8/CccEEKePXvWt29f8SfBo0aNmjp1anR0dGpqqkQCbNVLCHF2diaEdOrUiW3h\n8Xhubm7Pnj0jhJw+fTo2NtbY2JhhmC1btjwWQwdPs4cSnyIjI2Po0KESn8vLy+uff/4hhBQW\nFoqPrCgtLSWEODk5iefQvXv3Fy9eiL89LCwsIyNj5syZmzdvdnBw8PHxiYqKev78OVtfZmdn\ni3dCP5r41hg8Hq9Hjx6vXr0ihKSmpnp6ekr8luLp6UkzJIQYGRkNHTo0JSWF+y0jhHC/a9xv\nGQAAwNtBAQ1thLW1dVJSUlVVVVMBdXV1V69etba23rFjx4MHD6ysrAghVVVVUkY40W7Fy03q\nyy+/1NHRocNz2cYOHTqI17uGhob79u2TGCj87Nkzel4ejzd9+vSHDx/2799/7NixH3/88Zs3\nb7h8TGNj45KSEonGmpoaOr65W7duv/32W05ODm23tLQ0Nja+f/8+GykSiW7fvk1HiYgzNzf/\n+uuvMzIyVq9e/fTp0+DgYAcHBxsbm0mTJhFCHBwcbt26xU43vHnzJiGEjtVmPX782MHBgRDi\n7Ox8+/ZtibmJqampAoGAPXz58qVAIOB+ywgh3O8a91sG7yIkJKTRPwsIhcLw8HBHR0ctLS0H\nB4eNGzfimgNA24MCGtqIgICAjIyMgQMH/vLLLxIlZnl5+ZkzZ/z8/G7cuBEYGEgIsbKyCggI\n4NKth4fHpUuXDh8+LP7n/g4dOmzZsuXGjRvLly9n24cOHfr7779v3LiRPvdVU1ObN2+e+LTC\nuLi4K1euDBgwgG2xtLQ8efLkkSNHfvvtty5duvz000+NDirIyso6d+7c48ePa2pqhg8fLpFM\nVlbW+fPn6fPgL774Ij8/v3///vv27cvOzubxeKtWrVqwYAEtKKuqqkJCQu7fvy/xDJtla2u7\nYcOGly9fnjlzJigoSE1N7eeffyaETJ48+d69e0FBQdevX//ll18CAgIMDQ03btyYlZVF3xgd\nHZ2YmEhn5n344Yd37tyZN28eHVTNMEx8fPyKFSsGDhxICMnNzQ0JCfnzzz9HjBgh0y3jfte4\n3zIFkTLAvSGFZqI4OTk5sbGxjb4UHBy8evVqQgj97WvNmjVBQUEtmhwAQAvgPt8QQJnV1NTM\nmDGD/cbW19fv0KFDx44dDQwM2EZ/f//a2loaf/z4cdLcs0yGYV69ekV7cHBwmDNnDtteX1//\n8ccfE0I8PDzoasoFBQWOjo70RBs2bBDvJCYmho5C1tfXp0MjJOTk5IwdO5YQMmbMGDqKmn1J\n/KeVx+PRAdM//vgjffXgwYP0Ae358+dpS3R0NFu1a2trs4/Ara2t6bAKLy8vWq2KL+7RKJFI\nlJqayjBMZWUlLY6prl27Pn/+3NTUVFdX19vb283NjRDSvn17usBITU2Nl5cXzdbGxoYuqWFk\nZETX+qATDXv27Pnvv//Kess43jXut0z6xxdXXl7OPZhmrqen58IB926VQW1t7cmTJ1evXk2/\nrxrmT/8I07t374qKCoZhysvL6bIwT58+ld4zVuEAAMVRxCocKKChTXn06NFnn33m4eHh4ODA\n5/P5fL6Dg4Onp+fy5csfP34sHllbW/vmzRuRSNRsn69fv54xY4aVlVXHjh3F2+vr6zds2GBi\nYsL+LlpZWfnVV19NmDBh79694pHLli1r3779xx9/LGXBOJFIdPToUYFAoKGhIV7bFRcX3759\n+9ixYxEREYGBgd7e3tbW1pGRkfRVR0dHPT29mJgY8a6qqqqOHTs2YcKEvn372tjYaGpqmpiY\nuLm5jR8//sKFC/X19UyD1fGaVV9ff/LkybCwsIiIiLy8PIZh7t69S2sjIyOjCRMmZGdns8FC\noXDXrl0DBgwwMDCwsbGZMmUK+8FPnToVFRUlXgFzv2UM57vG8Zbdv3+fy2cXiUQmJiZxcXFc\nvlsYhunWrRvtv1evXuHh4Y8ePeLyLpUgMdCoYQG9fPlyQsjPP//MtsTFxRFCwsLCpPeMAhoA\nFEcRBTRW4QDgqrKysuGMt6qqqrt37z579kx8SWMJ1dXVWlpaXP5en5eXt2rVqpycnHPnzkkJ\nYxiG9hYfH+/p6SllHl5TcnNzaWEt6xslVFZW0rWu37EfBZF+ywIDA1etWhUaGkp/aWnUixcv\nZs+eTVdB8fHx2b17t/jsyaakpaUdP378xIkTycnJhJAuXbpMmDBhwoQJ3bt3V9prxQXDMDU1\nNfRrHR0dFxcXiXmuXl5eSUlJRUVF7FaaxcXFxsbGgwYN+vPPP6X0jFU4AEBxFLEKBwrotqxl\nNoUm2BcaVJNAICgsLOzRo0d0dHTPnj0lXhWJRPv27fv8888rKirs7e2NjIzu3r2rrq6+aNGi\ndevWcfyxevXq1YkTJ06cOHH9+nWGYRwdHWkl3a9fP5WupAkhPB6vYQHt6Oj45s0bOg2Apa+v\nb2VlRZe1YaWlpZ04cYI9TE5OPn78OApoAFAELGOn8mTaYfjd94WW2BSayLgvNMdNoYmM+0Ir\nblNoUDky/URwD+YY+fjxY39///v37/fr12/t2rW1tbVsQFpa2pAhQxYtWlRfX//ll18+efLk\n1q1b0dHRFhYWX3/9dadOnQ4dOsTlu71Dhw6ffPLJn3/+mZ2dvW/fPgcHhx07dri7u9P2a9eu\nyXrFlFxubq7Ef3MIISYmJg13sUlNTV0pho5uBwBQGYoYawKNkmmHYbnsCy2xKTQjy77Q3DeF\npt1y3BdaQZtCgyqS6SeCe7CsW3nHx8ezK1vfunWrrq5u+/bt2trahJDhw4dnZGSIv7GiomLD\nhg26urqEkH79+v3999+yfuqCgoLDhw+///77qv5fYNLYGGg+ny8x7pxhGFtbWx0dHYnGrKys\nODHz5s0jGAMNAIqBSYSq7ZNPPiGEhIWF0VUFLl++zOfzBQJBTk4OG8P+u073eGP3hR4xYoSa\nmlq3bt1evnzJMExtbW14eDghZPHixTL9siRRQIeEhGhra8fExNTV1TEMU1RUNGvWLEJIZGQk\n/UPq0qVL2clh3bt39/DwoPVxTU1NWFgYIeSTTz5hu01NTe3bt6+2tvbnn3/eVGVPu2U3hR4w\nYICampq5ubn4ptD0vAzD0FPQTaEZhqmvrz9+/LiBgcGlS5dqampOnz7du3dvQsjJkycVcb9A\n0WT6ieAeLFO3VFVV1erVqzU0NNTV1enaxu3bt//xxx+bmjWYk5MTHBxMf5/s1KkT94U1ampq\nLl68GBQUREefa2hovN2lUwaNFtAODg4GBgYSjXp6eo6OjtJ7wyRCAFAcTCJUOty3F5ZpU2iG\nYRwdHTnuC+3g4MBxU+jHjx/zeDyO+0K/ePGC46bQ//zzD9tts/s53l+SAAAgAElEQVRCK2hT\n6Bs3bnC/F9w9fvwY3crUrUzBMu25zf3Hp0uXLm+xlbdIJAoNDd26dSshREdH5/r16/TXs0bR\n5Va+/PLL6urqhq82/I9qbW1tfHz8L7/8curUqaKiIk1NzeHDh3/00UcffPBBwwEPqqLRMdB0\nEmFZWRldvpD87386Bg4cKH28CiYRAoDiKGIMNJ5Av5NPP/1UfH816ddZW1t77ty5Ej1MnTpV\nTU3t4cOH9JB9MKatrT1//nw2jI42TklJEX9vcHCwjo6OSCSim0JbWlqeOnVKPKDZIRza2tpJ\nSUkSKfn7+xsaGuro6EydOpVtpFtmSKyGGxgYqKur27BbOsqZrmwwZMiQgwcPPnv2jD7M09HR\nCQ4OZiPLy8sJIbdu3RLvNigoiM/nMwyjq6s7e/ZsifT+/vtvfX199nDGjBk0mPu9kOmuoVuZ\nupUpWNafCI7BMnVLpaamDho0iBBiaGg4YsQIQoiOjs6WLVvEl6CmRCLRjz/+SOfLmpmZRUdH\nS1nbrrq6+uzZs/QHiiY2bty4I0eOlJSUNPUWFUKaXsbu9OnTbMvp06cJIXT/GinwBBoAFAdD\nOJRRVlYWl+2FGYZxdHQcPny4ROPLly91dXUHDBhA/6lm/13v1KmTj48PG1ZfX79v377CwkLx\n9w4dOpT9w2h2dvaHH35ICJkyZcq///5LG5stoC0tLSXGhjIMM3nyZDMzs/79+5uamrLjN0Qi\nkbGxcWJionhKPXr06Nu3b8NuqVevXq1evZpeHEKIlZXVxIkT3dzcevbsSZciZhjmypUrhJDY\n2FjxN3p5eXXt2pVhmJ49e/bu3ZsNpg4fPmxnZ8ceDh482NbWln7N/V5wj0S3iguW6SeCe7BM\n3VZWVq5atYr+sjd27Fj6DX/q1ClLS0tCSLdu3f766y+2k1u3btE9YtTU1BYuXCjx88iqqqo6\nderU9OnT6X4ufD5/4sSJx44dKysra/bSqZBGC2j6QHrIkCFCoZBhGKFQ6OPjQ7CRCgC0KhTQ\nSooOgWi2sKDrBNPJ++LtkZGRhJClS5eKRCL233U6pWbDhg1NPaw6duwYIWTatGlsi0gkOnLk\niImJiampKR2+2WgB7erqevbs2UePHlVXV8+aNWvSpEni+WRmZurr6/v4+NDnRjY2Nnv37qVD\nkLdv396jRw+6kV5lZeXSpUsJIStWrGCk7sohFArpvtB0XteGDRsIIbNmzbp27drPP//csWNH\nQ0PDTp06ZWZm0vjDhw+T/x0DTXetmz17Nh1RLRKJLl++bGZmNn36dIZhcnJyPv30U0KI+G5z\nHO+FTJHoVkHBMv1EcA/mHvnbb7/RzSPNzc0l9kkpLCyk24bzeLwFCxY8efIkMDCQjglxd3e/\nffu2lM9FRy/o6el9/PHHx48fp3vytT2NFtAMw9DdJT08PFauXEk3rg8MDGy2NxTQAKA4KKCV\nlNw3hWYY5q33hZayKTQj477QHDeFZrhta0f3hVbQptCy3guZItGtgoJl+ongHsw9kvL398/P\nz280w4sXL3bo0IF+BxJCBALBd999J/EnkYaILLhcTOXUVAFdU1Ozfv16Ozs7DQ0Ne3v7TZs2\nNRwJ0xAKaABQHEwiVFJCobCkpEQgEDS7M0JmZmZYWFhCQoKGhsaLFy/YdpFItGnTpl27dtGx\nzvSmVFVV7d2796+//vL19Z0/fz4b/Nlnn/3www9Dhw7dsmVLo3uXMAzz008/LV68uLS0lP4h\nlX2ppKQkIyMjPT1d/P9DQ0Pp2hdOTk55eXl79+6lz5AIIdXV1WfOnImLi3v58mVubu6///6r\np6dnaWnp4uIye/bsYcOG0YUIJOYmSicSic6cOZOcnKynpxcUFGRubn7v3r3AwMC7d+8aGRn5\n+vp+88039A/ohJC6uro9e/b89NNPqampBgYGAwcO3Lp1K/3Up0+fLigomDp1Kl1xTNZ7wT0S\n3SouWKafCO7BHCM7dOiwf/9+dkW5RpWVla1cuXLv3r1z5syJiIjgMrxbpqmcEpPw/rMwiRD+\nUxQyp03Gzt+iZ9WFnQjbjrfeFJpw3hea46bQRGn2hVbyTaFBoWT6ieAeLD3yww8/lFi4pikH\nDhyYM2cO1w8DskMBDf8pKKBbGHYiVGrnzp0Tf9Yl3ZUrVxoG6+joeHp6NqyeJXrW1tZuqsoU\nj2zfvv13330npXpmg9ne/Pz8Gq2em/1oFhYWbPXM/TpIRPL5fCnVs0yX961zQLetFSzTTwT3\nYOmREtWzlGwlqmfFXTEAAFAJ6uvWrWvtHNoId3d3LS0tOuW8tYLRrZLkgG6VJAfV6va/7O7d\nu2fOnPnggw/69OnT2rkAKNzvj9dzjBzaeZ2COn+LnlWXIi44nkDLzYQJE+Lj40UiUSsGo1sl\nyQHdKkkOqtUtAACoChTQcvPNN98IBILg4OBHjx5VVVW1SjC6VZIc0K2S5KBa3QIAgKrAJEK5\nkT77jWmwhJwigtGtkuSAbpUkB9Xqtg0oKSkxMjJq2H7t2rWBAwdKeSMmEYJKk3WOGiYRtjBF\nXPB2b5sMSHJxcWn1YHSrJDmgWyXJQbW6bQMyMjIIIa6uruyy8RTdzBxAhSi0xlUe/5GPqQgo\noOVGpvVcFRSMbpUkB3SrJDmoVrdtQHp6OiFk27Zto0ePbu1cAEDOlOpBe6tDAQ0AAPJBn0A7\nOTm1diKgqhQ3/KBtF3PQ8lBAy019ff2WLVtOnz5dUlLS8FWJp1AKCka3SpIDulWSHFSr2zYg\nPT2dx+PZ29u3diIAAIqFAlputm3b9sUXX7RuMLpVkhzQrZLkoFrdtgEZGRn6+voLFiy4ePFi\nYWGhs7Pz8OHDV69ebWBgIBGZlpZ24sQJ9jA5ObllMwUAeCdYxk5uDh8+bGhomJCQIBQKmQZa\nJhjdKkkO6FZJclCtbtuA9PT00tLS8vLygwcPJiQkTJw4MTIysmfPnsXFxRKRqampK8UcP36c\nfWn1SR7H/7XshwMA+P/wBFpuXr16NWfOnKFDh7ZiMLpVkhzQrZLkoFrdqjqGYUJDQwUCweTJ\nk+nifZ6ennZ2dv7+/qtWrYqMjBQP7tevX1xcHHt45cqVb7/9tqUzbrsw3rctwd1UTiig5cbR\n0VFXV7d1g9GtkuSAbpUkB9XqVtXxeLyFCxdKNE6dOjU4ODg+Pl6i3crKauLEiexhaWnp251U\nqWoLrL/bkFLdIAA5whAOuRk5cuSvv/5aW1vbisHoVklyQLdKkoNqddsmqaurm5mZZWVltXYi\nIDcYYwNAsBOhHAmFwtGjR6urq69fv97NzY3P57d8MLpVkhzQrZLkoFrdqrr4+Ph58+aFhoYG\nBQWxjcXFxSYmJu7u7n/99ZeU94rvRCjTM0ulesAp0xNohX7MNt95CyxjpwwfU6GdK08mRPE/\nntiJULk0tUPvhQsXuHeioGB0qyQ5oFslyUFpu21LjzA8PT3z8vLWrVs3YsQIKysrQohQKAwJ\nCWEYZsKECa2d3f9Q3eocAJQKCui3pww79D5//lwRS66qVrfKkAO6VZIcVKvbNobP5+/Zs2fm\nzJmdO3ceOXKklpZWUlJSWlqaj4/Pp59+2trZQRuE33CgFaGAfnttbAcEAIB35O/vLxAItm7d\nGh8fLxQK3dzclixZMn/+fHV19dZODQBAnjCJUJ6ys7PXrl0bGxtLD7du3bpgwYLMzMyWDEa3\nSpIDulWSHFSr2zZg1KhRf/zxx5s3b4qLixMTExctWoTqGQCapXKTU1FAy83Tp0979+69YcOG\njIwM2lJRUbFv376ePXs+e/asZYLRrZLkgG6VJAfV6hYAAFQFCmi5WbNmzZs3b3755Zc1a9bQ\nlvXr1//xxx9lZWVsi6KD0a2S5IBulSQH1eoWAABUBcZAy821a9fGjBkjMdl88ODBY8aMuX79\nessEo1slyQHdKkkOqtUtAACoCjyBlpuSkhIzM7OG7cbGxvn5+S0TjG6VJAd0qyQ5qFa3AACg\nKlBAy42bm1tiYqJQKBRvrKurS0xM7Ny5c8sEo1slyQHdKkkOqtUtAACoChTQcjNx4sTU1FR/\nf//Xr1/Tlry8vKCgoNTU1A8//LBlgtGtkuSAbpUkB9Xqtm0QCoXh4eGOjo5aWloODg4bN26U\n+OUBAODdtfqqHdjKW27q6+vff//9+Ph4QoipqWm7du1yc3MJIf3797927ZqmpmYLBKNbJckB\n3SpJDqrVbdswc+bMmJgYBwcHT0/PxMTE58+fz5gxIyYmRvq7Wmwrb2XoXHkyUd3OlScT1e1c\neTJRws65wBNouVFXV7906dJ33303ePBgNTW1qqqqAQMGbNu27fr16w3/jVRQMLpVkhzQrZLk\noFrdtgFPnjyJiYnp3bt3SkpKbGxsSkpKr169YmNj09LSWjs1AAB5whNoAACQjxUrVmzbtu3n\nn3/+6KOPaMvPP/88adKksLCwTZs2SXkjnkC3Siaq27nyZKK6nStPJkrYORd4Ag0AAPKRmJhI\nCPHz82Nb3nvvPULItWvXWi0nAAAFwDrQAAAgH7m5ufr6+kZGRmyLkZGRnp5eXl6eRGRaWtqJ\nEyfYw+Tk5BZKEQBAHjCEAwAA5ENXV9fU1PTly5fijR07diwuLi4pKRFvPH369NixYyXeTodw\nKDxLAIB3hifQAAAgNzye5FhDhmEarmTXr1+/uLg49vDKlSvffvutwpMDAJATFNAAACAfFhYW\nDbdXLCoqsrKykmi0srKaOHEie1haWqrw5AAA5AeTCAEAQD4sLCxKS0vLy8vZlvLy8vLycktL\ny1bMCgBA7lBAAwCAfHh5eRFCrly5wrbQrz08PFotJwAABcAkQgAAkI8nT564uroOGTLk8uXL\n7dq1q6urGzZs2O+///706VNnZ2fpb/zjjz8GDx7s6uraYtkCALw1FNAAACA3/v7+sbGxHh4e\n3t7eV69evXHjRmBgYFRUVGvnBQAgTyigAQBAbmprazdv3nz48OGsrCwbG5vg4ODly5draGi0\ndl4AAPKEAhoAAAAAQAaYRAgAAAAAIAMU0AAAAAAAMkABDQAAAAAgAxTQAAAAAAAyQAENAAAA\nACADFNAAAAAAADJAAQ0AAAAAIAMU0AAAAAAAMmjX2gkAAAAQQsi9e/daOwUAANKzZ89mY1BA\nAwCAUqivr2/tFAAAOMEQDgAAAAAAGaCABgAAAACQAQpoAAAAAAAZoIAGAAAAAJABCmgAAAAA\nABmggAYAAAAAkAEKaAAAAAAAGaCABgAAAACQAQpoAAAAAAAZoIAGAAAAFda3b98JEybIqzeh\nUDhr1qy+ffsmJibKq09oe1BAAwAAAPwPDQ2NL7/80sDAICoqimGY1k4HlBQKaAAAAID/z9LS\nct26dQ8ePLhz505r5wJKCgU0AAAAwP8xePDggICA06dPt3YioKTatXYCAAAAAEpn4cKFrZ0C\nKC8U0AAAAMBJ3759O3bs+NVXX+3cuTMlJcXExMTd3X3RokV8Pp8GMAxz8eLFX3755eXLlzU1\nNVZWVqNGjZo6dWq7dv9Tb2RmZu7du/fRo0d5eXkWFhZDhgyZNWuWgYEBfXXChAkvX768detW\nw5MeP36c9n/y5Mlz585lZGSYm5v37t177ty5EklWVlbu37//77//zszMtLGxcXd3nzdvno6O\nDhvw22+/nThx4smTJ6ampp6ennPnzh08eDB7Co5JXr16df/+/cnJya9fv27fvv37778fGBjI\n8WNCG4ACGgAAALgqKiqaO3fuyJEjP/zwwzt37hw7duzvv/8+evSolpYWISQmJuabb74xMjLq\n1auXhobG7du3d+/eXVRUtHTpUkLI/fv3FyxYIBKJvLy8+vXr988//8TExCQkJERHRxsbG3M5\n++rVqy9evKivr+/u7t6uXbv4+Pjbt2+LB9TU1Pj7+7948cLFxWXEiBGPHj06cuRIUlLSDz/8\nQDPcuXPnkSNHjI2NPT09eTzehQsXHj9+LN4DxySXLFnSpUuXlStXVlVV7du378CBA9XV1UuW\nLJHLxwTlhwIaAAAAuCotLV20aNGsWbMIIb6+viYmJvv27Tt27Ji/vz8h5NixY3w+/+TJk/r6\n+oSQioqKESNGXLhwYenSpfX19eHh4VpaWlFRUfb29oQQhmGioqK+/fbbb7/9NjQ0tNlTJyYm\nXrx40d7efu/evWZmZoSQwsLC+fPni8ccOXLkxYsXH3744RdffKGmpiYSiSIiIk6dOvXTTz/N\nnDkzJSXlyJEjnTp12rt3r5GRESGkpKRkwYIF7Nu5J9m1a9eQkBD6tbW19fjx4//6668lS5a8\n+8cElYBJhAAAAMAVj8ebOHEiezh58mRCyNWrV+mhUCisqqp6/PgxXQBOV1f3zz//vHjxIiHk\n5cuXz58//+ijj2hZSbsKDAzU09P766+/uJw6Pj6eELJkyRJaPRNCTExMJEYq00wWLlyopqZG\nCFFTU6MVNm0/d+4cIWTx4sW0eiaEGBoaivfAPcnx48ezX9va2hJCamtr5fIxQSXgCTQAAABw\nZWpqqquryx7q6emZmpq+fv2aHi5dujQiImL+/PkODg59+/bt06ePh4cHHSH94sULQsihQ4cO\nHTok0WddXR2XUz9//pwQ0r17d/HGbt26iR9mZmaamJiYmJiwLQKBwNjYmGZIe3BzcxN/S5cu\nXdivuSdpY2PDfs3j8d6iB1BpKKABAACAq4ZVYG1trUgkol+PHj26f//+f/zxR3JyckJCQlxc\nnIGBwfr16wcNGkSn8c2ZM2fYsGHcT1ddXc1+raGh0TCAPmmWjsfjCYVCQgj9fyk9cE+SnS8o\n4e0+JqgcDOEAAAAAroqKiv7991/2MDMzs7S0tGPHjvTw/v371dXVEydO3Lp164ULF3bv3l1W\nVrZlyxZCCI0pKCiwE2NtbZ2amlpQUCB+CrYcJ4RkZGSwX3fo0IEQ8uDBA/Hghw8fih/a2NgU\nFhYWFhayLfSQnt3BwYEQkpqaKv4W8UmE3JNsyrv3ACoBBTQAAADIYO/evbTGraur2717NyFk\n8ODB9KVVq1YtXbqUPjZWU1Pr2bMnn8+ng4MtLS179Ohx5swZ8ZL3+++/X7NmTVpaGj2kgz3u\n3btHD4VC4f79+9lg+kx39+7d+fn5tKWoqOibb74Rz83b21s8Q5FIFBkZyWY4fPhwQkhkZGRJ\nSQmNLysr27NnD/t2LklK9+49gErAEA4AAADgysDAICkpafr06S4uLikpKS9evOjQocO0adPo\nq8OHD4+Ojp4yZUr//v2rq6tv3bpVUVHx0UcfEUJ4PN5nn302d+7cgICAgQMHtm/fPj09/e7d\nuz169Bg3bhx9++DBgx89ehQSEvLBBx9oa2tfv37dysqKPbW7u/vIkSPPnz8/adKkfv36tWvX\n7ubNm46OjuLpTZs27fz586dOnXry5Ennzp1TU1MfP35sZ2c3ffp0Qkj//v3HjRt38uTJiRMn\n9u/fX01NLTk52cvL6+HDh3RIBpckpXv3HkAl8Og8WQAAgNYlsaAvKCG6p8nOnTu/+uqrlJQU\nY2NjupEKO62wrq7u6NGjv/76a05ODiHExsZmzJgxkyZNYscZ5+TkREZGPnz48M2bN9bW1sOH\nD//444/ZfVhEIlFMTMyZM2dyc3P19fVHjhw5f/58T0/PRjdSUVdX9/Pz++STTyS2QamsrPz2\n229v3LiRnZ1tZWXl4eExd+5c8a1ezp49e+rUqfT09A4dOowYMWLUqFF+fn4DBgygz6qbTbLZ\n3V6a7QGUXJ8+fZqNQQENAABKAQW08pMoE9uGx48fT58+/YMPPli7dm1r5wJKgUsBjTHQAAAA\n8J9w+fLl/v37R0dHizeeP3+eENK3b9/WyQlUE8ZAAwAAwH+Cp6enlZXVoUOHbGxsPD09Kysr\nL126FBcXZ2tr+/7777d2dqBKUEADAADAf4Kuru6+ffsiIyNXrVpFF7TW0dEZMGDA8uXL1dXV\nWzs7UCUYAw0AAEoBY6ChxYhEovz8fA0NDSMjI/F9BAEItzHQeAINAAAA/y1qamrm5uatnQWo\nMEwiBAAAAACQAQpoAAAAAAAZoIAGAAAAAJABCmgAAAAAABm8awHNk0pTU9PS0tLX1zc8PJzu\n6gkAAKorMzPT39/f2dmZz+d369Zt2bJlxcXF7KslJSWN/ltw/fr1VswZAEDu3nUZO+6Lv2ho\naKxZsyY0NBRLLQIAqKKsrCw3N7eSkpJhw4bZ29snJyffuXPHxsbm7t27pqamhJA7d+706dPH\n1dXV2tpa/I07d+7s1q2blJ6fP39+69YtLS0tiTcCALQ8LsvYEebdyJrT3Llz3/GM/2WPHj3i\ncpG1tLS499nwO4FLi3wpuv93ocy5gay+//57Qgifz8/Pz5fpjRy/Dd68ecPn8wkh33///Tuk\nqbxmzJhBCImKiqKHIpFo5cqVhJCAgADacuzYMULI2bNnZe354MGDhJADBw7IM10AAIVp6THQ\n+/fvP3fuXAufFACgqKjos88+I4TMmzdPIBAo4hSmpqazZ88mhCxfvryoqEgRp2hdCQkJDg4O\nAQEB9JDH461fv15bW/vKlSu0JSMjgxDi5OTUaikCALQIOW+kcvfuXfZrhmEKCgoSExN37NhR\nWlrKtkdGRo4ePVq+5/2PUFdXl/iHv6CggH4h3q6lpdWiaQGogi+++OLNmzeampohISGKO8uy\nZcv27Nnz77//rlq1as+ePYo7UcurrKzU0NDw8/MTH7mnqalpZGTEDoNOT0/n8Xj29vatlCMA\nQAuRcwHds2dPiRY/P7/u3buPHz+ebbl586Z8T/rf4ezsnJ+fL97C/ksm0c7d5cuX3zUtAKX3\n8uXL/fv3E0ImT56s0FG2tra2kyZNOnr06P79+z///PMOHToo7lwtjM/nv3jxQqIxISEhNzd3\nxIgR9DAjI0NfX3/BggUXL14sLCx0dnYePnz46tWrDQwMJN745s2b+/fvs4epqamKzB0AQN7e\ncQgIl95KSkrEY/T19d/xpMCScuUrKiq++uord3d3c3NzHR0dFxeXGTNmPHz4sNkeuLTU19f/\n+OOPH330UadOnbS0tGxtbWfOnPnPP/9wyfnOnTu+vr66uroCgWD27Nni3x7iYRxPUV1dHRkZ\n6eXlZWVlRT/mqFGjTp48WV9f32wmqampc+fO7d+/v56enp2d3ciRI3/77bemrk9tbW1YWJil\npaW+vv6gQYOSkpIaDWMY5tixY927d7ezsxMPyM7O/uyzzzp37qyjo6Orq+vm5vbZZ59lZ2c3\n1cnFixeHDBliYGCgpaXVu3fvY8eOMQzz/PnzKVOmCAQCPT09d3f3RkeacjmRTNetZTJX9O3+\n9NNPaYYJCQkN0xaJRPv37+/Ro4e2trZAIBg/fvzdu3cb/YA1NTURERGurq5aWlrm5ubTpk1L\nT0+XONelS5do8LJly6Rnpep+/fVXPp+vpaWVnJxMW+gvJ5MmTTp//nxiYuKGDRu0tbXt7e2L\niook3nvq1KmG/x5hDDQAqIqWKKCTkpLEY4YOHfqOJwVWU1e+oqKi0Tnvmpqat2/flt5Dsy1C\nofCjjz5q2Hm7du1++OEH6QlfunRJT09P4vuh4Rk5nqK0tLRr164NwwghPj4+NTU1UjI5ffq0\npqZmwzfOmzev0evDjvuk1NTU7t271zDs0KFD9Atra2v21cTERGNj44bnMjExSUxMbNjJ4sWL\nGwZv2rTJzMxMovH8+fPi2XI8Effr1jKZK/p2l5WV6evrE0JsbGzES2327cuWLZPoUENDQ7zK\nZ9vHjBkjEamvr//333+Ln66urs7KyooQYmBgUFZW1lRWKu3ly5fTpk2j3wnsrRSJRJGRkT/+\n+KNIJGIjY2JiCCELFy6U6OHhw4efi6HPsFFAA4CqUGABLRKJ8vPzz5w54+DgIP7Pkvi/u/CO\nGr3yDMPQqfGEEDU1taFDh44YMcLQ0JC2+Pr6Su+h2ZYNGzbQQx6P99FHH4WEhPTo0YO9v/fv\n328q28rKyvbt29NI+nhSYrg2G8nxFPPmzaONRkZGI0aMmDNnzrBhw9q1+5+BSStWrGgqk6ys\nLFpREULMzMxGjRrF9k8IES/axNMzMjJycXFhD8ePH98wjL3ObAGdn5/PfmpNTc3Bgwd7e3uz\nH9zCwqKwsLDhuTQ1NTt37txwsIGJiUnnzp3Zw4EDB7I5cD8Rx+vWYpkr+nafPXuWxsyePVu8\nXSJDFxcX8ftrYWFRWVnZVGSnTp3Yw06dOtXV1Yn3HBwcTF86d+5cU1mpqPr6+r179+rq6qqp\nqQUEBOTk5EiPr6ur09TUdHFxkR6GVTgAQLW06DJ2hoaGv/76q1zyBoq9thLt7GD03bt305bn\nz5/TFhMTE+k9SG+pqKhga0R2Nau6urrhw4fTxsmTJzeV7a5du2iMnZ3dq1evGIZ5/fq1+Hwj\nGsb9FI6OjrQlIyODPcuFCxdoo5ubW1OZrF69msb069evtLSUYRiRSDRnzhzaOGDAgIaffezY\nsbSc2rp1K22xt7dvGGZhYfHzzz/n5OS8efOGvhQREUFfat++/YMHD2jjgwcPLCwsaPvmzZsl\nOhk6dGheXh7N6ptvvmHbp0+fXltbyzDMTz/9RFuMjIzYHLifiON1a5nMW+B2L126lMYcOnRI\nvJ1Nz9jY+M8//6SN165dYx+679y5U0pkQkICO7T3p59+Eu+ZloOEkE8//bSprFRRfX391KlT\nCSHu7u4Nx4M1xdraWk9PT3oMCmgAUC0tV0A7OjpmZWXJJWlgsZdXov3o0aOxsbGxsbHserfJ\nycmNBjdslN7C9mNgYCD+1O38+fO0XaJAF8eWRIcPH2YbDx8+LHFG7qfQ1tamLYGBgbdv36Z/\nna+rq7t8+fLly5fFR7tKGDZsGH3jmTNn2Ma0tDSBQCAQCBp99PjkyRPa8u+//9IWHo/X8BId\nP35c4lyjRo2iL+3Zs0e8PTIykrZ/8MEHEp2kpqayYTU1NdQngncAACAASURBVGz7s2fPaGNt\nbW3D28T9RByvW8tk3gK3u3///vSNEn8eYTNhC2Vq+/bttH3YsGESkbt27RKP3LJlC20fM2aM\neDu7HpH4L2NtAP1bwZIlSxodMHP58mVHR8eDBw+KNxYVFfF4vGavAwpoAFAtLfoEetSoUZmZ\nmXLJGyiJWkRCRkZGZGTkrFmzevTowf6lm7xbAX306NFmbzT7l30J7BPEly9fso3i8/ppC/dT\nDBkyRLzR3Nx8+vTpsbGx//77r/Trxq5Tm5ubKz2S7ZwdOysSiaRcooKCAokenJ2d6Uviz00Z\nhklLS6Ptrq6uTZ1LSnvDHLifiON1a5nMW+B2s0thSIw3YLuSmAj49OlT2u7o6CgRKXEpnjx5\nQts7d+4s3p6VlUXbJeaSqrSqqiqBQODh4SE+xFlcRUWFnp6ejY0N+6yktraWTh7Ytm2b9M5R\nQAOAamnpnQitra0bTlqHtyZRi7BKSkomTZokfuV1dHQaDW7YKL1l27Ztzd7lmzdvNpotO3y2\nqqqKbayqqpI4I/dTvH792s/Pr+Gr7dq1CwoKqqioaOq6sZlIn2jY1BXmctFY7HNT9qk2VVFR\nQdt1dHSkd8IxB+4n4njdWibzFrjd7De/+DeeeCZNfUBtbW2OkeylkGjn8/lNZaVyEhMTCSG2\ntra+jaExdK9HAwODKVOmzJw5k/4O5uPjIzFGvCEU0ACgWuS8DjTzf0vqurq658+fHzlyJCIi\nQigUEkKysrJ8fX0fPnyoq6sr31ODuDVr1sTFxRFCzMzM/P39fX19PTw8Gl1OQVa2trb0C3t7\ne/aP7BI6duzYaHv79u1fvXpFCHnz5g3bDzsi4i1OYWNjc/ny5SdPnvz8889nz55llxivq6uj\no2mjoqKkZ1JQUGBpadn4R5UTGxub9PR0QkhOTo74hNqcnBz6hbyWJeZ+Io7XrWUyb4Hb3azs\n7Gz2zyP0kH7RcBVniUj2STM725JiF2hnZH/KoLSePXtGCHn9+vXr16+bivH39xcIBFu3bo2P\njxcKhW5ubkuWLJk/f766unoLZgoAoHjvWIBz7G3dunXiYV9++eU7nheopq48+w//ixcvaMvD\nhw8bDW7YKL3l9u3b9GttbW06LYw7b29v+t6YmBi2ka5yJX5G7qe4e/fu3bt3X79+TQ9zcnK+\n++67Pn360LeLT7CT4OPjQ2NOnDjBNubn5y9cuHDhwoWLFi2qrq5u6mo02ijlR2DkyJH0pb17\n94q3syOJR48eLb0TjjlwPxHH69YymbfA7W52CIfEyGZ2DHTDDygxWpp9fP7++++Lt7fJIRwK\nhSfQAKBaWqiAZscUUr169XrH8wLV1JVn/2ZNB8zU1dVNnz6dYx0mvaWystLIyIgeitdVJ0+e\npKuAeXt7C4XCRrPdvHkzfaODgwMdDZ+ZmSn+PI+GcT8FXcGjS5cu5eXlbBi73oi5uXlT1+2z\nzz6jMd27d6fzLEUi0ZIlS2hj7969pV9hLheNtWnTJvqShYUFu5bFvXv3zM3NaXtERIT0Tjjm\nwP1EHK9by2TeAre72UmExsbG169fp41//PFHw3zYSPEFsH///Xd2/RCJMb737t2j7e7u7k1l\nBeJQQAOAammhArq8vFw8TFdX9x3PC1RTV55dxs7Q0HDkyJGurq5N3aaGjc22rF+/nh7yeLyJ\nEycuW7Zs+PDh7CTFHTt2NJVtcXExu/qytrZ2nz59xEdmi5+R4ylmzpxJW2xtbf39/efNmzdq\n1Ci2vBs3blxTmWRkZLADfE1NTT/44APxdaCjo6OlX2EuF42Vn5/PpqSlpeXt7S2xmjI777Cp\nTjjmwP1EHK9bi2Wu6Nv9ySef0Bh2mTyJTOipXV1dxX9S7O3t2SHyEpGdO3cWXzHa1tZWYmw0\nO5jkk08+aSorEIcCGgBUSwsV0Pfv3xcPk/LHVpBJU1ee3Q+PFRQUxFYk7B/BG+2h2Zba2trx\n48eTxoSEhEjfVDk6OlriLeI7JrJhHE9RUFDArqchwdzcXHytj4aOHj3a6LhMiYqnYW4cL5q4\n69evsw81xQkEgr/++qvZTjjmwP1E3K9by2Su6Nt97tw5GhYcHNxoJmPHjm3YYaMfsF+/fg0v\nxdWrVyXOOHv2bPpq29tIRUFQQAOAammJAlokEtFNX8X/EXrH8wLV1JUXiUTfffdd586ddXV1\nBw0a9N1334lEInYbZH9/fyk9cGmpr6+PiYkZN26co6Ojtra2k5PTlClTrl271tT6VuIuXrzo\n5eXF5/MdHR2DgoKKi4sb/RQcT1FeXv7NN98MHDiwQ4cOmpqa5ubmffr0WbduXbNLmzEMc+fO\nnVmzZvXq1YvP59vb248ZM+aPP/6QiGk0Ny6XSEJWVtaKFSuGDBliampqZmbm4+MTGhra1JDc\nt8uB+4kYWa5by2Su0NvNbuVtbW0tvhwEm0lVVdXatWsdHR01NDRMTEwmTpxIN/ppGFlWVrZq\n1SpnZ2e6u96sWbPYaQasuro6GxsbQoi+vn5b3cpb7lBAA4Bq4THvNkmcnWxOsdsHUNXV1c+e\nPTt06FBCQoJ4+7p169auXfsu5wUA4G7ZsmU7duwghFy+fJldC09Ba2UkJCTQU4SEhLDzEUG6\nqKio4ODgAwcOsA/vAQCUmZp8u+v1f3l4eEybNk2iejY0NFy0aJF8zwsAIMXixYvV1NQIIXSh\nYoWi45TU1dXZaaltSWZmpr+/v7OzM5/P79at27Jly8T/iEQIEQqF4eHhjo6OWlpaDg4OGzdu\npGuYAgC0JXIuoJs/n5rakSNHBAJBC58XAP7L7Ozs5syZQwg5duxYZmam4k6UmZl57NgxQsic\nOXOaWhBddWVlZXXt2jU2NtbBwcHf319TU3PHjh3dunXLz89nY4KDg1evXk0IoXs5rVmzJigo\nqNUyBgBQjBYtoG1tbRMSEkaNGtWSJwUAIIRs2rTJzMxMKBTSsRwKsmPHDqFQaG5uzi4C2JaE\nhoaWlJRERUVdunTp22+/vXXr1sqVKzMzM1esWEEDnjx5EhMT07t375SUlNjY2JSUlF69esXG\nxrLbvwMAtA0KL6D19fUdHBymTJkSGxubnp4+ZMgQRZ8RAKAhExMTuu/JgQMHCgoKFHGKgoKC\n/fv3E0K2bdsml40/lU1CQoKDg0NAQAA95PF469ev19bWvnLlCm2h6/eFhoby+XxCiK6ubmho\nKPnfYS0AAG3Gu27lLd/JNwAAijNz5kx2MWmigP98CQSCiooK+fapPCorKzU0NPz8/MTnjmtq\nahoZGbHDoBMTEwkh7DRNQsh7771HCLl27VrLJgsAoFjvWkADAMB/AZ/Pf/HihURjQkJCbm7u\niBEj6GFubq6+vr742uFGRkZ6enp5eXkSb3zz5o34/gCpqakKSRoAQDFQQAMAwNs4f/78xIkT\ntbS0NmzYQFtyc3NNTU0lwkxMTHJzcyUak5KSGu5fAwCgKlBAAwCAbF69ehUWFnbkyBETE5Mf\nfvihb9++7EsSmwMQQhiGabiSnbOz8+eff84ePnjw4MKFC4pLGABAvlBAAwAAVyKRaP/+/cuX\nL6+qqgoICIiIiLCwsGBftbCwEF/SjioqKrKyspJo7NKly+bNm9nDqKgoFNAAoEJaeh1oAABQ\nUSKRaMaMGQsWLOjatWtKSsqhQ4fEq2dCiIWFRWlpaXl5OdtSXl5eXl5uaWnZ4skCACgQCmgA\nAOBk06ZNR48eXbJkyZ9//tmlS5eGAV5eXoQQdlU79msPD48WSxIAoAWggAYAgOZVV1d//fXX\nHh4eu3bt0tTUbDSGbjq4c+fOuro6QkhdXd2uXbsIIbNnz27JVAEAFA1joJXIoUOHnj59um7d\nOm1t7dbOBQDg/7hz505BQUFmZiZd2llCfHw8IcTFxWXGjBmxsbGDBw/29va+evXqjRs3AgMD\nnZ2dWzxfAAAFQgGtROLi4i5duhQaGooCGgCUzbNnzwghr1+/fv36tZSwgwcPOjk5HT58ePv2\n7TY2Nps2bVq+fHlL5QgA0EIwhAMAAJo3ffp0pmlsmKam5po1a54/f15bW/vs2bOwsDANDY1W\nTBsAQBFQQAMAAAAAyEB5C+jMzEx/f39nZ2c+n9+tW7dly5YVFxdLiS8pKeE15vr16/JNTCgU\nhoeHOzo6amlpOTg4bNy4UWKPgKqqqvDw8O7du/P5fGdn57lz5zbchQsAAAAAVJSSjoHOysrq\n2rVrSUnJsGHDfH19k5OTd+zYERcXd/fu3Yb7xFIZGRmEEFdXV2tra/F2Q0ND+eYWHBwcExPj\n4OAwadKkxMTENWvWpKWlxcTE0FdFItGYMWPi4+N79Ogxbdq0tLS0AwcOnDlz5v79++bm5vLN\nBAAAAABagZQxba1oxowZhJCoqCh6KBKJVq5cSQgJCAho6i3Hjh0jhJw9e1ahiT1+/JgQ0rt3\n74qKCoZhysvLe/XqRQh5+vQpDTh58iQhZObMmfX19bRl27ZthJDFixc32/nw4cMJIcXFxYrL\nHwBACR08eJAQcuDAgdZOBACAEyUdwpGQkODg4BAQEEAPeTze+vXrtbW1xdfnl0CfQDs5OSk0\nsaioKEJIaGgon88nhOjq6oaGhhJCoqOjaUBycjIhJCgoSE3tf64tXRj15s2bCk0MAAAAAFqG\nMhbQlZWVGhoafn5+PB6PbdTU1DQyMpIyDDo9PZ3H49nb20vpOSMj4+OPP3Z2dtbR0enUqdOK\nFSuKiopkyi0xMZEQ4ufnx7bQJVGvXbtGD/X09AgheXl5bAAdAK2vry/TiQAAlFlISIirq6tE\nY4vNRQEAaF3KOAaaz+e/ePFCojEhISE3N3fEiBFNvSsjI0NfX3/BggUXL14sLCx0dnYePnz4\n6tWrDQwMaEBiYuJ7771XX18/cuTIoUOH3rx5c9u2bcePH79x44aZmZlEbzwez8XFhQ7YEJeb\nm6uvr29kZMS2GBkZ6enpsRVzUFDQ0aNHly9fbmpq2q9fv4yMjLlz5woEgvXr1zfM+ezZs6mp\nqezh8+fPpV8ZAABlkJOTExsbKxAIJNpbbC4KAEDrUsYCuqHz589PnDhRS0trw4YNTcWkp6eX\nlpaWl5cfPHjQ0NAwISEhIiLi+PHjd+7cMTIyqqurmz17tra2dmJiYufOnQkhDMOEh4evWbNm\nzZo1+/bt45hJbm5uw1mMJiYm7Dob5ubmZ8+e7dmzp4+PD21RU1O7ceNGv379GvZ27NixI0eO\ncDw1AEDrEgqFv/766507dw4dOpSfn9+wgE5PTyeEbNu2bfTo0a2RIABAC1H2AvrVq1dhYWFH\njhwxMTH54Ycf+vbt22gYwzChoaECgWDy5Ml04Ienp6ednZ2/v/+qVasiIyOfPn366NGjsLAw\nWj0TQng8XlhY2Pbt2y9duiRTSuIDS9izsyvZ3b9//7333jMzM9u9e3eXLl3++eefFStWjBs3\n7ubNm1ZWVhJvXLly5axZs9jD0NDQW7duEULIdslTNGIZ03wMAID8lJSUjBs3TkpAy8xFAQBo\ndcpbQItEov379y9fvryqqiogICAiIsLCwqKpYB6Pt3DhQonGqVOnBgcHx8fHE0IePXpECImI\niIiIiJAIY2vf169fV1RUsO01NTXsEA5jY+P27dsTQiwsLPLz8yV6KCoqYovjwMDAysrKO3fu\n2NjYEEL69u1rZGQ0bty4TZs27dmzR+KNXbt27dq1K3v41VdfNX09AABamUAgqKqqol/r6Og0\nDOAyFwUAoA1Q0gJaJBLNmDHj6NGj7u7uhw4d6tKly1t0oq6ubmZmlpWVRf53bt+6desmT57c\nVPzs2bPFn0a/ePGCfVy9dOnSXbt2EUIsLCyePXtWXl5OOySElJeXl5eX9+zZkxBCS2dvb29a\nPVNDhw4lhPz9999v8REAAJQHj8fT1taWEtDsXBTWmzdv7t+/zx6KzwYBAFB+SlpAb9q06ejR\no0uWLNm2bZumpmaz8fHx8fPmzQsNDaVrxlHFxcXZ2dnu7u6EkE6dOhFCcnNzxaeN19bWxsXF\n2djY0MaLFy+yLzU1idDLyyspKenKlStjxoyhLXRlPQ8PD0KIjo6OkZGRxCNqutCHiYmJTFcA\nAEDlSJ+LIh6ZlJQ0duzY1soTAOAdKeMydtXV1V9//bWHh8euXbukVM/5+fnsInSenp55eXnr\n1q3Lzs6mLUKhMCQkhGGYCRMmEELs7Oy8vLwOHTokvh7zli1bZsyY8eDBA+650QJ9586ddXV1\nhJC6ujr6ZHr27NmEEB6PN3To0IcPH37//fc0nmEYOjDDy8tLhksAAKBq6FyUH3/88aeffhox\nYoSnp+fq1asPHDjw/PnzVatWSQQ7Ozt/LkbKCksAAEqIxzBKNxctKSnJy8vL1taWPjaWQMc0\nkwYPiWNiYmbOnGlgYDBy5EgtLa2kpKS0tDQfH5/Lly+rq6sTQm7fvu3j41NZWTly5EhbW9uU\nlJRr1655eXldvny54WC+pp5AE0L8/f1jY2M9PDy8vb2vXr1648aNwMBAusEKISQ7O7t37955\neXkDBw50dXV98ODBzZs3nZ2d7969q6urK/2Dv//++5cuXSouLjY8aCQ9khBMIgSA1iTlP5Li\n6uvr+Xy+vb299MioqKjg4OADBw7QhxEAAEpOGYdwPHv2jBDy+vXr169fc3+Xv7+/QCDYunVr\nfHy8UCh0c3NbsmTJ/PnzafVMCOnTp09KSkpoaOjNmzfj4+Pt7e03btz4ySefNDoVRsrvFQcP\nHnRycjp8+PD27dttbGw2bdq0fPly9lUrK6uHDx9u2LAhISHhyJEjtra2ixcv3rBhQ7PV81vC\neh0AoMTE56IAALQZylhAT58+ffr06c2GNaxxR40aNWrUKClv6dix49GjR98pOUI0NTXp6tFN\nBQgEgq+//vodzwIAoFqanYsCANBmKOMYaAAAUDnNzkUBAGgzlPEJNAAAqBw+n79nz56ZM2d2\n7txZYi7Kp59+2trZAQDIEwpoAACQj2bnogAAtA0ooAEAQGZNzbRudi4KAEAbgDHQAAAAAAAy\nQAENAAAAACADFNAAAAAAADJAAQ0AAAAAIAMU0AAAAAAAMkABDQAAMgsJCXF1dW3YLhQKw8PD\nHR0dtbS0HBwcNm7cKBQKWz49AACFQgENAACyycnJiY2NbfSl4ODg1atXE0ImTZpECFmzZo34\nzt4AAG0DCmgAAOBEKBSeOnVqzZo1/fr1y8/Pbxjw5MmTmJiY3r17p6SkxMbGpqSk9OrVKzY2\nNi0treWzBQBQHBTQAADASUlJybhx4zZu3JiVldVoQFRUFCEkNDSUz+cTQnR1dUNDQwkh0dHR\nLZgmAIDCYSdCAADgRCAQVFVV0a91dHQaBiQmJhJC/Pz82Jb33nuPEHLt2rUWSRAAoIWggAYA\nAE54PJ62traUgNzcXH19fSMjI7bFyMhIT08vLy9PIvLNmzf3799nD1NTU+WbKgCAQqGABgAA\n+cjNzTU1NZVoNDExyc3NlWhMSkoaO3ZsS+UFACBnKKABAEBueDyeRAvDMA1XsnN2dv7888/Z\nwwcPHly4cEHhyQEAyAkKaAAAkA8LC4uGq3MUFRVZWVlJNHbp0mXz5s3sYVRUFApoAFAhWIUD\nAADkw8LCorS0tLy8nG0pLy8vLy+3tLRsxawAAOQOBTQAAMiHl5cXIeTKlStsC/3aw8Oj1XIC\nAFAAFNAAACAfdNPBnTt31tXVEULq6up27dpFCJk9e3YrZwYAIFcYAw0AAPLh4uIyY8aM2NjY\nwYMHe3t7X7169caNG4GBgc7Ozq2dGsB/Be9qIpcwZoiXojNp21BAAwCA3Bw8eNDJyenw4cPb\nt2+3sbHZtGnT8uXLWzspAAA5QwENAAAyYxim0XZNTc01a9asWbOmhfMBAGhJGAMNAAAAACAD\nFNAAAAAAADJAAQ0AAAAAIAMU0AAAAAAAMlDeAjozM9Pf39/Z2ZnP53fr1m3ZsmXFxcXS3yIU\nCsPDwx0dHbW0tBwcHDZu3CgUCuWeWLNnqaqqCg8P7969O5/Pd3Z2njt3bm5urtzTAAAAAIBW\noaSrcGRlZXXt2rWkpGTYsGG+vr7Jyck7duyIi4u7e/euqalpU+8KDg6OiYlxcHCYNGlSYmLi\nmjVr0tLSYmJi5Jub9LOIRKIxY8bEx8f36NFj2rRpaWlpBw4cOHPmzP37983NzeWbCQCAUikp\nKTEyMmrYfu3atYEDB7Z8PgDQVnFc7poobMVrJS2gQ0NDS0pKoqKiAgMDCSEMw4SFhW3evHnF\nihWHDh1q9C1PnjyJiYnp3bv3tWvX+Hx+RUXFoEGDYmNjV69eLcc1/Js9y5kzZ+Lj42fOnHno\n0CE1NTVCyFdffbV8+fLw8PDdu3fLKw0AACWUkZFBCHF1dbW2thZvNzQ0bKWMAJRRqxd/LaNt\nf0wlLaATEhIcHBwCAgLoIY/HW79+/a5du65cudLUW6KiogghoaGhfD6fEKKrqxsaGjpp0qTo\n6OhNmzbJK7Fmz5KcnEwICQoKotUz/Xr58uU3b96UVw4AAMopPT2dELJt27bRo0e3di6gFNp2\nCQX/Zco4BrqyslJDQ8PPz4/H+3/s3XlcVOUeP/DviOwgCI4iiyKLiGjiisBVAyUTy/QamF5B\nZTO1tCwtuMJ1AcqU1NRrC4tB2rUuN9NyuaLXxDFEc0NBZXEBWRIFbARxkPP74/l1fvMbZOYM\nzsCAn/erPziH73nOd+bMPH47POd5RPxOAwMDS0tLJcOgJRIJEU2aNInfExAQQETZ2dn8nuLi\n4tmzZ7u6uhobGw8cOHDlypU1NTVq5abyLGZmZkRUVVXFB7AB0Obm5mqdCACg02F3oF1cXDo6\nEQAA7dLFO9AmJiY3b95U2Hn06NHKysopU6a0dlRlZaW5ubn88DtLS0szMzO+lpVIJAEBAU+e\nPAkMDPT398/Nzd2wYUNmZmZOTo5YLFZoTSQSubm5Xb16Vd2zhIeH7969e8WKFb169Ro9enRx\ncfHChQutra3XrFmj3rsAANDZFBUViUSiAQMGdHQiXQ3u40KX1+k+5LpYQLd04MCBoKAgQ0PD\ntWvXthZTWVnZ8vlCKysrdgO4qakpMjLSyMhIIpG4u7sTEcdx8fHxbMnZHTt2CMxE+VmIqHfv\n3vv37/f09PTz82N7unXrlpOTM3r06JatLVy48Pvvv+c3pVKpwDQAAHRQcXGxubn54sWLDx06\ndP/+fVdX18mTJ8fGxvbo0UMh8u7duxcvXuQ38/Pz2zdTAIBnousF9O3bt2NiYnbt2mVlZfXN\nN9+MGjVKSbD8kA+G4zg2x9z169cLCgpiYmJY9cyCY2JikpKSDh8+rFZKSs5CRBcvXgwICBCL\nxZ999tngwYMvX768cuXKGTNm5Obm2traKhzYu3dvJycnfrOwsFAb8+4BALSPoqKiBw8eSKXS\n5ORkCwuLo0ePJiYmZmZmnjt3TmF2jlOnTk2fPr2j8gTQrE539xSene4W0M3NzV988cWKFSsa\nGhoWLFiQmJhoY2OjJN7Gxqa6ulphZ01NDStbCwoKiCgxMTExMVEhhq9ZS0tLHz58yO9vbGzk\nh3D07NmzT58+Ks9CRGFhYfX19efOnbO3tyeiUaNGWVpazpgxIyEhYfv27QoHrlu3bt26dfzm\nyy+/rG41L1SSYtH/FO9xWjk1ADwfOI6Ljo62traeNWsWu9Hg4+Pj6OgYGhq6atWqbdu2yQe7\nurp+8MEH/OalS5cOHjzIfkYt0s6ekzdc3Zf5nLwt0GY6WkA3NzeHhITs3r3by8srNTV18ODB\nKg+xsbEpKSmRSqXsMT4ikkqlUqnU09OT/ny2b/Xq1bNmzWqthcjISPn69ebNm/zt6mXLlm3e\nvFnlWVjpPGHCBFY9M/7+/kR0+vRp9d4CAIBORSQSLVmyRGHnnDlzIiIisrKyFPYPHjz4448/\n5jdTUlL4Avo5gfrsqQS+Lc/VewK6SRdn4SCihISE3bt3L1269MSJE0KqZyLy9fUlIvl57tjP\n3t7eRDRw4EAiqqysHCTHycnp7Nmz/PDlQ4cOcX8iIjc3N36TVc8qz2JsbGxpaalwi5pN9GFl\nZdXG9wIAoNPS09MTi8V37tzp6EQAADRJF+9AP3r0aMuWLd7e3ps3b2454JhXXV2tp6fXs2dP\nthkeHr5hw4ZNmzYFBgZ27969qamJVb2RkZFE5Ojo6Ovrm5qaumDBgjFjxrBD1q9fHxcXt2XL\nlhdffFFgbsrPIhKJ/P39//Of/3z99dfz5s0jIo7jNm7cSH9W3gAAXVVWVtabb74ZHR0dHh7O\n76ytrS0vL/fy8tLSSXEf96m0+rbgPQcg3Sygz507d+/evbKyMjbFsgL+T4FisVh+pjk3N7eQ\nkJCMjIzx48dPmDDh+PHjOTk5YWFhbIFAkUi0ZcsWPz8/Hx+fwMBABweHvLy87OxsX19fVvsK\npPwsRLR161aJRDJ//vzk5ORBgwZdunQpNzfX1dX1/ffff6Y3BQBAt/n4+FRVVa1evXrKlCns\nsRCZTLZ8+XKO42bOnNnR2f1fahV/Who1i7ISdIdWvxFdmy4W0CUlJURUWlpaWlqq1oHJycku\nLi5paWlJSUn29vYJCQkrVqzgfzty5Mi8vLzo6Ojc3NysrKwBAwasW7funXfeMTY2btkUG8XR\nhrPY2tpeuXJl7dq1R48e3bVrl4ODw9tvv7127VpTU1O1XgsAQOdiYmKyffv2efPmubu7BwYG\nGhoanjp1qrCw0M/P79133+3o7AAANEkXC+i5c+fOnTtXZVjLGtfAwIDN69zaIf3799+9e/cz\npqfyLNbW1lu2bHnGswAAdDqhoaHW1taffPJJVlaWTCbz8PBYunTpokWL9PT0Ojo1AABN0sUC\nGjqMkNnuCBPeAUCrpk6dOnXq1I7OAp4Lz8mIgufkZXY6OjoLBwAAAACAbkIBDQAAAACgBhTQ\nAAAAAABqQAENAAAAAKAGFNAAAAAAAGpAAQ0AABojAb+zQgAAIABJREFUk8ni4+OdnZ0NDQ2d\nnJzWrVsnk8k6OikAAA1DAQ0AABoTERERGxtLRMHBwUQUFxcnv7I3AEDXgHmgoU2EzBiN6aIB\nnjPXrl1LT08fMWJEdna2iYnJw4cPx40bl5GRERsb6+rq2tHZAQBoDO5AAwCAZqSkpBBRdHS0\niYkJEZmamkZHRxPRzp07OzYxAADNQgENAACaIZFIiGjSpEn8noCAACLKzs7usJwAALQAQzgA\nAEAzKisrzc3NLS0t+T2WlpZmZmZVVVUKkXfv3r148SK/mZ+f304pAgBoAgpoAADQjMrKyl69\neinstLKyqqysVNh56tSp6dOnt1deAAAaJuI4POmlK15++eXDhw/X1tZaJFuqjmaP6Al8mE+D\nYWo1CADPE1NTU7FYfPPmTfmd/fr1q66urq+vl9+Zn5+fnp7Ob166dOngwYNffvllZGRk+6QK\nAPAscAcaAAA0w8bGprq6WmFnTU2Nra2tws7Bgwd//PHH/GZKSsrBgwe1nh8AgIbgIUIAANAM\nGxubBw8eSKVSfo9UKpVKpX379u3ArAAANA4FNAAAaIavry8RHTt2jN/Dfvb29u6wnAAAtABD\nOECbMFQa4HkSHh6+YcOGTZs2BQYGdu/evampafPmzUSkcmTzoEGDoqKi3N3d2yVNAIBnhQIa\nAAA0w83NLSQkJCMjY/z48RMmTDh+/HhOTk5YWJjKZQh9fX3Z3WsAgE4BBTQAAGhMcnKyi4tL\nWlpaUlKSvb19QkLCihUrOjopAAANQwENAAAaY2BgEBcXFxcX19GJAABoER4iBAAAAABQAwpo\nAAAAAAA1oIAGAAAAAFADCmgAAAAAADWggAYAAAAAUAMKaAAAAAAANWAaO9ANWLMQAAAAOgnc\ngQYAAAAAUEMnuAO9fPnyAwcOXL16VXlYXV2dpaVly/3Z2dl/+ctfNJiPTCZbv359WlpaWVmZ\nnZ3dggULPvzwQ319fT6goaEhKSnpu+++KyoqsrOz8/f3X7NmjY2NjQZzAADoegoLCzs6BQAA\ncnV1VRmj6wV0RUVFRkaGtbW1ysji4mIiGjRokJ2dnfx+CwsLzaYUERGRnp7u5OQUHBwskUji\n4uIKCwvT09PZb5ubm6dNm5aVlTVs2LC//e1vhYWFX3755b59+y5evNi7d2/NZgIAGoNBRDrg\nwYMHHZ0CAIAgOlpAy2Syn3/++dy5c6mpqdXV1UIK6KKiIiLasGHDK6+8or3Erl27lp6ePmLE\niOzsbBMTk4cPH44bNy4jIyM2Npb9/8q+ffuysrLmzZuXmprarVs3Itq4ceOKFSvi4+M/++wz\n7SUGAAAAAO1DR8dA19XVzZgxY926dXfu3BF4CLsD7eLios28KCUlhYiio6NNTEyIyNTUNDo6\nmoh27tzJAs6cOUNE4eHhrHpmPxNRbm6uVhMDAAAAgPahowW0tbV1w58EHlJUVCQSiQYMGKAk\npri4ePbs2a6ursbGxgMHDly5cmVNTY1aiUkkEiKaNGkSvycgIICIsrOz2aaZmRkRVVVV8QGV\nlZVEZG5urtaJAAAAAEA36egQDpFIZGRkpNYhxcXF5ubmixcvPnTo0P37911dXSdPnhwbG9uj\nRw8WIJFIAgICnjx5EhgY6O/vn5ubu2HDhszMzJycHLFY3DIBNze3lk8uVlZWmpubyz+taGlp\naWZmxlfM4eHhu3fvXrFiRa9evUaPHl1cXLxw4UJra+s1a9a0zHn//v35+fn85o0bN9R6yQAA\nAADQ/nS0gG6DoqKiBw8eSKXS5ORkCwuLo0ePJiYmZmZmnjt3ztLSsqmpKTIy0sjISCKRuLu7\nExHHcfHx8XFxcXFxcTt27BB4lsrKyl69einstLKyYreZiah379779+/39PT08/Nje7p165aT\nkzN69OiWre3Zs2fXrl1tfMEAAAAA0BG6SAHNcVx0dLS1tfWsWbNEIhER+fj4ODo6hoaGrlq1\natu2bdevXy8oKIiJiWHVMxGJRKKYmJikpKTDhw+rdS7WvsLZZTIZ+/nixYsBAQFisfizzz4b\nPHjw5cuXV65cOWPGjNzcXFtbW4UD33777ddee43fTEhIuHjxolrJAAAAAEA76yIFtEgkWrJk\nicLOOXPmREREZGVlEVFBQQERJSYmJiYmKoTxtW9paenDhw/5/Y2NjfwQjp49e/bp04eIbGxs\nqqurFVqoqanhi+OwsLD6+vpz587Z29sT0ahRoywtLWfMmJGQkLB9+3aFA728vLy8vPjNlJQU\nFNAAAAAAOq6LFNBPpaenJxaL2Twe7Nm+1atXz5o1q7X4yMhI+bvRN2/e5G9XL1u2bPPmzURk\nY2NTUlIilUpZg0QklUqlUqmnpycRsdJ5woQJrHpm/P39iej06dMaf4EAAAAA0P50dBYOdWVl\nZbm4uLA55ni1tbXl5eVDhgwhooEDBxJRZWXlIDlOTk5nz57lhy8fOnSI+xMRubm58ZuseiYi\nX19fIjp27Bh/Fvazt7c3ERkbG1taWircomYTfVhZWWnrxQMAAABAO+rEBXR1dTU/CZ2Pj09V\nVdXq1avLy8vZHplMtnz5co7jZs6cSUSOjo6+vr6pqany8zGvX78+JCTk0qVLwk/KJnXetGlT\nU1MTETU1NbHaOjIykohEIpG/v/+VK1e+/vprFs9x3MaNG+nPyhueVZJI9X8AAAAA2tSJh3CI\nxWJ+pjkTE5Pt27fPmzfP3d09MDDQ0NDw1KlThYWFfn5+7777LhGJRKItW7b4+fn5+PgEBgY6\nODjk5eVlZ2f7+vqy2lcgNze3kJCQjIyM8ePHT5gw4fjx4zk5OWFhYfyy6Vu3bpVIJPPnz09O\nTh40aNClS5dyc3NdXV3ff/99bbwJAAAAANDOOvEdaAWhoaE//fSTp6dnVlbW3r17xWLx1q1b\njxw5oqenxwJGjhyZl5cXHBycn5+flpZ27969devWHTp0yNjYuGVrHMe1nASaSU5OXrNmTUVF\nRVJSUlVVVUJCwueff87/1tbW9sqVK0uXLq2pqdm1a1dtbe3bb7+dm5tramqqjVcNAAAAAO2s\nE9yBZiOSheyfOnXq1KlTlTTVv3//3bt3P2M+BgYGbPbo1gKsra23bNnyjGcBAACANhg1alT/\n/v0zMzM7OhHoyrrOHWgAAAAAgHaAAhoAAAAAQA2dYAgHQFsImY7jvaePDgIAgI4ikUgyMzOv\nXbt2//59Y2NjW1vbKVOmzJ49u1u3bkQ0c+bMW7dunT17Vv4QfszG3r174+PjiejWrVujRo2a\nN2/e22+/zQ6RSCSffPLJsWPH1q5dO27cOCIqKyv75z//WVBQUFVVZWNj8+KLL86fP79Hjx4d\n8qqh08EdaAAAANAJ+/fvX7Zs2YkTJ/r37z99+nQPD4/bt29v2rTpiy++EHL4yJEj165dS0TW\n1tZr16596aWX+F9FR0eXlJQEBwc7OzsT0cWLF2fNmvW///3P2dn5lVdeMTIySk9PDwkJ4afH\nBVAOd6ABBMD9bAAA7fvmm2+IKCoqKioqiu25ceNGUFBQdnb2okWLVB7u4ODg4OAQFxdnZmYW\nGBgo/ytLS8uNGzey29hPnjyJj483NDRMSUkZMGAAEXEcl5KS8vnnn3/++efR0dGaf2HQ5aCA\nBgAAAJ2QmJhIRH379uX3dO/enYgaGxufseW5c+ey6pmIbt26dePGjbCwMFY9E5FIJAoLC/vm\nm29+/fXXZzwRPCdQQMPzTeO3lnGvGgCgrZydnRsbG69evVpSUlJSUnLt2rXLly9rpGU7Ozv+\n55s3bxJRampqamqqQhhbZhhAJRTQAAAAoBPOnTv34Ycf3r9/38HBYdSoUS+//PI777wTGhqq\n5JBHjx4JadnQ0JD/mS2gFhUVJT9IGkAtKKABAABAJ8THxz948CAjI8Pd3Z3tqa+vbxnW3NzM\nj8coLi5W9yz9+/cnonv37jk6OvI7ZTLZkSNH+vTpI78ToDWYhQMAAAB0wr1798zMzNzc3Ngm\nx3HssUJ+7WETExMiunDhAtuUyWRPnaDjyZMnSs7St2/fYcOG7du378qVK/zOr7/+Oi4urrCw\nUBOvA7o+3IEGAAAAnTB+/PiDBw+GhYWNGDFCJBLl5ubW1tZaWVndvn37s88+i4qKGj9+fEFB\nwfLly1999VUjI6OTJ0/a2toqNGJoaHjnzp3t27ePHj16zJgxLc8iEonef//9hQsXLliw4C9/\n+UufPn2KiorOnz8/bNiwGTNmtMsLhU4Pd6ABAABAJ0RHR8+dO/f+/fv/+te/fv3111GjRu3Z\ns2ft2rX9+/f/6aefHj9+HB4e/tZbb1lZWWVmZu7bt2/s2LEfffSRQiNvvfWWpaXlrl275G8w\nK3B3d9+zZ09AQEBJScm+ffvq6uoWLVq0detW+aHSAErgDjQAAADoBBMTk3feeeedd96R3zl2\n7NjMzEx+c/78+fPnz5cPUFiYcPbs2bNnz+Y35Y+V17dv34SEhGfPGZ5PuAMNAAAAAKAGFNAA\nAAAAAGpAAQ0AAAAAoAYU0AAAAAAAakABDQAAAACgBhTQAAAAAABqQAENAAAAAKAGFNAAAAAA\nAGpAAQ0AAEKVlZWFhoa6urqamJgMHTr0vffeq62t5X9bV1cnepqTJ092YM4AABqHlQgBOkKS\nSHXMe5z28wBQw507d4YMGVJXV/fSSy9NnDjxzJkzn3766XfffXf+/PlevXoRUXFxMRENGjTI\nzs5O/kALCwvlLf/xxx93796tr683MTHRXv4AAJqCAhoAAASJjo6uq6tLSUkJCwsjIo7jYmJi\nPv7445UrV6amphJRUVEREW3YsOGVV15Rq+XvvvsuIiLiyy+/jIyM1EbmAACahSEcAAAgyNGj\nR52cnBYsWMA2RSLRmjVrjIyMjh07xvawO9AuLi4dliIAQLtAAQ0AAKrV19fr6+tPmjRJJPp/\nA5AMDAwsLS35YdBFRUUikWjAgAEdlCMAQDvBEA4AAFDNxMTk5s2bCjuPHj1aWVk5ZcoUtllc\nXGxubr548eJDhw7dv3/f1dV18uTJsbGxPXr0UDhQJpNJpVJ+s76+Xpu5AwBoGApoAABoiwMH\nDgQFBRkaGq5du5btKSoqevDggVQqTU5OtrCwOHr0aGJiYmZm5rlz5ywtLRWOnT59ekdkDQCg\nAZ1gCMfy5csHDRokJFImk8XHxzs7OxsaGjo5Oa1bt04mk2k8H5VnaWhoiI+Pf+GFF0xMTFxd\nXRcuXFhZWanxNAAAOsrt27fnzp07depUIyOjH374YdSoUUTEcVx0dPS33377r3/9a8qUKT4+\nPrGxsV9++eWNGzdWrVql0IJYLJ4kZ/DgwR3xOgAA2kjX70BXVFRkZGRYW1sLCY6IiEhPT3dy\ncgoODpZIJHFxcYWFhenp6ZpNSflZmpubp02blpWVNWzYsL/97W+FhYVffvnlvn37Ll682Lt3\nb81mAs8FTHgHuqS5ufmLL75YsWJFQ0PDggULEhMTbWxs2K9EItGSJUsU4ufMmRMREZGVlaWw\n38fH58iRI/xmSkpKRESEVjMHANAgHb0DLZPJ9u7dGxcXN3r06OrqaiGHXLt2LT09fcSIEXl5\neRkZGXl5ecOHD8/IyCgsLNRgYirPsm/fvqysrHnz5p07d+6rr746fvz4hg0bKisr4+PjNZgG\nAED7a25uDgkJWbx48ZAhQ/Ly8lJTU/nquTV6enpisfjOnTvtkyEAQPvQ0QK6rq5uxowZ69at\nE97tpqSkEFF0dDSbh9/U1DQ6OpqIdu7cqcHEVJ7lzJkzRBQeHt6t2/99b8PDw4koNzdXg2kA\nALS/hISE3bt3L1269MSJEy0HXWRlZbm4uLBOkldbW1teXj5kyJB2TBMAQOt0tIC2trZu+JPA\nQyQSCRFNmjSJ3xMQEEBE2dnZ/J7i4uLZs2e7uroaGxsPHDhw5cqVNTU1aiWm8ixmZmZEVFVV\nxQewAdDm5uZqnQgAQKc8evRoy5Yt3t7emzdvNjAwaBng4+NTVVW1evXq8vJytkcmky1fvpzj\nuJkzZ7ZvsgAA2qWjY6BFIpGRkZFah1RWVpqbm8s/6G1paWlmZsbXshKJJCAg4MmTJ4GBgf7+\n/rm5uRs2bMjMzMzJyRGLxS0TcHNzu3r1qrpnCQ8P371794oVK3r16jV69Oji4uKFCxdaW1uv\nWbNGrZcDoB4MlQYtO3fu3L1798rKythdAwVZWVkmJibbt2+fN2+eu7t7YGCgoaHhqVOnCgsL\n/fz83n333fZPGABAe3S0gG6DysrKXr16Key0srJiN4CbmpoiIyONjIwkEom7uzsRcRwXHx8f\nFxcXFxe3Y8cOjZyFiHr37r1//35PT08/Pz+2p1u3bjk5OaNHj27Z2sKFC7///nt+U35WVAAA\nnVJSUkJEpaWlpaWlrcWEhoZaW1t/8sknWVlZMpnMw8Nj6dKlixYt0tPTa8dMAQC0TkeHcLSN\n/PpYDMdxbI6569evFxQULFq0iFXPLDgmJsbCwuLw4cOaOgsRXbx4ccyYMWKx+Ouvvz5z5kxa\nWpq1tfWMGTP4v2nKMzU17Smne/eu8/8zANDFzJ07l2sdHzZ16tRffvnl7t27tbW1Eonkrbfe\nQvUMAF1P16nYbGxsWs7XUVNTY2trS0QFBQVElJiYmJiYqBDD176lpaUPHz7k9zc2NvJDOHr2\n7NmnTx+VZyGisLCw+vr6c+fO2dvbE9GoUaMsLS1nzJiRkJCwfft2hQM//fTTTz/9lN98+eWX\n1a3mAQAAAKCddakCuqSkRCqVssf4iEgqlUqlUk9PT/rz2b7Vq1fPmjWrtRYiIyPl69ebN2/y\nt6uXLVu2efNmlWdhpfOECRNY9cz4+/sT0enTpzX6cgEAAACgY3SdIRy+vr5EdOzYMX4P+9nb\n25uIBg4cSESVlZWD5Dg5OZ09e5Yfvnzo0CH5P0e6ubnxm6x6VnkWY2NjS0tLhVvUbKIPKysr\nrb10AAAAAGg/nbiArq6ulp+Ejk23vGnTpqamJiJqampiVW9kZCQROTo6+vr6pqamys/HvH79\n+pCQkEuXLgk/qfKziEQif3//K1eufP311yye47iNGzfSn5U3AAAAAHR2nXgIh1gslp9pzs3N\nLSQkJCMjY/z48RMmTDh+/HhOTk5YWJirqysRiUSiLVu2+Pn5+fj4BAYGOjg45OXlZWdn+/r6\nstpXIOVnIaKtW7dKJJL58+cnJycPGjTo0qVLubm5rq6u77//vsbfAQAAAABof534DnRLycnJ\na9asqaioSEpKqqqqSkhI+Pzzz/nfjhw5Mi8vLzg4OD8/Py0t7d69e+vWrTt06JCxsXHLpjiO\nazkJtJCz2NraXrlyZenSpTU1Nbt27aqtrX377bdzc3NNTU01/noBANpZWVlZaGioq6uriYnJ\n0KFD33vvvdraWvkAmUwWHx/v7OxsaGjo5OS0bt06/kFtAIAuoxPcgZafIEn5fgMDAzavc2tN\n9e/ff/fu3c+Yj8qzWFtbb9my5RnPAgCga+7cuTNkyJC6urqXXnpp4sSJZ86c+fTTT7/77rvz\n58/zE+RHRESkp6c7OTkFBwdLJJK4uLjCwsL09PSOzRwAQLM6QQENABojZMFCwpqF8HTR0dF1\ndXUpKSlhYWFExHFcTEzMxx9/vHLlytTUVCK6du1aenr6iBEjsrOzTUxMHj58OG7cuIyMjNjY\nWH6cGwBAF9ClhnAAAID2HD161MnJacGCBWxTJBKtWbPGyMiIn5goJSWFiKKjo01MTIjI1NQ0\nOjqaiHbu3NkxGQMAaAcKaAAAUK2+vl5fX3/SpEnyq7EaGBhYWlryw6AlEgkRTZo0iQ8ICAgg\nouzs7PZNFgBAuzCEAwAAVDMxMbl586bCzqNHj1ZWVk6ZMoVtVlZWmpubW1pa8gGWlpZmZmZV\nVVUKB8pkMqlUym/W19drJWkAAO1AAQ0AAG1x4MCBoKAgQ0PDtWvXsj2VlZX804Q8Kysrfr0q\n+WOnT5/eHlkCAGgBCmgAAFDP7du3Y2Jidu3aZWVl9c0334waNYr/lfwAD4bjuJYz2YnFYvmR\nHuXl5fn5+dpLGABAs1BAAwCAUM3NzV988cWKFSsaGhoWLFiQmJhoY2PD/9bGxqa6ulrhkJqa\nGltbW4WdPj4+R44c4TdTUlIiIiK0lzYAgGahgAaApxEy4R1mu3vONDc3h4SE7N6928vLKzU1\ndfDgwQoBNjY2JSUlUqnUzMyM7ZFKpVKp1NPTs92TBQDQIszCAQAAgiQkJOzevXvp0qUnTpxo\nWT0Tka+vLxHxs9rxP3t7e7dbkgAA7QB3oAHgGeBG9XPj0aNHW7Zs8fb23rx5c8uBzkx4ePiG\nDRs2bdoUGBjYvXv3pqamzZs3E1FkZGT7JgsAoF0ooAEAQLVz587du3evrKyMTe2sICsri4jc\n3NxCQkIyMjLGjx8/YcKE48eP5+TkhIWFYRlCAOhiUEADAIBqJSUlRFRaWlpaWqokLDk52cXF\nJS0tLSkpyd7ePiEhYcWKFe2VIwBAO8EYaAAAUG3u3Llc6/gwAwODuLi4GzduPH78uKSkJCYm\nRl9fvwPTBgDQBhTQAAAAAABqQAENAAAAAKAGFNAAAAAAAGpAAQ0AAAAAoAYU0AAAAAAAakAB\nDQAAalu+fPmgQYMUdtbV1Yme5uTJkx2SJACAlmAeaAAAUE9FRUVGRoa1tbXC/uLiYiIaNGiQ\nnZ2d/H4LC4v2Sw4AQPtQQAMAgCAymeznn38+d+5campqdXV1ywK6qKiIiDZs2PDKK690RIIA\nAO0EBTQAAAhSV1c3Y8YMJQHsDrSLi0t7ZQQA0DFQQAMAgCDW1tYNDQ3sZ2Nj45YBRUVFIpFo\nwIAB7ZsXAEB7QwENALokSaQ65j1OdQxogUgkMjIyUhJQXFxsbm6+ePHiQ4cO3b9/39XVdfLk\nybGxsT169FCIlMlkUqmU36yvr9dKxgAA2oECGgAANKOoqOjBgwdSqTQ5OdnCwuLo0aOJiYmZ\nmZnnzp2ztLSUjzxw4MD06dM7Kk8AgGeEAhoAADSA47jo6Ghra+tZs2aJRCIi8vHxcXR0DA0N\nXbVq1bZt2+SDxWLxpEmT+M3y8vL8/Pz2zhgAoK1QQAMAgAaIRKIlS5Yo7JwzZ05ERERWVpbC\nfh8fnyNHjvCbKSkpERERWk8RAEBDsJAKAABoi56enlgsvnPnTkcnAgCgSbgDDQDtAk8HdnVZ\nWVlvvvlmdHR0eHg4v7O2tra8vNzLy6sDEwMA0DgdvQPdtvVgZTJZfHy8s7OzoaGhk5PTunXr\nZDKZxnNTcpbKysqnps1cuHBB48kAQHtLEqn+77nk4+NTVVW1evXq8vJytkcmky1fvpzjuJkz\nZ3ZsbgAAmqWjd6Dbth5sREREenq6k5NTcHCwRCKJi4srLCxMT0/XbG5KzmJgYDBx4sSWh1y8\neLGurq7lql0AAF2GiYnJ9u3b582b5+7uHhgYaGhoeOrUqcLCQj8/v3fffbejswMA0CQdLaDb\nsB7stWvX0tPTR4wYkZ2dbWJi8vDhw3HjxmVkZMTGxrq6umoqMeVnsbKyavmsTEFBwYgRIxIT\nEx0cHDSVBgCADgoNDbW2tv7kk0+ysrJkMpmHh8fSpUsXLVqkp6fX0akBAGiSjg7haMN6sCkp\nKUQUHR1tYmJCRKamptHR0US0c+dODSam7llkMlloaKivr+/y5cs1mAYAQMfiOO7q1ast90+d\nOvWXX365e/dubW2tRCJ56623UD0DQNejowV0G9aDlUgkRCQ/sWhAQAARZWdn83uKi4tnz57t\n6upqbGw8cODAlStX1tTUqJWYkLPI27Bhw+XLl9PS0rp109G3GgAAAADUoqNDOISvB8urrKw0\nNzeXX+zK0tLSzMysqqqKbUokkoCAgCdPngQGBvr7++fm5m7YsCEzMzMnJ0csFiu0JhKJ3Nzc\nWt5fUXkWeb///vtHH320dOnS1gZvHDt2jA1WYcrKylp7dQAAAACgI3S0gBa+HiyvsrKyV69e\nCjutrKwqKyuJqKmpKTIy0sjISCKRuLu7ExHHcfHx8XFxcXFxcTt27BCYmPKzKEhMTNTT0/vg\ngw9aay01NXXXrl0CTw0AbYHp8wAAQNN0sYBWaz1YeSxYoSk2x9z169cLCgpiYmJY9cyCY2Ji\nkpKSDh8+rFZ6Ss4ir6ysbMeOHStXrrSysmqtqbCwsPHjx/Obn3322ZUrV9RKBgAAAADamS4W\n0GqtB8uzsbGprq5W2FlTU2Nra0tEBQUFRJSYmJiYmKgQw9e+paWlDx8+5Pc3NjbyQzh69uzZ\np08flWeR99VXXz1+/HjBggWtvk4if39/f39/fvM///kPCmgAAAAAHaeLBfRTqVwP1sbGpqSk\nRCqVmpmZsT1SqVQqlXp6ehIR27l69epZs2a11kJkZKT83eibN2/yt6uXLVu2efNmlWfhNTU1\npaSkTJgwwcnJqW2vFwBUwNiMDrV8+fIDBw60fFBEJpOtX78+LS2trKzMzs5uwYIFH374ob6+\nfockCQCgJbo4NURWVpaLiwubMI7H1oMdMmRIa0f5+voS0bFjx/g97Gdvb28iGjhwIBFVVlYO\nkuPk5HT27Fl++PKhQ4e4PxGRm5sbv8mqZ5Vn4R08ePDOnTtz5859hrcBAEBHVVRUZGRkPPVX\nERERsbGxRBQcHExEcXFx8it7AwB0DbpYQAtcD7a6ulp+EjrWR2/atKmpqYmImpqaWNUbGRlJ\nRI6Ojr6+vqmpqbm5ufwh69evDwkJuXTpkvDclJ+F9+OPPxLRU1clBADopGQy2d69e+Pi4kaP\nHt1yMBvJLTWVl5eXkZGRl5c3fPjwjIyMwsLC9s8WAEB7dHEIh8D1YMVisfxMc25ubiEhIRkZ\nGePHj58wYcLx48dzcnLCwsLYMoQikWjLli1+fn4+Pj6BgYEODg55eXnZ2dm+vr4Kta9yys/C\ncBx3+PBhW1tbR0dHzbwjAAA6oK6ubsaMGUqMZq78AAAgAElEQVQCnrrUVHBw8M6dOxMSEtop\nSwAA7dPFO9BEFBoa+tNPP3l6emZlZe3du1csFm/duvXIkSPKV7RKTk5es2ZNRUVFUlJSVVVV\nQkLC559/zv925MiReXl5wcHB+fn5aWlp9+7dW7du3aFDh4yNjVs21doiWyrPQkRXr14tKyvz\n9fVtOV8HAEDnZW1t3fCnpwaou9QUAEAnpYt3oJmpU6dOnTpVSQAbqSzPwMCAzevc2iH9+/ff\nvXv3Myam8izu7u4tcwMA6OxEIpGRkZGSAOFLTclkMqlUym/W19drNlUAAK3S3QIaAAA6F+FL\nTR04cGD69OntlRcAgIahgAYAAI0RuNSUWCyWH+lRXl6en5+v9eQAADQEBTQAAGiG8KWmfHx8\njhw5wm+mpKRERERoPT8AAA3R0YcIAQCg07GxsXnw4IH84Ga21FTfvn07MCsAAI1DAQ0AAJoh\ncKkpAIDODgU0AABohsClpgAAOjuMgQYAAM0QstQUAEAXgAIaAAA0Jjk52cXFJS0tLSkpyd7e\nPiEhYcWKFR2dFACAhqGABgAAtbW2XJTKpaYAALoAFNAAAAAAXUROrKCwseu0nEdXh4cIAQAA\nAADUgAIaAAAAAEANGMIBAAAA0H4EjrIgDLTQYbgDDQAAAACgBhTQAACgGXV1daKnOXnyZEen\nBgCgSRjCAQAAmlFcXExEgwYNsrOzk99vYWHRQRkBtAcMyXgOoYAGAADNKCoqIqINGza88sor\nHZ0LAIAWYQgHAABoBrsD7eLi0tGJAABoFwpoAADQjKKiIpFINGDAgI5OBABAu1BAAwCAZhQX\nF5ubmy9evNjOzs7Y2PiFF15YsWLFgwcPWkbKZLIaOfX19e2fLQBAm2EMNAAAaEZRUdGDBw+k\nUmlycrKFhcXRo0cTExMzMzPPnTtnaWkpH3ngwIHp06d3VJ4AAM8IBTQAAGgAx3HR0dHW1taz\nZs0SiURE5OPj4+joGBoaumrVqm3btskHi8XiSZMm8Zvl5eX5+fntnTEAaFPXnpwEBTQAAGiA\nSCRasmSJws45c+ZERERkZWUp7Pfx8Tly5Ai/mZKSEhERofUUAQA0BGOgAQBAW/T09MRi8Z07\ndzo6EQAATcIdaAAA0ICsrKw333wzOjo6PDyc31lbW1teXu7l5dWBiQFQVx9OAO0PBTQAAGiA\nj49PVVXV6tWrp0yZYmtrS0QymWz58uUcx82cObOjswNQDwpuUA4FNAAAaICJicn27dvnzZvn\n7u4eGBhoaGh46tSpwsJCPz+/d999t6Ozg85BYNmKmhU6HApoAADQjNDQUGtr608++SQrK0sm\nk3l4eCxdunTRokV6enodnRp0DNzHha5KdwtomUy2fv36tLS0srIyOzu7BQsWfPjhh/r6+po9\nRBuJtU8aAAA6aOrUqVOnTu3oLAC6DvxPiG7S3Vk4IiIiYmNjiSg4OJiI4uLi5B9M0dQh2kis\nfdIAAAAAgA6ho3egr127lp6ePmLEiOzsbBMTk4cPH44bNy4jIyM2NtbV1VVTh2gjsfZJAwCg\nq8L9tpbwnoCWqPXRwudQno4W0CkpKUQUHR1tYmJCRKamptHR0cHBwTt37kxISNDUIdpIrH3S\nAAAAXaM7D8BptdBBFQVAOltASyQSIpJf6DUgIICIsrOzn+WQ4uLiVatWnT17tqyszMHBYfr0\n6dHR0T179tRgYm3IHAAA2gbFHAB0CB0toCsrK83NzS0tLfk9lpaWZmZmVVVVbT5EIpEEBAQ8\nefIkMDDQ398/Nzd3w4YNmZmZOTk5YrFYoTWRSOTm5nb16lV1z9KGzAEAoH3oTsGtO5kAQBvo\nbgHdq1cvhZ1WVlaVlZVtO6SpqSkyMtLIyEgikbi7uxMRx3Hx8fFxcXFxcXE7duzQVGJqZb58\n+fIff/yR36yoqBCYBgCAbsI0RJ1O5y3lO2/m0AXoaAFNRCKRSGEPx3Eymaxth1y/fr2goCAm\nJoZVzyw4JiYmKSnp8OHDmk2sDZkreo/TcKRmw57DBvFCunyDGj/v8yoiIiI9Pd3JySk4OFgi\nkcTFxRUWFqanp3d0Xm2B+gwAWqOj09jZ2NjU1NQo7KypqWHLw7bhkIKCAiJKTEwUyenevXtd\nXR0/uKK0tPTqn4iosbGR3+RjVCamVuaffvppsZzx48e3+o4AAOg8fhqivLy8jIyMvLy84cOH\nZ2RkFBYWdnRqAACapKN3oG1sbEpKSqRSqZmZGdsjlUqlUqmnp2fbDmE7V69ePWvWrNZaiIyM\nlL8bffPmTf529bJlyzZv3iwksTZkDgDQNWAaIgBom073Bx8dvQPt6+tLRMeOHeP3sJ+9vb3b\ndsjAgQOJqLKycpAcJyens2fP8qOTDx06xP2JiNzc3PhNVj0LSawNmQMAdA2YhggAnhM6WkCz\npfs2bdrU1NRERE1NTayEjYyM5GOqq6vlB0soP8TR0dHX1zc1NTU3N5c/ZP369SEhIZcuXdJg\nYkIyBwDokoRPQySTyWrk1NfXt2+mAADPREeHcLi5uYWEhGRkZIwfP37ChAnHjx/PyckJCwuT\nX8xPLBbLzzSn/BCRSLRlyxY/Pz8fH5/AwEAHB4e8vLzs7GxfX1+1SluViQnJHACgSxI+DdGB\nAwemT5/eXnkBQFfT8UM+OF3V2Ni4Zs0aR0dHfX39AQMGJCQkPH78WD6A/v9RFkIOuXnz5uzZ\ns52dnY2NjQcPHrxu3bo//vhD44mpDGjN5MmTiai2tlbdlAAAdIGJiUn//v0Vdjo4OBgbGyvs\nlEgkk+QMHjyYiL788st2ShQA4NmIOA5TMumKl19++fDhw7W1tRYWFh2dCwCA2pydnaurq+vq\n6uR3mpub9+nTp6ioSMmBKSkpERERX375JUa7AUCnoKNjoAEAoNOxsbF58OCBVCrl97BpiPr2\n7duBWQEAaJyOjoF+ntXW1jY3N3d0FgDQTszMzLrMQn2+vr6nTp06duzYtGnT2B61piGqr69v\nOY8+AEA7MzU1NTAwUBHU0WNI4P956623jI2N2+WzAQC64uDBgx3d92gMe6r7xRdflMlkHMfJ\nZDI/Pz8iun79uvID9+7dq2SdLACA9pSWlqayu8MdaB2ydevWhoaGW7duKey/dOnS77//Pm7c\nOENDQyWH19TU/Pbbb46Oji4uLspP9OuvvzY2Nr744ovKw0pLS69du+bh4aHyz69ZWVkWFhaj\nR49WHnb16tWysjIvLy9zc3MlYQ8fPvz1119tbW3Zc0VK/PbbbzU1Nf7+/t26KRuMVFVVlZeX\nN3DgwH79+ilv8MSJE3p6emwybyVKSkpKSkqGDx9ubW2tJOzx48cnTpwQi8XDhg1T3mBeXl5V\nVdVf/vIXIyMjJWFqXeJHjx6x2kWJsrKyq1evavASX7t2rbS0dMyYMT169FASpvFL/Pvvv1+6\ndMnV1bV///7KG1TrEnt6eracU0KeTCb75ZdfevXqpXKlJCWXWPmnqHNp8zREr7322oMHD566\n4ndDQ4NEIrGxsRkyZIiQHC5evHj37t3x48ervoFEdP/+/XPnzg0YMMDZ2VlI4wI7T+bOnTsF\nBQWDBw8W+P8Gx44dMzU19fLyEhJcWFh469atkSNH9uzZU2VwY2NjdnZ27969X3jhBSGNC/xH\nhxHeLzGnT59++PChv7+/kODy8vL8/Hx3d3c7Ozsh8f/73/+MjIwE/sWjuLj4xo0bI0aMsLKy\nUhksvD9n2Ffe19dXyH2xurq6M2fO9OvXj61ZoVJubu4ff/wxceJEIcEVFRVXrlxxc3NzcHAQ\nEv/LL7/o6+v7+PgICRb4ryHDOkxra+vhw4cLafzKlSsVFRU+Pj5sYSblHjx4kJub6+Dg4Obm\nJqTxM2fO1NXVTZw4USQStfytoO+sRu46gFa9/vrrRFRaWqo8jP2pNDo6WmWDgwcP7tGjh8qw\nrVu3ElFGRobyMDbgxNvbW2WDixcvJqLffvtNedjly5eJKDw8XGWD7J+xhoYG5WF79uwhoqSk\nJJUNisViZ2dnlWH/+Mc/SMCNw99//52IXn31VZUNBgcHE9GtW7eUhx0/fpyIPvjgA5UNenh4\nmJubqwzbvn07EX399dcqI0UikZeXl8qwt956i3VMysPy8/OJaMGCBSobZP8PUF9frzzs+++/\nJ6INGzaobLBPnz4DBgxQGbZmzRoi+vnnn5WHVVdXE9HUqVNVNsjWQL1586bKyM6uzdMQtaak\npISIZs+eLTD+1VdfJaLff/9dSPCRI0eIaNWqVQIbF9h5Mp9//jkRpaamCow3NDQcPny4wOCV\nK1cS0S+//CIkuLS0lIhef/11gY3PnDmTiMrKyoQEC++XGE9PTyMjI4HBbG3LL774QmC8ubm5\nh4eHwOC///3vRJSVlSUkmE3F+Nprrwls/I033iCiGzduCAlmKxAtX75cYOOjR4/W09MTGMz+\nv3Tbtm0C462srAYOHCgwWOC/hsz9+/eJaMqUKQIbDwkJIQF/v2LOnDlDRG+99ZbAxtn/ITx5\n8kRgfEu4Aw0AABpjYGAQFxcXFxfX0YkAAGgRZuEAAAAAAFADCmgAAAAAADVgCEcn4O3tLRKJ\nVD6I0Lt376CgICHP2UyePPnevXsqw1xcXIKCglQ+lUVEQUFBQh59GD58eFBQkMpHXnr06BEU\nFDRq1CiVDU6YMEEsFuvp6SkPc3BwEJjhtGnThDzw4eHhERQUZGNjozzM0NAwKCho5MiRKhsc\nO3Ysx3Eqn5MQi8VBQUFDhw5V2eDkyZPZ8Fzl2CV2dHRUGRkUFCTkCSFtXOJevXqpvMT29vZB\nQUFCnh159dVXhTwXNXjw4KCgIJXPVhoYGAQFBY0YMUJlg2PHjm1ubhbyKAwoMDc3j4qKUvkA\nK2/y5Ml9+/ZV/jwuz87OLioqSshHkQkKChI+0Z67u3tUVJTAR5qIKDw8XPiE2V5eXlFRUQLj\nTU1No6KihHxQmYCAAGtra1NTUyHBffv2jYqKGjNmjMDGZ86c2XJp99a4ublFRUW5u7sLjF+w\nYIGQJwKZ0aNHR0VFCXw80djYOCoqSuAThEQ0ceLEHj16KH9intenT5+oqKixY8cKbHzGjBlC\n/mVhXF1do6KiPDw8BMbPmzfPzMxMYPDIkSOjoqIEPp5oaGgYFRUl8GlgIvLz8zM2Nha4tJxY\nLI6KilL5gDjvtddeGzJkyFOfIBQIKxECAAAAAKgBQzgAAAAAANSAAhoAAAAAQA0ooAEAAAAA\n1IACGgAAAABADSigATqlJ0+e1NbWaqnxCxcusKXLAACeB1rtUVtCH9sFoICG593169djYmLG\njx/v5uZmYWFhYmLi4uIybty4lStXFhYWqttadXV1WFiYo6OjpaXl1KlTz549qxAwadIkNm9O\nWVnZ4sWLvb29J06cePDgQSI6e/ZsUFCQp6fnxIkTV65cqbw3LywslJ8t7uTJk3l5efIBtbW1\nS5YsGTRokLm5ube3d1JS0pMnT4jo0aNHO3fu/OGHH/jIU6dO+fv79+zZ08XFJSoq6v79+8OH\nD3d2dk5KSmLrtCtXVVW1dOnSsWPHBgQEnDp1ioju3LmzZMmSUaNGvfTSS2vWrHn48CERcRwn\nkUjmzZs3cuRIW1tbAwODvn37jhgxYtq0aV9//TWL4Wnwogi/IgAgnGZ7TnlqfWefsSPlKfSo\npE6nyhPSu7L9avWxjMCellGrvyWduZqdTJsXAQfo7BobG+fOnct/F8zMzPr16+fg4CA/BWZ4\neLhMJhPY4P379+3t7YnIyMioT58+RGRoaHjw4EH5mIkTJxLRjRs35Dvr7t27//vf/zY2NhaJ\nRHZ2dgYGBkRkb29/79691s5VUFAg//0lookTJ/KbtbW1bHZnAwMDOzs71j35+/tXVFQMHz6c\niP7+97+zyKysrG7duhER62SJiM3o2atXLyMjo4kTJ96+fVvJSy4vL5efD9va2jo3N1dhXlVX\nV9fy8vKXXnqJbdrY2LDpkPX09MRiMcvN3Nz8iy++0PhFEX5FhLQGAJwWek55an1nn70j5Sn0\nqJzgTrWpqYkF/P7770J615qaGta4wD6WEdjT1tbWchwnlUoF9recLl3NTqdTJg2gEf/4xz+I\naMSIEd999x3rd3h//PHH3r17J0yYQESJiYkCG3znnXeIKCYm5vHjxxzHHTlyxMTExNrauqKi\ngo9hncW8efOIaP369dXV1Xfu3JkyZUq3bt2GDh1669YtjuMeP34cHx9PapLv65cvX25kZJSe\nns4695qamvnz5xMRm2R+2bJl5eXlLPKFF17w9vZmPXhjY2NMTAzfWn5+/qhRo4yMjD744IPW\n/gWKjIwkoo0bN96/fz8/P3/s2LHdunXr3bv3Dz/8UFdXd+PGjSVLlhARWwXjjTfeuHPnDsdx\nT548yczM7NGjx+HDhxsbG3/88Ue2xMMPP/yg2Ysi/IoIaQ0AOC30nPLU+s4K7EjffvttdqDw\n7pRrUUC31qlu27aNBbDOUGXv+s477/CNC+lj5RtX2dMuW7aM4zh2IiH9rU5dzU6nUyYNoBH9\n+/d3cnJ6+PBhawGPHz/28vJydnZ2E8bAwGDkyJHNzc18Cz/99BMRhYWF8XtYZ+Hk5OTv78/v\nPH36NBFlZmbye5qbm729vflVrGxtbRXOxe6F8JsKfb2Hh0d8fLzCa3F2djYwMJgwYQK/848/\n/iCiCxcuyJ+XrcrGWnv8+HFCQoKZmZmFhUVsbGxJSYnCW+Ts7Pziiy/ym+fPnyeinTt3KjRo\nZGTk4eGhcA9j06ZN3t7e7Oeamho7OzsvLy+BF0VfX1+zV6S10wGAAuE9J9sU2H+6ublxHDd4\n8GDh31mBHamHhwfbfPXVVwX2qFyLArq1TtXX15dt2tnZCeldWTLC+1hGYE87aNAgjuNGjx4t\nsL/ldOlqdjqdMmkAjdDX1w8JCVEe8+GHHxoYGLz77rvW1tZC7lssXLhQoYU5c+Z069btypUr\nbJN1FkZGRosWLeJj2MC4vLw8+QMjIiKMjY0zMjJ69uzZt2/fvXv3yv9W+RAOIyOjU6dOKWQS\nGhoqEonmzJnD77lz5w4RSaVS+bCwsDCF1tjYO319fSJ68cUXk5OTS0pKWJ9obGwcERHBR0ql\nUiI6e/asfIPh4eFEFBkZqZDP6dOnzc3N+c2QkBATExOBF0VPT0+zV0T5GQGAJ7znZD8L7z85\njjMyMhL+nRXekbKfm5ubBfaonOBO1cLCgv1sbGwspHc1NTVt2biSPpZvXEhPa2JiwnGcqamp\nwP6W06Wr2engIUJ4ftnZ2Z06daqhoaG1gKamp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FT89NNPrcVwHMcaycrK8vHxaZmMEpWVleyfDeGHtFRfX88mc32WRkCnCPxUP3tYfn5+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\nACDAAg0AAAEWaAAACLBAAwBAwE8uDWjO0k0LGgAAAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "# assemblage des panneaux avec cowplot\n", "# la zone fait 1 x 1, avec le point (0,0) en bas à gauche\n", "fig5 <- ggdraw() +\n", " draw_plot(fig5A, x = 0 , y = 0.5, w = 0.5, h = 0.5) +\n", " draw_plot(fig5B, x = 0 , y = 0 , w = 0.5, h = 0.5) +\n", " draw_plot(fig5C, x = 0.5, y = 0 , w = 0.5, h = 1 ) +\n", " draw_plot_label(c(\"A\", \"B\", \"C\"), c(0, 0, 0.5), c(1, 0.5, 1), size = 20)\n", "\n", "options(repr.plot.width=8, repr.plot.height=6.5)\n", "fig5\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 11.368 0.000 11.376 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "fig6A <- filter(gencode, type == \"transcript\") %>%\n", " # group_by groupe le tableau de donnée en fonction de colonnes particuliéres\n", " # les fonction dplyr appelées aprés un group_by s'appliquent individuellement pour chaque groupe\n", " group_by(gene_id, simple_gene_type) %>%\n", " # en l'occurence, pour chaque gène, on demande le nombre de transcrit via la fonction n()\n", " summarise(n_transcript = n()) %>%\n", " # pour rendre le plot plus lisible, la valeur maximale sera 20 transcrits\n", " mutate(n_transcript = if_else(n_transcript > 20, 20L, n_transcript)) %>%\n", " ggplot(aes(x = n_transcript, fill = simple_gene_type)) +\n", " geom_bar() +\n", " scale_x_continuous(breaks = c(1, 10, 20), labels = c(1, 10, \"\\u2265 20\")) +\n", " facet_wrap(~simple_gene_type, scales = \"free_y\", ncol = 1) +\n", " theme(legend.position = \"none\") +\n", " labs(x = \"Nombre de\\ntranscrits\", y = \"Nombre de gènes\", title = \"Transcrits\\npar gènes\")\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 11.872 0.000 11.884 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "fig6B <- filter(gencode, type == \"exon\") %>%\n", " mutate(exon_number = as.integer(exon_number)) %>%\n", " group_by(gene_id, simple_gene_type) %>%\n", " # cette fois, le nombre d'exon d'un gènes et définie comme le maximum du numéro d'exon de ce gène\n", " summarise(n_exon = max(exon_number)) %>%\n", " mutate(n_exon = if_else(n_exon > 20, 20L, n_exon)) %>%\n", " ggplot(aes(x = n_exon, fill = simple_gene_type)) +\n", " geom_bar() +\n", " scale_x_continuous(breaks = c(1, 10, 20), labels = c(1, 10, \"\\u2265 20\")) +\n", " facet_wrap(~simple_gene_type, scales = \"free_y\", ncol = 1) +\n", " theme(legend.position = \"none\") +\n", " labs(x = \"Nombre\\nd'exons\", y = \"Nombre de gènes\", title = \"Exons\\npar gènes\")\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 1.648 0.000 1.650 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "fig6C <- filter(gencode, type == \"gene\") %>%\n", " mutate(\n", " # on extrait le numéro de version de l'annotation de chaque gène\n", " gene_version = strsplit(gene_id, \".\", fixed = TRUE) %>%\n", " sapply(last) %>%\n", " as.integer()\n", " ) %>%\n", " mutate(gene_version = if_else(gene_version > 20, 20L, gene_version)) %>%\n", " ggplot(aes(x = gene_version, fill = simple_gene_type)) +\n", " geom_bar() +\n", " scale_x_continuous(breaks = c(1, 10, 20), labels = c(1, 10, \"\\u2265 20\")) +\n", " facet_wrap(~simple_gene_type, scales = \"free_y\", ncol = 1) +\n", " theme(legend.position = \"none\") +\n", " labs(x = \"Nombre de\\nrévisions\", y = \"Nombre de gènes\", title = \"Révisions\\npar gènes\")\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <e2>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <89>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <a5>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <e2>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <89>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <a5>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <e2>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <89>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <a5>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <e2>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <89>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <a5>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <e2>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <89>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <a5>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <e2>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <89>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <a5>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <e2>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <89>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <a5>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <e2>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <89>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <a5>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <e2>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <89>”Warning message in grid.Call(L_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :\n", "“erreur de conversion de '≥ 20' dans 'mbcsToSbcs' : le point est substitué pour <a5>”" ] } ], "source": [ "# plot_grid est une version simplifié de ggdraw(), dans lequel les plots sont rangés dans une grille\n", "fig6 <- plot_grid(fig6A, fig6B, fig6C, labels = LETTERS[1:3], ncol = 3, label_size = 20)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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fr103VdNEIoFB4/fly2/MCBAxKJRPv1AWgE5CkAPyCX+Q0N6JZl4cKFuq6CDrRq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\ng62yKdvn6ekZHh4u9ihExq4unT9/vlW2hnOgzcXufrRly5ampiYiampq2rp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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width=8, repr.plot.height=5)\n", "fig6" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 0.104 0.000 0.103 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "# la longueur exons + introns est triviale à obtenir grâce à la colonne width\n", "fig7A <- filter(gencode, type == \"gene\") %>%\n", " ggplot(aes(x = width, fill = simple_gene_type, color = simple_gene_type)) +\n", " # geom_denisty plot la distribution des longueurs\n", " geom_density(alpha = 0.2) +\n", " # on fixe la même échelle pour tout les plots\n", " scale_x_log10(limits = c(10, 10000000)) +\n", " annotation_logticks(sides = \"b\") +\n", " labs(x = \"Paire de bases\", y = \"Densité\", title = \"Longueur des gènes (exons + introns)\", fill = \"\", color = \"\")\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 25.536 0.008 25.567 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "# pour la longueur des transcrits, on somme tout les exons de chaque transcrits\n", "fig7B <- filter(gencode, type == \"exon\") %>%\n", " # on groupe par transcrits\n", " group_by(gene_id, transcript_id, simple_gene_type) %>%\n", " summarise(transcript_length = sum(width)) %>%\n", " # puis par gènes\n", " group_by(gene_id, simple_gene_type) %>%\n", " # et on prend la longueur du transcrit médian\n", " summarise(gene_length = median(transcript_length)) %>%\n", " ggplot(aes(x = gene_length, fill = simple_gene_type, color = simple_gene_type)) +\n", " geom_density(alpha = 0.2) +\n", " scale_x_log10(limits = c(10, 10000000)) +\n", " annotation_logticks(sides = \"b\") +\n", " labs(x = \"Paire de bases\", y = \"Densité\", title = \"Longueur des gènes (exons seuls)\", fill = \"\", color = \"\")\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 0.364 0.000 0.364 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "# la longueur des exons est triviale à obtenir\n", "fig7C <- filter(gencode, type == \"exon\") %>%\n", " ggplot(aes(x = width, fill = simple_gene_type, color = simple_gene_type)) +\n", " geom_density(alpha = 0.2) +\n", " scale_x_log10(limits = c(10, 10000000)) +\n", " annotation_logticks(sides = \"b\") +\n", " labs(x = \"Paire de bases\", y = \"Densité\", title = \"Longueur des exons\", fill = \"\", color = \"\")\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 44.968 0.148 45.162 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "# pour la longueur des introns, il faut ruser !\n", "fig7D <- filter(gencode, type == \"exon\") %>%\n", " # on groupe par transcrit\n", " group_by(gene_id, transcript_id, simple_gene_type) %>%\n", " # pour chaque transcrit, on trie bien chaque exons dans l'ordre\n", " arrange(start) %>%\n", " # nouvelle colonne qui contient le start de la ligne du dessous, cad de l'exon suivant,\n", " # via la fonction lead().\n", " # puis, dans le même mutate, nouvelle colonne qui contient la différence entre la fin\n", " # d'un exon, et le début de l'exon suivant.\n", " mutate(next_exon_start = lead(start), intron_length = next_exon_start - end) %>%\n", " ggplot(aes(x = intron_length, fill = simple_gene_type, color = simple_gene_type)) +\n", " geom_density(alpha = 0.2) +\n", " scale_x_log10(limits = c(10, 10000000)) +\n", " annotation_logticks(sides = \"b\") +\n", " labs(x = \"Paire de bases\", y = \"Densité\", title = \"Longueur des introns\", fill = \"\", color = \"\")\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“Removed 2 rows containing non-finite values (stat_density).”Warning message:\n", "“Removed 2 rows containing non-finite values (stat_density).”Warning message:\n", "“Removed 2141 rows containing non-finite values (stat_density).”Warning message in self$trans$transform(x):\n", "“production de NaN”Warning message:\n", "“Transformation introduced infinite values in continuous x-axis”Warning message:\n", "“Removed 199480 rows containing non-finite values (stat_density).”" ] } ], "source": [ "fig7 <- plot_grid(fig7A, fig7B, fig7C, fig7D, labels = LETTERS[1:4], ncol = 1, label_size = 20)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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RZwBjVDAm1Ce3GwLR+ri6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6va+vb0lJSVFRUUBAQGFhoVar9fOr9c7a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\n7HCg6jXolfu2bdu2bNmyvLy8tWvX5ufnr1q1asuWLXVeBCGEUNPG4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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width=6, repr.plot.height=6.5)\n", "fig7" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 0.248 0.088 0.333 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "# A ce stade de l'article, vous devriez commencer à saisir la démarche:\n", "# on ne garde que les lignes de type \"CDS\"\n", "fig8A <- filter(gencode, type == \"CDS\") %>%\n", " # nouvelle colonne contenant le modulo 3\n", " mutate(modulo_3 = width %% 3) %>%\n", " ggplot(aes(x = modulo_3)) +\n", " geom_bar(fill = \"darkorange\") +\n", " labs(x = \"\", y = \"effectif\", title = \"Modulo 3 des\\nCDS (exons)\")\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", " 11.652 0.028 11.693 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "fig8B <- filter(gencode, type == \"CDS\") %>%\n", " # on groupe par transcrit\n", " group_by(gene_id, transcript_id) %>%\n", " # on somme la taille de chaque CDS pour chaque transcrit\n", " summarise(total_CDS = sum(width)) %>%\n", " mutate(modulo_3 = total_CDS %% 3) %>%\n", " group_by(gene_id) %>%\n", " # pour chaque gène, on prends la mediane des modulos 3 des transcrits\n", " # qu'on arrondit au plus bas en cas de chiffre non entier\n", " summarise(modulo_3 = floor(median(modulo_3))) %>%\n", " ggplot(aes(x = modulo_3)) +\n", " geom_bar(fill = \"darkorange\") +\n", " labs(x = \"\", y = \"effectif\", title = \"Modulo 3 des\\nCDS (gènes)\")\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [], "source": [ "fig8 <- plot_grid(fig8A, fig8B, labels = LETTERS[1:2], ncol = 2, label_size = 20)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "options(repr.plot.width=4.5, repr.plot.height=3)\n", "fig8" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false, "hide_input": false, "run_control": { "marked": true }, "scrolled": true }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ " user system elapsed \n", " 0.864 0.000 0.865 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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WobeMKADGuk6mw5CHgitkwwsOWxddNggVH3DhmANxwkFa3x8fH56KOPBALB1q1b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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ptm <- proc.time()\n", "\n", "fig9 <- filter(gencode, type == \"gene\") %>%\n", " # on enléve les chiffres à la fin\n", " mutate(family_name = sub(\"[0-9]*$\", \"\", gene_name)) %>%\n", " # on compte\n", " count(family_name, gene_type = gene_type) %>%\n", " # on ungroup avant de trier\n", " ungroup %>%\n", " arrange(desc(n)) %>%\n", " # on ne guarde que les familles de plus de 100 membres\n", " filter(n >= 100) %>%\n", " ggplot(aes(x = factor(family_name, levels = rev(family_name)), y = n, fill = gene_type)) +\n", " geom_bar(stat = \"identity\") +\n", " geom_text(aes(label = gene_type, y = 10), hjust = 0, color = \"grey40\", size = 3) +\n", " coord_flip() +\n", " labs(x = \"\", y = \"Nombre de membres\", title = \"Pseudo-familles de gènes\") +\n", " theme(legend.position = \"none\")\n", "\n", "options(repr.plot.width=4.5, repr.plot.height=3.5)\n", "fig9\n", "\n", "proc.time() - ptm " ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " user system elapsed \n", "133.756 0.720 134.599 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "proc.time() - ptm.begin" ] } ], "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": "3.3.1" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "12px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": true, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }	gpl-2.0
NekuSakuraba/my_capstone_research	subjects/em/multivariate t - draft02 - EM Known degrees of freedom.ipynb	1	148673	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numpy.linalg import inv\n", "from numpy import log\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from multivariate_util import " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def log_likelihood():\n", " n = len(X)\n", " u_ = [(_-mu).T.dot(inv(cov).dot(_-mu)) for _ in X]\n", " return -.5nplog(2 * np.pi) -.5nlog(np.linalg.det(cov)) - .5 * sum(u_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Expectation Maximization with Known degrees of freedom\n", "### Generating a sample" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mu = [0,0]\n", "cov = [[1,0], [0,1]]\n", "df = 10" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "size = 300" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = multivariate_t_rvs(m=mu, S=cov, df=df, n=size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting the sample" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x, y = np.mgrid[-3:3:.1, -3:3:.1]\n", "xy = np.column_stack([x.ravel(),y.ravel()])\n", "xy.shape\n", "\n", "t = multivariate_t(mu, cov, df)\n", "\n", "z = []\n", "for _ in xy:\n", " z.append(t.pdf(_))\n", "z = np.reshape(z, x.shape)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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9Lz723TvyufeOjznrnFHc/cD5PrXrSHkDy37ezN7cEv75h3mcP2V4p/WJosgH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\ntmOVgkAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7c476d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(X.T[0], X.T[1], s=10, alpha=.5)\n", "plt.contour(x, y, z)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Estimating parameters" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# guesses\n", "mu = [.5, 1]\n", "cov = [[.5,0],[0,1.5]]\n", "# known variables\n", "df = 10\n", "p = 2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mu: [ 0.01975157 -0.04165135]\n", "cov: [[ 1.05576444 -0.03485003]\n", " [-0.03485003 0.92592019]]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x6f6f5f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "likelihood = []\n", "for z in range(200):\n", " # E-Step\n", " u = []\n", " for delta in X-mu:\n", " u.append(delta.dot(inv(cov)).dot(delta))\n", " u = np.array(u)\n", " tau = (df + p)/(df + u); tau = tau.reshape(-1, 1)\n", " tau_sum = tau.sum()\n", "\n", " # M-Step\n", " mu_ = (tau * X).sum(axis=0) / tau_sum\n", " cov_ = np.array([[0,0], [0,0]], dtype=np.float32)\n", " for idx, delta in enumerate(X - mu_):\n", " delta = delta.reshape(-1, 1)\n", " cov_ += (tau[idx]*delta).dot(delta.T)\n", " cov_ /= len(tau)\n", "\n", " mu = mu_\n", " cov = cov_\n", "\n", " likelihood.append(log_likelihood())\n", " if len(likelihood) > 1 and likelihood[-1] - likelihood[-2] <= 1e-10:\n", " break\n", "\n", "print 'mu: %s' % mu\n", "print 'cov: %s' % cov\n", "plt.plot(range(len(likelihood)), likelihood)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting with new parameters" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x, y = np.mgrid[-3:3:.1, -3:3:.1]\n", "xy = np.column_stack([x.ravel(),y.ravel()])\n", "xy.shape\n", "\n", "t_old = t\n", "t = multivariate_t(mu, cov, df)\n", "\n", "z = []\n", "z_old = []\n", "for _ in xy:\n", " z.append(t.pdf(_))\n", " z_old.append(t_old.pdf(_))\n", "z = np.reshape(z, x.shape)\n", "z_old = np.reshape(z_old, x.shape)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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KrbMGd9nmjjoOSX4aHnvhKw4eLmTB3BHc8etZPbZfdEleGXdN+ws15TU8s/av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N7rbONXP2MWBOE94HygVhRF+wcjJ/EKZi+/Z5pqmvnzpEcx6Iy8umMJUUkRTssM5Exw57Y8ljz5\nPUqlnMefuZjhoxIH2gXqWjT8d9k2Vuw6hkImIyomlvFZ6TTqPGPRvvZYNV8fzMck1VLQUodWbKPJ\n0N71d5VMRmpIKImBgYSr/YjwVROhVhPuqybU1xelTIaPTIaPVIZSJmNrXj1rjlYTEaCgolnLlEGh\njE4KotWgp06rpa5dS+3Jn5VtbRQ0NlKj1fS43/DIKMbFxDEuJpZR0TH4KRxbWruq4bBaRTYczOet\nlTvJq6gnKSqE+y+dwZShSTYFPODyIk2r0bP0H8vZtjmXc88bwZ//usChcd9AEUWRpTe9xtoPN/Hg\nh3cx99oZp+1eZxtnQjBPBzTAR17B7OX/M/p2A3+d/XeKskt5ceNTDB6X5lK5/uyYRVHkm8938vZr\n60hLj+Lpf11OWHjAgNpvNJn5dP1+3l21G5PFytWzRzFjdAYf7yrv126+u/Dy94W1hQVsKCpkd0U5\nGpMRAKXEh4lxcUxKiO3awcb4B3h8B9ubVoOe/MZG8hobyG2oZ19lBcfqarGcVHlnhoUzJSGRc1MH\nMTwyasBq8E4B/eqyrZTVNTNtWDL3XzoDkyDvIeDdXaRZrSKfvLeZj9/bQtaIeJ5ccilBwf0/U3aG\nyWji0YVLOLIlh3+ueYIRM3+9ADn/nzkjVtmCICQBK7yC2cv/V/Jr23jl2lc58csh/v7dX5l8wTi3\nyrtzxmwxW3ll6UpWLz/ItFkZPPjEYpTKgcXZ3nakiOe/2kB5XQszR6Ry78XTSYgIcqttvf1ol67N\npspQR4W+liZTCyKQEBDIpPgEEv3DCJIFMjYugrSIgbuPecJCWGM0cqC6kr2VFeyuKGdfVSVmq5Ug\nHxXTEpK5Imso42PjkA0gWYbRZObzDQd5Z9UuDCYz18wZza0LJ6Lq5ifsziKjs9/1OeV89OpaQkLV\nPPviVSQknb5kKJpmLfdMeYzGqmb+s3MJcekxzgt5cchvUjALgnArcCtAQkLCmJKSEo/c14uXX4OC\nOg1P/fUzqj5aS/Qf5vDki384bdaqRqOZ5/6+jK0bj3PNDVO57pYZbhtgdaetXc8/v9jAqj3HSYoK\n4cHLZzIx45Q63B2h/PqGfESslOlqqLPUUKppACBI5s+MxBT+NHEk6SGhNs9Cf4uuNwcrGnj2l91U\nGeqoMTRgEa2Eqny5fGgWV2UNJy4gsN/trm/RsuTLjWw8cILwID+W3LSAMYPiAPfHvFOIL4zz480l\nyxGtIs+9cjVp6QOzBbB1v852qbXt3DnuYcLiQvjPjiUofqXobf9f+U0K5u54d8xefm98+s0ePrzq\nBcLHpRN97yUsGBZ9WiKLGQwmnnrkG/bsLOD2e+Zy8RUTBlTfvhPlPPzeSpra2rl0xijuu2hKj9jU\n7uzePt+XzwcHD1Kmr0RvMRHq40eUPIp4VSQqqdJh2d9qBKjuauXSJg0xERby2ipYX1yIKIqMi0lA\n0h5MjDIMEdxW87++IZ+W5mZyjh9Hr9dx47njue38icilriXQsKX2HqyW8eA9n9KuNbDkpavIzIob\nwAj0bW/3Z1S/O5fHF/2TxXecy93/vcUj9zlb8foxe/HiQTTNWlY88D6yQDURtyxEhD5+o57YDer1\nJv724Jcc3FfMXx4+j4WLR/W7zSazhTdW7OCDn/egVKoYM3oMdfhT1qzv0T5X/IH3V1Xy3z072Vhc\nhIBAjDKcpIBY/nbuOCSC0EO1vS6nxuYY/FZjJnf3B5Yg4eKh6aSGj6OyrZUvj2bz0cFDtBhL8Zer\nSFElkFcT4XK7O/s8NDkGf39/dPUVvLd6NztzSnj2xgUkRjp3dbPlrxwX7sfLr1/Hg3/+lEfu/Ywl\nL1/F0GEDj8lu6xnNOW8Ml9x7Ht++8hMjZg5l+qWTBnwfL87xCmYvZy2uCtOXb3uTpspG7l/2MJLk\n6D7Xe2I3aDJZuoTyXx9fzNwFw/vdr6rGVv761gqOldQwfmgqqtBYEsL8bQpER4EqjtTWsHT7FraU\nlhCqUnHvhMlMiEpB006PMegMs+loDOzdx2y1UG9oo0rXRLW+mSpdM/WGVnRmE3qrEb3FhN5iwmAx\nIZVIUEkVKKVylJKOn4FyXyJVgUSrgolSBhGtCsJX5tNnTOw9a1v+wB3X6licOoKFyVk8uWYXBdoy\nDrXm8viOMhrEyVyemeV019tD6Eul/PXK2RSXZ/CPT3/hqiWf8MQ1c1kwfojDOuz5K0dGB/Hi69fy\nwF2f8MhfPmfpq39gcObAzoHtPaOb/3kNR3ec4MWb/0f62FSXvBC8DAxPWWV/DswEwoAa4ElRFN+1\nd71Xle3lTOOqMN38zQ6eufwlbnr2aq565CKbdQ00PKIoivzrmR/5ZXU2Dzy2iHPPG9Hvfh0qrOS+\n//2IyWzhyevmkRQX7bSfvYVWcXMTL+3Yxoq8XIKVSv40djzXDBuJr9y+8ZkrY5BT08zOqmLa5U3U\nWerJaSmntL3jXLc7AXIVaqkPSqkcn5OC2EcixyxaMJwU1HqrEZ3FRIuxHbNo6VE+RKFmcEAsmYGx\nBBNGU52Sffkal+Ko23NvKqrT0Gpt5Yvj+9lbWUFSUDD3T5zCgkHpDq25bSXz8JfDo++tZH9eBdfN\nHcOfL5zWbxuChvo27v3Th+h1Jl59+wa3A87Ya2/vBUx1cS23Dr+fjEnp/HP1497gI/3AGyvbixcn\nuCJIWupbuSXrPsLjQ/nPjiU9Ygh3Z6A75g/f2cQn723h+j/O4A83TnOrH90n0sLSSp74YDWRwf68\ncscFJEeF9LnGUbv0ZhP/2b2Tt/fvRS6RcPOosdwyeiwBPn13oLba0XsMUsLU5LZWsqXuONvqcslt\nrewSwiEKPzID40jzjyJGFUyUKohoZRCRqkCUUteNjCyilf2VNby18yhGSTs6QUtirECFoZYCTQ0i\nHfObYPQhRhKLjzacUUGpXDo6yeZYOHsvRFFkfXEhS7dv5URDPWOiY3h29lzSQ+1bSNsam4QQFS98\ntZGvNx/mnNGDePr6+f1OM1laXM89t31AcIiaf795A/4Bqn7V44zl/1vDq3e+w/3v3M78m2aflnv8\nf8YrmL14cYIrwvSf177Kxi+38/re50kZ7jioR3/PmNf8dIgXnl3OvIXDeeCxRW7tRDr7IADFJSUU\nFhYyIiWal26/gGA/9ybnraUlPLHhF0pamrkkYygPTp5GuNo9X9mCOg0Fda20KGrJ1xextS6XOkMr\nAgJZQfGMCUkmIzCOzMA4InwCPLbrsidMVx0t5/vc4+gVzeRpyrH6NyJKzQiihCBLBBcmD+fS1NGE\nK0/5hru6yLJYrXx3/Bj/3LoJjdHIrWPGcee4CShlfbUK9toniiKfrNvPK99tJispmlduX0ywv+Ow\nrvY4dKCER+79jIysWJ57+erTEoTEarXywOy/U3iohHeOvkxYTIjH73Em+LW8BbyC2YsXF3D0Qe5f\nl81Dc5/m2r9dxnV/v/y03P9Ydjn33fERI0Yn8uyLVyLrtSN3NmGsy6lhVXYV1eUlHMsrZMSgBN64\n+wJ85K5PyjqTiSVbN/Fp9iGSgoL5x6xzmByf4HZfavUtLCvbw/fle2gwtOErVTAxbBBTw4cQI4mj\nqQWPTHy2xqS7MG3RmZiRHs7E1I5c2N1/H+4vJ0dThtm/jiZZJQaZFqkgYUZEBpcmTGRMSAqCILg1\nUTfq2nlk7TrWFp8gISCI189bRGZ4zzNYZ8J+3f48Hv9gFVHBAbx136WEB/ZvjNb/fITn/r6MhYtH\n8ZeHz+tXHc6oyK/ithEPMHHRGB7/4r7Tco9fk1/TW8ArmL14GQBWq5W7xj9MS30b7x//t03/zYGu\nslua27n9hreRyaS8/v4t+Pn3zCDkyoSRX9vGA+/+TGlpKTExMbx860IGRdoP5NG7zTl1tfx59U8U\nNDVyy6gx3D9pKj4y14W6KIrsayzkm9KdbKrNwSqKTA5P5+L48UwIG4RCInPYD3fH0Fld3XNyt+iM\nTE+PIC5Y1XW2u7uogRd/PoGPTEAqkXLngmhqFEX8WL6XVpOOZHU4lyRMZGHsKPxkrmUJ62xTnbGR\nvc1HMWPmwcnTuHnUmB4aAWd9PZBfwV3//Z7IYH/euvdSlzN59ebdN9bzxUfbefCJgRkQOuLDJ7/k\nk2e+4T87lzBk/KDTco9fC0+mz3SG113Ki5cBsOmrHeTtL+LBD++yK5QHssq2WkWef/oHmpva+fdb\nN/QRyuCae9HaXdmUlpYycVgaf7lkplOh3L3NQ5Lh5T2bCPDx4aMLL2Vqguvxt0VRZFdDPq+f+Jnj\nrRUEyFVclTSFS+InEOvbU71prx/9GUNHY5Ia7kdxvZZAlQKVXMruIg1ao4WoAGWXAdePhypRyCQI\nAiSEqAiTBXPF4CHcmnYOv1Rn82nBNl7IWc5ruT9zXeo0rkqcQlWTyaFALa7XojWYCZIFMdF/LC0+\nZSzZuomD1VU8f865qE/G43aWPnFUWiyv3nkhd//3e27/97e8fd9lBLl5HAFwwy0zOXq4nFeXriJ9\nSDSJyeFu1+GMyx5YzIo3fuadhz9l6bonf9eGYJ5Mn+lJBp6exouX3zEFdRrW5dRQUNeR8MCoN/Le\nY5+RMjyR2VdPtVmmu4Do9ON1h88/2tYRQOTeeQwaHG3zGmcTxie/7OPtlbu4YPJQ/vun8x0K5e5t\njgpUcqQtj+d2rCMrIpLlV17rllA+2lzGHXve4c9736fFqOWxrItZMfNh/jx4QR+h7KgfroyhKIqY\nrQa05iaajZVEBusQhXYqm7U96up8hlJJh5DveJYiqeF+XXUX12sJVMoJVMqRSQSs1lPlKxoNNJeF\n4l88kVHac/A1hPJm3i8s3riUx7atZOWRSl7fkN/1jnRHKhE4VtXG4fJm8mv03DVyBg9Nmcbqgjwu\n+fpzSpqbXR7bMYPi+PcdF1JW18xd//0ejc7gctmu9sgkPPb0RShVCp5+7Ft07Ua367BH5zhX6S1c\n88SlHNp4lF0/7fdY/WeCTne0+VlRv5mgN+DdMXs5i7G1azv8+Waqi2r555rHkdrxUx3IKrswv4aP\n3t3EzHMyOf/C0Xavc5Rvd9PhAl76djPnjB7E49ecY9PVprfqNClMjdlqYVnxbir0tSweNJR/zZuL\nwsUIVI0GDa+dWMPyin2EKNTcn3E+F8WPRyFxPIXY60fHGFqp1lQj960Bv1w2VNfQaCyl1VSFwaLF\naNUi0tOVKv5kXEEpSja0qNnSHEhlfSBmQyRGfQRzh2dR3xzO5hNN6IyWHs9H7SMjMdSXFr2JW6en\n9ti5V7fqKW/UMSM9ggB9KJnxImuatlOoPEi9soik9lEU10f3mbgtVpHMaH/kUgkmixWrCLeNGU9m\neAT3rP6Jy775nA8vvJSMsL47V1vq7XGD41l66/nc/8ZynvhgNS/etthtV6rQMH8efepCHrrnUz54\neyO33zPPrfK22Jhby5ubCwhUylH7yLjt4knEv7aadx/5lPELRyEZQGzxM40zbcaZwCuYvZy19FaN\n5lc08+W/ljF8RiZj5tr3Je5vknqrVeTfS1fh76/i7vvnO1UB2powSmqaeOL91QyJj+Dp6+d35f2F\nnj6zyw9V9lhwxAT5UCnJp0Jfy60jJ/Lw9CkutRlgQ81Rnj3yHe1mI9cmT+em1FmobQTxcKUfOnML\npe37KTXvJX3kXvTWjljbR7SgkPgSrEggWjUUpcQPhVSNXKJCIfFFIVFhEU0YrTqMFm3HT6uWkuYK\nfNTFqIMPAJALSIPlzJiSicw4lPSg8aSEqREEweYz63wHUsP9KG9sp6BOQ1SgkumxaUyLSeUfW7dQ\nqDzAYd+NhGqbmWS+oEcAk6QwNWofGRJBQCGTdC0CpiUk8dWlV3Ld999w1bdf8v7iixkVfSoAiCNV\n/rRhKfzlkuks/Xoj763ezS0L3Q/LOmpsMuddOJplX+9h7oLhA4qpXVCn4a1NBVS16GiWm0gM9aWs\nxcAf/nYZz13zb7Z9v5tpl0zsd/1e+uIVzF7OWnrvfMvX7KOhsomHPrrbadn+rEzaoOsAACAASURB\nVLJ/XnmIY9nl3P/o+QQEuu8W0643cv+bPyKTSnjhtkU9/F67T/Tlze0EKuUMjgqgsllHbk0Lj2/e\nyt6qcpbMnsuVWa4ZBbWbDbyYs4LlFfsYEhDLU8MvI9nP/ahPLcYqclp+pki7i1p9HiDiI/EjXj2a\nWNUwQnwSCFEkopbZTn7hiAJZR7+lUiMSRS0LRgHyUkq1+6gWPqO65TP2akJIUI9mcMBsZg0Zg0Q4\npSXofAd0RguDowK6LLo7n+0T06aTVzeCvYb9LK/ZSf72Mm5LWIRUF9gl4O0t0tJCQvnqsiu59vtv\nuHbZN7y3+GLGx3bEtXZmQ3DlrJEcLanmfyu2k5EQwZSsZLfH/abbZrF1w3H+88JqXn7j+n4HMSmu\n1xKgktPUbkJnMtOiN5EUpibp8kl88vTXfPLMN0y5aPzvetf8W8Nrle3lrKZzlxnrr+DpyY8QmRTO\nS5ue9rhBS2urjhuveJ34xFBeer1jknTHKlkURR5+dyXr9ufx2t0XMSGj57lwd+vS3OpWWnUmYoN9\nMVktlIq5HK6r4qV5C1g8OMOl9q4rKeCFvG9oNLdwfcoM/pg2B7kTtXV3rKKFQs0ODjf9QFn7AQQk\nRKkySFSPI1E9hghleg8BORDsjWObqY5S7b6T//ait7bhJwsnK2ghw4LOx1cW7LB8b/Y1FPLYwS9p\nMmpIMg4j1jCYO2cNcvrs6rRarv7uK6o0bXx84aWMio5hY24tb20qIEDVoRq2db6pM5q4cemXVDW0\n8tmj1xAbFuj22KxddZh/PfPjgGKvdy76tAYzrToTt85IZebgjgXaL59s5vnr/sPfv/srUy4c36/6\nzxa87lJevLjJxi+38exVr/DsT48yfkH/k0fY453X1/PVp9t548M/kpIW6bZV8po9uTzy3kruumAK\nN83vOwH2rm/RiBjMFivfFuxlTVEuL85dwEUZmS619cu8/bycvwyZKGeIbiKPTJ/ksnbAbDVypPkn\nDjZ9R4upCrkYSorvuUyJWYS/3PMWwq5iEU0Utu3gSMtPlGr3IRXkDAk4hzGhVxCscD0704ojJfyv\naCV18jIiTIncnXo+52bGOi1Xq9VwxTdfojUZ+fc5F/HN7mq0ho7d523TTwm63pTXNXP1kk/JSIzk\njXsucXvBKIoi993xEZUVTXz41Z39zultb/FiMVu4If1uolIiWfrLk/2q+2zBHcHs1T148QKsfGcd\nkYnhjD23/3Gq7dFQ38ayr3cze24WKWkdPpLuWHY3aXQ8/9UGhiZGcv082991b+vSmYMjKDdWsqYo\nl7vHT3RZKH9ftpuXCr5BbQ1goWwRwdaIPm3rbckOHQKgoG0bnxTdzKba15CIgdSVXEvRkYdYu2s0\ntc2nJ0xkb2y1DUAqyBkUMJ2L4p/n2uT3yAg8l+Ot6/ik8Ga21L6JweKaZX1GZCiDdRNJMmRRKy/h\n86YVtJudW09HqP146/wL0ZvMPLphFSJWBkcFEBfki8Vqf3MUFx7EPRdPY09uGcu2HbHbP3sIgsBN\nt82isV7Dj9/2fzOUGu7HnIzIPgs0qUzK/JvncHD9ESoLqvtdv5eeeAWzlzOOu5ONp6ksqObAumzm\n3zTbY+dk3fv06ftbMZutXP/HGV1/d8ey+8WvN6LRGXjy2nk9jL16033y3FZWwrNbNjIvJY17Jkx2\nqc2fFW/luaPLGBGQyjDtTJpahD5t69yZrz5S3eVC1GAoYVnZw6yoeBKZ4MNF8c8Ta3oCQ+tIYoL8\n+uVS1h9stc0WIT4JzIm6lxtTP2VI4Fz2N37DR4U3cKx5DWKvhBq9SQ33485Zg7gtfQ53JC0mV1PG\n3Xvfp82kc9q+QaGhvHzuQkrbGtlSl83xqha7z777+3PRlGGMTY/jxW838/Lqo3365+z7GTYygXGT\nUvnio21o2vRO2+ku5944C4lUwqp31nm87rMVr/GXlzPKrxkSzx5r3t+ARCJw7o2zPFJf9z7pm7Wc\n+PEA510wiujYU5l/XLXs3nGshJW7j/PHhRNIi7WfKKE7VW1t3L1qBanBIbwwb4HD7EedfFa8lVeO\nr2R25FCeGXEFpQ16m23rYbTU0sau+vepb1iOQuLLjIg7GR68GIkgxRSmGXDgBncjg7mb81ktC2Zu\n9AMMD1rEptrXWFu9lOzmFcyLecihevuU4V8kicH+PHbwC+7c8y5vjP+jzZST3UnyjyTDL5VjmgJO\naEp5csI0m9m+en8Tj18zl0uf/oi8vDzmTxvf1T/Ape/npttmcfsN7/DNFzu54Y8zHbbRXcJiQphw\n3mjWfLCBG5650m6yFy+u490xezmjDDRYhyfY8t0uRs7OIjwu1CP1de9T4+ESRFHkquv6uifZUw92\nIooi//1hKzGhAdxs41zZXpknNvyC3mzmf+dfgJ/CeaamLbU5/Pv4KmZHDuUfI65ELpHZbVvnTr+6\nrZ6wpDepE35gSOBcrkv5gJEhF3UZdA0kcINobaOk9hhfbf+RA3nfsfnQG9TV/A+x/TNE/SpEw05E\nUy6ipRaxW8rH/vqXR6oGc1nCK8yLfpAmYzmfF99OsWa3S2VnRQ7l+VHXcKK1iicPf43VyY67uF7L\nEL8kkv0iKTeXUqVptXlN728iISKIi6ePpK6ujuOlNV39c/X7SUuPYvK0dH76fj9Go9mlvrnDnGum\n01TTwtHtuR6v+2zEu2P2ckY50yHxyk9UUna8gsV3nOuxOjv7VF6voeFoKSMnpBIeEeC8YC82HS4k\np7SWJ6+dh8LFxBQr806wvriQR6fOIDnIeW7e/LZqnjj0JYMDYvj78MuQSRzvdlLD/bh+upptTS9g\nFuqZG/0IQwLn2L3WFWtzLMVg3Ito3AumvWApIx54qLv7rghiXxkGghpRPhpBMZaUwLHcOTOFogaz\n2zHMBUFCRuA84nxHsrz8CX4sf5xZkX9mWPD5TstOixjCvUMW8tLxn3gj7xfuSLcf0CMpTI0IZPim\nU6at55v8vVw5OrWHUZe9b+KuRRNYufMIbfWVPLj4vK7+ufr9LL5kLNu3nGDLhhzmnDvMxZFxjXHz\nRyJXyNjx416GT3fNnsGLfbyC2csZpb/BOjzFjh87DGImLXLJWNIlOvu0/McDHNabuOoq9wNEWK0i\nb6zYQXx4EOdNcM3FqdWg5+nNG8iKiOTGkfajinXSZNRw//6P8ZX58MLoP7iUB7my/Qg72v6GRAYX\nxy4l1tf9CV4URTAdRtR9CYaNYK3v+IMQDIoxCKorWJ1jZdVRHSYxmEadLxePyeLysWFgber4JzaB\ntRHRlAemvYialwFIQk5SxCgE1cWI4gIEwT2jM395BJcmvMyqymdZX/MKzaYKpob/EUFwrFy8InEy\nBZoaPijcSIpfBPNjRtq8rvv7ntYk8urerSw7ntPDOM/eN+Gn8uHGc8fyn2Xb0La1wcmFj6vfz6ix\nycTGh/Djd/s8Lph9/VWMnJ3F9h/3cOvSa3/X8bN/C3gFs5czzpkMibfzp32kDE8kMtGzrjyp4X6U\n7C0kPjGUkWOS3C6/9WgRJ8rreOaG+cikrp04/Wf3Thp07by7+CKHRmKd/CP7OxoNbbw54VYilM59\nZCvas/m+7EH8ZRFcEL+EIIVzN6HeiIZdiJpXwbQHBF/wmYOgGAeKsSBN7Uq7+H3OcY5UtWCyWkmP\n8GdsSgKC1A+kPd2KOqd/0doMxv2Ipr2gX4fY+jBo/gXqP4LqKgSJ6wFdFFJfFsU9zeaa19nf+DUG\ni4Y5Ufc5FDaCIHBp+BwO11XxTPZ3ZAXFE+d76mik93l5argfs8QItpYXsGTrRs5NG4Sv/JQrk71v\n4ooZI/nkl/28u3oX/77jQrvX2jqfl0gEFl00hjdeXUtxYR1JKZ595yctGsurd75D+YlK4ge7/254\nOYX3jNnLWYvRYCJnZx6j5gzzuGV4U6OGo4fLmDV3aL92Dz9sO0JogC/njh3s0vWNunY+yz7EBYMz\nyIpwnrZuS+1xttQd57ZBc8kMdO7H22AoYXn5EwTIo7g88VW3hbJo3Iu14Q+ITdeCpQTB/zGE8K1I\ngl5E8L0SQZbWNU4dWaLknJMZSXqkPwuG9Y1RDT2tkQVJEIJyNhL/BxHCViMEfwSyTMS25xHr5yBq\n30UUbSd06F7PxtxaXvo5l80nGpgZdTfjQq/maMsqdjd84rB/HWEri4hsGIPVCv849AMFdRo+2VnC\ny2tP8PyqnD7W1BJB4JFpM2jQ6fjyaLZL4+irVHDhlCy2HSmmrtn2u+rIOj1pZEdgmuWrXLufO4yc\n3RHIPHvLcY/XfbbhFcxezlpyd+djMpgIG57kkpuNO+zYmocowuRprgnW7jS1tbMlu4iF4zNc3i1/\neOgAerOZ28c6NxIzWEy8lLOCZHU4VyY6d6XSW1r5sfwxpIKCC+OeQyVzPQKVKBqwti5BbLwaLMUI\n/o8jhK9DUF+PILGtJekeJjMqQMmk1L5GeY6EjyAICD4TkYS8hxDyOciGdAjohks6VN926vnbsiM8\n8l02Px6q4G8/HGVjbi2Twm4kI2AeO+s/JLd1vd1+dhphJQeGkGgYyv6WfB5dt4G3Nxfw2e4SjlS0\nopJL+xhojYmOZUJsHO/s34PRYrFbf3cWTxqKVRT5aXeOw7b0NggrqNPw2eFqfKOCWPvLUY+7J8al\nxxAUEcjhzUc9Wu/ZiFcwezlrObj+CIIgoBqc0C/LcEe77B1bThAZFUhKmvuxpVfuPo7ZamXxJNeM\naDRGIx8eOsDc1DTSQpxbln9ctJkKXSMPZC52auwlilZWV/4TrbmBRXFPE6BwPRmCaMpDbLgU2j8A\n32sRwtciqK9DEBy7FLli0d0pfFQKKdWtenYUNNisS1CMQRLyPkLQm2CtR2y4CFH7EZ0RD3tY0GuN\nWCwWIgNUSASR/SVNCILAnOj7iFFl8UvVSzQYim3ep7vBVrRxEMGSIMr9DuGrFAhQyjFZLBTUaWwa\naN0+dgJVGg3Ljh9zPqhAYmQwI1Ki+XHHMWxFbnSWajNuaBy6mhay82pdup8rFNRpWH+8lrRJgzmw\n7ojNdnlxHa9g9nLWkr01h9SRSQxJDXfbMtzRjs1stnBgbxETpgzqlxr7530nyEiIIDXGNb/l5SeO\n02ow8KcxznfLGpOej4u2MDsyi3GhqU6vP9byMyXa3UyPuJ0o1RCX2gMgmrIRG68CawNC8NsUGf7C\n+uOtLu/SnLmSJYWpadGZ2JRbS1Gdlm/3lbEx176gEZSzEEJXgM9kxLZ/IGpeQDz5rDuffYhagVQq\npaZVh1UUGJ3YYdUuFWQsjH0CuUTJL1Uv2BQ63RcTd81K547UBZhk7bSqSzFbRNIjA1g4LNrmQmNa\nQiKZYeF8fPigS2MDcP7ETIqrG8mrqHfYlu736+yrENOxeGsrqXP5fo7o/i1UhYfSWNXUIwrYmQ4g\n9HvEa/zl5axEFEUKDhYz5cLx/bIMdxTMorigDoPBTNbweLfb1dau52hxNTcvcD0hwKq8EyQHBTMi\n0vludmXlAXQWI9elTHd6rcmqZ2f9B0QphzAsaJHL7ekQyjeCJAAh5GMKGwP7FUTGUYCR1HA/ZqSH\nU9dmoEVnoqndyJubC4gP8bVbtyANhaA3EVufAu3bFDdowff+Hs++rLGd/SVNjE4M7hG/Wi0LZXL4\nzayrfpHvjy9nRNhsm23q/F1KWBafVUTRGl/HNZkzmJQWZr9dgsCFQzJZsnUTpS3NJAQGOR2bqSez\nTe3MKSE9rq8Rly2DsM73vKhWw6vf7aChzLaWwV26fwvtKdEAFB4qITYt+jcRQOj3iHfH7OV3hadW\n3w2VjbQ2tJEyosMYxtkOrTeO/K9zjlUAMCQzxl5xu+w+XoZVFJnYK3uUPZp0OnaUlzI/zfnuXBRF\nvi3bRUZArE2Dr95je7DpezTmeqZG3Oryzl80neghlAVpbL+CyLgSXnNiaigquQSraEUllxGolDut\nWxAEigz3s71yHonKz8gveg6g69nHh/gyIj6I+JC+Vtw+xskY9ZEUGj/j9Q25Dt9BQRC4KnkSDdZG\nsobg9L2anzYIgNX5eQ6v6yQy2J+U6BB25pS4dH0nqeF+nDM0iozMWHKPVbpV1h7dvwVFXBiCRKDg\nUDHw2wgg9HvEK5i9/G5wNRayS3UdLAYgbWRSv8o7OgfNPVZJYJAvUTHOdz692X6sGD+lgqxk185y\nfykqwCKKLEhLd3rtgaYiijS1XJrQN6l977HNqalkb8PnJPtNItbXtfzNolWD2HwnCD5dQhn6F0TG\nlQk9NdyPW2ekEh2kItRPQavOhNRBzuHOhcfOwkZ+KLiFo41zmZv4DZqWVTbHoPf7Vdqgp7X6fOQ+\n9fiH7uzRJlsLxnOjR+AnU/Jt6S6n/Y0LCCQrIpJV+SecXtvJpMwk9udVoDOaXC7TyeCMGAoLajAY\n3C/bmx5q/HMzSBgS2yWYz3QAod8rXlW2l98N7sZCdkT5iSoAEjJcT/nXG3u+psWFdaQOiuzX+fLh\nwipGpsUil7oWb3hHWSkRajVDw50bmW2sycFHImNudN/gEjsLGqhu0ZMa7ofOZCG7cR1GoZ1JYTe4\n3vj2jztcoUI+7RLK0DNghlRySsg6enauTuid6uY3NxcQqJKz/FBl1263uxq8u0q1RWcCRD49diN3\njsxjiN/riOJFTt+vpDA17dlDMLQnoA7ZSVLY9YD9eO8qmYJzooaxtuowZqvFqaHd7KQU/rN7Bxqj\n0W4o1e7q/bHpcXy6bj+5ZbWMTHXPfS0tPQqrRaSsuIG0wa4b9Nmj+7eQkBlH0eGSrt+fyQBCv1e8\nO2Yvvxs8ufquK29A6euDf4jnJ4rqqmai+7FbNlkslNQ2kRbjeszuY3W1ZIW7tgg42FREVlB8nwhf\nBXUaNp2oo7ypnU0n6mjRGTHK9xOsSCBc6dxADEAUdYjaD0AxvSNgSC9Sw/1IClOz/FClSxoPd2Jt\nW6wicUG+DI4KQCII7Cho6LPz7S50A1VypqdHMHdoPLKAPyOnAvSrnL5fHW0aRKJqOgpVFaGBLYDj\n3f2YkBS0FgN5bc5TIg6LiEQEjtfbNsrqvaOXKzuimhVVNTqtuzed2pyqqma3yzojPDaU+orGLiM5\nd4+JvHgFs5ffEQNJjNCb+ooGQmNDPB46UKs10NLc3iOTlKuU17VgtlhJiXZNMOvNJgqaGhka4Xy3\nrDHrOdFaxajg5B6/L6jT8MOBCiQCzBgcQVyIiqmDfWk0HyXNf6rrjW//GsQmBL8/2b2ky8VJLqW6\nRc9OOy5Ondia0G2pjHsLVAH6CMre10xKDWVORiRR4QtBNghR+yYpYb5O36/UcD8WpC3oaEvbVpv3\n7y7QR4YkAXCwqdipfUTmSa3HsbpT1uXdy/ReAGjNEpRyGQVV7htxxZx8P6sqmtwu64ywuFD0WgPa\nlnaP13224FVle/ld4anwnfUVjYTFhnigRT2pKu+Y6GL6IZgLT06wrgrm3IYGLKLYNaF3p7dFc3ZT\nKVZERgYn9bjm+VU51LUZqW7VMSIuiKgAJYmxhTRprKS6KJhF0YqofRfkYxEUY+1aU3e4OBnZXaQB\nRDadqGNiaqjLz9Oeyri3qry8SUeLriPKV6egtKdSFQQJqG9DbHkAjJtJDZ/ptD0B8kgifAZRoNnG\nmNArHKprI5WBxKiC2Vadz95StUPr5Cg/P0KUKo6eFMy9+7toREyPBUBKuB9JUSEU9mPH7OevJCBQ\nReXpEMwnv6u68gb8grxnyv3BK5i9/OZxNy+vK2iatCRkeD6eb3NzhxozONT9dja2duwwIoIcl+0c\nj3Jdh8ozzj+gz997C7AqXccEnOx3SojvLGggt1qD2keKgEC4vw83TEmmStyNVJAT4TPItYZbq8Ba\nheB3u0P3mNRwP6anR6A1WjrOso0Wt+wEiuu1aA1m5FIJJou1S2Xc+W4kham77g0CI+ODXBP8ygWI\nLQ9TUrkZq9/YrjNpR+9ctCqTnNa1Xf/vaMGY4hdJYUsDg5zYRwiCQFxgIDUaTVe/up95W6xinwVA\nZLAfVY1tLo1fb0JC/Ght9vyutvN4SNvstcDuLx4RzIIgzAf+DUiBd0RR/Kcn6vXi5XT5QRp0RhQq\n59mU3EWn67ByVfWjbq2+Y5fnp7IfGav7eBRpO9yyAnyUPa6xZcTUpOiYJIMUp9yAxG7/lUsFBkcF\nkBruR15lA2pZqOtqfnNhx09ZqlMDqkmpoRwobUJntLhtJyCVCByrakMiiFhFgdo2PSuzq6hp1VPf\nZmB8cmjXvQGiApVd93b0HhXWG/DVRtKmO8GHe/JZNCKG5YcqHb5zankYRms7RqsOhcRxBqsghS96\nscIl+4hAHx9aDHrAtoq89wJArVSg1RlcHsPuKFVydDrb8cMHgs/Jd99wGur+tTgdmwF3GLBgFgRB\nCrwGzAXKgT2CIPwoiqJr8eW8eHGAJy2xu2PUGfFRel4w609ORiqV3MmVfdHojUglAlvz60m2Z/Hd\nbTxOaDoS3gcqewpyWxP6ofp21DIf5JJTn/yk1FA2n6hDazQTF+zbFZNaa67HT+Za1DHglGCWppAU\n5uOCAVX/rHQtVpHMaH8UMglGs5WqZj01rXqyy1swW0Vq2irJjPYH6HNvhwFh6rWEW2KI9qtCIgjs\nL2ly+s51jo/W3IBC4diyP0iuRmvRcfvMVEoa2h32O8DHh/LWVpfHSq306VrQuYtSpTgtgrlzwWvU\nD9wV60zwWwiK4okd83ggXxTFQgBBEL4ALgC8gtnLgLElZDyxmjXqTShOh2A+ORn5KN0XzFVNWgSJ\nlDVHa7CK1TYnhO7jYRGtAPgpegpmWxN6S5WOAJmqz3UPLRjSZyy15iZCfVwLcAIgWkpBUIMkhNRw\nwakw6a+dQFKYGrWPDIkgIJdKGJ0YzM9HqzFbRVRyKQq5hORwP2YNjrB5vm1vwZAUpqakOIohIfux\nilZGJwaz/FClw92tWtZxjqo1NxDsRDAHKnwxWM3EhfqQFuHv8Fp/hQ9txlM7YGdj5auUo+2nL7JS\nJaetVdevso7o3DEbf6c75tO1GXAHTwjmWKCs2/+XA30ywwuCcCtwK0BCQoIHbuvlbKC3kAE8spqV\nyaVYzK5l83EHubzDV9Vssri9gGg3WbFarQ4nhO7jEVTbyrFDBRjMZlRyeZ/rupdVSeXorX0ncFsT\nv0Lii9Hi+tmjIAlBFNsBPaDymIFeb2wtOKpadLz08wnkMgkyiYRzMiJ7hNJ0VLb738KsYLaEcMes\nQV0RwBw9O4Ol42jAR+JcFa+zGJEgoJA4n271ZjNKmevTstFkwUfmms97b8wmC3KF582MzKYOTY5U\n3r92nWl+C0FRfjXjL1EU3wLeAhg7dqw39YgXl+k+0a/LqfHIalahUqDv59mcIzrPlk9UtrCsoMmt\nBUR0kC9Wq5XyRi0Igt0JoXM8aswdvrEtBj2VzQaHgiRIoabF2I5VtCIRHHtJ+snCaDaWu9LdDqTJ\ngAjmYpBnuF7ODbovcuZknMo3PT45lGsmJFKvNdgVyp04WjD4y0pBnkpqiJ/Ta6FjpwygdkHl32TU\nEqjwdTruAK0GA4G9bAYcodUbUffTVkKnM/Y5cvGENsrQ3rFT9jkNNhy/Br+FoCieEMwVQPdo/XEn\nf+fFy4DpPVF4ajXro1KcFlWb8uRkVFzd6vYCIj60Q805Mz2UzLgQu9d3jknn0eKx6mZ+2t/gcBEQ\nJPfFikibSU+gom8c6O74yUIpb3c90xGylI6f5sLTIpjtnfn1/r2t+NauIIpiR9tVrifq0JjrkQpy\nVFLnualbjFqC5K61rcWg72PM5wit3oC6n0cyOp2JwMBT7fLU2Wqn0dfpMK78tThdWh9X8USAkT3A\nIEEQkgVBUABXAj96oF4vv0M8meLNVuxiTwUZUfopaW/TD7h9vfvqH9AxqfoJotsLCH91x1nx8Bh/\nh0K5c0w2HOvYtR2raXQaVzrUp0Po1+idR3oKkEdhsGrQml30j5UlAXJE407XrncTe5G1PJYgwZwD\nYhuCbLDLRRqNpfjJIlyyXK/WtxDi4/hsuZP69naCla4L5maNHn8HVvyO0LTq8FWfKuup8dRrOr4r\npdr1fnjpyYAFsyiKZuAuYA2QA3wliuLRgdbr5feHJ5NMgP2JwhMh/kKjg2modD8wQyf2+hoR2bGD\nkulNbi8gEiM6gpIUV9tvV/cxCZJ31Kmzap0uAoYGdii1DjU5z0aU4DcWgMK27U6vBRAEJaguBt33\niBb7OZH7iz0tSWfAkr3FjbTojP3WnojaN0HwA+X5Ll1vsuoo0exDahzq9B3XmY3ktlYyLMhxCtCC\nOg0/ZZdT2tJMeqjrFvHFNY0kRrofKMdoNFNf19Yj0YqntFGd31VojPtBdrx04JEzZlEUVwIrPVGX\nl98vnrZmPJ1GGGGxIeTsdD2TT2/s9TU0zA8fHxkV5Y0sclMdlhzVMcEWVjcwY4TtGNU9UuxJFISo\nfKnRtXDHrOFOAmIEEaEM5EBTMZclTnLYjlBFEkHyWPI1WxgW3CGsnJ09Cuo/Iuq+Rmz/AMH/Qbt1\nd68HcOkcz/GZnwDCyZ/9QDQXgX41qG9FkLi2q91VuQUrRnKLUjmYne9w4ZXdUopFtDIyOMnuGHYu\n8hpNLYjg8u66WaOjobWdlGj3BXNNVQtWq9gjQp2nzlbryhuQSCUERzpX83uxjTfylxeP4WlBejqN\nMMLiQmmpb8OoN/bLbcpeXwVBIDI6iJrqFrfr9FP5EBnsR0Gl/djHvcekaUchR+tqnZ6JCYLAqOAk\n9jUWIoqiQxWsIAik+k/lQOM36C1tVDQKTs8eBVkConIhaD9E9L0eQRrZp96eGZ6MgECgSu7SeWZ3\nn+PO/+9YHEGoWoHRbO3XQlDUvgkoEHxvcLlMfts2LBZfQuWZVApGh/c92FiMBAF/Syivbz7V9+np\nEUw6GZWsc5EnyjpUwD6ia+fRRSc1K6kuhnDtTvXJ5BW9k6144my1vqKRuMHLWAAAIABJREFU0Jhg\npC5mSPPSF69g9uIxTocgPV1GGFFJHda7FfnVJGe5777nqK+xcSGUFNrOEOSMYcnR7MtzbBHdfUxG\nRUWzqbiIOq2WcLXjhdD40DTWVB3icHMpI4L7+il339ENDpjFvsYvOdj4Hdr6+S5pQgS/uxEN6xFb\nH4egN+iIPXSK3loGBMiIDnBJu2LLMKl3JDBHuZhtIep/Bt134HsTgtQ14dZirKJN2EV78zhamo12\nF6AFdRqK6jSsqj1MZmActU2WrgQeu4s0aI0WDpQ2ccestK5FXn5zDUqJD6Niw11qS3ZRR+rS9DjX\nru9OaXE9ADFxno8XX5Ff3fV9eekf3uxSXjyKJ85/fw1ShncI48JDzs9c7WGvr4MzYygrbUDTD+Oy\n0Wmx1DRpKK9zLR3fvNRBiMDPhflOrz0nahhqmQ/flPY10up9Zt7aFkWa/3T2N35NVIjRJU2IIEtG\n8H8IDJsQWx9DFHv6iXfXMqh9pKgVMpe1K7bsDTojgQ2LDSIz2h+L1XUvTNGwDbH5fpCPQPC/z+Z4\n2DJi3FH/PoIg4cK0m+3aD3SO5ZfHs6nQ1zM5aHhX3zvqE0kN9+vqR2q4HzdOTaTO1Mi5qWlOg5B0\nsv1oManRoYQH9VSLu2J8efxoBeGRAYT0I6a7I6xWK0WHS0gZ7nqAGi998e6YvZyVxA+JRa6QUXio\nmDnXTPNo3UMyYwDIzalkzPgUt8pOykwCYMexEi6b4Tync3pIKMlBwazOP8E1w0Y4vFYlU3BezGi+\nL9vNfRnnEaw4NSnbOjOfnHoThW3bqBG/5Y5Zt7ikCRF8rwJrPaLmP1Q269CrniY1ouOs0VawGHtn\nrr1/b+/ooDMSmEImcfnoRDRsQ2z6E8iSEILfpMOZ5BT23IZq9Xnktq5nbOhVqAgDbFstd45lk7oY\nmVVOrDWxq+87CxrYdKKuT6zwUm0tJquFK4YNdakPOqOJAwWVXDHj1DN3x93peE4lQzI9n8SluqgW\nnUZPyogkj9d9NuEVzF7OSmRyGQmZceTuLfB43YMzOgRzztEKtwVzQkQQ0SEB7Mwp4bIZjgUtdJwH\nL0hL5819u6lvbyfM1/H55MUJ4/mqdAffl+3hptRZXb+3JfiCFX4MCzqfQ80/MDRpIXPC013qQ6Hu\nRvKKq5mX9DUHq5sp5HlSIqKAvkcT9nabjlI7dhfY7h6diLoViC2PdAjlkA8RJH1VubYWKclhKjbX\n/A+lNIBQ6/m8vsm+AEwKU6OjnTJrCdHGVNIjgnr0fWJqaJ82r8w7QahKxbgY14Tl/hPlmMwWJmae\n2pm6anzZ1KilurKZRReNcele7lBwUgOVOsK7Yx4IXlW2l7OWrClDyNtX6PHQnH7+StLSo9i7y32h\nLwgCM4ansO1oMa1a11ThFw3JwCqKfHz4gNNrU/wimRo+hI8LN1Ovb+36vT3/8InhNxAgj2J15ZKu\nMJTOKK7Xsq7scrZW3cDw8J1Emq5ANO53uaw9X9rOADPF9Vq3XfFEaxutNfcittyHnsF2hTLYXqTs\nrv+ECt1hpoT/kYpGHPr7pob7IU8sRCIIPDjmnK6x7FQzlzX2DHda3trCmoI8LhiciVTi2pS8em8u\nfkoFo9NOxel21fiy870cMcp94elMVZ69+RgKpZzkYb9+2GVPxlA403gFs5ezlmHTM2lv07H+52yP\nf9CTpg7iWHY5TY3uB2lYNCkTo9nCmr25Ll2fGhLK3NQ0Pjx0gDaD8zCjfxlyHkarmVdzV/esx8aZ\nuVLqz7zoh2gxVrKm8jmsovNFTKeAWJ5/Hq8fehqZVILYeDXW1qcQLTU9JtDek6kj4dL7HHxjbq1T\nv3lRNCPqvsNUex4qy2q+y72I+9b+lcIG+5b4vRcpovIAuxo+ISNgHkMD5zsVgPsbi9jWeJTrU6cz\nOS6+R9u/3lvG3344ytd7y7ra/Na+PUgEgZtHubaD1egMrNufx7yxg1F2i3Xdvd2LRsTYXcDs2HKC\n0DB/Bg2Jdul+nbgSp+DA+myypg45LQliBtq23xNewezlrGXEzEwAPvp0u8c/6MnTBiOKsGt7nttl\nh8RHMCg2jOU7XU/QdvvYCbQaDHx+5LDTa+PVoVybMp3VVQfZ31jo9PpY32HMjLybIu1ONtS82hHC\n0gGdAmJkfBAhQROpkHwOqiuh/UssdXMoL32MbSeO8fyq4zy/KsflyG69d9Pd0zP23rl2COTvEesX\nILY8TKvBj7tXP8B7B88jp0rLjgL7LmmdfZiTEYlKXczqyueIUg5hdtQ9Ha5kNtrYucA4UdvKC8eW\nE60M4oaUGX3arpBJkAgicqkEiSBwqKKer44d4eKMoUT7u2b09cv+PPQmM4sn9T2P7tQqLD9UafOd\nNhrM7NlVwMSpg5C4acXuLDJYa0MbxUfKGDEzy616PYHHosD9RvAKZi9nLUHhgYSmRKLNKfX4B52a\nHklEZABbNhx3u6wgCCyaNJQjxdXklrkWSWtEZBRT4xN5e/9eWl3YNd+QMoM43xCeyv4Wjdm5ynx4\n8CLGhl7Fkeaf2FT7mks75/2lTRwqa+a1jdUUGR9ACFtDtWEuk2PW8PC4O7lu6KukBe4nKUR0KbJb\n753q6MTgnjvXUCWiKQdR8xqmmtmILQ9hsCgRgl5nTdXr5NR3Bm0RXApHUtF+mB/LH8dfFs7iuH8g\nk5wKX9m9jd13a49tX06+ppp7M85DKT21a+xsu9FsxSoKmCxWrKLItupczFYrt44Z50KLOuJ6f7vl\nMEmRwQxLjrJ5jSMhtXd3IXqdicnTXLMX6I4zTcGRrR3vetbUIW7XPVB+CxmhPInX+MvLWc2IOcPZ\n8MEGyqpbEHzkHvugBUHgnPnD+OLj7dRUNRMZ7dzCujuLJ2by1k87eeunnbz4p8UulfnrlGlc9OWn\nLN2+hWdmnQPYj9illCp4POsS7tzzLn879BVLR/8BqZPsR5PDbsJiNXKg6VtaTTXMj3kUhURl81rb\nhkjxGHyf4V875jM9diUjIzYxOXYnVlGgUpOEf8BERP0kkI8ESRiC0HN66mEAFupLSphAWrAGnWYX\n0b5H8RUPITa0AVDYNIwdlddxpGEMd8waxMRU2HSiDq3BQlywlImpjv2Wc1rW8kvViwQqYrgw/jlU\nMvtRrDr7ag2spcR6lBG+g5kZkWm37VKJgMUqYpS0c/fP67kyazjJQa6Fr9x+tJijJTU8evUcu0Fi\nHAmpn5btJyTUj9Hjkl26n70+2DK227liH77+KjImDnK77oHyW8gI5Um8gtnLWc38Kyax/u21DGpp\nYt4Vkz36QZ93wWi++Hg7K37Yz81/mu1Sme6C9A9zRvPGih0cK6khM7FvJK3eDIuI5LoRo/jw4H4u\nGpJJoCzAofvM6JBk7htyHktzlvP6iZ+5e/B8u21JDfdDEASmR95OoCKGTTWv8W3JfSyKewY/ed/Y\nzvaEQ2q4H1dOmkJx/UjqfJ6glUPotbuI8j+CSvwesfnzrjpEIRAkwSf/BYKoJ1loIjm0CaxNiLUm\nYgB8AWkqKBYiKMayrSSZH46IJ3eMHYuCORmRPLQgw+nELYoiu+o/YlfDx8T5juS82CdRSh2rmJPC\n1LQKTRyybMHfGsJ9gxfbFJqdVtkFdRoKatt4Ze9mQlW+PDh5qsP6u7ftfyt2EBMawAU21Njd72NL\nSFVVNLFnZz7X3DgNWT9zONsL+GO1Wtm5Yi/jFoxErpDbKHn6OdMZoTyJVzB7OasZNi0DvyA12v35\npN41z6N1R0QFMnHKIFb/eJBrb5qOwklS+t6uQjdMSuez9ft5Y8UOXr3zQpfued/EKazJz+ORdT/z\nl1HznLrPXJY4iUJNLR8XbSbZL4LzY0fbbEt3oT4i+AIC5JGsqvgHX5TcybzoB0lQ9zRccrSD6TmB\nzjn5D0TRCKb/a+++w6K60geOf8/M0DvSi6CgKHbFlpho7MYYNb23TUzdJLvpbU0zvfwSs2mm92aM\ncWNii11j7wUUBUF6h6FNub8/UIOKMMAAA76f5/FR5M69587Afc976l4w7UazFoBWE4CxFoIlG5Qb\n6CPAqQ/o/GpGVes7g/Ogk0ZYh3Yqw6odJDGrhJIK04kVwRp6cFeYi1mW9SbJZWuIcBmNt/FGjhYo\nYhpYWMvPG9L8/8LT4spLva6jZ/CZs9/j7+sBYyoHSvN48tyxNm/zuGrXIfamZjPz+vE4NRBYa1cC\nlu3LJjrAgz9/2YrSKS68eIBN12uM/RsPUphdzDkX29YkL+ongVmc1QxOBoZOHsi6XzdRXWXC2cW+\ntf2LL01g3eoklv6+kwunDqz32FObf3PKTNwwLoF35q9l/d5Uhsc3PL3F09mZWaPHccuvP/N76g4M\nWmiD/W4P9LyIVGMus3b/jIvOwLjQvg3Oie3iOYzLo97i94znmZf2CN29RjEiaAZeTn8vxdjYDEYp\nZ3DuT3Jx85okYwI9mdIvjA9WJePj5sSCHRlE+ruf8VxWzcKuogWsz/0Mk7WSnu43seivXuhUHlYt\nt96FOvKqSvnnpk8oNVcwZ+gMevjU3e97XEqekSJTCfvKDhHmEkhnt4ZbQgCqTGbemreGyEBfJg/t\n2eCmInBy5cpUUc3h+Vs557w4AoO8bbpmY6z6cT16g57Bk+wf9M9GMvhLnPXG3TCS0oIy1s7baPdz\nDxzchZ69wvnyk9VUVZnqPbau5t9rxwwkOtiP579eSnlltU3XHBXdhRmDBrPg4F6iIqsb3HrSoNPz\nysDr6OPbmad2fM+C9C02DaYJdI3h6uj3GNrpepLL1vHFoZvZkPcVZmvDg8/OxF7TXixWjQhfd+JC\nvOsc1Hc8k9yQsY5vUm5nRfY7BLl245ou76M3jkOndKcNnjp1ald2RRG3b/iQjIpCXh90PT18Tl8c\n5NTXeLrD+sIduOhc6O/Tky42Vjw+WriBlKwCHr1qNKkF5Ta9R7UrV3lbkykvq+Lam2xrNm+M6ioT\nS75YyTlTE/Dy6xhNyW1NArM46w0Y04eQ6EAWfrTU7udWSvGPOy8gL7eUX+duqffYuqbhuDgZeOq6\ncWQWlPDfX9fafN2Hho9gZFQ072xZi5e3qcHM09PgytsJNzG4UyzP7Z7LmtIt3Dkqps4pQbUDgZPO\nlWGBN3JD10/p4jmUv/I+44tDt7CjcD4ma4XN5T3OXtNe6qtYHMwp5eMNi9lUOou/Sv5DuamcyeFP\nMz3yFTq5RNf52lMrDH+mHuLWDR9QWG1kdsLNDOkUe1oZTn3NvqwiXvprKeisPDR4NP8a09OmFoHE\ntBw+X7yZKcPiCQrsxPxtRzFWmRt8j47fR+rRQnK2Hmbw+XHEdq8/o2+Kdb9spCS/lEm3jrX7uc9W\nEpjFWU+n0zHxljFs/3M3Rw9m2v38/QZGkzC0K999uRajsf5ssq6pQgNiw7liZD++W7GdHckZNl1T\nr9Px1sTJRHr7cPfCX0kvaXgbSle9M68Pup7xIX35b9Iivs5axLnd/U6aEvTj5jSemreLFYl/T+NK\nzi1j00GIc/43l0S+hrvBjxXZs/n44DWsyZlDUbVtZQb7TXupa7GNpJx89hUv4c/8BwiOfRdXj1SK\nsybRxfIysV4jTgzYqquCVLvCUOB0lP/s/xyrpvHekFvpW8dOXXByJUMBz63+kx3ZWbw54UJuHNLD\npqBsslh45ssl+Hi6Mn3UIN5dfpCk7FL2ZpaSmFVS73t0/D5ckzNQVit33zOmSe9lQ37/eBnBUYEM\nGte3Rc5/NpI+ZiGACbdcwFfP/cjcN/7Hve/eZvfz3zRjFPf84xO++mQ1t/+z8ZnFP6eNYM3uwzz2\n8UK+evQa/L0b3rPX28WVOVOmMe27r7nk++94a9xUhkfXnzE56ww81+9KOnsE8FHyn2wvSOUSv/Go\nCm+MVWZS88upMJn5YFUykf41ZTh1kNiVUbPJrNjLtsKf2FrwI1sKvifCvT/dvEYS5TEIH+ewM16/\nudNeTu17NWsVfLFlCe7e+9hp3YZOX4mHPoyCI5dQUTQYi9WJrr1OH6x1av94dIAHJs3EiorNZLol\n08UthNlDbiTI9fRpVMfLoNfVvCf7M4vZWZpEtiWLfw07hwkxtk8n+r+5q9mflsOrMy4iv7xm68i4\nkJo+4u7BXkwdEF7ve6QvrWD7in1MmtKf8BbY4vHg9sNsXbqLm567Cp2Ny4mKhklgFm3GlgEsrSUg\nzJ8JN13AH5/8ydWPX0JgROM3n69PXM8wJk3pz88/bGDcpD50jbVt0M9xHq7OvDrjIm557Xse/Xgh\n7957CQZ9ww9CzezMIK++rC3cxm2/zeX9C6czosuZA+Pxz2RMwDCCugXzWuIvvFPxPaHGnlDSmQqT\nGTcnAz6uTieaUOsaJBbm3osw916UmfLYW7yIvcWLWJ79FgA+TmF09hhElEcC4e59cNWfPBipqdNe\nknPLeHfFflzcsnDJSqRr5BHyTfsJ6mLBanWiorgvvXwncnG3URzyMzbqZ6/auZiUwBVkVhVwvs8g\nxvkMo7RUT9ApA6prD7gqrqgm2NuFVVn7ySeHWPfOTIo+fVWsM/0e/LZhH98u38Y1owcwZkA3knPL\nam2baWgwKGuaxtuv/Y67hws3337BGY9rjq+fn4uHjztT757Y8MHCZqqh5fVaQkJCgrZ58+ZWv65w\nHI3Zoq61ZKXkcFP3e7no9nHcM/sfjXqtLZWMkuJybrn6fcIj/XnzvRsbvSQiwIL1e5j5xWLOH9CD\nf04/r8H3bNm+bP7YnYXepYpfUv/CzcnAd5dfQVyn0+cen/qZDOzsx6a0bI54bSOdVAIIxDWrJ0H6\nADxcDNx1QU2/qi2fo6ZpFFWnk2rczBHjFtLLt2PSalYcc9P74u/cGX+Xzvg7R+HrHI6L3hNnnQfO\nOjecde446dywaCaqrRVUW42YrBVUW4yUmnMpqD5CYdUR0koPUUU2Sllrrlkdwf6UCJLTIsnKDadv\neADPTuvdqJ8zo7mKT5KX803KGjo5e3Jb54tYtcV8xvs9/n67OelZkZRDsUsqpaqAXt5diHPvyqQ+\noYzp+Xel7Ey/B4lpOdz86vf0ig7h3fsuwUmvP3G8rRWKxQt38OrzC/jXo5NbZIrUoZ2p3N7/Qa57\n6jJufOZKu5+/o1FKbdE0LcGWYyVjFm3C1i3qWlNIdBDjbhjJwo+WceUj02zOmm2tZHj7uHPrXaN5\n/YX/sfDXrVw0rfHb7sXHRhEeHs6qbftJKjDx8PRhjIoLOuPxx/tsqXLhPP+BbCvfxdVzv+f9yVMZ\nEh5x0rGnfiYaoNec6Vo6FHdDMFleu8kPWU2oZy+u6nJBo7ZeVErh5xKJn0sk/f2nY9FMZJbvIbsy\nkYLqNAqrj5BUsoIqa+NHYevQ4+Mcjr9LFIlp8ZirQqkoi8FV+bL/YC7ers64GcrpWkf5zhTozFYL\nCzO28cGBpeRWlTAlfBD3xk1ic3Ipxqo0nPQ6TBbraT+3x9/vxJwi8pySqVQl+JhCcTIGcdRUcWJO\n9Zne85Q8I74uigc/WIC3hysv3XrhiaAMtrcmFBUamfPOMuJ7RzDxov6Nfk9t8dVzP+Lu5cYl909u\nkfOfzSQwizbhqGvbXvvkpSz7ahWfPvktD392j02vaUwlY/yF/fhz8W7ef2sJvfpE0iXmzEH1TNcK\njYwmp7CErNRkXv/VQORN55/xeif32cbg5Nybm+f/zHXzfuTJ80Zxfd/+JwY9nfqZDI/pxPATewfH\nEuQ7gTkHl/HTkQ3s3LOfy0uHc0PXkXUuZtFQ8NArJyI8+hPh8XfQ0DSNckshxdUZVFvLqbYaj2XI\n5VRbyzEoZ5x0brjoPHDSueGs8yC/xIW8Im+6ePnUlMHr70CbVlDOygN5lFRW4+rsxJj4k7sP6qpQ\ndQlw58/sPXxwYAmpxjx6+UTw0oBr6ONbs42hXlfG3sxSdErDqqnTAm1MoCcXDerEI8s2UqUvZYB3\nD1xNnSivMuPjevqc6lPf8wAPA3e9/TP5peV8+K/L6eTd+N8Li8XKi0//grG8ivsfubBJLTMN2bMu\nkdVzN3D9fy6XKVItQJqyRZtxpD7m2uY8/CU/vr6A97e9Ste+DS/q0dhm+YL8Mu686SM8PFx45+Nb\ncPdwOeOxdV3ryV92kVFQRkXWISxVFfzj4lHcNdH2rKikqop/L1rInymHuLRnL54eORoPZ+cT56/9\nmdT1GR0tL2DOwWX8nrEdF72BiaH9GebVl982lLZq14Qt7/uKxBy2phYyMMrvtJaF483OYb5uHCkq\nxq9zPlvKd5NZVUC4awD39ZzIyKCeJy2vuWxfNj9uTsPZoKPabOXyhMiTmqb/OHiAh5f8gYtBz2Pn\njMVH50NWcSXb04pOVNwm9g45rTk7Jc9IoKcTr32ziMT0XN66ayrDejZ+v2SALz9ZxRcfrWqxJmxN\n07j/vKfIOpzDZ0lv4+Zh28plZ7vGNGVLYBbiFCUFpdwY+0/iz+nOrP89btNrGlvJ2LE1hYfv/Zrz\nR/fk8Wemn3j423KeFYk5fLgyGTcDHNi3m6rKCt6+aypDe0bZXA6rpvH2hvXM3rieKF8/3pxwIf2C\nTx6x3VDgSy7N5tvUtSzK2EGV1YS3OYBeTvEYSoK4sHf4ScGnJdQOrHUFvIYk55bxysrNZDkfJNsp\nFauy4GXuRLipGwGmCO6+oHudTd91vSfG6mqeXbWcH/fupm9wCO9eOIUwL+96X1NbWUUVd8+ex77U\nbF6ZcRGj+sVQnzN9zls3H+bR+75mzIQ+PPxU3Wt2N9e6+ZuYOf0V7n9/BpNnjLP7+TsqCcxCNNMP\nr85nziNf8cLvTzB4Qsv00X3z+Ro+/WAFd9w7jkuvGtqozPv4g9nfTc+LX/1OWk4h9142ms1ZZx6Y\nVJe/0tN4YPHv5JYbuXVAAvcOHYaroWZZUlsDX4mpgs8S1/FT6gYq9WUYrC6cGxjHhZG9GRrQDXeD\nbS0Cja3cNGUAoaZp7C/JYE3uflbn7GN/SQYGpWeEf296O8Wz64C1wfs9tZyrU1N4cvlS0kuKuTNh\nKPcOHY6zXl/va2rLLzFy/7vzSUzL5aXbJjO6/+mLldhy35kZhdx766f4+Lkz+6NbcHNzrvc8TVFd\nWc3t/R9EKcWcXW+gb+JmGGcjCcxCNFN1ZTV3DHiI6koTc3a9jptn3dsb1sXmrNWq8dyTc1m7cj9P\nzbqM6mC/JmWAhaXl3PfufPakZBHbrRtDe3cjs7jS5tcXV1by/OoVzN23hygfX567YCwjOkedCADG\nKjPFlSZuPz+m3oFmB3JKWZq+j/2mJHaVHqLMXImT0jOoU1fOC+zBQP+uRHsG1rm9ZFNH6dvyXhdU\nlbG7OI21OftZk5tIblUJCkVv30j6e3Ynmhjig2sG+jWmDHnl5Ty/ejm/Ju6nq58fL4wef9qAuoYc\nyszn3v/+QkFJOS/+40JGNpApQ90VpiHh3tx3+2cUFhh564Ob6Bx9+qh7e/j48W/47qV5LVph7agk\nMAthB7vX7uff5/+Hi++aYPP0qcYGmMpKEw/98ysOHcjmX89fyu8Zxib101ZUm/j3B/9jw94UIiIi\niImJ4Z4xpzfF1i7nqQFtfdoRnli+lJSiQsZ2ieHBc0aQkWflw5XJeLs5nZgiZcs5ozq5sb0whTW5\niazO2UdaeT4A7npn4rzD6OkTQbxPON28Qgl182VtUmGjKiV1ld+iWcmrLOFIeT57i9PZV5zO3uKj\nZFUWnbj2sIBujAjqSZgKZ++RSlYl5eDj5nzi/QYaDPTlJhNvrP2Lb/duw2y1cGfCUO5MGIKLoXFj\naTclpvHgBwtwMuh5666p9Gpg8Zfa9177Z+y2EdHMmfUr+3Yf5cX/u4Z+A5rWN92QA1sPcc/Qxxh3\n/Uge/OSuFrlGRyaBWQg7eff+T5n39kLeWPksfc7r2eDxTen3LCwwct+MT6kor+aBl67A6OTUpAFx\nVqvGM18vY8G6XfSLCefNO6bgW0emXzsTLqkwMWPk35lwldnMR9u28MGWjZSbTAwL7YK3KZRuAf71\n3s/xfu8zBfAjxjx2FR05FjCPklSaSbXVfOL7XgY3tCpXXK0eOFtdGRIVRIiXB656J1x1TrjonTBr\nViot1WSUGFl7KBurslBNJZ38NYospeRUFmPRrCfOGe7mT7xPOPE+EfT0Cae3b2ecdYYT959VUkl6\nQQUjuwdSYbI0+FlVWyz8uHc3b65fR0FlOaEuAfTyiuWRcf0a9VlpmsaPq3by2g8riAzyZfY90wjr\ndPoKYvU5XjGJ6uTOT+8uY9ni3Tz29DRGjz99AZOmnPe06WMmM3cPeZSinBI+2v2GjMRuAgnMQthJ\nhbGSGX0fAE3j3S2vNPhAamqTbFpqPvfd/hkeHs68+s71hIT6NrnMv6zdzYvf/YmfpxvP3jiBIT06\nn/T94yOLjy+vGerrxvPT+pxUzsKKCt7bvIHPd2zHbLUS5RZGN4/OPDyub50Dop78ZReZRRW4ORmI\n6uR+2mjlU5mtFpLLsjlclkNWRRGZlUUcKs4js6KIUouRas10UpCtix4DBqsTIW6+9PAPItTNlxBX\nX8Ld/YnzDsfXue5lS08sAuKsZ2ViDhF+7oT4uJ7xs6owmZi3fy/vbd7I0dISYn0DCddH0TcotNGD\nzorKKpj1zVKWbTvIiN5dmHXzRLzcmzaq2WrVeOf1P1gwbws33z6Ka25s3s5R9f3sfvToV3z/ynye\nmfcw50yVPZebotUWGFFKXQ48DfQEhmiaJtFWdChuHq7c8M4MXpv6As9c+zav/vZYvSNdm7rWc2RU\nJ15882oevf8bHrjrC15753pCw09fw9kW087tTfeIQJ789HfufHsu148dxF1TzsHZqebXPTrAg5IK\n02nLa9Yuq5+bG4+fN4qb+g/klTXrWHhwHykVR6lcm8E1ffoxpksMhmNrI6fkGfFxdaLIqeacJRWm\nBuelG3R64rzDiPOue3lQTdMwaxYqLaYTf5x0elz1TmQUVvPRyhTYB/7CAAAgAElEQVT0SlcTQAbb\n1rx+6tzhimoLcSHejOweyLCYTqdXOAry+Xr3Tn7et4eSqir6B4fy3AVjiXAP4L0VyY2eg79h/xH+\n89kfFJZVcO/0EdwwNqHJc4ytVo23XlnIwl+3ccW1w7n6hnObdJ7azjQff8NvW/j+lflMvm2sBOVW\n0qyMWSnVE7ACHwAP2hqYJWMW7cXxLCJv4UYyvljClbOu5dbHprXY9Q4kZvLIfd/g7Gzg5bevJaoZ\ng3gqqky8OXcVP63eSVxEILNumUTX0JpBTisSc/hgVTI+rg33HQPkGMv4fs8uvtu9k8yyMoI9PLmy\nVx+u7NWH8krVqEFi9lB7owiLVauzElRfBnimJtsqs5llh5P5etcO1qen4aTTMSG2G9f16c/gsPBG\nTWs7cU6TmXd/XceXS7cQHezHrFsm0bNz06eSWcxWXp31K8sW7ebqG87l5ttH2WVaVF3vl091Fbf3\nf4iACH/eXjcLFzfb59yLk7V6U7ZSagUSmEUHdLzZM9THlbWPf45x12Fmr59Ft4FdW+yah5NzeOS+\nr7FaNV5442q69wht1vlW7kjmma+WUFZRxc0TBnPThMG4OTs1aYEXs9XK8sOH+Gb3DlalpqABg0LD\nSAiJIsojmMGRwY3uG2+qhroNbO3vLzeZWH0khUUHD7D0cDJl1dVEeHtzde++XBbfm0B3jxPXs+X9\nqn1cXn4BL367jCM5RVx+fl/uv/R83JydmnzPVVUmXnz6F9auTLRL8/Wpapc92s+Nh8c9S9LmZN7d\n/DKRceF2vdbZxiEDs1JqBjADoHPnzoNSU1ObfV1w3NWjRMdQ++FfXWIk8+nPMSjF23+9QECY/bfR\nOy49rYBH7v2KkuIKHv7PVM4b1aNZ5ysoKef1n1by+6b9hPh7cf8l5zFuYPdmZVqpRUUsSNrPHweT\n2JuXC0CPgEDGdY1heERn+oeEnJgT3RIaCrxnCtyapnG4qJCNR9NZnnKIVampVFnM+Lq6MrZrDJNj\n4xjROQp9rW0MbR07cPy4qspKkg4cIC8vj4gAHx6/ZkyTV/I6riC/jJmP/sj+PUe56/7xTL9iSLPO\nVx9N05h990cseH8xD39+D+OuH9li1zpb2DUwK6WWAnWN439C07T5x45ZQRtkzI64Q5HoeGpX/sjI\n41/nPUVYbAhvrHwWdy/b5zc3Vu0H8U0zRnHNjec2u8lyS1I6r/64gqT0XAZ1i+DBy0cSF9n8Zucj\nxUUsTj7I4kMH2ZJxFA1w0unoExRMQngEg8PC6RMUTKC7h91Wo7Ll9z85t4zE7GIwVJFVUcimjKNs\nzjhKfkU5AKGenoyP6cb4rrEMDo840W9+Kluz74U70vno940cSUtDKcW4Ib2ZefX5uDjVP5ynoQTj\nYGIW/3nke0pLKnlk5lRGjGxeRa0hP76+gA8f+oIrH57KrS9d16iyNvXYjs4hM+ba7BWYm7sknxBN\nsfH3bTx18UskTOjHs7880qKrH1VXmXnjxf+xbPFuRo2N59+PXdTsFZ0sVivz1u7m3flrKTJWMm5g\nN269cBjdwu2zKEVxZSWbM4/WBMGj6ezKycZkrRlh7e3iQjf/TsQe+xPl40OghydB7h4EuLuftJOS\nLZJzyzicW0YnbwMebpBbbiSjtJTkgnwOFBRwsCCf9JJijj/lIr19GBwWTkJYOIPDwunq529TRaGh\nSoCxspofVm7ns8VbKC2vJCQkhK5du/Kvib1sCl71nXvlsr28NmsBXt6uPPvKlcR2t22+c1OtnvsX\nz17+OiOvGM7j39yPrgktB4099mxw1mz76Kg7FImObcikAdwz+x+8fdccZt/zMfe9d1uLrEkM4Oxi\n4JGZU4nuGsgnHywn5VAuTzx7CdFdA5t8Tr1Ox2Xn9WX8wO58tWwr3y7fxpKtBxjdP5ZbJg4hPqp5\nlVsfV1fGdIlhTJeaVawqTCZ2ZmeRmJ/HgYJ8Dhbks/TQQb7fs+u01/q7utHJ3R1XgwEXgwFXvQEX\ngx5nvQGL1UqVxUyV2UKl2UyVxUxxVSV5xnKqrZaTzuOs19PVz59+ISFcFt+LWP9ODAgJJcTTq9H3\nczzrm9Iv7LSBZqXllXy/cgffLNtKkbGSc+KjuWhEP5xcPWzOEs80Grq6ysycd5fxy4+biO8dwY0P\nTiLVAiq3rMUC3J51ibx0/dvED+/OQ5/efVJQrq+sjbkv0bDmTpeaDswGAoHflFLbNU2bYJeS2aCp\nU1OEaK4pd4wnOyWH71+Zj4e3G7e+fF2LBWelFFfdcC6xcSG8/Mx87r7lY267ewxTL0to1jW9PVy5\n6+JzuHbMQL75cyvfLt/On9sPMqxnZ64c1Z9ze3XBoK+7addWfzdl+jG0X+RJ38svLye9tIRcYxm5\n5eXkGo3klBvJLy8/EYDLTdUUVtYEYoNOVxOw9Qa8XVxwMbjT0zmQQA8PAt09CDr2d7CnJxHePmds\nlm5s+evK+lKzC/l5zS5+XrMLY2U1I3p3YcbkYfS2cfWu2upKMA4n5/Di079wODmHaZcPZuyVw/hw\nzeEWzT6TtiTz+IWzCIwM4JlfHq5zBHZjkiFJnJpOFhgRwgZ19ZVpmsbsez5mwXuLuGHmFVw/8/IW\nL0dhQRmvzfofG9cfZPDwGB58fAr+nezzgC6rqOKn1Tv55s9t5BUbCfTxYNq5vZl6Tm/COnk3+nwd\noSmzdndZWn4ZYa4mkg4fYXNSOnqdYuzA7tw8YTDdI5reggF//3x19ndn14p9fPTeMjw8XHnwiSkM\nPSe2xbvtDu8+wgOjZuLu5cYbq54lKPLM3RrSx9w0svKXEHZUX4CxWq28fut7LP5sBbe9fB1XPDS1\nxcujaRq//ryFD2cvxc3dmbvuH88F43rZLWM3WSys2XWYeWt3s3bPYQCG9YxiyrB4zuvTFQ9X2/q4\nO8IYkIM5pbw0fyu5ublkZmZiNpuJCPBh2rm9mTI8nkAf+wWbzKOF/N8rC9m66TBDz4nlgcen4Of/\n91StlqrkpCUe5YFRM9Hpdby56jlCu7avz6i9OGv6mIVoDfX1lel0Ov495w6qK6qZ88hXAC0enJVS\nTL00gf4Do3ht1gJefPoXlvy+k7v/PZGIyOZP4XLS67mgfyydw0M4/1AOSSlprN6RxOOf/I6zQc+w\nnlH0iY0kKDCQ+Ai/MwaI9tqUabFa2ZGcwbJtB1m+/SBZhaXodTqGxEdzw+j+DI7r3OQVu+pSXW1m\n7ncb+PrT1ej1Ou59aBIXTRt4UkWrpbrtUvem8cj459CsGq8snylB2UFIxixEA2zJVswmMy/fMJsV\n36/jigcvbtE+59osFiu/zt3MZx+uwGSycMW1w7nqhnNxdW3e/OFT7/mOkTGUlZawdOsBFm9NIr/Y\niFIKX19fJg2KZcLAGHp2Dj6tT7q9NGVmFZSyKbGmiXrd3hTyS8pxNugZHh/FmAHdGNm3a5PXtK7P\n5g3J/PfNRaQfKeDckXHcdf94goIbt6FFU+3bcIAnJr+Ak7OBlxY9SZc+LbMrlaghTdlC2JktAcZi\nsfDff37CgvcXM+7Gkfz7wzswNDB/1V7y80qZ899lLFu0m+AQH/5x52hGjolvcmZXXzP00r1Z/LD+\nABUlBaQczcZoNALg4erMgNhwErpHEB8VTPfwQLw97B/MGqOuz81ksZCSVUhiWg7bko+yKTGN9Nxi\nAHw9XBncozOj+8cyoncXm5vtGyv9SD6fvL+c1Sv2Exbhxz3/nsjgYQ3vxWwvG3/fxnNXvI5/iC8v\nLXpKMuVWIIFZiDaiaRpfPfsTXzzzAwkT+vGfHx/ArY6tF1vKjm2p/PeNRRxOziGmWzC33HEBg4fF\nNDp7b2id6drfu3ZwOPkFhWxKTGNzUhop2YUnzhPWyZu4iEDiIoOIDvEjIsCHiADfVgnY+zOLeWvR\nHiorKykvL6ezt46M3EKSM/KpNtdMr/J0c2FQt3ASukcyOC6S2LCAJlVmbG0ZyM0p4atPVvPH/7aj\nd9Iz8dLB3HHbSJxdWq9X8Y9Pl/PmjPfp2jeKWb89hn9I0zZLEY0jgVmINrZwzlLeuvNDonpF8sy8\nhwntGtxqzboWi5XlS/bw+UcrycooIr5PBDfeNpIBg6IbFaDrK2993ysoKWd/eg5JabnsT8shKT2X\n1JxCaj9qfDxciQz0JdTfC19Pd/y83PDzdMPPyw0fDzdcnPQ4Gww4G/Q4O+kx6PVYrFZMZgvVZgvV\nJgsms4WyyioKyyooKq2gsKyCgtJy8oqNpOcWkVFQSu3nm7urC32ig+keEUiPyCDiIgOJCvY7aenN\nprClqyM/r5TvvlzHb/O3YrVq+PbuTMiQ7ujdnVtttLrFYuGTx7/lh1fnM3BcX2b+9GCLrlwnTiaB\nWQgHsGnRdl64+v9QOsUt79/BcqtLq04dMpks/LFgO998voa83FLie0dwyVVDGHF+D/SGpgWjplYu\nKqpMpOcVk5ZbRHpu0bG/i8kuLKWwrIJiY2WTylObq5MBXy83Arw9iAjwwcPDnT3ZFXi4u+Pi6sr9\nE+KJDWr8AiMNqa/ZPzUlj3k/bGTJwp2YLRbGX9iP6BE9+CvL2Kqj1UsKSnnhmrfYsngHU+6cwJ1v\n3ohTMzbTEI0ngVkIB3H0YCZPT3+V1H3phF4zhn7XjSKzuNKuD+OGgmV1lZmFC7bx83cbyMwoIjDY\nm2mXDWbSlP54edueMbXklB2TxUJxWSWFZRWUGCupMpsxmY5lx2YLJosFg06Hk16Pk5MeZ4MeJ4Me\nT1fnY9m2O24upwea1milqGugXNHhHH7+fgObNxzCyVnPmAl9uOr6cwiP8G/1+d2Hd6Xy9CWvknMk\nj3/+9zYuvHVMi11LnJkEZiEcSHlpBU9f+zbb/rcZnyE9iLh9Mv+8qLddHsaNechbLFY2rD3Azz9s\nZMfWVFxdnRg7qQ+TpgygW1xIg83c9WWGjjr6urXKtSIxh7/2ZaFPz2Pf2kTSUvPxD/Dk4umDmDxt\nIL5+J08Va41yaZrG4s9XMPvuj/Dw9WDmTw8QPzyuRa4lGibzmIVwIO5ebrw8/2E+fP5n5j77A5kz\nczHG3AuB8c0+d2PWI9brdZxzfhznnB9H8oFsfv5+A4t/28n/5m2lS0wQEyb3Y8yE3qcFkePONC+5\npTLA5gav1shMLWYrC5bs4avvNlKSnIVm1YiKDebRmVM5f3Q8Tk51b8oRE+jZohWF0sIy3rrzQ1b+\nsJ5+o3rx+Df3ySCvdkQyZiFa0f6NB3jxurfJTM7mykemccPTlzerr6+5waestJLlS/ew+Lcd7N+b\ngV6vY9iIbpx/QU+GDI/F0+vk0dN1BcuWWOHLHkG1pVYes1is7N97lHWrkli2aDf5eaXo3ZyJ7BeF\nS2wY0y/o3qYrnO1YsYeXb5hNQVYRNzx9BVc+MhV9I3ftEvYnGbMQDqrHkG68v/UV3vvX53z30jy2\nLNnBw5/dQ3SvyIZfXIfmrgjl6eXKlOmDmDJ9ECmHcln02w7+XLybtSsT0et19B3QmeEjunPOed0J\nDvWtM9NriRW+7LEzkT3LVVlpYuumw6xfnchfaw9SVGhEr9cxeFgMl906krVGKwaDvk1XOKuqqOKL\nmT/w4+sLCIsN4a11s4hLaL250cJ+JGMWoo2smbeB/7v9A4zF5Vzz+KVc+eg0nOsYwNTarFaN/XuO\nsn5NEutWJ3EkJQ+AztEB9B8UzcCELvQbGHVSNt3UZuczva6xGXN952lKuSwWKweTsti26TBbNx9m\nz850qqvNuHu4MGR4DOecF8eQ4TF4eLo26zr2smPlHv7v9g9IT8pk8m1juf2NG3Fr48Vd2kJbfw71\nkcFfQrQTRbnF/Pe+T1nx3Voi48K4770Z9BvVq62LdZL0tALWr05i2+bD7Np+hMpKEzqdIq5nGP0G\nRhEXH0aP+DACAm3bger4w1OvUyzYkYFOKYorqjm/exDDYzqdtJCJLQ9ZezR7V1aaOJiURdK+DHbv\nSGP7lhRKS2umcHWJCaL/oGiGnhNL3wFRZ+w3bgvFeSV8+PCXLP5sBSHRgfzrwzsYOLZvWxerTTj6\nbmYSmIVoZzYt2s7bd80h63AO424cyYxXrsc3sHXWTG4Mk8nCvt3pbN18mG2bUkjcl4HFYgXAP8CT\nuB5hdO8ZSlR0AJFRAYRF+OHs/HePWe2HZ3pROT6uTgR5ubIyKZcIfzdCv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"text/plain": [ "<matplotlib.figure.Figure at 0x7f841d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 10))\n", "\n", "plt.subplot(211)\n", "plt.title('After Estimating')\n", "plt.scatter(X.T[0], X.T[1], s=10, alpha=.5)\n", "plt.contour(x, y, z)\n", "\n", "plt.subplot(212)\n", "plt.title('Actual Parameters')\n", "plt.scatter(X.T[0], X.T[1], s=10, alpha=.5)\n", "plt.contour(x, y, z_old)\n", "\n", "\n", "plt.show()" ] } ], "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": 2 }	mit
uwoseis/simplepicker	simplepicker.ipynb	1	665257	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example Autopicker\n", "Brendan Smithyman \| March, 2016" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "import numpy as np\n", "import sys\n", "sys.path.append('../zephyr/')\n", "\n", "from pygeo.segyread import SEGYFile\n", "from pygeo.analysis import energyratio\n", "from zephyr.middleware import FullwvDatastore\n", "\n", "%matplotlib inline\n", "matplotlib.rcParams['savefig.dpi'] = 300" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Set parameters\n", "projnm = 'toyani' # Project name\n", "pickOffset = -5\n", "\n", "# Parameters for STA/LTA\n", "windowLength = 20 # How long a window to use\n", "zeroStart = 50 # How far back to blank in the first pass\n", "zeroRel = -10 # How far back from the initial pick to blank in the second pass\n", "\n", "# How far back to shift picks from peak STA/LTA\n", "erPickOffset = pickOffset - windowLength\n", "\n", "# Parameters for inflection picker\n", "inflectionSign = +1 # Final pick on + or - inflection\n", "inflHalfLength = 10 # Half-width of search window for inflection picker\n", "\n", "# How far back to shift picks from inflection\n", "inflPickOffset = pickOffset \n", "\n", "unit = 1e3 # 1000 ms / s\n", "\n", "outputFormat = '%(index)8d %(tag)4d %(sx)0.6e %(sz)0.6e %(rx)0.6e %(rz)0.6e %(pick)e\\n'\n", "banner = \"\"\"Picks output for %s by simplepicker\n", "https://github.com/uwoseis/simplepicker/\n", "\n", "\"\"\"%(projnm,)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computation" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fds = FullwvDatastore(projnm)\n", "systemConfig = fds.systemConfig\n", "infile = '%s.ppre'%(projnm,)\n", "outfile = '%s.til'%(projnm,)\n", "nsrc = fds.ini['ns']\n", "nrec = fds.ini['nr']\n", "\n", "sf = SEGYFile(infile)\n", "data = sf[:]\n", "er = energyratio(data, windowLength)\n", "er0 = er.copy()\n", "er0[:,:zeroStart] = 0\n", "picks0 = np.argmax(er0, axis=1) + erPickOffset\n", "del(er0)\n", "er1 = er.copy()\n", "for itrace in xrange(sf.ntr):\n", " er1[itrace,:picks0[itrace]+zeroRel] = 0\n", "picks1 = np.argmax(er1, axis=1) + erPickOffset\n", "del(er1)\n", "\n", "der2 = np.diff(data)\n", "gathers = np.array_split(der2, nsrc)\n", "subpicks = np.array_split(picks1, nsrc)\n", "\n", "inflector = np.argmax if inflectionSign > 0 else np.argmin\n", "findInflection = lambda traces, picks: [pick + inflector(trace[pick-inflHalfLength:pick+inflHalfLength+1])-inflHalfLength for pick, trace in zip(picks, traces)]\n", "picks2 = np.concatenate(map(findInflection, gathers, subpicks)) + inflPickOffset\n", "\n", "clipScale = 0.5abs(data).max()\n", "def getInfo(isrc):\n", " trStart = isrc nrec\n", " trEnd = trStart + nrec\n", " return data[trStart:trEnd], er[trStart:trEnd], picks0[trStart:trEnd], picks1[trStart:trEnd], picks2[trStart:trEnd]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Review" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/brendan/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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yaQ82s5Nbln1I9jr/3B2zmouej8gmV8zRyT2D1wD5xcZH79FQFiIisjeUkZZA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+Kocoqj9Yq2OAaXXv3QnCqsgJxXbMjIecOybkgpYHuswALk2BRKqmZaTjVTU5WTcFEplUQVpUqn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a0XiMhozWczrHkTMZ/F5OOHIsRjCgwdS6/E9oz+7Af3AUlaVaic55oc7y2+1EWKxgy0CeJiCW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bFOuQcPMMz1Hi46Ey+1UIxG3a9WgHxGa75i53e/EbVWTDBzzKdieecGpIcZ6TwD\nk5wUpDoB6tkSdS3nRp6VsZFUD98SYiT1ZvDWRE2QOPfpRPTSFdCOp6aHR3Eo6oe0x0icHDnJ0q+i\nXqDGiZAnNHc49JjgaGecd5fBV0XPG4/kwEGDp4j1vi5aKibHBExWMHq7x847bwRWs6RXgYJRKHGL\nW06nepIfMVaz2CtS8NPeK8EJ5xpX75YsUqzx5qt1KxZ7EYusO5bTBA8J87mQJJMVq7vQj5sex1Df\nKY46wjjUfz3U13z08O4WfmL3//TKO/0IZRzcfbjSg5b7w1i3RQGXHeT5kuXcyvN5ou6Vb7EhSSBh\nWM8wV+KJ8seO2JguYbEAL9uSOdyrag6GFCIsBPimjC8VPO9V4SZQ4+M09zZkfdD9QaxbUKmaaDeG\nVQD9ltMnuU4sbQG/bOWTKcLyOZm19I1ZBQDJYpkHo8PdEEOCT1ZpY15Y5ttvDeU534WyzvTdkVl1\nzV0TmTrKPibX74iUbRL/WXtHPSVLv+MJmfcd7E02i9hovSiLp6fB0P8ZG/qW66kXEIKr2H3azlAz\nv2eY7gG5ASnA14i0TAghI8QMD1bZeEXI0uZ4UFFLDDKcCusvb8sULmJYZbZfYxC2dAFJXCuct/QE\nXz3TkHVaKy+quEgoNCavdW37Tb79NvMSFjwD7kgOhAwYx06pWGtHVl51U6ncfoxAwnqGubLVNO6R\nTmV8ZZa+5e40ZMgbysBIlIGRBkYaGEnyGxhpYCQMjDTkY5eBkSjUt8RCajHHMXxLj8QrYfzLMOIs\nJRw4YMRIBkQviimHqmdDgFuZ8NsdWGkjUZAQUQ7MsGWs3gWof9XFJcO00r3VEZ+tKLd1+bYrv8do\npcRmSV3Snbf09G0wY9nhS3eDz/U8wMt3AMD/z967xlrXtWdB1z3GnGs/7/eVisSGQ2vbrySAjah/\nDAqltaJIIjHBcDAmQhAkGAI/BEMwaIOHkpZoAqmRxKIcAp5rwUNqjLYeaG2iEWMs8APLZyhCW7C0\n3/e9z1pzjnH7Y4xrjmuONdd+9n72ft53P+877mRnrzXHcY05DtcY93XfYzK4zfC1Ku9UWaP49U6e\nKVaC/FfhWqpW2h+JxQ87lQI37Vj8Ac9FdNLFqne9cBQHUrfYfVeMpPiMmhtqrhU3U2F4T9VW+SOh\nhmNDcZHG0apxjeXfJHkvKJZ3CPBgyGtEWiMWevio2hE3dZtJS1Or3xp5T/8AGndqR9t77mA6Wseu\nmLtRtu+kxGpRPq+Y0Kz9btdNLQLNAI+lD1l2wBwhZbij3Itc92FX40bnLYXthr1HEJN0fViW76pg\npXOLHj/x52cMbZXIi24KM/s6AH8E7RX+K+7+P79FPt8A4F+TfH69uz/W5P99k1fd97eha/R8lA8+\nxnLeXnS9U6YkGU+8o4VXWihzgIQTMnSUKW7YE1ZYxknCeDhVw4jP1hmIlhEuFyzTjPOpMJbCDVb5\nsrGaGqu8xGquDi44CVM8C++isEaO0i9bGXUiR2OFm9AiFszgZcIEYoynFw8zbDvYCoCfai0IuMyx\nvD4hRasUjzpra9v2+IH33d4n6jWJ7X+fEFM8mczEyy7O2B/2KGX3IaI+wvUQ6Tmkdy7NDvshSt2J\nhnqG1DsQMgp7VpSSue6TbMAlIvsJZ6+uk8yLT/yp+fbXQ1Kyx5VVroe+3LCQfUjIY7tNR5FY23FC\nqOeahQXOMVLkgrkCpuab3La7BPq6BUlPhiLvmHHW3xzLNMMt4LRckIPDTw0ub/MNx89r7Pu445rN\npN1WmU/E3MooVaa4sqqipOOcyvJvMd+GPIsMjPQkGRiJMjDSwEhHMjASBkYaGGlgpPdXBkZ6kgyM\nROFhc8J+/aVOQrESPxuaFZ5gnG1u4LxCzwMX7O/Y5NRaywupEBDyDARznM6peCW4s4KVcMEqc6xi\no4wA3vXLM3XikxnLpgTQO7KAYnkXkDAL/jnjbourygpa3ilGWwSjlSe2swKkm03WcSu/Tv+rT4gh\nI9wt5VH1RuAp1NInlAsAsSdA6FzbQ4UjT5WU3nPBO7e0e4joAvIuLO/OKA31JtNvtaojEHkIRkoH\n6R5YNVpuqSLnSKhYevWGIhx13BlgoWirTg04pzUWHF51prMVqzhVfs1YkHf7gKYkK0WUShIHcYQQ\nP6lV3AmXKyUcMUDs250AACAASURBVFMZSe0Ha9zego8NQGtW1k3r1eq/IgfH5W6CLwl3OSNHwAMQ\nLkVBvjPCZLe75ZUg4xjj2EEYcRTzYdWIrY7w04R3DrnfJ3nRijsAvxXAN9XP/w/e/gLhHwTwcwF8\nVf3+zwH4lqdV7cVLz1h6G2cc/VA5YkF9VOW8tXzxS8BnyMxI2DPHlbnBzTIBwAR89jMSBuwJI9zk\n5S4fHmopw6rmWUlRWzY5OGJOOC0LUoxYQxQz6DIRZ0REpM2sGsCOFcVFhewq9SkekeoLKc+bf+X9\nxcElPd0fNMYrXSKoxO50SP2V68b/hMs28TtpHJubCYMtXhbJibSMemSX9l+3/xS2pzKiyGBdDuL3\nwnrwcHIXlxmuaLb6H9b/t9w6Ga6RIZ8/xg/OLdr7m0T9llPYyZX+oou3dmC6t+Bq+g5OEshwUjKW\nspb76lF0bOl7jQbPBuSA9VKWMR56hpjLAQ7mK8BSqtJcMSnjW92a8e6kWOM0l07AXA9ieRlwIw2R\nBa6XGqddGTz8iki7AzKgATCCMo3TfnZGCo5lmjBZQkwJaWJ6wOjyTs8dycrne9DDqYQ2JvXaniT5\n1HRf5CaTG1jOc53L+m081vy/eGuDNOQ5ZGCkt5eBkaoMjFTyGhhJ0g+MhIGRBkYaGOm9l4GR3l4G\nRqryxQ87jNQbGKnFT8AO73yWh9ZquA00LMSlhdbyQJvLL9hhJIuAparLiI6YMuZLwjo50hQQkDCh\n3M8LNIxDbETLO4BGMfOGY2hNB+zx01pn6eYis+CY5vpy2TBOiRsrVgIAw1oxGnEblRct3mVTMZQ6\nW8NDAcheG3kuFkI5ldrGu6V8BwBa3mnP4VxPjKTrIzGOrrFqEfQmjPQk4dH70cSvCjFVyhiOj+y1\n0m8j/PFHln5HFnhZ4kZ57l0c79K9Zf10vez/jsLuw0iNWVTxldWwgBwjks0IU+m3yzJtOGCyFcHy\nDrfEHaZHHVlp5+Ky4RhiG6Zt2KjlV8utZKTybO3i+lVceg5pbv+bQq9Ywfph3SYDgmWkyXHxCXHN\niGuGV3e8gdimt7wD9t5StfvQdThxEzEWCQ9TxUjs2uy6amWn+eX2/IsfidXr+yEvXXH3q+Xzn3D3\nt4K37r6Y2R8H8C/VR78Gn3zA9YXue89oeogoY8kP8nwX5Rzl+ST53J96+7T+h9GYUsq0InMgShiZ\nUnqnq6arIM2yEBdOQExlc3k+3SGFUCESp+4TlnrgFJGumE6UI6bUBSfQNLv+mpvpyTRnXIPjNV7t\n0jN+7hbznRk2fDsYU9/m22EZWRUGwBwpRSDm8kcH1G9a3/VQSZ99iDff6wI0pgjfz5XogdRrFHf7\nr/F2TCcihHcpPE1V0YbuhTQmnpzSp/pnsPe39IyiZHbg4Wdv/aIONBDOKOuEnOrhjTlmW5Bjc3Wm\nh6bA/lCVTEMe/hzFCfWOJGVDzZUNpc+sssovOEFdiZwq8FK3U6/wepeedaFVCMd7RkTe6lbLCI5L\nOMFtQUgZOTo8COGQrzWidAu9h4WgiF1DN6AfYs8Gzft8vux3vellDfkYZGCkt5eBkaoMjDQw0k4G\nRsLASEUGRsLASO+3DIz09jIwUpXP/bG3T+t/GNfKOiruDc3KRN1S08OgHo5Xo6awAp6Lniokxymt\ngEekKXQYpeGWiLR5HKBXAa58tMY74bLNzc1zgFX9yHyFf7h+tLvxGv4p+Im4ao/RAGxKwmaN56L6\nsdo0DjdDtrqGTA4Ew3I25GCIdxcgZLhXNJji9T1mRzwiYI9xVLgevDMhOAs4rpyaHvXh1O6qUDv5\nVFH/6gGlsx2BEMfequ5dY7dnkJ63tTMGNCAVSlH2gPmDMyxmrMu0s7w72R7jTPU/x8ArnDflmGKW\ngFxxDL0KEAftreJoTUfLUwO3S+Ue4bTlWbwSMC73NAnThoMAx6nir3I38GUjOfV4KsWAFAPuXq+I\nKSNPgAXAvJAEdtw29TxAUgHjvEbDSsRRCu1r2Jf97se8uCFH8mIVd9W9wdfKo+9+Ypb/ORrg+vlm\n9nPc/a8+Mc+XLD1o+exjEpuZ4WFAqH/2mceUU6Wv27MCricJ9+/cuClTnBbmeqkwsDcnrqx0rGj3\nuFYW80aKnoA0AVMqma9xgofry9lTZSz1TCeTOMqIBQr7acUEZZX36TVuAlnly459FeryclQGLxdW\nQMbPeikx2Spmjmla6+JpWC8T1uXGVNSTsV3a9rGih1lHOGdHCvLu/63KfYD9rapH/jOeS3oaCuXo\nx7Djsnf0fiTesfQMpxn3n3fxp53RCO2OvWcszfsCAAGwCZgSPOYdq3w+LQgxI1txw6Sul8gspKhv\ncrLMyQikxO5Zqdq6gSGyymcUBjoBE8uZOsYTWeUT1itWeTmMKumnOoKVVb4xzUPE+XTCnNaNVb4Z\nsRDLT2j3Ri/Y93+6iCLjXF20MEyYUkNelgyM9GQZGOk5ZGAkAAMj3a7cwEiHMjDSwEhD3qkMjPRk\nGRjpOYQWvWqxS2t2Tkj0OMh1gGG0nO+WKrOqwPPqKTJl4PWCdaICr1nVJcE4dzhv2KZ4IECNR4JF\nmecbjiGBRTFWs7wjtqECTi3vWAYrXpasRodS7wOKg0pdV8w1PrFRtIRTuFTLuxkpRYSYMb06I1l1\n5rmGYjKkQoykxBZa/nzs0mMkYA/E9DKwW+nvcPti47cRKufUsq4HJwQw1OSov9jeYJbpPyJ8xeqz\nG5ywrz5xlIalAJyBZDM8VaXclNpPotW+FWzp1u7NXjHjXBXPR9io4ZhmeaceA86iSaRSXZV6scNR\nwP7e7qUq1XvLuwkLaLGnWKk0UdjmhIiEdQ5wmzAtGdGrAk8t715t2e6vSiA2YnftXY4n+fw2e5Ih\nV/KSoebfLZ8/BPDnnpjf/4EyVHnzxt8D4JMMuP569/2rDmPdlp+Ja97Kjx/E65/NZvYV7v5jjyjr\nK7vvP/qItG+UH/7VwFdwXnyFNsGQvEJm5Sxh6kaFzChlipuEocZ3+a4b6yBh1sLMgVi/5wjENSHk\nDLd6ybrp1vm0Y4xzG90YTmq2rdAIG7O1sZ/8Kj22kBlhY6U3RhVA6NDubOnLUOY5QZ/WJSHC4AiW\nC3PKygGCZyuHUubFf7lZa29a66tJvJps3ydsa3WRwAVEz3V6c/t7M6ME7A966E/H5btmuDvxegth\nJR8Kzo5+TGPsPFwM17/9DcL3o2Mh4Br39e9UxxbHIJu5f0cOiQQgONIywb2yuK0wpFKI28YgWwnr\n3ZhpH9U+691vVkuJo2cMUdahwXdsxN7nuI7lXvSeAq85MZbBiwuRUJ6FnJEi4HXOCACMeFrnJnYB\ndkU9Q2W7s301bAK+8O+g3aNEYEymKDG8uoaqAO7Hvgh87o9e/bwhT5eBkZ4mAyNVGRhpYKSBkYCB\nkQZGGhjpEyUDIz1NBkaq8sO/DviKz6C8dR72Kw5SyxRiJfWISGzEqVWte03CODkdzTGoz6oxVPCi\nuHMDwuo4pZJBDobJEszKJFbUK2WupbvwZvHc5vXy3DZM04hPZV6+gDgmVYd+Vqu2n4cV9xQrvVIO\n52igNZXe10VpeKyFJMSC98yL63Cz8sMjYCHD3YpVuRuwWsM0PUZ6EJZ5V8KXq4tLj5EYpkBMSVI9\nRiKDBBL3KTiK5aiWhUo3xTkah8w79R2twkFyBDifSfS99p5DOU57C1at3mLAWi3vUgQMCGktmHuD\neI5gvo3JhonCrqhwgI16HOOSfu994HKFg041fbGYsy117+GDlncunT2IxwRiO45/orEAxxrLvGHu\nCNmQzcs2IwOmGAnYz1vAfv+XcY2VJP0X/hAa/qFXFmLbnsdHBWsGfuxLwOe+E0PwshV3XyufP+/u\nT+JGuPtqZn8ZwM+vjz73lPzeA/mL3feveWT6r+6+f97dr/zguPuHZvZ5yd/q58cArr6sv/CItG+U\nz0bgs7ohphsCrkNkfHPu5XdDuyxY7z3Qw6kJe/ZBz5RiHso4YFzupxMwn4F1BrI55mVFyI5lnuBm\nmz9yAFiFzd2zOgLyxjylGyZK8Zscr1jlxUcyQVhbSLiRXypHo11CDPBwKxws0FyAlIWl/s8BYtR2\nWLDJlMoiuaEsvP1dvIpldP088irABeTeGUZfIDPn9wmFIMg7XvoKsOCn0E14GPW206B20sewnsjm\nemAaYjm6E7pPEoqFBRfq+6qmB1aONibJKvcJmBM8O5ZLYyqRHcW82Y/Vt3h/nwpBE+8E0L4bu/Tl\nnHNv4QEAE2xjPJafyjtj0nYoRsY4x7KOB7KpyJRinFIGahmFPbXGCLc7zOuC4AescrKh6L6OB4Q8\nOOI8yDY+dXErq/yzd+3zlicvJ16wZ1+lFvdLL+Jy70+kfK18Hhjp8TIwUpWBkQZGkoYYGGlgpIGR\nBkb6JMjXyueBkR4vAyNV+WwGPkuFHOdY1blQoUdRIgw9NVOZp0uNLpEapta9scsHLb0ZEJfilSBP\nwLQmmDuWU0SOe68EzW66YRudx9XaWefsiIw7nAX7lFC9B3jBvFn+cH3gPE6y0oIJaXt+2ZQXmo5N\nuVxZBTqWSoqabIVFB06ArRPWRRbFSTWedV1SY7aPVfjC6XPwIRjpDg0AqBVcL4q/ntvXJ4GImJ69\nlUw1j8+g/C4uus8gxDqKf1Rx/iGudaI6E1HR5KEof+cMzxHra74jYJoSbEpYfdq62Fx7flFP0x3m\n8f6j4ZhmebdhlJ0Cu2Cj5qmgPI91d6N31Z1wAd2S03Wm1bGy1buq7jjeWZe9xd4KM8c6x7KnuiRE\nd6RTsewNen8dK/8mbMTvcv/dZ78crYvSUJTYiN+5SrO7LMCXXsT4fRnykhV3P00+/61nylPz+fJn\nyvOlSg9avv6R6f+uN+TXhymg+3oA/+s7KuvxwnXc0A6XuFYowVZZBGQKMI7iAOvyJNPjiEyDLk8t\nozJAKpEDseZvXgBMDoU1ZMErC2oFnTERCJE50XyW75ka6q5pz5QtT5geWxV9i6fpIjJ42bAhC0PE\ndvH27PNWDy4Q9LGcK3hLMSLEhDgl5Fz9lE+xkbOPtll6kEgXFBF7cEvmLNkfq/z1wvBDkhIZUXyB\npMSRviM3UF9R3XV6VdbUUQXeRP1iJd9WWP7RlE9K2iR/dG2gJhdvYEjxQILRjn4u2VBJvtNNiB5I\nkYHD98lxw02LjqXtfZR+ndYI5OojP7Rxsr1Cs5qlHpQeM8XJTlLwxfzoZqAfd2Q2WWVI8UCK4yrU\nw2Ee2mr6vvzSRGGLx/BF4nio1ieeAXeE4NuvC6jnvP2cpv/7ualnmCtDTRlXOt8xLiQO8/wIvY99\nymRgpKfJwEiUgZG2uAMjHeQ3MBIGRhoYaWCk904GRnqaDIxE4dqolltH1rn8U2zEOEfziM45ipV6\n/NXPOYxvaEZBBkR3WM7IodrTBd/i885QYo2GP0pFHBnFDXGZlUNdhMoM3+LzTP7IUk7dLPf3fQF7\njAVY/cm2rQnEY8zfkQVF1TWiWhLGmIqlXTbEmJCnBM8BjgBkL/eX8b3dsyy/eyF+YAegH0eaMXWA\nd4eR+kXfcYyV2PHui/O2QvzFDvu21nLETPwL2LxHPDY7NhH/dEyppSWbT6uvonreiFIflP+OAA+G\ndK4F3i3F4rPuO/jamjVdxgIqn7mD2BfYYxSXZ7qvOHXpy9jYp9c8Qx27vRUeSwgHZfTlGhweC1YK\n2QHLCMmLVW/9rWZodwTyHdy3N7wvjG1/hLH0XQ6MtJOXrLj7knx+LnD0ZfL5RXg3fofyQ2h6bAD4\nGjP7We7+1x6Y/pd03+9zMfHnAPxj8v0XA/jjDynEzH429mDtglL35xMywtkSGc1E906+9y6leShy\nRnPbrGGO/bUdq+TJtTlKPkqwaW7DN4ZCWAHLhVVu5jgtC9bouMzzttgUNmvGihntUt+2SSZDSf0e\nh22WBBrDapU9ZFsYylnAhIAjpha2eHqBMX/I/k4Z38rrN/QEZxYcPteyDbicZ6xr7xv7QLhw9Acf\n+zOyZ2JYkeL8Id48ZZBq8hrXB0j3UaUXvB1l/rlkRvG98UH9f1c/fwatw6qJxYFwLGTcvlacB03a\n728xzpVZyLg6NoF2sfQWNwK5unxKARe34ksEALyClRnIsQCa3P0WZWyX6haLjBMuV6xyk86Vqs0E\n73NhWu33dH+grCpe/s3NjcZRmbCCFxYzzlJZ5WUqqkypaYZbwGm5IAeHz+2t7dzPh9peZIr3Psk5\nb5EpzjuqyP6kpcAqn5nnUT4PGNJD3koGRnqaDIykOQ6MBGBgpMfLwEgDIw2MNDDSi5SBkZ4mAyOp\ncDxzXl7ku+IgVbL1GIfYiBiH6XuMdRQ2Ye+xANjmrGCAXYBcsdF8SQjZcbkzmGVMWJBEkUZLf2KV\nZvnfLOSahfNpw017rDSJsq0hIFXWkbzRpyv5NWx0qStJb3l3qTjOat1Y3836OzjmU7nH2A1I5xnp\nIRjpIxUuFhdc4x56T6DrhPsw0iRx77O842Lz3NOTdvhnUh8Y7oVNNyVj742AHkKARmDSMK7jVBbp\nXodxuH57VU7eOXDKyGtEzq9AQD2dVpiFYnlXo55wAV1V8scQH2WEnQeDHqMAFaMc4B9aw5b7gNlc\nxcJu3SaLVj6xEq3wLrIRYBm95Z264ZywIpjjcjdhWhJOOSFHhwcgLJXk1Ipt3ZZ6abYxsecZrYsr\nxlolHcO4VzSJi/qsXQP4qZeXrLhTE/mvNDNz97emD5hZBPB3yqMjP9ufGHH3nzKz/xHAL6uPDMA/\nCuBPvCltvVD4H+ke/xf3JPkvAfxu+d6nvU9+eff9e939S4cx31aUFaBsKLljYGO0AtemvxF7ggzQ\nDkSUYR4kLrq4nJy41jIt62GAVawTU4ng7nAkTGbI0eAhIFVAFCsCzIgbQ0kZ4NxEk72trHLG4x0t\njMNDqPLTvVa5rGpkajl4saltz6cNZTY2VWPOZqhPda1XsFz+QkaICWYyHXEx1wM9JV2Tfcy/cPD9\nsbIxPoiySQNJkiEZUnzW39kC7BEEKdbaeXrh8YJeevJRChnzR1QlNbe40agkut/HIu9dDvUEerJu\nmI9WS4n0bEJllW/vzbYDEE8BjhmIGYgZy1IA1rb5CPkmUCSIKdmXzQgPm3q3a+zfJU2AISJU4FVc\nS024SEFlU4IdK4tjjIdR6pJKy2Kclrc2gcMtA9Gx+ITJEkJKsKn1VON7UOClLH5m6BLG99KT/5Rh\nzvfIptF8OO8OeRcyMNITZGAkkYGRBkZ6iAyMhIGRBkYaGOm9kYGRniADI3XC8dovV2pUrvOwWt4x\nPnEN4+ghdY+xmI7KfsVoLFfiWAQs1bkslolnCgkWHWkKBSvBdoQMWgvR8pmH+7TIoyWPWuXssVIh\nZhRvA0VJYYK5KD3G4jN1m8l6ANhZ3lnFYFMlUzmKVVBGgAVH8ogQC0ZKPUYiTFFLdsVMqfv8LGSm\nvmD6Hw8SpniGYI7PgNsYiYs+n/fTmZZhN+K8rbB+R/mRAvNI0yjFJw8VHQdsQl1rOR65JzkqJ+Na\nYahj61wDzOAxANGRLlOLeFqL5Z3iOcOGg7jYExvRgwC20OUYo9QKNVfjzavBinkrbsYKB90xnLZK\nsHzuIyISFki90RMR9xZ4jMZ7thcH4poRU4bH2ltTzam3vANa19R3otcfKnlQcRTD1PEFx+Z9W4NP\nobxkxd3n5fOXA/gHAXz/E/L7Rdi7TfiRJ+T1vsifQQNcAPCb8ADABeCbAXytfP9r7v6D98T/fhQA\n+3fU719nZv+Qu3/fA8r6Td33P/2ANI8TXR+VDaUTPgHXLbconHheyXcyB5gPWZXKqiUog6RjPSiS\n3gIQF8AysJyAmBPCUmFWKIsxmUqGiKUCH2UzAbp4yEa8xtkfSpHYFbaFpEzk0y4O2axTZbNTzrhD\nuf9lz1in0H2NisFxfhN9guc9utD2zPHnFkNp4zxVxg1Fl1ZlM/WnX6/RqCYUsp7uqzDBDtO/Z8Kx\ncd8rJW59hdsMco4biuan40Y3Olz4+z4S6+Ha3QrEjHWZy4XDdeNhJ0e2cGXpAGBjSJE16DDc4byx\nmVwK4+cTLjWsgObGvopX+RSwdM14IlOqxWl9kHE4DgNy3VjN288+obhxWCqr/C7njSm1wWnOdcT2\nBK/qVWHqwrRr6p0MypTS+Y8suDfd3zPkqTIw0tNlYCRgYKSBkR4mAyO9nQyMNDDSwEgfhwyM9HQZ\nGAnYL09HngOUwKQ4CGjKBGA/H/DQ+j6MdZGwHqPpelCvWQ0r4BnIBgRznM4r1rncdxet4aBVMA7x\nRkIELd4WOR6mNR7nX8VK5ax93jDOihlesVLvyaC32KM1XdgUBwUvNRzVnlPJpxjNzYpnA79BmNF5\nvIcXtzDTsxj2BzSTIxagGly+wA/leb+Q3MJIPcY6sqpTxR0717uWGcULwTtmoVAR1DcRpR9bXGtV\ngUdRxR9FPYy4FW34XQZiQlpmeMVIMMd8cmQPWHzexnqzfGv45YTLprhTa9SbGAV7qzjmSWzDn9Hu\ntms/jjhMx1uxtG3miFTO8/685rq2yYwFKQbkEDDbipgy8lT3X75VtrUnr23U+wN1/CXsDUY1fR+m\nZM+Pqvu+R/KSFXc/iDKrfVC//048DXD9C/L5AuDPPiGv90X+QwDfCuCz9fs3mtk3u/v33kpQWVLf\n0j3+9+8rxN3dzP4ogN8lj78FwPfdl87MfhmAb5BHPwngP74vzVsJHT0QFOnGjIvAa1y7gVI2VM+m\n6l2dHLHR6dpgOghT8sx8HccMmJbi1SZNjinVgqMDYS5sbHg1mY7CRmpuZNQdk7KYyF6iy6YmnL5t\ntwHXOO0C+WWbS8vVqhPIGCF7nK5wlGWii8PG3IqAzwbPAXCr6+gEpDp790yZ58YFJKyQ1CQ1bH/c\nYdNFwRGbSTuQgrHeLxJRvKKFW5SjJH+35KriaKysozwZRndPdP10h/2p7SPlaHElACJB/0h0/HFM\nMp02i26CgP1ZoS7y6mqEvzVm+ORYLqd64GiI81p85MNAt2kUdR9QivDtcJcHtxmxErrKD9+Pv8Z+\nauzweZePdWUqY70xxvc0I26qLjiBlxq3OpaDr2gJKTgu8wkxrZhSQppaExo3lnpxM+9BYrvyUEkZ\niozL7sOwPt2py3McTr0rGRjp6TIwEjAwEgZGulcGRsLASAMjDYz03snASE+XgZGAshzRCEote9Q6\npHcZp0p+tUq5ZWl3lvjrjbBJwvSQHNh0OmaArUDwgo9iyjidE9YpIE0Bsc7DSpqISLjDecM0ExIc\nyzaPA42k1FvFAaiEjLYUJRSLvZNowpLgrtasE/aWd7atEYqtCgyddmuHwREsYw4VI50MqBgpYy6x\nUtivf2/6m9Dck3KNfYhs1u+hrr/KmFGrKAJqR1PuLRL/Poy04BoY9xiLouXeurz4OaVvwA/Qft8z\nCbsNiwCacg4SdqRMZ9gs34Hm9VObR/c2r1HxUKwYyZAuVePkhmleEaeE5FPrKkYcErBslnMNm/Q4\n6hCjVGyjLjY1/ca/qnirxfUtvV4H0FvelZ9Y8NsZdxtWKvuSALq4jZaRpoDL3VQs77wq8AwIxDaq\nQD3CRoTS/byl6fuw/jqGIZu8WMWdu1/M7L8D8Cvro19lZr/O3f+jx+ZlZr8OwD8pj77vnZjRvzBx\n9x8zs+/A3v3Ad5rZN7j7/3sj2e8B8Evl+08A+AMPKO7bAPxWNP/v32Rmv9vdv+0ospl9JYDv7B7/\nQXf/mw8o63GiVupKWlFWuZrj9ownrp1B8urZAMrcMPmswCuhMQy4flPiPn25ZLgEFeCVEHKtiBU3\nAaVKqVY37DbMu8tGK5QiCArIOOPuioXOdBT1hd7H2ccsQM42iOe7+MV1QmN0qGudgAyECrjYLG7I\nXgHYRyHENuejQP5KnmoSzSmbiULKiL5cnjiqsIP1nYDl6bN8q2JVCAL7OGRv6VvSU70Zx4dSc5fm\nLYU/gf2+Z0a5/CcbnZjPJZ02EbtD75t883GENha3V9NY5R4y1ssEzwaY4wRHCBlupUf2bKgjVjkP\nXUvxfVg7vGJPP2KVl1oVlqM+47jgoRTZWP3l3pqugLO4bZqc+QTHJYSyzySr3IRVTg8LeqDUd189\nBDwKM8mHYT3DimNryLPLwEhPl4GRqgyMBGBgpJsyMBIGRhoYaWCk90sGRnq6DIxUhToTteCickDn\ngd4ajpiJ451zvXolCBIG7MkZ1oURB13N41WqpV9wwHPROYTkOKUVwIQcQ7W8a27yOGfrvK14pM2v\nnLttw0qcz/s5uFhM5x02Kp4L7rO8E2s6wVFNrOKoDiNZBibAg8G9vho3uFsxPXzMeq3zMTHxkYIP\n3fe6jGLhl94LQbN4ul4kFAz3Cwmf9e4xVcl3yywpyI95LsVd3wB9GLU0dB3QN9pbCnFQxt76XceG\nFqX7DuotP0BTvPd7C9WTov6EzRqvbjjuHB4MiRgJDljBSCkEZLG8K14BFHcYXlVruH4f0axXO4yC\nZhUH6N14cx2jnG5y3Sm093KH8w5T0fLOpW8FLKCL3NIMpSGb23+DYQFiQIoBd69XhJyRa3NYBkwx\nEtDuFmRVFEfRmFTfkaZnWOjeH/Dk7vNJkheruKvy+9EAFwD8UTP7Mnf/Iw/NwMx+M4DvOMj3Yxcz\n+yVoTDCVv7f7/kFlFR113R9x9z9/TzHfDuA3APhZ9fvnAHy/mf0Od9/8jZvZVwH4vQB+S5f+33D3\nn7gnfwCAu/8NM/tWFGYW5feb2VcD+NcJ8MwsAPgnAPxB7H3F/wiAf/NN5by1cK3rtfm3CLcKyuJB\n+lsXBytjnSwsMmXng3wWKU+ZHzVOSMB06Vjl7ihkazKcvP7EiBXTBqbIWCLjifGUtbpUwDTLBB6x\nbks92eDFDYJvz/rJvixS2OqhYacblCWW/zKFbCYKb04l4mYHuENDAOwwehC14tppekQBNbzVVQ+w\n2Cke4g4qjirodQAAIABJREFUoZ2SvklINXrTQV9AO5x6wqGgjol+lVG3IZA4/TOgNY0CMuJPtexQ\nxhTRjF4YjgD4BMwZOTuWM1nl2M4byQbv2VDK6tZNBDc6yirPsplZrljl686yol083FwlkH2VEXDG\n3RUbqzSJbc+UKUU2ozK21hjhdsK8roheWOUbmYwbUZ3T2H0JdtX6hXMUN6XcE/BQsWdRkSA45F3K\nwEhFBkZ6qgyMNDDSo2RgpIGRBkYaGOnFy8BIRQZGeopQSaA4BmjzJ/HLkTUcl5ajlu/XAbU04VLH\nCUnDaDlE0gDrYS2OGRCWAodyBKY1wdyxzBHrZIh1QeAxfvlcKtks3lYkFAsc3jFHhULxapA3Sz1a\n91AWlFv1eoyzt6YjZppAV528865fa6baANqM7xQjcf5WxZAqffYVOdCN6SSvl87qIjGhDF9iHcbp\nOwDkGb0asJJ3tyrwzELcRi0lO+0trUpEsxa8w1urHHSMqZJc+/+KYlvNNZjpgL3yh+FsWlb9hDZ+\nIc92eRdrTswZngNWwUjTlBCnhNWnTeHU45fmTWC56tsNxyj+aZZ36jpfvQpwNNDybhXN8wmXDStx\nbDYloWGpY3PqylAFPr9HJKxzgNuEeUmI7sgnFMtexUhs595DgTrhYJdWY9DeUrLHWC91G/QxyItW\n3Ln7D5jZd6GxnO4A/Ltm9s8A+EMAvueI8WRmnwXwKwD8DuxZPwDwZ9z9f3iH1X6M/EkAX/2AeD8T\nwH97I+yPAfiNtxK6+/9XmWL/DZo/mq8B8KfN7CcA/GUAP73Wo9+Ffre7PwYEfRuAX4w9SP7nAfwW\nM/s8iguDzwH427p0XwLwa939Jx9R1uOEJGDV9ANt/eGEToYNsF+scU96Jc7owt6nO2Jc7slEe6KN\nlUcbqzwAk5eLhj0YkkVkC7JknrbNKFkdQDuEOmJGkVkFNPczPVO8MTYcvrktsB1DisxXukdgGOtS\nwuLGeiejY+ciqjJXyiXDGTlkIAYgWGkXMjOC/PXf9dljGRra/jDA1ScG/1hIljCKsqh6RriGA+1y\nYgVlSst7yKGU1k2FdVQhgNKG7BuPHZi7ggcIm6Rva7KjSMDS6urPY9qeochx2I8PyM/gGFNX8oyv\nryEAsFqQFc7gmq30tynBzHeHXj0bStl/Jes9U6q5PGvPGM9qDmRKeddY+/S+lVeatozNI4ZWz4TU\n8b5onADkEGDuMHd4KAwxNrnx/fH8TBlO/Ryn81yWODrfcU5ThvkTzjaH3C8DI20yMNJTZWCkgZHe\nJAMjYWCkgZEGRnp/ZGCkTQZGeorQskcxDoVju8MmO/yiy4diqD4O14B+HuFheG+VAuznI7V4MSDk\nWiUDwuqwnIoFdTAE8zK3A5uFjc7havGWkaEeCvYIiE0UJQ62fEoVr9frZtXXymzpQ53Zw5YvFRZE\nTdMBRnpW4dpMKEIFBCFFwr5fXLkR10mefq21I/QLiBKMtAPwjxpjxVYB+46ji3xfl1uWeQ8V1aCw\ncRQzcaEkTlOs9BZm5dokLOIWRtKxwS7FbnHkWEGbvx9/R5gqAcXMrPx3BHi28srNgbsFFsq9i1oH\ntaZT/FKi7MdQwzGt2B7rABCsZFucEyDP2qTSp88yplK1CKT1rcbR/JmPx+LJJGQHPCOYb5a9ttsb\nYD8PAvt5M0uYYz+n3cJRL1pb9dHK+9AUvxHALwDw9fLsG+tfMrO/COBHAXwBxbz+ZwL4edgbb1L+\nLwC//p3W9qOXN87C7v4/mdk/DuA/AfAzJOinA/j7biT7kwD+2UdVpPgo/zUovsz/KQmKAL7uRrIf\nB/Cr3f0HHlPWo0RbSP3vKtNJ2UtkWUDi6B0rTK8skN5HLzfZ/M5FRS8zzpKPTnan6/QhAbMD61wO\nb07LgjU7LvOMYBmnuhW17ZAo1uq3TSvQmE7KgNLJHyiHUwZeYIrtWbnsNG3xsIVNG3Aq4IoNUb7f\nz4ZqC0OMaXMXVNIBmcBAgfBx8r0QwD5G+N6UzORHgQFl7xLRKD6Mw5ebunTMTDN/qtA59ILG1Apo\nHbjfPxFE3QH4DJoLKLKhHnl6oJYRD8Fk/PkcBxSODfb7s3zf2OBo75rjj92Mza/jhkLvVgnAuYJX\nN2Bu7yClWFxqdKxy9S2u9x710u5tiRU+lfgnXIQpXkDQjHJnwNoxrQora9rGItlXdE1FoNazymkF\ncsZdjcN7l9hMhWm+xgluAfOyIFhCmtur27WhoXXxExqJj8CXLHJ2O967oIeK6qKfZLsh71IGRrpf\nBkZ6Y8Xk88BIAyPdkoGRHlf8wEgABkYaGOljl4GR7peBkd4kxDhqQctaca7lnKxzr+IgYE9cAsq8\n0GMlzjHMTzEW5xjvwiZcl1XjWAbiAuQJyAZMS7W8O0XkWCyAUsUonKtLVZtb4hOWDRsVi6Fy+h7F\n0lqtgAoOKrLAsVaLnzsBHb3HgxJ32mEyzuuM37vkbI36kkS1EKqwVIzEl7uivVz1GUi/gj1+IROH\nvgcVR7Fz9PhJy31uE+8Je5fivNvuaOp8pNAbxwkND/UYSTHW3IVl7C24mB/z0fSUI4zEMb2gYCMP\nwJ0Xy7s1YvW78twc01yIg2p5d8Jlwy+qpCZmOsJRxCjl2bphHe/SE9vweSH98R5hq9Uv5Wt6Wval\n2kDqTYF4qr/XeMKKYI7LacIUEuZL+a3JgFDv1NxZFrM7KjZSHKt3gvaWxoqx3rUh6XsmL15x5+4/\nZWa/AsB/BuDv74IjChD7+quE1/K/oCzs746x/Hg5oka8m4Lcv9fMvh7lst/fgLIrParP/47ikuC7\n37KcM4B/2sz+UxSXCbcA3RdQWF6/z91//G3KerAQZCnhRCcW/vXsR3VJzfslbrEIgsRnWM8wV5Kv\nxiWLZJFn7BU1f8stKuBw9zI1B0MKETmEbeKl72Ng2dgV3AxjK6IxNzSOSmFgFJczZFgVVlbhOU0y\nm/Kgq7msMeRqfq0gkAzzYpqdKmAsFxPnEAADcgxIUwJiLlR6EqH5Dvu/6eC7/im7jcJ4eoEqGVM7\nwq+hsMoD9r5uaMetbG7Sp486CNDQhTKliPBvuXIibeiINa4sKk7lynzid7KgZjRgRXCljfdI4biI\n3TN2i6Ms+3GU5NlBv99tjiL2Y4Tl9YeV+v50bE4AkgEpACEirxFpmeAxwaPBgu/SF4b3/kD1aAyR\nJRhqRRqrvBzQeh1FgO3GgvYTsgnLNNDicNzx0Dd2/UTHFON47SdtaivWJ24G8zKRhJwB9zI0MmB8\nj+xqQOvu2sb8rIQ/fUeQMMZ9Bvw+5LYMjPRMBQ2MNDDSwEhNBkbCwEgDIw2M9P7LwEjPVNCnGSNx\nHub6p+sl5wCG65jWeZjfmRefK8bp53liroj92sAw/leM1q8DDlhEcZ1pgHmZmHIolcshwK0gESrG\niHXoHaBk5bWKxEYLEnK1fmsKhdJcRGLYnhsCDPEKR3Fu5zztFRux7Fzxj5IyyrxOu6EFEeXuvhgD\n8pSQ5xVIATkZMAVgtr2FHN/TU4Xwg2vBNrdb/fF8uer7j1iEnYCLuZppzxJHwUBG61zUcqjyjnHV\nuo51eFvT7oBrcE42Ei3qFFDyt/amburV4AGiPxloa+nR2FCMo+NEw1il3iOpjhc+P7LS2/Y/NYCW\nd25INgPmgC/AqeCJLT5QvBdsP2SuWXqtYt7hqN7aTZ+pFWzDRAX/ML9Cg2L60xaX5bc78hRrWW2y\nkk4JjPQNklGUdBbrfJAdMEdIudwtWecvA/aK1n7e0vfJ7tvvG70Le/Haqo9O3oumcPe/Yma/FMDv\nA/DbcQwWbslPofjB/lfd/UXpbd39cx9xeT8K4LeZ2e9EcUXwC1DYUhcU3+A/6O7/9zOV9V0AvsvM\nfi6AXwTg56DMID8B4M8D+LPufnyxx3MLWUx6yKT3EZCxpCwAMjaYhouHMiWZniyO/sLhuUunjEtl\nlWs+nNj6db0uPMRn6wmIOSNcLljmGedwQqxHRouwooopdGE6vcLrbaLmvS1kdtA9k7oz0IWEoEr9\nLPPeFxWyR9KNqYWuDvgalJnOxeRZxNDeLbBnlvMsSMUBaG/kQp8j4D1Vtre/54rz4UGcFXuqHTvC\nuab/AHvWVC8EW+yUR8KDJa24pj+hscZ5IPUK9/smf0shiz/jzSxiZfwr4OJ/PSCuF25vrwFo46a/\nVJhjSePx5wKFcniJyH7C2Q2nVxeEeEFaY7lwuPo/J5tJ+6Rexl2ybodKPVPKYbgDKqu8XVIMkBUV\nsVRkV+KeJb1t5YXKEM8IWxwFdYyjTKnGlORUtMDMsUwz3AJOywU5OPwETAsQ1R0F57HX2LPKCZ44\nb8XarnzfSupjtx33t3wkMjDSs5U3MBIwMBIGRhoYaWCkgZEGRvqkyMBIz1bepxMjERMl3G98feQ5\ngPPw0VKScYxxiKP0bq8eY3FeoXWRYjRdB2p9QwI8A3kGgjlO51S8EtwZomUEXLBWCzu1vOP8znmV\n+GnGBYYJGXegdRzj0fJulpm4zLdhm48hzyllNWkXG9/yRmCglVD5bFzWuyXbHXBMRXOpkvA8S/st\nfdiKorzbFgguGAl7jBPQ7vPlYt8vMopx2N2jpPtQCuYic8ZtzPRYoecCNjK9EnyA48sbCS57/ETT\nubdseLVqpWUpcZQqw3Ufo2G9p44+jmIkVrPHTWzabKVPeQZOCWmNyPkViHfn04psAYvPW/844YKA\nDPUccIIhVHKgWt41d5h7zwNqBat5qjVqGQZLHUm2y5NxOcZ5V/BWb/Gm0O93CtYr917m6Li8muCX\nhLuct21AuBT95W6+Y7e95ZWApCh2cU1PPfW4426T90JxBwB1cf49ZvYHAPxmAL8cwD+AY/D1BQA/\nAOB7APx77v63PrKKvgfi7q8B/Pf1712X9ZcA/KV3Xc79lcCeBUVmEwETn+ldrwq4dBHQPPs/vQge\nXXn9gqKbPK0HwzhZEZzVcwdbWzYh1op4Au9VyWYbMYY+yyNWGNrdKRRO9GSoMr4yVItDKEO6Sl8Y\nHglhO9xq/soBA1njhcV+wmXLR8vo0z2bmPyRTH2H9g7JMNc/vjeNYyjsGo8dp5Ev9BUamteV6giN\nK21aK3nfQZz+kDfFUSG1iAdSPIgiYGS4Msnng/C3WCKO+J/a/7nBUNb5hP1GQw9J+BOBhgOVMcX0\nXPD5U1gWX8XurM7gKQDnGauVCscpAROwrtMGIiZbtwMflQK+Yq1CY5XPWOrBa6xFeq126SOrIJBY\n2YJ0n8BNkbosYP7KlCILqndJpYfMfRz+9GgZKTiWacJkCTElpFql4DUeQRS7bX/JsB5OcQ8C7Oe/\nJPkMwPWRyMBIzycDI2FgJAyMNDDSwEgDIw2M9EmRgZGeTz51GEnHuc7LQZ73uInzQMIeR+n3gGus\nha6MhOYOs8dKek/qhGuMpuuA1X9ei42OmDLmS8I6OdIUEOpsy3mRpIqEuGEbuummJ4AZFxQfAS2O\nWt71VtCpw1FUINByqN2Vx9U/bx4R6NWgNFFE7jCSmZe7+0KGhdzagUs2M9X52/EwAgX1V3xfhCtq\nhan5G4qCZ1Pg8bK83i+gLiioYawYFXgL9mSn3OVJF5n34SGW0TN5boliHP1M1+GTPKPP7VsEKT57\nAPFMm4j9nRiffby32lL8A1xb1XEcAvt9A+Mwb+0HHL89VYPpX6O8WwtAdPjkSJe2uZnmFSGm4jZT\nmoDjQfELsY7e10isRJLfhlEqtlJFn+bp27N1wzglru/iEk9FJCyyKfODMtqeB3WPAgTLSJPj4hPi\nmhFThk+lywd26d7yDthjI3bxFc0YtR8SHF9DALxHijuKu/9NAN8O4NvNbALws1D8bf80FFbU3wTw\n114aK2rIxyjKlOK1G8Desw4XB7I5lBX7wQPLIXOAaxOBmzKlGEZyMMtVNsjapSPDq4I0y/Un1N9h\nnmB1RnSzLZ9LZYwXhkU5dOJCQFB1wWmL05tqk92Brbr79AW7HF/cUdhQywa+KGfc7Ramj0xm7N/j\n0d0uvQW/AoIcy8HUFkDQxN/PF8jvSiOJeF7200OEdVMWeU/v7llUBF6a/plWS2UqKoscaExvLtzA\n3qsVxVF+RsDeCwTHxgX7cz9Nq1fqMCwFIIUyNN1w98EZIWasy1RY5bUpTrjs2Ex8RvdppWpWsy7g\npoW1EdCYTuV5Y1/tGes8XOrLU8b4K7zelQ8UkEfW1lQBW3FvELfmM5QLlC/hBLcFIWfk6PAgNgkE\ny+y2BFPcYHJuIrhiQjWmGEypj00GRhryaBkYaWCkgZEwMFL9PDDSwEifYBkYacijRRVnnCtfoZEs\neo8FJFmoAu10kJ7T0y2MYxIGNGWdrr1n7JWEitF6j4lWeTaGcjdVcpzSCnhEmooiLiBvqjAq4khK\n4qG/VwTkKJZ25SfFLX0pzvEarzb8xKZxzMI7ydt8TizVEy6o1GC5bOS8uwj2kUKLH87Xr++PDmDv\nKABo7d/Dlp3irs8gAvgS9kQmLhKbyTuapR0z4iKj2mHU7x9InPvkKP19opZ/TE+sRBxHjERPBc8g\nHD9cR4l7PkAbPxQqf/guNI4SpIifFGNlifMBrpWC/Hk6/oA9RnMDLAJ3GYgJ6TLDU21jc5xCRvaA\ni5+2ZrzDuWKdeRtnr3DeFHc91ikj5LQ1jaHdjddb3jU3mJxKiivbtOVpW/nlTm4q96YNBwGOIFip\npPKdxV5AufcyxYAUA+5er4gpI09Fj2lemmXrvsDe84Din4hmjEocpWFDdvJiFXdm9rOx90X+f7r7\nD2ucCqr+Sv0bMuRYaKKrkzlJs70o04Mg6DXagq0sEDLOlXTraAdOMxqo43qp6Tnx0yV0z9TUvFep\nh+3zCQmYL0CKQJqAeV2LqfIErFam5YBUiyhs7v7CXwIuujroD6kiCh2s30Azrl4uDJRJ/qGHT7yw\neGOUzxWcuWExx2rARk3jIvsc7mX0TAk1TyU18RlJTNumnBQepcwqrYT0uw/QbMT5Uo/uaplQCJ+8\nOPjInv8hou6gFGARkSit6COirxwxpY7O5vgzSSBT9qEynrjY69iiKIuqZ7T3LHRAWFgBfp6xmMPd\nEOcVFhzLZd6atGd4l+QrIlb0rHKguGCjb/AFpy2M+ZSx0dhPBbA1xt2MBeUupAC6SiAzSt0YTFJ+\na4ZS7hl3V3FM8kkh4jyfMKe1sMonmdq4sWQ36udNMqCUca7dlmHj2OOdycBIQ55NBkYaGOlIBkZ6\n9zIw0sBIQ96JDIw05FmFlm+cRxXH6Dyu/BUubYqNpoP0JmX0GIeTTm+o1VslfYhmFK5l9RhrwWZ1\nx7rOXhaUdY5IU0Ss83BCBC3eji3viI3WajgzbbiGuKfcQtesi6bakLwjVXFQ81jQFqeiWnjHx9UT\nmv6L0q+RSpzpRSGOvpsJxQQpEdCSAER2WkRbQHQR18WClVvQtMQs5EgBZ7juJCr3pVcNNLXOBBm6\njtIFAzuqpid+Ilh5S+1LP37U44DiIO3b9CqgGInVu29sWH1GXari55Oko/T7H6/tFDPcDOl8Au85\nnOaEOO0t79yaq8oVM87wmmU6wFHEOmqN1zwGqHt+xT9U6l1b3mFTylM5R4yllncFo+WtDCoZ41bH\nqWGzOcBtwrRkRK8KPLW8e7Vle31VworWJZcujBjrCTr6T5q8WMUdgF8F4DvqZwfwCz/Gugx5n4WD\nn2sg105OFC7P1HyX+3ZdKLiQm6RlmEm4Mi6VRd6zqJQ0o2msy6evo9TDAMRcHwWH+QrzwhBFKK6h\nCsvVa7J2uSndE9AdFE2n+YwuC1pYqD+3pYxorBBxWLDbLKsrg979ExkfDkO2AI+VgeuF5ZtSKOze\nHJp7gnpGtftT7NCLMoz5mXlA2lMZcny3uQvnAr0F9ki9f3HqE4MvXF8k03MF47SsgKunRx8JTzfZ\nufVQyuT5JN8fmvcjhW2lVhQ9M02roKyojcFfn/X9n8/6scXnBFz6fjUsd888ADkgGeBuMCvuNjxb\niwOUZ9jbR5gwE8vPaD8owOvP5yXAtvV5dZhGFlOuW6VWtVLR/hJh9XEeuvJVsoxVjrz2xsuuLYfy\nzHJxcQDzNky0a2uXRvdu+F3fm8a9b1wOeYoMjDTkeWRgpIGRBkaS5wMj7Z4NjDQw0vspAyMNeT6h\nlY6O4X7M90vFck8cxUZUBvbrgcZZD9ITK5HwwbB+jtH5qM7xwbCtJ5M7AhJgBg9ljk/W7grVuVbv\nvipVCABoqYeKiYqdEL0S8FmZZcstXy7YSrEOrakVL+WtId6RUDnANdm7P7Zn742S7c21lNXUdwMr\nyrvtJ1Apd7SAQ57pIk/8lLowLUgroGZNvdmlhh01xCxx+YyaF4JJmoorIDF5zri3GIAHi562t1ad\nWV2wr1LfRMrl6jGSjqMscdDF6ddoypGhItOn+sUMuHN4CEiLwR0oDMELQshIFovHD+KlWoDe7WsV\n6/R45QjH6B6Fn9vdeLbFaZZ35Y46/rA+fbt/r/WjUL0pNPe17Y/jM8CxRkMOBnNHcEM2h1nBSKYY\nie3ct38/b+l7mLAnEH7K5SUr7v52+fzX3f2HPraaDHn/RVlMXAAc1yxuZYX3bFbHNdF3kvTAfk0k\nm+qE6/I1vV642rNBmI+yrxiX32u9YyprRJoBt4zTshRT5Y1V3pgSytzgMzXRJlOKEzvNr8tPLC6i\nKMqwuiX9xcUqD0m/E56tqDvwktFtUTYU8yB5m9LfF6zXsRyyyglKeJiklWMnO5KAdivrQxnjD2GA\nK8NJD6R4YS7Q0CVZUKwv/ZU/k5CFRM9YR8Quji0yFZU9lWuV+nEDNCsMjgOgAWqOaa2HNi/H0Vme\nb0yfAPfCKs/ZMN8tyDlgOc9bc3FTob7FdWPSi7K5CdHoTo2MpdYcHId7phWZjgRiZF+RMX5UPtlU\nZEoxTnFDta/bGiPc7jCvC0I+YJWTDcUurd2ecyQ3jjpHMu4AXO9KBkYa8nwyMNLASAMjYWAkDIzU\n1W1gpPdWBkYa8jyic+Qt/HHqwlTXwvlb8QvnCE3PsnqvBkxvXfoO4xzisKN1hPNPXSMsAeEMzEgw\ndyyniByL1U2qM2E59r+Ad4QS45QqlDic/8sagJqueQyw+oxYqfcmQCXBixVDwz3K6zljj0P5nNbV\nM4pv0sRFmmvYGc1UUxfw3ixbLe24uCtGoleCMxpo63GUCsN6bNUzeRQr8UfTVSYVeOpf/yEslHuY\nZNq3tTn0zjpayinGeQhGos7q1h7l3MXpPRYcWd6d0KwBiXndgTnDc8T6+q4Q2QyYpgSbvFjeVaX6\njKVapYaqYsM2to4s7/q9yUyMUsck0xPbXFve0ZtBw0q0it3fH9zSTdWulmSokleqYaH+X2HmWOei\nnJwvCdEd6QSEtfztLIs5Nu7DRvw+vBLs5CUr7v6GfB4uDIY8TcjcoNZf73IA9hp+LhZ9HGUaK1OS\n7AFIHj0LxCVtv+D3BGOgTWa6ppKNpSxZydMcCF7PSuCwnJDNECvcClA2a9hMnEvWhaGREVAuIC0b\nZLLMyawolxE3FmvJYX9YEqAXDscNhJFNRZdTzLd3K/VGIamHiy0XagLlKOGMo+3X7mhtfYLtre9D\nyUzo/q81w+1OF6XisJOxo7ASSb5r5qTY6Y/ij2RFCHSORMMC9gdSd2gISDuXgsTmfmg/CB4CwLqf\nz58I7A/xVPRgj31cizsaN9oUqYvD96eMKj0n7H9KP+a2etQ+fi5tEYLDPRX2YfDd2KWLj5blnpm4\nYN6eqYVGg9M8orKtajwE7g9tlWm1wHflqWXGfWwsxmP4InE8GNwM5hlwRwhea1ab7oi9pv8Zxu7c\nM0tfMsp4v2VgpCHPJwMjDYw0MJLUb2Ck7fPASAMjvZ8yMNKQ5xHioyOLHZ1Xdd5WvNKTWhj/KD3L\nQFfGrXmkx1r5IKyfc7owi2Uem5Zq32a21SuEMgfmOhsT8yjGKfgnbzNsqviF83vBSsVhX4ahOOBs\n2Ip4Z++FABX9FFQVqwqxlJ8lTdo+R0tIFhFDRooZcUpIk8GTAaYv5B7he+qxEj186zslHMnyB+wh\nDMPdQPeJxRxrlhfGRYALs3YKvjQWvsh3dgZ+10LRxdEKKn5SUTBAS7s77BV11FbRuo4kJwXwjKuf\nDwC+9tejPQGw788P2T/gDWH9Pobjhq42gf0Y6bGRYt7+mdXIVn14BEOyuQyuu6V4JQi2myd4z6NX\n5VjJsuEf7IpSa7r9M/UU0qcvZMJ9eiqPWT73NKX5wi6fvgxV0MdtpHvxBmIGyw5YRkgO99Ld2TQ7\nr/mE8tqN+W65H+G7GQLgZcPFvyqfP/ux1WLI+y89oYRCxoZjz8pQl8wkvvA52dx0UU2iiwKwnk31\nGm3d0zDHNQmmt4rXM48e7J2k3rnkaQbEBbAMuLBqC6xasFRWxiQs5gtOm49jnaT397fohcPYsWBV\n9hO6Nkljo78zUT/I/TvnNSqlkiVef+inDI+AdjewYg2gvaMNW5GVxEsvVNiZ+lvp6duc1BLeXM2C\nlU11fsMPV6p8wJ5Fzvek7g8Y70gM7QbtRxxKAfu+HeXZEYucLilIIgu4PW70nfb+r1+jLfa9j2xH\nw5y9m3dND01vwBKR/YSLG+ZXF4S7C9Iai1uo+tvI8NY+Pdc7W4ACeugTvACmdm8LR9gME6ZVu6Nl\nxoL+0IsHVsqqmrGAFwVnhF0clQnrZg3COEutR2nKypSaZrgFnJYLPDjWdn1Na1N2UbW4UNYa258k\nvFsGFUOeQwZGGvI8MjDSwEgDI2FgJKnDwEgDI73/MjDSkOcRXY6A23Ot4h61/Af2ngOsi9OnRw3r\n53x+5hJJSz1dKxjGJTJL3N7ijxivGpWHFUB2nJAwTWWuXiaH3wGrzZtVXX/HL91ilrk2Xj1TBcQk\nOArwbW7vpaRvi1JZxnqFRBNDceG8ra3WUq4w4PzANZtkil5hQ9Hr6NqPatCkdy7A90GctKIo8LYF\nglrOjxmVAAAgAElEQVQcR8M2mvA+uQ8jrQdxerDRi+KgGe3+X4ax85IAxYbSMP5wDfsAh5iK+wG1\nUOWeQquk1qhH445Y/z6MpBiH2KfHWEsXR/cPFB3jlJ0VX22fOwdOGXmNyJleHRzTaYVZwOLz1pR0\nB56k43nFP7yHrhVFjNJGR8NPzSpOvREUrMW3sdTdTjOBY/llrIYtzkU2AixDsRIlI2KBbZZ3y90E\nXxJOOSHHci1BWApxcmd5x26rOmmF9uTrHU8Rn0p5yYq7/w1tu/E1ZnZy9wFxhzxeyJIhyxTYH/aQ\nCaVkXmAP0NR3OLC/MLgn8fSmvxF7ggxwzRYJXXzH9YX1jNuzQngIUOttEQgGxBWYgiOHFSlGpBB2\nm2ayLCas0PtWlKHKcPKZWlg7gCIjfELzV17Y62kr4/bJ4DPKEUtKJ3sye4DGyj9aEAiOeYioV6rk\n7jsB2NWdLmSNkzJykgRKwdXOowwl/pHddF/7vULzPx7RWOQ9vb4HTUq5V+nZ6fK97+sqOk6OsKES\nxIhTlTyv7CcWzXGjzaEMHR1bbCJl5mg6YA+0eobVlr4ypWzGag6YI8ZU3KmlaavbZNebF2V275vG\ncKrjCYi1Gs2nv1cOIvNw8OLhCRfJqxwcY8cq52ExD6N61mKpbovDvH1jSwYYItwyEB2LT3BLiKm4\nhOKrMOB648hxxDC2I8P6s9AhzykDIw15HhkYaWAkYGCkgZEGRhoY6ZMkAyMNeR6hJTPnYc7PavxN\njALsMY5aPNOgqp9zGafXtfQGUlwbtAzFWj1+o97kTesI15JYgxcv5KYAhEgEU2ZNKubUAo6uiGO1\nqCtYp8Slsi0gQd0aEyupROQtTwAbprr/z/ffLSOEDAsZISZYzEVjQAUQcQ69EaTuOz+zrRUz8TMV\noFniM73LH6GdlrNhaysNjEkScDFQRk+UDJTVxgx7jKRKNNUiPuTIn5iIC9PUPVNM1IeFLlwx0n69\n3YSvv+dzcdZmmF6DrOOO/Z5jij/xCCOxCv3e4L6xofuHHhsdKXeZ/lwDzOAxANGRLlK504oQMlab\nWhsYtv0H+4FaoyrhiCSotHunjlg7GBV9TO8wrJi3JqPyfe3wc/MwEjcctB/9TWm+Ytrhq20sG+CW\nYZNjcSCuGTFleKy9NdVm6y3vgP0+gkNilXYd8nIVd+7+I2b2fQC+GcVx768E8F0fa6WGvJ+ijG89\nE1DGa0KZRDg590ypJM/I0MgoZwHKxtANNCeeIGW8wn4hAfa+0XUNJrs5Yl++skJ0Iy7pzYApA4YE\nQ8b5NCOFE6bKXb0cMMXPuLvazDLsgtPNsH4TvuzoxB+zKItH8aF+7s9rzvJMN9ckLJE8zhWQWGqL\npNRbMszZJl96wg+4JWSPA9csKFK3j06SIsrUeuuUifkJPaZ3G9AL2VBHcQxtTNE3Ofstx1/ffAzr\n0+uF30sXxyQO60QX7P11OTfTB+ASkBzIbji9OiPEjHWZCqu81qMxDPeoQgHO3lUB/ZaHXdhpuzcg\nbN/bZcJhy4t3AeSOVcXDsYwgcfZMd40TtgkLWxknFDcOyzzDzRBy3phSWy/kfMTNzGtcW9Owu9D7\nh96bNOTZZGCkIc8mAyMNjAQMjHQlAyMNjDQw0vsqAyMNeVbhXEuMAew9Duhy9qoL03VBrUp6xZum\n78sA2nysruSypH8bjKTrSF1jQvVKkOT+TeKZkqzdw8UDeyruGnEJOMscO2FFRMIZd1dWz62MFSZz\n77N6IaDhF7Bf46x7ZigOAI6EihxV8JBEpjCG+fWOA7imU6e2EZqo/aM1v1aAGZyx74D0UtBLQAML\nzJcN8CasRO0TXYizfrQYI8g4SRjLYuOoSdUDRb14MqujvcXRuFOMovuHvm/fwji3MJYqjz7AtXcE\nEh17azzuf9wAi8BdBmJCWmbkVLXv5phPjuzV8q7+rmb5Nm+j6ITzprjT8RCQq1KtDVJDuxtPsZNi\nG4BvmZZ3rTPf4bzFpXKPsqJcKVDGfdjuzztyXz5jQYoBOQTMtiKmjDwV2BjZprp/JN4lLNdx88iu\n9EmXF6u4q/JvoQAuAPhWM/sed3/srm7Ip11Um3/E9ObCS2KKujMxSRclHRkaZ4lD5obmzcWF6bkY\nME/mo5faanqWMXV5k6CD+uw12lrZsdLNHVMqhaQ4IYdQJ22v5tCFz8GJd6oN0e5niXX5KMyNeUOQ\nkywCjWGuEpHEnNq2fPUQy7dpX54HwGeD5wC4lTXVAXgsi2GPVZTZxO98p+pbXmc8xr+gXS5PuaAd\nRtHEnoebbGMSVeg2wVFBmLLIY5cBK6J03B01HY0JDkmnwvRcbJVNrodS/M62JqhimDLXgdbJ+lXy\ngavmkcsnCvt6P37Yb2+NG+JMZXxreo4toG1QCAh0/OhP1AMrPmN69OkD/FxZ5TDEubpMWqatbhwv\n/SXCPFQC2rjgZocsJt7zUqrRXEUp+6mxw9udMGQk9q7aFLAdscoZR90u6Ijc0oWIy3xCTCumnlVO\ngNx3bWWVs4uSxTbkXcnASEOeLgMjDYw0MBIGRsLASAMjfdJkYKQhTxddNxUrqQVQ7/JN5+ELjufY\nPo6mV88GE64xWm9pd8bjMVK/jnQYLyzAZOX+rXUOSFMArZoVE0Uk3OGM5hazWOLM4k4vIV4p7Iix\n1IrnljLgyWI3/u4Lo3Eb0PBQT257JfnQolKXD/VKcIStEyvAzqQarCO3BoqDqGXaW161OK8kvyMf\nnxHXiraIvYvMOzSNJ/GbWtZpWm1U5v1Ik3KOpyOPG8y2H3esqrrR7Pt2g+VNOUQ5wliMw59EI8Te\nRSbTaf0Vv1E5GzMw1TuCqxeKaV4Rp4TV5f0ZcQjxC/FPwy+t2guKxw+1xmvYRnGMYhuOt1iV6RpX\nLe0Uf5VXUTq+Y0Gs+O2MO0xYQc8kjoAVE7Y7J6eAy91ULO+8KvAMCIqR6u/e2p/YiPBfx9OnXF60\n4s7d/ysz+7cB/DYAPw/Af21mv9bdf/RjrtqQ9004AVDDz82rknnIuND1U4XsCkdbl3QT3q+7nOx1\n8VnQziaUhanxNT3rDTQApvV2iXNUfv2dMSWEnHGBYa23EBcmeK4/O9YqNjdPnNhzzZSHUlYbwGG4\n7BrQazM0tlXhglxA91C+5XR9QKUHU9kCfKrHXAa4G1I9oNoxhpQR0x9IUdg22uaMo2ArYo8/CHQj\nyuKrbazvVN/DlTsonkY6jg+l+jik4TFTMqx6epAePOklJ2RKsQ76gxh2tAIqKtFnDzhR6A8Aj+RO\n4rD/KlO8Zwr2oE0PvNiEwDV7UMcRx6u6J/pA4mqTanotIwUghVIdN9xZ8aHvXvttPfM7YpXTN7my\nwUvWjnKNtx+Gte2KHbLKS3Vz/fntGUcPD6UKG2ufjrI/TD4YhcFxCaGczZFVbgescj2I6udNZSQO\neScyMNKQZ5OBkQZGGhgJAyNhYCQMjPRJkYGRhjyL6Hp45M05yzMqDPgduMZBhr3FEOPoXE2hFZda\n/PUYTefxHuO8CSMdrSNWDtfNUVVpK2ATcjREKxgny5xMAkWJOW3YZq6xejKSbUgnY5LGYfqPVOzG\nHxVErXJFPsTeNaNa2NFySK3DiI1U4cS+xP6y4SIFY2pyRA2GFnYrTEFfv7al7jvNEBXL9OSmO4kD\nicOOyecazvqr6egDRZXjkCKIeWipquOOVeK4CxLOMB23inH4Xu/DWMRGsUvTp9OfD/6OOpjuHB4M\naZngXgeXOULIyGFveVe8AkS4gASr1nD9WCLW0bsiiVlUSce78Zje4LWbZtA3COUOZ/TeDJpbzNIA\nActGXCzNUF5ac2BrCHCkCKQYcPd6LV4KOLdkwBQjAfuxATT4/wpDqrxoxR0AuPtvN7MvAPgXAXwj\ngB+qIOw/cPe/8PHWbsh7J8o0MlyzoCbsGRucM1OXrs8HXToFZQqUgLbYZ7RLgeeDPJWNpSwEniko\nU72vd01PolaegRQdU1oBd2ACluA7tqmyWfNuJm1hamrdSzmw2l8fYAcMq49EiCl6WdBYNmSD871c\navjcxeczk+9KgjpklSudVn2ME3HrzdFMqD4WSJWeUDy8KEog0GJc3srLMkIXV8OO3gU7vr7zgP2p\nwz1CNt8tJnkvfb9dUYDwCY3NpIwljhE2G5vi1rg5d3GUDUUgp+7cKPelTwE4z1jMkbNhPi3IOWC5\nzDtWec+GIgupZ5WXMLLCy6W+BFTNVVTPKl/R+zgv9wW0zk72VUbAGXfCxmrvkWx0ZUrRQqRsmppv\n8zVGuJ0wryuiF1b5RkZjt9W5VJlSOjaGvDMZGGnIs8rASAMjDYzUycBIwBvSD4w0MNILlYGRhjxZ\niIM4dZFn0mOcI/xEvsdRnPswji51Z8nnVhk9DroPI3EdUa8G/TqSsVvrpzXB3LHMEXkqa2hRzi07\na7qiyCNW2q+P6g4ZdS3orZ4/UunnX74jKg96Y36FKEDzRtDrvxY0HhDlluUd+0ECGiKlwotmmK+w\nx0EshAVfcH15m3YAYA/YtQGAPSmpt7jTOHxGHNVb7FHZeOqeU6hR7ll/FGrcRDLaWknLObpV1P2H\neuVQBeqtvq0Yh4rw+zCWxun7BtOt3TPFb16JdXOG54CVlncApik1y7uqb1X84tXybo9/Hm55d5aO\neJ/l3SoD4oQFxeNIsbxrHguYzjBhwYR2/x7rV15beTl0kbvOAW4T5iUhuiOfAFuBoGMCaGND578h\nm7xoxZ2ZfS/a9Pk3AHwFgJ8B4F8G8HvN7KcAfB7AT+J6er1X3P0fft7aDnmxQrckZAGrNl/Xjh37\nBY0wAuwZHD2LQ/M6YpFzsudnJahoPj3TwLv0ZB4wTA+6brDYzYBY61gIHgkWHSnGjXXBC33J0EiV\nUkJ2hbLI9wCrhEfkLdzgmGqOZXNsu4344+UBaYkf+MfFXdukZ5czXk8UYnwVZeh4F0ffX5Y/GOCk\n5jACK6UdUZlRhusXenQASBCnh1K36Cgzri/QUEoZcMwiJ3i0/eNe2A9vLazaz5V9qO44yGriGUw6\nSHs0NoHb7DWm94M42qw9RryZPsCzIdkJ7oYQSkU9GMx816RemUbYitgzpHrWIcOWigYbe8pqTBOm\n1L7C+7x9y7M0W9jG5DHbsQGrUMcw2VNOq5GA4jbOHeYOD4Uhxh5ibFPt0j2L82Pck33SZWCkIc8i\nAyMNjDQwksjASAMjDYz0SZCBkYY8i1DJxeUJ2M/DXCtv4SddT3uvBJyyjuZq4NqqSHEPuu9cMnuL\nIw3T+Yh5AtfrCABMZQ6zDMTVYTnBzZBDRjKH2/4OX8VEZdkrFSaG6nEU3ZDfkhJabPk0D6Ys3xMC\n4i48ICNYRgiOEDNCzPCY4cGAaO1d8p2wbdTVpWJKxU96f6+2PwlovTU85XXXxkfv0Q2N1MSXwoxu\nLeQK5JgRO4A+v2+hUYxEjSN/6BEO4vNeK01cRK3VkfB33JIOdBDz9BhHMYqOu6Om6fcmtzAO4wT5\nDjQFu8bpcbC+IkhcsP71i1WfArm4uIc5cLfAgsPNWj6m1nQBS4dfFK+U4ol19OfssQ6wt7xjnBMg\nzxQr5V36ZoXHPU+xrVMrPv0PqYdHg5vBsgOeEcwRHHCvTaNzWz82jrmQn0p50Yo7AN90T5gB+HIA\nv/At8n3KLnnI+yavUXoLWTWOa1YS0Eyd6RtZmeZkcSdJ18dR9hOJwkf5WFc+/SArY8O7/JQoo2xc\npu/js/wqIZW1YRXCcmE+NXrIKswnZUXRdUHPIg9b+jLJX3C6ivORSMTxuctZvnfnK0ANZztqemXj\n6DNHc39wRiMV9VesHB14boVcakKyqfTmelK3IJkfCfMiCHuF5n+8l6P3wcHQIxN+ZmeT5wp6KEcH\neEdFBezHzUMOKThuyAzsx13P+NZxo2OKCOaIfEbvWSv27+pmegOWCHfgYsB0d8HptCCtEZ5t+22F\nVR52vsU5lo4sK5oVR9zg2IwFJ1yEaVVA2oyljsc906qwsni3QGNfcdwSqPWscjIlycaaap/IWGtT\nFjb8Gie4BczLgmAJaW5TztamR2PjUccgQ95CBkYa8nQZGGlgpIGRugoOjDQw0sBInwAZGGnI04XK\ngzP2Ool+Hmbc/q5SWvz03gA4HxNbKcZR95Yz9uUfYSTFa0dWRf0cr2HTQT5VLAPxAuS5TPHTUizv\ncAKWOO/m7AkrAjIuOG1zaTngbwvSiulBVna05mOTqpDi0ZQPqE+MiYsIRFjh5R5gZSQR4xxXYH9/\nHUWt6RhGbwSsDCEC8dOrLj1xtEl++k4BSXjBNQ7iy13RXq6y6F7jGqTdJz1GUsu5WziIA8G6Z7wA\n922k1zyLsG+S79Vbs/b7Ft033OEYI3EfoO3u2O9N5i4/jdN7aT9ydbuz4quA786L5d0asfodKmsQ\n05zKHcFolncnLJhxqdZ07PnN8u4Wjmrwb92wjnfpiW34vJAJicOsVv8CujqnNwJKRsAF85XHEbpB\n770rmDmW0wQPCfMlAdXFeFiB0O83OaY4/w0B8PIVd0OGPF1oZq1rS+w+K2FF/YT3LAwylXtGh27a\nlWHFRQbyDBKH8TWPfo1UZpS6V9H6K9OE+ZH5UQ+kDOU/3BFzAqwwRQtfqbGg6KecvKVySWkDYHQz\nU5orVEZU2Jght4RMLOaRELe8mA+fk9WRLSDGhDitmOaAlK0cACQDJmuTPP2Hq9sHZXf3AFgXaDJ5\nmFbbul8smI+yyaWdd2UGVPYUOxj9kPOlMoO1C7vdgq2SekpJVHAU90iU2d4/17+DLBWgvIk0xTRA\nayf9iWwzVqfv2wRhhusq9+OtHwf9mNLnkLTMF9gDLY7bpU9fRoifgQTHao4QE3wyWPJd3QpDfP9e\neBdLQN7Cmu/wUqlli1vcjZSbBPZupGxrfNu+l3GLHSuLLChurOLWgbHF4ZjUe19aeM0nVKaUZ8CA\nkDPgvr0+a1VpjMLe7f6QIUNepgyMNDDSwEgiAyMNjIQtzsBIQ4Z8uiVnwFfAdOq/NQ8rHjGJE2o8\njcN08UY+kHy8e/4mjAPsMVY6COP/fr7vrLstApaAYChKO8sIXpBRrFio3e/b5uFYG6LMv83irhfO\nz3v8xHtIAyIy0oaEUv3LW5r+e0RCsIwYM1JMCFOCTRmIXv4m26+XhuYJkoodxbtHygPiGOIqVdQq\n/uF/6lf6NV+x0danQn1PfGFqDkiQoGBrknh0p/kYktgRuSmgLVC9Io1h2hnZSE9RLbBvHCjvVKen\n7aaQTC3vvAtTTxOQ7BmmazTTx4N4vUdS4iDIc/VWAUnvwJXlnRtSmGuZF+BU8MQWH4BZFtwx1yy9\nVjHvcFRv7abPiKX4bH8vZcmvwH+mP21xg4zlUHciWQaQSToq+eI2aiMyHGaAx5pPdkRzxJThDnht\nfwP211hz/A0B8OlV3Nmbowz5pIgTDOlhE5kyyjwCGnDiIsuewvQkAasfY1qFpy4d2Ryvunx6pg3L\nX6SMno3Fui1dnppey6fwmZBxgjtOy4I1O86n0240kEUekHc+kRlGVgWl33S/SRTYMc/y024MyYiy\ngAEw88K3dmBj+Rjaxb9duu0dO8pCe/T+GXbpwkj05ncKWeVM/xrX5zuMYwByLABsR7EiYyqi3M3y\nGsUBtvoPPxKr8bVjAsfTWc+UOsqrF6L1e6ZHAqfnEO2vbBL2f2UznrFnMTGNsp8Me7/jHBM67jUO\n35GjedTqWeXhVnoDLhHZTzi74fTqghAvjVVeyyCbSS0sZizbwRDvWClZN+sNMqX8/2fv7UFted4u\nofVUde99f38VGVBx/EpEQQb8CAZeMBHRVDARzEQZVFBBGb9AHBR9A0HBRAaZQE0EQ0ETAyc00UAR\nDBx4xRlmlAEZB9/f3bu76jGoWl2ra/feZ597z7333N+pBYdzTnd9dXVX1aqq9TwFwxmoqvJ2SDGV\nUuXcl8LsStiLxLctv4C8qcoZRtsbw6hSqoThollVcZljmWa4BZyWK3Jw+EnWnbRtaPsZ57f8jBgc\n6QNhcCQMjjQ40kFaPQZHGhxpcKQBAIMjfSjka9GYmPaxatTd98O81lvVJbT2zzC06mF/nLsw/Tig\n+R9xHP5NjsUhVbmO8i+1LlJjqbyPHxLguVjecfwpG3HNTFvPrGtuMdkP3wfdXgKN8zC97wIVp2nL\nvh6EeXSP8bVv57nBahRHXsV4DBNQxs/VapVyUy6iETm+UJpuf0IbmJVUv6QaUtC8XsHd3HsuG7Si\nZhybJ34JVPHUbxh26NsGox9xHM5NjjgS23JvefcJzf1p3/68C6O87+ia8ji3shvuGTglpCUip/YA\n82lFtoDF5+25TriiCJsafzmh+C4olm8nyWrPUYBmjUcBkqapHkPKHjbPtrNdmgyrbjaxxSoeRmgF\nSOvb9qoK15uxIEfH9dOE+ZoQU96mAeGKInLqz5Ec2PCuN+7c/TVygYGBQ6xrFWnogNkfPKom7vzq\nVIFOpYXG68kZ1Rzs+Klwojk3FR06lvZ/6339W/OApDnhNn8Nz7LWtEKdYOfosNDOaJmxbh1xP5Eu\nj59qVkUFReX5ESLS5q6GP6f6f5lcm9zZ/5DoqR/0YBlmpbwIvleHUSV1Pqg/VeJMD+5RJKQqck6u\n9SxgBZWyNLFXERTQFhgNRaLmUUgYX8prJSSGooh6ZpavUuqXwjDtXiIk4GLU7pVTbnaU3h1wMZdZ\n+cE1/W6JdCeMqp9cwrBNx4MwnJgwXz4y81PlVOjCbPENngJwmbEWEw3EKQETsK7TRsYnK0rAXg1l\ncCFJbWGWi1ZlotPuTZV57g//LqpCuk/gYpO6LGBYkicSNp7VomCbU6VUcwtXq9YyUnAs04TJEmJK\nSNsEDjB+9+T52ocOvDkGRxp4CwyO1NIaHOng3uBIGBwJgyMNjvTTYXCkgbeAO5DV+gzY97GE9sPc\nyNnGOdxuDvCa8iBNmzqRfhzo8+/5j97rOZaWUe+RA/TPIWGsls8cmJcEcyBNofCPGpkCppJM2ZLr\nN5EoxCCzYTjlOm+NEBPieUH28j6RrWygsG57662AxntYv2ohSc8Eek9FZIZmIc9NIp6fB7RxWi36\nkvxvBuRQq467vyws/+cLZ0QVFmlYYE+Ae5CwK3Hv7+1qs7v+Wgu/Z8DKFZ7W8yy+J20b2u6U45C3\nsIq0jeg94NaqjvMg5kFxk3LsCXtuRE+iKmLU+J/R1ADR4ZMjXVkAwzSvCDFjdSF6hk3YpPyFXEfd\n1pIrpW6rp1ivxt1Gn6bJacCEdeM4fVirYWnlmlGsbsv85TaP3hPCBCBYRpoc1/OEuGbENcOn0iyD\nciR9LwOjKgZ++1iW4jVotyhFF9EkPNpJH/koVnNoHdyBWzUGx8kg91SZ9SWgcoPkj8pNkirmz4Uv\nKqU6UhlXIGRgkXE9VBPsthbWKynahPUl9VMZQNYD0lWY5wVnHJ1h8TpI2jpgdrd26yW9UurS3dN3\nw3dHNRsHkD4+413QFqWUn/J93ajKe4mtroQ+gmEvtf5akJU+sWrQL6YC2K98Ao/V69hzSBJf4FYx\nRcGYrpGpUpEkmu+pV3zzvanCbenCBOzbvaOs9+nCM7rH8S5+CkAKpfhuOP9yKSRrmeBu2ys+VTJ1\nu9CbJenW5ugaJFeyyntF6RS3/5v6ar+IzLaq1xhWXT21PArmqrCiUoru3+gCrry2coDyNZzgtiDk\njBwdHmSZlX3O1/R3AwMD3w2DI7VkBkeqGBwJgyNhcKTBkQYGBgB4AlIC4qmstW8cqfcY8Miqjv2n\nbhSoVQ+wt+rhxh5wbIGdcWwVpOOIps2Nw9zdUyEIhVvMX+Nz4y475ms56y7HeTdEqleCz7vD3W7D\nkAeVM/HeduNnz7EcYV5hU6qvSAbYfrOS6HU7wF7M0lMD5Tgbr8FeHKM8lHkn7LnSNrZb2cXYCscP\n6A8lIeIqYchB+THyAWc8FjWRLD4rfHpLrnUEJVUyWOp7Igc6srw7ancJhceQ3/Qcqec4zH7CfY5F\nbsQwPa/W9gOJfwE2y7tzBmJCus7wVCcp5jiFjOwBVz9tz3XGpXKdedvm/oTLtnHXc53CY9o7NbSz\n8XquRK8C/HwLfwq7tnnGBRkZuZ5tF+WBV0QEGIJwpZKnb+UhZixIMSDFgPPntVjeTaVvNS/Vsr3f\n3uDzA2Ns3A18CKQE+BWYoixOZRTFw6NDhXXMpFk7J7zqcoBhNB6VHuzMP0v4IzXUUdoceDjjU6X6\nPWj5twqQdGr/G3LGablijRPWad8VlG7+KtHj3cWkErYop7hwld94RhqnOuq5bWNs5sOstidTR8U8\nOjjYu3saT5VSn3CsKue1CWXQpqsDoK07UWm+U5Urq+hlJY/ARN+q2+bQfJANcODuiQwzy/8vqOK4\nQKpZ6VpWryrnd0v3Bdo2IfFoRaFtQ107nSSdC1qbfBSG5A9oykN11+4SH3IvBfhlxmKO7EUlZdmx\nXOetbL3Cu2SxbqpEPWOF9+gbfMFpIz1MZxVpZFNaUdZXCFHxQB7Q3Dk1f+NceOJ5Se11FTU6XVT1\nYUzSSSHiMp8wp3VTlW+vlASZk9KBgYF3jcGRMDjS4EgdBkcaHGlwpIGBjw6XbnxneccFePbdE0q/\n11vMAXseoxzrIvGUh6hVD9MmV9I+vo/fcyx2Omqo1d8j+vhqjYdyL9R0/IDCsP/lSXM9aB3U+ui3\nwVQHqd1mndYbgLTGYu19hJ5icENHxWnkMYZS5xSo6T4XN2XpbYCboHzv5Dze5adeCQzdOzYgsS77\nDTP9qDiQMFOqq1gnn/DyphwJ8Asio92u5mugJOGlPDQOK7vjYxmNh+qGtRoDHnGce/MH3jvJPWZJ\nrtW3P4bhI7FtESeJp49EbvUrygYeIhAz3AzpcirXKl+KU9pZ3mULm5BoxYwLvCaZbnhU4zpqjdc8\nBqjLf7ZN3dSLlWspf1IedK2TBRVQAYYJC2h5R67EjTtHwIqpcbM5wG3CtGRErxt4R5Z3A2Pjbtg8\nk4EAACAASURBVOC3j5zrVDqXjoDngsIr6QJu1cSqeKVqJsvfJDxBwqji2yQdKox36uKal07UgVsV\niEm8Xqnep+kSl8/k2K8h1IHMHIi5HC7stp8QqxsmRzF/7s9XCZsWNW//03HTEVl7BkcuoViOGBJs\ndrhbOcg1h+JCgG4EOKZz8NZ3xvegVgAmP8DelQXRC336xRVe44DPhRXNg2FYhhxlHSdJAs90xVxN\ne8MFvyMhucnvraxaib0s7QG0DUi72wiMqtmShNW1L1WVqyIc2LcjtskmFWph+P9V/mf+2kb4HVDx\nxnfJb+kwfvkOkwHuhlBdhvDcIZbHrB0OjC163tqXng1QonktfimkKs7VKQmVUrnacWjajK9uCqg+\np6q8L9M+X0iYsH0SBgcCkENVZuXi5gHmW1Ubiek4v2Vg4F1jcCR5rsGRBkfqk7x3bXCkwZEwONLA\nwG8dycvmnRngCchedCZIwpEI5Sjazyof6cdgYM9H2EfrBgDT1jGjHwd0LNHxqal5Wji1woOU+Si+\nlM0cCF4MqokSvG3KqdUP7ylfSWKp/CxaGvu0lGO1EKXPzhaQY4CbwT0AK6pb8QwPobxE5UU9J6LV\noVIL1gk9Nuj77eOwApQ/MZwKbj7jmBtbTYCWdw60nSBu0JG4EWplp7zpE95OAHVEjLYCPrjHjcZ+\nd+0RWBksm+1vKedRjqRziSOOo22Q8XXeotlp9tq2IfGVW+k8A7idazA/vkLUidfZ4SEgLVbEAlYS\nCSEjWYSbNWGhbIIxQavWeEfzkUc8hn+3s/EalzrV+HTjfxS/lMN2c6OS7xUrJiTErX/gnEmPFfBo\nyMFg7ghuyOZlLppR3PMOjrRhbNwN/OZxvQLrDMwTkNamlgoBmGbAqHgASodIV81HqvJe8cqDe9mp\n+EE8VU/poahc3JokPcbTCfCpu6duEzR/hundXn+u92sZzYG4oPom39eVunG6p4aii4PyuFb1pl/f\nlZRzZHqWWkDz7UOoGsOwd/FEUGGmZNhR6oSD6xV7H/NAU08xvip17oVhGWhNoOfA7Ihb6BJ6Bl+i\ncLqDfhFOr99wLy5G3UjMn0M/GWGS/H6PXHRQofQrGk8F9ovC2m5UPcXy31NIkXCTjBv2akR9bK2L\nPj4k/hLgXlXlJ8N8XpBTPVy4tkmqEa93WEjo6pdq7ox2ngvPeSmqqqYqb0rzvdJqxrIjXFRfUTHO\ntI5U5VRKMUw524Vpl7KtsZDJeV0Q8oGqfGBg4F1jcCQMjjQ40i0GRxocaXCkgYEPj8+fgesJONdu\nyR3IS1lYDjOa2Il9pLoT570jizm1CiIP0fFXw2j/y7Q5DvTx7/Gwe8MjN/XUOojxNUzPnyoCEmY4\nEo5dHs/b4O5YcPoi8VLcBptbR+PPciwzRzgtMPPyOF4r+ILmDaBluN/nUk6km3K9y/iAkl5vjUfO\no+9PDeWYB7VKTJs0aNMEqZomopgBKhJuC4Ya7hnzbsNj8dM9azuW6d7mHQlkv9H4LI52RSsymuWd\nziPU48cRx+nnD8qRMsr7b5/dbdvu25iGYToE25a+lr6aPZTOZc7wHLF+PqO403RMU4JNXizv6ubi\njAUnXMVzQM9/9lzHahtt1ypHwbTxn57bqOVd4TjN8u6E62b5t2DeONayWex5bZnlHOOWVplkTvU7\nmLCWNjlXrnRNiO5IJxQL34ENY+Nu4DePNRc3UMGaYgqof4eiHAqqau3VqPz7SGnh3X2GwUEYVdGo\nmgPdPVVsqKKDMz2OXTqYMO/YhVnlusQLNQ9dlKKKyWqiSo2olOhVqP3ZEfdgyJtJdEmnmGjrNarI\nMwIi0jZw8MfgCCGXA4anBJ8ScgrlJWbbr5vw2Tmgsk7pjkDfkR4+7BKe0AWVLP8reqUUDsLp9ZWZ\nv627rFdBF4hehMqavgD9d8960nejExhgz03J02IXhumo4jx391Thpm1Lw/SKqoS94l3r6W780jbS\npRCaEBw+JbgbLPguLarHsUu2KQfZ9jY1Um0di4Rt91mQ/T1ClVZLvXeriLxVuquaSq1LmJsxXjC4\nGcwz4I4QvJasvtIf+IkPDAy8jMGRbuMNjiT1PjjSExgcacPgSIMjDQz8hrAkYFkLD4qh/Kb7TK/c\nwpT/9BY7RM9jGIbja2/h04fhRoGODX4nvvIrupwD9pb1vMcyk+/kg/gst3Aiy0BMtU8ODrWiIyfi\n34W3BKQXxC3s5ymeUP5TipYQqw0QOdARx2L+EQnJImJISDHAzJFgsDUXK+jkhR+RMypPYh3hoF5X\nCUde2Y+fyodZf0nu8bcKqwjW+Q1HqoEyB/x+AFFPBYpnXGW+BI5aR8RIH/IRtLJeA037juVdz3H6\neccRx+E7PuJIvNdbUPbzH8bXs8APinlDadVrwYI2+bLSCjwYks2AOXAuG85OK9H6LLQ+9bo5VrJp\n/EehHKW/Ri7Vx18wo4gR+/gtr1DnL70Vns6VNA9u8JU2GjcG5rFypeyAZYTkcEfznj8wNu4GPgbW\nBOQMnGaAR5VkB5al/L8tSjmaUoMHiFLx3YdxNGUSVRscE3MXhuNor0pmHlc8VrUqHuWvLg+o5tF0\nHqRdOt1y4Gm//BCRdkrX16rHY9Vj3BuoqbC9p7KtgRCnBDOXOIBfJiDX8lDh1GdDkQ/DUPGkahhV\nSh0pxlnH99RU6rZbyXQt+w0h+Io1njfBaznTW4CTASUzqnTSSQOV/Sc0bxCfDsL01h+qPiRJ61WM\nR2F6v/+O5lmCaRMaHxrfgCUi+wlXN0yfrjidr0hrhGfblIpUeOtkQxXbQCNctN7Yq7kL5Tnjgv6M\nlrmepbTItXP1/a+qKm3PDttcJPRte8K6WXMwTFFOsSoLmVymGW4Bp+WKHBw+1+7otQYTAwMD3x2D\nI7Ei7qc9OBIGR/rWGBxpcKSBgYF3h2UtHOnTGTjVbomWd2GqG3fALUfSfpxjZN/XMszyRJiHhZT4\nRxSkLxs3DB55LdRxpLe0yxmnz47lFHE97zMsbvaKq7yHvKUD+/PmCrA8+PWVlnrM38xL3VUrtmWZ\nkHornp6/Eve4rYpW7nGcgDL+cXOG4yit0ZVjaR76vQB3OJKV3YybPTLuNn7C7U11R/E1+FJipATm\nVaqoDr1aSUCuFHHLkRj1iOPwfR1xpDNuOQ65T8+x2LYYhhZ7uYunFnuz3FuAct5dAM4OnBLyGpHz\nJ5A0z6cV2aqnghqU7sBTZzl3whU8s5do7jDVGm8V3mL1WvNG8Bmfts93Egs71j/zX6vFLa3wrpvF\nnu/yIFdqr61wvYlc6TzBl4RTTsixWN4NFLzrjTsz+1N4eev+i+Du/+63SHfg/cFRyFXysjgFADHW\nLtCL0tysLEwFVYP3h8NzMq0qJV7jwb9UU6iCFbj9iqmOgoTz7n+G04NT1Se5xk9dHFVQ9emomgvl\nHJd5XZBCBELc3aRrpoSIFRPUd/ERqKSg9/GpakHoZkbPfTH5+166qrpyWPHxHAwhZljMxRRgqg+c\nQxnwVCmlPZyjDbwkXTSZZ51RhaOLgz2UdyTcTrpZ959we+Cw3v+WKltVx78Wu+8xY/+xHkElTa/I\nA2jfonpOiBImoS2uRuxdE2iYXv3EYnEdlPlFCc93cBSGYLvnNXVx0CustvhVKYUZyRyLOWJMwASs\nqS2eTlYO6u1V5fQlXrJvqicSI1SXUOVqaW+ODJM4Dh48XD48plMWnoEFXmlS3logF6N4YHFfJgDb\nolZLu1V3tIwUHYtPmCwhpoQ8YRCub4jBkQbeAoMjYXAk1tPgSC9jcKSGwZG2MgGDI703DI408Fbw\nyo+W2sdNsVne5dpnmlreAa1vppWcjq3a16rVf+rCaB8bcDs200Wgeg3QvHiPYTkOKH9Ta7ylu9bz\nr3otVK7lwWG+b2K0tMsIT222GTImJDhWqBX1kTXPcXyv1nftFDzNPyAjWIaFso1g5gjzCmRDzhOQ\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zvi4IJziyuIPqy7TPF2NR6ifB4EgfF4Mj1b8HRxocaXCklg4wOBIGRxooGBzp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\nE/N90rJB362qqPSME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Ie/IcbG3cBvBwpN/TrPu4IDEQ+s9Qs+r1WIq1JKz6hQNammww9W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\nCobZIqLlA5ehxRt5iPcMoNfIUfSMX4jl3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5QKJwq9wL4Ayk0852EB/DmovMfxTDMyHPkX\ncG99xzDemt7zId0U6XkleMlEsce7NN33hHpjUG6ieTKMWs/pGXfkSmqVpO/mEUdS/unr9rnEp+Vd\nNCDEjEssO1BpCpis8SByjalundHNZY+HKMdRa7iCSw0TsewWe40jBblGa7yj94HmDYEbhI0lmbjM\npOWdWONZdc+Zw73lXQrIE4BQX5BNbTPZ8xjlP3rNC8+0b9lJGM+xehxJ53jPsXhfORJ/Z/mZV63Z\nY+P4KOjHQqeT6hjkOZJvv7S802ftcaRvElazHxxpx9i4G/jtwHUb/f7s3dMxfgXwtyhj0rufk0nF\ncU9VTsXCmar8keKcSoezh+S1XnyfTg+vUUoRqrx9BDLb17iBoFLJkyp1kaXl92mz/gg1X6fJey8+\nF6w4IUc0VzfZpaMSO10UmIAQq/vrCYgzMG8bLGesc1GVL1JhsSq/SWaoKu8pvulqQF09sfhbDaPx\nqCo/uphqqvInXOv1jChq9p6q/Ki0aq6qdlV5rZu4TQhTwuX5hk1V5Tkc1TV0X6Dtk/Xv3ZCpUsrH\nV/VaLwzT4wKMqqfO2m1AO3BX3bprvCzXlfgpGf+Z6JFYtnu2cYZTNX0E8AeOA54fHFUpuKCR2Yx2\nMDnHr4GBgb8MBkfC4Ei8NzjS4EiDIw2ONDAwAOA4RL5mGn4EansWvJ43pVg298KMe5eZOsb7tXZv\nAeMLcsYDNG1diO9VQG9vgWG7D4O3iZt8mn/2BXjoM/q8gDbGq9UcLad0LvM8aMG9NVePIy0urM4j\naPHplSAlIM3AvJXz7tZlwjZPB46xYcaGeecv3itBCbMcwtx7GlDLu+bNQD0HkOfQoq5wLeVoEzbM\nwrfyHn9GhFUeRR62cyvLQABinhDzhGlpZGK7LojrDMwZsGp5Z3Zuede7Rm5Dy7ur1L9yFbW88xxL\n01GOxHueY2k/Uo5EARdw3FTfA3+0ygk1L2d5x0vaD9QNpo4fOu6wftknehyJG4CqeBgbdzvGxt3A\nbwd+fwH9NRUdh/Qav+1UKHFIN5dJ1Kpi+LX3DoOTqpl9Yd96r+fbXAdShVda6Qfs2Qc0B+rXLEo9\n+rhXeJXMI9Adlsbx6vLorvsFrOz+1jkxyH0fX69TKaL1wbT5TlVVVa97VfmcC/FKZvX+sXA9Nbj+\nfnOKbx4grP7KS/ZNiaUK8p5CnfGpjNJyMH+G9fkTB/WVuh8CEELxm57qeS45m6inagKqVPbqG68q\nVxWUj69t2dxPXbjJnXua3yS/856G0QVRVZUrPoqIqPpL89Vxwyvj2V79gAe0/uTrTevCjzWv6fcD\nAwNfBoMjYXAk5jU40v5zcCSXx+BIgyMNDPxmUAMpv2+UcOpcrntPDZT93lZG3ZgxHN1l1gLQO+CB\nL3Fc0nnWZ+Y5hFpBw933XMFb9U3u70deCXq85RF/egSOue89R+hcyvL68mf3u260wMXVedGLRHTz\nRq2yda7QtHVONpTzDlHaQdgyQozFQi0YsmXAjhwjIB023YKreOUh6rmg4dK5duRRnhv14gekw3Wm\n2eI39rSHs8rfzGBThplQmhSQDchmwJPVOdz6vEL5PhPQOdrzp0f8x3OsXhim2eNY7Ke+bWiVHV6R\nJ2U/2vh7DfURSJKAvfL4TL543tOJPnOv3UuS3Xav48oAgLFxNzDwamQ0McAz7jvPtgEpA5f53uVT\nrAqpeem4gyKowqAS85AAmlKnp6KiwqEX7zva2Pz0IG3UcK9xXQAcF9NeE/Y14AfzWwZpKp4Yv1cm\nqmI1bTJlXbxgHVMFZp34SeLR/URPMe5VWEwn4KDKDRFYMrAtQLCEy7ZiywnrckG2hIsckLFhhqEd\nCkylti4SqdJ8w4xbPRy4hcGukKJSvMzBTYXVVOVHpRZV5cW3uR18m/v81Ue6uoqCtfqL24R5jgjf\nrrhZLq9mncpXiiq+r7XO9H0Z7pVSQPM7fxaf9e9dpKkKyCQM/Zizb7KtUUWlyjptW1RqfZRv/kfQ\nxalee9f2q1YobO9PaM/Nfk9lYK/fkDC/u+nNwMDAV8XgSA6DIw2ONDjS4EiDIw0M/BYg/9HhgKBx\nSY++9JAS8P0KLAvwfDlu0OUEpLVu3ixu806JGF3XKXSOJzfw8XuWM3BhaGWEmsalEw547OGvl/9r\nwGf8GeDcpK6Slf8t7h5NJvn8ZxxH582Mo1UR5xjOo3THqXONt7y7lPc/3YC0VMu7NcJSxvo0IU2G\n2U2udM9tyLjhAu9xgJZyap1HHkQU/pLRzqwr/GXFcuBGmseRoy0w5D3fDLuzCgRyPT5O8q+bO2te\nYJaxXLZ9m3C7XhDTAsxV/UfLO1q66TnA5D/qjYBtWfsLOZFynOzCKMcy9DkS+1CPY5H39zhSF7rD\n+COWdwnN/YVWwGvAPDu78PRSMOOeIzGofhvwu+uCUrc9jsR+8FU44hfB2Lgb+OWhYt8/CxVcAsfv\n2pSLEmqrF0JoZCrnsjBFtwZ6b4dXGmjiVDd4FYLeo5qhdw84Egam0VOJvpVAvTd6kxaf6wy9e1oP\n+kxUiXk1PD/cqXjx8VkGui5gHpx46epKz73gIc1+0uG7tSKKsgxMNS/LJcFsGzBNSCFAD/oFmlIq\noxz821OB89qMbVdE6aHEqPFInujFh6RLSZVXmgNWhX1tgcyDPs0VEVOrS9aNAWYZ86WYBiQUsRRS\naI/MQif5HS3+4Z3yWsSRNGh8FRz1vq7YFlQl5XmSpjF1rk+SjlcV/lnB1FvRU5H1xg246+wHXl3J\nDxPG07HKW1R81DMODAz8EAZHqr8PjtR+HxwJgyNhcKTBkQYGfnu8tovSyL23h6VDZO8e9wj2YSYD\nWx1nggHzDExqeVcTynXcObjNZEE49kzunoLjlo/Pezom+jEQnXu9a482Iz6DU3keqODehD63chZf\nJxzb9fl7HKcXN6Od8aX3yId0ruC7vB3j2wRYLG3EcjnJLq/lwVIIgG31sQL8hhndYirv8R4HyG1U\naFQex+TvehZdrQS/KXfkOlYfrTVEcidu2i07VwNMeZjMoTmbcBNDzoYcA7JNwFOtyGxHrqPw+17a\npvU63423mO9xLOW6ypfVu4DnWLRG8xzJt6m9vJ60v5VA/Ghn08K5zqM8jlko73xkTadjlOdIvDcs\n7naMjbuBXx7f0QjRe0DFAH8HxyEwZ2CtqvInr4ICEOuhsstSJts7UKlApYEPQ8VCT0UV5d5Z2iuO\nPrCe/APg9S6bPhKPfJnzhSi8issrzPjcSoAY3vshZ3xVQamTe0KVUroAeEF7N8STXKvPFmJZnNpV\n5esNlhdcLxdMSAhOsRSQ8IQrbrjcKcb13JSAhJscDkyyVARGtodpj9HOfVFVuR5YnGuFZQmbXYNk\nOuojXV1F7ZP5CmzJMF9WhJBwAxC/A7jVrxVVSrHjafu/4bhIyPeWcP/B8h1HpTfVWMGFkbLtyiGq\n384WTXuKqUnieULzI2KpPwslg6w/HTdmd40qQrZtNaNhv6OFhPqEp5pKSe7AwMCXxOBIkvbgSIMj\nDY40ONLgSAMDAx8EHWL8pl+MwB8JeM7A9HS8t1veZWDyETn+kBv0xhk/jvn4Kqh5xDE4nimUR5zB\nz/sfBfKfRxyT5db68/yP854+v+Exx+HcTCUQNzVoecd7nE/0ns4jwM63QqxtYSkin+VW3IrfngzB\nIhYkbM6ajtyFbjSXykOKpdzTzmd04+3Ig2YcLfbygUep5d0VTw85Wrliu8XfBStCnRxX4Ji/Mf8F\nISQsT5Lm9yfEFIClqgAtAN+t1L23HiW3eXbXMu7brec4vm0r7wroc6RF7nmORR6gHMlzg4OnDe6I\nJfQJ1xkCGmF8pPQ7gyqNOoMK2/iM/tjCbyzWVUQ5K5jfDT2O5Ma83x1j427gl8fZt58qnHxH4Nyc\nO/c0zV66OQMpAqsVl0/z5O6loqKacKIqp+pDDznXe1RS9dTQwP0BwpOLz3ARR3JC909fUf35aF7y\nzwi0OuKz+YUHTqJ6noguJFgnPtBILCclryphmCxh2MgmHEmwnvOBtkg5bXXNImRMMeKy3hCnGZtT\nlXul1FYbivowj5Uq8VqsmTY1FPYwqqYiUfOHE+uBxVZZRS8+F658/j4drbcUJyAD8+VWnw3I2wTE\n0A4AbgW87xd8H7zPnyRsjM+Dh7mAFXGvxrqh9TGGARqponnKE479jmUxiWMuni5OUdX30X3OK/6A\nVn7fJ3RFnwRT6177DdDaP9v7Z3yUDQwMvBqDI7n4gyMNjjQ40uBILMPgSAMDAwLyH+BoKMJ7Og0G\nd0+HDDVg0zQ1Xs5N8AQDlgkHd+K5cp60ljnSehl6HnNWKIKF03G3xzHUK0FvjL6hvz+gljifwafU\nUrwHfTlHQ7Fj/Xn+wnBKjn0YbTh8N7P83bunfIp5q6hqRrO8A5CnjGlLWCximwPiHBAQazLthVBM\ntGF2lncGw9J4CO65UYk31XilsEwvYto3/phexLS7w2TBPUeje3PlUUCutF7yr+9iCzNinjAtlSvB\nAMtI24ycM3BJQA5Atrbh6sVkEW1jScU6FO4BR/6rXOXMGwE35XociRu3PY7FfqEc6WH/+BGSpB2S\njbN3jsAj8EOrY3nHZJmVH/D0G408iN8SLILnSF/xm+uTMDbuBn5bqBjSdwQVuHhS9qq0M3Bby7kc\nk1t4yhnY1qKOufRU4Vo4KmJ7rJDKZbh7+v3/6FBgThTEM95Pcv/e8GVV9FRT3u0OVTC8x49rdfHM\nxTxV+/r4rHfWKdPRMFRKUQxzw1FxqwuGQJugL4VwT1tRz6ULMKWI6RZxvQAxXDAjVhVSeXAuHE2I\noJsCVTqp+mlCxHc8SxjUME7NBM6rxTe5KqzoDoGLUrkWnOVQFVeG7fl7xfq1qrkC0j6Z37LBgmF5\nWmEhI+WaEhel9B1zVVgVgr1JH2gEzMcnib5JGM9nGEb9lgcXnxX2B46up7S9Mm0SFpb3M1Tl5Hd+\nQZskShdY2e/U3z7bP8NsLgzrnu39Z51DMDAw8FMxOBIGRxocaXCkwZEGRxoYGNiRcTSK8vTjitLl\n/w7u9624V/DcucehwnrxtiJosmfcnQOcY7HMmy5u4w44cgOOqS+FeXohDEFxRg+c23tgvB8x+nkP\n6O6qB8d93Zw7qz9PhJm2bsb1OI5uzHBzhhsYyrGuck8t9nQeAXZuGqrHimhAsIzLdYPlCXEKmKxw\njIwLeEad8hFullmdgMvUGw4chwKkxlUuNUzEBbEWu1jYMR7Tv+Jp3+RrHO3Iv0r1GZRHofKo1edv\nJWzKAdMUEQLLlLGmgBwMuORal9ORf2i7o4WYvr8rGn8xufYSx1FvINy08hzp0omvngoMfY7UBcnI\nizt8J9hQKqZ3Kvkj6EahH4xwfBbPkYD7fkNPJ9/Q50jjjLsdY+NuYOABIspY29MicLjruTbY4yfg\neiu+yWc3tqUErGshYNNZT6SpsB6OvieAo4pKJ5CzMGcHDfOBvrqqobcARVWSQhW8wPHZeI8vV+tm\nQVt40vicE/UAWobXcgCNqPG9LZ34Xn1Flzm1jDaVoGkG4gzMMcLyDds8IwfDIiuPsSql6KJpqy6b\n5kOYaQ8TkKpavLk4YBhVSBVB0OyUT8BaFVI8MFhJH/PVM2R4z6vKVyzgwcN8J6stiHFCmBKW5xui\n5RJjm4AtHBVQN6lHXqOKacL9giMV6L34NMvP9X1lF2aWtL2aivlygZkHHzNNDePT4fXPUJVzgYwf\nCEAzseGHgu8/M47uVxZJa3Vh6E5iYGDgl8XgSF8MgyMNjjQ40vtgcKSBgYEvhFwFTzkXd+KT4yop\nlnth7mzgAUdR0yPXl9519hn/8vwJaHsJjzblOI5+tstxPpvfeT17ftafv0euBLR5jPMAeVCP4+im\nHPkQ76nLQeW4fq4ht6tzsxkQahtJEzDFhOfvK9ZlQpwDprphF2XyIVcp3KhZxZWqEB6CxmPU0wAt\n7yZELNXyjlxL3YHToq7wqD7/0nDNmu9at/IcDzPAQsaWKw+bt8pfDNt1QVpn5CkDT7FY3pkd3ZcS\n3vKO18iJvqHxgB7H4TyvfJgca8U9R6LHSm5KTZKeCteUI5FvdPfouNv8VpXTjCIv+FEiwl26jvrR\nez7Rdqub4voBqS72B0fqYmzcDfz26H2bEUnu9daEOE9zXLqLn8o/WHX5hKYszxnY6rwVauQ7l1A6\nKTMDrwBV+EKoqtpw/5D6u35kf0X0yg8cXT7pAp26cFD3O1pHqhjn4oCGY1jGZx7qB4OLCaqU0vxo\n9q7vTxW9KrGrKiyb21yVAjDloibKZkhmMNOJ2apiu5AxKpVsn7ytapbaIcM9hbcPw4UlVYrzGn82\n9XlZlTnmi/0ez23RM2aYdkkni7LJinIspEJDqqo8J0M5fVnen6rAWadUMXn3Hfr+zuKHkzB+4Ujb\nD6+RlCVJS8PAhUmS1o/wrT+LR6pyoD2zEkUtK3DfR3zdDAwM/OUxOFLF4EiDIw2ONDjS4EgDA78V\ndChSPOJGj+Ix7o/cW7fCmehGPEigHIGcynU7KxTnYd1Ye8SbdKw297PHsc54yN2DvHD/I3DmcSHh\n+Iz6/Kvc000Iz590HjjjOExTOY6fP7wZZm+OhaRlQEg1mAFhywgxIpshB4NZRLR84DIztn2zLiHs\n11t1tGv3/Ak4ehWgxV4W/pIO3Og8/na4fuRRVlMt1/czhms9FMu7BLPWsNZU8s/Bap2FUim9zsXN\nbP99oC7y9Z/Wv74boFlKKr/27095gedovK9tS9vNHRfyjeq1RKmnZnwLWFjgriNxT4/F8xxJeR7r\nRj0WaD94aSz5jTA27gZ+e1DM0nMH9RpQIPCEB6ryCFwzsHRU5bEqpObl3v3BDlV4PFKeU6lwprTq\nqYj+KgOiVzp5aN1QzUWoOQDrQRcSgOOBqYxP39S9eAQXDjnxqpotopkcLJ181X95J//AZGYgWsa8\nrbCcse6q8nYob8SMXN0IUK3UFoEKVaLS/IIbNswuDPljUTOpUlwV6ry2YkasKqwWluUw+PNcNH+9\nt2LZywGrD2wL4jYhzBGXb1dsqioP4V7xrUo3tn8qnKiUgtS/j39z8djXIGFUMaUqdlUNUkVFtwhc\naNtcGCqrtI16UvdRILlSzqVjSa/faFtm22YYf37Ri64eBgYGvjIGRzpJ76thcKTBkQZHen8MjjQw\n8FvjDwD/Au69QurU5rkNeRM95ymNyGhr1N4buA4RZ1QlZeD7FVgS8HQ5bt4hA2nDy5Z33tPAKTnD\n0fK+Z9Xv0z6zpjPcu2X+TKilHEFSMbkwvefnPW9drfOAWsx5juPnEY3nOZZvFD4dPkcNYwmY1mJ1\nlxZgXmPhRsuENBtmmVx1U82QdyERw5QpfTlwHFrhHS3vZmS0M+uA4o78nkf1LO/If8Id/+HZeORQ\na81rz79ygy3PyDZhXlr+23VBvC3AnEul0PKO1lzaqdUbgPLeFW1Tz9DOAVaOk1088iByrDOO9Cz3\nlGPppi85Upa/Ty3vujt7PxnM8wXLO30O3SSlmIDtXt2KDo50wNi4G/gtQEtjFTAQ3PB/1Bl6320a\nn+fv6r9DmKoqD9YUUqoqj7EqpFBIVldVrgqNs4IwjCp5ggujeI0q6ivhTIIGd93XkeFYF95fOdNW\nhYz+9OoPqsijhPEqM1UoB9y/F30eCmVc/odbBlhOMGxItYFYIHsrie5noqAQsIgJPRU4yZOqqEie\nsqS2OGLHeHrgMJBFTWW4uTz0PBfCu4Tay2EZ2azVrxVVeU6llOl76S/F/ELqkRM7O7H6kKeFgAqR\nvHpQrS5U4aNuvjzRpoKKaWvbYDp6X8OoT3RVHPXws7mXF2ixwan6T/tNljCqAtMPGZ/OwMDAl8bg\nSBJGMTjS4EiDIw2OpD8HRxoY+K2gHuwU3KN5pAPy+wC8p1Oj9yypw0jPe3fO5bw7oAz183R0m5lr\nYbPOqcqbeuOOjvneyji6ex4v8SgN95X4FMdgf02vP+KLDNcb43UeUF76Go6jHEnLoGH1fWkekraR\nIwVgyhmWi1cCGJBCAKxZ2XFTTrmHofGnkn3jOD3vA+1x9O/KTRAO7jCVa7VXYDXXYwM6ekMgVwOg\n/I3fC8GQ7fhScwrlnxnwxIA1jneRqfXOZHpWdVpk/26UM3iOpWE0TeU/+jPg/v1Dwh/K4xrATydH\nBAsHnFreKZfkc3Dg899vanGq9TIwNu4Gfg9sAP4WRdhwJip6KT43/886Dc8W5dmaPaxbUUpdFmBy\n5OVFVXlGU2FQsXoWhmlTqdMjSl5x/dXBRYJHintVyqvETVVNu38lHJ+f9aSHy3LFsXd+jpoh0Fc1\nFSOqZlb1DMvk1Vhqot/Jn3PZtgDBEi7bii1n3Jal/C2q8g0L6FN8xYIbLgelE9VLVIy3MBvo4qAI\njOzOVdSKGQHioqBe2zCjHVhMtVYpx4L1QOpYBt4jqPBasBY3V1WZtqUZ87IhhNzO713ryq52ZiWy\n057RUSlucp1fU+Qa/uBhqrH8IuUm4f075j1tf1cJowcQM39VVvU410fxLiWm2m/8wMcxJuPok/0J\nbRFP+83AwMCXx+BInbCDIw2ONDjS4EjE4EgDAwPvCBqXLLi35suge+b7jT8iJuD7d+ByAb65BHIC\nkszfYQbsbLzhXM1NhTMix7HNW9M9stjz8ZWHfDZ6/M8/I5/NOvf82WRn1tUMoy+zx3H0HFiG1bmC\n95T/6ByrZ8NWFxchAXYrVndmuVjepYzbk8GmwjG2ylEA7LwjIB04B1A22fSaehq450FN7MR43BRU\n0VLhVgnhhKOVK3bwRhDqzk95FZK/Mf8FFhLmi6R5vSCmpZipmpXdzO9W6rBneUdrOOKsMzKsfzcm\n8ZRjKY9inN498iG+T382Ijcd7yxbdTfwozsad9k6ykm2cYrOvOcG/X6jpR2/H76Khe4XwNi4G/gt\noILOM0S08fdO3YTj+Khnkvs8OM721oKoHN+q0oA+ynkvpbY4BZTv7qADFudFPdT2LhP3oKpwUqn7\ne3/wqiL3LejJ73tQovNSOl59RJdXGl/VUcBxQUsnx8ndI/HkPZ4dM7t0VGHOcq2SjpYhPM7ftmNx\nLSdkRCxmiNOEGFSdXdhlrgoqqsoBoB04XCqGPskXrHsYVZXHSqd08Whz8ZhnrA95vHYfv6Uz36nK\nUfOcLLYHzkBKRTY2X0rvSgCyTUAMpd5JrkhkFD1po5Ie31EZlmRaw3BxU9PR9wb394Lj4KFKc5aX\n6fRU5R+1IKXQ/sExQgc+htEBc8LxTCO4eAMDA18agyPV3wdHGhwJgyMNjvQAgyMNDAwIuLfSs47T\ne37RNeOcZujQwmHQr1/nDMRczgK+WrW8k0BZ+EaOQKr7FXfuM7UQnkdwLO+FJTjWPQLn8ffkVRxn\nPZ/r8b4zKP8jfHwN4wkw3QD6eUD3TjgPAPdzhXKc3r0exwLuOa7yqNsxvk2AxUq5pjKZLtV1ZpxD\ntYUrG2BqVcdNtijuL8v9ykMQodZwPR40I8Jwg1reAUevBBHTIQ8WXLkRLeusksFL/ZIoVGVq+df3\nZGFGygF5qeQQBmRDigE5T8Al17ZobTPZb2xzE4lFgryHLD/VuwDDLA/CaB9TjsT8ddM4SVy2M5aN\n7//U8u69O9xL0MJ1LO94i0XzH5Xkef774ats9H8BjI27gYEKHg3yjAdqcLws5r6iuYzqCpYysK5F\nVf50cd+gGdjWtlA1TcCi7g2AxgJTLewjcuIV05cHD/dnoWdUvAXvpdTSZ6OMjeAL84pznVeomOLi\nAxcJniWeV0MFuceGo37IVSmuqvJnl87lJH+duJK4iL4AU4oIa8KKBTFcMCNWFRJQKNFlV4P3lE48\nN2XGhgkRVzzVMCtUVX6r8Y5qqqYGN8lTDwwuC196YPHxsGOSOK9Yv+IJuzur+o7WdUFKActlRQgZ\n11xTSuHeX/73WtdKwG44np/CRSaqfx7Fp3spqp90YVHfKd+xkjCm/QeOi1teRTWhKa0+E+R3Xiyl\ninFdfCOp1XoILswgXAMDvwQGR/qTGBxpcKTBkQZHGhxpYOCXgk6bfqrW6e9HFl11GDkzPFm34jrz\n2/OJNwIAKQKWgHDpbNwpMtq5XsC9NfajQj7Cgh9z5/AIEce5AijlPCOgHj2vCtwE8+O4pq08itZU\nah2uYfw8oB4DzjgO7/HZhOPs5dB7vbmGdVLrImxlIzcBCJax3DZYnpAmw2Tec0DhKvRKwE068p7i\nsvIJAYYLVqxVZNS8ChTcsCAIfzHknUcpN7riqW7cnXM0oHCitcOjSrVL/rWt3vIF05QQAncyM9bv\nT8gpAEusu9hT4TTkH9o36A1ABTmsd74jXlOrOr6XZxdG3xEt/c44EtsEP7z43pUjUYzX/bZgI074\nWJKhG4XT/S097863W6D1SfIs9bgwMDbuBn4feKX32aISxwtaJXtwTEknYfhNSgvfrqocRTl+W4tC\navZjWx3zUiqLVMGpqPZMXnNgsBKaM0fFWr3mAAAgAElEQVTt7wGvenpLvNuLoY6qpB702fhhrAtO\nwHEC5otUGZtXirOOKZXTBS0fX9W0VIxMkg7jccKdJaya2nulyfcarrpF2sVkMxDnjCkW5VGcZuRg\nB8WSVzGpW4PyuE3xXRasgA3TTopYJdFdY1okc+1w4ny4RlLG+FRzldK1e15VTldVE+KBQMc4IeSE\n5fmGaLnE2Dqqci4eTWjuD6jkUbUa0L6qZgmr8WmynyVM7oRRtxeqPFeiRfdTXLBUcRkHE2/x8NGC\nKaDVlaoMdXBkW9X+wvLT78ssYQYGBr48BkfC4EiDIw2ONDjSyxgcaWDgt4LuY515kTwDh4az+DqN\nnlGU3hR9CFMFT8jAsuBw5p2GyRsQKw+wUD0W9AiYL5y3QPPmhS+NY6/lMW/BmasI3ex4BM+DCC9g\n0gpXAqxCFLqONtyTZD8PMLzyJu9yscexOG+ic089JpB/aX0vReBGy7s8AVNMuFwj4hywzQGTcAzd\nbCtVUlqeciNaz1ndke3zoGnnPEu1vDNkbJjh3YErD0JN2XM0Qz5sJj7hupcxaf6VG2y58rB5q/zF\nsFlG2mbknIGniHIYpLUPF/IK8haK0rAXq3kI+IYjj/YcZ5E4QOM9T2gb5J4j6Qasciz1jKAcSVWQ\n5CZ7n/hMkkSJputgyv+58XjWp8ZO1QGjOgZ+K3BM9vMwkSWM9w7gw1AM0BMiUczM+bMbJpV/yIVg\nmQ8gYRYcXUbtmeikrGbycNeJ91Z3vsccwDI9UnMB7QPZh9PJ0C/+8AVy4tU6UhWVd43Fl+vTVMU3\nXHy9R6WypvNIKa5K9Uf556KY29fVAgqliRHXiyGZwUxV28uu5g5IoILbHyisPspTVXOr8vtWw/h4\nCVb7QDqkBRhCXZSiqlzj6+HG9JfuFeskhKoqRwbyZFhCghmQctV/UVWu7Z8LTdqJddGH5IDvkfGt\nE59KKd/+tE3xvWnbaK+huThg3rpwBck/SVzflj4Kvpy8xrozHJ/hrN3/LMuVgYGBn4LBkTA40uBI\ngyMNjvQYgyMNDPxW4DR05ikAuB/ievF3YYnc82vyvfg6jJyFWeumnBkQxKJaeVGSzGwqm3d3ab3E\nic6I3VvT+RG8NM5z48Ln30PPt6nf38g45z+6YRNxPHfOE2C+QF7zQqYb7jkO42njUNGI32CMLp6K\n0GrDCRnICYgGhJhxiRtumBGd5Z1u3LWNseYdAACiVF5jLsczfulVICBiOVjs2SEfALs1nu1sqcWn\n5wOf1+VgjYdj/jWRlEO1vGubhGsKyBOAYLXOpjaHs5Oyoyr/IXhNRW09juM/pvjhozzbcyRtM55j\n9dqVcom7zWw+zI8qB38UWoiO5Z1aEXqOxDpP/ei/M8bG3cDAD0LFAI/O+/1b7GfE9sMk4HoD5vle\nVb6HiUDOJUw4G8B65sQT+nL29wAVQu81D5zJ2AhOqJ74TZ14Spz8gfFKcqhY4TMYmqpJ1VCqgqEa\nivmq0pjxaPKeJJ0JRzUUTQ4W7Erxu3Qe5M+PhzQDcQbmbYPljHUuqnKewwIAsSq/SWaoKu8pvi+4\nYcN8cPXEqtkkjKqwqIZqLqaaqrypoTKiqNlVVa6kUEFXVbuqvNZN3CaEKeLy7YpNVeU5HP2K032B\ntg++P6rPlXhTKdWLz3i9MKuEoYqI6ilVjGv7CSgDQy8Mr2m7/CqqcrV+0EO4vZqQ5jQ/Y+wZGBj4\n8hgcCYMjDY40ONLgSIMjDQz8BtAh8kzURK8GPdeXr4mvQ2xv3z9n4LoW15lA4UyXpS98QgJSx1Vk\nmPGyO80bHo9dtAp6T3GC5yiP8Jr8e1aA5uKpAEx3XfX5xeL/MMbTuvqMAHt4jqMuFL01l3IsvXdW\nDrQwZsC0VuHbDMxbOe9uXSZsc+MqEdPOcchH1CuBISAKedB4R08DzfJOLfbUcwA5Di3qygaccrQJ\nG2bomXeMX87Ru9ZrJa89fwNyMMQ8IeYJ89LS3K4L4joDcwZMLO+A48YScHRpCrlGbkPLuyv6HEfb\ngOdYmo5yJN7z1pwqiPMc6RR8ts9QOf2g5R3w/mLKvzjGxt3AbwcdQh4JhlTZdJYOx8mztFQofWaR\nT8W4WRF+mN0TLA3DBAyOWPUGN6+QfU9suPct/mfglbo9nEnZtB54XyddnXiVaPFDP3TCqpqXH+c6\n72m+Z2paSJyeikobmVfqZHfP5U/+CBSuYbkQr1Qbj4Xji+mpwfX3myi+yyMV1ZMuFJFWqRr8LG3+\nVBU7akwlaAyrLqnUdUJTX+Wj+yEAISTkbEj1PJecaycCju3AjvH2n8H97duCj+9VfLpQ4/PQe7zG\nGZeuDfTdemHSmapcy/uzoeovoD1fb+CLElYtK4ZSamDgL4XBkd4JgyMNjjQ4Egs5ONLgSAMDf3kk\nnC5Ddw1HfBhdm/Zd/zXx1aiK8ZWH5QxsWxuC0ozdG4EBB88EdJ15QIdf3V3XMe4MYgX+bmC+L25S\nvDJ/69z3pJbzD+95bkJ4azjG5T3lRgxDsq2WXZ7j9NL2HEvNOZUT+flXnsFSDRaAacsIMSKblX8h\nA9a4inebuZ+xi7kyoPJPOY73SgDHn/iAngcxfc1P4zOfloLnVo097eGs8rdQPC7oe8spIBuQzeq5\ndfVF9YR+nL/1vXuOqnjEfzzH6oVhmny3aoWm1pvKkayTzqEwvnH9bJAkAXeVx2djsdTbA4PrMw+M\njbuB3w8Z7YxzFZMqVAT8kiDyNaIajv/POFeVbxuQMnCZcXq48Bax+yafAjCfqai0cN9feIAfxXuL\nNl6jonpC3zm8LgSoep4vkn+rjE0Vr3wpJIVVlXSqFL/g2Eg0LFXkbBRUw1A1RRcJTHeTNL2q6iz/\np5Y/p8FtAYIlXLYVW864LUv52525Ygj7gcNUausikSrNN8y41cOBm1Icu0JKfYsfVVgMO1c11CZk\n64Li29wOKi76LGc5dGGKC2a7qry+422bMc8R4dsVN8ul+axT+TK5SLu41oJr2zDcK6X4gFTq9eLz\n/fkVZg4qXg1HP+ZnHxqqrFN3GFRqqRpS8/sozsW8PGHV9q4WG9onfPkHBga+PAZHeicMjjQ40uBI\nDYMjDY40MPAXBrtvROEqvUXUjKNxzlupxWvisxw6bZ96LIjAH5XjGIDnJ2B5wXI/dSz3wwzYW1aN\n1VLtPfHaRfzX5N8jsHwBnI9oOcV7ylV0rL9KWmoppzyqx1/Ig17iWDpvsnHwGTnHMN8nHHmU5oUW\n3xIw3YC0AHkG5jXCUsb6NCFNhhkrYuUojeOUDbEbLnceB2gpR46jPIhYMcOQ0c6sK/xlxXLgRppH\n42gZGxYY8sGaz+cL5Dsexne1YoFZxnzZWprXC2JaquVdqkova5ZuT4Bk1izdlBOtuG9LnuP4Tq0c\ny9DnSBTQ9TgW+4L3eEBLyzvvmLrD+BmWd6quE9BLwYx7jvQzvs3+whgbdwO/HXSDn9+efg3Ih6Fw\n51F6HIq6inE0LsAwPr2UgRyBrUa+O68FQE4yzGYclOch4N61gT7IOyNn7OfP/BlYKGV/UVWhk5KC\nL4hhVC0V5HeqpCDXIo7+rFkOyDVVsHiFDN05eTW4fsBr+bwTew2rDeg1+edy32L5N9V2YDkVamOG\nbZqQQoAe9AsAGQGq4O6pwHltxrYrypuLg/Lwx8WjXIta7vK6qqFaWKuCtQD1hZ5qubzanIiYWl3U\nuok2VRJWelcC8P+z9+7B9nRlfefn6cs+v/cFRgQhIISbKRWNxCQzkkJBEBVLjaIxGknFGi+JNUk0\nw8wYr4mvM1YuOlbUkZik4kASJl6ozHiJEhUlQokhXqI1CWoSbkEGBRECvO/7O3t39zN/rH52P712\n79u57LPP/j2fql3ndPe69F69utd39/qutVSArlht9K38vePJ0ssXHm5ZH9/XkfxNtAml3CVlmkUY\nTwOQO8Rt26/34h16nkMapvxfq4v2/WA8z3vrwhxSFwZBcGlCI10NoZEIjRQaKTRSaKQgOBns8WVP\n6nV6ZuWducP6ZjbpIRt07B8ffgC0T9s/8qd0U9c/l0VgvkjaBJKumFo/WCd0hsp6+SHXqK1U+/PZ\n8zkpwnLw1OaA7mM6I/+izcQxr1Ws/fG6B8YXzC54Pjq7ZJz3Jo3l0/bxG7cNQ+X0Osp01nwcX3qN\nVAiIKkKHLlJmXVGAtKS9RZ9kmkDTT4s51j1WPOXyb240SmFl9L/Pw2ujhgpb42448XTMpznoIqVe\nhgHx+VuWBagKutQmkrbbApUSzvpvojLWOh6rk17T5voJhmuzbjSl11i+bvm0/ewCfsSd/fX1JD+3\n/HyXJ5CfyHXjT25i5J0dgvE9GTMSjIiOu+DkWfdI6kgmGCWZSTeF2bT+iuFNwetcWOek5/H9TL/k\nUk2LC3cKZ1uc4l0HC9cO1jOo1r05uwa6DhbzQQBelKruX0ptw5Ry7sr1E8VrdtzcVP6YXSQrW7+o\nsIU194f2+9osnh+OYM5xO5Y7pawC2XQGXZZm7p4xF8+u+fcULYgmV3kpHcVijmjN+WzWT65klaWm\noe73nTNnNuEYH5zmJe1ywWBzhYMZjJLDu3CtrV/3xbvKTWQN0yHUqAvrRVw+LYMxmqrKfqEsoOmE\natZQFJrckHeBuaQ3db7Rv8vg1JZlokPZ+qkOOlYXFfbxbRq0/JfSXUbntnQOFS78HZePPRS8q8qO\nlS5eLmgObZaC8ZQQVn6+blbZvnULNQRBcDSERroeQiMRGik0Umik0EhBcFKY7ukYD0a+yvjWbNox\nP/A9xz9iNukw1dRxt+g1QF3BfWebddTyfHoDzBRSQblNAF4QbUnr8O3bcVdCsW3GhbyQTYdMhZm5\nY6Z/cq1ko8JNo9hoOGsP/WwA9p0KVnUQbNdYcxffj7yzY15/2THTWFaWfX5FmzpHuxpElHqephWf\nnwmFtNR0NNSu867rZxxI+wpSGCCN+ieNdoOkSXJtlIqqYjxiT0dhvTnpnLNMo6WRdwXdcjSfrZmX\nZkNYUPS9XukyufzF8q8pi47izKV594y2K6Buh97ouzLWH4bXKvRlatfEj8azsFMax8KYXrU6NqWR\nancs11jWwec1Ut5pPRpFaz1ind95AHxv4kTnndXxirFGit6qJVEUwT2LPT7M4W1r3OdhYHjPkT+3\n87CdCzvlorLnUj4aehSmd0ctZBAcZTE9NZR/IdT6xngNRbn6AqjrBjfWPnRd74S65A/jtt0uepfn\nPeVO9S+hcqduy3gamtLFcc6bpWMJF19dfHs54R0y/kP21yqCuaHsHPz6HZamlb2dWz7U3efv02wZ\nOaek/y7LqdILpexaZos5bVnRFIMrCqDLLGUmsvzCwbaeiu1rKZcvhQo6Klpa0gzndSaoOgryxYkt\n/ow5NuXC1JotNiWUudArxm8ibeFj78bp2gIUqtm8d4GBNiW0xbAAMAwLB6+bTswLLhM6U/FtKIpN\nR+DdWHOGa2xhYJjOwFsxfZ1oGKcJQ522eP7l1Lp74rrJXeWw+uCz+yfc5EFwKwmNlAiNRGgkQiOF\nRtqD0EhBcNJYM7bu9jXtZM3RlNZpSO/h100jPtV8rssr9+T4x+IorNMkTQvn83HnlghUVdJUeSZr\nz6HrO9euAe2YHAG4lQ66Zk3HnfQjzZaZ9H99B6EVoOGf39auWOea1z++w800inXWVBPHcv2S57Ep\njKVdsaqV/DHLy4/48yPvKjfyDtBSKZuOWlqaqqCtUudc1SuOwnWqpXpXLUfeJW0xrLtrHXk2As/r\nmKa3PlW0CHPMnGQ6yI+8G/4OF6jINJqgS200aCvtL4vLv7/wTVHRaklZN/0+AVG6pkJVYdaBFqAy\n1h+GddTOlidA/wVSmdsUm3YdYaxV1s1GYJ1yUxrJG+ByjSUT+zbqi5sSSXajrBl550ezhkYaER13\nwT2PPRvvZ/0N4Y0KG91MjE3BU0ZK77TKzarLML0rypjV0y+lPG2bPusQgVomXkq1ML+Oech3pGth\nvuWlWD3b4Dj3CxX7qZdgcBrB4MI1B8sycYYXR7mwsfjesb4Lfli7OZxakrXO8vdOK2v4vXPYzDBz\nd8wcWz6eOW1I5qDlS6kZlG1L2bacz6AtCirapeDq+hrtFxvOnU7e/VTSctfZ120xYtw0BiaiUloz\nF4blfv9SKomw2fIFlgm9dH7DBR871uGcM5aLI/cCd6E1XVFQny2QQum0T8leSvnrZ9fG38x+bRzv\nKp9yWvmbd74mTOvC+LfQ5o6z9M1N9DDjqaf8PeFflN60q9z0nddb9ovR6qKf9izWbwmCW01opNBI\noZFCI4VG2pHQSEFwz2NNk3XcTeGbqKlOtn3xTb31/WxqltsWHs60RVHA/TLRcbcB3aKtboJNHX5S\npMHmK4XjC9DaFP+s9u0fDDoGFy7vsfX6xXSU77Ow0VReY91hWmP5EXPCuAJZp56N+raZB/x5+FFd\nvpOyF+NFk8qsFShEmZ03iJa0VTHSGP6v6RHrLBM6hskqGWkcMyANWmW2HLFXOL11ztkynp9VwPSR\n3SmmgabCmI6i11GLPH/pdZgWlGVHUQzGqkVXoIXATPuyLMf6I9c6Xj9pX+6mbcTtyzVOmcXLNdaU\nRppNxDeNRZ/flEaaxG6CQ/eO+Y7CcvWQ/y4t69c/vgeJjrvg5LkLPILtld0E1rr3D/53V/7czrF2\n1owuU2KsIbUD26Y2gCSIzvuXKyJQTbjCt6GaHOddJmTy7V3SWTSXd5EXRfoeu0zR0E7kV/QO+1F8\na/i88KrdMbuA/qJ4N5QwXsw3T9vHt2EBlod3OuVTHZjTyhrlmnElyZ1S9uaydmn7887dVxa2z1c6\nqBbQldBWULUtonOaqkILoaahpaTpa7rN/22LCdu6Lcvy711R+VorFtfCeIdUMgRVmfMJFuhy3xnn\nK/ErmqW7yh/rGLvh/VRV3lrYtiVF2TG7M6cRTTGaEppi7ICau3K0feZiKhlPY+Cv+1R8m8bC6g1Z\nGLtulo53U01h0yB4weLtk37kyE0bpgYNPZ4+5Cp+hQZBcO2ERkqERiI0Umik0EhXRWikIDgJ1j02\nWrZrlJah+dlkfHqY9SPvLIx/pO2qsewRMzVjwhSqSUs1U89eSSapah+TziVp2t6kteOzWwqYVZuN\nXKppNF7esVGUqY8GWF+ACxevYHVU3aZeWtMzwlgAe61kx/IpJ3Jt5tsRX8k2HauydLxxixRGBIp+\nHcSuhLLtuPPwgkVtHXjD2naGaZWkjSqq/sumJndaB/mZBmzkXUlL3Y+8M61l03ImPVYt01qn0ex/\nG3mXRvOdL7v8RvkLSKE02uuwqun1i9Cc13SLCi0Vzto08k5kPH2pkY+8s302c4HNh7tO49hN78OY\nxlqwqpEK0nk2DPXO9JCvm14jmX6f7KOzXuSbcDm1Ln+HzcoxNVz5HiY67oKTx56L9lhYd//nRpWp\nsN7Muykte39BH3bikUQH/czMQzu/Lt+2Sx9IL2OmXuSIbH/B0/qHen7OutuLpk6ToGv3fJmVU5Wk\nxXA3nLN9p65bfXlW9vGXhSX9v15smuvVLoB3Xy9t1wwX3v+oLlwY+8HvnU4wdlrlP9J92jB2Efs2\n0o75Cthmx3wcfz7eqW7fqQNpoeydQV0BlbYURYeK0IkgYi+WBOkdTeYGN6eSdzD5dVPMBW7iZwjD\nShjLo8z22V/vPsevzcIg8mze8oVLx+jcdAyja1WSvi8sXeXaSV/h3PWbcoH7srVtu9b5je/j59cI\nFyZ/ceTrj99neRbuf6+jTLj50Qq+rt2UqxyGss8ffEEQHDWhkVw6oZFCI4VGCo10FYRGCoKTxvd7\nmB9lSsdYs7VOF+Xv22HcTE6F8Y+OqTSV1cG8+eNmMl5vPJoaCCyyXZNcNU2TOhJ3NUIVRaZ7HEKv\nmeg77tbEl1ybLA8ybp9yE5K/yLneabNtP6tAHt+3HV6H+fbMyiPXOFO6aVsbOyogKLo+mEDRKEXb\noiJoIYi0tKK9Fhm0kZ9CXJaZ0IdIITuKSf00nlWg609l0EOmu2wKcj8Sr3OayjoVjTw+yzNz+ffl\n0GlBVbZL7Qew6NLF0UL6MitSofjyGzJbNbP50XiahfXl76+NHfd1Y+r6ec3uj3kNndctycIsySvV\noYTSUE9Wnp7Wpwf7zeZx4kTHXXBP4F3gmyq9Mh6pvM5V7tdq36RfvMFmnYsqn1L7zrZzVFgsoMky\nri/pgmraNQ6rPH/Si6nL0nZwvthcfmWZ3F1TdF0qB6OamirLnEYztrvK7aWQNbznDIsKezd3kcX3\n7hkTY7lTyse3t5FWgbyrvMji5+efn7d3bFlFsuNAoX0yFagoVbNAtGNR1b2rfCzNvTPKnNr+JZE5\nzdOixNVEmOScUmTpFE9t7+BQt7ALKlqqzFWuo7C5q9zyz9d6GbnK+3Jvm5KyapH7zseu8qJIYawO\nz105GnYdvVPK6kjBdHyzP065scxV7t1w5mL34e0czH1ngj2f6sycWv7y5aLuUOQiEsbO+ngxFQRH\nT2ik7YRGIjRSaCSWXyQ00m6ERgqCk6YBHiI9rtaNvNtFY/nBXPb426Sx7HFSbcjX55/3Q9lAnV37\n4VSTJlkccOq6TnfvtAOWIwbni9VjInA2g3rNBdA25VfUqY9mxNQF9NrE9Mc6jQRDb6rXLxamzvJY\nFz+f1cCfk58Nwc9mULt88yk3/Yg/hvjSQdnPStDVUC1aRJVFXdJVQuWUuTccCbo0MlUsVkbKmcZp\nem0zHnlXodnMAXNmk1prQZ1pHdNfq+sBN1ROW2k/GM7l32uDRis6Eap6yL85r2nnNVSaCsVG3tlI\ntzNwmQ36Q9w+r22GLzutcSxMrrHWaaQ72TGvkWGskdRtrx15N9mzd81YnmtG3h3yVI6c6LgL7gnM\nOLPtnY21ibBeBHkTgJlh1v3usvTs9+I6N5Y1W94gsW50sCq0Ew8xcxFdlKZNn13xhtZd8WWlutuc\n6E2Rfa/e8QXj+F5kmStseZJW8H6u8j6tZaF7lwqMTSDe9eTdTFOuqinziP1w9/H9SwRvwxMX3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sFu+uS7uZSCd3UTlXstWxroK2gqppEFUWVXKV15mrvKFaipncMe73zZjTUC2nehrCJOeUn6Ig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\nI+1G1OM9iY674Dbz69n2c3aNKCJPZNzbPgf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"text/plain": [ "<matplotlib.figure.Figure at 0x1097439d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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YEfFkL9B1Ng1CicQEVcy7sA/bMmCy4rYf5e77fIfVKlnxuO8YPQdKN/23EPOo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\n/JL12L4by4d5t5Xe2Wa0t77tbdGv/dj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XMPyH4279ZtpPtMjtSKBU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0UJWKE9LnSDW2xleOc7PrNpbFPw2V91Edc/9E9564ffyls//NZljV/3wet4zRNf0wItdDz6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\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pEaqPSUUFeaUgA4Zh5GkOaD5HoNNPSKR5m/Oy8D2BN2pPhD3dUyVcvIrLRE1Du+sD0qyd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\n0BthY1JJIZ2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BLgTekJk3zKteWkwR8XrgtVNk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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d742c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "shotGather = 10\n", "\n", "# 72a-[esc]\n", "\n", "fig = plt.figure()\n", "\n", "plotOpts = {\n", "}\n", "\n", "def makePlot(title, picks):\n", " plt.imshow(picks.reshape((nsrc, nrec)), plotOpts)\n", " plt.hlines(shotGather, 0, nrec, color='w')\n", " plt.xlabel('Receiver No.')\n", " plt.ylabel('Source No.')\n", " plt.title(title)\n", "\n", "ax = plt.subplot(1,3,1)\n", "makePlot('0: STA/LTA', picks0)\n", "\n", "plt.subplot(1,3,2)\n", "makePlot('1: STA/LTA', picks1)\n", "\n", "plt.subplot(1,3,3)\n", "makePlot('2: Inflection', picks2)\n", "\n", "fig.tight_layout()\n", "\n", "# 72a-[esc]\n", "\n", "dataGather, erGather, picks0Gather, picks1Gather, picks2Gather = getInfo(shotGather)\n", "\n", "fig = plt.figure()\n", "plt.subplot(1,2,1)\n", "plotOpts = {\n", " 'vmin': -clipScale,\n", " 'vmax': clipScale,\n", " 'cmap': cm.bwr,\n", "}\n", "plt.imshow(dataGather.T, plotOpts)\n", "axis0 = plt.axis()\n", "plt.plot(picks0Gather, 'm-')\n", "plt.plot(picks1Gather, 'g-')\n", "plt.plot(picks2Gather, 'k-')\n", "plt.axis(axis0)\n", "\n", "plt.subplot(1,2,2)\n", "plotOpts = {\n", " 'cmap': cm.jet,\n", "}\n", "plt.imshow(erGather.T, *plotOpts)\n", "plt.plot(picks0Gather, 'm-')\n", "plt.plot(picks1Gather, 'g-')\n", "plt.plot(picks2Gather, 'w-')\n", "axis0 = plt.axis(axis0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Output" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "timePicks = picks2 fds.ini['deltatt'] / unit\n", "if fds.ini['reduce']:\n", " reducedTime = True\n", " tbegin = fds.ini['tbegin']\n", " vel = fds.ini['vmin']\n", "else:\n", " reducedTime = False\n", "\n", "sx, sz, sw = fds.ini['srcs'].T\n", "rx, rz, rw = fds.ini['recs'].T\n", "offsets = np.sqrt((sx.reshape((nsrc,1)) - rx.reshape((1,nrec)))2 + (sz.reshape((nsrc,1)) - rz.reshape((1,nrec)))2).ravel()\n", "shifts = tbegin + offsets / vel\n", "\n", "with open(outfile, 'w') as fp:\n", " fp.write(banner)\n", " fp.write('%d\\n%d\\n'%(len(timePicks),7))\n", " for isrc in xrange(nsrc):\n", " for irec in xrange(nrec):\n", " index = isrcnrec + irec\n", " \n", " if reducedTime:\n", " pick = unit (timePicks[index] + shifts[index])\n", " else:\n", " pick = unit * timePicks[index]\n", " \n", " data = {\n", " 'index': index + 1,\n", " 'tag': 0,\n", " 'sx': sx[isrc],\n", " 'sz': sz[isrc],\n", " 'rx': rx[irec],\n", " 'rz': rz[irec],\n", " 'pick': pick,\n", " }\n", " line = outputFormat%data\n", " fp.write(line)" ] }, { "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 }	mit
jarvis-fga/Projetos	Problema 3/Julliana - Daniel/imoveis.ipynb	1	52318	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "dadosBrutos = pd.read_csv('communities.data.csv', header=None)\n", "dadosBrutos = dadosBrutos.replace(['?'], ['']) " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "Z = dadosBrutos.iloc[:, 3:4]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dadosBrutos.drop(dadosBrutos.columns[[3]], axis=1, inplace=True)\n", "X = dadosBrutos.iloc[:, :-1]\n", "Y = dadosBrutos.iloc[ :, -1:]\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0, 58.876629889669005, 59.027081243731196, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05015045135406219, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 0.0, 0.0, 0.0, 84.00200601805416, 84.00200601805416, 84.00200601805416, 84.00200601805416, 0.0, 84.00200601805416]\n", "126\n" ] } ], "source": [ "resultado = []\n", "\n", "for Y in X:\n", " Y = X[Y]\n", " nao_tem = 0\n", " \n", " for caso in Y:\n", " if caso == '':\n", " nao_tem = nao_tem + 1\n", " resultado.append(float(nao_tem)100/float(len(Y)))\n", "\n", "print resultado\n", "print len(resultado)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-4f0bb4e85d1f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\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[0mX\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----> 4\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mresultado\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mnew_X\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mnew_X\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "new_X = []\n", "\n", "for x in range(len(X)):\n", " if resultado[x] > 0:\n", " new_X.append(X[x])\n", "print new_X\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Found arrays with inconsistent numbers of samples: [ 101 1994]", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-baa7122d0fcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# particionar dados em treino e teste\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_validation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m.1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# .5 metade dos dados para teste\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python2.7/dist-packages/sklearn/cross_validation.pyc\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(arrays, *options)\u001b[0m\n\u001b[1;32m 1904\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtrain_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1905\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1906\u001b[0;31m \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1907\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstratify\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1908\u001b[0m cv = StratifiedShuffleSplit(stratify, test_size=test_size,\n", "\u001b[0;32m/usr/lib/python2.7/dist-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(iterables)\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 202\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/lib/python2.7/dist-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\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[0m\n\u001b[1;32m 175\u001b[0m raise ValueError(\"Found arrays with inconsistent numbers of samples: \"\n\u001b[0;32m--> 176\u001b[0;31m \"%s\" % str(uniques))\n\u001b[0m\u001b[1;32m 177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Found arrays with inconsistent numbers of samples: [ 101 1994]" ] } ], "source": [ "X = new_X\n", "y = Y\n", "\n", "\n", "# particionar dados em treino e teste\n", "from sklearn.cross_validation import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .1) # .5 metade dos dados para teste\n", "\n", "print(len(X_train))\n", "print(len(X_test))\n", "print(len(y_train))\n", "print(len(y_test))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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lzp1sPVDc3McdO56iZLKKysqmUzJZ5ejY5uZm4vEopPtMo+bm5qzrKBHjZvD3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\ntM9rAMbpfr8JoFpwrpwaIChUV1cLO7HZD9+MOPR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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff624e60890>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff624efb1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "a = X[0]\n", "b = X[120]\n", "plt.scatter(a,b)\n", "plt.show()\n", "\n", "sample = dadosBrutos.sample(n=200)\n", "\n", "x = sample[0]\n", "y = sample[120]\n", "\n", "plt.scatter(x,y)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }	mit
acuzzio/GridQuantumPropagator	Scripts/Calculate_ratios_3.ipynb	1	1106240	null	gpl-3.0
Ledoux/ShareYourSystem	Pythonlogy/ShareYourSystem/.ipynb_checkpoints/Overview-checkpoint.ipynb	1	12751	{ "metadata": { "name": "", "signature": "sha256:ba0b0105b52f7e79531e7d06dfa2ffe0acf3d4fa1937060bc5aca0eaa8974aa2" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Overview of the SYS framework" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#/###################/#\n", "# Global config\n", "#\n", "\n", "#ImportModules\n", "import ShareYourSystem as SYS\n", "\n", "#style for the notebook\n", "SYS.setStyle()\n", "\n", "#SYS config\n", "SYS.DebugPrintBool=False\n", "\n", "#Backend plot config\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", "@import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n", "@import url('http://fonts.googleapis.com/css?family=Kameron');\n", "@import url('http://fonts.googleapis.com/css?family=Lato:200');\n", "@import url('http://fonts.googleapis.com/css?family=Lato:300');\n", "@import url('http://fonts.googleapis.com/css?family=Lato:400');\n", "@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n", "\n", "/* Change code font /\n", ".CodeMirror pre {\n", " font-family: 'Source Code Pro', Consolas, monocco, monospace;\n", "}\n", "\n", "div.input_area {\n", " border-color: rgba(0,0,0,0.10);\n", " background: rbga(0,0,0,0.5);\n", "}\n", "\n", "div.text_cell {\n", " max-width: 180ex; / instead of 100%, /\n", "}\n", "\n", "div.text_cell_render {\n", " font-family: \"Crimson Text\";\n", " font-size: 16pt;\n", " line-height: 130%; / added for some line spacing of text. /\n", "}\n", "\n", "div.text_cell_render h1,\n", "div.text_cell_render h2,\n", "div.text_cell_render h3,\n", "div.text_cell_render h4,\n", "div.text_cell_render h5,\n", "div.text_cell_render h6 {\n", " font-family: 'Kameron';\n", " font-weight: 300;\n", " color : #3399CC ;\n", "}\n", "\n", "div.text_cell_render h1 {\n", " font-size: 50pt;\n", " font-weight: bold;\n", " color : #3399CC ;\n", " text-align: center;\n", "}\n", "\n", "div.text_cell_render h2 {\n", " font-size: 25pt;\n", " font-weight: bold;\n", " color : #3399CC ;\n", " text-decoration: underline;\n", "}\n", "\n", "div.text_cell_render h3 {\n", " font-size: 20pt;\n", " color : #3399CC ;\n", "\n", "}\n", "\n", ".rendered_html pre,\n", ".rendered_html code {\n", " font-size: medium;\n", "}\n", "\n", ".rendered_html ol {\n", " list-style:decimal;\n", " margin: 1em 2em;\n", "}\n", "\n", ".prompt.input_prompt {\n", " color: rgba(0,0,0,0.5);\n", "}\n", "\n", ".cell.command_mode.selected {\n", " border-color: rgba(0,0,0,0.1);\n", "}\n", "\n", ".cell.edit_mode.selected {\n", " border-color: rgba(0,0,0,0.15);\n", " box-shadow: 0px 0px 5px #f0f0f0;\n", " -webkit-box-shadow: 0px 0px 5px #f0f0f0;\n", "}\n", "\n", "div.output_scroll {\n", " -webkit-box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n", " box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n", " border-radious: 2px;\n", "}\n", "\n", "#menubar .navbar-inner {\n", " background: #fff;\n", " -webkit-box-shadow: none;\n", " box-shadow: none;\n", " border-radius: 0;\n", " border: none;\n", " font-family: lato;\n", " font-weight: 400;\n", "}\n", "\n", ".navbar-fixed-top .navbar-inner,\n", ".navbar-static-top .navbar-inner {\n", " box-shadow: none;\n", " -webkit-box-shadow: none;\n", " border: none;\n", "}\n", "\n", "div#notebook_panel {\n", " box-shadow: none;\n", " -webkit-box-shadow: none;\n", " border-top: none;\n", "}\n", "\n", "div#notebook {\n", " border-top: 1px solid rgba(0,0,0,0.15);\n", "}\n", "\n", "#menubar .navbar .navbar-inner,\n", ".toolbar-inner {\n", " padding-left: 0;\n", " padding-right: 0;\n", "}\n", "\n", "#checkpoint_status,\n", "#autosave_status {\n", " color: rgba(0,0,0,0.5);\n", "}\n", "\n", "#header {\n", " font-family: lato;\n", "}\n", "\n", "#notebook_name {\n", " font-weight: 200;\n", "}\n", "\n", "/ \n", " This is a lazy fix, we should fix the \n", " background for each Bootstrap button type\n", "/\n", "#site .btn {\n", " background: #fafafa;\n", " -webkit-box-shadow: none;\n", " box-shadow: none;\n", "}\n", "\n", "</style>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.HTML at 0x105c623d0>" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###1. Design scientific structure of objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####A. Thinking like a JSON data structure for building objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SYS proposes an API that builds architectures of objects like a JSON interface." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#/###################/#\n", "# Import modules\n", "#\n", "\n", "#ImportModules\n", "import ShareYourSystem as SYS\n", "\n", "#/###################/#\n", "# Build a neural network model\n", "#\n", "\n", "#Define\n", "MyLeaker=SYS.LeakerClass(\n", " ).mapSet(\n", " {\n", " '-Populations':{\n", " 'ManagingShareVariable':{\n", " 'LeakingSymbolPrefixStr':'V',\n", " 'LeakingNoiseStdVariable':0.1,\n", " 'LeakingThresholdVariable':'#scalar:V>-50mV',\n", " 'LeakingResetVariable':-70.,\n", " },\n", " '\|I':{\n", " 'LeakingUnitsInt':100,\n", " '-Interactions':{\n", " '\|/':{\n", " 'LeakingWeigthVariable':\"#array\",\n", " 'LeakingDaleStr':\"I\",\n", " 'LeakingInteractionStr':\"Spike\"\n", " }\n", " },\n", " 'RecordingLabelVariable':[0,1]\n", " },\n", " '\|I':{\n", " 'LeakingUnitsInt':20,\n", " '-Inputs':{\n", " '\|Rest':{\n", " 'LeakingWeigthVariable':'#scalar:-60mV'\n", " },\n", " '\|External':{\n", " 'LeakingWeigthVariable':'#scalar:11mV'\n", " }\n", " },\n", " '-Interactions':{\n", " '\|toE':{\n", " 'LeakingWeigthVariable':\"#array\",\n", " 'LeakingInteractionStr':\"Rate\"\n", " }\n", " },\n", " 'RecordingLabelVariable':[0,1]\n", " }\n", " }\n", " }\n", " )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "#print\n", "print('MyLeaker is ')\n", "SYS._print(MyLeaker)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####B. Using an ontology of classes for defining derived methods setting the whole structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the example here, now we have established the vertical and horizontal relationships between objects.\n", "We can call top methods that will parse the structure to instanciate new objects at particular nodes, helpful next to ensure a scientific computation.\n", "\n", "Here we show the example of two structuring successive methods :\n", "\n", " leak, that establishes a differential equation framework suggested by the structure, setting essentially LeakedModelStr in the Population and Interaction Managersn that can be then used by scientific modules interpreting expression strs like brian.\n", "\n", "* brian, that instanciates Network, NeuronGroup, Synapses, State or Spike Monitor at the good levels.\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "#leak and brian\n", "MyLeaker.leak(\n", " ).brian(\n", " )\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#print\n", "print('MyLeaker is ')\n", "SYS._print(MyLeaker)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "We can simulate" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "#simulate\n", "MyLeaker.siimulate(\n", " )\n", "\n", "\n", "MyLeaker['-Populations/\|I/-Traces/\|*V/-Samples/\|Default/BrianedSpikeMonitorVariable']\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "####C. Design views" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can set a view and call pyplot for proposing a view" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#leak and brian\n", "MyLeaker.mapSet(\n", " '-Panels':\n", " '\|Run':{\n", " '-Charts':{\n", " \n", " }\n", " }\n", " ).view(\n", " ).pyplot(\n", " )" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "####D. Design models" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Store in mongo" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#leak and brian\n", "MyLeaker.mapSet(\n", " '-Panels':\n", " '\|Run':{\n", " '-Charts':{\n", " \n", " }\n", " }\n", " ).model(\n", " )" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
Kaggle/learntools	notebooks/machine_learning/raw/tut_titanic.ipynb	1	18733	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In the final exercise of the [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) course, you learned how to make a submission to a Kaggle competition. But some of the work was already completed for you, since you were provided a notebook with partially completed code. \n", "\n", "In this tutorial, you'll explore a full workflow that you can use to get started (from the very beginning!) with creating a submission to any Kaggle competition. We'll use the [Titanic competition](https://www.kaggle.com/c/titanic) as an example.\n", "\n", "# Part 1: Get started\n", "\n", "In this section, you'll learn more about the competition and make your first submission. \n", "\n", "## Join the competition!\n", "\n", "The first thing to do is to join the competition! Open a new window with [the competition page](https://www.kaggle.com/c/titanic), and click on the \"Join Competition\" button, if you haven't already. (_If you see a \"Submit Predictions\" button instead of a \"Join Competition\" button, you have already joined the competition, and don't need to do so again._)\n", "\n", "![](https://i.imgur.com/rRFchA8.png)\n", "\n", "This takes you to the rules acceptance page. You must accept the competition rules in order to participate. These rules govern how many submissions you can make per day, the maximum team size, and other competition-specific details. Then, click on \"I Understand and Accept\" to indicate that you will abide by the competition rules.\n", "\n", "## The challenge\n", "\n", "The competition is simple: we want you to use the Titanic passenger data (name, age, price of ticket, etc) to try to predict who will survive and who will die.\n", "\n", "## The data\n", "\n", "To take a look at the competition data, click on the <a href=\"https://www.kaggle.com/c/titanic/data\" target=\"_blank\" rel=\"noopener noreferrer\"><b>Data tab</b></a> at the top of the competition page. Then, scroll down to find the list of files. \n", "\n", "![](https://i.imgur.com/LiM3JA7.png)\n", "\n", "There are three files in the data: (1) train.csv, (2) test.csv, and (3) gender_submission.csv.\n", "\n", "### (1) train.csv\n", "\n", "train.csv contains the details of a subset of the passengers on board (891 passengers, to be exact -- where each passenger gets a different row in the table). To investigate this data, click on the name of the file under the \"Data Sources\" column (on the left of the screen). Once you've done this, all of the column names (along with a brief description of what they contain) are listed to the right of the screen, under the \"Columns\" heading. \n", "\n", "![](https://i.imgur.com/w5HFxp8.png)\n", "\n", "You can view all of the data in the same window. \n", "\n", "![](https://i.imgur.com/CEPZi6z.png)\n", "\n", "The values in the second column (\"Survived\") can be used to determine whether each passenger survived or not: \n", "- if it's a \"1\", the passenger survived.\n", "- if it's a \"0\", the passenger died.\n", "\n", "For instance, the first passenger listed in train.csv is Mr. Owen Harris Braund. He was 22 years old when he died on the Titanic.\n", "\n", "### (2) test.csv\n", "\n", "Using the patterns you find in train.csv, you have to predict whether the other 418 passengers on board (in test.csv) survived. \n", "\n", "Click on test.csv (under the \"Data Sources\" column) to examine its contents. Note that test.csv does not have a \"Survived\" column - this information is hidden from you, and how well you do at predicting these hidden values will determine how highly you score in the competition! \n", "\n", "### (3) gender_submission.csv\n", "\n", "The gender_submission.csv file is provided as an example that shows how you should structure your predictions. It predicts that all female passengers survived, and all male passengers died. Your hypotheses regarding survival will probably be different, which will lead to a different submission file. But, just like this file, your submission should have:\n", "- a \"PassengerId\" column containing the IDs of each passenger from test.csv.\n", "- a \"Survived\" column (that you will create!) with a \"1\" for the rows where you think the passenger survived, and a \"0\" where you predict that the passenger died.\n", "\n", "## Your first submission\n", "\n", "As a benchmark, you'll download the gender_submission.csv file and submit it to the competition. Begin by clicking on the download link to the right of the name of the file. \n", "\n", "![](https://i.imgur.com/Pl1DIA8.png)\n", "\n", "This downloads the file to your computer. Then:\n", "- Click on the blue \"Submit Predictions\" button in the top right corner of the competition page. (_This button now appears where the \"Join Competition\" button was._)\n", "- Scroll down to \"Step 1: Upload submission file\". Upload the file you just downloaded. Then, click on the blue \"Make Submission\" button. \n", "\n", "In a few seconds, your submission will be scored, and you'll receive a spot on the leaderboard. Next, we'll walk you through how to outperform this initial submission!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 2: Your coding environment\n", "\n", "In this section, you'll train your own machine learning model to improve your predictions. \n", "\n", "## The Notebook\n", "\n", "The first thing to do is to create a Kaggle Notebook where you'll store all of your code. You can use Kaggle Notebooks to getting up and running with writing code quickly, and without having to install anything on your computer. (_If you are interested in deep learning, we also offer free GPU and TPU access!_) \n", "\n", "Begin by clicking on the <a href=\"https://www.kaggle.com/c/titanic/kernels\" target=\"_blank\">Notebooks tab</a> on the competition page. Then, click on \"New Notebook\".\n", "\n", "![](https://i.imgur.com/DHPyh7s.png)\n", "\n", "Next, click on \"Create\". (_Don't change the default settings: so, \"Python\" should appear under \"Select language\", and you should have \"Notebook\" selected under \"Select type\"._)\n", "\n", "![](https://i.imgur.com/qUVvr8k.png)\n", "\n", "Your notebook will take a few seconds to load. In the top left corner, you can see the name of your notebook -- something like \"kernel2daed3cd79\".\n", "\n", "![](https://i.imgur.com/64ZFT1L.png)\n", "\n", "You can edit the name by clicking on it. Change it to something more descriptive, like \"Getting Started with Titanic\". \n", "\n", "![](https://i.imgur.com/uwyvzXq.png)\n", "\n", "## Your first lines of code\n", "\n", "When you start a new notebook, it has two gray boxes for storing code. We refer to these gray boxes as \"code cells\".\n", "\n", "![](https://i.imgur.com/q9mwkZM.png)\n", "\n", "The first code cell already has some code in it. To run this code, put your cursor in the code cell. (_If your cursor is in the right place, you'll notice a blue vertical line to the left of the gray box._) Then, either hit the play button (which appears to the left of the blue line), or hit [Shift] + [Enter] on your keyboard.\n", "\n", "If the code runs successfully, three lines of output are returned. Below, you can see the same code that you just ran, along with the output that you should see in your notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_kg_hide-input": false }, "outputs": [], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", "\n", "import os\n", "for dirname, _, filenames in os.walk('/kaggle/input'):\n", " for filename in filenames:\n", " print(os.path.join(dirname, filename))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This shows us where the competition data is stored, so that we can load the files into the notebook. We'll do that next.\n", "\n", "## Load the data\n", "\n", "The second code cell in your notebook now appears below the three lines of output with the file locations.\n", "\n", "![](https://i.imgur.com/OQBax9n.png)\n", "\n", "Type the two lines of code below into your second code cell. Then, once you're done, either click on the blue play button, or hit [Shift] + [Enter]. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_data = pd.read_csv(\"../input/titanic/train.csv\")\n", "train_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your code should return the output above, which corresponds to the first five rows of the table in train.csv. It's very important that you see this output in your notebook before proceeding with the tutorial!\n", "> _If your code does not produce this output_, double-check that your code is identical to the two lines above. And, make sure your cursor is in the code cell before hitting [Shift] + [Enter].\n", "\n", "The code that you've just written is in the Python programming language. It uses a Python \"module\" called pandas (abbreviated as `pd`) to load the table from the train.csv file into the notebook. To do this, we needed to plug in the location of the file (which we saw was `/kaggle/input/titanic/train.csv`). \n", "> If you're not already familiar with Python (and pandas), the code shouldn't make sense to you -- but don't worry! The point of this tutorial is to (quickly!) make your first submission to the competition. At the end of the tutorial, we suggest resources to continue your learning.\n", "\n", "At this point, you should have at least three code cells in your notebook. \n", "![](https://i.imgur.com/ReLhYca.png)\n", "\n", "Copy the code below into the third code cell of your notebook to load the contents of the test.csv file. Don't forget to click on the play button (or hit [Shift] + [Enter])!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test_data = pd.read_csv(\"../input/titanic/test.csv\")\n", "test_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As before, make sure that you see the output above in your notebook before continuing. \n", "\n", "Once all of the code runs successfully, all of the data (in train.csv and test.csv) is loaded in the notebook. (_The code above shows only the first 5 rows of each table, but all of the data is there -- all 891 rows of train.csv and all 418 rows of test.csv!_)\n", "\n", "# Part 3: Improve your score\n", "\n", "Remember our goal: we want to find patterns in train.csv that help us predict whether the passengers in test.csv survived.\n", "\n", "It might initially feel overwhelming to look for patterns, when there's so much data to sort through. So, we'll start simple.\n", "\n", "## Explore a pattern\n", "\n", "Remember that the sample submission file in gender_submission.csv assumes that all female passengers survived (and all male passengers died). \n", "\n", "Is this a reasonable first guess? We'll check if this pattern holds true in the data (in train.csv).\n", "\n", "Copy the code below into a new code cell. Then, run the cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "women = train_data.loc[train_data.Sex == 'female'][\"Survived\"]\n", "rate_women = sum(women)/len(women)\n", "\n", "print(\"% of women who survived:\", rate_women)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before moving on, make sure that your code returns the output above. The code above calculates the percentage of female passengers (in train.csv) who survived.\n", "\n", "Then, run the code below in another code cell:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "men = train_data.loc[train_data.Sex == 'male'][\"Survived\"]\n", "rate_men = sum(men)/len(men)\n", "\n", "print(\"% of men who survived:\", rate_men)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code above calculates the percentage of male passengers (in train.csv) who survived.\n", "\n", "From this you can see that almost 75% of the women on board survived, whereas only 19% of the men lived to tell about it. Since gender seems to be such a strong indicator of survival, the submission file in gender_submission.csv is not a bad first guess, and it makes sense that it performed reasonably well!\n", "\n", "But at the end of the day, this gender-based submission bases its predictions on only a single column. As you can imagine, by considering multiple columns, we can discover more complex patterns that can potentially yield better-informed predictions. Since it is quite difficult to consider several columns at once (or, it would take a long time to consider all possible patterns in many different columns simultaneously), we'll use machine learning to automate this for us.\n", "\n", "## Your first machine learning model\n", "\n", "We'll build a [random forest model](https://www.kaggle.com/dansbecker/random-forests). This model is constructed of several \"trees\" (there are three trees in the picture below, but we'll construct 100!) that will individually consider each passenger's data and vote on whether the individual survived. Then, the random forest model makes a democratic decision: the outcome with the most votes wins!\n", "\n", "![](https://i.imgur.com/AC9Bq63.png)\n", "\n", "The code cell below looks for patterns in four different columns (\"Pclass\", \"Sex\", \"SibSp\", and \"Parch\") of the data. It constructs the trees in the random forest model based on patterns in the train.csv file, before generating predictions for the passengers in test.csv. The code also saves these new predictions in a CSV file my_submission.csv.\n", "\n", "Copy this code into your notebook, and run it in a new code cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_kg_hide-output": false }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "y = train_data[\"Survived\"]\n", "\n", "features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\"]\n", "X = pd.get_dummies(train_data[features])\n", "X_test = pd.get_dummies(test_data[features])\n", "\n", "model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)\n", "model.fit(X, y)\n", "predictions = model.predict(X_test)\n", "\n", "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})\n", "output.to_csv('my_submission.csv', index=False)\n", "print(\"Your submission was successfully saved!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure that your notebook outputs the same message above (`Your submission was successfully saved!`) before moving on.\n", "> Again, don't worry if this code doesn't make sense to you! For now, we'll focus on how to generate and submit predictions.\n", "\n", "Once you're ready, click on the blue \"Save Version\" button in the top right corner of your notebook. This will generate a pop-up window. \n", "- Ensure that the \"Save and Run All\" option is selected, and then click on the blue \"Save\" button.\n", "- This generates a window in the bottom left corner of the notebook. After it has finished running, click on the number to the right of the \"Save Version\" button. This pulls up a list of versions on the right of the screen. Click on the ellipsis (...) to the right of the most recent version, and select Open in Viewer. \n", "- Click on the Output tab on the right of the screen. Then, click on the \"Submit to Competition\" button to submit your results.\n", "\n", "![](https://i.imgur.com/kKKnHpx.png)\n", "\n", "Once your file is successfully submitted, you should receive a message saying that you've moved up the leaderboard. Great work!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 4: Keep learning!\n", "\n", "Can you use what you learned about random forests in the [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) course to generate even better predictions? \n", "\n", "Check out the [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) course to learn about more advanced techniques!" ] } ], "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 }	apache-2.0
the-deep-learners/TensorFlow-LiveLessons	notebooks/live_training/multi_convnet_architectures_LT.ipynb	1	6814	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multi-ConvNet Architectures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we concatenate multiple parallel convolutional nets together to classify IMDB movie reviews by their sentiment." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Load dependencies" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import keras\n", "from keras.datasets import imdb\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.models import Model # new!\n", "from keras.layers import Input, concatenate # new! \n", "from keras.layers import Dense, Dropout, Embedding, SpatialDropout1D, Conv1D, GlobalMaxPooling1D\n", "from keras.callbacks import ModelCheckpoint\n", "import os\n", "from sklearn.metrics import roc_auc_score \n", "import matplotlib.pyplot as plt \n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Set hyperparameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# output directory name:\n", "output_dir = 'model_output/multiconv'\n", "\n", "# training:\n", "epochs = 4\n", "batch_size = 128\n", "\n", "# vector-space embedding: \n", "n_dim = 64\n", "n_unique_words = 5000 \n", "max_review_length = 400\n", "pad_type = trunc_type = 'pre'\n", "drop_embed = 0.2 \n", "\n", "# convolutional layer architecture:\n", "n_conv_1 = n_conv_2 = n_conv_3 = 256 \n", "k_conv_1 = 3\n", "k_conv_2 = 2\n", "k_conv_3 = 4\n", "\n", "# dense layer architecture: \n", "n_dense = 256\n", "dropout = 0.2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Load data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "(x_train, y_train), (x_valid, y_valid) = imdb.load_data(num_words=n_unique_words) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Preprocess data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_train = pad_sequences(x_train, maxlen=max_review_length, padding=pad_type, truncating=trunc_type, value=0)\n", "x_valid = pad_sequences(x_valid, maxlen=max_review_length, padding=pad_type, truncating=trunc_type, value=0)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Design neural network architecture" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "input_layer = Input(shape=(max_review_length,), dtype='int16', name='input') # supports integers +/- 32.7k \n", "embedding_layer = Embedding(n_unique_words, n_dim, input_length=max_review_length, name='embedding')(input_layer)\n", "drop_embed_layer = SpatialDropout1D(drop_embed, name='drop_embed')(embedding_layer)\n", "\n", "# CODE HERE\n", "\n", "# start with conv_1 only and no concat\n", "# add conv_2\n", "# add conv_3\n", "\n", "dense_layer = Dense(n_dense, activation='relu', name='dense')(concat)\n", "drop_dense_layer = Dropout(dropout, name='drop_dense')(dense_layer)\n", "dense_2 = Dense(64, activation='relu', name='dense_2')(drop_dense_layer)\n", "dropout_2 = Dropout(dropout, name='drop_dense_2')(dense_2)\n", "\n", "predictions = Dense(1, activation='sigmoid', name='output')(dropout_2)\n", "\n", "model = Model(input_layer, predictions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.summary() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Configure model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "modelcheckpoint = ModelCheckpoint(filepath=output_dir+\"/weights.{epoch:02d}.hdf5\")\n", "if not os.path.exists(output_dir):\n", " os.makedirs(output_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Train!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# start with conv_1 only and no concat: 89.1% validation accuracy in epoch 2, as earlier notebook\n", "# add conv_2: 89.5% in epoch 3\n", "# add conv_3: ditto\n", "# add dense_2: ditto in epoch 2\n", "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_valid, y_valid), callbacks=[modelcheckpoint])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Evaluate" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.load_weights(output_dir+\"/weights.01.hdf5\") # zero-indexed" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_hat = model.predict(x_valid)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(y_hat)\n", "_ = plt.axvline(x=0.5, color='orange')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"{:0.2f}\".format(roc_auc_score(y_valid, y_hat)*100.0)" ] }, { "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
rringham/deep-learning-notebooks	4_convolutions.ipynb	1	31921	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4embtkV0pNxM" }, "source": [ "Deep Learning\n", "=============\n", "\n", "Assignment 4\n", "------------\n", "\n", "Previously in `2_fullyconnected.ipynb` and `3_regularization.ipynb`, we trained fully connected networks to classify [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) characters.\n", "\n", "The goal of this assignment is make the neural network convolutional." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "tm2CQN_Cpwj0" }, "outputs": [], "source": [ "# These are all the modules we'll be using later. Make sure you can import them\n", "# before proceeding further.\n", "from __future__ import print_function\n", "import numpy as np\n", "import tensorflow as tf\n", "from six.moves import cPickle as pickle\n", "from six.moves import range" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 11948, "status": "ok", "timestamp": 1446658914837, "user": { "color": "", "displayName": "", "isAnonymous": false, "isMe": true, "permissionId": "", "photoUrl": "", "sessionId": "0", "userId": "" }, "user_tz": 480 }, "id": "y3-cj1bpmuxc", "outputId": "016b1a51-0290-4b08-efdb-8c95ffc3cd01" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set (200000, 28, 28) (200000,)\n", "Validation set (10000, 28, 28) (10000,)\n", "Test set (10000, 28, 28) (10000,)\n" ] } ], "source": [ "pickle_file = 'notMNIST.pickle'\n", "\n", "with open(pickle_file, 'rb') as f:\n", " save = pickle.load(f)\n", " train_dataset = save['train_dataset']\n", " train_labels = save['train_labels']\n", " valid_dataset = save['valid_dataset']\n", " valid_labels = save['valid_labels']\n", " test_dataset = save['test_dataset']\n", " test_labels = save['test_labels']\n", " del save # hint to help gc free up memory\n", " print('Training set', train_dataset.shape, train_labels.shape)\n", " print('Validation set', valid_dataset.shape, valid_labels.shape)\n", " print('Test set', test_dataset.shape, test_labels.shape)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "L7aHrm6nGDMB" }, "source": [ "Reformat into a TensorFlow-friendly shape:\n", "- convolutions need the image data formatted as a cube (width by height by #channels)\n", "- labels as float 1-hot encodings." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 11952, "status": "ok", "timestamp": 1446658914857, "user": { "color": "", "displayName": "", "isAnonymous": false, "isMe": true, "permissionId": "", "photoUrl": "", "sessionId": "0", "userId": "" }, "user_tz": 480 }, "id": "IRSyYiIIGIzS", "outputId": "650a208c-8359-4852-f4f5-8bf10e80ef6c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set (200000, 28, 28, 1) (200000, 10)\n", "Validation set (10000, 28, 28, 1) (10000, 10)\n", "Test set (10000, 28, 28, 1) (10000, 10)\n" ] } ], "source": [ "image_size = 28\n", "num_labels = 10\n", "num_channels = 1 # grayscale\n", "\n", "import numpy as np\n", "\n", "def reformat(dataset, labels):\n", " dataset = dataset.reshape(\n", " (-1, image_size, image_size, num_channels)).astype(np.float32)\n", " labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n", " return dataset, labels\n", "train_dataset, train_labels = reformat(train_dataset, train_labels)\n", "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n", "test_dataset, test_labels = reformat(test_dataset, test_labels)\n", "print('Training set', train_dataset.shape, train_labels.shape)\n", "print('Validation set', valid_dataset.shape, valid_labels.shape)\n", "print('Test set', test_dataset.shape, test_labels.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "AgQDIREv02p1" }, "outputs": [], "source": [ "def accuracy(predictions, labels):\n", " return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n", " / predictions.shape[0])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5rhgjmROXu2O" }, "source": [ "Let's build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we'll limit its depth and number of fully connected nodes." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": false, "id": "IZYv70SvvOan" }, "outputs": [], "source": [ "batch_size = 16\n", "patch_size = 5\n", "depth = 16\n", "num_hidden = 64\n", "\n", "graph = tf.Graph()\n", "\n", "with graph.as_default():\n", "\n", " # Input data.\n", " tf_train_dataset = tf.placeholder(\n", " tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n", " tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n", " tf_valid_dataset = tf.constant(valid_dataset)\n", " tf_test_dataset = tf.constant(test_dataset)\n", " \n", " # Variables.\n", " layer1_weights = tf.Variable(tf.truncated_normal(\n", " [patch_size, patch_size, num_channels, depth], stddev=0.1))\n", " layer1_biases = tf.Variable(tf.zeros([depth]))\n", " layer2_weights = tf.Variable(tf.truncated_normal(\n", " [patch_size, patch_size, depth, depth], stddev=0.1))\n", " layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\n", " layer3_weights = tf.Variable(tf.truncated_normal(\n", " [image_size // 4 * image_size // 4 * depth, num_hidden], stddev=0.1))\n", " layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\n", " layer4_weights = tf.Variable(tf.truncated_normal(\n", " [num_hidden, num_labels], stddev=0.1))\n", " layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n", " \n", " # Model.\n", " def model(data):\n", " conv = tf.nn.conv2d(data, layer1_weights, [1, 1, 1, 1], padding='SAME')\n", " hidden = tf.nn.relu(conv + layer1_biases)\n", " hidden = tf.nn.max_pool(hidden, [1,2,2,1], [1,2,2,1], padding='SAME')\n", " conv = tf.nn.conv2d(hidden, layer2_weights, [1, 1, 1, 1], padding='SAME')\n", " hidden = tf.nn.relu(conv + layer2_biases)\n", " hidden = tf.nn.max_pool(hidden, [1,2,2,1], [1,2,2,1], padding='SAME')\n", " shape = hidden.get_shape().as_list()\n", " reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n", " hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)\n", " return tf.matmul(hidden, layer4_weights) + layer4_biases\n", " \n", " # Training computation.\n", " logits = model(tf_train_dataset)\n", " loss = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n", " \n", " # Optimizer.\n", " optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n", " \n", " # Predictions for the training, validation, and test data.\n", " train_prediction = tf.nn.softmax(logits)\n", " valid_prediction = tf.nn.softmax(model(tf_valid_dataset))\n", " test_prediction = tf.nn.softmax(model(tf_test_dataset))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 37 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 63292, "status": "ok", "timestamp": 1446658966251, "user": { "color": "", "displayName": "", "isAnonymous": false, "isMe": true, "permissionId": "", "photoUrl": "", "sessionId": "0", "userId": "" }, "user_tz": 480 }, "id": "noKFb2UovVFR", "outputId": "28941338-2ef9-4088-8bd1-44295661e628" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialized\n", "Minibatch loss at step 0: 2.887852\n", "Minibatch accuracy: 6.2%\n", "Validation accuracy: 10.0%\n", "Minibatch loss at step 50: 2.051110\n", "Minibatch accuracy: 12.5%\n", "Validation accuracy: 29.9%\n", "Minibatch loss at step 100: 1.185204\n", "Minibatch accuracy: 62.5%\n", "Validation accuracy: 51.3%\n", "Minibatch loss at step 150: 0.624291\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 71.7%\n", "Minibatch loss at step 200: 0.955439\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 74.3%\n", "Minibatch loss at step 250: 1.174336\n", "Minibatch accuracy: 68.8%\n", "Validation accuracy: 76.7%\n", "Minibatch loss at step 300: 0.375701\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 80.0%\n", "Minibatch loss at step 350: 0.705345\n", "Minibatch accuracy: 93.8%\n", "Validation accuracy: 76.4%\n", "Minibatch loss at step 400: 0.281616\n", "Minibatch accuracy: 100.0%\n", "Validation accuracy: 80.5%\n", "Minibatch loss at step 450: 1.062438\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 79.2%\n", "Minibatch loss at step 500: 0.610115\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 81.4%\n", "Minibatch loss at step 550: 0.876767\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 81.6%\n", "Minibatch loss at step 600: 0.271369\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 81.8%\n", "Minibatch loss at step 650: 0.766848\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 81.8%\n", "Minibatch loss at step 700: 0.815853\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 82.6%\n", "Minibatch loss at step 750: 0.094008\n", "Minibatch accuracy: 100.0%\n", "Validation accuracy: 83.7%\n", "Minibatch loss at step 800: 0.632895\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 83.1%\n", "Minibatch loss at step 850: 0.845280\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 83.7%\n", "Minibatch loss at step 900: 0.678683\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 82.9%\n", "Minibatch loss at step 950: 0.518502\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 84.3%\n", "Minibatch loss at step 1000: 0.376357\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 84.2%\n", "Minibatch loss at step 1050: 0.493665\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 83.4%\n", "Minibatch loss at step 1100: 0.616195\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 84.2%\n", "Minibatch loss at step 1150: 0.454042\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 84.7%\n", "Minibatch loss at step 1200: 0.823288\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 85.0%\n", "Minibatch loss at step 1250: 0.589719\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 84.6%\n", "Minibatch loss at step 1300: 0.276481\n", "Minibatch accuracy: 93.8%\n", "Validation accuracy: 84.1%\n", "Minibatch loss at step 1350: 0.729200\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 85.3%\n", "Minibatch loss at step 1400: 0.252959\n", "Minibatch accuracy: 93.8%\n", "Validation accuracy: 85.3%\n", "Minibatch loss at step 1450: 0.213688\n", "Minibatch accuracy: 100.0%\n", "Validation accuracy: 85.3%\n", "Minibatch loss at step 1500: 0.729057\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 85.2%\n", "Minibatch loss at step 1550: 0.555658\n", "Minibatch accuracy: 93.8%\n", "Validation accuracy: 85.1%\n", "Minibatch loss at step 1600: 1.003705\n", "Minibatch accuracy: 68.8%\n", "Validation accuracy: 85.1%\n", "Minibatch loss at step 1650: 0.614646\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 85.3%\n", "Minibatch loss at step 1700: 0.526734\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 85.2%\n", "Minibatch loss at step 1750: 0.517184\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 86.0%\n", "Minibatch loss at step 1800: 0.242730\n", "Minibatch accuracy: 93.8%\n", "Validation accuracy: 86.1%\n", "Minibatch loss at step 1850: 0.540403\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 85.9%\n", "Minibatch loss at step 1900: 0.385952\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 85.2%\n", "Minibatch loss at step 1950: 0.452086\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 86.3%\n", "Minibatch loss at step 2000: 0.094848\n", "Minibatch accuracy: 100.0%\n", "Validation accuracy: 86.2%\n", "Test accuracy: 92.6%\n" ] } ], "source": [ "num_steps = 2001\n", "\n", "with tf.Session(graph=graph) as session:\n", " tf.initialize_all_variables().run()\n", " print('Initialized')\n", " for step in range(num_steps):\n", " offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n", " batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n", " batch_labels = train_labels[offset:(offset + batch_size), :]\n", " feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n", " _, l, predictions = session.run(\n", " [optimizer, loss, train_prediction], feed_dict=feed_dict)\n", " if (step % 50 == 0):\n", " print('Minibatch loss at step %d: %f' % (step, l))\n", " print('Minibatch accuracy: %.1f%%' % accuracy(predictions, batch_labels))\n", " print('Validation accuracy: %.1f%%' % accuracy(\n", " valid_prediction.eval(), valid_labels))\n", " print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "KedKkn4EutIK" }, "source": [ "---\n", "Problem 1\n", "---------\n", "\n", "The convolutional model above uses convolutions with stride 2 to reduce the dimensionality. Replace the strides by a max pooling operation (`nn.max_pool()`) of stride 2 and kernel size 2.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "klf21gpbAgb-" }, "source": [ "---\n", "Problem 2\n", "---------\n", "\n", "Try to get the best performance you can using a convolutional net. Look for example at the classic [LeNet5](http://yann.lecun.com/exdb/lenet/) architecture, adding Dropout, and/or adding learning rate decay.\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [], "source": [ "initial_learning_rate_value = 0.05\n", "batch_size = 16\n", "patch_size = 5\n", "depth = 16\n", "depth2 = 32\n", "num_hidden = 64\n", "\n", "graph = tf.Graph()\n", "\n", "with graph.as_default():\n", "\n", " # Input data.\n", " tf_train_dataset = tf.placeholder(\n", " tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n", " tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n", " tf_valid_dataset = tf.constant(valid_dataset)\n", " tf_test_dataset = tf.constant(test_dataset)\n", "\n", " # learning rate decay\n", " global_step = tf.Variable(0)\n", " learning_rate = tf.train.exponential_decay(initial_learning_rate_value, global_step, 1, 0.9999)\n", " \n", " # Variables.\n", " layer1_weights = tf.Variable(tf.truncated_normal(\n", " [patch_size, patch_size, num_channels, depth], stddev=0.1))\n", " layer1_biases = tf.Variable(tf.zeros([depth]))\n", "\n", " layer2_weights = tf.Variable(tf.truncated_normal(\n", " [patch_size, patch_size, depth, depth], stddev=0.1))\n", " layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\n", "\n", " layer3_weights = tf.Variable(tf.truncated_normal(\n", " [patch_size, patch_size, depth, depth2], stddev=0.1))\n", " layer3_biases = tf.Variable(tf.constant(1.0, shape=[depth2]))\n", "\n", " layer4_weights = tf.Variable(tf.truncated_normal(\n", " [image_size // 7 * image_size // 7 * depth2, num_hidden], stddev=0.1))\n", " layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\n", "\n", " layer5_weights = tf.Variable(tf.truncated_normal(\n", " [num_hidden, num_labels], stddev=0.1))\n", " layer5_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n", " \n", " # Model.\n", " def model(data):\n", " # data ---> conv2d \| layer1 ---> relu --> max_pool\n", " #\n", " # data input tensor: [16, 28, 28, 1] 4D tensor: (batch, h, w, ch)\n", " # layer1 filter tensor: [5, 5, 1, 16]\n", " # conv2d output tensor: [16, 28, 28, 16]\n", " # relu output tensor: [16, 28, 28, 16]\n", " # max_pool output tensor: [16, 14, 14 16]\n", " \n", " conv = tf.nn.conv2d(data, layer1_weights, [1, 1, 1, 1], padding='SAME') \n", " hidden = tf.nn.relu(conv + layer1_biases)\n", " hidden = tf.nn.max_pool(hidden, [1,2,2,1], [1,2,2,1], padding='SAME')\n", " \n", " # hidden ---> conv2d \| layer2 ---> relu --> max_pool\n", " #\n", " # hidden input tensor: [16, 14, 14, 16]\n", " # layer2 filter tensor: [5, 5, 16, 16]\n", " # conv2d output tensor: [16, 14, 14, 16]\n", " # relu output tensor: [16, 14, 14, 16]\n", " # max_pool output tensor: [16, 7, 7, 16]\n", " \n", " conv = tf.nn.conv2d(hidden, layer2_weights, [1, 1, 1, 1], padding='SAME')\n", " hidden = tf.nn.relu(conv + layer2_biases)\n", " hidden = tf.nn.max_pool(hidden, [1,2,2,1], [1,2,2,1], padding='SAME')\n", " \n", " # hidden ---> conv2d \| layer3 ---> relu --> max_pool\n", " #\n", " # hidden input tensor: [16, 7, 7, 16]\n", " # layer3 filter tensor: [5, 5, 16, 32]\n", " # conv2d output tensor: [16, 7, 7, 32]\n", " # relu output tensor: [16, 7, 7, 32]\n", " # max_pool output tensor: [16, 4, 4, 32]\n", " \n", " conv = tf.nn.conv2d(hidden, layer3_weights, [1, 1, 1, 1], padding='SAME')\n", " hidden = tf.nn.relu(conv + layer3_biases)\n", " hidden = tf.nn.max_pool(hidden, [1,2,2,1], [1,2,2,1], padding='SAME')\n", " \n", " # hidden ---> relu --> dropout\n", " #\n", " # hidden input tensor: [16, 4, 4, 32]\n", " # reshape tensor: [16, 512]\n", " # layer4 tensor: [512, 64] (512 = 4432)\n", " # relu tensor: [16, 64]\n", " \n", " shape = hidden.get_shape().as_list() \n", " reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n", " hidden = tf.nn.relu(tf.matmul(reshape, layer4_weights) + layer4_biases)\n", " hidden = tf.nn.dropout(hidden, 0.75)\n", " \n", " # hidden ---> layer5\n", " #\n", " # hidden input tensor: [16, 64]\n", " # layer5 tensor: [64, 10]\n", " # ouput tensor: [16 x 10]\n", " output = tf.matmul(hidden, layer5_weights) + layer5_biases\n", " return output\n", " \n", " # Training computation.\n", " logits = model(tf_train_dataset)\n", " loss = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n", " \n", " # Optimizer.\n", " optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)\n", " \n", " # Predictions for the training, validation, and test data.\n", " train_prediction = tf.nn.softmax(logits)\n", " valid_prediction = tf.nn.softmax(model(tf_valid_dataset))\n", " test_prediction = tf.nn.softmax(model(tf_test_dataset))" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialized\n", "Minibatch loss at step 0: 5.236138\n", "Learning rate at step 0: 0.049995\n", "Minibatch accuracy: 12.5%\n", "Validation accuracy: 9.9%\n", "\n", "Minibatch loss at step 50: 2.147182\n", "Learning rate at step 50: 0.049746\n", "Minibatch accuracy: 6.2%\n", "Validation accuracy: 21.2%\n", "\n", "Minibatch loss at step 100: 1.249267\n", "Learning rate at step 100: 0.049497\n", "Minibatch accuracy: 62.5%\n", "Validation accuracy: 49.4%\n", "\n", "Minibatch loss at step 150: 0.540573\n", "Learning rate at step 150: 0.049251\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 65.2%\n", "\n", "Minibatch loss at step 200: 0.937742\n", "Learning rate at step 200: 0.049005\n", "Minibatch accuracy: 62.5%\n", "Validation accuracy: 70.7%\n", "\n", "Minibatch loss at step 250: 1.064317\n", "Learning rate at step 250: 0.048760\n", "Minibatch accuracy: 68.8%\n", "Validation accuracy: 74.1%\n", "\n", "Minibatch loss at step 300: 0.627920\n", "Learning rate at step 300: 0.048517\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 76.8%\n", "\n", "Minibatch loss at step 350: 0.415646\n", "Learning rate at step 350: 0.048275\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 75.8%\n", "\n", "Minibatch loss at step 400: 0.240822\n", "Learning rate at step 400: 0.048034\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 77.6%\n", "\n", "Minibatch loss at step 450: 0.937572\n", "Learning rate at step 450: 0.047795\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 78.7%\n", "\n", "Minibatch loss at step 500: 0.680249\n", "Learning rate at step 500: 0.047556\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 80.2%\n", "\n", "Minibatch loss at step 550: 1.324346\n", "Learning rate at step 550: 0.047319\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 79.2%\n", "\n", "Minibatch loss at step 600: 0.295159\n", "Learning rate at step 600: 0.047083\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 80.6%\n", "\n", "Minibatch loss at step 650: 1.080894\n", "Learning rate at step 650: 0.046848\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 80.8%\n", "\n", "Minibatch loss at step 700: 1.045246\n", "Learning rate at step 700: 0.046614\n", "Minibatch accuracy: 62.5%\n", "Validation accuracy: 80.7%\n", "\n", "Minibatch loss at step 750: 0.046020\n", "Learning rate at step 750: 0.046382\n", "Minibatch accuracy: 100.0%\n", "Validation accuracy: 81.9%\n", "\n", "Minibatch loss at step 800: 0.465275\n", "Learning rate at step 800: 0.046150\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 81.3%\n", "\n", "Minibatch loss at step 850: 0.930939\n", "Learning rate at step 850: 0.045920\n", "Minibatch accuracy: 68.8%\n", "Validation accuracy: 82.2%\n", "\n", "Minibatch loss at step 900: 0.631694\n", "Learning rate at step 900: 0.045691\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 83.0%\n", "\n", "Minibatch loss at step 950: 0.647490\n", "Learning rate at step 950: 0.045463\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 82.1%\n", "\n", "Minibatch loss at step 1000: 0.653211\n", "Learning rate at step 1000: 0.045236\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 82.2%\n", "\n", "Minibatch loss at step 1050: 0.587030\n", "Learning rate at step 1050: 0.045011\n", "Minibatch accuracy: 87.5%\n", "Validation accuracy: 81.6%\n", "\n", "Minibatch loss at step 1100: 0.564651\n", "Learning rate at step 1100: 0.044786\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 83.2%\n", "\n", "Minibatch loss at step 1150: 0.468530\n", "Learning rate at step 1150: 0.044563\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 82.9%\n", "\n", "Minibatch loss at step 1200: 0.933110\n", "Learning rate at step 1200: 0.044340\n", "Minibatch accuracy: 68.8%\n", "Validation accuracy: 83.6%\n", "\n", "Minibatch loss at step 1250: 0.926923\n", "Learning rate at step 1250: 0.044119\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 83.2%\n", "\n", "Minibatch loss at step 1300: 0.381629\n", "Learning rate at step 1300: 0.043899\n", "Minibatch accuracy: 93.8%\n", "Validation accuracy: 82.2%\n", "\n", "Minibatch loss at step 1350: 0.864118\n", "Learning rate at step 1350: 0.043680\n", "Minibatch accuracy: 68.8%\n", "Validation accuracy: 82.9%\n", "\n", "Minibatch loss at step 1400: 0.351495\n", "Learning rate at step 1400: 0.043462\n", "Minibatch accuracy: 93.8%\n", "Validation accuracy: 84.2%\n", "\n", "Minibatch loss at step 1450: 0.113925\n", "Learning rate at step 1450: 0.043245\n", "Minibatch accuracy: 100.0%\n", "Validation accuracy: 84.2%\n", "\n", "Minibatch loss at step 1500: 0.531653\n", "Learning rate at step 1500: 0.043030\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 84.2%\n", "\n", "Minibatch loss at step 1550: 0.796815\n", "Learning rate at step 1550: 0.042815\n", "Minibatch accuracy: 68.8%\n", "Validation accuracy: 83.7%\n", "\n", "Minibatch loss at step 1600: 0.930998\n", "Learning rate at step 1600: 0.042601\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 84.0%\n", "\n", "Minibatch loss at step 1650: 0.791512\n", "Learning rate at step 1650: 0.042389\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 83.5%\n", "\n", "Minibatch loss at step 1700: 0.803234\n", "Learning rate at step 1700: 0.042177\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 84.1%\n", "\n", "Minibatch loss at step 1750: 0.481777\n", "Learning rate at step 1750: 0.041967\n", "Minibatch accuracy: 81.2%\n", "Validation accuracy: 84.1%\n", "\n", "Minibatch loss at step 1800: 0.334682\n", "Learning rate at step 1800: 0.041758\n", "Minibatch accuracy: 93.8%\n", "Validation accuracy: 84.5%\n", "\n", "Minibatch loss at step 1850: 0.801326\n", "Learning rate at step 1850: 0.041549\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 84.9%\n", "\n", "Minibatch loss at step 1900: 0.306579\n", "Learning rate at step 1900: 0.041342\n", "Minibatch accuracy: 93.8%\n", "Validation accuracy: 83.9%\n", "\n", "Minibatch loss at step 1950: 0.563700\n", "Learning rate at step 1950: 0.041136\n", "Minibatch accuracy: 75.0%\n", "Validation accuracy: 84.9%\n", "\n", "Minibatch loss at step 2000: 0.068546\n", "Learning rate at step 2000: 0.040931\n", "Minibatch accuracy: 100.0%\n", "Validation accuracy: 84.7%\n", "\n", "Test accuracy: 90.9%\n" ] } ], "source": [ "num_steps = 2001\n", "\n", "with tf.Session(graph=graph) as session:\n", " tf.initialize_all_variables().run()\n", " print('Initialized')\n", " for step in range(num_steps):\n", " offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n", " batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n", " batch_labels = train_labels[offset:(offset + batch_size), :]\n", " feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n", " _, l, predictions = session.run(\n", " [optimizer, loss, train_prediction], feed_dict=feed_dict)\n", " if (step % 50 == 0):\n", " print('Minibatch loss at step %d: %f' % (step, l))\n", " print(\"Learning rate at step %d: %f\" % (step, learning_rate.eval()))\n", " print('Minibatch accuracy: %.1f%%' % accuracy(predictions, batch_labels))\n", " print('Validation accuracy: %.1f%%\\n' % accuracy(\n", " valid_prediction.eval(), valid_labels))\n", " print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "colab": { "default_view": {}, "name": "4_convolutions.ipynb", "provenance": [], "version": "0.3.2", "views": {} }, "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
jorgemauricio/INIFAP_Course	algoritmos/Analisis_Crimes.ipynb	1	115977	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Crimes\n", "Para el siguiente ejercicio vamos a utilizar la base de datos de los crimenes en Chicago de 2001 a presente del siguiente [Link](https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# librerias\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import statsmodels.formula.api as sm\n", "import seaborn as sns\n", "%matplotlib inline\n", "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('../data/CrimesNoNA.csv')" ] }, { "cell_type": "code", "execution_count": 4, "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>Unnamed: 0</th>\n", " <th>ID</th>\n", " <th>Case Number</th>\n", " <th>Date</th>\n", " <th>Block</th>\n", " <th>IUCR</th>\n", " <th>Primary Type</th>\n", " <th>Description</th>\n", " <th>Location Description</th>\n", " <th>Arrest</th>\n", " <th>...</th>\n", " <th>Ward</th>\n", " <th>Community Area</th>\n", " <th>FBI Code</th>\n", " <th>X Coordinate</th>\n", " <th>Y Coordinate</th>\n", " <th>Year</th>\n", " <th>Updated On</th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " <th>Location</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>2753874</td>\n", " <td>HJ394416</td>\n", " <td>05/01/2003 12:00:00 PM</td>\n", " <td>080XX S DREXEL AVE</td>\n", " <td>0890</td>\n", " <td>THEFT</td>\n", " <td>FROM BUILDING</td>\n", " <td>APARTMENT</td>\n", " <td>False</td>\n", " <td>...</td>\n", " <td>8.0</td>\n", " <td>44.0</td>\n", " <td>06</td>\n", " <td>1183627.0</td>\n", " <td>1851969.0</td>\n", " <td>2003</td>\n", " <td>04/15/2016 08:55:02 AM</td>\n", " <td>41.748989</td>\n", " <td>-87.602691</td>\n", " <td>(41.748988677, -87.602691353)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2753875</td>\n", " <td>HJ376019</td>\n", " <td>05/20/2003 04:55:00 PM</td>\n", " <td>048XX S PRAIRIE AVE</td>\n", " <td>1506</td>\n", " <td>PROSTITUTION</td>\n", " <td>SOLICIT ON PUBLIC WAY</td>\n", " <td>STREET</td>\n", " <td>True</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>38.0</td>\n", " <td>16</td>\n", " <td>1178856.0</td>\n", " <td>1873092.0</td>\n", " <td>2003</td>\n", " <td>04/15/2016 08:55:02 AM</td>\n", " <td>41.807062</td>\n", " <td>-87.619531</td>\n", " <td>(41.807062178, -87.619531469)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>2753876</td>\n", " <td>HJ384436</td>\n", " <td>05/24/2003 03:25:00 PM</td>\n", " <td>036XX S WENTWORTH AVE</td>\n", " <td>1130</td>\n", " <td>DECEPTIVE PRACTICE</td>\n", " <td>FRAUD OR CONFIDENCE GAME</td>\n", " <td>STREET</td>\n", " <td>True</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>34.0</td>\n", " <td>11</td>\n", " <td>1175539.0</td>\n", " <td>1881005.0</td>\n", " <td>2003</td>\n", " <td>04/15/2016 08:55:02 AM</td>\n", " <td>41.828851</td>\n", " <td>-87.631460</td>\n", " <td>(41.828851109, -87.631460054)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>2753877</td>\n", " <td>HJ378088</td>\n", " <td>05/21/2003 04:50:00 PM</td>\n", " <td>122XX S HALSTED ST</td>\n", " <td>1811</td>\n", " <td>NARCOTICS</td>\n", " <td>POSS: CANNABIS 30GMS OR LESS</td>\n", " <td>SIDEWALK</td>\n", " <td>True</td>\n", " <td>...</td>\n", " <td>34.0</td>\n", " <td>53.0</td>\n", " <td>18</td>\n", " <td>1173211.0</td>\n", " <td>1823609.0</td>\n", " <td>2003</td>\n", " <td>04/15/2016 08:55:02 AM</td>\n", " <td>41.671401</td>\n", " <td>-87.641694</td>\n", " <td>(41.671401054, -87.641694057)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>2753878</td>\n", " <td>HJ377036</td>\n", " <td>05/21/2003 09:01:47 AM</td>\n", " <td>023XX N KARLOV AVE</td>\n", " <td>2027</td>\n", " <td>NARCOTICS</td>\n", " <td>POSS: CRACK</td>\n", " <td>STREET</td>\n", " <td>True</td>\n", " <td>...</td>\n", " <td>31.0</td>\n", " <td>20.0</td>\n", " <td>18</td>\n", " <td>1148661.0</td>\n", " <td>1915297.0</td>\n", " <td>2003</td>\n", " <td>04/15/2016 08:55:02 AM</td>\n", " <td>41.923513</td>\n", " <td>-87.729189</td>\n", " <td>(41.923512556, -87.729188885)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 23 columns</p>\n", "</div>" ], "text/plain": [ " Unnamed: 0 ID Case Number Date \\\n", "0 0 2753874 HJ394416 05/01/2003 12:00:00 PM \n", "1 1 2753875 HJ376019 05/20/2003 04:55:00 PM \n", "2 2 2753876 HJ384436 05/24/2003 03:25:00 PM \n", "3 3 2753877 HJ378088 05/21/2003 04:50:00 PM \n", "4 4 2753878 HJ377036 05/21/2003 09:01:47 AM \n", "\n", " Block IUCR Primary Type \\\n", "0 080XX S DREXEL AVE 0890 THEFT \n", "1 048XX S PRAIRIE AVE 1506 PROSTITUTION \n", "2 036XX S WENTWORTH AVE 1130 DECEPTIVE PRACTICE \n", "3 122XX S HALSTED ST 1811 NARCOTICS \n", "4 023XX N KARLOV AVE 2027 NARCOTICS \n", "\n", " Description Location Description Arrest \\\n", "0 FROM BUILDING APARTMENT False \n", "1 SOLICIT ON PUBLIC WAY STREET True \n", "2 FRAUD OR CONFIDENCE GAME STREET True \n", "3 POSS: CANNABIS 30GMS OR LESS SIDEWALK True \n", "4 POSS: CRACK STREET True \n", "\n", " ... Ward Community Area FBI Code \\\n", "0 ... 8.0 44.0 06 \n", "1 ... 3.0 38.0 16 \n", "2 ... 3.0 34.0 11 \n", "3 ... 34.0 53.0 18 \n", "4 ... 31.0 20.0 18 \n", "\n", " X Coordinate Y Coordinate Year Updated On Latitude \\\n", "0 1183627.0 1851969.0 2003 04/15/2016 08:55:02 AM 41.748989 \n", "1 1178856.0 1873092.0 2003 04/15/2016 08:55:02 AM 41.807062 \n", "2 1175539.0 1881005.0 2003 04/15/2016 08:55:02 AM 41.828851 \n", "3 1173211.0 1823609.0 2003 04/15/2016 08:55:02 AM 41.671401 \n", "4 1148661.0 1915297.0 2003 04/15/2016 08:55:02 AM 41.923513 \n", "\n", " Longitude Location \n", "0 -87.602691 (41.748988677, -87.602691353) \n", "1 -87.619531 (41.807062178, -87.619531469) \n", "2 -87.631460 (41.828851109, -87.631460054) \n", "3 -87.641694 (41.671401054, -87.641694057) \n", "4 -87.729189 (41.923512556, -87.729188885) \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# checar la informacion del dataframe\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 5764119 entries, 0 to 5764118\n", "Data columns (total 23 columns):\n", "Unnamed: 0 int64\n", "ID int64\n", "Case Number object\n", "Date object\n", "Block object\n", "IUCR object\n", "Primary Type object\n", "Description object\n", "Location Description object\n", "Arrest bool\n", "Domestic bool\n", "Beat int64\n", "District float64\n", "Ward float64\n", "Community Area float64\n", "FBI Code object\n", "X Coordinate float64\n", "Y Coordinate float64\n", "Year int64\n", "Updated On object\n", "Latitude float64\n", "Longitude float64\n", "Location object\n", "dtypes: bool(2), float64(7), int64(4), object(10)\n", "memory usage: 934.5+ MB\n" ] } ], "source": [ "# información del dataframe\n", "data.info()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Unnamed: 0', 'ID', 'Case Number', 'Date', 'Block', 'IUCR',\n", " 'Primary Type', 'Description', 'Location Description', 'Arrest',\n", " 'Domestic', 'Beat', 'District', 'Ward', 'Community Area', 'FBI Code',\n", " 'X Coordinate', 'Y Coordinate', 'Year', 'Updated On', 'Latitude',\n", " 'Longitude', 'Location'],\n", " dtype='object')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# columnas que existen en el dataframe\n", "data.columns" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Unnamed: 0 5764119\n", "ID 5764119\n", "Case Number 5764119\n", "Date 5764119\n", "Block 5764119\n", "IUCR 5764119\n", "Primary Type 5764119\n", "Description 5764119\n", "Location Description 5764119\n", "Arrest 5764119\n", "Domestic 5764119\n", "Beat 5764119\n", "District 5764119\n", "Ward 5764119\n", "Community Area 5764119\n", "FBI Code 5764119\n", "X Coordinate 5764119\n", "Y Coordinate 5764119\n", "Year 5764119\n", "Updated On 5764119\n", "Latitude 5764119\n", "Longitude 5764119\n", "Location 5764119\n", "dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.count()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# eliminar datos nulos\n", "data = data.dropna()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# data.to_csv('../data/CrimesNoNA.csv')" ] }, { "cell_type": "code", "execution_count": 10, "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>Unnamed: 0</th>\n", " <th>ID</th>\n", " <th>Beat</th>\n", " <th>District</th>\n", " <th>Ward</th>\n", " <th>Community Area</th>\n", " <th>X Coordinate</th>\n", " <th>Y Coordinate</th>\n", " <th>Year</th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>5.764119e+06</td>\n", " <td>5.764119e+06</td>\n", " <td>5.764119e+06</td>\n", " <td>5.764119e+06</td>\n", " <td>5.764119e+06</td>\n", " <td>5.764119e+06</td>\n", " <td>5.764119e+06</td>\n", " <td>5.764119e+06</td>\n", " <td>5.764119e+06</td>\n", " <td>5.764119e+06</td>\n", " <td>5.764119e+06</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>2.985964e+06</td>\n", " <td>6.427085e+06</td>\n", " <td>1.190543e+03</td>\n", " <td>1.129892e+01</td>\n", " <td>2.262782e+01</td>\n", " <td>3.765230e+01</td>\n", " <td>1.164481e+06</td>\n", " <td>1.885503e+06</td>\n", " <td>2.008571e+03</td>\n", " <td>4.184142e+01</td>\n", " <td>-8.767194e+01</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.790435e+06</td>\n", " <td>2.667375e+06</td>\n", " <td>7.027939e+02</td>\n", " <td>6.938395e+00</td>\n", " <td>1.381158e+01</td>\n", " <td>2.153224e+01</td>\n", " <td>1.742568e+04</td>\n", " <td>3.306369e+04</td>\n", " <td>4.350682e+00</td>\n", " <td>9.099525e-02</td>\n", " <td>6.297949e-02</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.000000e+00</td>\n", " <td>6.340000e+02</td>\n", " <td>1.110000e+02</td>\n", " <td>1.000000e+00</td>\n", " <td>1.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>0.000000e+00</td>\n", " <td>2.001000e+03</td>\n", " <td>3.661945e+01</td>\n", " <td>-9.168657e+01</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.454616e+06</td>\n", " <td>4.002952e+06</td>\n", " <td>6.220000e+02</td>\n", " <td>6.000000e+00</td>\n", " <td>1.000000e+01</td>\n", " <td>2.300000e+01</td>\n", " <td>1.152873e+06</td>\n", " <td>1.858937e+06</td>\n", " <td>2.005000e+03</td>\n", " <td>4.176835e+01</td>\n", " <td>-8.771406e+01</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>2.944914e+06</td>\n", " <td>6.440010e+06</td>\n", " <td>1.034000e+03</td>\n", " <td>1.000000e+01</td>\n", " <td>2.200000e+01</td>\n", " <td>3.200000e+01</td>\n", " <td>1.165933e+06</td>\n", " <td>1.890186e+06</td>\n", " <td>2.008000e+03</td>\n", " <td>4.185440e+01</td>\n", " <td>-8.766637e+01</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>4.387150e+06</td>\n", " <td>8.708174e+06</td>\n", " <td>1.731000e+03</td>\n", " <td>1.700000e+01</td>\n", " <td>3.400000e+01</td>\n", " <td>5.800000e+01</td>\n", " <td>1.176352e+06</td>\n", " <td>1.909179e+06</td>\n", " <td>2.012000e+03</td>\n", " <td>4.190659e+01</td>\n", " <td>-8.762844e+01</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>6.458127e+06</td>\n", " <td>1.112554e+07</td>\n", " <td>2.535000e+03</td>\n", " <td>3.100000e+01</td>\n", " <td>5.000000e+01</td>\n", " <td>7.700000e+01</td>\n", " <td>1.205119e+06</td>\n", " <td>1.951573e+06</td>\n", " <td>2.017000e+03</td>\n", " <td>4.202271e+01</td>\n", " <td>-8.752453e+01</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Unnamed: 0 ID Beat District Ward \\\n", "count 5.764119e+06 5.764119e+06 5.764119e+06 5.764119e+06 5.764119e+06 \n", "mean 2.985964e+06 6.427085e+06 1.190543e+03 1.129892e+01 2.262782e+01 \n", "std 1.790435e+06 2.667375e+06 7.027939e+02 6.938395e+00 1.381158e+01 \n", "min 0.000000e+00 6.340000e+02 1.110000e+02 1.000000e+00 1.000000e+00 \n", "25% 1.454616e+06 4.002952e+06 6.220000e+02 6.000000e+00 1.000000e+01 \n", "50% 2.944914e+06 6.440010e+06 1.034000e+03 1.000000e+01 2.200000e+01 \n", "75% 4.387150e+06 8.708174e+06 1.731000e+03 1.700000e+01 3.400000e+01 \n", "max 6.458127e+06 1.112554e+07 2.535000e+03 3.100000e+01 5.000000e+01 \n", "\n", " Community Area X Coordinate Y Coordinate Year Latitude \\\n", "count 5.764119e+06 5.764119e+06 5.764119e+06 5.764119e+06 5.764119e+06 \n", "mean 3.765230e+01 1.164481e+06 1.885503e+06 2.008571e+03 4.184142e+01 \n", "std 2.153224e+01 1.742568e+04 3.306369e+04 4.350682e+00 9.099525e-02 \n", "min 0.000000e+00 0.000000e+00 0.000000e+00 2.001000e+03 3.661945e+01 \n", "25% 2.300000e+01 1.152873e+06 1.858937e+06 2.005000e+03 4.176835e+01 \n", "50% 3.200000e+01 1.165933e+06 1.890186e+06 2.008000e+03 4.185440e+01 \n", "75% 5.800000e+01 1.176352e+06 1.909179e+06 2.012000e+03 4.190659e+01 \n", "max 7.700000e+01 1.205119e+06 1.951573e+06 2.017000e+03 4.202271e+01 \n", "\n", " Longitude \n", "count 5.764119e+06 \n", "mean -8.767194e+01 \n", "std 6.297949e-02 \n", "min -9.168657e+01 \n", "25% -8.771406e+01 \n", "50% -8.766637e+01 \n", "75% -8.762844e+01 \n", "max -8.752453e+01 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Year\n", "2001 3850\n", "2002 345009\n", "2003 471992\n", "2004 467128\n", "2005 449870\n", "2006 445495\n", "2007 435527\n", "2008 419787\n", "2009 385828\n", "2010 368409\n", "2011 350470\n", "2012 334394\n", "2013 304243\n", "2014 269320\n", "2015 259583\n", "2016 250472\n", "2017 202742\n", "Name: Arrest, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.groupby('Year').count()['Arrest']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x193b3a9e8>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1190f05f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.groupby('Year').count()['Arrest'].plot.bar()" ] }, { "cell_type": "code", "execution_count": 13, "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>Unnamed: 0</th>\n", " <th>ID</th>\n", " <th>Case Number</th>\n", " <th>Date</th>\n", " <th>Block</th>\n", " <th>IUCR</th>\n", " <th>Primary Type</th>\n", " <th>Description</th>\n", " <th>Location Description</th>\n", " <th>Arrest</th>\n", " <th>...</th>\n", " <th>Ward</th>\n", " <th>Community Area</th>\n", " <th>FBI Code</th>\n", " <th>X Coordinate</th>\n", " <th>Y Coordinate</th>\n", " <th>Year</th>\n", " <th>Updated On</th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " <th>Location</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>2753874</td>\n", " <td>HJ394416</td>\n", " <td>05/01/2003 12:00:00 PM</td>\n", " <td>080XX S DREXEL AVE</td>\n", " <td>0890</td>\n", " <td>THEFT</td>\n", " <td>FROM BUILDING</td>\n", " <td>APARTMENT</td>\n", " <td>False</td>\n", " <td>...</td>\n", " <td>8.0</td>\n", " <td>44.0</td>\n", " <td>06</td>\n", " <td>1183627.0</td>\n", " <td>1851969.0</td>\n", " <td>2003</td>\n", " <td>04/15/2016 08:55:02 AM</td>\n", " <td>41.748989</td>\n", " <td>-87.602691</td>\n", " <td>(41.748988677, -87.602691353)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>2753875</td>\n", " <td>HJ376019</td>\n", " <td>05/20/2003 04:55:00 PM</td>\n", " <td>048XX S PRAIRIE AVE</td>\n", " <td>1506</td>\n", " <td>PROSTITUTION</td>\n", " <td>SOLICIT ON PUBLIC WAY</td>\n", " <td>STREET</td>\n", " <td>True</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>38.0</td>\n", " <td>16</td>\n", " <td>1178856.0</td>\n", " <td>1873092.0</td>\n", " <td>2003</td>\n", " <td>04/15/2016 08:55:02 AM</td>\n", " <td>41.807062</td>\n", " <td>-87.619531</td>\n", " <td>(41.807062178, -87.619531469)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>2753876</td>\n", " <td>HJ384436</td>\n", " <td>05/24/2003 03:25:00 PM</td>\n", " <td>036XX S WENTWORTH AVE</td>\n", " <td>1130</td>\n", " <td>DECEPTIVE PRACTICE</td>\n", " <td>FRAUD OR CONFIDENCE GAME</td>\n", " <td>STREET</td>\n", " <td>True</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>34.0</td>\n", " <td>11</td>\n", " <td>1175539.0</td>\n", " <td>1881005.0</td>\n", " <td>2003</td>\n", " <td>04/15/2016 08:55:02 AM</td>\n", " <td>41.828851</td>\n", " <td>-87.631460</td>\n", " <td>(41.828851109, -87.631460054)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>2753877</td>\n", " <td>HJ378088</td>\n", " <td>05/21/2003 04:50:00 PM</td>\n", " <td>122XX S HALSTED ST</td>\n", " <td>1811</td>\n", " <td>NARCOTICS</td>\n", " <td>POSS: CANNABIS 30GMS OR LESS</td>\n", " <td>SIDEWALK</td>\n", " <td>True</td>\n", " <td>...</td>\n", " <td>34.0</td>\n", " <td>53.0</td>\n", " <td>18</td>\n", " <td>1173211.0</td>\n", " <td>1823609.0</td>\n", " <td>2003</td>\n", " <td>04/15/2016 08:55:02 AM</td>\n", " <td>41.671401</td>\n", " <td>-87.641694</td>\n", " <td>(41.671401054, -87.641694057)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>2753878</td>\n", " <td>HJ377036</td>\n", " <td>05/21/2003 09:01:47 AM</td>\n", " <td>023XX N KARLOV AVE</td>\n", " <td>2027</td>\n", " <td>NARCOTICS</td>\n", " <td>POSS: CRACK</td>\n", " <td>STREET</td>\n", " <td>True</td>\n", " <td>...</td>\n", " <td>31.0</td>\n", " <td>20.0</td>\n", " <td>18</td>\n", " <td>1148661.0</td>\n", " <td>1915297.0</td>\n", " <td>2003</td>\n", " <td>04/15/2016 08:55:02 AM</td>\n", " <td>41.923513</td>\n", " <td>-87.729189</td>\n", " <td>(41.923512556, -87.729188885)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 23 columns</p>\n", "</div>" ], "text/plain": [ " Unnamed: 0 ID Case Number Date \\\n", "0 0 2753874 HJ394416 05/01/2003 12:00:00 PM \n", "1 1 2753875 HJ376019 05/20/2003 04:55:00 PM \n", "2 2 2753876 HJ384436 05/24/2003 03:25:00 PM \n", "3 3 2753877 HJ378088 05/21/2003 04:50:00 PM \n", "4 4 2753878 HJ377036 05/21/2003 09:01:47 AM \n", "\n", " Block IUCR Primary Type \\\n", "0 080XX S DREXEL AVE 0890 THEFT \n", "1 048XX S PRAIRIE AVE 1506 PROSTITUTION \n", "2 036XX S WENTWORTH AVE 1130 DECEPTIVE PRACTICE \n", "3 122XX S HALSTED ST 1811 NARCOTICS \n", "4 023XX N KARLOV AVE 2027 NARCOTICS \n", "\n", " Description Location Description Arrest \\\n", "0 FROM BUILDING APARTMENT False \n", "1 SOLICIT ON PUBLIC WAY STREET True \n", "2 FRAUD OR CONFIDENCE GAME STREET True \n", "3 POSS: CANNABIS 30GMS OR LESS SIDEWALK True \n", "4 POSS: CRACK STREET True \n", "\n", " ... Ward Community Area FBI Code \\\n", "0 ... 8.0 44.0 06 \n", "1 ... 3.0 38.0 16 \n", "2 ... 3.0 34.0 11 \n", "3 ... 34.0 53.0 18 \n", "4 ... 31.0 20.0 18 \n", "\n", " X Coordinate Y Coordinate Year Updated On Latitude \\\n", "0 1183627.0 1851969.0 2003 04/15/2016 08:55:02 AM 41.748989 \n", "1 1178856.0 1873092.0 2003 04/15/2016 08:55:02 AM 41.807062 \n", "2 1175539.0 1881005.0 2003 04/15/2016 08:55:02 AM 41.828851 \n", "3 1173211.0 1823609.0 2003 04/15/2016 08:55:02 AM 41.671401 \n", "4 1148661.0 1915297.0 2003 04/15/2016 08:55:02 AM 41.923513 \n", "\n", " Longitude Location \n", "0 -87.602691 (41.748988677, -87.602691353) \n", "1 -87.619531 (41.807062178, -87.619531469) \n", "2 -87.631460 (41.828851109, -87.631460054) \n", "3 -87.641694 (41.671401054, -87.641694057) \n", "4 -87.729189 (41.923512556, -87.729188885) \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1613152" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(data[data['Arrest']==True])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4150967" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(data[data['Arrest']==False])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5764119" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Arrest'].count()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10c16e630>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x113bfb518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data.groupby(['Year','Arrest']).count()['ID'].plot.bar(figsize=(20,5))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Unnamed: 0', 'ID', 'Case Number', 'Date', 'Block', 'IUCR',\n", " 'Primary Type', 'Description', 'Location Description', 'Arrest',\n", " 'Domestic', 'Beat', 'District', 'Ward', 'Community Area', 'FBI Code',\n", " 'X Coordinate', 'Y Coordinate', 'Year', 'Updated On', 'Latitude',\n", " 'Longitude', 'Location'],\n", " dtype='object')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data2 = data.filter(items=['Year','Primary Type', 'Arrest','Location Description','District'])" ] }, { "cell_type": "code", "execution_count": 21, "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>Year</th>\n", " <th>Primary Type</th>\n", " <th>Arrest</th>\n", " <th>Location Description</th>\n", " <th>District</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2003</td>\n", " <td>THEFT</td>\n", " <td>False</td>\n", " <td>APARTMENT</td>\n", " <td>6.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2003</td>\n", " <td>PROSTITUTION</td>\n", " <td>True</td>\n", " <td>STREET</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2003</td>\n", " <td>DECEPTIVE PRACTICE</td>\n", " <td>True</td>\n", " <td>STREET</td>\n", " <td>9.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2003</td>\n", " <td>NARCOTICS</td>\n", " <td>True</td>\n", " <td>SIDEWALK</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2003</td>\n", " <td>NARCOTICS</td>\n", " <td>True</td>\n", " <td>STREET</td>\n", " <td>25.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year Primary Type Arrest Location Description District\n", "0 2003 THEFT False APARTMENT 6.0\n", "1 2003 PROSTITUTION True STREET 2.0\n", "2 2003 DECEPTIVE PRACTICE True STREET 9.0\n", "3 2003 NARCOTICS True SIDEWALK 5.0\n", "4 2003 NARCOTICS True STREET 25.0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data2.head()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in np.unique(data2['Primary Type']):\n", " data2[i] = [1 if x==i else 0 for x in data2['Primary Type']]" ] }, { "cell_type": "code", "execution_count": 23, "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>Year</th>\n", " <th>Primary Type</th>\n", " <th>Arrest</th>\n", " <th>Location Description</th>\n", " <th>District</th>\n", " <th>ARSON</th>\n", " <th>ASSAULT</th>\n", " <th>BATTERY</th>\n", " <th>BURGLARY</th>\n", " <th>CONCEALED CARRY LICENSE VIOLATION</th>\n", " <th>...</th>\n", " <th>OTHER OFFENSE</th>\n", " <th>PROSTITUTION</th>\n", " <th>PUBLIC INDECENCY</th>\n", " <th>PUBLIC PEACE VIOLATION</th>\n", " <th>RITUALISM</th>\n", " <th>ROBBERY</th>\n", " <th>SEX OFFENSE</th>\n", " <th>STALKING</th>\n", " <th>THEFT</th>\n", " <th>WEAPONS VIOLATION</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2003</td>\n", " <td>THEFT</td>\n", " <td>False</td>\n", " <td>APARTMENT</td>\n", " <td>6.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</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>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2003</td>\n", " <td>PROSTITUTION</td>\n", " <td>True</td>\n", " <td>STREET</td>\n", " <td>2.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</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>2</th>\n", " <td>2003</td>\n", " <td>DECEPTIVE PRACTICE</td>\n", " <td>True</td>\n", " <td>STREET</td>\n", " <td>9.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</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>3</th>\n", " <td>2003</td>\n", " <td>NARCOTICS</td>\n", " <td>True</td>\n", " <td>SIDEWALK</td>\n", " <td>5.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</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>4</th>\n", " <td>2003</td>\n", " <td>NARCOTICS</td>\n", " <td>True</td>\n", " <td>STREET</td>\n", " <td>25.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</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", "<p>5 rows × 39 columns</p>\n", "</div>" ], "text/plain": [ " Year Primary Type Arrest Location Description District ARSON \\\n", "0 2003 THEFT False APARTMENT 6.0 0 \n", "1 2003 PROSTITUTION True STREET 2.0 0 \n", "2 2003 DECEPTIVE PRACTICE True STREET 9.0 0 \n", "3 2003 NARCOTICS True SIDEWALK 5.0 0 \n", "4 2003 NARCOTICS True STREET 25.0 0 \n", "\n", " ASSAULT BATTERY BURGLARY CONCEALED CARRY LICENSE VIOLATION \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "\n", " ... OTHER OFFENSE PROSTITUTION PUBLIC INDECENCY \\\n", "0 ... 0 0 0 \n", "1 ... 0 1 0 \n", "2 ... 0 0 0 \n", "3 ... 0 0 0 \n", "4 ... 0 0 0 \n", "\n", " PUBLIC PEACE VIOLATION RITUALISM ROBBERY SEX OFFENSE STALKING THEFT \\\n", "0 0 0 0 0 0 1 \n", "1 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 \n", "4 0 0 0 0 0 0 \n", "\n", " WEAPONS VIOLATION \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "\n", "[5 rows x 39 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data2.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x115957160>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x115989470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data2.groupby('Year').sum()[['PROSTITUTION','BATTERY']].plot.bar()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Year', 'Primary Type', 'Arrest', 'Location Description', 'District',\n", " 'ARSON', 'ASSAULT', 'BATTERY', 'BURGLARY',\n", " 'CONCEALED CARRY LICENSE VIOLATION', 'CRIM SEXUAL ASSAULT',\n", " 'CRIMINAL DAMAGE', 'CRIMINAL TRESPASS', 'DECEPTIVE PRACTICE',\n", " 'GAMBLING', 'HOMICIDE', 'HUMAN TRAFFICKING',\n", " 'INTERFERENCE WITH PUBLIC OFFICER', 'INTIMIDATION', 'KIDNAPPING',\n", " 'LIQUOR LAW VIOLATION', 'MOTOR VEHICLE THEFT', 'NARCOTICS',\n", " 'NON - CRIMINAL', 'NON-CRIMINAL', 'NON-CRIMINAL (SUBJECT SPECIFIED)',\n", " 'OBSCENITY', 'OFFENSE INVOLVING CHILDREN', 'OTHER NARCOTIC VIOLATION',\n", " 'OTHER OFFENSE', 'PROSTITUTION', 'PUBLIC INDECENCY',\n", " 'PUBLIC PEACE VIOLATION', 'RITUALISM', 'ROBBERY', 'SEX OFFENSE',\n", " 'STALKING', 'THEFT', 'WEAPONS VIOLATION'],\n", " dtype='object')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data2.columns" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11a246320>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11a2501d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data2.groupby('Year').sum()[['PROSTITUTION','CRIM SEXUAL ASSAULT']].plot.bar()" ] }, { "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": 2 }	mit
ajitkoduri/Chicago-Crime-Analysis	Chicago Sex Crimes Analysis.ipynb	1	441052	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Dataset on my machine\n", "Crime_5_7 = pd.read_csv('C://Users/kodur/Documents/Chicago_Crimes_2005_to_2007.csv.',\n", " na_values = [None, 'NaN','Nothing'], header = 0) \n", "Crime_8_11 = pd.read_csv('C://Users/kodur/Documents/Chicago_Crimes_2008_to_2011.csv.',\n", " na_values = [None, 'NaN','Nothing'], header = 0) \n", "Crime_12_17 = pd.read_csv('C://Users/kodur/Documents/Chicago_Crimes_2012_to_2017.csv.',\n", " na_values = [None, 'NaN','Nothing'], header = 0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Demographic data of each zipcode\n", "ZipCode_Data = pd.read_csv('C://Users/kodur/Documents/Chicago_Zip_Data.csv.', header = 0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Crime_Data = [Crime_5_7, Crime_8_11, Crime_12_17]\n", "del Crime_5_7\n", "del Crime_8_11\n", "del Crime_12_17\n", "Crime_Data = pd.concat(Crime_Data,axis = 0)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#removing duplicate rows\n", "Crime_Data.drop_duplicates(subset=['ID', 'Case Number'], inplace=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#removing non-predictive columns & redundant columns\n", "Crime_Data.drop(['Unnamed: 0','Case Number','IUCR','FBI Code','Location',\n", " 'X Coordinate','Y Coordinate'], inplace = True, axis = 1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#converting index to date of crime\n", "Crime_Data.Date = pd.to_datetime(Crime_Data.Date, format = '%m/%d/%Y %I:%M:%S %p')\n", "Crime_Data['Updated On'] = pd.to_datetime(Crime_Data['Updated On'], format = '%m/%d/%Y %I:%M:%S %p')\n", "Crime_Data.index = pd.DatetimeIndex(Crime_Data.Date)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Groups = Crime_Data.groupby(Crime_Data['Primary Type'])\n", "Groups = dict(list(Groups))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#grouping sex crimes together to look into\n", "SexCrimes_Data = [Groups['CRIM SEXUAL ASSAULT'], Groups['HUMAN TRAFFICKING'],\n", " Groups['OFFENSE INVOLVING CHILDREN'], Groups['PROSTITUTION'],\n", " Groups['SEX OFFENSE'], Groups['STALKING']]\n", "SexCrimes_Data = pd.concat(SexCrimes_Data, axis = 0)\n", "del Groups\n", "del Crime_Data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Zipcode of each sex crime, found through geocoding\n", "SexCrimeZipData = pd.read_csv('C://Users/kodur/Documents/ChicagoSexCrimesZipCodeData.csv',header = 0)\n", "SexCrimes_Data = SexCrimes_Data.dropna(axis = 0, how = 'any')\n", "SexCrimeZipData.index = pd.DatetimeIndex(SexCrimes_Data.index)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#adding zipcode data\n", "SexCrimes_Data['ZipCode'] = SexCrimeZipData['ZipCode']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#converting data for each zipcode into list zipdata.\n", "G = dict(ZipCode_Data)\n", "keys = list(G.keys())\n", "listdata = []\n", "zipdata = []\n", "for i in range(len(G[keys[0]])):\n", " for j in range(1,len(keys)):\n", " listdata.append(G[keys[j]][i])\n", " listdata = []\n", " zipdata.append(listdata)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#binary search algorithm to find zip code\n", "def BinarySearch(item, L, data, low, high):\n", " mid = int(low + (high - low) / 2)\n", " if (item == L[mid]):\n", " return data[mid]\n", " elif (low == high):\n", " return None\n", " else:\n", " if (item < L[mid]):\n", " return BinarySearch(item, L, data, low, mid - 1)\n", " else:\n", " return BinarySearch(item, L, data, mid + 1, high)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#creating a list of the zipcode demographics of each location that the crime occurred in\n", "SC_Data_Zip = list(SexCrimes_Data.ZipCode)\n", "zdata = [None] * len(SC_Data_Zip)\n", "for i in range(SexCrimeZipData.ZipCode.shape[0]):\n", " z = BinarySearch(SC_Data_Zip[i], ZipCode_Data.ZipCode, zipdata, 0, 84)\n", " zdata[i] = z" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#concatenating the original data with the additional demographics data\n", "zdata = pd.DataFrame(zdata[:], columns=keys[1:])\n", "zdata.index = SexCrimes_Data.index\n", "Data = [SexCrimes_Data, zdata]\n", "Data = pd.concat(Data, axis = 1)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Turning non-int variables into categorical variables\n", "Data['Primary Type'] = pd.Categorical(Data['Primary Type'])\n", "Data['Description'] = pd.Categorical(Data['Description'])\n", "Data['Location Description'] = pd.Categorical(Data['Location Description'])\n", "Data['ZipCode'] = pd.Categorical(Data['ZipCode'])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Removing variables that are redundant about the geography of the location\n", "Data.drop(['ID','Date','Block', 'Updated On','Beat','District','Ward','Community Area'], inplace = True, axis = 1)\n", "#Adding variables about the time that each crime was reported at\n", "Data['Month'] = Data.index.month\n", "Data['Hour'] = Data.index.hour\n", "Data['Day'] = Data.index.day\n", "Data['Minute'] = Data.index.minute\n", "SC_Data = Data.groupby('Primary Type')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Splitting data by crime type\n", "Data_By_Crime = [None] * 6\n", "Data_By_Crime[0] = SC_Data.get_group('CRIM SEXUAL ASSAULT')\n", "Data_By_Crime[1] = SC_Data.get_group('HUMAN TRAFFICKING')\n", "Data_By_Crime[2] = SC_Data.get_group('OFFENSE INVOLVING CHILDREN')\n", "Data_By_Crime[3] = SC_Data.get_group('PROSTITUTION')\n", "Data_By_Crime[4] = SC_Data.get_group('SEX OFFENSE')\n", "Data_By_Crime[5]= SC_Data.get_group('STALKING')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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FORjghpUZu32v6dXuUR9XyfEzXZO6kEgkEolrzDqPaMzhdGJz0eXeu9uQvEBR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\n+ZmkPfUhPljcOsBXhGHcEcxF6QwB0ZgQQUYMfYOyVjlsUQAxq21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4i5v2HcC89m4IwrTh\nqvH4JRMz7jr47Va6m8Mgtk/LUj67xmBYy3oJn3+/3cF/9lg8Sk9XkVcaL/bCRWWSaLFZwKz1cdZE\nH9K907IT3reeJ2XatQ3c0MDVLgtgvZR/rYVZLsBFBnvW4OF3n8F8/8eq1L9ba+wqXKsonccYNxvc\ntuDNNnoYjoKlyYK9JiH7RiMUo3dwVHgF/iCAnyOi3wXw3wP414nol6/53H8DwPeZ+XNm7gH8bQD/\nqt73KRF9GQD038/e7GnfQQT/Xz+O7SNXU7uiZrGAWa2k47HUCNGwkWjX0uhGhzSTIIs0FdumYYgd\nGJgD6xfljAavRQSxkyEE71IyIzeVskzWSEiZCa9v7X5+9oQPGjo0JktjkUn6fcHOg5oBtu4lWcp5\nsb1J0tv/nXI15rV3A0BZBrtajhOH6X1pJt3Kw4JVJxgcLNbaDjjbCqfz6XNkP3omivaPP8Lwz38d\n9OEj0GoJKnK4VS4/J8IvNY8ewD68L+fx6CHsR1+C+eChhFsAV3YwpVvb7vG67YP7Yq6fpZGbzcMg\nsaLPz8FVLWuuyEH9AHO2BTXqynEEY/uA6+7u8xjjJkGvHsOVG2WZfNh/79dBP/sN2I8/khFAksqC\n1dFh5GIedHXif4dF7GW3vy+EDfKIYkqZ+dvM/FVm/jpkwvD3mPlCx+UK/BDAv0xEC5Lq4g9jnHT8\nzwD+lP78pwD8T2/wtO8uDkZ4pixEBKQFJ5WFdE8WpfBF03QsFEOXM8+lGF0U4DIXPhvzGEWYJtIR\nNZMu5qQjy9phjReegUZgrYihrBh+myIfM+4Bvc9E7lv0LtVzoywTzmo6fmfEpKdFif7RAt2DyTiS\nLrGOukOY197NQOgk0nIRVfHxPu1mTsfh450kt1sjE4dGgiX82Qb+B5+Auw7u/hK7jwu4R2tgvQQX\nOXxmwalcGHI/gBcFsCjjefB6Ab9ayLp1wc3m4p7Ew7Bntk/WSic00HRS5WXrevSbLfx2K/cZM/oS\nm9Fd41jwUp/S6RiDiP7Qqxz8YIzxHMDfIqJfYOa9K05mZiK6cowB4DsAcEIPjqciCZhuAmbkoSBV\ndWGRA7lYwXz/50qAgNPfvo8v/YMS9PiZqP2adlwM/bDXLYl+hd5HLht7lo2qkLERd50mxigmHcvo\na/q2DfIngQDHbsZPRD8P4L8G8AGAv0NE/4iZ/wgRfQXALzHzt5j5/yKi/xHArwMYAPwGdB0B+CsA\n/gci+tMAfgDg33337+KWIthCpQlouZQuZdh4clmLqBsdtxsQ0j0rJ7AHbAZeluDESA59UPx6BqCC\nJiJw78XKpsg1W1u7opcVg1rMkrXS6fFeFPrDAHRdpAoQIBthUOAbAiVSKIcurMmkmKY8B907Qfv1\nh/jhH8lhOuBnfnRPohf74coN+S5jXnvvFtz1kiZ2egKqa6Cmvb0A1oLKEgYQz1CFyXOYhw9G7maR\nS2hEVcPXNcha2E2D/FmmwRIGYIZtBlDdwtWNuF48fqb0lx78/Bw0LEGJBeoG7B0oz6/ejya3cdeB\nn52NfHEAYA/74BH8hw9gfvcTOF2nfL6Ri9pFKWlsR8IlDbiOeX4YY3wLQAHghIh++ZpXjXGMAQBE\nFMYYvwwdYzDzT+YxxjURlLGBn5alQJ6B8wzdaYr8953DGo/d9j6GkwLpeSYfaDuMhacJggvdpMIV\nG3sx2gbEQLssJD/bs/i5hYUxtZUCxm7MZeKqt/H+gaMaZQQw868B+DX9+VcA/Molj/kxgG9Nfv9L\nAP7SJY97AunezHgVqC0UjIzukavCt9fijwhsDWjyeOk4EsBByCSG3b5UbqgZLzrF3V4v+JJkjCEE\ntCMqHVI2+0UpM2uxqV2VNJHxu/fgvpOuaORhm+ge4Ls+WkqBSDoz0PE/M6jI4dclqg8z0Dd26OoU\nXGYiEumPw54GmNfeO0dwtPBOEpO6Xj7bSSIen2HsrSmFsGZsugRYK+tTmzBc5BIlOgwwmmRGzzfI\n06ngj2FqTTzTtcCbrdgkDj3c2TkseyDPwdudrJ1r+obyMMCdncs6nDZzliW6hyWKT0uYfhiFjEUG\nXuSgdr+JdAx4aVHKzN8G8G0A0E7pX3idMQaAGrIYv6v3hTHGX8E8xng52EtwhEOMC6XWit9ammGR\nWNi//xCuAE5/5JE83oA3G7nCm3Q5eerDBoBr4Y6FcR4VOczJGv7RKfqTAuQZdtOMi37woH4AtZ2Y\nexNJJ5bo+h5sX+BvMGPGe0FIZJlMKPyiAAAYHyI/GdR0UkgmdlwbXvmZXjqTnFiwNTCtJs2kKZhU\nda8Z9mJlU8hzs1Q6m70HygImrNcgaBwG4aMlCZgZfL6RUX/XA30vrx1M8odBDPonYLWeYuelQLVW\nNs1SvBuT2gP/ZImiIlBzJk8KfFQ+jsJ0xrtD4GpHwZ1zsq6SBPToIfh0BfN8IwXjbrcXlxvAXQ9+\n+kw+01kKAsBVLYXtwyXc4yfwZ+cg70GLElzoBWbVRMsmShKYkzW47+GeqsJeU89Q5PIah/ZOL4I2\nU8iSGPZ3HajpkOx6IM9Av+dj9A8WsK0DE+DzBD41KKoGODs/mqnEa8eMzmOMd4hocK0+acHCBQCa\nVnhkw4Av/58ZhmUKW/XAszNJstAc3+mx9mq7YRB/NkB4ciQxh5xacCL522wtOA2pT/pY52RsGTpB\nYaN7nXzeaSbwdf4Or/s6M2a8LshoElIqnqJFDiQy8oORLs50pAhAOp3aqYwj9zBO9AzqBhnfKzjE\neToHWjOQBm9SI9+eZGRDtBYUhFes3aKuB5YLkHPw250c0Lmx0CSS3712VdVeLXiXhlE+rI0dV7Yi\ngkq3DuvvWySN37ugjTGn8zqc8QYh3p8ZqGlBNpUReaPxuGUCXySweQY0icZ1slJq9oVQXkfwcA5I\n5LFUFhLjSwRf1zBebJp4WQKWNHSCo5BR3Ccmn+8sFY5rkoC7s3F/DWIr7/Z/1vuCvdqeoNF7cNvB\nPt0BRHCrHN39DKZndCcW3ZKQVoz8n+2/r7uOVypK5zHGOwZd0Y3Y8x91YLW1ML/zCTJVDfqzjSyY\n62wYk6LX1w2IGaZqkOfZ2HEJIwTPMbfbB0sbVeS+tgjpRed4SGAPXxjWqrpy7tTMeEfwGs+bJODE\nytSg64VDmiTgRQFOE5hdDTTtPhfbGtmsvHR9zMaCmnZMeeklpSkUh5IWY7Qg1Q5LnoH7fqQHOC/3\nhySaIE5KEymSnQeCWML70YIqeKYmSSygg9I+/AvvZCLSDTBdguKZhxlYRqLTkSUZyJXrXJjOeLMg\na2A+/EDERp98KiP1toWpav2cpzDr1dWfPc9qeu8j7Yy3O1DXq22iJpp5D851egEIbzvLpBje7uIo\nH2RAqyXcwzVM1YGqSm8nmFKmJr5uYJYL2R/1flPkEjpR5PBPn8PvKnBdC497GIAnz4CigGl6ZOcJ\nXG7x2b9I4K/VOPk/Sp26HM9U4rU7pTPeIsKmEZSy3Uu+9JmF8/LsbLztNYs17jspZnfVSx44abdO\niedvEsrhI41i5H4ASFWXRIDz4LkonfGuEJSw2l0MNJYQNyjpSdph9D7GGCIo3XUUyW0nYgmno/em\njQJDACpE4liM8jCMI/jJZIO7brR/YpaLQ1XiC/9cBItIE7mwDOBJvOh0lM88Xnw6B7QdqC8AA9iW\nQY7hswRG06GYDMj4Y9krZ7wrOCdrI00lArTIQEGE6zxQNzCna1HkL0qgqscOZIjtdQ46S5RJRd+N\neojAz1bPURgDcgwcaq2NutxAbIpYC9kLRvrKLWXn4vQhPoJIBIPhdab7pK5nv21kTbUDbDXApQY+\nY5yuajSPFvDLPH6/HIPIdy5KbxpCIVbkyiVjeM8v73oyv7krqdc51lvIuCdrYZal2Oi0LZjERYDy\nXDb4pgX3Lz/UjBlfFGRITfCl4KR+kKKx7UQhnGWgRjhikWudJnHczc4BraQ5Ga8dxzQBGoqZ1pSk\no2l3mFI0jfA9U+18xgQlTYbq+kgN4DDaTxJwp6NLa2GKQgrpXjiosfMDRKs4DkJILXAjF7Ys4JM1\niBnkGUiMnFubiT9yfweS4mbcKPiuhwHE69czTNNFNwgYoxoGIwXrIoM538BvtlK4LZdA24odU9g3\nAdlD1VmCsgy+hhS26xUwOJgzGaEHa7YwuaCvPQInBua8gv/pZ/CPn8I0rRyj66MF2zSgwlfV3tSQ\nilzW79PnMolMEymok2ScXvhxHREDj36dsHn6EPXHA5ovlSgz+W7wu1rCO+4w5qL0pmJqfH2sAh8y\nUpCWhTgDBF/FPNOrYS9Cr3lTnPGOEJXuYSQ+DNKhHJwYXfPYndEf9lTq3Guns2lAKLTbz5FzRkkY\nvUuiE/pBOz0DgEE3Pr9n5M9OC0tV0wPSRYVz0knVzhMHQ/Gp/+mhrVPgqQLSmTEEMEAOsJ3aUiXj\nFOdov5tmvD14Bx5IeNeVJBl554RrSiQXc0YmZdTK5IG7bnJBpr9PLZmGXpwzptSTYLLfdUCjjw3d\nWO9kEuG8cMehnO+qAnW9NEt67Yh4Hi3SgH3RVfAiVccLoeRMziFLYbCQzm3Xg9oe6Tlw/6xFvlng\n08KCXB9dcsyyBDdmz/7qrmEuSm8adBTPzglfDXh5y/66QqFbCEoScJaCglExSfQhASLumDHjHYC9\ndA8pY+GPlrlsmE64mtS2YK9fp8H43onHIPfDWLwxw7ce5Dxou1MRlI1RpMFiLWTVB9N77ge5bVpU\nTmFIHKX6sdMDAOgHjR71Y0b9tGDuOh3DS6qTb1VQkmWg1Qq+yGGbAcYZUOdhmiF6sEbR0x3+/pnx\nfsAq2KO6lt/1IgsaFENJAmx2QNPC1zVCsISvqiv3SxnVk9Jlevjz7Xgh6X10oAndVe468I8/BVkD\nH8zsdX/2Wx8vNn2Ni4LiCy9OoKIYKTjqZUzLBbAowc+eg7c7GCfexOg7rD8/Qf7sEdLPttIZ7jqY\nB/dBRQF+PLxdp5v3iLkovYnQ8fkr8SXvGhE6eCs6t+/XSEY6NSFuccaMdwGe2M5kqXTpIy+TJt1M\nALmNnrqk7hTssF8M9p10gxJV9Geqsg/3d71slGkK8ogF7rgOggjSx4ISQDThD/6JoTND+h6A8dxG\nSpAHe6PvyY/qYCKQczCdw5BZwO4Xn6KSziOfTgrpu895m/GWoWp1HnqhhygibxQAnyxBbS/CpYnd\n2ZWFmvr3ir9wDnr6PI7Bzb1TeUzTSKrSZJ367XZP3xFuj69j7AV7NEqz8fuCPbhp5MLSSQy4rO/d\n6DeepvG46HtJn2paoG6QVg0osWCjUcTDAOQZzMMH8JP3cJcwF6W3CYc2SAdpTy+cpN02C6Ug5mga\n+RIKYpGwb1e1WH6Ex96m9zbjdiL6A7egnVrHTKM7nQOMkXhDIFJO4mf3kotGSsXzlNI0fo65nWw0\nmry0l55kxhx77gfZ3Ioc6IyMFI0Zlfjqqcp9LxngXi2iPMNXPiZN7Qk11KgcfQ+qCJQlcIUFGwtb\n97BnQgeg5QJUijqYnId/+gwIDgKzAHHGa8Jkoqlw5+7CmuGhB7oew/0FyDOSuhGaS1W9oCBV4bCx\n4HtrEU5Vws00ZQH/1S+BvId5uhHj/HCckN6WZaAsheuHC+dDaQKzWoJ3lUxAVAchgqxaOr5nmzgV\nMYuFvA/P4F0lFIFk8h1grXRUndAHeLMBTk/UuYPhz85hvvQI/OGD+B7uGuai9DbhssJrkg7xUtyy\n4o196BipKjikdzStdGVCp+dtKP9nzJggxu6q9yf6XruJZm98TUTSSWUWxfC0w2Ls3jFNlo4c0iIX\ngdQwAFkqz9XjxYhSo6b2ocMyVc5PlfsmeAqrpVSWiqew89FEH2Ez004pMHopUpGLmAlASKViAtgS\nfJ7AZinQGDnXsIGHopn9zDOd8YUQ9QKX3wnftrDnQjPhwe37ZF8CU5YwpycS1eu8+JGGpgdL8hn1\nTrqUfrzwM0Wu4ioanTSA0cM08Fy10DThYjDPYZIEWC7gnjyTqUgvz2OrCVJKo+FW6D3WWiBT8aP6\nqUZB7+kaSCyMIbGf6wdQ08v7uYOYi9JbjtCV8XeJXzkZTfqmBdlBNsssEwuNutlTEN+mQnvGLYW1\nMMuFiCpCMZamOloLynURYXApOfJhdB8QO6habNJyoYWmEd60iiWoLKXLGugpzCCoyl67n3sG/aGT\n6n20gIqbaOjiqt0TLUvh4VWTmFAVY4VNmIpc3sckvtH0Hp4MfGbhFwVMVcNtlJMHCGf1ur7IM2a8\nANHf11/OWeZ+AP3gkzhFoNVy/wDTxxOB1iu4jx/BfvIYqFuYNImffe462Mdn4KaB2+7kNYlgilwK\n2WUJ2tVw59sxkSlJRXGvwTW8KORiMUli4hOfLMF5CtpVYzeTWfivwcItvmEPXzewy4Wo9IcBtFwK\nb915dB+ewKcGOQA628p4//nZOCm8Y5iL0lsMslY2NudeLvoJufG3hXcaBRle1MA2lY21dzGZZt4A\nZ7wzeJYOaeBvGu14WE1dcn40sNfReeSG6lgcar9EykGjVD/TgY8aLJnCVGDQ0bw1YC9cNMpzOZ8Q\neZiNaS8cHp9l4pPoefQ7DR2gppVN7XDUyaqsD0V0P4B6RJ5ruk3gygTkpLPEwyDiq4mAa8aMN4KJ\nJSGlMjqPojzPMFkqY3adIJi62Ru5m9UKZI0UkiyJTa5MYdZLUbjX7ViU9gP8+UaOPRX5LUpJbhpc\nFEZNz4dV4BgmIhHKa6W2AOt3AzXZXmE63YMpUZ5r4IA7LwmKwwDyGfy6hGkH2K0HVY1MCOsmirvu\nIuai9DZiEmdIq6UUpOfbFz8lcNBeZsR/E3B4fmGj1QhGvunnP+POQXxGW+lChrF7EPzkOtbv+6jk\nDQb13MgGGPxAoWlLFMQNfS8bUN0gxpFqQcpdD1gvU4KwaRU5eHDiX+oczKKU0X/bAbWO5/NMzkGF\nFggRor2D2+5GZf5UHKnnS87JWLDrIm2G2g6WCORKsCXQTvncl7kAzJjxpkAEsyxlctCL8I8A+X27\nA0gEfVFgpzDLhYiBNJQCxoATgj8pYTYUu/thjO5DUIy6Zpg8l9dILPB0I8lMZCS4ZeoX6hzIOeGX\nG1m3rOlsBoDReFNKkyu5n5RlI7WAWdaxWsLBe7hljvTTM/jPn8CpA8FdFxPOReltxJRHaS2QvOQD\naizM6RogA//s2e36UKsII1ppBIXwzFub8Q5BRkbzVBTqk2j2k2RCt7TvxCpKRVFBzR4nFYDk1ycW\nCJujtWL/lOiovWnleWH03g/C81wtwUUOajv1UQR4vQQvcpiNkUKS/di1VfN8FLl0fIJjxdSnNJ4U\njRxZz1F0MbV/MlUHTgy4asZ0mxkz3hbIgFYryaV//DSq1MO6sicrsS17/GSv8y9qdwtz7xTc9+Bc\nLwAdy3g8saBqDZOmEgfatLCrJWAt/GYjx6oqkJdUKbNe7YkPQxwwpUKj4boWRf+iAC0K+M/lfKjr\nx8nJpe+PQIsSfG8NPD0DV2J/BWuBjx6hf7CUNVYfuALccVzNDp5xsxH4NWaft3YZyFrg/ilw/2SM\nJbwl4qBx7BnMyvt9JfKMGe8C4cIoS8FZCk6sbHhVLR1Ua9T6yUvRVlVSuAUhYlijIS7UmNjxpyQZ\nTbWNkS4otCtDagWTJODVAlxm4DwDFYV4+OYZ3CKTzVajd9H3Y6fVs2SHK591WhxP1xDpNAJWuNwo\ncvCiAK8X8Gvh7FHVwGxqHf/fIQ77jJsDYyM9JvA1ucyETpKlEpyiegJaLuEfnQoHesIjdedb8G4H\nXi9lkmgtaJBkKAAYTgrQegmzXERLM1qUkZvq6xr+7Bz+fAOUBXDvBOZkLR1YAF4vGinLxMO0bmS9\n5RncoxPQcjkKEV/oL25AZQm3FrW9ryrxCTaE7qM1tl+VJDYebgnl7g1h7pTeZjgHavtRAXsFyBoM\nj9YAAfYnEwXw+1bjTwRNV50Hewa6Dhw257kYnfGuQcrxrBsZxw2i1PWhE6IRoNIBTbSL2cfRPdhH\nWzOzXoGDOr8sLqqMwxgydC2ha7ttQdtKI0H1Na2B2dVxhBi6tpHeYi1gDTgRb8fQJaUkHX0UafRK\nlYJUeahtB2LpLA33CqQ/bcGb0cR7Xocz3jiMhX1wT37ueqGODA7YCWfULBeSgDQM8hl1Eg/Kgd6l\nnFB4UbSbfhAx0uCQJhb0fKOfcxIRkiZBsXNycWm7Pa0ChfVgVMxU6/GDcr7rpGjsxQyfzjawgwMW\nRbRl42dncnF4CCIx81c7NaiHqTk5AX/8AfplInZyCcUY4WPBXJTedFzW0QzdjrBBvCzZwVoMywSc\nGCSLElZtZsIYAtcVK0zP5VJ7Krl/L5nmCqpAVBB7Bg8vGMV7B+4BH441Y8b7gCpkjXYkebuLXOfo\nMarCBu575YqOI252TgRCZQ7OElDdgXO1lekHxGhPaOc0GIIbsa+RLPozoRCsV9oZNeCqFj5q3wsF\nILMioBoGKUStFccnVeizZ1BKIEomHqVmTzAFQN7D4IA8g88tyDP8died3P4l3zczZrwGonCXSBKP\nwsWRc/BKRWGl0LATU3o+34wBDpNj8TCIqK+q5cKLCLyrgMbCEMW1FwrZmFcfFP/WAmkmayCxsetJ\nehEnxv6DCgodfF2Dug5U1aAPHwmvu+1G68LL3quuOdPIJMQsFsDpCu2jEsSMdMtyATq1lTsCzEXp\nTUYwyQ5FoypzKcuiz1pU2F6G2IlkZGcd+nUG/7UvyaHrHubpmRj4DsMYhxgw5WyGEaSKNdhNFfAj\nP40SIYHTogQtF+CzcyGEH5C8KRHDYaSZjCprjyuNjwEpTPl2eazOuOE4mBJQKhtENH4/tKHxDJNN\naC+pjBID95LLXIQYtZpiu8AlxbjRMYtVlPPSpUkT6ZIo35OyFMiz/XSmJAFMHy/u4ljQKI812ESl\nKYBKNuzTlQiWuk420c0OXDUqwFILKTKxYxrBLJzZLAX6Qbh5mx2yxxmgQiwRQM0XhzPePNg5sAp2\nueu0ENTPp2fw+QaoE5lQTBwgjFcl/IXjqaOEihNRyphcRIGNXGRB4keF6jLZp4KwcbUQPna1lUI5\nz8Vw/+nzaNE2jTlF0wpNoB+iOf7hvkVJIkKqYQCqWs7vg/vy3dAPKH68AadW/ksmcb5Hgrkovakg\nmnDRVHkbujFZOvLHgrr24LnyrxaT3sM+2QBYY1hncJlBurFI61ZMwMPztMjkaZEbhEZqhRPTazyD\njMYTwo8dl0SvZJclUDWAOfBSC4VynssG20v29ksxF6Qz3iRIP7eTqFBSNS7z2PEPa4H0YjDGh2ap\nFHBBLZ9YwAU7pT4+Px4vPK/tATMAg5Nxv26uUggrNzSFdk85io1IDfMpy6R74r2M5vNMXntwQnMp\ncwz3SqTbSrq1zgPbnUxFJl1bkFjrwAQuqgNggUQLbGuk01PXMI8NeBAHAd/dvQSZGTcE3sGdncdf\nQ5gDyICdg9tsRroXMF4wDmFKkY/TOd3LqMhlP8pTUJeCIVMArlWsFzqWTiZywDjFY+fkO6Hr4Z8+\nF2V+WcgxrdE0s0T8hbXxInG7Mr2MoikgNm1YO76UZ/FxAOBOCpitATY78OOnoCIXqs+yHF09jgRz\nUXpToYbWe11QQ6OyNhh265j8sOMh8GCnBeTjp7B1A1sWYGtAneQGc91c7HzuHUeOAQe5WjWDejYe\njv31ypUZ5hyyoVXVxVGfvq8w/vQHdh4zZrwTHPKYtXsYofnvpApdAPtjtOmkgln4Zt7LONAYKTi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7X1mTHjKmjTg6x0L72vwVO/zywDZSlcP4z71V43UtatWSxA907EwilOGcZwi3i8VAzzua4l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\nGRifZ+ZvJ6I3AHiGmZ926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"text/plain": [ "<matplotlib.figure.Figure at 0x1cfff80d160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Heatmap of sex crimes in Chicago between 2005 and 2017.\n", "fig = plt.figure(figsize = (10,8))\n", "d = dict(zip(range(6),Data['Primary Type'].unique()))\n", "plt.axis('off')\n", "for i in range(6):\n", " Data_By_Crime[i] = Data_By_Crime[i].dropna(how = 'any', axis = 0)\n", " x = Data_By_Crime[i]['Longitude']\n", " y = Data_By_Crime[i]['Latitude']\n", " a = fig.add_subplot(2,3,1+i)\n", " a.set_title('%s'%d[i])\n", " heatmap, xedges, yedges = np.histogram2d(x, y, bins=120)\n", " extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n", " a.imshow(heatmap.T, extent = extent, aspect = 'auto', interpolation = 'gaussian')\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#animating graphs code\n", "import matplotlib.animation as animation\n", "from IPython.display import HTML\n", "plt.switch_backend('tkagg')\n", "Writer = animation.writers['ffmpeg']\n", "#will save animations as mp4\n", "writer = Writer(fps=1, metadata=dict(artist='Me'), bitrate=1800)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#I segment by year and see if the clusters are moving/changing in anyway.\n", "#I can then see if there is any overlap between the different sex crime types that I have selected, \n", "#and if there is, it might mean that the centroids of these clusters are hits for where the crime originates from.\n", "#couldn't figure out how to get them simultaneous, so I just did them all individually instead of in a for loop\n", "#Animation for Sexual Assault\n", "Year_Data = Data_By_Crime[0].groupby(['Year'])\n", "YData = []\n", "for j in range(2005,2018):\n", " for k in range(len(list(Year_Data))):\n", " if(j == list(Year_Data)[k][0]):\n", " YData.append(Year_Data.get_group(j))\n", "fig = plt.figure(figsize = (6,5))\n", "listyears = []\n", "for k in range(len(list(Year_Data))):\n", " listyears.append(list(Year_Data)[k][0])\n", "dyear = dict(zip(range(len(list(Year_Data))),listyears))\n", "ims = []\n", "for j in range(len(YData)):\n", " YData[j] = YData[j].dropna(how = 'any', axis = 0)\n", " x = YData[j]['Longitude']\n", " y = YData[j]['Latitude']\n", " heatmap, xedges, yedges = np.histogram2d(x, y, bins=100)\n", " extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n", " im = plt.imshow(heatmap.T, extent = extent, cmap = 'hot', aspect = 'auto', interpolation = 'bessel', animated = True)\n", " plt.title('%s'%d[0])\n", " ims.append([im])\n", "anim = animation.ArtistAnimation(fig, ims, interval=1000, blit = True)\n", "HTML(anim.to_html5_video())\n", "anim.save('sex_assault.mp4', writer=writer)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Animation for Human Trafficking\n", "Year_Data = Data_By_Crime[1].groupby(['Year'])\n", "YData = []\n", "for j in range(2005,2018):\n", " for k in range(len(list(Year_Data))):\n", " if(j == list(Year_Data)[k][0]):\n", " YData.append(Year_Data.get_group(j))\n", "fig = plt.figure(figsize = (6,4))\n", "listyears = []\n", "for k in range(len(list(Year_Data))):\n", " listyears.append(list(Year_Data)[k][0])\n", "dyear = dict(zip(range(len(list(Year_Data))),listyears))\n", "ims = []\n", "for j in range(len(YData)):\n", " YData[j] = YData[j].dropna(how = 'any', axis = 0)\n", " x = YData[j]['Longitude']\n", " y = YData[j]['Latitude']\n", " heatmap, xedges, yedges = np.histogram2d(x, y, bins=50)\n", " extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n", " im = plt.imshow(heatmap.T, extent = extent, cmap = 'binary', aspect = 'auto', interpolation = 'nearest', animated = True)\n", " plt.title('%s'%d[1])\n", " ims.append([im])\n", "anim = animation.ArtistAnimation(fig, ims, interval=1000, blit = True)\n", "HTML(anim.to_html5_video())\n", "anim.save('human_traff.mp4', writer=writer)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Animation for Child Offenses\n", "Year_Data = Data_By_Crime[2].groupby(['Year'])\n", "YData = []\n", "for j in range(2005,2018):\n", " for k in range(len(list(Year_Data))):\n", " if(j == list(Year_Data)[k][0]):\n", " YData.append(Year_Data.get_group(j))\n", "fig = plt.figure(figsize = (5,7))\n", "listyears = []\n", "for k in range(len(list(Year_Data))):\n", " listyears.append(list(Year_Data)[k][0])\n", "dyear = dict(zip(range(len(list(Year_Data))),listyears))\n", "ims = []\n", "for j in range(len(YData)):\n", " YData[j] = YData[j].dropna(how = 'any', axis = 0)\n", " x = YData[j]['Longitude']\n", " y = YData[j]['Latitude']\n", " heatmap, xedges, yedges = np.histogram2d(x, y, bins=200)\n", " extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n", " im = plt.imshow(heatmap.T, extent = extent, cmap = 'hot', aspect = 'auto', interpolation = 'nearest', animated = True)\n", " plt.title('%s'%d[2])\n", " ims.append([im])\n", "anim = animation.ArtistAnimation(fig, ims, interval=1000, blit = True)\n", "HTML(anim.to_html5_video())\n", "anim.save('child_offenses.mp4', writer=writer)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Animation for Prostitution\n", "Year_Data = Data_By_Crime[3].groupby(['Year'])\n", "YData = []\n", "for j in range(2005,2018):\n", " for k in range(len(list(Year_Data))):\n", " if(j == list(Year_Data)[k][0]):\n", " YData.append(Year_Data.get_group(j))\n", "fig = plt.figure(figsize = (7,5))\n", "listyears = []\n", "for k in range(len(list(Year_Data))):\n", " listyears.append(list(Year_Data)[k][0])\n", "dyear = dict(zip(range(len(list(Year_Data))),listyears))\n", "ims = []\n", "for j in range(len(YData)):\n", " YData[j] = YData[j].dropna(how = 'any', axis = 0)\n", " x = YData[j]['Longitude']\n", " y = YData[j]['Latitude']\n", " heatmap, xedges, yedges = np.histogram2d(x, y, bins=120)\n", " extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n", " im = plt.imshow(heatmap.T, extent = extent, cmap = 'hot', aspect = 'auto', interpolation = 'hamming', animated = True)\n", " plt.title('%s'%d[3])\n", " ims.append([im])\n", "anim = animation.ArtistAnimation(fig, ims, interval=1000, blit = True)\n", "HTML(anim.to_html5_video())\n", "anim.save('prostitution.mp4', writer=writer)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Animation for Sex Offenses\n", "Year_Data = Data_By_Crime[4].groupby(['Year'])\n", "YData = []\n", "for j in range(2005,2018):\n", " for k in range(len(list(Year_Data))):\n", " if(j == list(Year_Data)[k][0]):\n", " YData.append(Year_Data.get_group(j))\n", "fig = plt.figure(figsize = (5,5))\n", "listyears = []\n", "for k in range(len(list(Year_Data))):\n", " listyears.append(list(Year_Data)[k][0])\n", "dyear = dict(zip(range(len(list(Year_Data))),listyears))\n", "ims = []\n", "for j in range(len(YData)):\n", " YData[j] = YData[j].dropna(how = 'any', axis = 0)\n", " x = YData[j]['Longitude']\n", " y = YData[j]['Latitude']\n", " heatmap, xedges, yedges = np.histogram2d(x, y, bins=100)\n", " extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n", " im = plt.imshow(heatmap.T, extent = extent, cmap = 'hot', aspect = 'auto', interpolation = 'bessel', animated = True)\n", " plt.title('%s'%d[4])\n", " ims.append([im])\n", "anim = animation.ArtistAnimation(fig, ims, interval=1000, blit = True)\n", "HTML(anim.to_html5_video())\n", "anim.save('sex_offenses.mp4', writer=writer)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Animation for Stalking\n", "Year_Data = Data_By_Crime[5].groupby(['Year'])\n", "YData = []\n", "for j in range(2005,2018):\n", " for k in range(len(list(Year_Data))):\n", " if(j == list(Year_Data)[k][0]):\n", " YData.append(Year_Data.get_group(j))\n", "fig = plt.figure(figsize = (5,5))\n", "listyears = []\n", "for k in range(len(list(Year_Data))):\n", " listyears.append(list(Year_Data)[k][0])\n", "dyear = dict(zip(range(len(list(Year_Data))),listyears))\n", "ims = []\n", "for j in range(len(YData)):\n", " YData[j] = YData[j].dropna(how = 'any', axis = 0)\n", " x = YData[j]['Longitude']\n", " y = YData[j]['Latitude']\n", " heatmap, xedges, yedges = np.histogram2d(x, y, bins=100)\n", " extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]\n", " im = plt.imshow(heatmap.T, extent = extent, cmap = 'hot', aspect = 'auto', interpolation = 'bessel', animated = True)\n", " plt.title('%s'%d[5])\n", " ims.append([im])\n", "anim = animation.ArtistAnimation(fig, ims, interval=1000, blit = True)\n", "HTML(anim.to_html5_video())\n", "anim.save('stalking.mp4', writer=writer)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1cf99bcfc18>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1cfc61d5c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#return matplotlib back to inline viewings\n", "%matplotlib inline\n", "#Creating heatmaps for visualizing crime by zip code\n", "import seaborn as sns\n", "#Graph of Number of Crimes per zipcode and crime by zipcodes\n", "Crime_By_Zip = Data.pivot_table('Arrest', aggfunc = np.size, columns = 'ZipCode',\n", " index = Data.index.year, fill_value = 0)\n", "plt.figure(figsize = (20,8))\n", "plt.title('Number of Crimes By Year In Each Zip Code')\n", "hm = sns.heatmap(Crime_By_Zip, cmap = 'YlOrRd', linewidth = 0.01, linecolor = 'k')\n", "#Graph of Number of Arrests per zipcode and crime by zipcodes\n", "Arrests_By_Zip = Data.pivot_table('Arrest', aggfunc = np.sum, columns = 'ZipCode',\n", " index = Data.index.year, fill_value = 0)\n", "plt.figure(figsize = (20,8))\n", "plt.title('Number of Arrests By Year In Each Zip Code')\n", "hm = sns.heatmap(Arrests_By_Zip, cmap = 'YlOrRd', linewidth = 0.01, linecolor = 'k')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#converting factors into their levels to be used for classification algorithms\n", "Data['Primary Type'] = Data['Primary Type'].cat.codes\n", "Data['Description'] = Data['Description'].cat.codes\n", "Data['Location Description'] = Data['Location Description'].cat.codes\n", "Data['ZipCode'] = Data['ZipCode'].cat.codes" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#In the previous part, I tried to see if there was any way to discern whether an arrest would be made or not;\n", "#therefore, in this one I will also try to do that given the new information about the zip code data.\n", "#First, a GLM because the data has concrete boundaries on values\n", "from sklearn import linear_model\n", "#Importing PCA library\n", "from sklearn.decomposition import PCA\n", "pca = PCA()\n", "reg = linear_model.LinearRegression()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\kodur\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " after removing the cwd from sys.path.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[ 7.81712590e-01 2.02142403e-01 1.27447839e-02 3.40001566e-03\n", " 6.91592954e-08 4.71678323e-08 2.89953576e-08 1.96060171e-08\n", " 1.26366339e-08 8.83067348e-09 6.77194760e-09 4.50546061e-09\n", " 4.41824338e-09 1.49506814e-09 9.59873620e-10 9.19323111e-10\n", " 9.05504365e-10 3.10505989e-10 2.78849215e-10 1.68738403e-10\n", " 1.14456301e-10 1.16647339e-11 9.54814717e-13 7.08705292e-13\n", " 4.07321293e-13 2.28521728e-13 1.04280591e-13 6.83447570e-33]\n" ] } ], "source": [ "#Separating Data into predictors and response dataframes\n", "Data = Data.dropna(axis = 0, how = 'any')\n", "Y = Data['Arrest']\n", "Data.drop(['Arrest'], inplace = True, axis = 1)\n", "#Using PCA to optimize fit for each algorithm\n", "pca.fit(Data[0:28])\n", "PCAData = pca.fit_transform(Data)\n", "X = PCAData\n", "print(pca.explained_variance_ratio_)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Splitting Data randomly 80-20 training-test\n", "from sklearn.model_selection import train_test_split\n", "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 30)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#GLM Least Squares Regression split along the decision border, converting probability of prediction to True or False\n", "reg.fit(X_train,Y_train)\n", "Reg_Expected_Y = reg.predict(X_test)\n", "for i in range(len(Reg_Expected_Y)):\n", " if (Reg_Expected_Y[i] >= 0.5):\n", " Reg_Expected_Y[i] = True\n", " else:\n", " Reg_Expected_Y[i] = False" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Importing Confusion matrix and accuracy score reporting libraries\n", "from sklearn.metrics import confusion_matrix\n", "import itertools\n", "from sklearn.metrics import accuracy_score\n", "cm = confusion_matrix(Y_test, Reg_Expected_Y)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#copied confusion matrix plotting algorithm from scikit documentation\n", "def plot_confusion_matrix(cm, classes,\n", " normalize=False,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues):\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", " with sns.axes_style(\"white\"):\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\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", " fmt = '.2f' if normalize else 'd'\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, format(cm[i, j], fmt),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " plt.ylabel('True Arrest')\n", " plt.xlabel('Predicted Arrest')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized confusion matrix\n", "[[ 0.81863769 0.18136231]\n", " [ 0.18792581 0.81207419]]\n" ] }, { "data": { "image/png": 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B4cOHq/TLH5kPdoREJGtqtRqWlpYICAiAhYUFLCwssHTpUoYgVRo7QiIiMmucLENERGaN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\n/PhxBAcHQ6PRoFu3blCr1dV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"text/plain": [ "<matplotlib.figure.Figure at 0x1cfe32f19b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of Model is: 0.815194\n" ] } ], "source": [ "#Accuracy greatly increased compared to previous part of analyzing crime as a whole, population data for each zipcode\n", "#is a helpful predictor to whether an arrest will be made\n", "plt.figure()\n", "plot_confusion_matrix(cm, normalize = True, classes = Y.unique(), title = 'Arrest Predictions Confusion Matrix Using GLM')\n", "plt.show()\n", "print(\"Accuracy of Model is: %f\"%accuracy_score(Y_test, Reg_Expected_Y))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Will now do the Naive Bayes to predict arrest probability\n", "#Using Bernoulli because it can deal with discrete values better than the other types in scikitlearn\n", "from sklearn.naive_bayes import BernoulliNB\n", "BNB = BernoulliNB()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Fitting Naive Bayes Model to data and creating predictions\n", "BNB.fit(X_train, Y_train)\n", "BNB_Expected_Y = BNB.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized confusion matrix\n", "[[ 0.75376097 0.24623903]\n", " [ 0.20855413 0.79144587]]\n" ] }, { "data": { "image/png": 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olIiIDBonyxARkUGTWQ7yHCERERk2doRERCQpDo0SEZFBYxASEZFBk1kOMgiJ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\n+GyIKzoM+Fq/7GTcTQzs/Q6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"text/plain": [ "<matplotlib.figure.Figure at 0x1cf80889f28>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of Model is: 0.773535\n" ] } ], "source": [ "cm = confusion_matrix(Y_test, BNB_Expected_Y)\n", "plt.figure()\n", "plot_confusion_matrix(cm, normalize = True, classes = Y.unique(), \n", " title = 'Arrest Predictions Confusion Matrix Using Bernoulli Naive Bayes')\n", "plt.show()\n", "print(\"Accuracy of Model is: %f\"%accuracy_score(Y_test, BNB_Expected_Y))" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Will now do Random Forest\n", "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#testing oob score of random forests with sizes between 50 to 100 trees\n", "OOB_Err = list(range(50,100))\n", "for i in range(50,100):\n", " rfc = RandomForestClassifier(n_estimators = i, oob_score = True, n_jobs = -1)\n", " rfc.fit(X_train,Y_train)\n", " OOB_Err[i-50] = 1 - rfc.oob_score_" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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89NBDCAZ7fgM/K2pERESkNc2C2rvvvgu/348tW7Zg8eLFWLlyZYev7927F/Pnz0dNTY3y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"text/plain": [ "<matplotlib.figure.Figure at 0x1cfeee86630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plotting OOB Scores\n", "plt.figure(figsize = (10,10))\n", "with sns.axes_style(\"white\"):\n", " plt.plot(list(range(50,100)), OOB_Err)\n", "plt.title('OOB Errors Over Number of Trees')\n", "plt.xlabel('Number of Trees')\n", "plt.ylabel('OOB Error')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rforest = RandomForestClassifier(n_estimators = 90, n_jobs = -1)\n", "rforest.fit(X_train, Y_train)\n", "RF_Expected_Y = rforest.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normalized confusion matrix\n", "[[ 0.96207689 0.03792311]\n", " [ 0.20410674 0.79589326]]\n" ] }, { "data": { "image/png": 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e3h7Dhw+Hl5eXIQR1Oh0sLCyg0+mKDfnMzEyMHz8en3/+OTp16oTJkycXul6bNm2wfft2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\nI8DVAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1cff244ec50>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of Model is: 0.874876\n" ] } ], "source": [ "#Very accurate compared to previous project and much faster\n", "#Running for cumulative crimes led to a 4-hour wait and an accuracy below 50%\n", "cm = confusion_matrix(Y_test, RF_Expected_Y)\n", "plt.figure()\n", "plot_confusion_matrix(cm, normalize = True, classes = Y.unique(), \n", " title = 'Arrest Predictions Confusion Matrix Using Random Forest')\n", "plt.show()\n", "print(\"Accuracy of Model is: %f\"%accuracy_score(Y_test, RF_Expected_Y))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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SUlIA2L17N/Xq1QPAZDLdcn6DBg348ccfAfjpp59uOW4ymbBYLAC4uLiQkpKC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"text/plain": [ "<matplotlib.figure.Figure at 0x1cfc62c8f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Find most important variables in determining arrest rates Using Out of Bag Error\n", "Col_Imp =[]\n", "Col_Imp.append(list(Data.columns))\n", "Col_Imp.append(list(rforest.feature_importances_))\n", "Col_Imp = list(map(list, zip(*Col_Imp)))\n", "Col_Imp = pd.DataFrame(Col_Imp, columns = ['Predictors','Feature Importances'])\n", "\n", "#plot feature importance\n", "Col_Imp.index = Col_Imp['Predictors']\n", "colors = plt.cm.RdYlGn(np.linspace(0,1,len(Col_Imp)))\n", "plt.title('Feature Importances of Each Predictor')\n", "plt.xlabel('Importance')\n", "with sns.axes_style(\"white\"):\n", " Col_Imp['Feature Importances'].sort_values().plot(figsize = (10,10), kind = 'barh', color = colors)\n", "plt.show()" ] } ], "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
wcmckee/wcmckee	vote2014nz.ipynb	1	46195	{ "metadata": { "name": "", "signature": "sha256:516d448df3f20d766e6528268ba523f5e5918983d2dfb7f77e29862130bbe3be" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>vote2014nz</h1>\n", "\n", "Python script to get data on Election 2014 New Zealand Results\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from bs4 import BeautifulSoup\n", "import dominate\n", "import requests" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "elecreq = ('http://www.electionresults.govt.nz/electionresults_2014/partystatus.html')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "elecreq.upper()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "elecaz = requests.get(elecreq)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "eletextg = elecaz.text" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "eletextg" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "u'<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\">\\n<html>\\n<head>\\n<title>Official Count Results -- Overall Status</title>\\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\">\\n<meta name=\"generator\" content=\"Catalyst IT gen-web-ovstatus-index.p\">\\n<meta name=\"author\" content=\"New Zealand Electoral Commission\">\\n<link rel=\"stylesheet\" href=\"public.css\" type=\"text/css\">\\n</head>\\n<body>\\n<table cellSpacing=\"0\" cellPadding=\"0\" width=\"100%\" border=\"0\" bgcolor=\"#FF850C\">\\n<tr class=\"menu\"><td valign=\"bottom\" align=\"right\" colspan=\"2\" class=\"menu\"><a href=\"index.html\">HOME</a> \| <a href=\"partystatus.html\">OVERALL STATUS</a> \| <a href=\"electoratestatus.html\">ELECTORATE STATUS</a> \| <a href=\"electorateindex.html\">ELECTORATE DETAILS</a> \| <a href=\"partystatus-early.html\">ADVANCE VOTES</a> </td><td width=\"7%\" class=\"menu\" align=\"right\" valign=\"bottom\"><img src=\"img/oMan.jpg\" width=\"59\" height=\"37\" alt=\"oMan.jpg\"></td></tr>\\n<tr class=\"menu\">\\n<td class=\"bannerbar\" height=\"73\" rowspan=\"3\" align=\"left\"><img src=\"/img/eclogo.gif\" alt=\"Elections Logo\" style=\"width:150px; height:73px; align:left; padding-left:10px; padding-top:1px; padding-bottom:2px;\"></td><td class=\"bannerbar\" height=\"42\"></td></tr>\\n<tr class=\"menu\"><td height=\"10\" class=\"bannerbar\"></td></tr>\\n<tr class=\"menu\"><td height=\"21\" class=\"bannerbar\"></td></tr>\\n</table>\\n<table border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"70%\" align=\"center\">\\n<tr><td align=\"center\"><h3>Official Count Results -- Overall Status</h3>\\n</td></tr>\\n<tr><td align=\"center\"><span class=\"topmsg\">This is not the formal declaration of results.<br>The Electoral Commission declares the results of the official count by publishing a notice in the NZ Gazette.</span></td></tr>\\n<tr><td height=\"10\"> </td></tr>\\n</table>\\n<table class=\"maintable\" border=\"1\" cellpadding=\"1\" cellspacing=\"0\" align=\"center\"><tr><td style=\"padding:5px;\"><table class=\"eledata\" border=\"0\" cellpadding=\"3\" cellspacing=\"0\" align=\"center\"><tr><td colspan=\"6\" height=\"10\" class=\"orhdg\"> </td></tr>\\n<tr><th class=\"orhdg\">Results Counted:</th><td class=\"orhdg\" colspan=\"5\">7,554 of 7,554 (100.0%)</td></tr>\\n<tr><th class=\"orhdg\">Total Votes Counted:</th><td class=\"orhdg\" colspan=\"5\">2,416,479</td></tr>\\n<tr class=\"headrowR\"><th class=\"headrowL\">Party</th><th class=\"headrowR\">Party<br>Votes</th><th class=\"headrowR\">%<br>Votes</th><th class=\"headrowR\">Electorate<br>Seats</th><th class=\"headrowR\">List<br>Seats</th><th class=\"headrowR\">Total<br>Seats</th></tr>\\n<tr class=\"hhevy\"><th>National Party</th><td align=\"right\">1,131,501</td><td align=\"right\"> 47.04</td><td align=\"right\">41</td><td align=\"right\">19</td><td align=\"right\">60</td></tr>\\n<tr class=\"hlite\"><th>Labour Party</th><td align=\"right\">604,535</td><td align=\"right\"> 25.13</td><td align=\"right\">27</td><td align=\"right\">5</td><td align=\"right\">32</td></tr>\\n<tr class=\"hhevy\"><th>Green Party</th><td align=\"right\">257,359</td><td align=\"right\"> 10.70</td><td align=\"right\">0</td><td align=\"right\">14</td><td align=\"right\">14</td></tr>\\n<tr class=\"hlite\"><th>New Zealand First Party</th><td align=\"right\">208,300</td><td align=\"right\"> 8.66</td><td align=\"right\">0</td><td align=\"right\">11</td><td align=\"right\">11</td></tr>\\n<tr class=\"hhevy\"><th>M\\xc4\\x81ori Party</th><td align=\"right\">31,849</td><td align=\"right\"> 1.32</td><td align=\"right\">1</td><td align=\"right\">1</td><td align=\"right\">2</td></tr>\\n<tr class=\"hlite\"><th>ACT New Zealand</th><td align=\"right\">16,689</td><td align=\"right\"> 0.69</td><td align=\"right\">1</td><td align=\"right\">0</td><td align=\"right\">1</td></tr>\\n<tr class=\"hhevy\"><th>United Future</th><td align=\"right\">5,286</td><td align=\"right\"> 0.22</td><td align=\"right\">1</td><td align=\"right\">0</td><td align=\"right\">1</td></tr>\\n<tr class=\"hlite\"><th>Conservative</th><td align=\"right\">95,598</td><td align=\"right\"> 3.97</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\\n<tr class=\"hhevy\"><th>Internet MANA</th><td align=\"right\">34,094</td><td align=\"right\"> 1.42</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\\n<tr class=\"hlite\"><th>Aotearoa Legalise Cannabis Party</th><td align=\"right\">10,961</td><td align=\"right\"> 0.46</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\\n<tr class=\"hhevy\"><th>Ban1080</th><td align=\"right\">5,113</td><td align=\"right\"> 0.21</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\\n<tr class=\"hlite\"><th>Democrats for Social Credit</th><td align=\"right\">1,730</td><td align=\"right\"> 0.07</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\\n<tr class=\"hhevy\"><th>The Civilian Party</th><td align=\"right\">1,096</td><td align=\"right\"> 0.05</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\\n<tr class=\"hlite\"><th>NZ Independent Coalition</th><td align=\"right\">872</td><td align=\"right\"> 0.04</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\\n<tr class=\"hhevy\"><th>Focus New Zealand</th><td align=\"right\">639</td><td align=\"right\"> 0.03</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\\n<tr><td colspan=\"3\"> </td><td class=\"totals\" align=\"right\">71</td><td class=\"totals\" align=\"right\">50</td><td class=\"totals\" align=\"right\">121</td>\\n</tr>\\n</table>\\n</td></tr></table><table border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"><tr><td class=\"refrmsg\">Updated 12:00 pm on 10/10/2014</tr>\\n<tr><td align=\"center\" class=\"bannerbar\" style=\"vertical-align:middle;text-align:center\" height=\"25\"> </td></tr>\\n<tr class=\"menu\"><td style=\"text-align:center;padding-top:5px;padding-bottom:5px;\"><td></tr>\\n<tr><td><p class=\"copyr\">Copyright ©2014 New Zealand Electoral Commission, Wellington<br>All Rights Reserved</p></td></tr>\\n</table></body>\\n</html>\\n'" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "soup = BeautifulSoup(eletextg)\n", "print(soup.prettify())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\">\n", "<html>\n", " <head>\n", " <title>\n", " Official Count Results -- Overall Status\n", " </title>\n", " <meta content=\"text/html; charset=utf-8\" http-equiv=\"Content-Type\">\n", " <meta content=\"Catalyst IT gen-web-ovstatus-index.p\" name=\"generator\">\n", " <meta content=\"New Zealand Electoral Commission\" name=\"author\">\n", " <link href=\"public.css\" rel=\"stylesheet\" type=\"text/css\">\n", " </link>\n", " </meta>\n", " </meta>\n", " </meta>\n", " </head>\n", " <body>\n", " <table bgcolor=\"#FF850C\" border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\">\n", " <tr class=\"menu\">\n", " <td align=\"right\" class=\"menu\" colspan=\"2\" valign=\"bottom\">\n", " <a href=\"index.html\">\n", " HOME\n", " </a>\n", " \|\n", " <a href=\"partystatus.html\">\n", " OVERALL\u00a0STATUS\n", " </a>\n", " \|\n", " <a href=\"electoratestatus.html\">\n", " ELECTORATE\u00a0STATUS\n", " </a>\n", " \|\n", " <a href=\"electorateindex.html\">\n", " ELECTORATE DETAILS\n", " </a>\n", " \|\n", " <a href=\"partystatus-early.html\">\n", " ADVANCE\u00a0VOTES\n", " </a>\n", " </td>\n", " <td align=\"right\" class=\"menu\" valign=\"bottom\" width=\"7%\">\n", " <img alt=\"oMan.jpg\" height=\"37\" src=\"img/oMan.jpg\" width=\"59\"/>\n", " </td>\n", " </tr>\n", " <tr class=\"menu\">\n", " <td align=\"left\" class=\"bannerbar\" height=\"73\" rowspan=\"3\">\n", " <img alt=\"Elections Logo\" src=\"/img/eclogo.gif\" style=\"width:150px; height:73px; align:left; padding-left:10px; padding-top:1px; padding-bottom:2px;\"/>\n", " </td>\n", " <td class=\"bannerbar\" height=\"42\">\n", " </td>\n", " </tr>\n", " <tr class=\"menu\">\n", " <td class=\"bannerbar\" height=\"10\">\n", " </td>\n", " </tr>\n", " <tr class=\"menu\">\n", " <td class=\"bannerbar\" height=\"21\">\n", " </td>\n", " </tr>\n", " </table>\n", " <table align=\"center\" border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"70%\">\n", " <tr>\n", " <td align=\"center\">\n", " <h3>\n", " Official Count Results -- Overall Status\n", " </h3>\n", " </td>\n", " </tr>\n", " <tr>\n", " <td align=\"center\">\n", " <span class=\"topmsg\">\n", " This is not the formal declaration of results.\n", " <br>\n", " The Electoral Commission declares the results of the official count by publishing a notice in the NZ Gazette.\n", " </br>\n", " </span>\n", " </td>\n", " </tr>\n", " <tr>\n", " <td height=\"10\">\n", " </td>\n", " </tr>\n", " </table>\n", " <table align=\"center\" border=\"1\" cellpadding=\"1\" cellspacing=\"0\" class=\"maintable\">\n", " <tr>\n", " <td style=\"padding:5px;\">\n", " <table align=\"center\" border=\"0\" cellpadding=\"3\" cellspacing=\"0\" class=\"eledata\">\n", " <tr>\n", " <td class=\"orhdg\" colspan=\"6\" height=\"10\">\n", " </td>\n", " </tr>\n", " <tr>\n", " <th class=\"orhdg\">\n", " Results Counted:\n", " </th>\n", " <td class=\"orhdg\" colspan=\"5\">\n", " 7,554 of 7,554 (100.0%)\n", " </td>\n", " </tr>\n", " <tr>\n", " <th class=\"orhdg\">\n", " Total Votes Counted:\n", " </th>\n", " <td class=\"orhdg\" colspan=\"5\">\n", " 2,416,479\n", " </td>\n", " </tr>\n", " <tr class=\"headrowR\">\n", " <th class=\"headrowL\">\n", " Party\n", " </th>\n", " <th class=\"headrowR\">\n", " Party\n", " <br>\n", " Votes\n", " </br>\n", " </th>\n", " <th class=\"headrowR\">\n", " %\n", " <br>\n", " Votes\n", " </br>\n", " </th>\n", " <th class=\"headrowR\">\n", " Electorate\n", " <br>\n", " Seats\n", " </br>\n", " </th>\n", " <th class=\"headrowR\">\n", " List\n", " <br>\n", " Seats\n", " </br>\n", " </th>\n", " <th class=\"headrowR\">\n", " Total\n", " <br>\n", " Seats\n", " </br>\n", " </th>\n", " </tr>\n", " <tr class=\"hhevy\">\n", " <th>\n", " National Party\n", " </th>\n", " <td align=\"right\">\n", " 1,131,501\n", " </td>\n", " <td align=\"right\">\n", " 47.04\n", " </td>\n", " <td align=\"right\">\n", " 41\n", " </td>\n", " <td align=\"right\">\n", " 19\n", " </td>\n", " <td align=\"right\">\n", " 60\n", " </td>\n", " </tr>\n", " <tr class=\"hlite\">\n", " <th>\n", " Labour Party\n", " </th>\n", " <td align=\"right\">\n", " 604,535\n", " </td>\n", " <td align=\"right\">\n", " 25.13\n", " </td>\n", " <td align=\"right\">\n", " 27\n", " </td>\n", " <td align=\"right\">\n", " 5\n", " </td>\n", " <td align=\"right\">\n", " 32\n", " </td>\n", " </tr>\n", " <tr class=\"hhevy\">\n", " <th>\n", " Green Party\n", " </th>\n", " <td align=\"right\">\n", " 257,359\n", " </td>\n", " <td align=\"right\">\n", " 10.70\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 14\n", " </td>\n", " <td align=\"right\">\n", " 14\n", " </td>\n", " </tr>\n", " <tr class=\"hlite\">\n", " <th>\n", " New Zealand First Party\n", " </th>\n", " <td align=\"right\">\n", " 208,300\n", " </td>\n", " <td align=\"right\">\n", " 8.66\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 11\n", " </td>\n", " <td align=\"right\">\n", " 11\n", " </td>\n", " </tr>\n", " <tr class=\"hhevy\">\n", " <th>\n", " M\u00c4\u0081ori Party\n", " </th>\n", " <td align=\"right\">\n", " 31,849\n", " </td>\n", " <td align=\"right\">\n", " 1.32\n", " </td>\n", " <td align=\"right\">\n", " 1\n", " </td>\n", " <td align=\"right\">\n", " 1\n", " </td>\n", " <td align=\"right\">\n", " 2\n", " </td>\n", " </tr>\n", " <tr class=\"hlite\">\n", " <th>\n", " ACT New Zealand\n", " </th>\n", " <td align=\"right\">\n", " 16,689\n", " </td>\n", " <td align=\"right\">\n", " 0.69\n", " </td>\n", " <td align=\"right\">\n", " 1\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 1\n", " </td>\n", " </tr>\n", " <tr class=\"hhevy\">\n", " <th>\n", " United Future\n", " </th>\n", " <td align=\"right\">\n", " 5,286\n", " </td>\n", " <td align=\"right\">\n", " 0.22\n", " </td>\n", " <td align=\"right\">\n", " 1\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 1\n", " </td>\n", " </tr>\n", " <tr class=\"hlite\">\n", " <th>\n", " Conservative\n", " </th>\n", " <td align=\"right\">\n", " 95,598\n", " </td>\n", " <td align=\"right\">\n", " 3.97\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " </tr>\n", " <tr class=\"hhevy\">\n", " <th>\n", " Internet MANA\n", " </th>\n", " <td align=\"right\">\n", " 34,094\n", " </td>\n", " <td align=\"right\">\n", " 1.42\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " </tr>\n", " <tr class=\"hlite\">\n", " <th>\n", " Aotearoa Legalise Cannabis Party\n", " </th>\n", " <td align=\"right\">\n", " 10,961\n", " </td>\n", " <td align=\"right\">\n", " 0.46\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " </tr>\n", " <tr class=\"hhevy\">\n", " <th>\n", " Ban1080\n", " </th>\n", " <td align=\"right\">\n", " 5,113\n", " </td>\n", " <td align=\"right\">\n", " 0.21\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " </tr>\n", " <tr class=\"hlite\">\n", " <th>\n", " Democrats for Social Credit\n", " </th>\n", " <td align=\"right\">\n", " 1,730\n", " </td>\n", " <td align=\"right\">\n", " 0.07\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " </tr>\n", " <tr class=\"hhevy\">\n", " <th>\n", " The Civilian Party\n", " </th>\n", " <td align=\"right\">\n", " 1,096\n", " </td>\n", " <td align=\"right\">\n", " 0.05\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " </tr>\n", " <tr class=\"hlite\">\n", " <th>\n", " NZ Independent Coalition\n", " </th>\n", " <td align=\"right\">\n", " 872\n", " </td>\n", " <td align=\"right\">\n", " 0.04\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " </tr>\n", " <tr class=\"hhevy\">\n", " <th>\n", " Focus New Zealand\n", " </th>\n", " <td align=\"right\">\n", " 639\n", " </td>\n", " <td align=\"right\">\n", " 0.03\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " <td align=\"right\">\n", " 0\n", " </td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"3\">\n", " </td>\n", " <td align=\"right\" class=\"totals\">\n", " 71\n", " </td>\n", " <td align=\"right\" class=\"totals\">\n", " 50\n", " </td>\n", " <td align=\"right\" class=\"totals\">\n", " 121\n", " </td>\n", " </tr>\n", " </table>\n", " </td>\n", " </tr>\n", " </table>\n", " <table border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\">\n", " <tr>\n", " <td class=\"refrmsg\">\n", " Updated 12:00 pm on 10/10/2014\n", " </td>\n", " </tr>\n", " <tr>\n", " <td align=\"center\" class=\"bannerbar\" height=\"25\" style=\"vertical-align:middle;text-align:center\">\n", " </td>\n", " </tr>\n", " <tr class=\"menu\">\n", " <td style=\"text-align:center;padding-top:5px;padding-bottom:5px;\">\n", " <td>\n", " </td>\n", " </td>\n", " </tr>\n", " <tr>\n", " <td>\n", " <p class=\"copyr\">\n", " Copyright \u00a92014 New Zealand Electoral Commission, Wellington\n", " <br>\n", " All Rights Reserved\n", " </br>\n", " </p>\n", " </td>\n", " </tr>\n", " </table>\n", " </body>\n", "</html>\n", "\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "soup.find_all('a')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "[<a href=\"index.html\">HOME</a>,\n", " <a href=\"partystatus.html\">OVERALL\u00a0STATUS</a>,\n", " <a href=\"electoratestatus.html\">ELECTORATE\u00a0STATUS</a>,\n", " <a href=\"electorateindex.html\">ELECTORATE DETAILS</a>,\n", " <a href=\"partystatus-early.html\">ADVANCE\u00a0VOTES</a>]" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "mydivs = soup.findAll(\"tr\", { \"class\" : \"hhevy\" })\n", "\n", "devsa = soup.findAll(\"td\", { \"class\" : \"orhdg\"})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "votelis = []" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "divlist = []\n", "\n", "for divs in mydivs:\n", " print divs.findAll('th')\n", " divlist.append(divs.findAll('th'))\n", " #votelis.append(divs.findALL('th'))\n", " print divs.findNext('td')\n", " divlist.append(divs.findNext('td'))\n", " \n", " #votelis.append(divs.findNext('td'))\n", " #print divs.findNext('tr')\n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[<th>National Party</th>]\n", "<td align=\"right\">1,131,501</td>\n", "[<th>Green Party</th>]\n", "<td align=\"right\">257,359</td>\n", "[<th>M\u00c4\u0081ori Party</th>]\n", "<td align=\"right\">31,849</td>\n", "[<th>United Future</th>]\n", "<td align=\"right\">5,286</td>\n", "[<th>Internet MANA</th>]\n", "<td align=\"right\">34,094</td>\n", "[<th>Ban1080</th>]\n", "<td align=\"right\">5,113</td>\n", "[<th>The Civilian Party</th>]\n", "<td align=\"right\">1,096</td>\n", "[<th>Focus New Zealand</th>]\n", "<td align=\"right\">639</td>\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "for devz in devsa:\n", " print(devz)\n", " votelis.append(devz)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<td class=\"orhdg\" colspan=\"6\" height=\"10\">\u00a0</td>\n", "<td class=\"orhdg\" colspan=\"5\">7,554 of 7,554 (100.0%)</td>\n", "<td class=\"orhdg\" colspan=\"5\">2,416,479</td>\n" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "votelis" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "[<td class=\"orhdg\" colspan=\"6\" height=\"10\">\u00a0</td>,\n", " <td class=\"orhdg\" colspan=\"5\">7,554 of 7,554 (100.0%)</td>,\n", " <td class=\"orhdg\" colspan=\"5\">2,416,479</td>]" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "votezcont = []" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "for votez in votelis:\n", " print votez.contents\n", " votezcont.append(votez.contents)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[u'\\xa0']\n", "[u'7,554 of 7,554 (100.0%)']\n", "[u'2,416,479']\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "for numz in votezcont:\n", " print(numz)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[u'\\xa0']\n", "[u'7,554 of 7,554 (100.0%)']\n", "[u'2,416,479']\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "votaz = votez.getText()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "numadd = []" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "for lets in votaz:\n", " print lets\n", " numadd.append(lets)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2\n", ",\n", "4\n", "1\n", "6\n", ",\n", "4\n", "7\n", "9\n" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "for numza in numadd:\n", " print numza" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2\n", ",\n", "4\n", "1\n", "6\n", ",\n", "4\n", "7\n", "9\n" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "for numz in numadd:\n", " if int in numz:\n", " print numz" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "coercing to Unicode: need string or buffer, type found", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-26-4a071c9bca0a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnumz\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnumadd\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mint\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnumz\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mnumz\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: coercing to Unicode: need string or buffer, type found" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "soup.find_all('tr')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "[<tr class=\"menu\"><td align=\"right\" class=\"menu\" colspan=\"2\" valign=\"bottom\"><a href=\"index.html\">HOME</a>\u00a0\| <a href=\"partystatus.html\">OVERALL\u00a0STATUS</a>\u00a0\| <a href=\"electoratestatus.html\">ELECTORATE\u00a0STATUS</a>\u00a0\| <a href=\"electorateindex.html\">ELECTORATE DETAILS</a>\u00a0\| <a href=\"partystatus-early.html\">ADVANCE\u00a0VOTES</a>\u00a0</td><td align=\"right\" class=\"menu\" valign=\"bottom\" width=\"7%\"><img alt=\"oMan.jpg\" height=\"37\" src=\"img/oMan.jpg\" width=\"59\"/></td></tr>,\n", " <tr class=\"menu\">\n", " <td align=\"left\" class=\"bannerbar\" height=\"73\" rowspan=\"3\"><img alt=\"Elections Logo\" src=\"/img/eclogo.gif\" style=\"width:150px; height:73px; align:left; padding-left:10px; padding-top:1px; padding-bottom:2px;\"/></td><td class=\"bannerbar\" height=\"42\"></td></tr>,\n", " <tr class=\"menu\"><td class=\"bannerbar\" height=\"10\"></td></tr>,\n", " <tr class=\"menu\"><td class=\"bannerbar\" height=\"21\"></td></tr>,\n", " <tr><td align=\"center\"><h3>Official Count Results -- Overall Status</h3>\n", " </td></tr>,\n", " <tr><td align=\"center\"><span class=\"topmsg\">This is not the formal declaration of results.<br>The Electoral Commission declares the results of the official count by publishing a notice in the NZ Gazette.</br></span></td></tr>,\n", " <tr><td height=\"10\">\u00a0</td></tr>,\n", " <tr><td style=\"padding:5px;\"><table align=\"center\" border=\"0\" cellpadding=\"3\" cellspacing=\"0\" class=\"eledata\"><tr><td class=\"orhdg\" colspan=\"6\" height=\"10\">\u00a0</td></tr>\n", " <tr><th class=\"orhdg\">Results Counted:</th><td class=\"orhdg\" colspan=\"5\">7,554 of 7,554 (100.0%)</td></tr>\n", " <tr><th class=\"orhdg\">Total Votes Counted:</th><td class=\"orhdg\" colspan=\"5\">2,416,479</td></tr>\n", " <tr class=\"headrowR\"><th class=\"headrowL\">Party</th><th class=\"headrowR\">Party<br>Votes</br></th><th class=\"headrowR\">%<br>Votes</br></th><th class=\"headrowR\">Electorate<br>Seats</br></th><th class=\"headrowR\">List<br>Seats</br></th><th class=\"headrowR\">Total<br>Seats</br></th></tr>\n", " <tr class=\"hhevy\"><th>National Party</th><td align=\"right\">1,131,501</td><td align=\"right\"> 47.04</td><td align=\"right\">41</td><td align=\"right\">19</td><td align=\"right\">60</td></tr>\n", " <tr class=\"hlite\"><th>Labour Party</th><td align=\"right\">604,535</td><td align=\"right\"> 25.13</td><td align=\"right\">27</td><td align=\"right\">5</td><td align=\"right\">32</td></tr>\n", " <tr class=\"hhevy\"><th>Green Party</th><td align=\"right\">257,359</td><td align=\"right\"> 10.70</td><td align=\"right\">0</td><td align=\"right\">14</td><td align=\"right\">14</td></tr>\n", " <tr class=\"hlite\"><th>New Zealand First Party</th><td align=\"right\">208,300</td><td align=\"right\"> 8.66</td><td align=\"right\">0</td><td align=\"right\">11</td><td align=\"right\">11</td></tr>\n", " <tr class=\"hhevy\"><th>M\u00c4\u0081ori Party</th><td align=\"right\">31,849</td><td align=\"right\"> 1.32</td><td align=\"right\">1</td><td align=\"right\">1</td><td align=\"right\">2</td></tr>\n", " <tr class=\"hlite\"><th>ACT New Zealand</th><td align=\"right\">16,689</td><td align=\"right\"> 0.69</td><td align=\"right\">1</td><td align=\"right\">0</td><td align=\"right\">1</td></tr>\n", " <tr class=\"hhevy\"><th>United Future</th><td align=\"right\">5,286</td><td align=\"right\"> 0.22</td><td align=\"right\">1</td><td align=\"right\">0</td><td align=\"right\">1</td></tr>\n", " <tr class=\"hlite\"><th>Conservative</th><td align=\"right\">95,598</td><td align=\"right\"> 3.97</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\n", " <tr class=\"hhevy\"><th>Internet MANA</th><td align=\"right\">34,094</td><td align=\"right\"> 1.42</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\n", " <tr class=\"hlite\"><th>Aotearoa Legalise Cannabis Party</th><td align=\"right\">10,961</td><td align=\"right\"> 0.46</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\n", " <tr class=\"hhevy\"><th>Ban1080</th><td align=\"right\">5,113</td><td align=\"right\"> 0.21</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\n", " <tr class=\"hlite\"><th>Democrats for Social Credit</th><td align=\"right\">1,730</td><td align=\"right\"> 0.07</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\n", " <tr class=\"hhevy\"><th>The Civilian Party</th><td align=\"right\">1,096</td><td align=\"right\"> 0.05</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\n", " <tr class=\"hlite\"><th>NZ Independent Coalition</th><td align=\"right\">872</td><td align=\"right\"> 0.04</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\n", " <tr class=\"hhevy\"><th>Focus New Zealand</th><td align=\"right\">639</td><td align=\"right\"> 0.03</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>\n", " <tr><td colspan=\"3\">\u00a0</td><td align=\"right\" class=\"totals\">71</td><td align=\"right\" class=\"totals\">50</td><td align=\"right\" class=\"totals\">121</td>\n", " </tr>\n", " </table>\n", " </td></tr>,\n", " <tr><td class=\"orhdg\" colspan=\"6\" height=\"10\">\u00a0</td></tr>,\n", " <tr><th class=\"orhdg\">Results Counted:</th><td class=\"orhdg\" colspan=\"5\">7,554 of 7,554 (100.0%)</td></tr>,\n", " <tr><th class=\"orhdg\">Total Votes Counted:</th><td class=\"orhdg\" colspan=\"5\">2,416,479</td></tr>,\n", " <tr class=\"headrowR\"><th class=\"headrowL\">Party</th><th class=\"headrowR\">Party<br>Votes</br></th><th class=\"headrowR\">%<br>Votes</br></th><th class=\"headrowR\">Electorate<br>Seats</br></th><th class=\"headrowR\">List<br>Seats</br></th><th class=\"headrowR\">Total<br>Seats</br></th></tr>,\n", " <tr class=\"hhevy\"><th>National Party</th><td align=\"right\">1,131,501</td><td align=\"right\"> 47.04</td><td align=\"right\">41</td><td align=\"right\">19</td><td align=\"right\">60</td></tr>,\n", " <tr class=\"hlite\"><th>Labour Party</th><td align=\"right\">604,535</td><td align=\"right\"> 25.13</td><td align=\"right\">27</td><td align=\"right\">5</td><td align=\"right\">32</td></tr>,\n", " <tr class=\"hhevy\"><th>Green Party</th><td align=\"right\">257,359</td><td align=\"right\"> 10.70</td><td align=\"right\">0</td><td align=\"right\">14</td><td align=\"right\">14</td></tr>,\n", " <tr class=\"hlite\"><th>New Zealand First Party</th><td align=\"right\">208,300</td><td align=\"right\"> 8.66</td><td align=\"right\">0</td><td align=\"right\">11</td><td align=\"right\">11</td></tr>,\n", " <tr class=\"hhevy\"><th>M\u00c4\u0081ori Party</th><td align=\"right\">31,849</td><td align=\"right\"> 1.32</td><td align=\"right\">1</td><td align=\"right\">1</td><td align=\"right\">2</td></tr>,\n", " <tr class=\"hlite\"><th>ACT New Zealand</th><td align=\"right\">16,689</td><td align=\"right\"> 0.69</td><td align=\"right\">1</td><td align=\"right\">0</td><td align=\"right\">1</td></tr>,\n", " <tr class=\"hhevy\"><th>United Future</th><td align=\"right\">5,286</td><td align=\"right\"> 0.22</td><td align=\"right\">1</td><td align=\"right\">0</td><td align=\"right\">1</td></tr>,\n", " <tr class=\"hlite\"><th>Conservative</th><td align=\"right\">95,598</td><td align=\"right\"> 3.97</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>,\n", " <tr class=\"hhevy\"><th>Internet MANA</th><td align=\"right\">34,094</td><td align=\"right\"> 1.42</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>,\n", " <tr class=\"hlite\"><th>Aotearoa Legalise Cannabis Party</th><td align=\"right\">10,961</td><td align=\"right\"> 0.46</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>,\n", " <tr class=\"hhevy\"><th>Ban1080</th><td align=\"right\">5,113</td><td align=\"right\"> 0.21</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>,\n", " <tr class=\"hlite\"><th>Democrats for Social Credit</th><td align=\"right\">1,730</td><td align=\"right\"> 0.07</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>,\n", " <tr class=\"hhevy\"><th>The Civilian Party</th><td align=\"right\">1,096</td><td align=\"right\"> 0.05</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>,\n", " <tr class=\"hlite\"><th>NZ Independent Coalition</th><td align=\"right\">872</td><td align=\"right\"> 0.04</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>,\n", " <tr class=\"hhevy\"><th>Focus New Zealand</th><td align=\"right\">639</td><td align=\"right\"> 0.03</td><td align=\"right\">0</td><td align=\"right\">0</td><td align=\"right\">0</td></tr>,\n", " <tr><td colspan=\"3\">\u00a0</td><td align=\"right\" class=\"totals\">71</td><td align=\"right\" class=\"totals\">50</td><td align=\"right\" class=\"totals\">121</td>\n", " </tr>,\n", " <tr><td class=\"refrmsg\">Updated 12:00 pm on 10/10/2014</td></tr>,\n", " <tr><td align=\"center\" class=\"bannerbar\" height=\"25\" style=\"vertical-align:middle;text-align:center\">\u00a0</td></tr>,\n", " <tr class=\"menu\"><td style=\"text-align:center;padding-top:5px;padding-bottom:5px;\"><td></td></td></tr>,\n", " <tr><td><p class=\"copyr\">Copyright \u00a92014 New Zealand Electoral Commission, Wellington<br>All Rights Reserved</br></p></td></tr>]" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "X" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'X' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-28-253bcac7dd80>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "for divs in mydivs:\n", " print divs.findAll[('tr')" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-29-36651929cc2c>, line 2)", "output_type": "pyerr", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-29-36651929cc2c>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m print divs.findAll[('tr')\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "for divs in mydivs:\n", " print divs.find_next('tr')" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'mydivs' is not defined", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-7-9bbb1e065ae6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mdivs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmydivs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mdivs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind_next\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tr'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'mydivs' is not defined" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "divlist" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
alimanfoo/anhima	benchmark/ped.ipynb	2	12120	{ "metadata": { "name": "", "signature": "sha256:de98bf60ab63a4dfcd2a3bfd86cff663a55e7db5c33d839e1225b442b4538e11" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import print_function\n", "import numpy as np\n", "np.random.seed(1)\n", "import scipy.stats\n", "import sys\n", "import cProfile\n", "%reload_ext memory_profiler\n", "# dev imports\n", "sys.path.insert(0, '../src')\n", "%reload_ext autoreload\n", "%autoreload 1\n", "%aimport anhima.sim\n", "%aimport anhima.gt\n", "%aimport anhima.ped" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "# simulate random biallelic genotypes in a cross with 2 parents and 10 progeny\n", "n_samples = 12\n", "n_variants = 10**6\n", "ploidy = 2\n", "af_dist = scipy.stats.beta(a=.9, b=.1)\n", "p_missing = .1\n", "# progeny totally unrelated to parents, should give plenty of mendel errors\n", "genotypes = anhima.sim.simulate_biallelic_genotypes(n_variants, n_samples, \n", " af_dist=af_dist, \n", " p_missing=p_missing, \n", " ploidy=ploidy)\n", "parents = genotypes[:, :2, :]\n", "progeny = genotypes[:, 2:, :]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "me = anhima.ped.diploid_mendelian_error(parents, progeny)\n", "me" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "array([[0, 1, 0, ..., 0, 0, 0],\n", " [0, 1, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ..., \n", " [0, 0, 0, ..., 0, 0, 0],\n", " [1, 1, 1, ..., 0, 1, 1],\n", " [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit anhima.ped.diploid_mendelian_error(parents, progeny)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 447 ms per loop\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "%memit anhima.ped.diploid_mendelian_error(parents, progeny)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "peak memory: 193.02 MiB, increment: 31.57 MiB\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "cProfile.run('anhima.ped.diploid_mendelian_error(parents, progeny)', sort='time')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 146 function calls in 0.449 seconds\n", "\n", " Ordered by: internal time\n", "\n", " ncalls tottime percall cumtime percall filename:lineno(function)\n", " 5 0.247 0.049 0.247 0.049 necompiler.py:662(evaluate)\n", " 2 0.115 0.058 0.216 0.108 gt.py:704(as_012)\n", " 2 0.041 0.021 0.041 0.021 {method 'reduce' of 'numpy.ufunc' objects}\n", " 2 0.034 0.017 0.034 0.017 gt.py:300(is_het)\n", " 1 0.010 0.010 0.010 0.010 {method 'astype' of 'numpy.ndarray' objects}\n", " 2 0.001 0.001 0.001 0.001 {method 'fill' of 'numpy.ndarray' objects}\n", " 1 0.001 0.001 0.449 0.449 ped.py:123(diploid_mendelian_error)\n", " 5 0.000 0.000 0.000 0.000 necompiler.py:462(getContext)\n", " 21 0.000 0.000 0.000 0.000 {numpy.core.multiarray.array}\n", " 2 0.000 0.000 0.036 0.018 gt.py:486(is_hom_alt)\n", " 2 0.000 0.000 0.029 0.015 gt.py:395(is_hom_ref)\n", " 21 0.000 0.000 0.000 0.000 numeric.py:392(asarray)\n", " 5 0.000 0.000 0.000 0.000 {sorted}\n", " 2 0.000 0.000 0.041 0.021 fromnumeric.py:2048(amax)\n", " 11 0.000 0.000 0.000 0.000 necompiler.py:611(getType)\n", " 1 0.000 0.000 0.449 0.449 <string>:1(<module>)\n", " 2 0.000 0.000 0.041 0.021 _methods.py:15(_amax)\n", " 2 0.000 0.000 0.000 0.000 {numpy.core.multiarray.empty}\n", " 5 0.000 0.000 0.000 0.000 {isinstance}\n", " 10 0.000 0.000 0.000 0.000 {sys._getframe}\n", " 5 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", " 5 0.000 0.000 0.000 0.000 {zip}\n", " 5 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects}\n", " 5 0.000 0.000 0.000 0.000 {method 'copy' of 'dict' objects}\n", " 10 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}\n", " 11 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}\n", " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", "\n", "\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "me2 = anhima.ped.diploid_mendelian_error_multiallelic(parents, progeny)\n", "me2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "array([[0, 1, 0, ..., 0, 0, 0],\n", " [0, 1, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ..., \n", " [0, 0, 0, ..., 0, 0, 0],\n", " [1, 1, 1, ..., 0, 1, 1],\n", " [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "np.array_equal(me, me2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "True" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "%timeit anhima.ped.diploid_mendelian_error_multiallelic(parents, progeny)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 loops, best of 3: 1.62 s per loop\n" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "%memit anhima.ped.diploid_mendelian_error_multiallelic(parents, progeny)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "peak memory: 419.62 MiB, increment: 120.59 MiB\n" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "cProfile.run('anhima.ped.diploid_mendelian_error_multiallelic(parents, progeny)', sort='time')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " 97 function calls in 1.596 seconds\n", "\n", " Ordered by: internal time\n", "\n", " ncalls tottime percall cumtime percall filename:lineno(function)\n", " 12 0.982 0.082 0.982 0.082 {method 'reduce' of 'numpy.ufunc' objects}\n", " 1 0.489 0.489 1.596 1.596 ped.py:251(diploid_mendelian_error_multiallelic)\n", " 2 0.092 0.046 0.402 0.201 gt.py:763(as_allele_counts)\n", " 2 0.022 0.011 0.022 0.011 {method 'astype' of 'numpy.ndarray' objects}\n", " 1 0.008 0.008 0.008 0.008 necompiler.py:662(evaluate)\n", " 2 0.003 0.001 0.003 0.001 {numpy.core.multiarray.zeros}\n", " 11 0.000 0.000 0.000 0.000 {numpy.core.multiarray.array}\n", " 5 0.000 0.000 0.440 0.088 fromnumeric.py:1621(sum)\n", " 6 0.000 0.000 0.590 0.098 _methods.py:23(_sum)\n", " 1 0.000 0.000 0.008 0.008 gt.py:117(is_missing)\n", " 1 0.000 0.000 0.000 0.000 necompiler.py:462(getContext)\n", " 6 0.000 0.000 0.000 0.000 {isinstance}\n", " 2 0.000 0.000 0.323 0.161 fromnumeric.py:1842(all)\n", " 2 0.000 0.000 0.031 0.015 fromnumeric.py:1762(any)\n", " 4 0.000 0.000 0.000 0.000 numeric.py:462(asanyarray)\n", " 7 0.000 0.000 0.000 0.000 numeric.py:392(asarray)\n", " 1 0.000 0.000 1.596 1.596 <string>:1(<module>)\n", " 2 0.000 0.000 0.030 0.015 _methods.py:31(_any)\n", " 2 0.000 0.000 0.040 0.020 _methods.py:15(_amax)\n", " 2 0.000 0.000 0.040 0.020 fromnumeric.py:2048(amax)\n", " 2 0.000 0.000 0.323 0.161 _methods.py:35(_all)\n", " 2 0.000 0.000 0.323 0.161 {method 'all' of 'numpy.ndarray' objects}\n", " 2 0.000 0.000 0.031 0.015 {method 'any' of 'numpy.ndarray' objects}\n", " 1 0.000 0.000 0.149 0.149 {method 'sum' of 'numpy.ndarray' objects}\n", " 1 0.000 0.000 0.000 0.000 {sorted}\n", " 1 0.000 0.000 0.000 0.000 {max}\n", " 2 0.000 0.000 0.000 0.000 necompiler.py:611(getType)\n", " 1 0.000 0.000 0.000 0.000 {range}\n", " 1 0.000 0.000 0.000 0.000 {zip}\n", " 2 0.000 0.000 0.000 0.000 {sys._getframe}\n", " 1 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects}\n", " 1 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", " 2 0.000 0.000 0.000 0.000 {len}\n", " 1 0.000 0.000 0.000 0.000 {method 'copy' of 'dict' objects}\n", " 2 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}\n", " 2 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}\n", "\n", "\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }	mit
deepmind/open_spiel	open_spiel/colabs/deep_cfr_pytorch.ipynb	1	25270	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "OpenSpiel - DeepCFR", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "6fs6SIL3QgE-", "colab_type": "text" }, "source": [ "# OpenSpiel: Deep CFR in PyTorch\n", "In this notebook we present an example of how to use OpenSpiel with PyTorch as the learning library. This is possible since most of the components in OpenSpiel are agnostic of the learning library used.\n", "\n", "In this notebook we implement the Deep Counterfactual Regret algorithm in PyTorch on the game of Kuhn Poker. Adapted from [this example](https://github.com/deepmind/open_spiel/blob/master/open_spiel/python/examples/deep_cfr.py).\n" ] }, { "cell_type": "markdown", "metadata": { "id": "_4pUODpi0Lnl", "colab_type": "text" }, "source": [ "## Build and Install OpenSpiel\n", "Clone the latest version of OpenSpiel from Github." ] }, { "cell_type": "code", "metadata": { "id": "2e9hMgKSg3e-", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title\n", "%cd /usr/local\n", "!git clone https://github.com/deepmind/open_spiel;\n", "%cd open_spiel" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "3dvGiEYFChct", "colab_type": "text" }, "source": [ "Install the requirements and build the library." ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "x2iMd1KbT5gO", "colab": {}, "cellView": "form" }, "source": [ "#@title\n", "!./install.sh\n", "!pip install -r requirements.txt;\n", "!mkdir -p /usr/local/open_spiel/build\n", "%cd /usr/local/open_spiel/build\n", "!CXX=g++ cmake -DPython_TARGET_VERSION=3.6 -DCMAKE_CXX_COMPILER=${CXX} ../open_spiel;\n", "!make -j$(nproc);" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "UqqrOKZGCypH", "colab_type": "text" }, "source": [ "Set up the PYTHONPATH to enable the Python bindings for the current instance." ] }, { "cell_type": "code", "metadata": { "id": "KIFkxD2NhK7J", "colab_type": "code", "colab": {}, "cellView": "form" }, "source": [ "#@title\n", "%cd /content\n", "import sys\n", "sys.path.append('/usr/local/open_spiel')\n", "sys.path.append('/usr/local/open_spiel/build/python')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Ss3qlgexEhV9", "colab_type": "text" }, "source": [ "## Import dependencies" ] }, { "cell_type": "code", "metadata": { "id": "TJaIzvZLlelO", "colab_type": "code", "colab": {} }, "source": [ "from open_spiel.python import policy\n", "from open_spiel.python.algorithms import exploitability\n", "from open_spiel.python.algorithms.deep_cfr import AdvantageMemory, StrategyMemory, ReservoirBuffer\n", "import pyspiel\n", "from absl import logging\n", "import collections\n", "import numpy as np\n", "\n", "import torch\n", "import torch.nn as nn" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "qnFDt18N2ZjA", "colab_type": "code", "colab": {} }, "source": [ "def reset_params(m):\n", " if isinstance(m, nn.Linear):\n", " m.reset_parameters()\n", "\n", "# Defining fully-connected network to use as policy and advantage network\n", "class MLP(nn.Module):\n", " def __init__(self, layers):\n", " \"\"\"\n", " Creates a MLP with hidden units defined by `layers`.\n", " \"\"\"\n", " super(MLP, self).__init__()\n", " layers = [nn.Linear(layers[i], layers[i+1]) for i in range(len(layers[:-1]))]\n", " self._model = nn.Sequential(layers)\n", " \n", " def forward(self, input):\n", " return self._model(input)\n", " \n", " def reset(self):\n", " self._model.apply(reset_params)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "P35t4vpCapIz", "colab_type": "text" }, "source": [ "## Implement DeepCFR\n", "We follow the structure in the [Tensorflow implementation](https://github.com/deepmind/open_spiel/blob/master/open_spiel/python/algorithms/deep_cfr.py), rewiting the optimization steps in PyTorch. " ] }, { "cell_type": "code", "metadata": { "id": "TMXvG7O80u50", "colab_type": "code", "colab": {} }, "source": [ "class DeepCFR(policy.Policy):\n", " \"\"\"Implements a solver for the Deep CFR Algorithm with PyTorch.\n", "\n", " See https://arxiv.org/abs/1811.00164.\n", "\n", " Define all networks and sampling buffers/memories. Derive losses & learning\n", " steps. Initialize the game state and algorithmic variables.\n", "\n", " Note: batch sizes default to `None` implying that training over the full\n", " dataset in memory is done by default. To sample from the memories you\n", " may set these values to something less than the full capacity of the\n", " memory.\n", " \"\"\"\n", "\n", " def __init__(self,\n", " game,\n", " policy_network_layers=(256, 256),\n", " advantage_network_layers=(128, 128),\n", " num_iterations: int = 100,\n", " num_traversals: int = 20,\n", " learning_rate:float = 1e-4,\n", " batch_size_advantage=None,\n", " batch_size_strategy=None,\n", " memory_capacity: int =int(1e6),\n", " policy_network_train_steps: int = 1,\n", " advantage_network_train_steps: int = 1,\n", " reinitialize_advantage_networks: bool = True):\n", " \"\"\"Initialize the Deep CFR algorithm.\n", "\n", " Args:\n", " game: Open Spiel game.\n", " policy_network_layers: (list[int]) Layer sizes of strategy net MLP.\n", " advantage_network_layers: (list[int]) Layer sizes of advantage net MLP.\n", " num_iterations: (int) Number of training iterations.\n", " num_traversals: (int) Number of traversals per iteration.\n", " learning_rate: (float) Learning rate.\n", " batch_size_advantage: (int or None) Batch size to sample from advantage\n", " memories.\n", " batch_size_strategy: (int or None) Batch size to sample from strategy\n", " memories.\n", " memory_capacity: Number af samples that can be stored in memory.\n", " policy_network_train_steps: Number of policy network training steps (per\n", " iteration).\n", " advantage_network_train_steps: Number of advantage network training steps\n", " (per iteration).\n", " reinitialize_advantage_networks: Whether to re-initialize the\n", " advantage network before training on each iteration.\n", " \"\"\"\n", " all_players = list(range(game.num_players()))\n", " super(DeepCFR, self).__init__(game, all_players)\n", " self._game = game\n", " if game.get_type().dynamics == pyspiel.GameType.Dynamics.SIMULTANEOUS:\n", " # `_traverse_game_tree` does not take into account this option.\n", " raise ValueError(\"Simulatenous games are not supported.\")\n", " self._batch_size_advantage = batch_size_advantage\n", " self._batch_size_strategy = batch_size_strategy\n", " self._policy_network_train_steps = policy_network_train_steps\n", " self._advantage_network_train_steps = advantage_network_train_steps\n", " self._num_players = game.num_players()\n", " self._root_node = self._game.new_initial_state()\n", " self._embedding_size = len(\n", " self._root_node.information_state_tensor(0))\n", " self._num_iterations = num_iterations\n", " self._num_traversals = num_traversals\n", " self._reinitialize_advantage_networks = reinitialize_advantage_networks\n", " self._num_actions = game.num_distinct_actions()\n", " self._iteration = 1\n", "\n", " # Define strategy network, loss & memory.\n", " self._strategy_memories = ReservoirBuffer(memory_capacity)\n", " self._policy_network = MLP(\n", " [self._embedding_size] + list(policy_network_layers) + [self._num_actions])\n", " # Illegal actions are handled in the traversal code where expected payoff\n", " # and sampled regret is computed from the advantage networks.\n", " self._policy_sm = nn.Softmax(dim=-1)\n", " self._loss_policy = nn.MSELoss()\n", " self._optimizer_policy = torch.optim.Adam(\n", " self._policy_network.parameters(), lr=learning_rate)\n", "\n", " # Define advantage network, loss & memory. (One per player)\n", " self._advantage_memories = [\n", " ReservoirBuffer(memory_capacity) for _ in range(self._num_players)\n", " ]\n", " self._advantage_networks = [\n", " MLP([self._embedding_size] + list(advantage_network_layers) + \n", " [self._num_actions])\n", " for _ in range(self._num_players)\n", " ]\n", " self._loss_advantages = nn.MSELoss(reduction='mean')\n", " self._optimizer_advantages = []\n", " for p in range(self._num_players):\n", " self._optimizer_advantages.append(torch.optim.Adam(\n", " self._advantage_networks[p].parameters(), lr=learning_rate))\n", "\n", " @property\n", " def advantage_buffers(self):\n", " return self._advantage_memories\n", "\n", " @property\n", " def strategy_buffer(self):\n", " return self._strategy_memories\n", "\n", " def clear_advantage_buffers(self):\n", " for p in range(self._num_players):\n", " self._advantage_memories[p].clear()\n", "\n", " def reinitialize_advantage_network(self, player):\n", " self._advantage_networks[player].reset()\n", "\n", " def reinitialize_advantage_networks(self):\n", " for p in range(self._num_players):\n", " self.reinitialize_advantage_network(p)\n", "\n", " def solve(self):\n", " \"\"\"Solution logic for Deep CFR.\n", "\n", " Traverses the game tree, while storing the transitions for training\n", " advantage and policy networks. \n", "\n", " Returns:\n", " 1. (nn.Module) Instance of the trained policy network for inference. \n", " 2. (list of floats) Advantage network losses for \n", " each player during each iteration.\n", " 3. (float) Policy loss. \n", " \"\"\"\n", " advantage_losses = collections.defaultdict(list)\n", " for _ in range(self._num_iterations):\n", " for p in range(self._num_players):\n", " for _ in range(self._num_traversals):\n", " self._traverse_game_tree(self._root_node, p)\n", " if self._reinitialize_advantage_networks:\n", " # Re-initialize advantage network for player and train from scratch.\n", " self.reinitialize_advantage_network(p)\n", " # Re-initialize advantage networks and train from scratch.\n", " advantage_losses[p].append(self._learn_advantage_network(p))\n", " self._iteration += 1\n", " # Train policy network.\n", " policy_loss = self._learn_strategy_network()\n", " return self._policy_network, advantage_losses, policy_loss\n", "\n", " def _traverse_game_tree(self, state, player):\n", " \"\"\"Performs a traversal of the game tree.\n", "\n", " Over a traversal the advantage and strategy memories are populated with\n", " computed advantage values and matched regrets respectively.\n", "\n", " Args:\n", " state: Current OpenSpiel game state.\n", " player: (int) Player index for this traversal.\n", "\n", " Returns:\n", " (float) Recursively returns expected payoffs for each action.\n", " \"\"\"\n", " expected_payoff = collections.defaultdict(float)\n", " if state.is_terminal():\n", " # Terminal state get returns.\n", " return state.returns()[player]\n", " elif state.is_chance_node():\n", " # If this is a chance node, sample an action\n", " action = np.random.choice([i[0] for i in state.chance_outcomes()])\n", " return self._traverse_game_tree(state.child(action), player)\n", " elif state.current_player() == player:\n", " sampled_regret = collections.defaultdict(float)\n", " # Update the policy over the info set & actions via regret matching.\n", " advantages, strategy = self._sample_action_from_advantage(state, player)\n", " for action in state.legal_actions():\n", " expected_payoff[action] = self._traverse_game_tree(\n", " state.child(action), player)\n", " for action in state.legal_actions():\n", " sampled_regret[action] = expected_payoff[action]\n", " for a_ in state.legal_actions():\n", " sampled_regret[action] -= strategy[a_] expected_payoff[a_]\n", " sampled_regret_arr = [0] * self._num_actions\n", " for action in sampled_regret:\n", " sampled_regret_arr[action] = sampled_regret[action]\n", " self._advantage_memories[player].add(\n", " AdvantageMemory(state.information_state_tensor(),\n", " self._iteration, sampled_regret_arr, action))\n", " return max(expected_payoff.values())\n", " else:\n", " other_player = state.current_player()\n", " _, strategy = self._sample_action_from_advantage(state, other_player)\n", " # Recompute distribution for numerical errors.\n", " probs = np.array(strategy)\n", " probs /= probs.sum()\n", " sampled_action = np.random.choice(range(self._num_actions), p=probs)\n", " self._strategy_memories.add(\n", " StrategyMemory(\n", " state.information_state_tensor(other_player),\n", " self._iteration, strategy))\n", " return self._traverse_game_tree(state.child(sampled_action), player)\n", "\n", " def _sample_action_from_advantage(self, state, player):\n", " \"\"\"Returns an info state policy by applying regret-matching.\n", "\n", " Args:\n", " state: Current OpenSpiel game state.\n", " player: (int) Player index over which to compute regrets.\n", "\n", " Returns:\n", " 1. (list) Advantage values for info state actions indexed by action.\n", " 2. (list) Matched regrets, prob for actions indexed by action.\n", " \"\"\"\n", " info_state = state.information_state_tensor(player)\n", " legal_actions = state.legal_actions(player)\n", " with torch.no_grad():\n", " state_tensor = torch.FloatTensor(np.expand_dims(info_state, axis=0))\n", " advantages = self._advantage_networks[player](state_tensor)[0].numpy()\n", " advantages = [max(0., advantage) for advantage in advantages]\n", " cumulative_regret = np.sum(\n", " [advantages[action] for action in legal_actions])\n", " matched_regrets = np.array([0.] * self._num_actions)\n", " for action in legal_actions:\n", " if cumulative_regret > 0.:\n", " matched_regrets[action] = advantages[action] / cumulative_regret\n", " else:\n", " matched_regrets[action] = 1 / self._num_actions\n", " return advantages, matched_regrets\n", "\n", " def action_probabilities(self, state):\n", " \"\"\"Computes the action probabilities for the current player \n", " in the given state.\n", " \n", " Args:\n", " state: (pyspiel.State)\n", " \n", " Returns:\n", " (dict) action probabilities for a single batch.\"\"\"\n", " cur_player = state.current_player()\n", " legal_actions = state.legal_actions(cur_player)\n", " info_state_vector = np.array(state.information_state_tensor())\n", " if len(info_state_vector.shape) == 1:\n", " info_state_vector = np.expand_dims(info_state_vector, axis=0)\n", " with torch.no_grad():\n", " logits = self._policy_network(torch.FloatTensor(info_state_vector))\n", " probs = self._policy_sm(logits).numpy()\n", " return {action: probs[0][action] for action in legal_actions}\n", "\n", " def _learn_advantage_network(self, player):\n", " \"\"\"Compute the loss on sampled transitions and perform a Q-network update.\n", "\n", " If there are not enough elements in the buffer, no loss is computed and\n", " `None` is returned instead.\n", "\n", " Args:\n", " player: (int) player index.\n", "\n", " Returns:\n", " (float) The average loss over the advantage network.\n", " \"\"\"\n", " for _ in range(self._advantage_network_train_steps):\n", "\n", " if self._batch_size_advantage:\n", " if self._batch_size_advantage > len(self._advantage_memories[player]):\n", " ## Skip if there aren't enough samples\n", " return None\n", " samples = self._advantage_memories[player].sample(\n", " self._batch_size_advantage)\n", " else:\n", " samples = self._advantage_memories[player]\n", " info_states = []\n", " advantages = []\n", " iterations = []\n", " for s in samples:\n", " info_states.append(s.info_state)\n", " advantages.append(s.advantage)\n", " iterations.append([s.iteration])\n", " # Ensure some samples have been gathered.\n", " if not info_states:\n", " return None\n", " self._optimizer_advantages[player].zero_grad()\n", " advantages = torch.FloatTensor(np.array(advantages))\n", " iters = torch.FloatTensor(np.sqrt(np.array(iterations)))\n", " outputs = self._advantage_networks[player](\n", " torch.FloatTensor(np.array(info_states)))\n", " loss_advantages = self._loss_advantages(iters * outputs, \n", " iters * advantages)\n", " loss_advantages.backward()\n", " self._optimizer_advantages[player].step()\n", "\n", " return loss_advantages.detach().numpy()\n", "\n", " def _learn_strategy_network(self):\n", " \"\"\"Compute the loss over the strategy network.\n", "\n", " Returns:\n", " (float) The average loss obtained on this batch of transitions or `None`.\n", " \"\"\"\n", " for _ in range(self._policy_network_train_steps):\n", " if self._batch_size_strategy:\n", " if self._batch_size_strategy > len(self._strategy_memories):\n", " ## Skip if there aren't enough samples\n", " return None\n", " samples = self._strategy_memories.sample(self._batch_size_strategy)\n", " else:\n", " samples = self._strategy_memories\n", " info_states = []\n", " action_probs = []\n", " iterations = []\n", " for s in samples:\n", " info_states.append(s.info_state)\n", " action_probs.append(s.strategy_action_probs)\n", " iterations.append([s.iteration])\n", "\n", " self._optimizer_policy.zero_grad()\n", " iters = torch.FloatTensor(np.sqrt(np.array(iterations)))\n", " ac_probs = torch.FloatTensor(np.array(np.squeeze(action_probs)))\n", " logits = self._policy_network(torch.FloatTensor(np.array(info_states)))\n", " outputs = self._policy_sm(logits)\n", " loss_strategy = self._loss_policy(iters * outputs, iters * ac_probs)\n", " loss_strategy.backward()\n", " self._optimizer_policy.step()\n", "\n", " return loss_strategy.detach().numpy()\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "F6g8XAmdgQuZ", "colab_type": "text" }, "source": [ "## Train the model" ] }, { "cell_type": "code", "metadata": { "id": "46ZnC9qGq3bT", "colab_type": "code", "colab": {} }, "source": [ "game = pyspiel.load_game('kuhn_poker')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "WqVsQUOqm9kp", "colab_type": "code", "colab": {} }, "source": [ "solver = DeepCFR(game,\n", " policy_network_layers=(32, 32),\n", " advantage_network_layers=(16, 16),\n", " num_iterations=400,\n", " num_traversals=40,\n", " learning_rate=1e-3,\n", " batch_size_advantage=None,\n", " batch_size_strategy=None,\n", " memory_capacity=1e7)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "m-TnC_tuE5oP", "colab_type": "code", "colab": {} }, "source": [ "_, advantage_losses, policy_loss = solver.solve()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ljaGnchaLlfV", "colab_type": "code", "colab": {} }, "source": [ "for player, losses in list(advantage_losses.items()):\n", " print(\"Advantage for player:\", player,\n", " losses[:2] + [\"...\"] + losses[-2:])\n", " print(\"Advantage Buffer Size for player\", player,\n", " len(solver.advantage_buffers[player]))\n", "print(\"Strategy Buffer Size:\",\n", " len(solver.strategy_buffer))\n", "print(\"Final policy loss:\", policy_loss)\n", "conv = exploitability.nash_conv(\n", " game,\n", " policy.tabular_policy_from_callable(game, solver.action_probabilities))\n", "print(\"Deep CFR - NashConv:\", conv)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "E9BRbD1-OINm", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] } ] }	apache-2.0

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